Show HN: Topos – Structural code quality metrics for agent-written programs (krv.ai)

Krv-Labs has announced the release of Topos, an open-source framework designed to address the challenges of reviewing code generated by AI agents. As developers increasingly rely on AI for coding tasks, they face the overwhelming prospect of sifting through massive diffs that often obscure the true quality and intention behind the code. Topos shifts the evaluation paradigm from traditional line-by-line scrutiny to a more structured assessment of code quality through mathematical graph representations, focusing on control flow, module dependency, and data flow. This change enables developers to measure the "true cost" of code diffs and set concrete quality targets for agents, transforming vague requests into actionable feedback. Topos introduces a system of "Code Quality Medals," categorizing file quality into tiers based on three independent metrics: code complexity, module coupling, and data flow safety. By providing a nuanced scoring mechanism—ranging from GOLD to SLOP—Topos allows agents to understand and optimize for specific quality criteria, rather than just passing tests. This structured approach is significant for the AI/ML community as it not only enhances code maintainability but also narrows the judgment gap in code generation, enabling cleaner, more efficient development processes as AI tools evolve. With Topos, teams can proactively manage code quality, ensuring that the outputs from coding agents are contextually sound and architecturally robust.