# Leila Vi

- **Event:** [Meta Global AI Developer Hackathon](https://cerebralvalley.ai/e/meta-ai-developer-hackathon)
- **When:** Oct 26, 12:00 PM – Nov 4, 9:00 PM (EST)
- **Where:** Online
- **Team:** [Leila V](https://cerebralvalley.ai/u/VibuildsAI)
- **GitHub:** https://github.com/leilavi/firerisk-intelligence
- **Demo video:** https://youtu.be/NZYxLzR92qw
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/441

FireRisk Intelligence: finding the fire risks hidden between a building’s everyday alerts

In a real building, fire risk rarely shows up as one big alarm. It builds up across records that nobody reads together: an electricity spike, an overdue panel repair, a missed generator run, a blocked extinguisher seen on a walk-through video. Each looks minor on its own. Together they can mean the building would not stay safe during a power cut.

I manage operations for a 6-floor commercial building in Vietnam. FireRisk Intelligence reads our real operational records, with written approval from the data owner: meter readings, incident and repair logs, generator and diesel logs, asset registers, checklists and on-site survey videos. It turns them into one dashboard that explains which fire-safety risks are building up, where they are, and why.

How it works:

Deterministic rules decide the risk level. Meter spike and drop detection, nine fire-hazard pathway rules, and a compound-risk rule set every alert level. AI never changes a level.
Meta Muse Spark 1.3 investigates each alert as an agent. It uses tool calling over 7 read-only tools to look up records, then returns a strict JSON briefing: situation, competing scenarios with supporting and weakening evidence, limitations and actions. Every citation is checked against real record IDs, and claims without a valid record are downgraded.
Muse Spark reviews the survey videos. It has reviewed 14 real videos and produced 34 timestamped fire-safety findings.
SAM 3.1 visually grounds those findings in video frames, so a reviewer can see where the problem is. The app also reports honestly when SAM finds nothing.
A “butterfly effect” chain links the risks together. For example, the building has one shared ATS, a generator that powers all 6 floors, and emergency lights with batteries of uncertain age. The chain shows how a small equipment fault becomes a life-safety risk.

Privacy by design:

Personal data is pseudonymised before any AI call.
Technical drawings never leave the local machine.
Faces and signs are blurred in demo frames.
The public repo contains code only.
A fictional sample building (“Acme Riverside Plaza”) lets judges run the full dashboard. AI analysis runs only with the judge’s own Meta API key and is never faked.

Track fit: Build for Your World. All code was written during the hackathon. My earlier prompt-only workflow, which contained no code, is disclosed in the repo.

## More from Meta Global AI Developer Hackathon

- [workwell](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/438)
- [HeoCoin Family](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/439)
- [Ekumahost](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/440)
- [Spray](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/442)
- [Lim](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/443)
- [Naija AI Team](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/444)

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