prove-it-better Brings Empirical Verification to Claude Code

prove-it-better targets one of AI coding’s biggest weaknesses: agents confidently declaring subjective improvements without measuring them. Its methodology is promising, but the independent judge is still another model, so teams should treat results as structured evidence—not proof of correctness.

• –Establishes baseline behavior and success criteria before implementation
• –Uses blind pairwise comparisons with swapped presentation order to reduce judging bias
• –Combines model-based evaluation with deterministic checks and live metrics where available
• –Keeps changes reversible and explicitly allows the existing version to win
• –Its self-reported evaluation is directionally useful but small, so broader benchmarks would strengthen the claims