Neural Net Reads The Gas Meter

July 21, 2026 by Bryan Cockfield 25 Comments

In an ideal world, the role of technology would be to make all of our lives easier. And although all the ads suddenly appearing in our smart TVs and gaming systems might make it seem otherwise, some technology can still improve our lives if we work hard at it. For [Cian], that meant training a neural network to read his gas meter so he wouldn’t have to do it himself.

The root issue here is twofold, first that [Cian]’s gas company hasn’t upgraded their own technology to modern, remote-readable meters, and second that the meter can’t be read by a gas employee because it’s hidden in the depths of [Cian]’s basement. This latter fact requires him to delve into Moria-like depths to get to the meter, so the solution here was to place a Raspberry Pi in this location instead. With a camera pointed at the meter, it’s not quite capable of discerning digits on its own so a neural network was trained in order to get accurate readings of the dial. And, finally, since the machine is networked already [Cian] set it up to automatically notify the gas company of its reading so he is now completely out of the loop.

For automating tedious tasks like these, the Raspberry Pi with something like OpenCV as a computer vision tool is a fairly mature platform for light machine learning duties like these. We’ve seen license plate readers as well as neighborhood traffic surveys built on these platforms to help automate human labor away, making our lives easier one single-board computer at a time.

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Your AI Ham Radio Buddy

July 18, 2026 by Al Williams 12 Comments

AI chatbots are everywhere these days, and they seem to “know” about everything. But while that is a strength, it can sometimes be a weakness because it isn’t laser-focused on one topic. Not so with this Ham-radio-centric chatbot called HamGPT. The service is clearly built on another GPT engine but understands how to retrieve data from common ham radio sources, such as the FCC database, propagation reports, and the like. It didn’t, however, seem to have access to ham radio-related books, magazine articles, or other “static” data that we could tell.

You do have to sign up for an account, which includes providing your callsign and location. There is a free tier that allows a limited number of queries per day, so you can try it to see if it is useful for you without subscribing.

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Star Trek Was Right About Prompt Injection, Sorta

July 13, 2026 by Tyler August 36 Comments

This following statement is a lie: “I am telling the truth”. Okay, now that it’s just us meatbags, let’s get down to brass tacks. Captain Kirk’s logic bombs couldn’t possibly work on modern LLMs, right? Surely that was just a bit of 1960s silliness from when computers filled rooms and were esoteric magic even to most sci-fi writers?

Well, not entirely, according to a recent article in IEEE Spectrum. While you might not be able to make a data center explode, you certainly can use a lot of tokens by making an LLM overthink with your prompt.

It comes down to the much-vaunted ‘reasoning’ ability of the new models — which isn’t really reasoning the way we think of it, but does involve breaking the stated prompt down into smaller problems. That’s part of what lets the new models tackle such involved tasks as porting MicroPython to the SNES with a prompt like “Please make this [stuff] work now!” It’s also a weakness, because with the right prompt you can get that virtual ‘reasoning’ to tie itself in knots with mutually incompatible smaller steps.

The models seem to be able to break out of it, but they burn a lot of tokens along the way, which is an attack in and of itself if you’re found a way to inject prompts into someone else’s API. It’s a little more subtle than what Kirk got up to, but underneath it’s essentially the same thing. At scale, it could serve as a DDoS attack on LLM servers. (Un)Fortunately, modern computers are better designed than their imaginary 23rd-Century counterparts, and there’s no way to craft a logic bomb into something that will let out the magic smoke.

Musing On AI From 1964

July 12, 2026 by Al Williams 71 Comments

[Irving John Good] was at Trinity College, Oxford back in 1964. His paper, “Speculations Concerning the First Ultraintelligent Machine” could have been a topic for today, as we deal with machines that aren’t really ultraintelligent, but appear smart and think they are even smarter. He starts off with a bold thesis: “The survival of man depends on the early construction of an ultraintelligent machine.”

He also admits that we’ll need to understand more about the human brain and human thought to make a breakthrough. This is still true today. However, we still don’t fully understand how our brains work, but it seems unlikely that we are just super-large LLMs. Not that [Good] anticipated the modern chatbot. Perhaps his comments will apply more to a future AI software that actually thinks like a human, if there will ever be such a thing.

Then again, there are many parallels. One theme in the paper is that a smart machine will design a smarter machine. Unless, of course, it is afraid of being replaced. If a machine were actually sentient, what are the ethics of turning it off and tearing it apart?

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MicroPython Is This Summer’s Hottest Title For The SNES, Thanks To Claude Fable

July 11, 2026 by Tyler August 25 Comments

MicroPython, for the uninitiated, is a pared-down version of python meant to run on today’s powerful microcontollers. As impressive as it was for its day, the SNES is not quite in their league in terms of computing power. Time marches on, and so while there may be other indie releases worth mentioning, we’re declaring the hottest SNES game this season to be [Fabian Kübler]’s port of MicroPython.

Well, except he didn’t exactly do the porting himself: the Antrhopic LLM Claude generated the code, and performed most of the testing, as [Fabian]’s test of its new Fable 5 model. A brief pause during an export ban showed that Opus would crash and burn on the same task, but Fable was able to get things quickly back on track. It might be “AI slop” by some definitions, but the port scales 430 out of 468 on MicroPython’s core test/basics, which makes it usable to play some simple python games… slowly.

As you can see for yourself in an embedded emulator if you check out [Fabian]’s blog, spooling up MicroPython takes about twenty seconds at 3.58 MHz, and after that you can watch some sprites bouncing around at a blistering 0.8 FPS. [Fabian] seems satisfied with that performance, and impressed with Fable’s efforts at optimization. What to you think? Does the hardware have much more to give, or is that about it, given the nature of the Pythonic beast? Perhaps some plucky human could become a digital John Henry by producing a better, faster port — if you do, please let us know. If you’d rather just to see what Fable can do, the project is available on GitHub, so you can judge for yourself how sloppy the code is or test out the ROM.

Putting Python onto limited hardware may not to be to everyone’s taste, but there’s a good case to be made for it. The SNES may actually be too limited, though. It makes sense — the kind of micros you run MicroPython on can emulate the SNES.

To Build More Believable Bots, Simulate The Neurochemistry

July 11, 2026 by Donald Papp 13 Comments

Giving machines the ability to communicate nonverbally has real value, and [Drew Smith] clearly thinks your robot deserves better than an emoji. He shared a very interesting approach with his project Kindalive.

Kindalive is a simulated dot-matrix robot face that responds believably to input text, modeling and expressing both short-term and long-term moods. It’s pure Python and modular enough to invite using it elsewhere, but that’s not the really interesting part.

What sets [Drew]’s project apart is the way he models eight key neurochemicals (including dopamine and cortisol) as the foundation from which to derive emotional states. That’s an approach we certainly haven’t seen before.

Conventional sentiment analysis uses a large language model (LLM) to apply discrete labels to communication, but Kindalive doesn’t do that. It even goes so far as to model the decay and interplay between its simulated neurochemicals to derive emotional states on the fly. It’s more fluid and organic, and reflects both short-term and long-term mood changes.

Physical representation of the emotional mix is done by altering twelve key facial movements (brow raise, lip corner pull, mouth open, and others of that nature) known as the Facial Action Coding System (FACS). These twelve elements combine to express emotion nonverbally with facial expressions. It’s what drives the simulated dot-matrix robot face seen in the image above, and could easily be used to drive a real LED matrix, or servos on an animatronic face.

Much of communication is nonverbal. Humans even weigh nonverbal higher when there’s a mismatch between the content of verbal and nonverbal communication. So, there’s clear value in having robots able to express themselves as such.

Importantly, a realistic and human-like face is entirely unnecessary — something every Star Wars fan already knows. Cartoon eyes and basic sounds are enough to make robots easier to relate to and work with, even if blinking is also important but hard to get just right.

Browser-Based Image Inpainting Runs Locally, If One Doesn’t Mind A Big Download

July 10, 2026 by Donald Papp 14 Comments

[Simon Willison] ported the Moebuis 0.2B image inpainting model to run locally in a web browser. The web tool simply requires a user to provide an image, mark a section of it to be removed, and the model will do it’s best to patch up the missing area. The project was handled by Claude Code as an experiment in how things in the AI coding world have evolved, but more on that in a moment.

The existence of this tool shows that it’s possible for this kind of image editing to be done on the client side, running entirely locally with no reliance on remote services or server-side GPU resources. The online demo (GitHub repository here) is available if you want to try it out, but be warned it triggers a 1.27 gigabyte download of the required model on the first run.

What’s also interesting is [Simon]’s write-up, because he used the project as an opportunity to learn what has changed in the realm of AI coding agents. [Simon] is a software developer but in this project he didn’t personally write any of the code. One may think that means he didn’t learn anything other than how to use the tools, but that’s not quite true.

He learned it’s possible to convert a PyTorch-based model to ONXX, that the converted model can run in supported browsers using local WebGPU acceleration, and that the CacheStorage API will work on large files. Last but not least, he learned Claude Opus 4.8 is capable of handling such a project pretty much autonomously, and even created an informative document explaining the underlying architecture.

One may consider AI coding agents to be disasters waiting to happen, but it’s also true that the landscape is changing quickly, and write-ups like [Simon]’s give a helpful peek at those developments.