bvandeusen
0ed9cbf666
Per user clarification: previous over-rotation dropped the LLM-generated
prep prose entirely (just a phase greeting) and made the chat persona
extremely sparse ("you are a place where words go down"). User actually
wanted only the chat replies pulled back, NOT the prep dropped, and the
chat to behave largely like normal /chat — asking follow-ups and
verifying earlier details.
services/journal_prep.py — restored:
- _render_sections_for_prompt
- _PREP_SYSTEM_PROMPT (the direct, briefing-style prompt from 590a07b)
- _generate_prep_prose
- _fallback_prep_text
- ensure_daily_prep_message now calls _generate_prep_prose again
- removed _phase_for_now / _phase_prompt helpers (no longer needed)
services/journal_pipeline.py — persona rewritten:
- Old: "You are the user's journal. Be quiet. Listen. You are not helpful."
- New: "You are the user's assistant. Behave like the rest of the app's
chat: respond conversationally, ask follow-up questions, verify details
from earlier turns, use tools naturally."
- Calibration block reorganized: PEOPLE/PLACES (ask first), MOMENTS
(silent + use *_names), STATE-CHANGING TOOLS (confirmation flow),
OTHER, RESPONSE STYLE.
- RESPONSE STYLE keeps the no-apologizing / no-option-menus /
no-verbatim-repetition / match-user-length rules but drops the "be
quiet, one short sentence" framing.
Net behavior:
- Open journal → LLM-generated prep prose with today's tasks/events/weather
- Reply → assistant responds conversationally like /chat, asks follow-ups,
verifies details, uses tools
- Background: silently records moments via *_names, asks before creating
new people/places
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.