How I recreated classic gaming magic with modern AI assistance
The Game That Started It All
When I set out to build a retro-style game with AI assistance, Pong was the obvious choice. But not just any Pong - I wanted to create something that captured the essence of tennis with realistic court surfaces and physics that would make Wimbledon proud.
Why Tennis Pong?
- Nostalgic Appeal: Pong is the grandfather of video games, released in 1972
- Perfect Complexity: Simple enough to complete, complex enough to be interesting
- Tennis Twist: Adding realistic court surfaces (grass, clay, hard court) made it unique
- Physics Playground: Each surface affects ball speed, bounce, and friction differently
The AI-Powered Development Process
Effective Prompting Techniques I Discovered
Through this project, I learned that successful AI collaboration requires strategic prompting:
1. Context-Rich Requests
Instead of: "Make a Pong game"
I used: "Create a tennis-themed Pong game with three court surfaces (grass, clay, hard court) where each surface has different physics properties affecting ball speed and bounce"
2. Iterative Enhancement
Phase 1: Basic Pong mechanics
Phase 2: Add tennis court visuals
Phase 3: Implement surface physics
Phase 4: Add AI opponents
Phase 5: Tournament mode
3. Specific Technical Requirements
- "Use HTML5 Canvas for rendering"
- "Implement realistic physics with different friction coefficients"
- "Add sound effects using Web Audio API"
- "Create responsive controls for both keyboard and touch"
How AI Handled Classic Programming Challenges
Challenge 1: Game Loop Architecture
The Problem: Creating smooth 60fps gameplay with proper state management
AI Solution: Generated a clean game loop with requestAnimationFrame:
function gameLoop() {
if (!gameState.gamePaused && gameState.gameRunning) {
update();
render();
}
requestAnimationFrame(gameLoop);
}
Challenge 2: Collision Detection
The Problem: Accurate ball-paddle collision with realistic physics
AI Generated Solution:
function checkPaddleCollision(ball, paddle) {
if (ball.x < paddle.x + paddle.width &&
ball.x + ball.width > paddle.x &&
ball.y < paddle.y + paddle.height &&
ball.y + ball.height > paddle.y) {
// Calculate hit position for spin effect
const hitPos = (ball.y - paddle.y) / paddle.height;
const spinFactor = (hitPos - 0.5) * 2;
ball.velocityX *= -1;
ball.velocityY += spinFactor * 2;
soundManager.play('paddleHit');
return true;
}
return false;
}
Challenge 3: Court Surface Physics
The Problem: Making each tennis surface feel authentically different
AI's Elegant Solution:
const courtSurfaces = {
grass: {
name: 'Grass Court',
ballSpeedMultiplier: 0.85,
frictionCoefficient: 0.98,
bounceReduction: 0.7,
color: '#2d5016',
description: 'Wimbledon Style - Slower, softer bounce'
},
clay: {
name: 'Clay Court',
ballSpeedMultiplier: 0.75,
frictionCoefficient: 0.95,
bounceReduction: 0.6,
color: '#8B4513',
description: 'French Open Style - Slowest, lowest bounce'
},
hard: {
name: 'Hard Court',
ballSpeedMultiplier: 1.0,
frictionCoefficient: 0.99,
bounceReduction: 0.8,
color: '#4169E1',
description: 'US Open Style - Fastest, highest bounce'
}
};
Development Automation That Saved Hours
1. Instant Code Generation
What used to take hours of research and implementation:
- Sound system with Web Audio API: Generated in minutes
- AI opponent with multiple difficulty levels: Created instantly
- Responsive UI with court switching: Automated completely
2. Bug Detection and Fixes
AI caught issues I would have spent hours debugging:
- Ball getting stuck in paddle collision loops
- Inconsistent frame rate causing physics glitches
- Memory leaks in sound generation
3. Feature Enhancement
When I asked for "tournament mode," AI generated:
- Complete bracket system
- Score tracking across matches
- Victory animations
- Statistics dashboard
Interesting AI-Generated Solutions
Smart AI Opponent
The AI created a sophisticated opponent system with realistic imperfections:
class AIPlayer {
constructor(difficulty = 'medium') {
this.difficulty = difficulty;
this.targetY = 0;
this.reactionTime = 0;
this.accuracy = 0;
this.speed = 0;
this.setDifficulty(difficulty);
}
setDifficulty(difficulty) {
const settings = {
easy: { reactionTime: 300, accuracy: 0.6, speed: 0.7 },
medium: { reactionTime: 200, accuracy: 0.8, speed: 0.85 },
hard: { reactionTime: 100, accuracy: 0.95, speed: 1.0 },
expert: { reactionTime: 50, accuracy: 0.98, speed: 1.2 }
};
Object.assign(this, settings[difficulty]);
}
update(ball, paddle) {
// Predict ball position with reaction delay
const predictedY = ball.y + (ball.velocityY * this.reactionTime / 16);
// Add human-like inaccuracy
const error = (Math.random() - 0.5) * (1 - this.accuracy) * 100;
this.targetY = predictedY + error;
// Move paddle with speed limitations
const diff = this.targetY - paddle.y;
paddle.y += Math.sign(diff) * Math.min(Math.abs(diff), this.speed * 5);
}
}
Dynamic Sound System
Instead of loading audio files, AI created a procedural sound system:
createTone(frequency, duration, waveType = 'sine') {
return () => {
const oscillator = this.audioContext.createOscillator();
const gainNode = this.audioContext.createGain();
oscillator.connect(gainNode);
gainNode.connect(this.audioContext.destination);
oscillator.frequency.setValueAtTime(frequency, this.audioContext.currentTime);
oscillator.type = waveType;
// Create natural sound envelope
gainNode.gain.setValueAtTime(0, this.audioContext.currentTime);
gainNode.gain.linearRampToValueAtTime(this.masterVolume * 0.3, this.audioContext.currentTime + 0.01);
gainNode.gain.exponentialRampToValueAtTime(0.001, this.audioContext.currentTime + duration);
oscillator.start(this.audioContext.currentTime);
oscillator.stop(this.audioContext.currentTime + duration);
};
}
The Final Creation
Game Features Achieved:
- ✅ Three authentic tennis court surfaces
- ✅ Realistic physics for each surface
- ✅ AI opponents with 4 difficulty levels
- ✅ Tournament mode with brackets
- ✅ Procedural sound effects
- ✅ Responsive controls (keyboard + touch)
- ✅ Pause/resume functionality
- ✅ Score tracking and statistics
Technical Highlights:
- Pure JavaScript: No external libraries
- HTML5 Canvas: Smooth 60fps rendering
- Web Audio API: Dynamic sound generation
- Responsive Design: Works on desktop and mobile
- Clean Architecture: Modular, maintainable code
Key Takeaways
What AI Excelled At:
- Rapid Prototyping: From idea to playable game in hours
- Complex Logic: Physics calculations and game state management
- Code Quality: Clean, well-commented, maintainable code
- Feature Completeness: Comprehensive implementations, not just basics
What Required Human Guidance:
- Creative Vision: Defining the tennis theme and court surfaces
- User Experience: Fine-tuning gameplay feel and balance
- Quality Assurance: Testing edge cases and user interactions
- Project Direction: Deciding which features to prioritize
The Magic Formula:
Clear Vision + Specific Prompts + Iterative Refinement = AI Development Success
Try It Yourself!
The complete Tennis Pong game is available in this repository. Open tennis-pong.html in your browser and experience the different court surfaces:
- Grass Court: Feel the Wimbledon grass beneath your virtual feet
- Clay Court: Experience the slow, strategic play of Roland Garros
- Hard Court: Enjoy the fast-paced action of the US Open
Controls:
- Player 1: W/S keys
- Player 2: Arrow keys
- Space: Pause/Resume
- Court buttons: Switch surfaces mid-game
This project demonstrates how AI can accelerate game development while preserving the creative joy of building something uniquely yours. The future of coding isn't about replacing developers—it's about amplifying our creativity and productivity.
Ready to build your own retro masterpiece? The only limit is your imagination!
Github link to project: https://github.com/ultrasage-danz/tennis-pong-championship
Full Youtube Link: https://www.youtube.com/watch?v=N8Me-FDBmNo
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