bvandeusen
bccee7f192
Buried smoking gun: every CI run since the ci-python:3.14 migration
has silently failed to push the `:dev` tag. The build logs for commit
2a374d9 show:
/var/run/act/workflow/tags.sh: 4: [[: not found
/var/run/act/workflow/tags.sh: 6: [[: not found
act_runner invokes the workflow's `run:` block with `sh -e` (dash on
Debian-based ci-python:3.14, NOT bash). The original bash-only `[[ ]]`
syntax failed silently, the `:dev` tag never got appended to TAGS,
and only the SHA-tagged image was pushed. The `:dev` tag in the
registry has been stuck on whatever build last managed to push it —
likely back when CI ran on a bash-y Ubuntu runner before the migration.
This is why the deployed stack has been running a stale image despite
multiple successful "CI passed" runs: it pulls `:dev`, and `:dev` was
months out of date.
POSIX `case` is dash-compatible AND bash-compatible. Same intent
(decide which extra tags to append based on ref); no behaviour change
other than actually executing correctly.
This commit itself touches .forgejo/workflows/ci.yml, so it triggers
a fresh CI run that — for the first time in a while — should push
both :<sha> AND :dev. After this lands, redeploying the stack will
finally pull the recent code.
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.