- User Guide — Walkthrough: first papers to projects to AI features
- Costs & Privacy — What goes to OpenAI, cost estimates, data ownership
- FAQ & Troubleshooting — Common questions and fixes
- Getting Started — Prerequisites, environment setup, database, running the app
- Deployment — VPS, Docker, Vercel+Railway deployment options
- Architecture Overview — Two-plane design, data model conventions, service layer patterns
- Schema Reference — All tables with columns, types, relationships, and ER overview
- Migrations — Migration history and how to apply them
- Conventions — Naming, JSONB patterns, ID formats, CamelModel mapping
- Overview — Base URL, error format, camelCase convention, CORS
- Papers — Import, CRUD, BibTeX, PDF, metadata extraction, author linking
- Websites — Website and GitHub repo endpoints
- Projects — Projects, experiments, research questions, project notes
- Tasks — Task columns, tasks, custom field definitions
- Notes — Notes CRUD and AI generation across all scopes
- Chat — AI copilot chat for papers, websites, repos, and notes
- Search — Lexical/semantic search and library map
- Agents — Workflows, runs, proposals, activity feed
- Routing — All routes with components and layout nesting
- State Management — Context, localStorage, data fetching patterns
- Components — Key shared components and their props
- Agent Architecture — pydantic-ai setup, shared infrastructure, all workflows
- AI Copilot — Chat copilot flows, agentic notes copilot, suggestion mechanism
- Gap Analysis — Experiment gap detection, token budget, planning board
- Adding a New Entity — End-to-end: migration to frontend
- Adding an Agent — Creating a new pydantic-ai workflow
- Import Pipeline — How paper/website import works
- Testing — Test strategy, running tests, CI setup