# Memory Mirror - Personal Context Trainer

- **Event:** [Agentic Memory & Context Engineering Hackathon](https://cerebralvalley.ai/e/mongoDB-hackathon)
- **When:** Sat, Oct 11 at 9:00 AM – 10:00 PM (PDT)
- **Where:** Shack15, San Francisco, CA
- **Team:** [Ishita Jindal](https://cerebralvalley.ai/u/Ishitaj)
- **GitHub:** https://github.com/ijindal1/memory-mirror
- **Demo video:** https://www.loom.com/share/b66ceabc5074440babe03b30059e6803?sid=0043d9fc-753c-48db-9ee0-5ca679a82c79
- **Gallery:** https://cerebralvalley.ai/e/mongoDB-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/mongoDB-hackathon/hackathon/gallery/72

A real-time visualization tool that demonstrates how AI perception dramatically changes when given personal context/memories. 

Built for Problem Statement #3 (Memory-Informed Experiences), Memory Mirror extracts and stores structured memories from user input, then shows side-by-side comparisons of AI responses with and without this context.

Key insight: Users can immediately see why AI fails without memory and succeeds with it. Built by a non-technical founder in 6 hours while "vibe coding" - proving accessible AI development.

Features:
- Real-time memory extraction and categorization
- Visual memory graph with confidence scoring
- Side-by-side response comparison ("The Mirror Effect")
- Memory timeline and type analytics
- One-click demo mode for easy testing

Impact: Demonstrates the critical importance of memory in AI interactions and showcases the potential of memory-informed AI agents.

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Markdown version of https://cerebralvalley.ai/e/mongoDB-hackathon/hackathon/gallery/72. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt