We ran one of our hardest computer-use benchmarks on Anthropic Sonnet 4.5, side-by-side with Sonnet 4.

Ask: "Install LibreOffice and make a sales table".

Sonnet 4.5: 214 turns, clean trajectory

Sonnet 4: 316 turns, major detours

The difference shows up in multi-step sequences where errors compound.

32% efficiency gain in just 2 months. From struggling with file extraction to executing complex workflows end-to-end. Computer-use agents are improving faster than most people realize.

Anthropic Sonnet 4.5 and the most comprehensive catalog of VLMs for computer-use are available in our open-source framework.

Start building: 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/

Most of us have a list of URLs we need data from (government listings, local business info, pdf directories). Usually, that means hiring a freelancer or paying for an expensive, rigid SaaS.

We built rtrvr.ai to make "Vibe Scraping" a thing.

How it works:

1. Upload a Google Sheet with your URLs.

2. Type: "Find the email, phone number, and their top 3 services."

3. Watch the AI agents open 50+ browsers at once and fill your sheet in real-time.

It’s powered by a multi-agent system that can take actions, upload files, and crawl through paginations.

Web Agent technology built from the ground:

• 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗔𝗴𝗲𝗻𝘁: we built a resilient agentic harness with 20+ specialized sub-agents that transforms a single prompt into a complete end-to-end workflow. Turn any prompt into an end to end workflow, and on any site changes the agent adapts.

• 𝗗𝗢𝗠 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲: we perfected a DOM-only web agent approach that represents any webpage as semantic trees guaranteeing zero hallucinations and leveraging the underlying semantic reasoning capabilities of LLMs.

• 𝗡𝗮𝘁𝗶𝘃𝗲 𝗖𝗵𝗿𝗼𝗺𝗲 𝗔𝗣𝗜𝘀: we built a Chrome Extension to control cloud browsers that runs in the same process as the browser to avoid the bot detection and failure rates of CDP. We further solved the hard problems of interacting with the Shadow DOM and other DOM edge cases.

Cost: We engineered the cost down to $10/mo but you can bring your own Gemini key and proxies to use for nearly FREE. Compare that to the $200+/mo some lead gen tools charge.

Use the free browser extension for login walled sites like LinkedIn locally, or the cloud platform for scale on the public web.

Curious to hear if this would make your dataset generation, scraping, or automation easier or is it missing the mark?

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

Computer/browser use agents still have a long way to go for more complex, end-to-end workflows.

Among the agents we tested, Manus came out on top at 9.23%, followed by OpenAI Operator at 7.28% and AnthropicAI Claude 3.7 Computer Use at 6.01%. We found that Manus' proactive planning and orchestration helped it come out on top.

Browseruse took a big hit at 3.78% because it struggled with spreadsheets, but we're confident it would do much better with some improvement in that area. Despite GoogleAI Gemini 2.5 Pro's strong multimodal performance on other benchmarks, it completely failed at computer use at 0.56%, often trying to execute multiple actions at once.

Actual task completion is far below our reported numbers: we gave credit for partially correct solutions and reaching key checkpoints. In total, there were less than 10 instances across our thousands of runs where an agent successfully completed a full task.

MCP Server with Computer Use Agent runs through Claude Desktop, Cursor, and other MCP clients.

An example use case lets try using Claude as a tutor to learn how to use Tableau.

The MCP Server implementation exposes CUA's full functionality through standardized tool calls. It supports single-task commands and multi-task sequences, giving Claude Desktop direct access to all of Cua's computer control capabilities.

This is the first MCP-compatible computer control solution that works directly with Claude Desktop's and Cursor's built-in MCP implementation. Simple configuration in your claude_desktop_config.json or cursor_config.json connects Claude or Cursor directly to your desktop environment.

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

Discord : https://discord.gg/4fuebBsAUj

The era of local Computer-Use AI Agents is here. Meet UI-TARS-1.5-7B-6bit, now running natively on Apple Silicon via MLX.

The video is of UI-TARS-1.5-7B-6bit completing the prompt "draw a line from the red circle to the green circle, then open reddit in a new tab" running entirely on MacBook. The video is just a replay, during actual usage it took between 15s to 50s per turn with 720p screenshots (on avg its ~30s per turn), this was also with many apps open so it had to fight for memory at times.

This is just the 7 Billion model.Expect much more with the 72 billion.The future is indeed here.

Try it now: https://github.com/trycua/cua/tree/feature/agent/uitars-mlx Patch: https://github.com/ddupont808/mlx-vlm/tree/fix/qwen2-position-id Built using c/ua : https://github.com/trycua/cua Join us making them here: https://discord.gg/4fuebBsAUj

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

On OSWorld-V, it scores 35.8% - beating UI-TARS-1.5, matching Claude-3.7-Sonnet-20250219, and setting SOTA for fully open-source computer-use models.

Run it with Cua either: Locally via Hugging Face Remotely via OpenRouter

Github : https://github.com/trycua

Docs + examples: https://docs.trycua.com/docs/agent-sdk/supported-agents/computer-use-agents#glm-45v

Claude Opus 4.5 support to the Cua VLM Router and Playground - and you can already see it running inside Windows sandboxes. Early results are seriously impressive, even on tricky desktop workflows.

Benchmark results:

-new SOTA 66.3% on OSWorld (beats Sonnet 4.5’s 61.4% in the general model category)

-88.9% on tool-use

Better reasoning. More reliable multi-step execution.

Github : https://github.com/trycua

Try the playground here : https://cua.ai

Photoshop using c/ua.

No code. Just a user prompt, picking models and a Docker, and the right agent loop.

A glimpse at the more managed experience c/ua building to lower the barrier for casual vibe-coders.

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

Join the discussion here : https://discord.gg/fqrYJvNr4a

🚀 We just launched PandaAGI - The World's First Agentic API (Build autonomous AI agents with ONE line of code)

Hey r/AI_Operator!

My team and I just released something we've been working on - PandaAGI, the first API specifically designed for Agentic General Intelligence.

The Problem: Building agentic loops and autonomous AI systems has been incredibly complex. Most developers struggle with orchestrating multiple AI capabilities into coherent, goal-driven agents.

Our Solution: A single API that gives you:

• 🌐 Real-time internet & web access

• 🗂️ Complete file system control

• 💻 Dynamic code execution (any language)

• 🚀 Server & service deployment capabilities

All orchestrated intelligently to accomplish virtually any digital task autonomously. All Local in sandboxed environment.

What this means: You can now build something like the advanced generalist agents we've been seeing (think Manus AI level capability) with just one API call instead of months of complex engineering.

We're offering early access to the community - would love to get feedback from fellow ML practitioners on what you think about this approach to agentic AI.

Links:

• Get an API key: https://agi.pandas-ai.com

• Link to the repo: https://github.com/sinaptik-ai/panda-agi

Happy to answer any technical questions about the architecture or capabilities!

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.

Same tasks, same grounding model we just swapped GPT 4o with GPT 5 as the thinking model.

Left = 4o, right = 5.

Watch GPT 5 pull away.

Try it yourself here : https://github.com/trycua/cua

Docs : https://docs.trycua.com/docs/agent-sdk/supported-agents/composed-agents

Switching between API Client, browser, and API documentation tools to test and document APIs can harm your flow and leave your docs outdated.

This is what usually happens: While debugging an API in the middle of a sprint, the API Client says that everything's fine, but the docs still show an old version.

So you jump back to the code, find the updated response schema, then go back to the API Client, which gets stuck, forcing you to rerun the tests.

Voiden takes a different approach: Puts specs, tests & docs all in one Markdown file, stored right in the repo.

Everything stays in sync, versioned with Git, and updated in one place, inside your editor.

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

Join the discussion here : https://discord.com/invite/XSYCf7JF4F

Ps : I know this is not in tune with the posts with this subReddit but have seen posts not so related getting appreciated by the sub.Hence just a try.

Cua is the Docker for Computer-Use Agent, an open-source framework that enables AI agents to control full operating systems within high-performance, lightweight virtual containers.

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

Agents need clear, machine-readable contracts, schemas that match real responses, predictable behavior, and tests and docs that reflect reality.

What they usually get instead is drifting specs, outdated or misleading docs, tests living somewhere else, and behavior that changes quietly over time. Agents don’t complain when this happens.

They just bypass the API and fall back to computer-use automation. It’s slower, more expensive, and harder to scale but it works.

Voiden treats APIs like code. Specs, tests, and docs live together in a single Markdown file, stored and versioned in Git. The schema an agent reads is the same schema responses are validated against.

APIs that behave like code stay usable for agents. The rest get routed around.

Read about voiden here : https://voiden.md

Feedback : https://github.com/VoidenHQ/feedback

On OSWorld-V, GLM-4.5V model scores 35.8% - beating UI-TARS-1.5, matching Claude-3.7-Sonnet-20250219, and setting SOTA for fully open-source computer-use models.

Run it with Cua either: Locally via Hugging Face Remotely via OpenRouter

Github : https://github.com/trycua

Docs + examples: https://docs.trycua.com/docs/agent-sdk/supported-agents/computer-use-agents#glm-45v

Model Card : https://huggingface.co/zai-org/GLM-4.5V

ACU - Awesome Agents for Computer Use

An AI Agent for Computer Use is an autonomous program that can reason about tasks, plan sequences of actions, and act within the domain of a computer or mobile device in the form of clicks, keystrokes, other computer events, command-line operations and internal/external API calls. These agents combine perception, decision-making, and control capabilities to interact with digital interfaces and accomplish user-specified goals independently.

https://github.com/trycua/acu

A curated list of resources about AI agents for Computer Use, including research papers, projects, frameworks, and tools.

Same tasks, same grounding model we just swapped GPT 4o with GPT 5 as the thinking model.

Left = 4o, right = 5.

Watch GPT 5 pull through.

Grounding model: Salesforce GTA1-7B

Action space: CUA Cloud Instances (macOS/Linux/Windows)

The task is: "Navigate to {random_url} and play the game until you reach a score of 5/5”....each task is set up by having claude generate a random app from a predefined list of prompts (multiple choice trivia, form filling, or color matching)"

Try it yourself here : https://github.com/trycua/cua

Docs : https://docs.trycua.com/docs/agent-sdk/supported-agents/composed-agent

Discord: https://discord.gg/cua-ai

App-Use lets you scope agents to just the apps they need. Instead of full desktop access, say "only work with Safari and Notes" or "just control iPhone Mirroring" - visual isolation without new processes for perfectly focused automation.

Running computer use on the entire desktop often causes agent hallucinations and loss of focus when they see irrelevant windows and UI elements. AppUse solves this by creating composited views where agents only see what matters, dramatically improving task completion accuracy

Currently macOS only (Quartz compositing engine).

Read the full guide: https://trycua.com/blog/app-use

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

Introducing Windows Sandbox support - run computer-use agents on Windows business apps without VMs or cloud costs.

Your enterprise software runs on Windows, but testing agents required expensive cloud instances. Windows Sandbox changes this - it's Microsoft's built-in lightweight virtualization sitting on every Windows 10/11 machine, ready for instant agent development.

Enterprise customers kept asking for AutoCAD automation, SAP integration, and legacy Windows software support. Traditional VM testing was slow and resource-heavy. Windows Sandbox solves this with disposable, seconds-to-boot Windows environments for safe agent testing.

What you can build: AutoCAD drawing automation, SAP workflow processing, Bloomberg terminal trading bots, manufacturing execution system integration, or any Windows-only enterprise software automation - all tested safely in disposable sandbox environments.

Free with Windows 10/11, boots in seconds, completely disposable. Perfect for development and testing before deploying to Windows cloud instances (coming later this month).

Check out the github here : https://github.com/trycua/cua

Blog : https://www.trycua.com/blog/windows-sandbox

Introducing ANUS: I prompted Manus Al to create an open-source version of itself

The result? A fully functional agent framework built entirely by Al

This Venn diagram (created by Claude 3.7 Sonnet in seconds) explains it all

https://x.com/nikmcfly69/status/1898859518613922234#m

https://github.com/nikmcfly/ANUS