# BeeJEPA

- **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:** [Finn Hertsch](https://cerebralvalley.ai/u/FinnHrts)
- **GitHub:** https://github.com/F1nnSBK/beeJEPA-metaai-hackathon
- **Demo video:** https://youtu.be/wC5tftlY2Zs
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4137

Continuous 60 fps video monitoring of honeybee hive entrances produces high-volume video streams dominated by routine traffic, while critical events unfold in brief, localized bursts on the landing board. Streaming raw 60 fps video directly into cloud vision-language models is computationally and financially impractical, while conventional motion detectors fail in dense insect swarms.

beeJEPA, live at https://beejepa.finn-hertsch.de with dataset artifacts published on Hugging Face at F1nnSBK/bee-jepa-entrance-anomalies, solves this with a two-stage Active Perception architecture on Java 25 and GraalVM Native Image:

System 1 builds upon a pre-existing university coursework dataset in which a frozen V-JEPA 2.1 ViT-B/16 encoder processes 60 fps side-by-side hive entrance recordings over a 43x43 spatial token grid. Across 96 thirty-second sequences and 2,688 sliding windows, it tracks frame-to-frame token-delta scores and board token peaks against a 99th-percentile threshold to isolate 184 peak anomaly windows.

System 2, engineered from scratch during the hackathon, turns those anomaly windows into a grounded inspection console:
- A pixel-measured hotspot extractor decodes exact keyframes via ffmpeg, isolates the highest-energy 5x3 token window on the landing board above the ambient noise floor, and renders a 688x688 overview alongside a 384x384 bicubic hotspot crop.
- A Java 25 and Micronaut 4.7 runtime stores up to 64 keyframes outside the JVM heap via the Foreign Function and Memory API and evaluates 64-float measured board-energy vectors with the SIMD Vector API to build saccadic trajectories.
- Meta SAM 3.1 segments individual bees on the landing board, and our SamGeometry engine intersects those bounding boxes with the measured V-JEPA hotspot using Intersection-over-Area containment at IoA >= 0.25.
- Meta Muse Spark Open synthesizes single-event or cohort-wide visual digests and can invoke QuerySam31Concept tool calls on clean keyframes to test open-vocabulary predator or threat concepts before emitting structured Markdown reports and spoken briefings.
- Hands-free operator queries are transcribed via Meta Muse Voice Transcribe 1.0 and paired in a Three.js and Vue 3 console with neural speech synthesis, 22.05 kHz bee acoustic synthesis, and a 1024-point FFT acoustic mirror.

## More from Meta Global AI Developer Hackathon

- [Count Before You Post](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4134)
- [Dunham Computer Services](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4135)
- [Ozay](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4136)
- [Sitema ARKON](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4138)
- [Provenance](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4139)
- [INVICTUS](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4140)

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