# Project export: Recall

This document was generated by HackStack to give an AI agent context about a hackathon project. Sections are labeled with their provenance; content marked as truncated was cut to keep this document small.

## Project metadata

- Hackathon: Cal Hacks 12.0
- Tagline: Recall is your individual map of social interactions. Powered by a wearable device, it keeps track of who you meet and what you talk about so it can help you recall.
- Devpost: https://devpost.com/software/sd-32s1zj
- GitHub: not linked
- Video: https://www.youtube.com/embed/a9Ip1ZKou9Q?enablejsapi=1&hl=en_US&rel=0&start=&version=3&wmode=transparent
- Team: contributor stats unavailable

## Devpost submission (written by the team)

### Inspiration

At hackathons, conferences, and networking events, we meet dozens of people — yet often struggle to remember who they were, what we talked about, or even where we met them. We wanted to build a tool that helps you recall meaningful human interactions naturally — without needing to pull out your phone, take notes, or exchange LinkedIn profiles mid-conversation.

### What it does

Recall is a wearable device that helps you capture and organize real-world social interactions in a privacy-respectful, effortless way. • When you say a natural voice cue like “Hi, I’m Ina!”, Recall automatically activates. • It uses a Raspberry Pi-based camera to detect the person you’re speaking with and generates a unique facial embedding (no images are stored — only anonymized vectors). • It then records and transcribes the conversation locally until the interaction naturally ends. • The transcript is sent to Gemini, which summarizes the discussion and extracts key details — names, affiliations, topics, and interests. • All this structured data — transcript summary, keywords, and face embedding — is securely stored in Supabase. • On your connected web dashboard, you can later query interactions conversationally: “Who was the girl who mentioned working at Amazon and loved pizza?” Recall then surfaces the right person and summary — so you can follow up or reconnect effortlessly.

### How we built it

Hardware: Raspberry Pi 5 with ArduCam camera module and on-device microphone. • Triggering Logic: Voice activation detects natural conversation cues to start and stop recording. • AI Stack: • Face recognition: OpenCV + FaceNet for generating embeddings. • Speech-to-Text: Google Speech API. • LLM summarization & extraction: Gemini 1.5 Flash for concise structured summaries. • Backend: Supabase (PostgreSQL + Auth + Storage). • Frontend: Next.js web app for recall search and interaction visualization.

### Challenges we ran into

Achieving low-latency face embedding generation on the Pi while maintaining real-time voice responsiveness. • Handling privacy responsibly — we designed Recall so it only activates when explicitly triggered, and only embeddings (not raw video) are stored. • Fine-tuning Gemini prompts to extract useful context (e.g., company names or shared interests) without losing nuance.

### Accomplishments we're proud of

Built a fully functional wearable prototype within 36 hours that performs face recognition, transcription, and summarization in real time. • Created a natural, consent-based trigger system that respects privacy and minimizes passive surveillance. • Designed a clean, searchable dashboard that makes human memory queryable.

### What we learned

Integrating multimodal AI (vision + audio + language) on embedded hardware is hard — but combining them thoughtfully can make technology feel human-centric again. We also learned that designing for consent and trust must come before cool tech.

### What's next

for Re:call Add spatial mapping of interactions (time + location) for timeline visualization. • Improve on-device summarization for offline mode. • Build an API for CRM or personal knowledge base integration (Notion, Obsidian, etc.). • Explore smaller form factors (badge, glasses, or lapel pin).

## README (from the GitHub repository)

No README available.

## Detected evidence (automated analysis)

No repository was indexed for this project. Claimed technologies below could not be checked against code.
- Google Gemini (technology) — claimed on Devpost, not found in the code
- Python (language) — claimed on Devpost, not found in the code
- Supabase (technology) — claimed on Devpost, not found in the code

## Codebase structure

No repository index available.

## Key source files

No repository index available; no source files included.