autoresearch

autoresearch generalizes Karpathy's autonomous ML iteration loop to any domain. The pattern is dead simple: modify, verify, keep or discard, repeat. Claude iterates autonomously with mechanical verification and automatic rollback — works on backend code, frontend UI, content, performance, anything with a measurable outcome.

The genius is the constraint: one metric, one direction, fast verification, git as memory. 608 stars in 3 days because developers recognized the pattern immediately. Compared to AutoResearchClaw (heavier, academic papers only) and ARIS (cross-model collaboration), this is the lightest and most general-purpose. It's a Claude Code skill, not a framework.

Use this when you want to optimize anything measurable overnight — test speed, bundle size, Lighthouse scores, training loss. Skip this if your problem can't be reduced to a single improvable metric.

The catch: autonomous iteration without clear guard rails can burn through API credits fast. And "overnight optimization" sounds magical until your agent makes 50 commits that each improve the metric by 0.01%.