My First AI Project: A Cool Mashup of Two Childhood Classics

The Spark of an Idea

For many of us, the sounds of our childhood are etched in 8-bit. The iconic zap of a light gun, the triumphant fanfare of a level complete... and, of course, the mocking laughter of a certain pixelated dog.

So, for my first major project with Amazon’s new AI coding assistant, Amazon Q, I decided to resurrect that legend. My goal wasn't just to develop a clone of "Duck Hunt" – I had a score to settle. It was time to finally do something about that infamous dog. It was time to teach the dog new tricks.

The twist? I'm not a game developer. With zero experience in Pygame, this project became a true test: could I go from a blank file to a fully playable game with an AI as my only mentor? This post is the story of that journey, creating a unique game I'm calling "Duck Hunt: Reloaded".

The core idea was to blend the classic shooting with a frantic catching mechanic from another beloved Eastern European LCD game, "Nu, pogodi!", where a wolf catches eggs in a basket. That way, instead of just laughing at my missed shots (well, he still does that), the dog is now my hard-working partner.

Getting started was surprisingly simple. After a quick setup of the Amazon Q Developer extension in my Visual Studio Code, I was ready. With no idea where to even begin in Pygame, I opened the chat and just described my idea. What amazed me was the flexibility of the conversation – I could mix English and Polish, and Q seamlessly understood what I wanted to achieve.

Even more impressive was its ability to act as a true mentor. I'd bring up a specific memory from the original "Duck Hunt", and Q not only remembered it but helped me translate that classic feature into code. It was an awesome experience to develop a game with a partner who knows exactly what you need.

Why a Duck Hunt Mashup? The Perfect First Project

I chose "Duck Hunt" because, as a well-known classic and one of my favorite retro games, it gave me a clear vision from the start. This meant I didn't lose time designing core mechanics from scratch. The project scaled naturally, starting with the absolute basics and progressively moving to more advanced topics like sprite animation, collision detection, and implementing unique animation sequences like the dog's intro.

The Art of the Prompt: How to Collaborate with an AI Mentor

This project taught me that collaborating with an AI is a skill. An incorrectly defined prompt often led to confusing or buggy code. But I discovered a few techniques that made our partnership incredibly effective. Instead of asking for the whole game at once, I learned to break the problem down into smaller, actionable requests. Prompts like,

Let's add a crosshair that will follow the mouse movement

when a duck hits the basket area, play the sound "duck-caught.mp3"

yielded much better results than vague questions.

How AI Handled Classic Programming Challenges

There were many challenges, like implementing the game's flow or loading and animating sprite sheets with precise coordinates. When the game's structure became a tangled mess of if statements, the AI proposed and helped implement a State Machine (menu, pre_round, playing). This architecture was key to organizing the code and making future development manageable.

The real time-saver wasn't getting one-line fixes; it was getting complete, integrated solutions. When I struggled with a series of bugs related to the new intro sequence, the AI provided a complete, cleaned-up version of the entire main.py file. This saved me from the painstaking process of manually patching multiple issues and gave me an immediate, working foundation.

Of course, it wasn't all smooth sailing. I was facing errors like SyntaxError or ValueError. But here, too, I didn't need to google them. I simply asked Q to fix them.

My Favorite AI-Generated Solutions

Here are two examples of code that showcase how the AI translated my ideas into clean, functional algorithms.

1. The State Machine Architecture in update()
This simple dispatcher in the main update loop is the heart of the game's clean architecture.

def update(self):
    # A simple switch that calls the appropriate logic for the current state
    if self.state == "pre-round":
        self.update_pre_round()
    elif self.state == "playing":
        self.update_playing()

2. The "Frightened" Duck Spawn Algorithm
To capture the classic feel, ducks had to burst out of the grass as if startled. The AI translated this idea into a specific physics behavior, giving the duck a strong initial upward velocity.

class Duck:
    def __init__(self, difficulty=1):
        # ...
        self.vx = random.uniform(-1.5, 1.5)
        self.vy = -6 - self.difficulty * 0.2 
        # ...

Final Thoughts

This project started as a simple test: could I, a non-game developer, build a playable game from scratch? The answer is yes, but the journey revealed something far more interesting than just the final product.

This experience changed my perception of AI in development. It's not just a tool for automating tedious tasks; it's a partner that can accelerate learning, unblock creativity, and handle the heavy lifting of implementation, allowing you to focus on the "what" and the "why" of your project. If this is the future of software development, I can't wait to see what we build next.

The Final Result

Want to dive into the code yourself? The full repository for this project is available on GitHub!

Jacek @jacullah