Artificial Intelligence

318 Articles

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.

Revisiting Using AI Coding Assistants: You’re Holding It Wrong Edition

June 8, 2026 by 115 Comments

After scathing accusations of skimping on due diligence, as well as other feedback to my article on trying to use an ‘AI coding assistant’ for the first time, the only rational, academic response is to lick one’s wounds following a particularly bruising peer review and try to address the raised issues. Reality after all does not care about one’s feelings, and there may be more to this AI assistant technology that can be coaxed out with a more in-depth look.

To this end I’ll do my best to try and work through each raised point, criticism and accusation, to see what I – and perhaps others – can learn of this endeavor. Said points include the use of the wrong frontend – i.e. Copilot – and the wrong model – being Claude Haiku 4.5 – as well as the egregious flaw on my end of ‘prompting wrong’.

For the sake of due diligence the best frontend and models will be investigated for particular tasks, with finally the verbal minefield of ‘prompt engineering’ examined for industry-standard approaches.

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But Just What Is This ‘Artificial Intelligence’?

June 3, 2026 by 58 Comments

In the world of buzzwords, the acronym ‘AI’ has absolutely been the buzziest of buzzing buzzwords for at least a few years now. Where previously terms like ‘smart’ and ‘intelligent’ sufficed to promote a product, we are now being told that we are living in an age where this supposedly newfangled ‘artificial intelligence’ is doing literally everything faster and better while also curing cancer on the side. Yet, as a wise man once said: “You keep using that word. I do not think it means what you think it means.”

The obvious implication of using a term like ‘artificial intelligence’ in this manner is that it brings to mind a modern version of early last century’s ‘electronic brain’ vernacular alongside the rise of digital computers. Yet rather than electrons in vacuum tubes and semiconductors propelling us into a brave new world of super-intelligence, we now just use said devices to doom scroll and to engage in passive-aggressive online communications like the typical primate groups in a virtual jungle defending their turf.

Similarly, the term AI is massively oversold today, least of all in the inherent presupposition that we somehow have finally cracked the mystery of the brain and have created an intelligence that can go toe-to-toe with humans and even our corvid dinosaur friends. Perhaps the worst part is that there is a veritable mountain of fascinating algorithms and other constructs that help us automate many tasks today, making it somewhat rude to just give up and call everything ‘AI’ like we learned nothing from the 1980s AI craze.

So what is exactly being smoothed over by the glossy marketing of ‘everything is AI’?

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Why The Smart Home Bubble Popped

May 21, 2026 by 113 Comments

Circa 2015 or so, it seemed like you couldn’t move a finger without being bombarded with ads and articles about ‘smart homes’ and the ‘internet of things’ — all of which would make our lives so much easier and more automated. Fast-forward a decade and this dream has mostly evaporated along with many of the players in the space. Why this happened is the topic of a recent video by [Caya].

An interesting bit of context that the video starts off with is that home automation really kicked off back in 1975, when the X10 protocol and related devices using power lines for signaling began being sold. These fully integrated solutions generally worked reasonably well, but what all changed when the IoT and ‘smart home’ craze kicked off and brought with it an explosion of new standards.

Over the past decade we have seen the concept of a ‘smart home’ collapse into a nightmare of abandoned IoT devices, subscription services, forced ads, privacy violations, and an increasingly more congested 2.4 GHz spectrum that everything from WiFi and Zigbee to Bluetooth and others ended up competing for, with a corresponding collapse in reliability of data transmissions.

As raised in the video, a big issue is that of the financial viability of running the remote services for a smart home solution, even if this is the part that should make it as plug-and-play as a 1990s-era smart home solution. To the average user setting up their own locally hosted smart home solution isn’t really a straightforward option.

Although at the end [Caya] demonstrates using Home Assistant (HA) as a locally hosted alternative, this is still not something that a non-techie will be able to set up or maintain. Even if you shell out a cool two-hundred clams for the Home Assistant Green plug-and-play hardware solution, the average person will be lost the second any of the prescribed steps in provided documentation do not work. Woe to whoever is the person who is ‘good with computers’ in those cases.

Ultimately another problem with ‘smart homes’ is that they’re really not that smart, as you can definitely set up all kinds of rules in HA and similar solutions, but this is more painstaking manual automation with all the excitement of programming PID controllers. Having an actual intelligence behind the system that could react to what’s happening would make it a far easier sell, yet which is where all the ‘smart assistants’ like Alexa keep falling flat.

Currently [Caya] has set up his HA-based lighting configuration to be used by OpenClaw ‘agentic AI’, as a way to add some actual ‘smarts’, but it’s telling that he hasn’t integrated the smart lock of his apartment into the system yet. Nobody wants to have the OpenClaw agent tell you that it ‘cannot open the front door’ for you, after all.

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Prolog Via Pokémon

May 18, 2026 by 12 Comments

Like many people who read Hackaday, we are fairly fluent in a number of computer languages, but we have to admit it is easier to pick up languages that look like they group with things like Fortran. Sure, modern languages have all sorts of features, but the idea that you have a text file that executes in some order, variables, statements, and so on runs through most popular languages, but not all of them. Lisp and its variants are a different way of looking at things. And then there’s Prolog. [Alexander Petros] has an interesting way of explaining Prolog as a Pokémon game.

Prolog was “the next big thing” when AI meant expert systems. It is more of a specialized database where you define facts and rules that the computer can infer answers to queries. For example, if the facts say that Paul and Anna both have Mary as a parent, and a rule says that people with the same parent are siblings, then a query asking whether Paul and Anna are siblings will indicate that they are.

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