bvandeusen
30dfbce426
Per the design rule "italic is for emphasis, not for design", removed
every chrome `font-style: italic` declaration across the frontend.
42 declarations gone across 24 files: empty-state placeholder copy,
loading messages, "no results" hints, ghost text, voice/role labels,
field placeholder text, brand wordmark, et al.
Markdown content emphasis is unaffected — `<em>` and `<i>` tags from
`*emphasis*` markup still render italic via browser default styling
(prose.css doesn't override em behavior). User-typed emphasis in
notes, journal entries, and chat messages keeps its italic.
Specific spots that lost the decorative italic:
- KnowledgeView .filter-label, .empty-narrator
- NoteEditorView .ef-label, link-suggest related
- ChatPanel .empty-msg, .empty-greeting, .role-label
- ChatMessage .role-assistant .role-label (the "Fable" voice tag —
was italic per the doc's Illuminated Transcript spec, but per the
new typography rule the speaker tag stays regular and lets the
border-left + glow do the bubble framing on their own)
- AppHeader .brand-text ("Fabled" wordmark)
- editor-shared.css .title-input::placeholder
- ProjectView .project-title-input::placeholder
- HomeView .urgency-loading
- WorkspaceTaskPanel .empty-group
- WeatherCard .weather-unavailable
- SharedWithMeView .empty-msg
- DiffView empty-state spans
- ToolCallCard .tool-event-more
- SettingsView 7 spots (.you-label, .geo-pending, hint text, etc.)
- + several other empty-state / hint text spots
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.