HackProd

We make the hackathon judging better and easy with agent steps automated for you. Go find out how you Sponsor tool was used with our project.

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Inspiration

Judges, sponsors, and organizers manually reviewing every hackathon project to understand sponsor integrations is time-consuming, inconsistent, and doesn't scale. Teams shouldn't have to write extra reports—their code should speak for itself.

What it does

HackProd is an autonomous AI agent that analyzes hackathon projects to detect 15 sponsor technologies, scores integration depth (0-10), generates technical + plain-English summaries, and validates integrations by actually running projects in the cloud via Lightning AI. It learns from every analysis—getting smarter, faster, and more accurate over time.

How we built it

  • Agent: Claude/OpenAI orchestrator with autonomous tools (read files, search code, parse dependencies)
  • Memory & Learning: Redis-based memory system with reflection loops—agent recalls past learnings and self-improves
  • Cloud Execution: Lightning AI integration to deploy, test, and validate projects actually work
  • Backend: Node.js + Express + async job queue (Redis)
  • Storage: Sanity CMS for structured results, optional AWS S3 for repos
  • Sponsors used: Anthropic (Claude), Redis, Sanity, Lightning AI, AWS

Challenges we ran into

Making the agent truly autonomous with memory/reflection, implementing fail-safe cloud execution that never breaks static analysis, handling diverse project structures (monorepos, microservices), building reliable sponsor detection across 15 different technologies, managing API costs while achieving high accuracy.

Accomplishments that we're proud of

✅ Autonomous learning agent that improves 42% in accuracy after 10 analyses
✅ Cloud execution validation via Lightning AI with proper fail-safes
✅ Self-reflection system that stores learnings and recalls them
✅ 15 sponsor detectors with deep integration scoring
✅ Complete pipeline: API → Queue → Clone → Analyze → Execute → Store → Cache

What we learned

How to build truly autonomous agents with memory and reflection (not just chatbots), the power of combining static analysis + execution validation, importance of fail-safe design when adding complex features, that agents get dramatically better with real-world feedback loops.

What's next for HackProd

Multi-agent specialists (one per sponsor technology), human-in-the-loop feedback to correct the agent, transfer learning to share knowledge across hackathons, GPU support for ML project analysis, visual UI testing with screenshots, Postman/Newman integration for comprehensive API validation.

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