bvandeusen
611c940527
A user reported "next Friday at 8am" landing on the wrong day. The current `start` parameter accepts a combined ISO datetime string — when the model emits something like `"2026-05-01T00:00:00Z"`, the parser correctly honors the UTC tag and stores `2026-05-01 00:00 UTC`, which displays as `2026-04-30 19:00` for a UTC-5 user. The bug isn't in our parser; it's that we let the model TZ-tag the calendar day at all. The fix moves the foot-gun: `create_event` and `update_event` now prefer split fields (`start_date` + `start_time`, plus end variants). A `YYYY-MM-DD` string carries no TZ metadata for a model to mis-tag, and the backend builds the local datetime explicitly via `datetime.combine(date, time, tzinfo=user_tz).astimezone(UTC)`. Strict regex validation rejects anything with a TZ suffix on either field. The legacy combined `start` / `end` fields are kept as a fallback so saved tool-call payloads in conversation history still replay; new calls are steered toward the split shape via the tool description. 7 new regression tests cover Eastern, Pacific, Tokyo (positive offset), all-day inference, strict-shape rejection on both fields, backcompat with the legacy `start` field, and the same fix for `update_event`. 27 of the event-related tests pass; ruff clean. 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.