ck:loop

You can automate iterative code optimization by running an autonomous loop that explores the metric space, applies changes, and rolls back regressions automatically. The loop enforces atomic changes per iteration using a git-tracked commit-before-verify workflow.

An autonomous iterative optimization loop is a repeatable experiment protocol that improves measurable code metrics with minimal manual intervention. It iterates across multiple changes, learns from git history to identify effective modifications, and discards ineffective ones to continuously improve metrics.

Yes, you need a git-tracked software project to run autonomous optimization iterations. The loop leverages git history to learn which changes effectively improve metrics and uses a commit-before-verify workflow to enforce atomic changes, ensuring safe rollbacks on regressions.

Yes, you can use this autonomous loop to improve measurable code metrics including bundle size and performance. It applies to any software project where metrics can be measured and improved through iterative changes driven by the autonomous loop, automatically rolling back any regressions.

The loop handles regressions by rolling back ineffective changes automatically. It enforces atomic changes per iteration using a commit-before-verify workflow, meaning each change is committed and verified before proceeding, and regressions are discarded based on git history analysis.

The best way to start autonomous iteration is to run the loop on your git-tracked project. It will immediately begin exploring the metric space through autonomous iterations, applying atomic changes and learning from git history to continuously improve your measurable code metrics.