title	Cachy
emoji	🧠
colorFrom	purple
colorTo	blue
sdk	docker
pinned	false

Cachy 🧠

Reel-to-knowledge. Share an Instagram Reel, TikTok, YouTube Short, or article — get back a structured knowledge card: a one-liner, TL;DR, and typed content blocks (steps, facts, checklists, tables), plus the concepts and artifacts (books, movies, products) mentioned in it.

Your cards link to each other through a semantic knowledge graph, and a reel-style Feed replays your own saved knowledge back to you — so your library becomes a knowledge garden, not a bookmark graveyard.

Try it: vatxzz-cachy.hf.space · Android APK: latest release

Features

• Share → card in seconds — share sheet on Android, paste a link on web; watch the pipeline stream progress live (SSE)
• Typed knowledge blocks — steps, key-value facts, checklists, callouts, maps, tables — not a wall of summary text
• Knowledge graph — Obsidian-style force-directed graph linking cards by semantic similarity, shared tags, and referenced artifacts, with auto-labeled clusters
• Feed — insights, highlights, quizzes, and serendipitous cross-card connections replayed reel-style, at zero extra LLM cost
• Chat — with a single card, or across your whole library
• Concepts & catalog — extracted concepts get on-demand AI definitions; mentioned books/movies/products collect into a browsable catalog
• Free-first — every AI dependency has a fallback chain (Gemini → Cerebras → Groq → local); missing keys degrade gracefully, never fail the job

Architecture

Dual-client: a Flutter app (/app, web + Android) talking to an async FastAPI backend (/backend) over REST + Server-Sent Events.

• Single SQLite DB, in-process async job worker — no Redis, no Celery, deploys as one free HF Space
• Ingestion via yt-dlp / instaloader / trafilatura; keyframe OCR with pytesseract + OpenCV; transcription via Groq Whisper with local faster-whisper fallback
• Card generation LLM chain: Gemini 2.5 Flash → Cerebras Llama 3.3 70B → Groq Llama 3.3 70B → plain-paragraph fallback
• Pure-Python graph clustering (label propagation); force-directed layout computed client-side in Flutter

See CACHY_OVERVIEW.md for the full technical breakdown.

Run it yourself

Full stack (backend + web frontend)

./start.py

Backend alone

cd backend && .venv/bin/uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

API keys (all optional — missing ones fall back) load from .env; see backend/app/config.py.

Flutter app

cd app
flutter pub get
flutter run -d chrome --dart-define=CACHY_API_BASE=http://localhost:8000

Docker (as deployed on HF Spaces)

docker build -t cachy . && docker run -p 7860:7860 cachy

License

MIT — see LICENSE