# Focal point

- **Event:** [AI Engineer World's Fair Hackathon 2026](https://cerebralvalley.ai/e/aiewf-hackathon-2026)
- **When:** Jun 27 at 9:00 AM – Jun 28 at 5:00 PM (PDT)
- **Where:** San Francisco, CA
- **Placement:** Finalist
- **Team:** [Yash Patodkar](https://cerebralvalley.ai/u/ypatodkar), [Jay Patil](https://cerebralvalley.ai/u/jaypatil588)
- **GitHub:** https://github.com/ypatodkar/FocalPoint
- **Demo video:** https://youtu.be/bv-V5vNh_0A
- **Gallery:** https://cerebralvalley.ai/e/aiewf-hackathon-2026/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/aiewf-hackathon-2026/hackathon/gallery/50

FocalPoint is a chat interface that watches how you read and adapts in real time — no feedback buttons, no ratings, no extra effort from the user.

Using your webcam and MediaPipe iris tracking, FocalPoint records where your eyes linger, what you skim, and where you re-read. Every paragraph in every response is a gaze zone. After each turn, a Reward Agent scores those zones: smooth reading earns +1.0, confusion gets −0.5, skimming −0.3, skipped zones −0.2. That signal updates your reading profile on the spot.

A Prompt Agent then rebuilds the system prompt from your profile — adjusting complexity, response length, format, and flagging topics you've struggled with. The model doesn't get fine-tuned. The system prompt does. We call this Recursive Self-Improvement: the AI rewrites its own instructions based on how well its last response was actually read.

A Meta-Agent runs at session end to consolidate long-term memory across six layers — episodic, semantic, procedural, working, sensory, and prospective — so the adaptation carries forward into every future conversation.

The result: a model that starts verbose and expert-level, then learns to meet you exactly where you are. Purely from your eyes.

Stack: React · FastAPI · Gemini · MediaPipe · MongoDB · Antigravity (multi-agent memory)

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