pi-autoresearch

pi-autoresearch brings the Karpathy autoresearch loop to the pi agent platform. Edit, commit, benchmark, log, keep or revert, repeat — fully autonomous. Works for any measurable target: test speed, bundle size, build time, Lighthouse scores, training loss.

The confidence scoring after 3+ experiments is smart — it distinguishes real gains from benchmark noise. Correctness checks via autoresearch.checks.sh prevent optimizations that break things. A built-in dashboard lets you visualize experiment history. Compared to uditgoenka's autoresearch (Claude Code-specific), this is pi-native. Compared to ARIS (research-focused), this is more general-purpose.

Use this when you're on the pi platform and want overnight autonomous optimization of any measurable metric. Skip this if you're not using pi — the extension is platform-specific.

The catch: autonomous agents making commits in a loop can create messy git histories. And the experiment loop assumes your benchmark is deterministic — flaky tests or variable CI environments will produce misleading results.