Building Love into My ‘Birdie’ Game for Valentine’s Day.

VERSION 1.0 – PRE-LAUNCH (Planning Phase)

Valentine’s Day is just around the corner, and I’m in the thick of planning some exciting new features for Birdie. I’m documenting this entire process because it’s a perfect example of how AI-assisted development can turn a creative idea into a real feature, and I want to show you exactly how it works.

It started with a simple thought: what if I could celebrate Valentine’s Day by adding features to Birdie that teach people something romantic and educational? I was thinking about birds and relationships, and something clicked—most bird species are actually monogamous. About 90% of them! That’s way higher than mammals, and it’s genuinely beautiful. Why not build a game feature around that?

But here’s where I resisted the urge to jump straight into coding. A good idea isn’t the same as a shipped feature. Before touching any code, I needed to sit down and ask tough questions: What exactly is the gameplay? How many questions should there be? Should I use my existing 27 birds or generate new ones? How do I structure this so it’s engaging but not overwhelming?

This is where I brought in Claude, my AI pair programmer. We didn’t jump straight to building. Instead, we planned. We brainstormed different approaches—some too complex, some too simple. We narrowed it down to three Valentine’s features I want to build:

We figured out the data structure I’ll need: 14 monogamous birds as correct answers and 28 non-monogamous birds as wrong answers. We discovered that my existing 27 birds already have enough monogamous ones, but I need to generate 20 new non-monogamous birds to round out the pool. Smart bonus: these 20 new birds will live on in the game after Valentine’s Day, becoming part of the main bird identification challenge. No waste—just smart resource planning.

The planning is solid. I know exactly what I’m building, why I’m building it, and how it all fits together. Next comes the execution phase:

This is the part I’m about to start, and I’ll be documenting the journey as I go. Over the next two weeks, you’ll see the real messy, iterative process of building features with AI. The changes, the adjustments, the decisions made along the way.

Here’s what I’ve learned: the quality of your planning directly impacts the quality of your execution. By spending time upfront to brainstorm with an AI partner, ask hard questions, and nail down the details before coding, I’m setting myself up to avoid countless hours of rework later.

Too many developers (including past me) jump straight to building without a plan. Then they realize halfway through that the feature doesn’t quite work, or the data structure isn’t right, or the UX is clunky. With proper planning, those issues get caught and solved before a single line of production code is written.

It also shows something powerful about AI-assisted development: it’s not about the AI replacing you—it’s about amplifying your ability to think clearly and build faster. Claude helped me ask better questions, catch edge cases, and plan things I might have overlooked. But I still made all the decisions. I still directed the vision. The AI was a thinking partner, not a replacement for judgment.

I’m committing to documenting this entire build and launch as it unfolds. Over the next couple of weeks, I’ll be sharing:

If you’re interested in how AI and automation can accelerate your projects—whether you’re a developer, a creator, or someone with an idea—this journey might inspire you to explore similar workflows.

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And if you’re curious about exploring AI and automation in your own work, I’d love to hear your ideas and share what I’m learning.

The future of building is collaborative between humans and AI. Let’s explore it together.

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