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Codebase optimization requires a baseline-first experiment loop that captures current metrics, applies one mutation at a time, and uses binary evaluation to keep only changes that demonstrably improve performance. This replaces guesswork with measurable, iterative refactoring.

Reducing performance bottlenecks involves running repeated experiments to isolate issues, applying single mutations, and verifying improvements through benchmarking. This systematic approach ensures changes address actual bottlenecks without breaking existing functionality.

Codebase experimentation requires the Bun runtime and local read, edit, and shell access to execute proof commands. This environment allows the iterative optimization process to record experiment artifacts and validate mutations directly within your repository.

Binary evaluation scores code mutations using strict yes/no evaluations, preventing subjective bias during optimization. It ensures only changes that demonstrably improve performance metrics compared to the baseline are kept, making refactoring results measurable.

Yes, baseline-first experimentation applies to measurable codebase cleanup and reliability improvements, not just performance. By isolating variables and testing one mutation at a time, you can systematically enhance codebase stability and reduce bottlenecks.