bvandeusen
c33cab7020
Two related gaps in the journal weather panel:
1. Saving locations via PUT /journal/config didn't trigger a weather
fetch, so newly-entered sites had no cache row (or a stale one) until
the user manually clicked the panel's refresh button. The panel
rendered "two sites with empty values" against pre-existing cache
rows that no longer matched what the user had configured.
2. get_cached_weather_rows returned every WeatherCache row for the user
regardless of whether the location was still in journal_config.
Briefing-era rows survived migration 0040 (which only deleted the
briefing_config setting, not the cache table) and showed up as
ghost tabs in the UI.
Changes:
- get_cached_weather_rows accepts an optional valid_keys filter; rows
whose location_key is not in the set are excluded.
- routes/journal.py:
- put_config kicks off a background refresh_location_cache for any
saved location with valid lat/lon.
- GET /weather and POST /weather/refresh both pass valid_keys derived
from the current config so orphaned rows don't surface.
- services/journal_prep.py filters the weather section to currently-
configured locations as well; uses a lazy import of get_journal_config
to avoid a cycle (journal_scheduler imports journal_prep).
153 tests pass.
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.