# Aftershock

- **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:** [Shobhit Prabhakar](https://cerebralvalley.ai/u/Shobhit)
- **GitHub:** https://github.com/GeekLord/aftershock
- **Demo video:** https://youtu.be/2h6WUWHl3es
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1267

Aftershock is a disaster triage console for the first hours after a disaster. It turns a drone sortie and multilingual radio calls into one priority queue for the whole mission and a draft plan for three rescue teams, so responders reach the worst damage first and can see the evidence behind every item.

How it works:
- Every building, zone and radio call gets a stable ID (F03-B04, F03-Z2, R1), with its damage grade, whether two independent graders agreed, any review flags, and road-access evidence.
- Radio reports of trapped people attach to the matching sector and raise its priority. Head-counts show the exact words they came from and stay marked "needs human confirmation" until a person confirms them.
- Muse Wonder drafts the team assignments. A validator rejects any task that cites a missing ID, claims people with no radio report behind it, misstates a count, or claims a collapse with no severe grade behind it. A Tamper Test button shows those rejections live.

Measured on 200 RescueNet buildings, and what each result changed:
- Exact four-level grading is 66% accurate, but 95.5% of grades land within one level and severe-damage recall is 90%. Aftershock ranks by severe damage and never claims certified grades.
- The error rate is 15% when the two graders agree and 43% when they disagree, so any disagreement raises a human-review flag.
- The model's own "needs review" flag fired 0 times in 60, so review flags come from code instead.
- SAM misses flattened buildings (it found 3 of 13), so zone scans look for destruction separately.

Demo:
- Live at https://aftershock.busy.one, with a precomputed 12-frame Hurricane Michael sortie (RescueNet, CC0) that loads instantly.
- Run Live re-surveys a frame and re-plans with Muse Wonder, streaming every model call with its latency, tokens and cost.
- The radio calls are synthetic TTS, labelled as synthetic everywhere they appear.

What's next: the road to field use
- Validate on more disasters and regions: earthquakes, floods, wildfires and cyclones, using xBD, FloodNet and new partner imagery, so the published accuracy numbers hold beyond one hurricane.
- Run trials with real responders and a live drone feed, keeping the human-confirmation step for every head-count and every severe grade.
- Add more radio languages and dialects through Muse Voice Transcribe, and test them with real responder radio audio collected with consent.
- Deploy at the edge: the planner only needs structured outputs, so a command post with poor connectivity could run it on self-hosted open-weight models.
- The goal: a triage tool any response team can use in any country and any disaster, which earns that trust by publishing measured accuracy rather than claiming it.

## More from Meta Global AI Developer Hackathon

- [Brak](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1264)
- [bAIwor](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1265)
- [Truth Layer - Exauce Kilonga](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1266)
- [Aurif](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1268)
- [Aegis Home](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1269)
- [Calibrate](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1270)

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