Show HN: Proof Loop – I make my coding agents prove they finished the task (github.com)

Proof Loop has introduced a groundbreaking verification protocol for AI coding agents, designed to ensure that their work is genuinely complete before being accepted. The protocol involves freezing acceptance criteria prior to implementation, separating roles between builders and verifiers, and storing durable proof artifacts in the repository. This meticulous process addresses common issues where AI agents often claim task completion without sufficient verification or evidence. By requiring a fresh verifier to confirm every acceptance criterion, Proof Loop enhances accountability and auditability in coding tasks. This innovation holds significant implications for the AI/ML community by establishing clearer boundaries in collaborative coding environments, especially when multiple agents are involved. It promotes best practices in task management, including the maintenance of role fidelity and the prevention of "completion drift" during implementation. The user-friendly toolkit includes sample commands and structures to create a standardized workflow, making it compatible with various AI frameworks like OpenClaw and Codex. As AI coding capabilities continue to evolve, mechanisms like Proof Loop are critical to ensuring that assertions of task completion maintain a high standard of rigor and reliability.