LLM

99 Articles

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 28 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” →

Can Claude Write Z80 Assembly Code?

April 19, 2026 by 28 Comments

Betteridge’s law applies, but with help and guidance by a human who knows his stuff, [Ready Z80] was able to get a functioning game of Wordle out of the French-named LLM, which is more than we expected. It’s not like the folks at Anthropic spent much time making sure 40-year-old opcodes were well represented in their training data, after all.

For hardware, [Ready Z80] is working with the TEC-1G single-board-computer, which is a retrocomputer inspired by the TEC-1 whose design was published by Australian hobbyist magazine “Talking Electronics” back in the 1980s. Claude actually seemed to know what that was, and that it only had a hex keypad — though when [Ready Z80] was quick to correct it and let the LLM know he’s using a QWERTY keyboard add-on, Claude declared it was confident in its ability to write the code.

As usual for a LLM, Claude was overconfident and tossed out some nonexistent instructions. Though admittedly, it didn’t persist in that after being corrected. It’s notable that [Ready Z80] doesn’t prompt it with “Give me an implementation of Wordle in Z80 assembly for the TEC-1G” but goes through step-by-step, explaining exactly what he wants each section of the code to do. As [Dan Maloney] reported three years ago, it’s a bit like working with a summer intern.

In the end, they get a working game, but that was never in question. [Ready Z80] reveals over the course of the video he has the chops to have written it himself. Did using Claude make that go faster? Based on studies we’ve seen, it probably felt like it, even if it may have actually slowed him down.

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New Linux Kernel Rules Put The Onus On Humans For AI Tool Usage

April 14, 2026 by 17 Comments

It’s fair to say that the topic of so-called ‘AI coding assistants’ is somewhat controversial. With arguments against them ranging from code quality to copyright issues, there are many valid reasons to be at least hesitant about accepting their output in a project, especially one as massive as the Linux kernel. With a recent update to the Linux kernel documentation the use of these tools has now been formalized.

The upshot of the use of such Large Language Models (LLM) tools is that any commit that uses generated code has to be signed off by a human developer, and this human will ultimately bear responsibility for the code quality as well as any issues that the code may cause, including legal ones. The use of AI tools also has to be declared with the Assisted-by: tag in contributions so that their use can be tracked.

When it comes to other open source projects the approach varies, with NetBSD having banished anything tainted by ‘AI’, cURL shuttering its bug bounty program due to AI code slop, and Mesa’s developers demanding that you understand generated code which you submit, following a tragic slop-cident.

Meanwhile there are also rising concerns that these LLM-based tools may be killing open source through ‘vibe-coding’, along with legal concerns whether LLM-generated code respects the original license of the code that was ingested into the training model. Clearly we haven’t seen the end of these issues yet.

AI For The Skeptics: Pick Your Reasons To Be Excited

April 8, 2026 by 89 Comments

It’s odd being a technology writer in 2026, because around you are many people who will tell you that your craft is outdated. Like the manufacturers of buggy-whips at the turn of the twentieth century, the automobile (in the form of large language model AI) is on the market, and your business will soon be an anachronism. Adapt or go extinct, they tell you. It’s an argument I’ve found myself facing a few times over the last year in my wandering existence, and it’s forced me to think about it. What are the reasons everyone is excited about AI and are those reasons valid, what is there to be scared of, and what are the real reasons people should be excited about it?

If We Gotta Take This Seriously, How Can We Do It?

A couple in a horse drawn buggy, circa 1900ish
The futures looking bright in the buggy-whip department! Public domain.

I’ll start by repeating my tale from a few weeks ago when I asked readers what AI applications would survive when the hype is over. The reaction of a friend with decades of software experience on trying an AI coding helper 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. I agree with her that this has the potential to be a transformative technology, and while it’s entertaining to make fun of its shortcomings as I did three years ago when the idea of what we now call vibe coding first appeared, it’s already making itself useful in some applications. Simply dismissing it is no longer appropriate, but equally, drinking freely of the Kool-Aid seems like joining yet another hype bandwagon that will inevitably derail. A middle way has to be found. Continue reading “AI For The Skeptics: Pick Your Reasons To Be Excited” →