bvandeusen
d3d4294c30
Standalone tool to measure Ollama model performance under the two workload shapes the chat+curator architecture would impose: - chat scenario: short user message, short reply, no thinking. Mirrors the no-tools chat companion's expected load. - curator scenario: ~700-token journal transcript with an extraction prompt, thinking enabled. Mirrors the curator's expected load. Defaults to CPU-only inference (num_gpu=0). Streams responses; reports TTFT, total wall time, tokens/sec (from Ollama's eval_count/eval_duration so it excludes client-side stream overhead), and prompt token count. First request per (model, num_gpu) is a warm-up to load the model into memory; not counted in the measured runs. Designed for cross-server comparison: --server points at any Ollama instance, --out writes a markdown table. Comparing the two CPU servers becomes a matter of running the same command on each and diffing the output. Lives outside the chat/curator architecture commitment — measurement tool only. Tells us "is qwen2.5:32b on CPU fast enough for a 10-20 min curator cadence?" without writing any of the architecture code yet. 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.