Jev & computer-use

Current work: jev-computer-use

My TL for the last few days has been taken over by Jev, a system one model from typesafe.ai. So i applied for the waitlist and got in.

To me a interesting use-case for this kind of model is on browser/computer-use, lot of decision to be taken which may not need long reasoning before doing so, the UX improves with the speed we get and automations feel right.
Checkout the jev-ultrafast by browser-use.

Take a general purpose model (GPT / Claude), the lifecycle of a understand user request looks like:

• plan
• generate code / text
• produce tool calls
• something broke / unexpected ? - give the agent new state ask it to redo stuff.

This is useful when the task isn't fully known in advance, involves long reasoning and time. But think of structured inputs like forms or OS accessibility, the decisions of what needs be done given a goal is more local than you think.

What is Jev?

This could be very simply put as a classifier. The model accepts a state and questions returns a typed decision. A state is your structured text input and questions have three primitives:

• Choice: picks one of the options you provide + confidence
• Score: rate something you provide + confidence
• Noul: yes / no with a probability

General purpose LLMs are also capable of it but the main difference here is - jev exposes a decision interface rather text generation interface, give the context and decision to make, received a typed result.

Think of making your agent logic as state machines, give that state, goal and the choices it can trigger, this works really well.

On computer-use

There are multiple ways to do this:

• Screenshot, use a model for segmenting and OCR then convert to structured format pass it to JEV for decision
• OR the OS itself provides accessibility APIs to navigate, its already structured use it directly

I'm currently experimenting on this: jev-computer-use

A example computer-use flow:

Claims and things to look out for

Although the model is refreshing in the age of LLMs, i think the magic lies in the data, they do claim they are a data research company first and it's been trained on 100% synthetic data.

There is one more strong claim on almost no hallucination, being type safe does not guarantee that.
If you think this run deterministically, NO, same prompt gives you different probabilities every run.

Confidence score is a derived metric which is tightly coupled with the classification scores. So it may not be the right measure for whatever you are doing and you are are open to come up with any definition for the confidence using the probabilities list.