An experiment exploring whether AI-generated code can be trusted without human line-by-line review, by applying automated verification constraints instead. The approach uses property-based testing (via Hypothesis) to confirm requirements are met, mutation testing to ensure only the requirements are met, side-effect checks, and type/lint enforcement. The author shifts from 'must review' to 'must verify' AI code, treating verified output like compiled code. A FizzBuzz demo repo in Python implements all checks. The setup overhead currently exceeds just reading the code, but it establishes a foundation for future tooling improvements.
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