Despite Penalties, Lawyers Can’t Stop Using AI

April 5, 2026 by Maya Posch 31 Comments

Despite a few high-profile cases in recent years with lawyers getting caught using LLM-generated documents and facing disciplinary action due to this, it would seem that this is not deterring many other lawyers from following them off this particular cliff, per reporting from NPR.

We reported back in the innocent days of 2023 about the amusing case of Robert Mata v. Avianca, Inc. In this case, the plaintiff’s lawyer decided to have ChatGPT ‘assist’ with the legal filing, which ended up being filled with non-existent cases being cited, despite the chatbot’s assurance that these were all real cases. Now it would seem that this blind trust in cases cited by LLM chatbots is becoming the rule, rather than the exception.

Last year a record number of lawyers fell into the same trap, with many lawyers getting fined thousands of dollars for confabulated case citations. According to a researcher at the business school HEC Paris, who is keeping a worldwide tally, the count so far is 1,200, of which 800 originate from US courts.

Unsurprisingly, penalties are also increasing in severity, with monetary penalties passing the $100,000 and some courts demanding that any use of ‘AI’ be declared up-front. Whether or not the popularity of LLM chatbots among US lawyers is simply due to the massive caseload that digging through cases in Common Law legal systems entails has not yet been addressed, but that undesirable shortcuts are being taken is undeniable.

Remember that it’s easy to point and laugh, but the next case could involve the lawyer handling your delicate situation.

DC In The Data Center For A More Efficient Future

April 3, 2026 by Jenny List 51 Comments

If you own a computer that’s not mobile, it’s almost certain that it will receive its power in some form from a mains wall outlet. Whether it’s 230 V at 50 Hz or 120 V at 60 Hz, where once there might have been a transformer and a rectifier there’s now a switch-mode power supply that delivers low voltage DC to your machine. It’s a system that’s efficient and works well on the desktop, but in the data center even its efficiency is starting to be insufficient. IEEE Spectrum has a look at newer data centers that are moving towards DC power distribution, raising some interesting points which bear a closer look.

A traditional data center has many computers which in power terms aren’t much different from your machine at home. They get their mains power at distribution voltage — probably 33 KV AC where this is being written — they bring it down to a more normal mains voltage with a transformer just like the one on your street, and then they feed a battery-backed uninterruptible Power Supply (UPS) that converts from AC to DC, and then back again to AC. The AC then snakes around the data center from rack to rack, and inside each computer there’s another rectifier and switch-mode power supply to make the low voltage DC the computer uses.

The increasing demands of data centers full of GPUs for AI processing have raised power consumption to the extent that all these conversion steps now cost a significant amount of wasted power. The new idea is to convert once to DC (at a rather scary 800 volts) and distribute it direct to the cabinet where the computer uses a more efficient switch mode converter to reach the voltages it needs.

It’s an attractive idea not just for the data center. We’ve mused on similar ideas in the past and even celebrated a solution at the local level. But given the potential ecological impact of these data centers, it’s a little hard to get excited about the idea in this context. The fourth of our rules for the responsible use of a new technology comes in to play. Fortunately we think that both an inevitable cooling of the current AI hype and a Moore’s Law driven move towards locally-run LLMs may go some way towards solving that problem on its own.

header image: Christopher Bowns, CC BY-SA 2.0.

Ask Hackaday: Using CoPilot? Are You Entertained?

April 1, 2026 by Al Williams 55 Comments

There’s a great debate these days about what the current crop of AI chatbots should and shouldn’t do for you. We aren’t wise enough to know the answer, but we were interested in hearing what is, apparently, Microsoft’s take on it. Looking at their terms of service for Copilot, we read in the original bold:

Copilot is for entertainment purposes only. It can make mistakes, and it may not work as intended. Don’t rely on Copilot for important advice. Use Copilot at your own risk.

While that’s good advice, we are pretty sure we’ve seen people use LLMs, including Copilot, for decidedly non-entertaining tasks. But, at least for now, if you are using Copilot for non-entertainment purposes, you are violating the terms of service.

Continue reading “Ask Hackaday: Using CoPilot? Are You Entertained?” →

Repurposing Old AMD APUs For AI Work

March 18, 2026 by Zoe Skyforest 22 Comments

The BC250 is what AMD calls an APU, or Accelerated Processing Unit. It combines a GPU and CPU into a single unit, and was originally built to serve as the heart of certain Samsung rack mount servers. If you know where to find cheap surplus units of the BC250, you can put them to good use for AI work, as [akandr] demonstrates.

The first thing you’ll have to figure out is how to take an individual BC250 APU and get it up and running. It’s effectively a full system-on-chip, combining a Zen 2 CPU with a Cyan Skillfish RDNA 1.5 GPU. However, it was originally intended to run inside a rackmount server unit rather than a standalone machine. To get it going, you’ll need to hook it up with power and some kind of cooling solution.

From there, it’s a matter of software. [akandr] explains how to get AI workflows running on the BC250 using Ollama and Vulkan, while noting useful hacks to improve performance like disabling the GUI and tweaking the CPU governor. The hardware can be used with a wide range of different models depending on what you’re trying to achieve, it just takes some careful management of the APU’s resources to get the most out of it. Thankfully, that’s all in the guide on GitHub.

We’ve already seen these AMD APUs repurposed before for gaming use. Unfortunately the word is out already about their capabilities, so prices have risen significantly in response to demand. Still, if you manage to score a BC250 and do something cool with it yourself, be sure to let us know on the tipsline!

Ask Hackaday: What Will An LLM Be Good For In The Plateau Of Productivity?

March 9, 2026 by Jenny List 73 Comments

A friend of mine has been a software developer for most of the last five decades, and has worked with everything from 1960s mainframes to the machines of today. She recently tried AI coding tools to see what all the fuss is about, as a helper to her extensive coding experience rather than as a zero-work vibe coding tool. Her reaction stuck with me; she referenced her grandfather who had been born in rural America in the closing years of the nineteenth century, and recalled him describing the first time he saw an automobile.

Après Nous, Le Krach

The Gartner hype cycle graph. Jeremykemp, CC BY-SA 3.0.

We are living amid a wave of AI slop and unreasonable hype so it’s an easy win to dunk on LLMs, but as the whole thing climbs towards the peak of inflated expectations on the Gartner hype cycle perhaps it’s time to look forward. The current AI hype is inevitably going to crash and burn, but what comes afterwards? The long tail of the plateau of productivity will contain those applications in which LLMs are a success, but what will they be? We have yet to hack together a working crystal ball, but perhaps it’s still time to gaze into the future. Continue reading “Ask Hackaday: What Will An LLM Be Good For In The Plateau Of Productivity?” →

The Requirements Of AI

February 19, 2026 by Al Williams 77 Comments

The media is full of breathless reports that AI can now code and human programmers are going to be put out to pasture. We aren’t convinced. In fact, we think the “AI revolution” is just a natural evolution that we’ve seen before. Consider, for example, radios. Early on, if you wanted to have a radio, you had to build it. You may have even had to fabricate some or all of the parts. Even today, winding custom coils for a radio isn’t that unusual.

But radios became more common. You can buy the parts you need. You can even buy entire radios on an IC. You can go to the store and buy a radio that is probably better than anything you’d cobble together yourself. Even with store-bought equipment, tuning a ham radio used to be a technically challenging task. Now, you punch a few numbers in on a keypad.

The Human Element

What this misses, though, is that there’s still a human somewhere in the process. Just not as many. Someone has to design that IC. Someone has to conceive of it to start with. We doubt, say, the ENIAC or EDSAC was hand-wired by its designers. They figured out what they wanted, and an army of technicians probably did the work. Few, if any, of them could have envisoned the machine, but they can build it.

Does that make the designers less? No. If you write your code with a C compiler, should assembly programmers look down on you as inferior? Of course, they probably do, but should they?

If you have ever done any programming for most parts of the government and certain large companies, you probably know that system engineering is extremely important in those environments. An architect or system engineer collects requirements that have very formal meanings. Those requirements are decomposed through several levels. At the end, any competent programmer should be able to write code to meet the requirements. The requirements also provide a good way to test the end product.

Continue reading “The Requirements Of AI” →

Bruteforcing Accidental Antenna Designs

February 17, 2026 by Zoe Skyforest 20 Comments

Antenna design is often referred to as a black art or witchcraft, even by those experienced in the space. To that end, [Janne] wondered—could years of honed skill be replaced by bruteforcing the problem with the aid of some GPUs? Iterative experiments ensued.

[Janne]’s experience in antenna design was virtually non-existent prior to starting, having a VNA on hand but no other knowledge of the craft. Formerly, this was worked around by simply copying vendor reference designs when putting antennas on PCBs. However, knowing that sometimes a need for something specific arises, they wanted a tool that could help in these regards.

The root of the project came from a research paper using an FDTD tool running on GPUs to inversely design photonic nanostructures. Since light is just another form of radio frequency energy, [Janne] realized this could be tweaked into service as an RF antenna design tool. The core simulation engine of the FDTD tool, along with its gradient solver, were hammered into working as an antenna simulator, with [Janne] using LLMs to also tack on a validation system using openEMS, an open-source electromagnetic field solver. The aim was to ensure the results had some validity to real-world physics, particularly important given [Janne] left most of the coding up to large language models. A reward function development system was then implemented to create antenna designs, rank them on fitness, and then iterate further.

The designs produced by this arcane system are… a little odd, and perhaps not what a human might have created. They also didn’t particularly impress in the performance stakes when [Janne] produced a few on real PCBs. However, they do more-or-less line up with their predicted modelled performance, which was promising. Code is on Github if you want to dive into experimenting yourself. Experienced hands may like to explore the nitty gritty details to see if the LLMs got the basics right.

We’ve featured similar “evolutionary” techniques before, including one project that aimed to develop a radio. If you’ve found ways to creatively generate functional hardware from boatloads of mathematics, be sure to let us know on the tipsline!