Pixel Perfect Power: Building Tetris with Amazon Q CLI

INTRODUCTION

I’ve always loved the challenge and strategic thinking of Tetris, and I wanted to recreate that experience using Python and Pygame. But instead of building it from scratch, I leveraged Amazon Q CLI to accelerate the development process – a surprisingly effective approach! This project was a fantastic learning experience, demonstrating how AI can be a powerful tool for developers.

I started by setting up a Linux environment with WSL on Windows, then used Amazon Q to help generate code for core game logic. Breaking down the game into smaller components (piece generation, movement, collision detection, scoring) made it much easier to build a fully playable Tetris experience. Amazon Q isn’t just a chatbot; it's a developer assistant that helps with coding, debugging, and automation through natural language. It significantly reduced my time spent on boilerplate code and allowed me to focus on the core gameplay mechanics.

And now, I’m excited to share how I built this Tetris game using Python, Pygame, and Amazon Q CLI! This guide will walk you through the setup – WSL and all – and show you exactly how I used prompts to generate and refine the code. You can even check out my GitHub repo https://github.com/ImSaMPro/Tetris-Amazon-Q-CLI-DEMO and try it yourself! I’ll also highlight some key improvements we made along the way, focusing on making the game more visually appealing and engaging.

PREREQUISITES

Windows 10 or 11 with WSL enabled
Ubuntu installed via WSL (version 2 is highly recommended)
Python 3.9+ installed on WSL

INSTALLATION AND SETUP

Setup Windows Subsystem for Linux (WSL) version 2

Step 1: Open Windows Terminal
Step 2: Type wsl.exe --list --online (This will list your installed distributions.)
Step 3: Type wsl.exe --install Ubuntu-24.04 (Installs the latest Ubuntu release – adjust if you want a different version)
Step 4: To launch Ubuntu type wsl.exe -d Ubuntu-24.04
Step 5: You will be asked to “create a default unix user account”, type “ubuntu”
Step 6: You will be asked to “create new password”, try to use a string password with letter, numbers, capital letters, and symbols. (Important for security!)
Step 7: First command type sudo apt update
Step 8: Second command type sudo apt upgrade -y

Screenshot of Amazon Q Developer for CLI captured by Soumyadeep Mandal @imsampro #imsampro

Installing Amazon Q for command line

Step 1: To install Amazon Q for command line for Ubuntu, type wget https://desktop-release.q.us-east-1.amazonaws.com/latest/amazon-q.deb
Step 2: Install the package: sudo apt-get install -f followed by sudo dpkg -i amazon-q.deb
Step 3: Now connect and login with your AWS Builder account, type q login and hit enter.
Step 4: Select “Use for Free with Builder ID” using spacebar
Step 5: Type q and hit enter, you will be greeted with Amazon Q Cli Chat interface.

Screenshot of Amazon Q Developer for CLI approved captured by Soumyadeep Mandal @imsampro #imsampro

COMMIN ISSUES

Here are some common issues you might encounter when using Amazon Q for command line:

Authentication failures: If you're having trouble authenticating, try running q login to re-authenticate. Double-check your AWS credentials in the AWS console.
Autocomplete not working: Ensure your shell integration is properly installed by running q doctor. This will check for and fix common configuration issues.
SSH integration issues: Verify that your SSH server is properly configured to accept the required environment variables. (This is less common, but can happen.)

TROUBLESHOOTING STEPS

Follow these steps to troubleshoot issues with Amazon Q for command line:

Run q doctor to identify and fix common issues.
Check your internet connection.
Verify that you're using a supported environment (WSL 2 is crucial).
Try reinstalling Amazon Q for command line.
If the issue persists, report it using q issue.

PROMPT I USED IN AMAZON Q CLI CHAT INTERFACE

GAME WINDOWS

Tetris Game Screenshots by Soumyadeep Mandal @imsampro #imsampro

GAME IMPLEMENTATION & HOW TO PLAY INSTRUCTION BY AMAZON Q CLI

This code implements a basic Tetris game with the following features:

1. A 1000x1000 pixel window with a black background

2. All seven standard Tetris pieces (I, 0, T, L, J, S, Z) with different colors

3. Basic game mechanics:

Falling blocks that move down automatically
Left/right movement with arrow keys
Rotation with up arrow key
Fast drop with space bar
Collision detection with walls and other blocks
Line clearing when a row is filled
Game over detection when a new piece can't be placed

4. Score tracking:

Current game score
Last game score
High score
Scoring system based on number of lines cleared at once

5. Visual elements:

Grid display
Colored blocks
Score display on the right side
Game over message

To play the game:

Use left/right arrow keys to move the piece horizontally
Use up arrow key to rotate the piece
Use down arrow key to move the piece down faster
Use space bar to drop the piece instantly
Press R to restart after game over

The game runs at 30 FPS as requested, and the board is sized appropriately within the 1000x1000 window.

WHAT I LEARNED

What I learned is that even complex projects can be tackled with a strategic approach and the right tools.

Prompt-Driven Code Synthesis: I discovered the art of “prompt-driven code synthesis” – crafting highly specific, iterative prompts for Amazon Q that yielded surprisingly functional and reusable code snippets. It’s about teaching the AI exactly what you need, not just asking vague questions.
Bridging the Gap Between Intent & Implementation: The biggest challenge wasn't simply generating code; it was translating my high-level game design ideas into precise instructions for Amazon Q to execute. This honed my ability to break down complex tasks into smaller, AI-digestible steps.
Adaptive Debugging with AI Assistance: Instead of traditional debugging, I used Amazon Q to diagnose and suggest fixes for unexpected errors – a surprisingly effective way to accelerate the development process. It shifted from finding bugs to collaborating on solutions.
Reframing "Learning" as a Collaborative Process: I realized that learning isn't just about absorbing information; it’s about actively shaping an AI’s output through feedback and refinement – a truly symbiotic approach to software development.

Want to Try It Yourself?

Ready to build your own Tetris adventure, guided by the power of AI? Here’s how:

Download the Code: Grab the project from my GitHub repository https://github.com/ImSaMPro/Tetris-Amazon-Q-CLI-DEMO
Set Up Your Environment
Launch & Experiment
Become a Prompt Engineer: Don’t just run the code; talk to it.

Feel free to star the repo, share your feedback, or contribute enhancements.

Till that time .. Keep Learning... Keep Growing!!

Happy Generating! Happy Re-Generating!!

Thanks for reading!
Soumyadeep Mandal
https://www.linkedin.com/in/imsampro

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