Show HN: Evidence Graph – type checking for the specs your AI agent implements (github.com)

The Evidence Graph has been introduced as a significant tool for enhancing accountability and integrity in AI-driven coding environments. This system ensures that when AI models like Claude Code or Codex generate code, each requirement from specifications is explicitly acknowledged and documented, reducing the risk of oversight or misinterpretation. The framework sets a strict policy where any failure to meet these obligations results in a build failure, thereby enforcing adherence to configured prototypes and ensuring transparency in how components are implemented and tested. This development is particularly notable for the AI/ML community, as it provides a robust mechanism to link code functionality directly to documentation, thereby enhancing the trustworthiness of AI-generated outputs. The technical implications are profound; every exported function must reference and justify its claims to requisite documentation or tests, effectively minimizing the chance for ambiguous or erroneous outputs. The Evidence Graph functions as a verification layer by demanding concrete evidence alongside every code declaration, thus fostering a more systematic and reliable coding practice that aligns closely with user expectations and specified requirements.