GoKawiiltypesafe-computer-use drives a Mac toward a goal you type in plain English, for about a fiftieth of a cent per step. It never sends a screenshot to a big model. Instead it reads the screen deterministically, asks a small classifier which action comes next, and only calls a writing model when a text field genuinely needs free text.
clicker "go to techcrunch and take me to the checkout page for the cheapest tickets to their next upcoming event" --act
Why
Frontier-model computer use is capable and expensive: every step ships a screenshot and waits several seconds for a plan. Most steps do not need a plan. They need one choice from a short list, made quickly and cheaply, with a confidence number you can gate on.
TypeSafe sells exactly that: a decision model that answers a Choice over up to 255 options with a full probability distribution and a calibrated confidence, in a few hundred milliseconds, with free output tokens. This project is a computer-use loop built around it.
Measured on the same screenshot and goal, one decision each:
typesafe (jev) Claude Opus 5, bare screenshot multiplier input tokens 4,882 4,785 same cost per decision $0.0002 $0.032 155x cheaper cost per decision, realistic loop with history $0.0002 $0.035 to $0.08 170x to 390x cheaper cost per 12-step task $0.003 $0.40 to $0.90 130x to 300x cheaper model latency 0.13 to 0.38 s 5.2 s 14x to 40x faster end-to-end step, with capture and OCR about 1.5 s about 5.5 s 3.7x faster
The honest caveat: the big model read the event dates off the pixels and compared them unaided. The classifier needed the date parsing described below. Every piece of reasoning the frontier model does for free has to be rebuilt here as deterministic state.
Install
macOS 14 or newer, Python 3.12 or newer, uv.
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