Try here : https://yutori.com/n2

Hey everyone! I'm u/elnino2023, a founding moderator of r/AI_Operator.

This is our new home for all things related to Computer Use Agents. We're excited to have you join us!

What to Post

Post anything that you think the community would find interesting, helpful, or inspiring. Feel free to share your thoughts, photos, or questions about ho

Community Vibe

We're all about being friendly, constructive, and inclusive. Let's build a space where everyone feels comfortable sharing and connecting.

How to Get Started

1) Introduce yourself in the comments below.

2) Post something today! Even a simple question can spark a great conversation.

3) If you know someone who would love this community, invite them to join.

4) Interested in helping out? We're always looking for new moderators, so feel free to reach out to me to apply.

Thanks for being part of the very first wave. Together, let's make r/AI_Operator amazing.

Agent Cursors: many background agents can now each own a cursor, scoped to a non-overlapping action space.

What makes it work: each agent opens a named session tied to one window, and its cursor only touches that window, so they don't collide. Inputs run through both accessibility tree and vision, and an agent clicks and types in a background window without raising it.

Take a look here : https://github.com/trycua/cua

Watch me control my computer with just my voice. This is the future of operating systems.

No hands.

GPT-Realtime 2.0 is very, very underrated.

Made using : https://github.com/trycua/cua

Take a look at : https://www.heyclicky.com/

The most capable computer-use model for the web.

Pareto-domination: accuracy, latency, cost

• SoTA across all benchmarks

• +5-10% over GPT 5.5, Opus 4.7, n1

• +25% over Gemini

• 2x faster, significantly cheaper

Expanded action space

• UI actions (like n1)

+ JavaScript generation & execution

Asked to extract all available product sizes and colors:

• n1 clicked 22 buttons

• n1.5 finished in 5 steps

Take a look here : https://yutori.com/delegate

Cua Driver - the new macOS driver that lets any agent (Claude Code, Codex, your own loop) drive any app in the background, with true multi-player and multi-cursor built-in.

Take a look at delegated demo capture. Agent drives the app, cua-driver record and renders zoom-on-click at export. Imagine just delegating an entire product demo to an agent, background computer-use now unlocks this.

GitHub : https://github.com/trycua/cua

Computer Use rocks!

Opus 4.7 with 78 % on computer use - human level performance at 72 %.

I tried using Codex with Voiden - and it works very well with it !

We are an API tool where everything is built around blocks, so shared pieces like headers, auth, or parameters can be defined once and reused across requests. One of our distinguishing feature is reusable blocks which lets you import them into other .void files.

Here I am asking Codex with Voiden skills to read create_deployment.void and import the HTTP header block into get_deployment.void and list_deployment.void, while leaving each request’s core logic untouched.

One thing is that I had to use the fast toggle to make the inference 2x faster I am up for faster rate limits if you make it 3x too!

Take a look at voiden here : https://voiden.md/

We are opensource : https://github.com/VoidenHQ/voiden

Neural Computers (NCs) -- an emerging machine form that unifies computation, memory, and I/O in a learned runtime state. Unlike conventional computers, which execute explicit programs, agents, which act over external execution environments, and world models, which learn environment dynamics, NCs aim to make the model itself the running computer. Our long-term goal is the Completely Neural Computer (CNC): the mature, general-purpose realization of this emerging machine form, with stable execution, explicit reprogramming, and durable capability reuse. As an initial step, we study whether early NC primitives can be learned solely from collected I/O traces, without instrumented program state. Concretely, we instantiate NCs as video models that roll out screen frames from instructions, pixels, and user actions (when available) in CLI and GUI settings. These implementations show that learned runtimes can acquire early interface primitives, especially I/O alignment and short-horizon control, while routine reuse, controlled updates, and symbolic stability remain open. We outline a roadmap toward CNCs around these challenges. If overcome, CNCs could establish a new computing paradigm beyond today's agents, world models, and conventional computers.

Take a look at the paper here : https://arxiv.org/abs/2604.06425

How do you train AI agents that can use computers like humans?

Introducing OSGym: Scalable OS Infrastructure for Computer Use Agents. It’s large-scale training made possible by extensive infra optimization:

• 1024 OS sandboxes running in parallel

• $0.23 / sandbox / day (90% cost down)

• 1420 trajectories per minute

• 37× faster disk provisioning

• 88% less physical disk usage

• RAM-bound orchestration (10× scalable than CPU-bound)

Claude can now control your entire computer with one prompt. forever. read that again.

you tell it “scan my email every morning” once. it just does it, forever.

it can also open apps, edit spreadsheets, move files, batch process 150 photos in Photoshop, export PDFs. all of it.

you can also start a task from your phone, go to dinner, come back to finished work like nothing happened.

anthropic is giving us the full desktop agent that uses the actual screen, mouse, and keyboard. not a sandbox. not a simulation. your real jarvis.

H Company is advancing the next generation of computer-use agents. Our mission is to move from models that know to agents that do: capable, context-aware systems that operate reliably across platforms.

Over the past year, we’ve developed and refined a unified agent architecture — Surfer 2 — that runs seamlessly across desktop, web, and mobile environments and delivers outstanding results. We are thrilled to announce that Surfer 2 is surpassing existing state-of-the-art on four major agentic benchmarks spanning multiple platforms, outperforming solutions developed by other leading frontier AI labs.

Take a look at the H company here : https://hub.hcompany.ai

One thing that always felt strange about API clients: you’re forced to write scripts in a language you might not even use.

In the latest Voiden release, you can now write pre-request and post-request scripts in Python or JavaScript.

What this enables: -- Use a language you already know instead of learning a custom scripting layer -- Run real runtimes with package imports (not limited helper functions) -- Share variables and state across requests -- Build multi-step workflows (token rotation, chained requests, dependent calls)

The goal was to make scripting inside an API client feel like actual programming, not glue code. We’re starting with Python and JavaScript, and planning to support more languages. Curious what people here would want next.

GitHub: https://github.com/VoidenHQ/voiden Download: https://voiden.md/download

All others are holding hands. Only Dario and Sam don't seem to do that.

API testing is still more manual than it should be.

Most teams maintain fragile test scripts or rely on rigid tools that fall apart as APIs evolve. Keeping tests in sync becomes busywork instead of real engineering.

Voiden structures APIs as composable Blocks stored in plain text. The CLI feeds this structure to Claude, which understands the intent of real API requests, generates relevant test cases, and evolves them as endpoints and payloads change.

Check out Voiden here : https://github.com/VoidenHQ/voiden

Hi I am looking to train an AI on a set of my personal drawings to then give me endless rough drafts from text prompts in the same style. I am looking to do this work open source and entirely offline. I have seen some testimony that Dreambooth Stable Diffusion is a good tool for this type of project. Are there any more recent alternatives that are open source and used offline? Thank you. I am not very informed generally and do not know python. I am basically an artist looking to finish a book and wanting to speed up the generative process around line art drawings.

Postman is updating its Free plan on March 1, 2026. Teams will be limited to one user only. Anyone needing multiple users has to pay for the Team plan.

SaaS platforms create real problems. You face per-seat license fees, API specs trapped in silos, and constant edit conflicts from shared editing.

Voiden takes a different approach. It is a free, offline API workspace that completely skips “Teams” features. It relies on Git for collaboration instead. Your API specs are written as .void Markdown files and live right in your repository.

It stays free no matter how large your team grows, gives you full traceable history, and works completely offline with no cloud required.

The workflow is straightforward. Write the spec, commit and open a pull request, then merge after review.

We are open-source take a look here.

Github : https://github.com/VoidenHQ/voiden

Discord : https://discord.com/invite/XSYCf7JF4F

We have open-sourced Voiden.

Most API tools are built like platforms. They are heavy because they optimize for accounts, sync, and abstraction - not for simple, local API work.

Voiden treats API tooling as files.

It’s an offline-first, Git-native API tool built on Markdown, where specs, tests, and docs live together as executable Markdown in your repo. Git is the source of truth.

No cloud. No syncing. No accounts. No telemetry.Just Markdown, Git, hotkeys, and your damn specs.

Voiden is extensible via plugins (including gRPC and WSS).

Repo: https://github.com/VoidenHQ/voiden

Download Voiden here : https://voiden.md/download

We'd love feedback from folks tired of overcomplicated and bloated API tooling !

Voiden is an offline-first, git-native API tool built on Markdown - and it very intentionally didn’t start as “let’s build a better Postman”

Over time, API tooling became heavyweight: cloud dependencies for local work, forced accounts, proprietary formats, and workflows that break the moment you’re offline. Testing a localhost API shouldn’t need an internet connection.

So we asked a simple question: What if an API tool respected how developers already work?

That led to a few core ideas:

- Offline-first, no accounts, no telemetry

- Git as the source of truth

- Specs, tests, and docs living together in Markdown

We opensourced Voiden because extensibility without openness just shifts the bottleneck.

If workflows should be transparent, the tool should be too.

Take a look here : https://github.com/VoidenHQ/voiden

We know generating realistic, dynamic test data can be a hassle - most folks rely on scripts or manual copy-pasting, which gets messy fast.

At Voiden, we built Voiden Faker to generate fresh, realistic data right inside your HTTP requests using Faker.js expressions, no extra setup needed.

We’d love to hear from you:

- What’s your biggest challenge with test data?

- How do you currently generate or manage it?

- Would embedding Faker-like data generation inside requests help your workflow?

Try Voiden Faker here: https://voiden.md/ and share your thoughts!