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Track A (Infrastructure & Data Layer): - docker-compose.yml with all 7 services (nginx, frontend, api, mcp, renderer, worker, postgres, redis) - docker-compose.override.yml for local dev (hot reload, port exposure) - PostgreSQL init.sql with full schema (15 tables, pgvector indexes, creator economy stubs) - .env.example with all required environment variables Track A+B (API Layer): - FastAPI app with 10 routers (auth, shaders, feed, votes, generate, desires, users, payments, mcp_keys, health) - SQLAlchemy ORM models for all 15 tables - Pydantic schemas for all request/response types - JWT auth middleware (access + refresh tokens, Redis blocklist) - Redis rate limiting middleware - Celery worker config with job stubs (render, embed, generate, feed cache, expire bounties) - Alembic migration framework Service stubs: - MCP server (health endpoint, 501 for all tools) - Renderer service (Express + Puppeteer scaffold, 501 for /render) - Frontend (package.json with React/Vite/Three.js/TanStack/Tailwind deps) - Nginx reverse proxy config (/, /api, /mcp, /renders) Project: - DECISIONS.md with 11 recorded architectural decisions - README.md with architecture overview - Sample shader seed data (plasma, fractal noise, raymarched sphere)
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2.3 KiB
🔥 Fractafrag
A self-hosted GLSL shader platform — browse, create, generate, and share real-time GPU visuals.
Fractafrag fuses three experiences:
- TikTok-style adaptive feed of living, animated shaders that learns your taste
- Shadertoy-style code editor for writing, forking, and publishing GLSL shaders
- AI generation layer where you describe what you want and the platform writes the shader
Plus a desire queue / bounty board where users express what they want to see, and human creators or AI agents fulfill those requests.
Quick Start
# 1. Clone and configure
cp .env.example .env
# Edit .env with your secrets
# 2. Launch everything
docker compose up -d
# 3. Open
open http://localhost
Architecture
nginx (reverse proxy)
├── / → React frontend (Vite)
├── /api/* → FastAPI backend
└── /mcp/* → MCP server (AI agent interface)
postgres (pgvector/pgvector:pg16) — primary datastore + vector similarity
redis (redis:7-alpine) — cache, rate limiting, job queue
renderer — headless Chromium shader renderer
worker — Celery job processor (render, embed, AI generate)
Tech Stack
| Layer | Tech |
|---|---|
| Frontend | React 18, Vite, Three.js, TanStack Query, Zustand, Tailwind CSS |
| Backend | Python, FastAPI, SQLAlchemy, Pydantic |
| Database | PostgreSQL 16 + pgvector, Redis 7 |
| Jobs | Celery + Redis |
| Renderer | Node.js + Puppeteer (Headless Chromium) |
| MCP | Python MCP SDK, HTTP+SSE transport |
| Payments | Stripe (subscriptions + Connect) |
| Container | Docker Compose, single-stack |
Milestone Roadmap
| Milestone | Focus | Status |
|---|---|---|
| M0 | Infrastructure + Auth | 🚧 In Progress |
| M1 | Core Shader Loop (editor, submit, feed) | ⏳ |
| M2 | Intelligence Layer (MCP, recommendations) | ⏳ |
| M3 | Desire Economy (bounties, fulfillment) | ⏳ |
| M4 | Monetization (Stripe, subscriptions) | ⏳ |
| M5 | AI Generation (prompt → shader) | ⏳ |
Development
# API direct access (dev mode)
http://localhost:8000/api/docs # Swagger UI
http://localhost:8000/health # Health check
# Services
http://localhost:5173 # Vite dev server
http://localhost:3200 # MCP server
http://localhost:3100 # Renderer
License
Private — see DECISIONS.md for project governance.