LocalDeskAgent

A local, pay-per-use-free desktop computer-use agent for Mac

Problem

A developer spent three weeks, $600 and 20+ training rounds on two small models dedicated to computer use, wanting a fully local version that does not go through the cloud or charge per action. Current cloud-based options are per-use priced and need connectivity, which frustrates privacy- and cost-sensitive users.

Solution

An open-source desktop agent that runs on Mac and scores options at each step with local small models instead of generating free text. It acts quickly when confident, defers to a larger local model when unsure, and asks the user when the task is ambiguous. Fully offline and no per-action billing.

Users

Privacy- and cost-sensitive developers and power users who want to automate repetitive Mac desktop tasks.

MVP

A basic Mac desktop agent framework; local small models for step scoring and execution; a confirmation dialog on ambiguity; local recording and replay of common action sequences; an open-source repo with a one-click install script.

Revenue

Open-source core; paid pre-trained model weights, enterprise task packs and priority support.

Why now

Cloud computer-use services are expensive and privacy-invasive, while small-model local inference just crossed the usability threshold. An indie developer with modest compute can train domain-specific small models.