Building Snake Evolution with Amazon Q CLI
Discover how I built Snake Evolution — a modern twist on the classic Snake game — using Amazon Q CLI to tackle feature complexity and game logic.
Published May 30, 2025
Last Modified May 31, 2025
🎮 Watch the gameplay video here:
Growing up, I loved playing Snake — the simplicity, the increasing tension, the pure arcade joy. But I always wondered: what if Snake could evolve? That thought became Snake Evolution, a modern reimagining where new mechanics, adaptive AI, and evolving visuals transform the retro snake experience into a dynamic strategy game. Building it brought unique challenges — and Amazon Q CLI helped me conquer them all.
When I typed q chat inside my Snake project folder, I wasn’t expecting this level of interaction — but Q CLI immediately impressed me with its welcoming interface and tailored support:
In Snake Evolution, the player still controls a growing snake in a bounded grid — but this time, each level introduces new rules:
- Some apples grant mutations: speed boosts, double vision, or size reduction.
- Hazards appear over time — poison fruits, moving walls, and AI snakes.
- Strategic decisions are needed: Do you chase a power-up or avoid the growing threat?
As the player progresses, the snake’s behavior and environment evolve, demanding quick reflexes and smart choices.
Creating a responsive grid that handles growing body parts, hazards, and multiple moving objects wasn’t easy. I relied on a tile-based system, updating each frame with checks for overlaps and collisions.
Amazon Q CLI helped me debug subtle bugs where new segments weren’t spawning properly after collisions, saving hours of guesswork.
Power-up and Mutation System
Adding randomness while keeping balance required controlled generation of power-ups and tracking their effects.
Q CLI offered suggestions on managing dynamic attributes, and even proposed ways to serialize state for future save/load features.
Making enemy snakes that act semi-intelligently meant implementing a simple pathfinding logic. I used a greedy approach with fallback movement in case of blockages.
Q CLI helped identify edge cases like infinite loops or collisions during AI movement. It also recommended better naming and modularization for clarity.
Classic Snake is rigid — but I wanted mine to feel fluid. I used pygame.time.Clock() with delta-time calculations to decouple movement from FPS.
Amazon Q CLI provided sample code to implement smooth timing and even recommended a few libraries for interpolated movement, though I stuck with Pygame for full control.
After refining for days, Snake Evolution now includes:
- Classic + Evolution Mode
- Mutation-based gameplay (speed, shrink, clone, immunity)
- AI snakes and environmental hazards
- Save & resume feature (coming soon)
- Resolution-independent UI and key remapping
- Sound FX for actions and alerts
- Dynamic difficulty scaling
- Fullscreen and mobile compatibility (via Pygbag for web)
What made Amazon Q CLI stand out was how it didn’t just hand over code — it taught me. From game loops to bug fixes, BFS logic to modular architecture, it acted like a knowledgeable teammate. It explained concepts clearly and always gave me just enough to understand why, not just what.
Whether I was stuck on collision bugs, implementing AI logic, or improving the UI system, Q CLI’s guidance helped me stay focused and avoid burnout.
Snake Evolution started as a weekend experiment but grew into a full-featured game with modern mechanics, all thanks to a mix of persistence, creative coding — and Amazon Q CLI’s timely assistance.
Try the game, explore the code, and maybe even build your own version.
Check it out on GitHub
Happy coding!
Made with ❤️ and Amazon Q CLI