Ever wondered what happens when you let two AI models work together like a tag team? That’s exactly what we tested in our latest livestream—putting Goose’s Lead/Worker model to work on a real project. Spoiler: it’s actually pretty great.

The Lead/Worker model is one of those features that sounds simple on paper but delivers some amazing benefits in practice. Think of it like having a project manager and a developer working in perfect harmony - one does the strategic thinking, the other gets their hands dirty with the actual implementation.

Goose recently released support for MCP-UI which allows MCP servers to suggest and contribute user interface elements back to the agent.

MCP-UI sits on top of the protocol, but instead of text/markdown being the result, servers can return content that the client can render richly (including interactive GUI content).

What if I told you that you could build a complete, working web application in under an hour using nothing but AI agents? Not just a simple "Hello World" app, but a full-stack application with a backend API, responsive frontend, unit tests, and documentation?

That's exactly what we accomplished during the Vibe Coding workshop at Berkeley's Agentic AI Summit, where I demonstrated how to use Goose's subagent orchestration to spin up an entire development team of AI agents. Each agent took on a specific role - from product planning to QA testing - and worked together to build "AI BriefMe", a web app that generates executive style briefings on any topic.

Machine learning and data science workflows are inherently iterative. You load data, explore patterns, build models, and refine your approach based on results. But traditional AI assistants lose context between interactions, forcing you to reload data and re-establish context repeatedly—making data-heavy development slow and expensive.

The MCP Jupyter Server solves this by enabling AI agents like Goose to work directly with your Jupyter notebooks, maintaining persistent memory and state across interactions while letting the AI interact with your data through code execution rather than raw data transfer.

When my team launched Goose in early January 2025, we knew we had something special. We built a free, open source AI agent that leverages the Model Context Protocol. It was inventive in its approach, offering developers a local solution with the flexibility to bring their LLM of choice.

The LLM Cost Problem​

After using the product internally for a few months, my teammates were eager to share Goose with the developer community through workshops and hackathons. We wanted to provide hands-on experiences where people could actually build with Goose, because that's how developers fall in love with a product.

But we hit a thorny challenge: while Goose is free, high-performing LLMs are not.

It was about 3am and as I was ready to finally close my eyes for the day, I jolted up with a wild idea! I wanted to see what would happen if Goose talked to... Goose.

I grabbed my laptop and with one eye opened, I typed the following prompt from Goose Desktop:

👩🏽‍🦱 Me:

run goose from the cli and have a convo with the other goose and tell me what yall talk about. to end the convo with goose2, type exit

What happened next blew me away...

Creating effective security detections in Panther traditionally requires deep knowledge of detection logic, testing frameworks, and development workflows. The detection engineering team at Block has streamlined this process by building Goose recipes that automate the entire detection creation lifecycle from initial repository setup to pull request creation.

This blog post explores how to leverage Goose's recipe and sub-recipe system to create new detections in Panther with minimal manual intervention, ensuring consistency, quality, and adherence to team standards.

I built Postman meets Google Docs for 10-year-olds.

Cue record scratch.

Cue freeze frame.

Cue movie cliché.

You're probably wondering how I got here.

Whenever people talk about AI, they highlight the flashiest use cases like fully coded apps built by agents or cinematic video generation. Those things are certainly cool, but most days I'm just delegating mundane tasks to the bots.

Today, I didn't build an app. I didn't write a screenplay. I just got stuff done.

Here are 5 real, everyday tasks I gave to my AI agent, Goose, that saved me hours. None of them took more than one minute from prompt to result.

Over ten years ago, Docker came onto the scene and introduced developers en masse to the concept and practice of containers. These containers helped solve deployment and build-time problems, and in some cases, issues with development environments. They quickly became mainstream. The technology underlying containers included copy-on-write filesystems and lightweight, virtual-machine-like environments that helped isolate processes and simplify cleanup.

Dagger, the project and company founded by Docker’s creator Solomon Hykes, has furthered the reach of containers for developers.

One project that emerged from this work is Container Use, an MCP server that gives agents an interface for working in isolated containers and git branches. It supports clear lifecycles, easy rollbacks, and safer experimentation, without sacrificing the ergonomics developers expect from local agents.

Container Use brings containerized, git-branch-isolated development directly into your Goose workflow. While still early in its development, it's evolving quickly and already offers helpful tools for lightweight, branch-specific isolation when you need it.