prodlint addresses the emerging quality gap in AI-generated code by targeting the specific failure modes that LLM-based coding tools produce. Traditional linters like ESLint catch syntax and style issues, while SAST tools like Semgrep detect known vulnerability patterns. Neither catches the unique class of bugs that arise when AI generates code without full context: importing npm packages that do not exist, writing database mutations outside transaction boundaries, exposing API keys through NEXT_PUBLIC environment variable prefixes, or omitting authentication middleware on sensitive routes.
The 52 AST-based rules are organized across four categories: Security, Reliability, Performance, and Best Practices. Each rule targets a specific pattern observed in code generated by popular AI tools. The analysis runs entirely locally using static AST parsing with no LLM calls, completing in approximately one second for typical projects. Zero configuration means running npx prodlint in any JavaScript or TypeScript project immediately surfaces production-readiness issues.
Launched via Show HN with active community discussion, prodlint fills a niche that is growing rapidly as vibe coding becomes mainstream. The open-source CLI is free, with a paid web dashboard at prodlint.com offering team analytics and CI integration. For teams shipping AI-generated code to production, prodlint provides the safety net that catches what traditional tooling misses.
