🚀 Building “Neon Dash” with Amazon Q CLI

When I first heard about the Build Games with Amazon Q CLI campaign, I was curious but a little skeptical — how could an AI coding assistant really help speed up the game development process?

I decided to test it out with a fun idea:
✨ Neon Dash — a reflex-based racing game built with Python and Pygame, where players dodge glowing obstacles and collect neon orbs in an endless, fast-paced challenge.

Check project repository: https://github.com/Mstr-Creta/Neon_dash_game

In this blog, I’ll Walk you through how I used Amazon Q CLI, what the workflow looked like, and why it made my dev process surprisingly smooth, creative, and efficient.

🛠️ What is Amazon Q CLI?
Amazon Q CLI is a command-line interface tool powered by Amazon’s generative AI. It can help developers:
✅ Generate code from detailed prompts
✅ Create modular, readable project structures
✅ Suggest fixes, optimizations, or improvements
✅ Iterate on projects quickly, even for creative or experimental builds

*Read the blog here to get started with Q on your machine: *

docs.aws.amazon.com

For this campaign, I wanted to test if Q CLI could handle an interactive GUI-based game — not just backend or scripts, but something visual and real-time.

🎮 My Game Idea: Neon Dash

Here’s the concept I fed into Q CLI:
I want to build a Pygame-based GUI game called "Neon Dash."

Game concept:

A fast-paced reflex racing game where the player controls a glowing neon vehicle racing down an endless track filled with moving obstacles.
The goal is to survive as long as possible, dodging obstacles and collecting neon orbs for points.
The game gets progressively faster and harder over time.

Key features I want the code to include:

Game Window & Setup:

Pygame window with dimensions (e.g., 800x600).

Neon color palette: black background with bright glowing colors (cyan, magenta, yellow, lime).

FPS set for smooth gameplay (e.g., 60 FPS).

Player Vehicle:

Represent the player’s vehicle as a glowing rectangle or triangle.

Control with left/right arrow keys (or A/D) to shift between lanes or smoothly move left/right.

Add a subtle trail effect behind the vehicle for a glowing streak.

Track & Obstacles:

Generate a series of incoming neon obstacles (blocks, walls, or barriers) that the player must dodge.

Randomize obstacle positions and sizes.

Gradually increase obstacle speed as the player survives longer.

Pickups & Scoring:

Generate collectible neon orbs or pickups that increase the player’s score.

Track and display current score on-screen.

Optional: Add combo multipliers for streaks or near-misses.

Visual Effects:

Add glow/shadow effects to obstacles, player, and pickups.

Background animations (moving neon grid, flashing patterns, or starfield effect).

Smooth screen shake or pulse effect when the player narrowly dodges or collects pickups.

Game Over & Restart:

Detect collisions with obstacles — triggers game over.

Show game over screen with final score.

Add a “press R to restart” or “press Q to quit” option.

Sound & Music:

Looping synthwave/neon-style background track.

Sound effects for dodging, collisions, and collecting pickups.

Difficulty Scaling:

Gradually increase the speed of obstacles and frequency as time progresses.

Optional: Introduce new types of obstacles or lane patterns at certain score thresholds.

Optional Bonus Features:

Add leaderboard system to track top scores.

Add custom color themes players can unlock.

Add a turbo/boost mode the player can activate when a meter fills up. Please generate:

Main game loop with proper event handling.

Code for rendering the player vehicle, obstacles, pickups, and score display.

Collision detection between player, obstacles, and pickups.

Functions to spawn obstacles and pickups with random properties.

Code for scaling difficulty and updating speed.

Code for the game-over screen and restart handling. Bonus if possible:

Code for simple particle effects when the player collects pickups or crashes.

Code for a neon-style animated background (moving grid or pulse). Make sure the code is modular, readable, and split into functions or classes where appropriate to make future expansion easy.

I prepared a detailed prompt that included:
✅ Game window setup (size, colors, FPS)
✅ Player controls (left/right, smooth movement)
✅ Obstacle generation and scaling difficulty
✅ Score system and pickups
✅ Visual effects like glowing trails
✅ Game over and restart functionality

⚡ Using Q CLI: Workflow Breakdown

Here’s how the workflow went:
1️⃣ Crafting the Prompt
Instead of jumping straight into code, I focused on describing what I wanted in as much detail as possible.

With Q CLI, the more structured and specific your prompt, the better the results.

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

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.

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.

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).

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.

💡 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 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: