LLM

101 Articles

LLM Moats Quickly Evaporating

August 30, 2026 by 22 Comments

In the business world, a moat is a quality of a business that makes it difficult for competitors to take that company’s profits. With how hard it is to train models for large language models (LLMs) and generative AI, it might seem like Anthropic, Open AI, and other LLM companies would have huge moats given the amount of compute it takes to build models. But open source models are quickly draining that moat, and now the only thing standing in the way of a customer using one of these models on their own hardware instead one from the larger companies is physical computing resources. [TerminalBytes] demonstrates a few of these models on personally owned computers to show the current state of the art.

[TerminalBytes] started off running the 27B version of the Qwen3.8 on a Mac Studio with 256 GB of unified RAM, which is plenty for this task. But it’s also enough to benchmark a few different models. Qwen3.6 is compared to 3.8, and then the different quants of each model are also compared. Quants are compressed versions of models that need fewer bits to store weights, meaning that the same models can run in less memory with smaller losses in fidelity. Many of these quants run on machines with 32 GB of RAM or less, encompassing many average gaming PCs. There’s even a 1-bit quant that [TerminalBytes] tested which can easily run on a machine with 16 GB, although with mixed results.

Keep in mind that this is just the current state of affairs with open LLMs. Future versions of these models are likely to optimize the number of tokens produced per unit time, or otherwise increase quality of responses while requiring less computer resources. We don’t really think that the ease of running local models will be the sole reason that the AI bubble pops, though. The fact that not every computer user is running Linux is proof enough of that.

Same As It Ever Was

August 29, 2026 by 114 Comments

Whether you like it or not, the use of LLMs to write code is kind of a big deal at the moment. We’ve been asking ourselves what, if anything, this means for us here at Hackaday. Should we try to figure out what percentage of a project was done by an actual human and how much was done by a machine? Does it really matter? What is our AI policy anyway?

Clearly, Hackaday is pro-human. We’re in it for the hackers as much as for the hacks. Our community is, like Soylent Green, made of people. It’s your inspirations and innovations that keep us reading and writing every day. And we produce 100% of our content the old-fashioned way, with projects selected through the taste and judgement of our writers, and their own words telling the story.

What about the hacks? We’ve seen a lot of projects recently that were coded with the help of an LLM. Does that diminish the work? In the end, what rings truest to us is what has always been Hackaday’s editorial guiding star: Is there something special in the hack that makes it worth talking about? Then we write about it. Was it written using vim or emacs? Did the author consult friends or a chatbot while working on the project? That’s not really relevant.

But in the past few years, the BS-generation machines have found our hobby, and we’re finding a lot more projects that don’t have any spark to them. We’re seeing circuits that make no sense, and claims that defy physics. Of course, we always have. The LLM-nonsense project is today’s version of the perpetual motion machines of old. Just like we never trust a hardware project that is all renders, seeing only AI-generated images is a huge red flag. It’s our job to separate out the wheat from the chaff for you all, but it’s something that you must be doing everyday as well.

We’ve seen amazing hacks over Hackaday’s 22-year history. Hackaday is older than YouTube and older than Stack Overflow. We’ve seen technology come and go. We’ve seen C-beams glitter in the dark near the Tannhäuser gate. (OK, maybe not.) And in the end, our AI policy is our same-old policy: we write up hacks that inspire us in the hope that they inspire you.

So if you’re using Claude to help you with the UI bits, or if you’re hand-writing it all in assembly, or wiring up the logic in diodes, we just want to see your cool hacks. And we hope that our collective signal will be so loud that we drown out the noise, at least in our own little corner of the hacker universe.

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This Week In Security: BugTraq, AI Hacks, And Being Dumb On Planes

August 14, 2026 by 17 Comments

After a multi-year hiatus, the venerable BugTraq mailing list is back!

For decades, BugTraq was the place where vulnerabilities were disclosed, from the early days when nearly all vendors viewed all security research as a hostile force, through to the modern era of working with vendors to coordinate disclosing bugs. With the rise of bug bounty programs and other social changes, the mailing list slowly died: what started in 1993 ended in 2021 is returning. The new maintainer, Jonathan Brossard, says in his announcement “The mission is unchanged: full disclosure, researcher-first, no corporate filter.”

Don’t Be Dumb on Planes

In the unlikely event anyone here needs to be told: Don’t do dumb things on planes.

It seems that someone coming home from from the DEF CON hacker conference in Las Vegas decided to mess with the in-plane WiFi, and is likely now in the “find out” phase of doing something dumb. There hasn’t been any public followup beyond the original reports: a passenger on a Delta flight leaving Las Vegas brought up a fake WiFi hotspot named “Delta WiFi FAST” to trick other passengers into connecting, and attempted to disable the in-flight WiFi using a denial of service attack.

Continue reading “This Week In Security: BugTraq, AI Hacks, And Being Dumb On Planes” →

Wrangling Datacenter GPUs Into A Desktop

July 30, 2026 by 20 Comments

As we’ve seen many times before, there’s usually some way wrangle a bit more life out of what would otherwise be considered old and obsolete technology. Perhaps one thing that has been passed over by the masses a bit to early is older datacenter GPUs, which is understandable in one sense because of the rate NVIDIA is pumping out new ones, but these cards have plenty of useful life left in them for the average person, as [Andrew] demonstrates.

The cards [Andrew] is using are Tesla V100s of 2017 vintage. Despite being older hardware they have high-speed memory which allows them to run modern LLMs locally, competitively with online models. In this test, Gemma 4 26B and Qwen3 35B are run, with Gemma being a bit faster because it fits entirely in GPU memory and Qwen3 being a bit more capable but more hungry for resources. [Andrew] built a PCI card that can host two V100s, allowing these larger models to fit completely in memory.

Even though these don’t perform at the same level as the latest top-tier online models, they’re surprisingly capable and also have the benefit of running completely locally. This might be concerning for those looking at the global economy being propped up by companies that essentially have no moat for motivated users, especially as more and more datacenter hardware becomes available on the secondhand market. While this build by [Andrew] goes into detail on getting the software stack up and running, we recently featured another build using the same GPUs that focuses a bit more on hardware for those looking to get started with local hosting.

Chain-of-Thought Spoofing Targets Reasoning AI Models

July 2, 2026 by 23 Comments

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. Continue reading “Chain-of-Thought Spoofing Targets Reasoning AI Models” →

NVIDIA’s New AI Servers Run On Hotub Coolant And Don’t Need Evaporators

June 26, 2026 by 85 Comments

When people start ranting about AI, you can be sure a few things are going to come up during the two-minutes hate: job loss, higher power bills, the neverending tide of low-effort slop, and wasting precious freshwater. Well, NVIDIA wants to take away that last one, beacause the all-water cooled Ruben architecture won’t need any evaporative cooling— coolant can stay in a closed loop, and never needs to be cooled below 45 C, or 113 F.

This sort of coolant loop should be familiar to anyone who has ever built a water-cooled PC or PlayStation: there’s a glycol-water mix, water blocks, and a radiator to reject heat to the environment. NVIDIA doesn’t mention if their new servers come with RGB lighting, but we’d like to imagine it’s an option. The big difference — aside from the rainbow LEDs– between a Ruben server and your old gaming rig is that in these racks, everything is on a waterblock. If there’s a chip on the motherboard generating heat, it’s getting rid of it into the same cooling water. Cooling water, that we have to emphasize, needs only be cooler than the chips themselves: in this case, they’re talking 45 C on the cold side, and 55 C headed out of the racks. (That’s 113 F to 131 F for all the bald eagles reading this.)

Given the required temperature drop is so modest, there’s no need for the evaporative chillers that have given AI data centers such a bad name in water conservation circles. Just like in a water-cooled PC, ambient-temperature air running over dry heat exchangers– also known as big honkin’ radiators–is able to handle the cooling, so no water is lost. Since everything is on waterblocks, there’s no need for cooling air, either, and the server farms need only be air conditioned to the degree required to make them comfortable to work in.

If you think NVIDIA is making this change because they suddenly care about water conservation, think again. The press release makes their motivations very clear: cooling costs money, and running this hot saves a lot of it. We’re talking four mil US a year for a 50 MW hyperscaler. One might suspect that this sort of thermal regime could limit the lifetime of the hard-working NPUs, but since they’ll be obsolete in a few years anyway, that’s not likely a big concern, especially not for NVIDIA.

We’ve actually seen hotter fluids used to cool computers before– coffee, for one. Water cooling also isn’t new in the data center world; we took a look at it a few years back. Things are clearly heating up now, though.

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Bavarian Court Tells Gemini It Can’t Be A Real Boy Until It Tells The Truth

June 14, 2026 by 63 Comments

Does anyone like Google’s AI summaries? If so, they weren’t on the Judge’s bench in a specific Bavarian courtroom recently, where it was ruled that yes, Google is liable for the hallucinations of its search engine AI.

This was a civil case brought by a pair of Munich companies, both of whom were wrongfully slandered by LLM hallucinations. Google took the position that this information must have existed somewhere, and like presenting links to libelous websites — something they have no obligation to avoid — they should not be held accountable for what the summary at the top of the search results says.

Continue reading “Bavarian Court Tells Gemini It Can’t Be A Real Boy Until It Tells The Truth” →