# Linked

- **Event:** [Gemini 3 Hackathon SF](https://cerebralvalley.ai/e/gemini-3-hack-sf)
- **When:** Sat, Dec 6 at 9:00 AM – 10:00 PM (PST)
- **Where:** 365 Toni Stone Xing, San Francisco, CA
- **Team:** [ChaeJung Maeng](https://cerebralvalley.ai/u/cjmaeng)
- **GitHub:** https://github.com/mcj9587/VisionDataComm.git
- **Demo video:** https://youtu.be/6nhT-3qE9j8?si=yQbUQgPSmbkIrDyo
- **Gallery:** https://cerebralvalley.ai/e/gemini-3-hack-sf/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/gemini-3-hack-sf/hackathon/gallery/52

Modern manufacturing environments depend on accurate visual and contextual data to diagnose machine failures, assess wear, and build AI models for predictive maintenance. However, factory floor engineers and data/AI engineers operate in completely different worlds:
• Field engineers collect images and videos under noisy, harsh conditions
• Data scientists require high-quality, well-labeled, diverse datasets
• Miscommunication leads to poor datasets, delayed model training, and production downtime

There is currently no multi-modal system capable of guiding field engineers in real time while simultaneously generating structured, high-quality datasets for AI teams.

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