W Ian Douglas

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.

As Goose users, we have two main ways to provide persistent context to our AI assistant: the .goosehints file and the Memory Extension MCP server. Today, I'll share what's in my .goosehints file, why some of it should probably move to the Memory Extension, and how you can make that choice.

I taught Goose how to drive (a rover)​

Goose has no hands, no eyes, and no spatial awareness, but it can drive a rover!

I came across a demo video from Deemkeen, where he used Goose to control a Makeblock mbot2 rover using natural language commands like "drive forward/backward," "beep," and "turn left/right" powered by a Java-based MCP server and MQTT.

Inspired and excited to take it further, I taught the rover to spin, blink colorful lights, and help me take over the world!

Development teams can operate like sports teams. Each member has a role and a shared playbook helps coordinate efforts. Let's explore how AI-driven "plays" can form a starter "playbook" for your dev team, helping with common technical tasks. You can use recipes with Goose to leverage the Model Context Protocol (MCP) to make you more productive.

As developers, we're always looking for ways to build more efficient, scalable, and intelligent applications. For years, RESTful APIs have been our go-to for connecting services. Here are some ways you can integrate AI agents and MCP into your existing API infrastructure to make it smarter, more efficient, and easier to maintain.