bvandeusen
c9f2134ad4
The structured prep card was data-rich but voiceless. Replaced with an LLM-generated conversational opener — same shape the briefing's compilation slot had — that renders as a normal assistant chat bubble at the top of the day's conversation. Backend (services/journal_prep.py): - Renamed generate_daily_prep -> gather_daily_sections (still pure data fetching, no LLM); kept the old name as a backwards-compat alias. - New _generate_prep_prose: hands the gathered sections to generate_completion with a warm-conversational system prompt; returns prose. Falls back to a plain greeting if the LLM call fails or no model is configured. - ensure_daily_prep_message now persists the prep as role='assistant' with the prose as content. Structured sections stay on msg_metadata for provenance. Auto-upgrades legacy system-role preps in place on next call. Frontend: - Drop the <article class="daily-prep"> structured block from JournalView. The prep is now just the first chat bubble — picks up the existing Illuminated Transcript styling automatically. - Drop dayMessages / prepMessage / prepSections / asArray helpers — no longer needed. - ChatMessage hideMessage filter: comment refined to clarify it only catches LEGACY system-role prep rows. Current preps are assistant-role and render normally. Net effect: open /journal -> first thing you see is a warm assistant bubble that talks about your day -> input bar below to reply. 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.