bvandeusen
1b65c44339
Three coordinated changes per operator request 2026-05-24:
1. Settings UI rename matching the language we actually use:
- Chat Model -> Chat & Voice Model
- Worker Model -> Curator Model
Setting KEYS (default_model / background_model) unchanged on
purpose; renaming them requires a migration touching 50+ call
sites for purely UX-facing benefit.
2. Settings UI help text rewritten:
- Chat & Voice: documents that it handles chat AND small
conversational automations (titles, tags). Recommends
OLLAMA_NUM_PARALLEL=2+ on the Ollama server so background
automations get their own KV-cache slot and don't evict
the chat model's working state.
- Curator: notes the app enforces SERIAL execution regardless
of NUM_PARALLEL — only one curator pass runs at a time. This
matters most for 70b CPU models where a second instance
would waste system RAM.
3. Enforce serial curator execution globally:
- New module-level _CURATOR_RUN_LOCK in services/curator.py.
- run_curator_for_conversation now wraps its body in 'async
with _CURATOR_RUN_LOCK' — every entry point (scheduler sweep,
manual route trigger, future hooks) is serialized through it.
- is_curator_running() helper exposes the lock state.
- routes/journal.py manual trigger checks is_curator_running()
first and returns 409 {busy: true} immediately rather than
blocking the HTTP request for minutes waiting for a 70b CPU
pass to finish. The user can retry once the curator clears.
Why a 409 instead of queue: a curator pass on a 70b CPU model
can take 5+ minutes. Tying up an HTTP worker that long is bad;
making the user wait without feedback is worse. 409 surfaces
the busy state immediately and the user retries when they want.
Co-Authored-By: Claude Opus 4.7 (1M context) <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.