In modern software engineering, security data is fragmented. Your code lives in GitHub, your ticket ownership in Linear (or Jira), and your incident context in Slack. When a critical CVE like Log4j drops, engineers don’t just need to know what is broken; they need to know who owns it, how critical it is, and where to ping the on-call engineer.

Traditionally, connecting these dots requires writing brittle API glue code, handling pagination, managing auth tokens, and dealing with rate limits. It’s slow, expensive, and prone to errors.

I built PatchPoint to solve this. It’s an enterprise-grade Vulnerability Impact Mapper that unifies these silos into a single, SQL-queryable intelligence layer using Coral SQL.

🛠️ Tech Stack

• Frontend: Vite, React, Tailwind CSS, Framer Motion


• Backend: Python Flask, Groq API (Llama-3)


• Data Engine: Coral SQL, GitHub MCP, Linear MCP, Slack MCP


• Deployment: Vercel (Frontend), Local/Render (Backend)

Key Learnings

Building PatchPoint under hackathon constraints taught me the value of declarative data layers. By offloading API complexity to Coral, I focused on UX and business logic. I also learned the importance of resilient architecture—implementing a "Smart Fallback" ensured that even if the backend failed, the frontend could still demonstrate the product’s value using curated mocks.

🔗 Try It Yourself

• Live Demo:
  


• Video Demo: Watch on YouTube
  

Acknowledgments

Huge thanks to wemakedevs and coral for organizing the #CoralBeanHackathon. The opportunity to build with Coral SQL was a game-changer, showing how unified data layers can transform DevOps workflows from reactive panic to proactive governance.

Have you tried using SQL for cross-tool data resolution? Let me know in the comments! 👇

CoralSQL #DevOps #CyberSecurity #Python #React #AI #SoftwareEngineering #FullStack #Hackathon #TechCommunity