# SECA

- **Event:** [Gemini 3 서울 해커톤](https://cerebralvalley.ai/e/gemini-3-seoul-hackathon)
- **When:** Sat, Feb 28 at 9:00 AM – 10:00 PM (GMT+9)
- **Where:** 서울 Seocho District, Olympic-daero, 2085-14 세빛둥둥섬
- **Team:** [jjun kim](https://cerebralvalley.ai/u/seca), [Okchul Jung](https://cerebralvalley.ai/u/ok2ee), [Sung Jin Kwon](https://cerebralvalley.ai/u/suyugi), [Seungwoo Lee](https://cerebralvalley.ai/u/seungwoo)
- **GitHub:** https://github.com/jokc28/circuit-knowledge-graph
- **Demo video:** https://drive.google.com/file/d/1huQwhvNnBLeQnURLYHbXt2epsbTLXIBw/view?usp=sharing
- **Gallery:** https://cerebralvalley.ai/e/gemini-3-seoul-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/gemini-3-seoul-hackathon/hackathon/gallery/90

1. The Problem (Pain Points)
We address the limitations of inefficiency of manual analysis in hardware design.

Static Data Limitations: Complex schematics often exist as static images, making it impossible to immediately utilize component data or understand connectivity digitally.

Manual Bottlenecks: When a component needs replacement (due to EOL, cost, or stock issues), engineers must manually cross-reference BOMs (Bills of Materials) and datasheets.

Low Productivity & High Cost: This manual process requires analyzing traceability and circuit impact, taking an average of 3 hours per component. This creates a significant bottleneck, wasting valuable engineering resources and increasing costs.

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Markdown version of https://cerebralvalley.ai/e/gemini-3-seoul-hackathon/hackathon/gallery/90. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt