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now answering what is enzo so enzo is an opensource platform where i clubbed all the free available api for anyone use under one hood with more than 2000 models available to use for chatting coding researching and much more now answering the most common question of why you should put your time looking the project so it has few distinct feature meaning

it has a dedicated agents tab where you can describe your need and create a special agent just for one specific task with master ability in that domain

second it has the ability to connect your gmail drive and calendar and then you can ask it to perform some specific tasks like reading you the most important mail of the day or finding recruiter mails and creating personalized reply based on your data which it stores locally on your device

third the coding mode offers a dedicated preview window where you can see your code running and have a look of it feels and edit it in realtime as well as all the modes are packed with dedicated skills which delivers promising results

fourth the ui features some additional things such as music tab where you can listen to any music want and it has a custom personalized feature which runs in background and an llm understands your taste and recommends similar kind of music you like

fifth the most important why your trust it with your api key then to explain i would say enzo a dedicated vault which manages all your api and to secure it the vault as aes 256 bit encryption which prevents any person or any middle man to look at your api key and since the whole program runs locally on your device you have complete freedom to oversee all the backend work happening and it also features password lock which if you enable saves a backup key and then locks your whole platform work behind a pass screen though it is not foolproof as any third party or malware containing extension can still fetch login tokens from your browser so its security also depends upon how you access it concluding all of it.

i urge to anyone who reads this to have a look at the platform even if you hate it just curse it in the comment its fine or if you would like to drop any feedback i would highly encourage that and since its my first work open source platform i know it has a lot of errors and bugs so i apologize upfront for it and if you consider my work worthy please drop a star on the repo that'll make my day

We were wondering how many computer use tasks actually need a full general purpose LLM (e.g. gpt-6-astra, claude-opus-5 etc.) to think through all their decisions and steps. Some tasks require thinking about a plan, exploring different paths, recovering from failure. Other tasks are a question of making local decisions, like this value should go in this box, or should I check this box, or this element should be ignored.

We wondered how far we could go with a small model trained to only make these kinds of decisions.

Our inspiration was Typesafe's Jev and its System One Model framing. This is a nod to the dichotomy between thinking quickly, automatically, and intuitively (system 1) vs. thinking slowly, analytically (system 2), as described by Daniel Kahneman.

The interesting question for us was: what happens if you give a model an interface of current context, and a set of possible choices, and you ask it to return a probability for each choice? This kind of model does not generate output token by token like most LLMs do, but rather scores the options you give it, which you can check, trust, and use to drive your app's behavior.

CUA-S1 is our answer for narrow, specialized decision models for computer use. Our first release is CUA-S1-FORMS. We built this from ideas and code in jevlike, and then trained a second model just to handle form interactions. It has 706k parameters, and the original checkpoint is 2.8 MB.

The first training iteration took less than 30 minutes on synthetic data. Given a set of structured elements and values extracted from a document, it predicts whether to use the given value, CHECK, CLICK, or SKIP for each element. It does not predict new values for text fields, and does not consider screenshots. Element decisions are scored together, and your code can order the actions, and Cua Driver will execute them one at a time.

A first evaluation of this specialist vs. hosted Jev on our form task:

- For the whole decision set: 99.7% correct vs 83.6%.

- For the subset of steps that require an action: 100% correct vs 96%.

- For the subset of steps that are just leaving already-filled fields alone: 100% correct vs 74%.

The specialist was trained specifically for this task and convention (just press skip for already filled boxes), while hosted Jev has not been fine-tuned for it, so this is an experiment in scoped specialization.

We measured 7-9 ms to score a form locally vs. 260-280 ms per call to hosted Jev including network latency, though those samples measure different things and are not end-to-end form completion times.

Our interest here is in the space between a brittle script and a general agent loop. The content and layout of form fields vary enough that scripts get unwieldy, but the set of available decisions can remain narrow and well scoped. We want to explore the possibility of a general agent encountering something novel, and passing well understood decisions over to specialists like this.

That is a direction we are looking into. The current release is for forms only. We're open sourced the synthetic data generation, training, evaluation, and Driver integration under libs/cua-s1 with an MIT license.

Comments welcome! Especially if you are building computer-use agents and have run into a recurring decision that is too variable to script but is too narrow to call another LLM for.

https://www.nature.com/articles/s41593-026-02433-7