The Requirements Of AI

February 19, 2026 by Al Williams 76 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.

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Bruteforcing Accidental Antenna Designs

February 17, 2026 by Lewin Day 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!

Habit Detection For Home Assistant

February 8, 2026 by Lewin Day 15 Comments

Computers are very good at doing exactly what they’re told. They’re still not very good at coming up with helpful suggestions of their own. They’re very much more about following instructions than using intuition; we still don’t have a digital version of Jeeves to aid our bumbling Wooster selves. [Sherrin] has developed something a little bit intelligent, though, in the form of a habit detector for use with Home Assistant.

In [Sherrin]’s smart home setup, there are lots of things that they wanted to fully automate, but they never got around to implementing proper automations in Home Assistant. Their wife also wanted to automate things without having to get into writing YAML directly. Thus, they implemented a sidecar which watches the actions taken in Home Assistant.

The resulting tool is named TaraHome. When it detects repetitive actions that happen with a certain regularity, it pops up and suggests automating the task. For example, if it detects lights always being dimmed when media is playing, or doors always being locked at night, it will ask if that task should be set to happen automatically and can whip up YAML to suit. The system is hosted on the local Home Assistant instance. It can be paired with an LLM to handle more complicated automations or specific requests, though this does require inviting cloud services into the equation.

We’ve featured lots of great Home Assistant hacks over the years, like this project that bridges 433 MHz gear to the smart home system. If you’ve found your own ways to make your DIY smart home more intelligent, don’t hesitate to notify the tipsline!

[Yang-Hui He] Presents To The Royal Institution About AI And Mathematics

February 2, 2026 by John Elliot V 2 Comments

Over on YouTube you can see [Yang-Hui He] present to The Royal Institution about Mathematics: The rise of the machines.

In this one hour presentation [Yang-Hui He] explains how AI is driving progress in pure mathematics. He says that right now AI is poised to change the very nature of how mathematics is done. He is part of a community of hundreds of mathematicians pursuing the use of AI for research purposes.

[Yang-Hui He] traces the genesis of the term “artificial intelligence” to a research proposal from J. McCarthy, M.L. Minsky, N. Rochester, and C.E. Shannon dated August 31, 1955. He says that his mantra has become: connectivism leads to emergence, and goes on to explain what he means by that, then follows with universal approximation theorems.

He goes on to enumerate some of the key moments in AI: Descartes’s bête-machine, 1617; Lovelace’s speculation, 1842; Turing test, 1949; Dartmouth conference, 1956; Rosenblatt’s Perceptron, 1957; Hopfield’s network, 1982; Hinton’s Boltzmann machine, 1984; IBM’s Deep Blue, 1997; and DeepMind’s AlphaGo, 2012.

He continues with some navel-gazing about what is mathematics, and what is artificial intelligence. He considers how we do mathematics as bottom-up, top-down, or meta-mathematics. He mentions about one of his earliest papers on the subject Machine-learning the string landscape (PDF) and his books The Calabi–Yau Landscape: From Geometry, to Physics, to Machine Learning and Machine Learning in Pure Mathematics and Theoretical Physics.

He goes on to explain about Mathlib and the Xena Project. He discusses Machine-Assisted Proof by Terence Tao (PDF) and goes on to talk more about the history of mathematics and particularly experimental mathematics. All in all a very interesting talk, if you can find a spare hour!

In conclusion: Has AI solved any major open conjecture? No. Is AI beginning to help to advance mathematical discovery? Yes. Has AI changed the speaker’s day-to-day research routine? Yes and no.

If you’re interested in more fun math articles be sure to check out Digital Paint Mixing Has Been Greatly Improved With 1930s Math and Painted Over But Not Forgotten: Restoring Lost Paintings With Radiation And Mathematics.

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Can Skynet Be A Statesman?

January 21, 2026 by Tyler August 64 Comments

There’s been a lot of virtual ink spilled about LLMs and their coding ability. Some people swear by the vibes, while others, like the FreeBSD devs have sworn them off completely. What we don’t often think about is the bigger picture: What does AI do to our civilization? That’s the thrust of a recent paper from the Boston University School of Law, “How AI Destroys Institutions”. Yes, Betteridge strikes again.

We’ve talked before about LLMs and coding productivity, but [Harzog] and [Sibly] from the school of law take a different approach. They don’t care how well Claude or Gemini can code; they care what having them around is doing to the sinews of civilization. As you can guess from the title, it’s nothing good.

"A computer must never make a management decision." — Somehow the tl;dr was written decades before the paper was.

The paper a bit of a slog, but worth reading in full, even if the language is slightly laywer-y. To summarize in brief, the authors try and identify the key things that make our institutions work, and then show one by one how each of these pillars is subtly corroded by use of LLMs. The argument isn’t that your local government clerk using ChatGPT is going to immediately result in anarchy; rather it will facilitate a slow transformation of the democratic structures we in the West take for granted. There’s also a jeremiad about LLMs ruining higher education buried in there, a problem we’ve talked about before.

If you agree with the paper, you may find yourself wishing we could launch the clankers into orbit… and turn off the downlink. If not, you’ll probably let us know in the comments. Please keep the flaming limited to below gas mark 2.

Great Trains, Not So Great AI Chatbot Security

January 13, 2026 by Jenny List 13 Comments

A joy of covering the world of the European hackerspace community is that it offers the chance for train travel across the continent using the ever-good-value Interrail pass. For a British traveler such a journey inevitably starts with a Eurostar train that whisks you in comfort through the Channel Tunnel, so a report of an AI vulnerability on the Eurostar website from [Ross Donald] particularly caught our eye. What it reveals goes beyond the train company, and tells us some interesting tidbits about how safeguards in AI chatbots can be circumvented.

The bot sits on the Eurostar website, and is a simple HTML and JavaScript client that talks to the LLM back-end itself through an API. The API queries contain the whole conversation, because as AI toy manufacturers whose products have been persuaded to spout adult context will tell you, large language models (LLM)s as commonly implemented do not have a context memory for the conversation in hand.

The Eurostar developers had not made a bot without guardrails, but the vulnerability lay in those guardrails only being applied to the most recent message. Thus an innocuous or empty message could be sent, with a payload concealed in a previous message in the conversation. He demonstrates the bot returning system information about itself, and embedding injected HTML and JavaScript in its responses.

He notes that the target of the resulting output could only be himself and that he was unable to access any data from other customers, so perhaps in this case the train operator was fortunately spared the risk of a breach. From his description though, we agree they could have responded to the disclosure in a better manner.

Header image: Eriksw, CC BY-SA 4.0.

Automatically Remove AI Features From Windows 11

December 18, 2025 by Maya Posch 66 Comments

It seems like a fair assessment to state that the many ‘AI’ features that Microsoft added to Windows 11 are at least somewhat controversial. Unsurprisingly, this has led many to wonder about disabling or outright removing these features, with [zoicware]’s ‘Remove Windows AI’ project on GitHub trying to automate this process as much as reasonably possible.

All you need to use it is your Windows 11-afflicted system running at least 25H2 and the PowerShell script. The script is naturally run with Administrator privileges as it has to do some manipulating of the Windows Registry and prevent Windows Update from undoing many of the changes. There is also a GUI for those who prefer to just flick a few switches in a UI instead of running console commands.

Among the things that can be disabled automatically are the disabling of Copilot, Recall, AI Actions, and other integrations in applications like Edge, Paint, etc. The reinstallation of removed packages is inhibited by a custom package. For the ‘features’ that cannot be disabled automatically, there is a list of where to toggle those to ‘off’.

Naturally, since Windows 11 is a moving target, it can be rough to keep a script like this up to date, but it seems to be a good start at least for anyone who finds themselves stuck on Windows 11 with no love for Microsoft’s ‘AI’ adventures. For the other features, there are also Winaero Tweaker and Open-Shell, with the latter in particular bringing back the much more usable Windows 2000-style start menu, free of ads and other nonsense.