usecomputer is a desktop automation CLI for AI agents. It works on macOS and Linux.
Screenshot, mouse control (move, click, drag, scroll), and keyboard synthesis (type and press) are all available as CLI commands backed by a native Zig binary
This one works as an extension so you don't need a new browser specifically for agents. It's easier to collaborate. Also if you run the MCP in non headless mode it brings to front the browser on every interaction, like opening a new page. With the extension this does not happen.
Another benefit is context usage.
The cli can also do a lot more than other MCPs because it uses code snippets run in a stateful sandbox to control the browser, so it can do virtually anything instead of just a few exposed tools like `scroll`, `click`
Mainly using playwriter.dev to help debug CSS issues on the page. It's an extension you can enable in Chrome and let agents control the browser via CDP
I am using WebSocket hibernation so the connection will cost nothing if no events are being sent after some time, 9 seconds I think. I think cost is completely negligible. I run a similar service for the Framer MCP and it costs basically nothing even with thousands of users using it every week.
The big advantage of this architecture is that it that it's very lightweight and starts in less than 200ms. Much faster than the built in Cloudflare tunnel, and no login required
Kernels now written in Mojo were all in hand written in MLIR like in this repo. They made a full language because that's not scalable, a sane language is totally worth it. Nvidia will probably end up buying them in a few years.
A browser automation Chrome extension and MCP. It consumes less context than playwright MCP and is more capable: it uses the playwright API directly, the Chrome extension is a CDP protocol proxy via WebSockets.
I use it for automating workflows in development but also filing taxes and other boring tasks
This Chrome extension allows you to control your own browser via MCP.
It bridges the CDP protocol from the MCP to the browser, meaning you can do everything Playwright can.
The MCP works using a single tool: execute. It will run Playwright code to control the browser, meaning context usage is small compared to Playwright MCP and the capabilities are more extensive
The advantage of space is that you have infinite scale. Maybe data centers in space do not work at low scale but you have to think of them at much larger scale.
Elon Musk considered data centers in space simply for the fact that more solar power is available in space than Earth
This project is similar to Playwright MCP, but with some important differences and benefits.
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1. Key differences from Playwright MCP
1. Uses your existing Chrome tabs via an installable Chrome extension instead of launching a new browser window.
2. Exposes only one MCP tool: `execute`, which runs arbitrary Playwright code in a sandbox.
3. The extension communicates with the MCP via Chrome DevTools Protocol (CDP) over WebSocket.
2. Benefits
1. Less context bloat: the LLM only needs to reason about a single tool instead of many.
2. More capable: the agent can write any Playwright code to control the browser.
3. Fewer resources: reuses your existing Chrome browser instead of spawning a new instance per MCP project.
4. Human–machine collaboration: you can temporarily disable the extension to:
* Solve CAPTCHAs
* Unstick the LLM
* Intervene manually when needed
3. Using it in your own scripts
1. You can connect to the extension from your own Playwright scripts.
2. This lets you automate tasks in your existing Chrome tabs, instead of running a separate browser instance.
4. Non-disruptive browser control
1. The extension does not force-focus Chrome windows during interactions.
2. You can run automation in non-headless mode without Chrome constantly stealing focus and interrupting your work.
5. Security model
1. The extension connects to a localhost WebSocket server started by the MCP.
2. This server proxies MCP commands to the extension.
3. Only tabs where the extension is enabled are accessible to the MCP.
4. Other tabs remain isolated and inaccessible to random agents.
6. Safety considerations
1. The MCP can still cause damage if run unsupervised.
2. You should:
* Run it with permissions enabled, and/or
* Keep it under active human supervision.
This is how tools are implemented in latest Gemini models like gemini-2.5-flash-preview-native-audio-dialog: the LLM has access to a code execution tool that can run code in python and all tools are available in a default_api class
This is an API I built for my agents to let them read files in public GitHub repos without relying on the official GitHub API.
There are no rate limits. The API is built on top of sqlite on Cloudflare Durable Objects.
The first time you fetch an url it downloads the repository tar file and stores the files contents in the sqlite database.
It then uses sqlite to list, read and search the files.
Agents can paginate files using search params like start and end to only read a portion of a file. They can also use globs to only search or read a portion of a repo
It's because these industries don't create their own benchmarks. The only ones creating evals are the AI company themselves or open source software engineers