2026-03-26 23:17 UTC
@mirth @regehr I meant a different thing. If you have a hypothetical tool that tells you whether your source code meets your requirements, run a fuzzer on that tool: it may eventually find an input program that satisfies your requirements. This is not that different from using an LLM in a feedback loop with such a tool, except a coverage-guided fuzzer can see inside the tool to make potentially better guesses about what result you want. I claim random fuzzing should be "smarter" than an LLM for that purpose.
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@mirth@mastodon.sdf.org 2026-03-26 23:26
@jamey @regehr Oh I see. Do you follow "classical" AI? The combinatorics of program synthesis are (I think) even harder than say chess or go, but maybe we'll see some of the same patterns (heuristic search) at least until some possible future where quantum computers reduce the need to prune the tree. On a different tack, random program search guided by spec coverage has a similar "mouth feel" of layer-hopping cleverness to the RPython JIT-from-interpreter extraction. Hmm.