bvandeusen
03d725ea3e
A user asked Fable to schedule "this Friday at 8am" on Wednesday 4/29
2026. The model picked 4/30 (Thursday) and confidently labeled it
"Friday." The TZ pipeline did everything correctly given the model's
date — the bug was upstream: the model was guessing weekdays from ISO
dates without an anchor, and the calendar tools had no way to verify.
Three layered fixes:
1. **System prompts now name the weekday alongside the ISO date.**
Both the journal-conversation prompt and the general chat prompt
used to say "Today is 2026-04-29 (America/New_York)." They now say
"Today is Wednesday, 2026-04-29 (...)." LLMs are unreliable at
deriving weekday names from ISO dates; supplying the name removes
the guess.
2. **`expected_weekday` parameter on create_event / update_event.**
When the model passes `expected_weekday="friday"`, the backend
computes the resolved start_date's weekday in the user's local
timezone and rejects mismatches with a self-correcting error
("Date 2026-04-30 falls on Thursday, not Friday. Recompute..."),
without creating the event. The check is local-aware: a Friday
23:00 event in Tokyo crosses midnight UTC but the local view
stays Friday, and the validator respects that.
3. **Tool descriptions instruct echo-and-confirm.** create_event and
update_event descriptions now tell the model: when the user names
a weekday, state the resolved date in the reply BEFORE calling
the tool, and pass `expected_weekday`. Costs nothing in code,
reinforces the validator.
6 new tests — match success, mismatch rejection (with create/update
not invoked), omitted-param backcompat, invalid weekday name, local-
not-UTC weekday computation, and the update_event variant. All 18
calendar-tool tests + 33 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.