bvandeusen
4192a64c0f
Per the surface-phase spec for the Chat cluster (ChatView, ChatPanel, ChatMessage, ChatInputBar, ToolCallCard): Long-form line-height - ChatMessage: assistant-bubble .message-content jumps from 1.55 to 1.7 — chat is a reading surface, not a snippet stream. User bubbles stay tighter. - JournalView: dropped its :deep(.role-assistant .message-content) override; ChatMessage handles it now and Journal inherits. ToolCallCard borders - Removed the outer 1px border. ToolCallCard always renders inside an assistant bubble; the bubble already contains it. Background tint differentiates without re-bordering. Per the structural-not-decorative rule. - Error state preserved as a 3px left-edge accent in --color-danger, mirroring the assistant bubble's own left-edge pattern. Button reclassification per Hybrid rule - ChatView .bulk-link "All"/"None": accent text → --color-text-secondary (these are tertiary list-control affordances, not brand moments) - ChatView .bulk-delete-btn: --color-danger (Error terracotta) → --color-action-destructive (Oxblood) per Hybrid; paired with a Trash2 icon since destructive should always be reinforced by an icon, not just color - ChatView .btn-delete-conv hover: same Error → Oxblood swap - .btn-new-conv stays accent (brand moment, correct already) - .btn-send stays accent gradient (primary brand moment, foundation) Two-weights-only - Snapped every font-weight: 600/700 to 500 across ChatView, ChatPanel, ChatMessage, ToolCallCard per the doc rule. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Fabled Scribe
A self-hosted second brain and project management application with integrated LLM capabilities. Write, organise, and act on your notes and tasks with the help of a local AI assistant — all running on your own hardware.
Features
Notes and tasks with a Markdown editor, sub-tasks, milestones, and kanban project workspaces. AI chat with streaming responses, RAG over your notes, and tool use (web search, calendar, weather). A daily briefing that digests your tasks, RSS feeds, and weather on a schedule. Knowledge graph, per-user/group sharing, PWA with push notifications, an MCP server for external AI clients, and an Android companion app.
Quick Start
Prerequisites: Docker and Docker Compose. 8 GB+ RAM recommended for LLM inference.
Download docker-compose.quickstart.yml from this repo, then:
# Optional but recommended — set a secret key
export SECRET_KEY=your-random-secret-here
docker compose -f docker-compose.quickstart.yml up -d
Open http://localhost:5000. The first user to register becomes admin. Go to Settings → General to pull an LLM model — qwen3:8b or llama3.1:8b are good starting points.
GPU: Ollama runs CPU-only by default. See the comments in
docker-compose.quickstart.ymlto enable NVIDIA GPU passthrough.
Development: To build from source, see Development.
Documentation
| Doc | Contents |
|---|---|
| Architecture | Stack, design decisions, data models, key services |
| Configuration | Environment variables, Docker Compose, production setup, security |
| Features | Detailed feature breakdown and keyboard shortcuts |
| Development | Dev workflow, CI/CD, migrations, release process |
| API Keys & MCP | API key management and Fable MCP install guide |
| SSO / OAuth | OIDC setup for Authentik, Keycloak, and other providers |
| API Reference | All REST API endpoints |
| Android App | Flutter companion app architecture and feature status |
License
This project is privately maintained.