LLM

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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.

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Is A CS Degree DOA Thanks To LLMs? IEEE Says TBD.

June 14, 2026 by 63 Comments

The ongoing AI apocalypse is hitting prices for high-end components from RAM to GPUs to storage hard, which is bad enough when you have a job to try and budget for those now-pricier items — but what if you don’t? Once upon a time, it might have been good advice to tell a jobless friend to “learn to code,” but is that still true in the era of AI? [Brian Jenney], writing for IEEE Spectrum, says the death of the CS degree has been vastly exaggerated, but your take might differ. Let’s look at the numbers.

Unemployment is higher amongst new Computer Science grads than ever: in the US, it’s at 6.1%, while 7.5% of Computer Engineering graduates are on the dole. That’s a record high, and while various EU countries have their own numbers, they all have one thing in common: they’ve all shot up like a rocket in the past few years. In the USA, Philosophy grads report only 3% unemployment. Let that sink in: the folks you used to bully as being the most useless on campus are twice as likely to get a job as you would be if you were in school today.

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AI The Truly Environmentally Friendly Way

June 11, 2026 by 36 Comments

A common complaint about the rise of commercial AI services is that they are power-hungry and thus damage the environment. If this concerns you then [Squeezlabs] has the solution, in the form of an AI powered by a handcrank.

The guts of the system is a Raspberry Pi 5 running llama.cpp and appropriate speech conversions, but it and the Large Language Model (LLM) side are not the most interesting part of this system. The power comes from a hand crank charger of the type you’ll see for sale on the likes of AliExpress, designed for USB charging. That in itself is not enough to power the Pi though, as upticks in the processing can cause brownouts that crash the machine. Thus there’s a custom-made capacitor board to take up the strain, and even with that the handle resistance varies significantly depending on the computing load.

We can see that this is not the ideal way to experience an LLM, but maybe that’s not the point. It does however point towards a future in which the power demands of processing decrease and less effort is required. Meanwhile, this is by no means the first hand cranked project we’ve seen.

Automatic Tutorial Generator Is Perhaps The Best-Case For Vibe Coding

June 9, 2026 by 34 Comments

Quick question: how did you learn to code? It probably wasn’t bribing someone a year or two ahead of you in CS to finish all your homework, but that’s exactly what ‘vibe coders’ are doing — even in class. Odds are, you learned by working through exercises, following tutorials, and doing it yourself. Finding good tutorials isn’t getting any easier in the age of LLMs, and that’s where [Deven Jarvis]’s Lathe comes in: it’s a project to get an LLM to make the tutorial for you. Instead of doing the work for you, it gets the clanker to show you how to do it yourself.

Everyone’s different, so this may not apply to you, but it’s a journey/destination sort of problem. Some people just want a piece of software, and they can vibe code until the oceans dry up and will have no interest in this project. Other people take great joy in learning how to do things; [Deven] is one of those. A good tutorial is a great way to learn, since it artificially softens the learning curve compared to just jumping into a project with a man page or a datasheet.

Of course you’re still faced with the hallucination problem, something [Deven] admits in his excellent write-up. As he points out, the advantage is that you can call whatever model you plug into Lathe on its BS, and try and get a correct answer. Try that on Reddit, or most other places online. Sure, the tutorials aren’t going to match the best human-generated content, and [Deven] admits that. He’s using it for topics (like slicer design) that don’t have easy tutorials online — and sadly, his prediction that nobody is going to bother making good learning resources like they used to when they’ll just be scraped by LLMs is very likely true. It’s not that your options are vibe code or vibe-generated tutorial, but if that’s the direction the world is going, we’ll take the tutorial, thanks.

Getting the LLM to hold your hand through a tutorial might not appeal to the most Butlerian among us, but it’s a big step from that to the full cognitive surrender some people worry about.

An LLM From “Scratch”

May 7, 2026 by 12 Comments

Reading a book about bowling is not the same as actually bowling. If that resonates with you and you want to learn more about large language models, check out the LLM From Scratch project. The hands-on workshop lets you use a Mac, Linux, or Windows PC running Python and common libraries like numpy and torch to build your own bare-bones LLM.

The project takes inspiration from nanoGPT but scales it down so you can train the model in around an hour on a typical computer. It will use an Apple or NVIDIA GPU, if available.

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AI On Every Machine: The LLM You Probably Didn’t Want

May 6, 2026 by 72 Comments

It’s been a story of the last week or so if you follow the kind of news channels a Hackaday scribe does, that Google have quietly installed an LLM as part of the Chrome browser. Reports vary as to when they did this because there’s a lot of confusion online with their online Gemini features also present in the browser, but it seems Chrome users are noticing its effect through slower performance and hefty disk access. Given that Chrome is by far the most popular web browser, this means that billions of users will have downloaded the four gigabyte Gemini Nano model, and now have an LLM they didn’t know about. It will be used to provide advanced auto-correct and other text suggestion features that their online version of Gemini would presumably be overburdened with, and since it’s available through a set of in-browser APIs we expect that it will find its way into a lot of websites, online applications, and plugins.

It’s caused a bit of a fuss in some circles, and we think, with some justification. When billions of computers unwittingly install an extremely energy intensive software component the effect on global power consumption will be significant, with a consequent uptick in the carbon footprint of computing. It’s not a phenomenon restricted to Chrome, as an example Siri has used a local LLM on Apple devices for a while now. We’ve seen rumblings of discontent and talk of getting European climate regulators involved, but perhaps instead it’s time to have a conversation about local AI models. The key is not whether or not they are a good thing to have, but when and how they operate.

While many of us are sick to death of AI slop and have not been lured into AI psychosis by an over-reinforcing chatbot, the fact remains that LLMs can do some useful things, they’re here to stay whether we like it or not, and having one under your control on your own computer doesn’t have to be a bad thing. Install Llama.cpp on your machine, and you’ve got an LLM of your very own, upon which your usage data isn’t going to be sold, and your content isn’t going to reinforce the finest plagiarism device the world has ever seen.

Opt-In and Opt-Out

The concerning development with the Chrome LLM is that not only has it been installed without the user’s consent, it runs without their consent too, and they can’t use it for anything except what Google Chrome wants it to be used for. Unlike the Llama.cpp mentioned above, it’s not under their control, instead it’s a compute-hungry monster ultimately controlled by Google. The prospect of a future in which multiple pieces of everyday software install their own similarly out-of-control multi-gigabyte CPU-munchers is a concerning one. Anyone who remembers Microsoft’s Clippy grabbing all the resources in a 1990s desktop as its stuttering animation played its course will know where this is going.

If local LLMs are an inevitability, what’s needed is a way to make them like any other application, one that the user chooses and installs themselves. Such an LLM could make its services available to applications such as a web browser if the user allows it to, but not run unless asked. It’s fairly obvious that installing Llama.cpp or similar is beyond many users, but it shouldn’t lie beyond the bounds of possibility to package something like it as an application they can install.

We know that the previous paragraph is pie-in-the-sky wishful thinking, and that as the person who knows computers in your family your next few Christmases will be spent wrestling with six different LLMs running on some elderly family member’s PC. But perhaps in Clippy lies the answer. If the consumer can learn to associate built-in AI features with their computer grinding to a halt just as they did with an office assistant thirty years ago, then perhaps they’ll demand change. We can hope.

AI For The Skeptics: The Universal Function For Some Things Only

April 22, 2026 by 29 Comments

It’s a phrase we use a lot in our community, “Drink the Kool-Aid”, meaning becoming unreasonably infatuated with a dubious idea, technology, or company. It has its origins in 1960s psychedelia, but given that it’s popularly associated with the mass suicide of the followers of Jim Jones in Guyana, perhaps we should find something else. In the sense we use it though, it has been flowing liberally of late with respect to AI, and the hype surrounding it. This series has attempted to peer behind that hype, first by examining the motives behind all that metaphorical Kool-Aid drinking, and then by demonstrating a simple example where the technology does something useful that’s hard to do another way. In that last piece we touched upon perhaps the thing that Hackaday readers should find most interesting, we saw the LLM’s possibility as a universal API for useful functions.

It’s Not What An LLM Can Make, It’s What It Can Do

When we program, we use functions all the time. In most programming languages they are built into the language or they can be user-defined. They encapsulate a piece of code that does something, so it can be repeatedly called. Life without them on an 8-bit microcomputer was painful, with many GOTO statements required to make something similar happen. It’s no accident then that when looking at an LLM as a sentiment analysis tool in the previous article I used a function GetSentimentAnalysis(subject,text) to describe what I wanted to do. The LLM’s processing capacity was a good fit to my task in hand, so I used it as the engine behind my function, taking a piece of text and a subject, and returning an integer representing sentiment. The word “do” encapsulates the point of this article, that maybe the hype has got it wrong in being all about what an LLM can make. Instead it should be all about what it can do. The people thinking they’ve struck gold because they can churn out content slop or make it send emails are missing this. Continue reading “AI For The Skeptics: The Universal Function For Some Things Only” →