bvandeusen
49325816a3
Two architectural bugs in the conversation+curator rollout that
explain the no-response chat in dev:
1. Journal system prompt still instructed tool calls.
JOURNAL_CALIBRATION instructed the model to CALL record_moment,
search_notes, save_person, etc. — but the chat surface ships tools=[]
per the new architecture. The model received contradictory orders
('use these tools' + 'you have no tools') and produced either empty
output or tool-call-shaped text that gets stripped to empty content,
surfacing as status=error or stuck status=generating messages.
Replaced with a chat-only calibration: ~25 lines focused on tone,
length, anti-coaching, and the load-bearing rule 'never claim to
have done anything for the user' (the curator handles capture
silently and separately). JOURNAL_PERSONA also rewritten to drop
the 'use tools to act on their behalf' line.
2. Pre-warm warmed Config.OLLAMA_MODEL ahead of user's real choice.
_pull_model(Config.OLLAMA_MODEL, warm=True) at boot pushed the
system default (qwen3:latest) into VRAM before _warm_user_models()
ran for each user's actual default_model setting. On a single-GPU
setup the second warm could swap the first out — so the user's
chat model wasn't necessarily resident when their first message
landed. Now we just pull the supporting models without warming
them; only user-configured chat models get warm.
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.