[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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The CURL Project Drops Bug Bounties Due To AI Slop

January 26, 2026 by Maya Posch 44 Comments

Over the past years, the author of the cURL project, [Daniel Stenberg], has repeatedly complained about the increasingly poor quality of bug reports filed due to LLM chatbot-induced confabulations, also known as ‘AI slop’. This has now led the project to suspend its bug bounty program starting February 1, 2026.

Examples of such slop are provided by [Daniel] in a GitHub gist, which covers a wide range of very intimidating-looking vulnerabilities and seemingly clear exploits. Except that none of them are vulnerabilities when actually examined by a knowledgeable developer. Each is a lengthy word salad that an LLM churned out in seconds, yet which takes a human significantly longer to parse before dealing with the typical diatribe from the submitter.

Although there are undoubtedly still valid reports coming in, the truth of the matter is that the ease with which bogus reports can be generated by anyone who has access to an LLM chatbot and some spare time has completely flooded the bug bounty system and is overwhelming the very human developers who have to dig through the proverbial midden to find that one diamond ring.

We have mentioned before how troubled bounty programs are for open source, and how projects like Mesa have already had to fight off AI slop incidents from people with zero understanding of software development.

LLM-Generated Newspaper Provides Ultimate In Niche Publications

January 26, 2026 by Tyler August 18 Comments

If you’re reading this, you probably have some fondness for human-crafted language. After all, you’ve taken the time to navigate to Hackaday and read this, rather than ask your favoured LLM to trawl the web and summarize what it finds for you. Perhaps you have no such pro-biological bias, and you just don’t know how to set up the stochastic parrot feed. If that’s the case, buckle up, because [Rafael Ben-Ari] has an article on how you can replace us with a suite of LLM agents.

The AI-focused paper has a more serious aesthetic, but it’s still seriously retro.

He actually has two: a tech news feed, focused on the AI industry, and a retrocomputing paper based on SimCity 2000’s internal newspaper. Everything in both those papers is AI-generated; specifically, he’s using opencode to manage a whole dogpen of AI agents that serve as both reporters and editors, each in their own little sandbox.

Using opencode like this lets him vary the model by agent, potentially handing some tasks to small, locally-run models to save tokens for the more computationally-intensive tasks. It also allows each task to be assigned to a different model if so desired. With the right prompting, you could produce a niche publication with exactly the topics that interest you, and none of the ones that don’t. In theory, you could take this toolkit — the implementation of which [Rafael] has shared on GitHub — to replace your daily dose of Hackaday, but we really hope you don’t. We’d miss you.

That’s news covered, and we’ve already seen the weather reported by “AI”— now we just need an automatically-written sports section and some AI-generated funny papers. That’d be the whole newspaper. If only you could trust it.

Story via reddit.

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.

A photo of the cats and the generated image

The Cutest Weather Forecast On E-Ink And ESP32

January 17, 2026 by Tyler August 9 Comments

There’s a famous book that starts: “It is a truth universally acknowledged that a man in possession of a good e-ink display, must be in want of a weather station.” — or something like that, anyway. We’re not English majors. We are, however, major fans of this feline-based e-ink weather display by [Jesse Ward-Bond]. It’s got everything: e-ink, cats, and AI.

The generated image needs a little massaging to look nice on the Spectra6 e-ink display.

AI? Well, it might seem a bit gratuitous for a simple weather display, but [Jesse] wanted something a little more personalized and dynamic than just icons. With that in the design brief, he turned to Google’s Nano Banana API, feeding it the forecast and a description of his cats to automatically generate a cute scene to match the day’s weather.

That turned out to not be enough variety for the old monkey brain, so the superiority of silicon — specifically Gemini–was called upon to write unique daily prompts for Nano Banana using a random style from a list presumably generated by TinyLlama running on a C64. Okay, no, [Jesse] wrote the prompt for Gemini himself. It can’t be LLM’s all the way down, after all. Gemini is also picking the foreground, background, and activity the cats will be doing for maximum neophilia.

Aside from the parts that are obviously on Google servers, this is all integrated in [Jesse]’s Home Assistant server. That server stores the generated image until the ESP32 fetches it. He’s using a reTerminal board from SeedStudio that includes an ESP32-S3 and a Spectra6 colour e-ink display. That display leaves something to be desired in coloration, so on top of dithering the image to match the palette of the display, he’s also got a bit of color-correction in place to make it really pop.

If you’re interested in replicating this feline forecast, [Jesse] has shared the code on GitHub, but it comes with a warning: cuteness isn’t free. That is to say, the tokens for the API calls to generate these images aren’t free; [Jesse] estimates that when the sign-up bonus is used up, it should cost about fourteen cents a pop at current rates. Worth it? That’s a personal choice. Some might prefer saving their pennies and checking the forecast on something more physical, while others might prefer the retro touch only a CRT can provide.

Great Trains, Not So Great AI Chatbot Security

January 13, 2026 by Jenny List 12 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.

The map in action, along with a sample of the video feeds.

Hardware Store Marauder’s Map Is Clarkian Magic

December 20, 2025 by Tyler August 23 Comments

The “Marauder’s Map” is a magical artifact from the Harry Potter franchise. That sort of magic isn’t real, but as Arthur C. Clarke famously pointed out, it doesn’t need to be — we have technology, and we can make our own magic now. Or, rather, [Dave] on the YouTube Channel Dave’s Armoury can make it.

[Dave]’s hardware store might be in a rough neighborhood, since it has 50 cameras’ worth of CCTV coverage. In this case, the stockman’s loss is the hacker’s gain, as [Dave] has talked his way into accessing all of those various camera feeds and is using machine vision to track every single human in the store.

Of course, locating individuals in a video feed is easy — to locate them in space from that feed, one first needs an accurate map. To do that, [Dave] first 3D scans the entire store with a rover. The scan is in full 3D, and it’s no small amount of data. On the rover, a Jetson AGX is required to handle it; on the bench, a beefy HP Z8 Fury workstation crunches the point cloud into a map. Luckily it came with 500 GB of RAM, since just opening the mesh file generated from that point cloud needs 126 GB. That is processed into a simple 2D floor plan. While the workflow is impressive, we can’t help but wonder if there was an easier way. (Maybe a tape measure?)

Once an accurate map has been generated, it turns out NVIDIA already has a turnkey solution for mapping video feeds to a 2D spatial map. When processing so much data — remember, there are 50 camera feeds in the store — it’s not ideal to be passing the image data from RAM to GPU and back again, but luckily NVIDIA’s “Deep Stream” pipeline will do object detection and tracking (including between different video streams) all on the GPU. There’s also pose estimation right in there for more accurate tracking of where a person is standing than just “inside this red box”. With 50 cameras, it’s all a bit much for one card, but luckily [Dave]’s workstation has two GPUs.

Once the coordinates are spat out of the neural networks, it’s relatively simple to put footprints on the map in true Harry Potter fashion. It really is magic, in the Clarkian sense, what you can do if you throw enough computing power at it.

Unfortunately for show-accuracy (or fortunately, if you prefer to avoid gross privacy violations), it doesn’t track every individual by name, but it does demonstrate the possibility with [Dave] and his robot. If you want a map of something… else… maybe check out this backyard project.

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