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🎮Building Neon Dash — A Reflex Racing Game Using Amazon Q CLI

🎮Building Neon Dash — A Reflex Racing Game Using Amazon Q CLI

My experience on creating Neon Dash, a fast-paced reflex racing game using the incredible powers of Amazon Q CLI. Read the full experience here.

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Creta
Published May 26, 2025

Hey Amazon Builders!

I’m thrilled to share my experience creating Neon Dash, a fast-paced reflex racing game, using Pygame and the incredible power of Amazon Q CLI.
This project was part of the Build Games with Amazon Q CLI campaign, and it blew me away how much faster, smoother, and more creative my workflow became thanks to Q CLI.

GitHub | Demo Video

Here’s the full journey for anyone curious or looking to jump in!

🚀 Project Idea

Neon Dash is a visually striking GUI game where the player controls a glowing neon cube racing through an endless track of obstacles. The goal? Dodge barriers, survive as long as possible, and break high scores — all set in a pulsating, cyberpunk-styled world.
I wanted this game to feel snappy, addictive, and stylish, combining reactive gameplay with vibrant visuals and music.

🎮 My Game Idea: Neon Dash

Here’s the concept I fed into Q CLI:
“A fast-paced reflex racing game with a neon color theme, where the player controls a glowing vehicle, dodges obstacles, and collects pickups. The game gets faster over time and features glowing visuals, sound effects, and smooth controls.”

⚙ Step 1: Setting Up with Amazon Q CLI

amazon q welcome screen

Installation Guide

  • ✅ Installed Amazon Q CLI
  • ✅ Connected Q CLI smoothly into my Python dev environment
  • check status
The moment I had the idea, I knew Q CLI would let me skip the boilerplate and jump right into building fun mechanics.

🏗 Step 2: Using Q CLI for Game Architecture

⚡ Using Q CLI: Workflow Breakdown

1️⃣ Crafting the Prompt
Instead of jumping straight into code, I focused on describing what I wanted in as much detail as possible. The initial prompt was like:-
  • Build a Pygame-based reflex racing game called "Neon Dash."
  • - The player controls a runner or vehicle on a three-lane track.
  • - Pressing left/right keys switches between lanes.
  • - Obstacles (barriers) appear on the track; colliding ends the game.
  • - Collectible orbs increase the score and give temporary speed boosts.
  • - Background scrolls to create a fast-paced racing feel.
  • - Include neon-style visuals, glowing effects, and an energetic soundtrack.
  • - Add a high-score screen showing top scores.
With Q CLI, the more structured and specific your prompt, the better the results.
I prepared a detailed prompt that included:
  1. ✅ Game window setup (size, colors, FPS)
  2. ✅ Player controls (left/right, smooth movement)
  3. ✅ Obstacle generation and scaling difficulty
  4. ✅ Score system and pickups
  5. ✅ Visual effects like glowing trails
  6. ✅ Game over and restart functionality
  • prompt
Q CLI Result: It scaffolded a Pygame project with window setup, frame rate control, and input handling
Instead of writing each function from scratch, I iterated interactively with Q CLI, refining prompts to get polished, working code.

2️⃣ Generating Core Code

I ran the prompt through Q CLI, and in seconds, it generated:
  • ✅ A Pygame window setup
  • ✅ Player object and movement
  • ✅ Obstacle spawning system
  • ✅ Basic collision detection
  • ✅ Score tracking
prompting and correcting

3️⃣ Iterating & Improving

I used Q CLI iteratively:
  • Asked it to add sound effects.
  • Asked for visual glow effects on the player and obstacles.
  • Asked to introduce difficulty scaling — increasing obstacle speed over time.
Instead of spending hours debugging, I could refine features and try new ideas quickly, letting Q CLI handle the repetitive or boilerplate parts.
workflow

4️⃣ Testing & Polishing

Once the base game worked:
✅ I added custom neon color themes.
✅ Polished the background with moving grids and flashes (with Q CLI suggestions).
✅ Implemented a simple leaderboard (again, assisted by Q CLI).
output
game
By the end, I had a playable, visually appealing game ready for submission — in much less time than if I’d coded it all solo.
What impressed me was that the generated code wasn’t just functional — it was well-structured and modular, with clear functions and comments. I could easily tweak or expand parts without breaking things.

💡 Why Q CLI Was a Game-Changer

Here’s what stood out to me:
  • ⚡ Speed — Q CLI helped generate usable code in seconds, giving me a playable foundation fast.
  • ⚡ Creativity — I could experiment freely because the AI handled the heavy lifting.
  • ⚡ Structure — The output was modular and clean, making it easy to expand.
  • ⚡ Focus — I spent more time on design and gameplay decisions, less on boilerplate setup.
For devs used to grinding through long setup phases or struggling with game loops, Q CLI acts like an intelligent coding partner — boosting your flow and creativity.

📦 Final Thoughts

Participating in the Build Games with Amazon Q CLI campaign showed me that AI tools like Q CLI aren’t just productivity boosters — they’re creative enablers.
If you’re a developer curious about:
  • Building interactive games quickly
  • Exploring AI-assisted workflows
  • Experimenting with creative coding
I highly recommend giving Q CLI a try. It helped me turn Neon Dash from an idea to a playable project faster and smoother than I expected.
If anyone wants to collaborate, swap tips, or see my Neon Dash repo, drop a reply! Let’s build cool stuff together 💥
✨ Thanks, Amazon Q CLI team, for making this campaign fun and empowering! I’m excited to explore even more projects with this tool.
 

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