bvandeusen
4c58603009
Structural fix for the "end before start" bug class observed on prod
2026-04-29. Bad data became inexpressible at the schema level instead
of getting trapped in defensive read-path filters.
The hotfix that landed earlier today (94b169f) is reverted by the
preceding revert commit; this commit supersedes it cleanly with a
proper data-model change.
## Schema (migration 0043)
- Add `duration_minutes INTEGER NULLABLE` column on `events`.
- CHECK constraint: ``duration_minutes IS NULL OR duration_minutes >= 0``.
- Backfill from existing `end_dt`:
- end_dt valid (end > start) → duration_minutes = total minutes
- end_dt == start → duration_minutes = 0 (zero-duration point)
- end_dt NULL or end_dt < start → duration_minutes = NULL
(the corrupt prod row collapses cleanly to a point event)
- Drop the `end_dt` column. The wire format is preserved — `to_dict()`
emits `end_dt` as a derived `start_dt + duration_minutes`. Existing
API consumers (Flutter app, web frontend, CalDAV sync) keep
receiving the same response shape; they just no longer have a way
to PUT a stored `end_dt` that disagrees with `start_dt`.
## Service layer
- `Event.end_dt` becomes a `@property`. Setting it would require a
setter we deliberately don't define — writes always go through
`duration_minutes`.
- `_normalize_duration` is the single source-of-truth for input
reduction. Accepts (start, end_dt, duration_minutes), returns the
canonical `duration_minutes`, raises `ValueError` for negative
durations, end-before-start, or end/duration disagreement.
- `create_event` and `update_event` accept either `end_dt` or
`duration_minutes` for ergonomic compat; both convert via
`_normalize_duration`. Update validates the post-update state when
the patch includes either.
- `list_events` filter is simpler now: a coarse SQL prefilter
(`start_dt <= date_to`) plus Python-side refinement using the
derived `end_dt`. Avoids Postgres-specific interval arithmetic in
the WHERE clause; refinement runs over a per-user result set so
there's no scan-cost concern at personal scale.
- Recurring-event expansion uses `event.duration_minutes` directly
instead of computing `end - start`. No more negative-timedelta
hazard.
## CalDAV sync (incoming + outgoing)
- `caldav_sync.py` (pull) and `calendar_sync.py` (Radicale upsert)
both convert iCal `DTEND` → `duration_minutes` on the way in.
Outbound iCal still emits `DTEND` as `start_dt + duration_minutes`
via the model's derived property. iCal interop is unchanged.
## Behavioral upgrade for `update_event`
Pure end_dt model: moving start past the existing end_dt would either
silently corrupt or hard-reject. Duration model: the duration is
preserved by default, so moving start slides the effective end
forward — which is what users mean when they "move" an event.
Explicit clear is still possible via `end_dt=None`.
## Tests
`tests/test_events_service.py`:
- 6 new `_normalize_duration` unit tests (sugar conversion, zero
duration valid as point event, end-before-start rejected, negative
duration rejected, inconsistent end+duration rejected, none → None)
- New behavioral test: `update_event` preserves duration when only
start_dt changes (sliding semantics)
- New: clearing `end_dt=None` on update collapses to point event
- New: list_events surfaces a point event in the upcoming window
- New: list_events excludes a timed event whose effective end has
already passed
- Existing mock-event helper updated to use `duration_minutes`
instead of stored `end_dt`.
44 event-related tests pass; ruff clean.
## Out of scope (separate task)
Fable #161 — `find_events_by_query` returning multiple matches and
silently picking matches[0]. The exact root cause of how event id=2
got mutated in the first place; orthogonal to the storage model.
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.