How I built an AI GitHub repo auditor in one day using Llama 3 + FastAPI

The Problem Code audits are expensive. Tools like SonarQube cost $500+/month, and most dev teams skip them entirely. I wanted to build something affordable and instant. What I Built GitPulse AI analyzes any public GitHub repository in under 60 seconds and gives you: Overall health score (A-F grade) Security posture assessment Technical debt estimate Team performance metrics Prioritized AI recommendations Tech Stack FastAPI (Python) — backend Groq API + Llama 3 70B — AI analysis GitHub REST API — repo data PostgreSQL — storing results WeasyPrint — PDF generation TronScan API — blockchain payment verification How the AI Analysis Works I fetch ~15 data points from GitHub API: Commit frequency and patterns PR merge rate and open PR count Contributor distribution Issue resolution rate Branch count and release history All this gets sent as structured context to Llama 3 70B which returns scores across 5 dimensions: code activity, community health, documentation, maintenance, and security post