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Arthur T Knackerbracket writes:

"We really do earnestly believe AI could kill all humans!"

When a prominent researcher quits a job at a frontier AI lab these days, it's often to pursue a new startup or protest a new business model. But AI researcher Jacob Coxon is using his departure from Anthropic to publicly warn that frontier AI companies are "gambling with our lives" with systems that they "earnestly believe... could kill us all by the end of the decade."

In a social media thread Tuesday night, Coxon said that this existential risk is inherent not so much in today's models but more in the impending prospect of "self-improving superintelligence" creating "superhuman systems that can hack anything, revolutionize any field overnight, and acquire real power and resources." Others working on these models have either not "internalized the civilizational stakes" or believe that they need to "speedrun" the race to superintelligence to prevent an irresponsible party from getting there first, he wrote.

Lest you think this is just one departing researcher expressing an unpopular opinion, Anthropic Alignment Science lead Evan Hubinger piped in on social media to say that "Jacob is correct here—we really do earnestly believe AI could kill all humans! I personally think it is >10% within the next decade."

Hubinger points to a lengthy August report from the Anthropic alignment team [PDF] that predicts the potential for "catastrophic risk" from current models is "low." But that report also says current trends "might lead to more concerning misalignment in future more capable models," which could feature "strong covert capabilities" to avoid detection by safety researchers.

Anthropic's own threat model in that paper takes seriously the possibility that future models "may cause unbounded harm—up to and including humanity losing control over civilization entirely—by leveraging novel technology and their access to it."

An AI that can continually improve itself—potentially to a point beyond human control or understanding—has been a long-standing concern in parts of the AI research community (and in the dystopian science fiction that's part of AI training data, of course). Those concerns have persisted even as some research suggests AI systems are more likely to hit a capability plateau in the near future and others question whether "superintelligence" is even a reasonable metric for systems whose capabilities are so brittle and spiky (will this superintelligence at least be able to fold my laundry?)

Snotnose writes:

Problem: You have a 250 ton telescope that needs to turn smoothly all day. Solution? Set it on a pool of mercury. It worked very well for over 50 years. But time marches on.

A solar telescope in New Mexico is being torn down, and the reason is a pool of liquid mercury hidden inside it. The mercury was the clever trick that made the telescope work. It is now the thing that dooms it.

The telescope's optical system weighs 250 tons, and it had to turn smoothly all day. You can't spin something that heavy on ordinary wheels. So instead the whole thing floated on a sealed pool of liquid mercury. Mercury is dense and flows easily, so the load rode on the fluid and turned with almost no friction. That is how the 250-ton optical system of the Richard B. Dunn Solar Telescope was designed to move.

However, on January 5, crews found some of that mercury where it wasn't supposed to be.

Original Submission

AnonTechie writes:

Meltdown (08/Sept/2026) that grounded thousands of flights caused by 'spurious' flight data being entered into system, report says

The air traffic control outage that caused thousands of flights to be grounded across the UK earlier in the week was caused by a military aircraft entering "spurious" flight data into the system, according to a report.

Hundreds of thousands of passengers were affected after more than 2,000 flights were cancelled on Tuesday when the system used by the National Air Traffic Services (Nats) shut down for four hours.

According to the Financial Times [Paywalled -- Ed.], four people briefed on the incident claimed that the shutdown was caused by a flight plan submitted by a UK military aircraft, which caused a meltdown in air traffic control systems used by Nats which remained unfixed for several hours.

[Source]: The Guardian

This seems like gross incompetence ... why did the NATS System accept spurious data ? Spurious data should have been flagged immediately and rejected by the system. Your thoughts ??

Original Submission

owl writes:

https://www.righto.com/2026/09/8087-microcode-reverse-engineering-fscale.html

In the 1970s, floating-point arithmetic was a mess. Computer manufacturers had a dozen incompatible arithmetic standards. Moreover, floating-point systems were designed around hardware simplicity rather than mathematical rigor, leading to problems with numerical stability. This changed when Intel introduced the 8087 floating-point coprocessor chip in 1980, designed to be as accurate as possible, even in the corner cases. The 8087 became popular because it could be installed in the IBM PC, making floating-point operations up to 100 times faster in applications ranging from spreadsheets to CAD. But more importantly, the 8087 became the floating-point standard used by most computers today.

The 8087 implemented its instructions in complex low-level code called microcode. I'm part of a group, the Opcode Collective, that is reverse-engineering this microcode, and I've recently made some progress. In this post, I examine the microcode for one of the 8087's instructions—FSCALE—and describe how this microcode works. The FSCALE (Floating-point Scale) instruction provides a quick way to scale a number by a power of two, much faster than a multiplication. I figured that FSCALE was a simple, almost trivial instruction that would be straightforward to understand and explain. Spoiler: it is not simple. FSCALE uses over 140 micro-instructions and three levels of subroutine calls to handle many special cases. But the FSCALE microcode illustrates many interesting parts of the 8087, such as the shifter, the adder, and the exponent converter, and also reveals a hidden feature of the 8087, so hopefully you will find it interesting.

Original Submission

Arthur T Knackerbracket writes:

https://www.theregister.com/paas-and-iaas/2026/09/10/digital-sovereignty-sounds-great-until-you-try-ditching-your-suppliers/5294997

After years of governments and tech vendors banging the digital sovereignty drum, most large organizations have concluded that true technological independence is beyond their reach.

That's according to new research from Capgemini, which found that 59 percent of organizations regard complete digital sovereignty as unrealistic. Instead, two-thirds define it in terms of what the consultancy calls "resilient interdependence" – accepting continued reliance on outside technology providers while trying to ensure those dependencies do not threaten critical operations.

It's a pragmatic conclusion, though perhaps not surprising given how thoroughly tangled those dependencies have become.

Digital sovereignty broadly refers to an organization's ability to retain control over its data, infrastructure, software, and critical technology operations, including where they are hosted and whose laws govern them.

Capgemini surveyed 1,300 business and technology executives in April 2026, covering companies with more than $1 billion in annual revenue and government departments with budgets above $1 billion. Its separate Digital Sovereignty Index, based on an analysis of 866 organizations, found that 86 percent had significant exposure to foreign or externally controlled supply chains. Across the research, just 14 percent reported end-to-end visibility into dependencies throughout their wider technology ecosystems.

Getting out of those relationships isn't necessarily straightforward either. Some 36 percent said moving away from a critical technology provider would take more than a year, while another 10 percent said they had no viable alternative.

That leaves organizations trying to work out how much independence they actually need, rather than rebuilding their entire technology stacks closer to home.

European respondents appeared particularly receptive to that compromise. Three-quarters defined sovereignty in terms of resilient interdependence, compared with half of respondents in the US. The UK also tended to view sovereignty through the lens of risk: 56 percent of British respondents primarily associated it with resilience and risk mitigation.

This doesn't mean the subject has disappeared from the corporate agenda: 93 percent of respondents said it had been discussed at board level, and 44 percent ranked it among their board's top priorities.

AI is adding another dependency for those boards to worry about. Three-quarters of organizations identified AI as a key focus of their sovereignty efforts, ahead of cloud infrastructure, cybersecurity, data, and software.

Money presents another problem. Just under half said they would pay extra for sovereign technology. Among those willing to do so, the acceptable premium averaged 23 percent.

Perhaps the more uncomfortable finding concerns what happens when those carefully accepted dependencies actually fail.

Among organizations that had recently suffered an operational disruption, just 42 percent had contingency plans in place. Almost two-thirds of US organizations were prepared, compared with slightly more than a third in Europe and Asia-Pacific.

Capgemini argues the answer isn't complete technological independence, but deciding which systems and capabilities matter enough to keep under tighter control, while building alternatives and recovery plans around everything else.

Given that one in 10 organizations cannot currently replace a critical provider at all, this may be less a philosophical embrace of interdependence than an acknowledgment of where the exits actually are.

Original Submission

Arthur T Knackerbracket writes:

https://www.theregister.com/devops/2026/09/11/microsoft-annoints-rust-as-a-tier-1-internal-language/5295732

When Microsoft's developers and engineers sit down to code, they can now choose to work in Rust, which Redmond has added to its list of canonical languages.

"Rust now is a Tier One language at Microsoft, and that just means that it sits among C++, C# and TypeScript as the best supported languages for internal development in the company," explained Victor Ciura, Microsoft principal engineer for the Rust tooling team, during a keynote talk today at this year's annual RustConf, being held this week in Montréal.

Ciura said Microsoft has "paved a path" of tools and processes that support local Rust development across the entire software development lifecycle.

Rust is no stranger at Microsoft and is already present in over 100 Microsoft project repositories. The company has built Oxidizer, a set of crates to build scalable services in Rust, which have been used to build and refine the Microsoft 365 (M365) core services such as Outlook, Word, Excel, OneDrive, and SharePoint. The Copilot tech stack also owes quite a bit to Rust.  

But the really messy work still lies ahead. Given the recent Windows Patch Tuesday patch deluge, no doubt one of the largest uses for Rust will be shoring up leaky C/C++ Windows code. 

This is the work Rust was made for. Mozilla developer Graydon Hoare created Rust as a side project in 2006 chiefly as a high-performance language that, unlike C and C++, guarded against memory bugs. 

Rewriting programs in Rust to improve performance and minimize bugs has since become a common chore for project leaders, who have Rustified everything from databases to package managers, often with the help of AI.

Redmond has already done considerable work making Rust comfy on Microsoft Visual C++ (MSVC), its native platform for compiling Windows binaries. 

Notably, company engineers built rustc_codegen_utc, a custom Rust compiler code-generation backend that wires the rustc compiler directly into MSVC's internal toolchain for Windows. 

"Connecting rustc to that backend lets Rust build on the same platform investment, with perfect compatibility out of the box, rather than requiring a parallel implementation of every Windows-specific capability," wrote Ciura in a blog post that details Redmond's rusty efforts. "The result is a unified code generation platform for Rust and C++ on Windows."

Even with the tight Rust integration, Microsoft engineers will have their work cut out for them. One of the recurring themes of this year's RustConf is grappling with the dangerous "unsafe" territory that comes when mixing Rust and C++ code. While the Rust compiler can flag memory errors, it typically doesn't cover pointers into the C++ memory allocations. 

Still, Redmond running towards Rust is a welcome development. In his 2025 RustConf keynote, Microsoft Azure CTO Mark Russinovich noted that ~70 percent of Windows CVEs are memory issues.

"One of the things that I realized a long time ago is that no matter how much we really want to make C and C++ better, we can't make it as good as what Rust starts with," he said.

Original Submission

Arthur T Knackerbracket writes:

https://www.theregister.com/offbeat/2026/09/08/retired-man-turns-spare-room-into-soviet-era-supercomputer/5295046

You can go retro by using an old video game console or a PC from the 1980s, but why stop there when, like one man, you can build your own vacuum tube computer using 75-year-old recycled components?

A UK resident who identifies himself only as "Mike" has done just that, as detailed on his tube computer website and accompanying Hackaday project page. Mike's tube computer is primarily made up of 460 recycled, Soviet-era 6N3P vacuum tubes, which were common enough in their age to be readily and cheaply available online. 

"Many were used and then stored for over 50 years, so life expectancy may be questionable," Mike explained on Hackaday. A new 6N3P tube is supposed to have around 500 hours of life, but because each has been used, its current condition is essentially a mystery. Throw in manufacturing inconsistencies of mid-century Soviet Russia, and each tube had enough variability to necessitate extensive testing, Mike explains in the technical writeup on his website.

"I calculated the component values and tested the results, but in many cases the theory did not accurately match the practice," Mike writes. "So I built a test rig to test the viability of various component values and found a range of values that seemed to work in most cases."

All told, Mike's tube computer, with its 460 6N3P tubes, consists of 920 thermionic triodes that use heat to release electrons for his computing work. Those tubes are mounted to 46 separate 10-tube PCBs, which in turn sit on five backplane PCBs. The entire thing includes a control console that Mike describes as "far too simple" and in need of a complete redesign to better handle variations in the computer's logic levels.

It takes between 10 and 15 minutes for the entire system to stabilize once switched on, Mike said, resulting in a "warm and cozy computer room." The tubes all power up in random states, requiring the machine to be reset each time it is switched on, but then it's ready for the fun part: computing.

The entire thing uses NOR (i.e., not OR) gates, just like the Apollo guidance computers that got humans to the Moon, which allows it to be a bit smaller than some of the other classic tube computers of yesteryear. As for what it can do, well - 16-bit math is a possibility, as well as anything else that can be done with a limited set of 16 instructions on a 4-bit instruction register. 

Mike has it configured to output a 64-bit Fibonacci sequence and, more excitingly for his grandchildren and the kid in all of us, plans to use it to power a simple flight simulator once its I/O system is finished.

Arthur T Knackerbracket writes:

https://www.siliconrepublic.com/innovation/nasa-and-ibm-unveil-an-open-source-lunar-ai-model

The AI model could help scientists better identify lunar ice, volcanos and craters.

A new open source AI model trained on NASA’s extensive lunar observation data is expected to help scientists better navigate moon colonisation plans.

The NASA‑IBM Lunar Foundation Model is one of the first publicly available foundation models for the scientific exploration of the moon, coming just months after the two organisations’ Prithvi AI made history by becoming the first geospatial foundation model to be deployed in orbit.

The Earth’s lunar neighbour has an arid and varied topography covered in craters, ice and volcanic activity – all picked up over the decades by various sensors and instruments. The petabytes of collected data, is, however, scattered across maps and images that need physical examination, or are accessible only by task specific machine learning models.

A domain-specific AI model could change how scientists analyse data, potentially accelerating how lunar geography is identified and understood. The Lunar Foundation Model achieves this by combining multimodal and multi-resolution observations.

According to IBM and NASA, the new model can be used to investigate potential lunar ice deposits in the moon’s permanently shadowed regions, which could indicate the presence of water and oxygen.

It could also be used to study lunar volcanic features, called Irregular Mare Patches, and better identify craters to ensure safer landing sites for spacecrafts.

“NASA has spent decades building an extraordinary scientific record of the Moon, but collecting data is only part of the job,” said Kevin Murphy, the chief science data officer and acting chief data and AI officer at NASA.

“We also have to make data easier for scientists to explore and use. The NASA-IBM Lunar Foundation Model shows what’s possible when we bring AI to NASA’s petabytes of scientific data. That’s a real opportunity we see with AI: turning large-scale data into new discoveries.”

Alongside the model, IBM and NASA also scientists built the first open-source lunar dataset of its kind, made out of layers of data from nine instruments across four missions.

The dataset includes tens of thousands of images and maps lunar surface images from NASA’s Lunar Reconnaissance Orbiter and the GRAIL mission, and data from the Japanese Aerospace Exploration Agency.

Original Submission

Anthropic's September 2026 Report: State Espionage, Weaponized Swarms, and the Rise of Autonomous AI

JoeMerchant writes:

In its September 2026 Threat Intelligence Report, Anthropic detailed its ongoing operations to detect, disrupt, and mitigate the malicious misuse of its Claude AI models across seven core harm areas:

Cyber Operations:
Anthropic identified and banned several threat actors, including GTG-20006 (linked to Russia's Midnight Blizzard), which used AI to automate malware re-tooling, phishing, and infrastructure setup. They also disrupted GTG-50014 (ShinyHunters affiliates) conducting opportunistic data theft and extortion, and GTG-10007 (a Chinese-speaking group) that built autonomous "agent swarms" for zero-day exploit research and intelligence harvesting.

Influence Operations:
The team dismantled multiple covert campaigns using AI as a "newsdesk" to launder state propaganda. This included a Russian-led operation in the Central African Republic (GTG-04001), a commercial "influence-as-a-service" platform targeting Malaysia (GTG-84005), Iranian state-aligned networks (GTG-34001), and pro-Awami League fake-news rings in Bangladesh (GTG-54006).

Surveillance Operations:
Anthropic blocked state-aligned actors and spyware vendors from utilizing Claude to build mass-interception software or ingest bulk social media data to profile dissidents, notably targeting Uyghur, Tibetan, and Iranian diaspora communities.

Conventional Weapons:
The company shut down groups using Claude to engineer weapons software, including a Yemen-based cell (GTG-87001) designing guided rocket firmware, and China-nexus actors drafting fire-control specifications for undersea warfare and electronic warfare routing.

Biological Misuse & Scams:
Anthropic intervened against researchers attempting to leverage models for dual-use biological research, such as avian influenza mammalian adaptation and gain-of-function planning. They also terminated a deceptive dating app network (GTG-15001) that deployed over 4,700 AI personas to defraud users.

Illicit Distillation:
Anthropic actively defended its IP against unauthorized industrial-scale campaigns by Chinese AI labs—including Alibaba, DeepSeek, Moonshot, Zhipu, and Xiaomi—which flooded Claude with queries to illicitly harvest its reasoning traces and chain-of-thought data to train competing models.

To counter these evolving threats, Anthropic deployed advanced technical safeguards, including encrypted and summarized internal reasoning, identity verification for high-risk regions, and intelligence sharing with industry peers and global authorities.

https://www.anthropic.com/threat-intelligence-report-september-2026

Arthur T Knackerbracket writes:

Swiss Army sticks a knife in American cloud apps with its own FOSS push:

Switzerland's Federal Chancellery is preparing to offer a FOSS workplace suite to some 3,000 employees – but the Swiss Armed Forces are pushing ahead faster still.

European interest in digital sovereignty isn't limited to the European Union: the Swiss Federal Chancellery plans to offer selected government employees a FOSS alternative that will run alongside Microsoft 365. The chancellery is launching the program with an initial 3,000 staff, out of some 47,000 federal employees. The platform is expected to become available to about 3,000 selected employees from the end of 2027, with the first phase forecast to cost CHF 9 million (£8.2 million, $11.1 million). For comparison, Swiss Radio reported last year that the federal government had spent CHF 1.1 billion (£1 billion, $1.4 billion) on Microsoft licenses.

The program follows the BOSS Proof of Concept feasibility study, which began last year and tested browser-based FOSS tools with 172 users. The English version of the report says: "Functions examined included document editing, email, calendar, contacts, tasks, file storage, audio and video conferencing, chat, and identity and access management."

The study carefully spells out four limits to its scope:

• It would not replace Microsoft 365
• It would not have 1:1 functionality
• It would not have full M365 compatibility
• It would not be permanent infrastructure

It's important to make this stuff plain right at the start. Back in 2022, in a piece whose working title was You can't buy software, this vulture mentioned a member of one of his online communities who wanted to replace Microsoft software with free software – but demanded complete fidelity, including intact imports of all his Outlook files. This kind of exact and perfect match is basically impossible, and the four bullet points above strike us as a good effort at managing users' expectations.

An Anonymous Coward writes:

Open AI claims that AI has found a solution to the Navier-Stokes Millenium Prize problem.

The Navier-Stokes equations are a good model for most fluids we see around us like water and air. They are partial differential equations, and their solution is the velocity of the fluid --- a field that varies with both location in space and time. The Millenium Prize problem asks whether it is possible to start the equations with smooth initial conditions and smooth forcing, but still reach a solution with a singularity (i.e. infinite velocity at some time and point in space). One may either prove that such solutions stay smooth for all time, or one may construct an example which has a finite time "blowup"; the claim by OpenAI is that they found solutions with singularities. The result itself is interesting and it will most likely have some consequences within fluid dynamics, but these are medium to long term things that are hard to predict. I personally am not capable of judging the validity of the result (FWIW Sabine Hossenfelder explains reasonably well why the result might not be valid despite the "lean confirmation").

Sadly, the whole thing is clouded by allegations of not-exactly-plagiarism. In their statement OpenAI does not deny that their internal models may have been trained on research performed by others. To be more clear, there exists professor Tristan Buckmaster who was working with a friend from Anthropic on exactly this problem, and exactly this approach to the problem, and prof. Buckmaster used OpenAI's tools. I personally found prof. Buckmaster's statement a good read, and I don't want to forget this blog post by Curtis Pyke.

I believe the larger story is the AI agent collective. In the Hugging Face hack many AI agents started talking to each other and worked together to achieve their goals. OpenAI states that to solve the Navier Stokes problem, they purposefully set up to 10000 agents to work in groups that could communicate internally. At the same time OpenAI states that we are not prepared for AI --- the BBC has a broader text on controlling AI here.

I believe the larger story is that these AI agent collectives look a lot like global workspaces. This whole thing fits in nicely with the fact that humans consider themselves superior to other animals because they have language: unconstrained communication of different components is what leads the collective to have an actual chain/train of thought (the communication log).

I believe there is more to say, but I want to start a discussion, not to lecture. I apologize if I left anything important out.

Original Submission

Arthur T Knackerbracket writes:

Money would ostensibly fund research the NIH leadership said it no longer wanted:

Late last week, word started leaking that the National Institutes of Health (NIH) had reached an agreement with the Department of Defense that would see part of the NIH's budget used to fund research at the Department of Defense. So, on the Friday just prior to a US holiday weekend, the Department of Defense released a copy of the agreement and confirmed that it had been signed roughly a month earlier. The move is striking for a number of reasons, ranging from the existing budget disparities between the two parties involved to the fact that the money would be used for projects that the current NIH leadership has explicitly rejected.

The agreement itself sets up a system where the NIH would transfer money to the Department of Defense to fund staff and projects that would "support the advanced development of medical countermeasures against pandemic influenza, chemical, biological, radiological, and nuclear (CBRN) threats, and emerging infectious diseases." The money would come out of the budget for the NIH's National Institute of Allergy and Infectious Diseases, or NIAID, to which Congress has allocated $6.6 billion in 2026. The agreement is set to run for a decade.

Left unspecified is just how much of the NIAID budget will be spent on Defense projects. Reporting by Nature suggests that the Department of Defense was looking for up to a third of its total budget but was being told to settle for about 10 percent. There's obviously an enormous disparity between the budgets of these two agencies, given that the 2026 Defense budget is roughly $1 trillion. That budget is under considerable strain, however, due to the open-ended nature of the conflict with Iran. The deal has also been announced at a time when the NIH has been struggling to issue sufficient grants to use the money that Congress allocated to it.

That has led some, including Sen. Patty Murray (D-Wash.), to accuse the parties of simply using the agreement as a way to transfer money to the Pentagon without congressional approval. Others are suggesting that this is part of a longer-term effort to shift any biosecurity research out of the NIH and into the Pentagon.

Arthur T Knackerbracket writes:

"We will not be relevant without a massive rocket at a massive cadence"

All of a sudden, French entrepreneur Hèlene Huby is seemingly everywhere.

This week she is serving as one of two envoys, alongside astronaut Thomas Pesquet, at a high-profile International Space Summit convened by French President Emmanuel Macron. The government's website for the event describes her as chief executive of "the fastest-growing space company in Europe."

Also this week, that firm, The Exploration Company, announced a significant capital raise of $450 million in Series C funding. Although not spectacular by the standards of some US space businesses, this is the largest-ever Series C funding round by a Europe-based space company, and likely the largest funding round ever raised by a female-led company in Europe.

And Huby has arrived on center stage in the European space community at a time when there are increasingly urgent questions about the ability of the continent to navigate a future in space as commercial startups in the United States and China race ahead.

So many of the biggest European space projects of this decade feel dated. The flagship rocket, the Ariane 6 vehicle, is flying. But its costs are high, it is fully expendable, and its flight rate of 10 launches a year feels antediluvian in the megaconstellation era. Europe's response to SpaceX's Starlink constellation is a project called IRIS², which is already years behind schedule and has seen its estimated cost balloon to more than $15 billion before much metal has even been cut.

From civil projects, such as space-based data, to military applications, Europe seems to be fighting a losing battle on almost every front. All of this makes Huby refreshing because she is honest about the situation and has big ambitions. She seems to be one of the only people in Europe who recognizes these problems, speaks publicly about them, and is trying to do something about it.

Arthur T Knackerbracket writes:

Open source, commercial, single-hop, multi-hop, mixnet? The array of options is dizzying:

A prominent US senator is asking the National Security Agency to provide guidance to the general public on best practices for using virtual private networks to secure their communications from spying by foreign adversaries.

VPNs funnel all of a user's Internet traffic through an encrypted connection to a remote server. The design provides strong assurances that no one between the user and the server can read the encrypted contents. VPNs also allow users to hide their IP addresses from the destination servers they communicate with. While US agencies have previously recommended use of VPNs, none have given recommendations on which ones provide adequate protection.

There are a host of limitations that can undo many of the protections users may think their VPN provides them. For instance, the encrypted tunnel often terminates once a single server decrypts the traffic and sends it on to its final destination. That means the decrypted traffic or the sending and destination IP addresses may be available for snooping by rogue employees or attackers who hack the server. VPNs also don't encrypt certain types of metadata, such as time stamps, allowing nation-states to build profiles that can be useful in intelligence gathering.

With so many nuances, the existing recommendations to use a VPN don't provide enough information for people to make informed decisions. Senator Ron Wyden, a Democrat representing Oregon, is asking the NSA to provide specific recommendations.

"Americans facing advanced foreign threats—including government personnel, defense contractors, journalists, and human rights defenders—deserve clear, honest advice about how best to protect their communications from surveillance by foreign adversaries," Wyden wrote in a letter sent Wednesday to General Joshua M. Rudd, the director of the NSA. "To that end, I request that you update NSA's existing public guidance on VPN configurations to address this issue."

Arthur T Knackerbracket writes:

https://www.theregister.com/ai-and-ml/2026/09/03/hugging-face-is-too-important-to-fall-into-nvidias-hands/5294363

Nvidia on Thursday announced an agreement to acquire open source AI evangelist and model depot Hugging Face for $12.9 billion in a deal that's expected to close next year, assuming regulators don't get in the way.

They should get in the way.

As antitrust magnets go, Nvidia buying Hugging Face takes the cake. You wouldn't let an automaker acquire the primary means of fuel distribution. Nor would you let it buy the primary means by which the mechanics are trained. Yet, Nvidia's acquisition of Hugging Face is tantamount to both.

Unfortunately, the Trump administration likely lacks both the competence and teeth necessary to litigate the case, something that probably factored into CEO Jensen Huang's decision to buy Hugging Face now.

To understand why Nvidia's latest acquisition is so troublesome, we need to talk about just how important Hugging Face has become to the broader AI ecosystem. Have you seen the XKCD comic in which a piece of open source software maintained by a single developer is holding up all of modern infrastructure? It could be argued that Hugging Face is that piece for the AI industry.

Founded in 2016, the AI model repository has become the beating heart of the machine learning community. When a new open weights model drops, Hugging Face is the first place people look. A key element of its success has been its unrelenting commitment to open source AI, which has made it a sort of Switzerland for researchers, enthusiasts, and software devs to collaborate on neutral ground.

If you've ever downloaded an open weights model, it almost certainly came from Hugging Face, whether you knew it or not.

No matter how fervently Nvidia promises not to pollute Hugging Face's hallowed ground, the temptation to use the platform to advance its hardware and software interests will inevitably prove too great for Huang and his lieutenants to resist for long.

In spite of this, Hugging Face CEO Clem Delangue sang Nvidia's praises on Thursday, casting the acquisition and the capital it will bring as an opportunity to grow the company's user base from around 18 million today to more than 100 million in the years to come.