bvandeusen
a73dd17a1b
Frontend half of the conversation+curator architecture. Pairs with the
backend in commit a7002a8. With this commit, you can have a journal
conversation (chat model has no tools, doesn't try to capture), then
press a button and see what the curator extracts.
JournalView.vue:
- New "Captures" section in the right rail, above the existing
"Upcoming" events block. Shows moments from the selected day with
timestamp, content, and entity/task/note chips.
- "Process captures" button (Sparkles icon). Disabled for non-today
days because we're not back-running the curator over historical
conversations. Toast on success/failure with timing + tool-call
count from the CuratorRunResult.
- Captures auto-load on day change AND immediately after a curator
run completes — the right rail reflects current state without a
page reload.
- Bound CSS scoped to the rail: cards with a primary-color left
border, monospaced timestamps, chips for people/places/tasks/notes.
api/client.ts:
- CuratorRunResult type matching the backend dataclass.
- runJournalCurator(convId) helper.
- Pass empty body to apiPost() to satisfy the 2-arg signature
(caller-side fix, not a backend change).
What's not in this commit (deferred):
- The captures panel doesn't show captures from days where the curator
hasn't run yet, even if they would later be captured. Visible only
AFTER a curator pass. (Phase 2's scheduler closes this gap by
running automatically.)
- No edit/delete affordances on captures yet — that comes when we
add the moment-editing UI (out of scope for the conversation+curator
architecture commit chain).
Co-Authored-By: Claude Opus 4.7 (1M context) <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.