I submitted 316 AI-generated PRs to open source

Show HN: I submitted 316 AI-generated PRs to open source

Based, I submitted 316 AI-generated PRs to open source works in show hn launch.

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Every chatbot handles context overflow the same way — summarize everything into one block and throw away the sources. I replaced that with a union-find forest: messages merge into clusters, each cluster has its own summary, and you can trace any summary back to the messages that produced it.Ran seven trials against flat summarization. UF led by 15-18pp on fact recall in every trial. One hit significance (p=0.039), the rest are directional. The interesting finding: flat summaries drop "footnote" facts (cron schedules, webhook paths) because they compete against headline facts for space. Per-cluster summaries don't have that pressure.Code and trial logs: https://github.com/kimjune01/union-find

The company sits in the show hn launch segment, where it competes on its core product or service.

It is recorded at the Show HN launch stage.

Website: june.kim/speedrunning-open-source