bvandeusen
4668c0950b
The prep was generating a multi-sentence recap of tasks/events/weather/
projects/recent moments via an LLM call. Per user direction, that's
redundant — the right-side widgets already show today's data — and the
verbosity made the journal feel chatty when the user wanted quiet.
Replaces the prep prose generator with a single phase-aware check-in
question (drawn from a static map: morning="How are you starting the
day?", midday="How's it going so far?", evening="How did the day shake
out?"). No LLM call. The structured `sections` are still gathered and
persisted on msg_metadata for provenance and possible future tooling
(e.g., search), they just don't render in the prep message.
Also pulls the journal persona way back. The prior framing pushed the
model toward stock therapy-template patterns ("I'm sorry you're feeling…"
+ numbered option lists). The new persona is "you are the user's journal —
listen, be quiet, stay out of the way." RESPONSE STYLE rules now lead the
calibration block and explicitly forbid:
- apologizing for the user's feelings
- offering to help / pitching tools
- multi-option menus
- verbatim repetition of prior replies
- padding short replies into paragraphs
Most replies should be one short sentence. Sometimes the right reply is
"Got it" + a record_moment tool call.
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.