Building a Human vs AI Pong Game Using Amazon Q CLI and Python

In the world of game development, creating a simple yet engaging game can be a rewarding experience. Recently, I embarked on a project to build a classic Pong game where a human player competes against an AI opponent. This project not only allowed me to explore game mechanics but also introduced me to the Amazon Q CLI, which streamlined my development process. In this blog post, I’ll share my journey of creating the "Human vs AI Pong" game, the challenges I faced, and how Amazon Q CLI helped me along the way.
Why Amazon Q CLI?
Amazon Q CLI is Amazon’s AI-powered assistant designed to boost developer productivity. It helps generate, debug, and improve code right from the terminal. For this project, I used Amazon Q CLI to:
Understand Pygame module structures
Generate boilerplate for game loops and menus
Improve AI paddle logic for different difficulty levels
Add sound and music integration
Create glowing visual effects and animated transitions
About the Game
Human vs AI Pong is a two-player game — except the second player is a smart AI. You can choose from Easy, Medium, and Hard difficulties, and the AI responds accordingly. The game includes:
Dynamic ball effects (rainbow ball, trailing particles)
Background music and hit sound effects
Game states: Main Menu → Difficulty Select → Gameplay
Restart Option after a match ends
How Amazon Q CLI Helped
Prompt used for creating game
It generated the full loop with music loading, event checks, and clean structure. I then customized the rest to fit the game.
Screenshots
Lessons Learned
Using AI tools like Amazon Q CLI accelerates development, especially for game mechanics and visual polish.
Structuring your game into modules (paddle, ball, effects) keeps it clean and extendable.
Adding polish like audio, menus, and effects makes even a simple game feel professional.
Final Thoughts
Building this game was a fun dive into Pygame and AI-powered coding. Thanks to Amazon Q CLI, I was able to focus more on creativity and less on boilerplate.
Github Repository #AmazonQCLI
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