bvandeusen
b728acd841
Three real bugs surfaced from inspecting today's journal turns:
1. record_moment was getting fed hallucinated person_ids (the LLM passed
[1, 2] instead of the IDs save_person had just returned). Result: the
moment was linked to two random old test-data notes ("test task 2",
"Tell a joke"), not the people the user actually mentioned.
2. The calibration rule "ask before save_person" was being silently
ignored — model just called save_person on first mention of Victoria
and Mother without asking the user.
3. The model produced a verbatim-identical reply to its previous turn when
the user mentioned "overwhelmed" twice — same numbered-list of 4
options, same closing line. The "warm listener / ask gentle questions"
persona was pushing toward stock therapy-template patterns.
Fixes:
services/tools/journal.py — record_moment now accepts *_names parameters
(person_names, place_names, task_titles, note_titles). Server resolves
each name to a note ID via case-insensitive title match, scoped by
note_type or task-status. *_ids parameters still exist but are now
documented as DISCOURAGED. The LLM physically cannot invent the wrong ID
when using names — names with no match are silently dropped. Resolution
happens via _resolve_entity_ids_by_name helper.
services/journal_pipeline.py — JOURNAL_PERSONA tightened (no more
"warm/curious listener" framing that pushed toward stock comfort
patterns). JOURNAL_CALIBRATION rewritten as scannable sections with
imperative language: PEOPLE/PLACES require asking before save_person;
TASK/NOTE state changes use the confirmation flow; MOMENTS are silent
but MUST use *_names not *_ids; OTHER notes the no-set_rag_scope and
no-auto-notes invariants. Added a RESPONSE STYLE section that explicitly
forbids verbatim repetition and stock multi-option menus.
After deploy, force-regenerate today's prep via fable_trigger_journal_prep
to also pick up the tighter prep prompt from 590a07b.
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.