deskmind-ai / deskmind

# DeskMind · 得心
**Small enough to run on your Mac. Smart enough to ask.**
Open-source computer use for your Mac. DeskMind reads the screen, works out the next step and acts, all with small models running on your Mac. When a task could mean two things, it asks you instead of guessing.
中文 · Website · Docs · Download for Mac · Models · Discussions · Roadmap · Changelog
**Watch the 56-second demo on deskmind.dev**: a real recording with the released model. Two orders match "Lisa Wong", so it asks which one before writing.
How it was built, and what went wrong on the way: 3 weeks, 20 training rounds, $600. If DeskMind is useful or interesting to you, a star on this repo helps other people find it.
## What makes it different
**A small model, on your Mac.** A 0.8B model decides each step and hands the unsure ones to a 4B. Both run on your Mac: no cloud round-trip, no per-step bill. The 0.8B decides in about 0.5 s; the 4B answers in about 3.6 s (median decision times).
**System One: choices, not guesses.** Each step is a multiple-choice question. The model scores every option instead of writing text, so every option gets a probability. Unsure steps go to the 4B or to you, and any agent can call it through `POST /v1/systemone`. In 39 real-desktop runs it never said "done" when the task was not done.
**Open, from eyes to hands.** Eyes, Brain, Hands and the Mac app are open source, along with Bench, which grades them. Every result below comes with its sample size, so you can reproduce it.
## Get started
**Use the Mac app.** Download DeskMind for Mac (macOS 15+, Apple Silicon, signed and notarized). It ships the G18b release models. On first run it downloads the models (about 5.3 GB) and walks you through the permissions: Install the app.
**Or run the model yourself.** This runs the 4B alone (release G18b). It answers one step of a desktop task; it does not drive the desktop by itself.
 [code] 
The 4B is about 4.5 GB to download. If the download fails with a `CAS Client Error`, rerun it with `HF_HUB_DISABLE_XET=1` in front. In mainland China, ModelScope carries the same files:
`uvx modelscope download --model gxcsoccer/brain-4b --revision g18b-q8 --local-dir models/brain-4b`.
 [code] 
Success looks like a typed decision with a probability for every option, e.g.
`{"answers": {"operation": {"choice": "CLICK", "probabilities": {"CLICK": 0.96, "OPEN": 0.005, …}}, "click_target": {…}}}`.
A probability is the model's weighting of the options, not a guarantee that the step is right.
**The router (0.8B → 4B), as released:** also download `deskmind/brain-0.8b --revision g18b-q8` into `models/brain-0.8b` (0.8 GB), then serve
`uv run deskmind-brain-serve --predictor mlx:models/brain-0.8b --escalate-to mlx:models/brain-4b --two-stage --port 8796`.
The threshold (0.96) ships with the weights; each reply adds a `routing` record such as `{"by": "strong", "reason": "low_conf", "fast_conf": 0.956}`.
Step by step, with the full reply explained: Quickstart. To drive the deskto