EkaEva/locallens

LocalLens is an extremely early-stage project (1 day old, 1 star) that addresses the 'privacy gap' in AI-assisted coding by running security audits locally. While the 'local-first' value proposition is resonant for enterprises concerned about data leakage, the project currently lacks any technical moat. It functions primarily as an orchestrator for local LLMs (likely via Ollama or similar) to perform tasks traditionally handled by static analysis tools (like SonarQube or Snyk) or secret scanners (like TruffleHog). The defensibility is low because the 'local AI' wrapper pattern is easily reproducible. Furthermore, frontier labs and platform incumbents like GitHub (with Copilot Autofix and Advanced Security) are aggressively moving into AI-driven remediation. GitHub could easily implement a 'local compute' mode for its security suite, which would immediately marginalize standalone local wrappers. Without a proprietary fine-tuned model or a unique dataset of security exploits, the project remains a thin layer over commodity local inference engines.