🎮 How I Built “Simon Says Pro” with Python and Amazon Q Developer CLI 🚀

When I started building “Simon Says Pro,” I had one goal: to create an engaging, colorful, and responsive memory game — and build it smarter, faster, and better.

That’s when I discovered the power of Amazon Q Developer CLI — a command-line AI assistant that helped me brainstorm, structure, debug, and write clean code, all from my terminal.

This post is a walk-through of the development journey, tools used, challenges solved, and how Q CLI became my go-to coding companion.

🧠 Game Concept: What is Simon Says Pro?
"Simon Says Pro" is an upgraded version of the classic memory game — where colored buttons light up in a pattern and the player must repeat the sequence correctly.

Every round adds a new button, increasing difficulty. It’s a fun way to test and improve memory, focus, and reaction time.

🔥 Key Features:

• 🎨 Visually engaging, animated UI with colored buttons
• 🔊 Custom audio for each button, generated with NumPy
• ⏱️ Timer-based gameplay for added challenge
• 💥 Particle effects for wins and losses
• 🧠 Dynamic memory pattern that gets harder each round
• 🏆 Persistent high score

⚙️ Tech Stack

• Python 3.10+
• Pygame – for rendering graphics and input
• NumPy – for generating sound
• Amazon Q Developer CLI – for AI-powered coding help

🧰 Setting Up Amazon Q Developer CLI
Before starting the project, I set up the Amazon Q CLI — and it was game-changing.

📦 Installation
bash
npm install -g @aws/q
🔐 Authenticate
bash
qcli login

💬 Start an AI-Powered Terminal Session
bash
qcli chat
Once inside the chat, I could:

Ask for Python game architecture suggestions
Generate class templates
Request fixes for layout and animation bugs
Discuss color layouts and button positioning
Write audio generation logic using NumPy
It was like pair-programming with an expert who never sleeps.

🏗️ Project Structure
Thanks to Q CLI, the game is cleanly split into multiple files:

bash
simon-says-pro/
├── main.py # Entry point
├── constants.py # Global constants and colors
├── button.py # Interactive button class
├── audio.py # Audio generation using NumPy
├── effects.py # Particle system
├── game.py # Core game loop and logic
└── README.md # Documentation
Q CLI helped generate boilerplate and also ensured that modules were decoupled and reusable.

🧩 Game Development Breakdown
1️⃣ Layout & Button Logic
With the help of Q CLI, I structured the buttons in a hexagonal layout using calculated x/y offsets. Each button has:

Dark and light color versions
A sound frequency
Click detection and hover animation
Q CLI even helped with subtle animations using scale and color interpolation.

2️⃣ Audio
I asked Q CLI:
"Can you help me generate sound for each button using NumPy sine waves?"
It returned a precise make_tone() function, which generates real-time sound based on frequency, duration, and volume — exactly what I needed.

3️⃣ Game State Management
The game runs on four states: START, SHOW, PLAY, GAMEOVER. Transitions are handled using a centralized update loop. Q CLI helped me refactor logic to avoid spaghetti code.

4️⃣ Particles & Effects
The visual feedback on winning or losing was important. Q CLI guided me through implementing a simple particle system using pygame.draw.circle, velocity, and lifespan.

5️⃣ Score Persistence
Using pathlib and json, the game reads and writes the highest score locally. Again, Q CLI suggested a minimal and elegant implementation.

🖥️ How to Run the Game
✅ Prerequisites
Python 3.10+
pip

🧪 Install Dependencies
bash
pip install pygame numpy
▶️ Run the Game
bash
python main.py

💬 Challenges Solved with Q CLI
Challenge How Q CLI Helped
Layout math for hex buttons Provided correct spacing logic
Audio wave generation Generated accurate NumPy sine functions
Modular project structure Suggested clean file-based architecture
Debugging animation flickers Found and resolved update timing issue
Clean particle burst logic Designed a reusable effect class

🧠 Lessons Learned
AI-powered development is real. Amazon Q CLI didn’t just help write code — it helped me think through problems.

Modular design makes debugging and adding features effortless.

Python + Pygame + NumPy is still a powerful stack for creative games.

Particle systems are fun — they add life to any game!

📣 Why You Should Try Amazon Q CLI
If you’re a developer who:

Spends time Googling solutions
Wants real-time help in the terminal
Loves writing clean, testable code
Then Amazon Q CLI is for you.
It’s your co-pilot — minus the tab overload.

🔗 Final Thoughts
Simon Says Pro was more than a game — it was an experiment in AI-assisted creativity. Thanks to Amazon Q Developer CLI, the development process became smoother, smarter, and surprisingly fun.

👉 Check out the repo https://github.com/ekantverma/Amazone_Q_CLI_Challenge

🔧 Want to build your own project with Q CLI? Just run:

bash
npm install -g @aws/q
qcli login
qcli chat
Let the AI guide you through your next build.

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