When you're working on complex projects with goose, you'll often need to break work into multiple tasks and run them with AI agents. Goose gives you two powerful ways to do this: subagents and subrecipes. Both can run multiple AI instances in parallel, but they work differently. Picking which one to use can be confusing, so we're going to guide you to a decision.

I've been using both approaches, and the choice between them depends on what you're trying to accomplish. Let me break down when to use each method and show you real examples.

Remember when you first learned to cook? You probably started with simple recipes like scrambled eggs or toast. But eventually you wanted to make something more complex, like a full dinner with multiple dishes. That's how subrecipes work in goose: each recipe can run stand-alone for a dedicated task, and a main recipe can orchestrate how they run.

Let's explore goose subrecipes together! You're about to learn know how to orchestrate multiple AI models, coordinate tasks, and build workflows that will turn you into a "head chef" user with goose.

MCP-UI is in its infancy, and there's something addictive about being this early to the party. We're at this fascinating point where both the spec and client implementations are actively developing, and I find it thrilling to build alongside that evolution.

I wanted to see how far I could push it. So I grabbed two open source MCP servers, Cloudinary and Filesystem, and gave them a UI. Instead of boring text, I now get rich, interactive interfaces right inside goose.

Over the years, Block's iOS engineers have felt the pain of technical debt accumulating. Feature flags are a specific example of this. Even when successfully rolled out, they tend to linger in the codebase, each one a small weight slowing down development.

In the early stages of 2025, with a renewed focus on developer acceleration, the Foundation iOS team decided to organize 'Feature Flag Removal Month' - an opportunity for teams in the large iOS monorepo to come together and delete possibly hundreds of thousands of lines of dead code.

With the serendipitous launch of Goose recipes around the same time, the team wondered could a dedicated recipe aid this effort? Gemma Barlow, a newer iOS engineer on the team, wanted to find out.

Data visualization just got a major upgrade in Goose. With the new MCP-UI auto visualiser feature, you no longer need to manually request charts, graphs, or visual representations of your data. Goose now automatically detects when data would benefit from visualization and renders interactive visual components directly in your conversation.

You shouldn’t need a credit card to vibe code with goose. While goose is completely free to use, the reality is that most performant LLMs aren't. You want to experience goose in action without breaking the bank or jumping through hoops. We've been thinking about how to make that first step easier for newcomers to goose.

That's why we're thrilled about our newest provider integration: Tetrate's Agent Router Service. New goose users can get $10 in credits to use goose with any model on the Tetrate platform.

Remember when people shared recipes by passing handwritten cards between neighbors? You trusted Grandma's apple pie recipe because you knew Grandma. But what happens when strangers start sharing recipes online? You need someone to taste-test them first.

That's exactly the challenge we face with Goose recipes. We're building a community cookbook where you can try Goose recipes from other users with confidence that they're safe. But we needed a way to make sure every recipe was safe to run.

The days of endless text walls in AI agent conversations are numbered. What if instead of reading through paragraphs of product descriptions, you could browse a beautiful, interactive catalog? What if booking a flight seat could be as simple as clicking on your preferred spot on a visual seat map? This isn't science fiction. It's happening right now with MCP-UI.

In a recent Wild Goose Case episode, we dove deep into MCP-UI with its creators Ido Salomon and Liad Yosef from Monday.com, alongside Block's own Andrew Harvard, to explore how this groundbreaking technology is reshaping the future of agentic interfaces.

Kim is fresh out of the academy and has spent only 18 months learning development. When I asked her how she felt about Goose she had mixed reactions. While she found it cool that it could do so much for her, she wasn’t actually sure of what it was doing for her half the time, and also why. When she asked Goose to fix a broken build, or chase a bug, It would complete the task and claim ‘Success!’. Which is great, however she felt she wasn’t actually learning as much as when she was in the academy. Add on that sometimes she didn’t even know what to ask Goose to do sometimes.

That afternoon I started to see if I could get Goose to be more than just a ‘magic box’ for my Junior Devs. What if Goose could instead act as a mentor and also teach as well as speeding up development?

Working with AI tools can feel like working with a flaky, chaotic, but overconfident coworker. You know, the kind who forgets tasks, lies unprovoked, starts new projects without telling you, then quits halfway through. It's enough to make you say: "Forget it. I'll do it myself." But before we write off AI entirely, it's worth understanding what's actually happening under the hood so we can avoid common pitfalls and make AI tools worth using.