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338 Articles

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

May 6, 2026 by 60 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.

Building A C-3PO You Can Really Talk To

May 2, 2026 by 7 Comments

C-3PO is one of the more famous movie robots out there. However, we don’t see a lot of replicas built, perhaps because in speech and mannerisms, he’s quite hard to replicate. Of course, that feat has become much more achievable with modern AI tech, as [Samuel Potozkin] demonstrates.

We’re not looking at a full C-3PO build here, it’s just the head—but for the project’s purposes, that’s all that was really required. The build relies on a Raspberry Pi 5 as the brains of the droid. It’s running a mic hooked up to a real time speech to text engine, and that text is then sent to a large language model for interpretation. Responses are then generated, passed through a processing layer to capture C-3PO’s general tone and vibe, and then handed off to a text-to-speech synth to imitate the iconic voice, played via speaker. The end result is a C-3PO you can actually talk to, which is something that might have knocked a few socks off when the movie first launched in 1977. In-depth materials for the build can be had via Google Drive and on Github.

This ersatz C-3PO isn’t an exact dupe of the movie ‘bot.  The protocol droid is a little slow to respond, and the patter isn’t quite on point, even if the voice synth makes a good effort at mimicking the original. Overall, it’s a little… robotic… something you wouldn’t say of the character in the movies. Still, it’s a great effort, and something we haven’t really seen much of before. If you like more classic droid replicas, though, we’ve featured those too. Video after the break.

Continue reading “Building A C-3PO You Can Really Talk To” →

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” →

AI For The Skeptics: Attempting To Do Something Useful With It

April 15, 2026 by 78 Comments

There are some subjects as a writer in which you know they need to be written, but at the same time you feel it necessary to steel yourself for the inevitable barrage of criticism once your work reaches its audience. Of these the latest is AI, or more specifically the current enthusiasm for Large Language Models, or LLMs. On one side we have the people who’ve drunk a little too much of the Kool-Aid and are frankly a bit annoying on the subject, while on the other we have those who are infuriated by the technology. Given the tide of low quality AI slop to be found online, we can see the latter group’s point.

This is the second in what may become an occasional series looking at the subject from the perspective of wanting to find the useful stuff behind the hype; what is likely to fall by the wayside, and what as yet unheard of applications will turn this thing into something more useful than a slop machine or an agent that might occasionally automate some of your tasks correctly. In the previous article I examined the motivation of that annoying Guy In A Suit who many of us will have encountered who wants to use AI for everything because it’s shiny and new, while in this one I’ll try to do something useful with it myself.

Continue reading “AI For The Skeptics: Attempting To Do Something Useful With It” →

AI For The Skeptics: Pick Your Reasons To Be Excited

April 8, 2026 by 87 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” →

Are We Surrendering Our Thinking To Machines?

April 8, 2026 by 82 Comments

“Once, men turned their thinking over to machines in the hope that this would set them free. But that only permitted other men with machines to enslave them.” — so said [Frank Herbert] in his magnum opus, Dune, or rather in the OC Bible that made up part of the book’s rich worldbuilding. A recent study demonstrating “cognitive surrender” in large language model (LLM) users, as reported in Ars Technica, is going to add more fuel to that Butlerian fire.

Cognitive surrender is, in short, exactly what [Herbert] was warning of: giving over your thinking to machines. In the study, people were asked a series of questions, and — except for the necessary “brain-only” control group — given access to a rigged LLM to help them answer. It was rigged in that it would give wrong answers 50% of the time, which while higher than most LLMs, only a difference in degree, not in kind. Hallucination is unavoidable; here it was just made controllably frequent for the sake of the study.

The hallucinations in the study were errors that the participants should have been able to see through, if they’d thought about the answers. Eighty percent of the time, they did not. That is to say: presented with an obviously wrong answer from the machine, only in 20% of cases did the participants bother to question it. The remainder were experiencing what the researchers dubbed “cognitive surrender”: they turned their thinking over to the machines. There’s a lot more meat to this than we can summarize here, of course, but the whole paper is available free for your perusal.

Giving over thinking to machines is nothing new, of course; it’s probably been a couple decades since the first person drove into a lake on faulty GPS directions, for example. One might even argue that since LLMs are correct much more than 50% of the time, it is statistically wise to listen to them. In that case, however, one might be encouraged to read Dune.

Thanks to [Monika] for the tip!

The Heat Island Effect Is Warming Up The AI Data Center Controversy

April 7, 2026 by 58 Comments

There’s been a lot of virtual ink spilled in environmental circles about the cooling water requirements of data centers, but less consideration of what happens with all the heat coming out of these buildings. Naturally, it’s going to warm the surrounding environment, but how much? Around 2 C (3.6 F) on average, and potentially much more than that, according to a recent study on the data heat island effect.

It’s common sense, of course: heat removed from the data center doesn’t go away. That heat might go into a body of water if one is available, but otherwise it’s out into the atmosphere to warm up everybody else’s day. In some places — like a Canadian winter — that might not be so bad. In others, where climate change and urban heat islands are cranking up the summertime temperatures, it very much could be. Especially if you’re in the worst-case scenario micro-climate described by the paper, which saw a predicted increase of 9.1 C (16 F).

Now, these results are theoretical and need to be ground-truthed, but anyone who has huddled next to the air-exchange unit of a large building for warmth knows there’s something to them. Unfortunately there don’t seem to be before-and-after measurements available for existing data-centers — AI or otherwise — to show exactly what their heat output is doing in the real world, but the urban heat island effect from all the dark asphalt in our cities is well known. Cooling paint and green roofs can help with that, but they won’t do much for the megawatts being pumped out to keep your cousin’s AI girlfriend online.

Some would argue that all this heat wouldn’t be a problem if we could launch the data centers outside the environment — just have a care the front doesn’t fall off.

Image of data center cooling by Анна from Pixabay