Hackaday Links: September 27, 2026

September 27, 2026 by Al Williams No comments

It isn’t quite hailing frequencies open, but researchers from Harvard claim they’ve picked up a radio signal directly from a nearby exoplanet. Before you get too excited, planets in our solar system also emit RF, so no one credible is claiming these are extraterrestrial reruns of their version of I Love Lucy, but it is the first time they’ve localized a radio signal to an exoplanet, in this case, Beta Pictoris B.

Speaking of space, the asteroid formerly known as 1981 EC26 is now sporting a new moniker: (14331) Alyankovic. If you think that sounds like (Weird) Al Yankovic, you aren’t wrong. The Tucson Star reports that, thanks to the efforts of several planetary scientists who are also Weird Al fans, the International Astronomical Union made the name official. Apparently, another asteroid now bears a name in honor of Weird Al’s predecessor, Tom Lehrer.

If you follow open mobile phone software, you probably know the name postmarketOS, a Linux distribution based on Alpine aimed at mobile phones and tablets. Well, now you can forget it. The project announced a name change, so we’re now talking about Nura. Why Nura? According to the team, it is a shortened form of Nuraghe, some granite structures in Sardinia that are over 5,000 years old. The FAQ mentions that postmarketOS was hard to remember. We aren’t sure Nura is that much more memorable. Perhaps they should have pivoted to Phonz OS.

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

July 10, 2026 by Donald Papp 15 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.

Try Image Classification Running In Your Browser, Thanks To WebGPU

May 20, 2024 by Donald Papp 3 Comments

When something does zero-shot image classification, that means it’s able to make judgments about the contents of an image without the user needing to train the system beforehand on what to look for. Watch it in action with this online demo, which uses WebGPU to implement CLIP (Contrastive Language–Image Pre-training) running in one’s browser, using the input from an attached camera.

By giving the program some natural language visual concept labels (such as ‘person’ or ‘cat’) that fit a hypothetical template for the image content, the system will output — in real-time — its judgement on the appropriateness of such labels to what the camera sees. Again, all of this runs locally.

It’s maybe a little bit unintuitive, but what’s happening in the demo is that the system is deciding which of the user-provided labels (“a photo of a cat” vs “a photo of a bald man”, for example) is most appropriate to what the camera sees. The more a particular label is judged a good fit for the image, the higher the number beside it.

This kind of process benefits greatly from shoveling the hard parts of the computation onto compatible graphics cards, which is exactly what WebGPU provides by allowing the browser access to a local GPU. WebGPU is relatively recent, but we’ve already seen it used to run LLMs (Large Language Models) directly in the browser.

Wondering what makes GPUs so very useful for AI-type applications? It’s all about their ability to work with enormous amounts of data very quickly.

WebGPU… Better Than WebGL?

March 9, 2022 by Al Williams 19 Comments

As the browser becomes more like an operating system, we are seeing more deep features being built into them. For example, you can now do a form of assembly language for the browser. Sophisticated graphics have been around using WebGL since around 2011, but some people find it hard to use. [Surma] was one of those people and tried a new method that is just surfacing to do the same thing: WebGPU.

[Surma] liked it better and shares a lot of information in the post and — oddly — the post doesn’t use WebGPU for graphics very much. Instead, the post focuses on using GPU cores for fast computation, something else you can do with WebGPU. If your goal is to draw on the screen, though, you need to know the basics and the post links to a site with examples of doing this.

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