# Runtime Terror

- **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:** [Utkarsh Agrawal](https://cerebralvalley.ai/u/utkarsh16)
- **GitHub:** https://github.com/Utkarsh281308/MotionTwin_AI
- **Demo video:** https://youtu.be/KwAdQgpZzTc
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4126

MotionTwin AI turns a fixed-camera video of a physical process, like a packing line or a kitchen into a simulation you can run what-if tests on. SAM 3.1 tracks the objects and you draw boxes over the work stations. The app then measures arrival times, station times and queues straight from the video. A SimPy model replays those measured timings and gives every result a 95% range. Before trusting any prediction, it checks itself against what actually happened in the clip. You can test changes like "+50% demand" or "add a second packer" and see where the bottleneck moves. Muse Spark explains the outcome in plain language, using only the computed numbers. On our demo line it tracked 21 of 21 parcels, measured station times within 2% of the truth, and predicted completed parcels within 7% of the video.

## More from Meta Global AI Developer Hackathon

- [PMO (Permanent Memory Optimizer)](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4123)
- [WattDocket](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4124)
- [Padhai Saathi](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4125)
- [ChangeProof AI](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4127)
- [Rewind Labs](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4128)
- [Team ID: 656295919540867](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4129)

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