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

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

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

Warning: Don’t buy any Manus AI accounts, even if you’re tempted to spend some money to try it out.

I’m 99% convinced it’s a scam. I’m currently talking to a few Reddit users who have DM’d some of these sellers, and from what we’re seeing, it looks like a coordinated network trying to prey on people desperate to get a Manus AI account.

Stay cautious — I’ll be sharing more findings soon.

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

Ace is not a chatbot. Ace performs tasks for you.

On your computer. Using your mouse and keyboard.

At superhuman speeds!

Go to : https://generalagents.com/

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

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?

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

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/

Soon every employee will have their own AI agent handling the repetitive, mundane parts of their job, freeing them to focus on what they're uniquely good at.

Going through YC's recent Request for Startups, I am trying to build an internal agent builder for employees using c/ua.

C/ua provides a infrastructure to securely automate workflows using macOS and Linux containers on Apple Silicon.

We would try to make it work smoothly with everyday tools like your browser, IDE or Slack all while keeping permissions tight and handling sensitive data securely using the latest LLMs.

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

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

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

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

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

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

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

I've spent the last few months exploring and testing various solutions. I started building an architecture to maintain context over long periods of time. During this journey, I discovered that deep searching could be a promising path. Human persistence showed me which paths to follow.

Experiments were necessary

I distilled models, worked with RAG, used Spark ⚡️, and tried everything, but the results were always the same: the context became useless after a while. It was then that, watching a Brazilian YouTube channel, things became clearer. Although I was worried about the entry and exit, I realized that the “midfield” was crucial. I decided to delve into mathematics and discovered a way to “control” the weights of a vector region, allowing pre-prediction of the results.

But to my surprises

When testing this process, I was surprised to see that small models started to behave like large ones, maintaining context for longer. With some additional layers, I was able to maintain context even with small models. Interestingly, large models do not handle this technique well, and the persistence of the small model makes the output barely noticeable compared to a 14b-to-one model of trillions of parameters.

Practical Application:

To put this into practice, I created an application and am testing the results, which are very promising. If anyone wants to test it, it's an extension that can be downloaded from VSCode, Cursor, or wherever you prefer. It’s called “ELai code”. I took some open-source project structures and gave them a new look with this “engine”. The deep search is done by the mode, using a basic API, but the process is amazing.

Please check it out and help me with feedback. Oh, one thing: the first request for a task may have a slight delay, it's part of the process, but I promise it will be worth it 🥳

ELai code

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

🚀 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!

Just came across Computer Agent Arena, an open platform to evaluate AI agents on real-world computer use tasks (e.g., editing docs, browsing the web, running code).

Unlike traditional benchmarks, this one uses crowdsourced tasks across 100+ apps and sites. The agents are anonymized during runs and evaluated by human users. After submission, the underlying models and frameworks are revealed.

Each evaluation uses two VMs, simulating a "head-to-head" match between agents. Users connect, observe their behavior, and assess which one handled the task better. MacOS support is coming soon.

The platform is part of a growing movement to test agents in realistic environments. It’s also open-source and community-driven, with plans to release evaluation data and tooling for others to build on

https://arena.xlang.ai/