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.

[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.

Like so many large and popular open source projects these days, the Godot game engine struggles with an influx of pull requests. The situation has become increasingly dire due to the advent of AI-generated code. More specifically, the issue involves the inverse relationship between PR code quality and the number of PRs, which wastes a lot of time on the side of a limited number of (volunteer) reviewers. This has now forced the project to update its contribution policy.

An interesting point raised in the announcement article is that of the demoralizing effect of AI-generated PRs on reviewers. Often the human behind such a PR isn’t interested in being educated, or may even be an automated agent which isn’t capable of productive discussion on pros and cons of certain coding approaches — never mind in becoming a more permanent maintainer for the project.

This problem has led to new rules being instated, which include a ban on autonomous AI agents and vibe coding, a ban on substantial AI generating of code, and a ban on AI-generated text in human-to-human communication. It also codifies the requirement that all PRs are to be reviewed and approved by a human being before merging.

In many ways this new policy is similar to that of the Mesa project, which demands code comprehension on the side of the submitter, although it doesn’t go as far as NetBSD, which just outright treats LLM-generated code as ‘tainted’ due to potential licensing and other concerns. Other projects like the Linux kernel opt to make the human submitter responsible for any AI tool usage by forcing them to declare it.

Meanwhile there are also indications that such ‘AI tool’ usage is reducing useful interactions with open source projects. What the future will bring here remains to be seen, but at least as far as open source projects go these tools are clearly increasingly being banished.