Rizel Scarlett

You've heard the buzz: AI is reshaping our work. Maybe you've tinkered with ChatGPT, or your company is pushing you to "level up." But between the hype and the endless tutorials, a gnawing question remains: how do you move from theory to building something real?

The answer is practice. Not just following steps, but creating, problem-solving, and learning by doing.

That's why we're launching Advent of AI, a 17-day challenge series starting December 1st. Whether you're a beginner taking your first steps or an advanced developer exploring AI agents, this is for you. Each weekday, you'll get a new, hands-on project designed to transform you from an AI spectator into a confident builder.

"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.

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.

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.

"The best AI agent workflows go beyond demos. They deliver real productivity."

The DevRel team at Block is a huge fan of PulseMCP. Their weekly newsletter has been an amazing way for us to discover trending MCP servers and stay in the loop with any changes within the ecosystem. When the PulseMCP creators, Mike and Tadas, shared their goals of using Goose to help automate the boring parts of their newsletter workflow, we were excited to see what they'd build.

Their implementation showcased exactly why we built Goose's feature set the way we did, and they documented the entire journey to help others learn from their experience.

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.

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.

Developers deserve to have fun. There was a time when the internet felt magical. I remember going to the library just to create a character on The Doll Palace. At home, I'd spend hours changing fonts with WordArt. But as I grew up, the industry did too. We've shifted away from marquees and glittery cursors. Grown-up me started using ones and zeros to build reliable systems for insurance, banking, and healthcare companies. There's pride in that, but it's harder to justify doing something just because it's fun.

That's why I tapped into my inner child and used Goose to build a UI that reacts to users' emotions.

Localhost Ports Hoarding​

I'm perpetually drowning in open tabs. Yes, I do need Bluesky, ChatGPT, Claude, Goose, Cursor, Discord, Slack, Netflix, and Google Docs all open at the same time. I've learned that tab management isn't my only vice.

"Hi, my name is Rizel, and I'm a localhost ports hoarder. 👋🏿"