typesafe-computer-use

**typesafe-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](https://docs.typesafe.ai) 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 do