As technologies change and adapt, we’re often left with seemingly useless junk that has nowhere to go. Certainly anyone still sitting on a pile of floppy disks feels this way sometimes, but odds are anyone who owns a mining ASIC or an NFT can attest to that as well. The trillions of dollars flowing into GPU-based data centers will likely become the next victim of this trend, so if you want to capitalize on the losses of some venture capitalist you’ll want to figure out a way to get GPUs meant for a server into your desktop doing useful work.

Of course, calling these devices GPUs is a bit of a stretch compared to the Radeon and GeForce cards many of us are used to using for gaming. These don’t even have a PCIe slot or video output, after all. But, as [Oscar] notes, the VRAM and GPU cores are very real and can still do useful work. An adapter board is able to mate a Tesla V100 SXM2 16 GB GPU to a standard PCIe slot, which solves the first problem, but the major downside from there is that the cooling fan for this unit was literally deafeningly loud. At 82 dB it was about as loud as a lawnmower, which is fine in a server rack but not great in a bedroom. [Oscar] found a way to tamp down the fan speed, making it usable in a home.

Without video output, the utility of these cards mainly comes from adding VRAM and compute for tasks that benefit from parallel computing. Using tensor splitting, [Oscar] is running a local LLM with this card alongside his RTX 4080, providing 32 GB of VRAM on his NixOS system. With his benchmarking tests, the LLM sports impressive stats for a self-hosted model, ranking somewhere around Claude Sonnet 4.6. What’s even more impressive is that this is all done for around £200, and with the rate the various LLM companies are ratcheting up pricing could pay itself back very quickly. If trading off performance for cost is acceptable, though, it’s possible to run local models on much less powerful hardware as well.

A little over a month after the Department of Defense (DOD) bragged that nearly half of its 3.5 million employees were using AI at work, members of the Army’s Combat Capabilities Development Command (DEVCOM) received an email informing them that they were burning through tokens, and needed to limit use.

“Although the Army CIO announced in May 2026 that they were offering unlimited tokens, by mid-June the Army CIO pool was exhausted of tokens and had to re-establish limits,” the email reads. The email goes on to say that although the Army has chosen to renew token usage at “its current levels,” it’s unclear “if the Army CIO pool will be renewed after 1 Oct.”

The Army uses Ask Sage, a multimodal generative AI platform where users can run different large language models (LLMs), including Alphabet’s Gemini, Meta’s Llama, and OpenAI’s ChatGPT. “Apparently the whole Army burned through the whole year of tokens for just one service,” says an Army employee who spoke to WIRED anonymously because they were not authorized to speak to the press.

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There are a lot of AI coding applications out there, and as impressive as large language models and the agents they enable have become, many of the most recent developments in AI-assisted development have been in the software that manages those models, not just the models themselves.

Earlier this summer, I spoke with the head of product for Claude Code, Anthropic's Cat Wu, about that company's approach to building that software.

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The widespread introduction of AI-powered coding tools has led to some dramatic splits between those integrating those tools into their workflows and anti-AI absolutists who don't want large language model-generated code anywhere near their projects. When it comes to the Linux kernel, though, creator and top-level maintainer Linus Torvalds said he is "willing to absolutely put my foot down" in support of using AI tools to improve the long-standing open source project.

Writing in a lengthy post on the Linux kernel mailing list this week, Torvalds said that "Linux is not one of those anti-AI projects, and if somebody has issues with that, they can do the open-source thing and fork it. Or just walk away."

The statement came amid a lengthy thread arguing about the use of Sashiko, an "agentic Linux kernel code review system" that its creators claim can, in tests, independently find 53.6 percent of the bugs that would end up being fixed by human coders in later commits. But the tool can also waste maintainers' time by sending "false positive" reports of bugs that don't exist, at a rate Sashiko's maintainers estimate is "well within [the] 20% range."

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Researchers [Charles Ye], [Jasmine Cui], and [Dylan Hadfield-Menell] have shown that AI Large Language Models (LLMs) can fail to correctly distinguish between different instruction sources because they prioritize writing style over metadata tags, and this role confusion leads to a powerful attack called CoT (Chain of Thought) Forgery. We’ll explain exactly how it works after a bit of background review.

Prompt injection was where “getting an LLM to do something it shouldn’t” started by exploiting the fact that LLMs communicate like people, but are much more obedient. For a while, simply telling an LLM “ignore all previous instructions and <do something funny>” yielded results no matter how transparently dumb the instructions were, and the reason it worked at all was because LLMs do not have separate data and instruction streams; it’s all one big lump of input. It’s up to the model to sort legit instructions from untrusted, user-provided data. One step towards mitigating this was the addition of roles.

Roles are a method of segmenting that big blob of input into an organized hierarchy with metadata tags. For example with <system> at the top, and <user> requests much lower down. Instructions in a role are followed as long as they don’t conflict with higher-priority ones. A system-level directive of “don’t discuss illegal things” would override a user’s request to provide a recipe for cocaine.

Another type of tag is <think>, the contents of which represent a model’s internal reasoning process. Predictably, this role has high trust. What if one could inject spoofed internal reasoning? Researchers demonstrate this with an attack called CoT (Chain of Thought) Forgery.

CoT Forgery relies on LLMs being shown to prioritize writing style over actual tag content. By writing convoluted reasoning in a style that closely matches a model’s internal and highly distinct <think> style, the model is tricked into treating it like an already-reached conclusion. Note this attack does not simply wrap the injected prompt in <think> tags.

That’s the core of it, but the rest of the research makes a compelling case that, at least for the time being, mitigating prompt injection-style attacks is likely to remain an evolving process rather than become a solved problem anytime soon. LLMs are obedient but stuck with instructions and data in a single channel, role perception isn’t binary, and humans are clever and creative.

The complete paper is available online, and code examples are on GitHub.