bvandeusen 9f8b451d15 fix(journal-prep): bucket tasks + drop non-proximate events (#159)
Two filtering issues that made the daily prep noisy and trained the
user to ignore it.

## Tasks: bucket into due-today / upcoming / overdue

The prep was calling `list_notes(due_before=day_date)` and labeling the
result as "tasks due today". That filter is strictly less-than, so it
returned only OVERDUE tasks (a single 68-day-stale task in this user's
case), while the prompt still framed them as fresh today's work. Each
day of the prep treated the same overdue task as new — the user
learned to ignore the line entirely.

`gather_daily_sections` now runs three queries:
- `tasks_due_today` — `due_after=day_date AND due_before=day_date+1`
- `tasks_upcoming` — next 7 days, exclusive of today
- `tasks_overdue` — strictly before today
Overdue entries carry a `days_overdue` count. `_render_sections_for_prompt`
emits three labeled headers ("TASKS DUE TODAY", "UPCOMING TASKS",
"OVERDUE TASKS (still on the list, not currently due)"). The system
prompt has a new TASK BUCKETS rule telling the model: don't call
overdue items "due today"; surface them with their staleness duration
("still on the list 68 days") and frame as a backlog reminder rather
than today's work.

Backwards-compat: `sections["tasks"]` still exists, now as the union
of all three buckets — strictly more useful than the prior overdue-
only behavior any frontend consumer was getting before.

## Events: tz-aware window + proximity filter

The user's "Birthday — 2026-09-29 (FREQ=YEARLY)" event was surfacing
in every daily prep, 5 months out. Root cause: `gather_daily_sections`
built `day_start`/`day_end` as NAIVE datetimes; `list_events` then
called `rrulestr(...).between(naive_from, naive_to)` against an
aware `dtstart`, which throws TypeError, hits the `except Exception`
fallback, and appends the canonical event row — regardless of whether
today is anywhere near a recurrence.

Fix:
1. Construct the day window as TZ-aware in the user's local timezone
   and convert to UTC before the query. RRULE expansion now runs
   correctly.
2. Defense-in-depth `_filter_proximate_events` drops events whose
   start_dt is more than 7 days from `day_date` (in the user's local
   TZ — not UTC, so a Friday 23:00 NY event isn't misclassified as
   Saturday). If list_events ever leaks a far-future row again, the
   prep doesn't surface it.

10 new tests in `tests/test_journal_prep_filtering.py` cover task
bucketing (overdue marker, due-today no-marker, no-due-date), the
proximity filter (the 4/29 reproducer, in-window keeps, local-vs-UTC
boundary, unparseable dates kept rather than suppressed), and the
rendering (overdue staleness shown, due-today doesn't repeat the date,
correct section ordering).

53 tests pass across journal_prep + journal_search + record_moment +
calendar_tool + events. Ruff clean.

Closes Fable task #159.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-04-29 09:31:12 -04:00

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.yml to 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.

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