Quoting Armin Ronacher
Simon Willison · Simon Willison · 2026-05-24
Flask creator Armin Ronacher argues that AI-generated GitHub issue reports filed against open-source projects are degrading maintainer experience by producing overconfident but inaccurate root cause analyses and fake minimal reproductions.
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Extraction
Topics: ai-generated-contentopen-source-maintenancellm-qualitydeveloper-experience
Claims
- AI-assisted issue submissions systematically produce inaccurate root cause guesses presented with high confidence.
- LLM-reworded issues frequently include fake minimal reproductions and lists of error classes that may be irrelevant.
- Ronacher advocates limiting issue reports to four human-observed elements: command run, expected behavior, actual behavior, and exact error or log output.
- The core problem is that issues are not written in the human reporter's own voice, obscuring what was actually observed.
Key quotes
The most frustrating failure mode right now is that people submit issues that are not in their own voice. They contain an observed problem somewhere, but it has been thrown into a clanker and the clanker reworded it and made a huge mess of it.
The result is complete guesswork on root causes, fake-minimal repros, suggested implementation strategies, analogies to adjacent but often the wrong code, and long lists of error classes that might or might not matter.
I increasingly want issue reports to be condensed to what the human actually observed: I ran this command. I expected this to happen. This happened instead. Here is the exact error or log.