When it's the maintainer who's AI-pilled

22 June 2026

I had an odd experience the other day with an open source project. That project was dirge. It's clearly and unashamedly developed with heavy use of generative AI, both in the code and in the PRs.

Normally, as a first impression, this would turn me off a project. There's a lot of low-grade slop out there after all and most of the time I'm looking for the "mature" option if it exists. In this case I came to learn about dirge through an article the author wrote, describing some novel ideas in making effective use of smaller LLMs for coding, and how they were attempting to put all of these into practice in their own CLI harness. Sounds interesting.

I feel this is an underappreciated sweet spot for AI-assisted development. When you take someone who cares about software, cares about the craft, and has some specific goals they want to achieve, then a well-supervised agent can offer dramatic speedups in actually getting the coding done. I have at least as much confidence in AI-assisted code overseen by a strong programmer as I do their own manually-typed code. This is how I try to use coding LLMs myself—faster when possible, same or better quality—so I trust others who work the same way.

Having established interest in the project, I installed it and tried it out. I experienced two minor bugs. One was a straightforward unpolished feature. The other was a very woolly situation where my model sometimes became confused about the current state after compaction. I filed issues for both, apologising for the lack of concrete detail. I made a mental note to try making a PR for the simpler bug, and also get some better logs to support the other when I found a free moment.

What actually happened is that the simple issue was fixed 1.5 hours after I submitted it, and the complex one 3 hours after I submitted it, complete with comprehensive regression test. Wow, okay then.

I suddenly realised that my own PRs are scarcely necessary—if anything it'd just slow things down. The maintainer doesn't have to waste time understanding my contribution, whether it's AI or human-generated. If I write up a good issue then it's quite literally a prompt for their own AI. They can quickly generate a solution on their own, massage the output to meet their own standards for mergeability then commit as soon as they're ready. The whole process is delightfully efficient.

Most of the discourse at the moment concerns what happens when people with LLMs show up to traditional open source projects that either don't use them, or use them in only a limited manner. Much less has been said of projects where the maintainers are possibly more enthusiastic about AI than the contributors. It's only my first experience of this but I expect the workflow to become more common. Have a problem? Just write the issue. Low stress for everyone.

Serious Computer Business Blog by Thomas Karpiniec
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