"Migrate my app from x language to y language." You hit enter, watch your AI agent spin its wheels, and eventually every success story you've heard feels like a carefully orchestrated lie.

Most failures have less to do with the agent's capability and more to do with poor prompt and context strategy. Think about it: if someone dropped you into a complex, unfamiliar codebase and said "migrate this," you'd be lost without a plan. You'd need to explore the code, ask questions about its structure, and break the work into manageable steps.

Your AI agent needs the same approach: guided exploration, strategic questions, and decomposed tasks.

I code best when I sit criss-cross applesauce on my bed or couch with my laptop in my lap, a snack nearby, and no extra screens competing for my attention. Sometimes I keep the editor and browser side by side; other times, I make them full screen and switch between applications. I don't like using multiple monitors, and my developer environment is embarrassingly barebones.

The described setup allows me to fall into a deep flow state, which is essential for staying productive as a software engineer. It gives me the focus to dig beneath the surface of a problem, trace its root cause, and think about how every fix or improvement affects both users and the system as a whole. While quick bursts of multitasking may work well for other fields, real productivity in engineering often comes from long stretches of uninterrupted thought.

Recently, my workflow changed.

My mom was doing her usual Sunday ritual she had her pen, paper, calculator, and a pile of receipts. I’ve tried to get her to use every budgeting app out there, but she’s old school and always says the same thing:

“They’re all too complicated.”

You've been using goose for weeks, maybe months. You have dozens of successful sessions where you asked for help with blog posts, code reviews, documentation, or data analysis. Each time you think "Didn't I already do this?" but never get around to checking. Sound familiar?

I myself had over a hundred goose sessions and as many megabytes of conversation data. I was sitting on a goldmine of potential automation. A coworker suggested something brilliant: "What if goose could analyze your sessions and build recipes automatically?" Wait, wait, wait!! Create a personalized cookbook based on my own session history? Yes, please! Let's build a "cookbook generator" recipe!

October is around the corner, which means spooky season is upon us, and with that crispy fall air that gives you "goose" bumps...it's finally time for Hacktoberfest 2025! The goose team is beyond excited to celebrate with you all for the first time this year. Let's get into how you can participate, and what prizes you can win. 👀

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.