Reviewing AI-generated code before shipping: why I built Sego

A developer built Sego, a local-first review layer for AI-generated code that sits after coding agents to assess safety before committing. The MVP reviews staged changes and produces structured findings with severity, evidence, and suggested fixes. The project is seeking early users who work with AI coding tools like Cursor, Claude Code, and Copilot.

AI coding tools are changing how software gets built. Cursor, Claude Code, Codex, Copilot and Lovable can generate code very quickly. For solo builders and small teams, this is powerful. But it also creates a new problem: Who reviews the code after the AI writes it? Generated code can look correct while still introducing risks: That is why I am building Sego. Sego is a local-first review layer for AI-generated code. It does not try to replace coding agents. It sits after them. text AI writes code. Sego reviews whether it is safe enough to commit. The current MVP reviews staged changes and produces structured findings: severity file and line evidence risk suggested fix It also saves local review artifacts so the review process can be inspected later. Current focus: structured code review local review history crash recovery review-trust workflow early sidecar integration with AI coding tools I am looking for the first 20 AI coding users. If you use Cursor, Claude Code, Codex, Copilot, Lovable or another AI coding tool, you can send a small AI-generated project or diff and I will run a free Sego audit. Website: https://sego-8dw.pages.dev/ GitHub: https://github.com/007M7/Sego-Agent I would love feedback from developers using AI coding tools in real projects.