autoloop

Automating code optimization requires an autonomous experiment loop that modifies source code, measures target performance metrics, and retains beneficial changes while discarding regressions. This eliminates manual trial-and-error by logging all attempts to a tracking file for post-run review.

Reducing bundle size while maintaining passing tests is achieved using a bundle-size preset that automatically tests configuration and code changes. The optimization loop keeps only modifications that reduce the production bundle size and enforces hard constraints to prevent regressions.

Autonomous experimentation loops support machine learning metric tuning through pre-configured ML optimization presets. You provide a target metric and an evaluation command, and the loop iteratively modifies code to maximize model performance while discarding any changes that degrade quality.

Setting up iterative performance tuning requires a target metric, editable file glob patterns, an evaluation command, and optional hard constraints. Once provided, the system runs indefinite experiment loops that automatically keep improvements and revert losses.

Autonomous code optimization works for maximizing test coverage through built-in presets designed for test coverage improvement. The experiment loop iteratively modifies code, runs evaluations, and retains only changes that increase coverage while respecting any defined constraints.

Tracking code optimization attempts during an experiment loop is handled by logging all modifications to a dedicated tracking file. This allows developers to review both successful improvements and reverted regressions after the optimization run completes.