Dynamic Pong: Territory Conquest Meets Classic Physics

Because watching other people’s cool pong implementations was getting old.

Let me hit you with some facts right off the bat:

4 competing areas. Unlimited strategic depth. One AI assistant doing all the heavy lifting.

Here’s the Thing

I was scrolling through my feed when I saw yet another dynamic pong implementation. Cool concept - pong, but with a twist. Then I saw another. And another.

The problem? They were all interesting, but none of them did exactly what I wanted.

We fixed that.

By “we,” I mean me and GitHub Copilot. Here’s the story.

What Is Dynamic Pong?

Forget everything you know about traditional pong. No paddles. No two-player setup. No boring back-and-forth.

Dynamic Pong is a territory conquest game disguised as physics simulation.

The Setup

Square playing field (you control the dimensions)
4 areas by default (configurable)
Each area gets a unique color pair - one for the background, a complementary color for its ball
Each ball starts in a random position within its home territory
Hit start, and chaos begins

The Rules

Here’s where it gets interesting:

Bouncing: Ball hits the outer border? It bounces back at the corresponding angle. Real-world physics.

Conquering: Ball enters enemy territory? It starts removing square bricks the same size as the ball itself. Each brick removed becomes the color of the attacking ball’s area. The ball bounces back at the physics-appropriate angle.

Defending: A ball can move freely through its own colored territory. No destruction there.

Battle: Two balls collide? They bounce off each other like they hit a wall. Simple. Effective.

Defeat: When a ball’s territory shrinks below 9 times the ball’s size (width × height), that ball is eliminated. Its remaining territory gets absorbed by whichever ball currently has the smallest area. Strategy meets survival.

Why I Built This (And How Copilot Did Most of It)

I already noticed people implementing dynamic pong variations. I liked the concept. But I wanted more:

Configurable everything - field size, number of areas, ball dimensions
Real physics - proper angle calculations, realistic bouncing
Strategic depth - territory conquest with actual stakes
Clean implementation - maintainable code that makes sense

Could I have coded this from scratch? Sure. Would it have taken me way longer? Absolutely.

So I decided to test GitHub Copilot’s limits. Could it build this entire game from just a description of the rules?

Spoiler: Yes.

The Development Process 🤖

Here’s how it went down:

Step 1: Define the Rules

I wrote out the game mechanics in plain English. No pseudocode. No detailed specifications. Just the rules you read above.

Step 2: Let Copilot Build It

I started with a basic React component and Canvas setup. Then I prompted Copilot with the rule descriptions.

It generated:

The game loop
Physics calculations for bouncing
Territory detection logic
Brick removal mechanics
Collision detection between balls
Loss condition checking
Configuration panel integration

Step 3: Iterate and Refine

Copilot got about 80% right on the first pass. The remaining 20%? Minor tweaks:

Fine-tuning collision angles
Optimizing the brick removal algorithm
Balancing the loss condition
Polishing the UI

Total development time? A few hours. Most of that was me playing the game to test edge cases.

Technical Highlights

Built with modern web tech:

React 19 for component management
TypeScript for type safety
HTML Canvas for rendering
Custom physics engine (Copilot-generated)
Responsive design that works on any screen size

The entire codebase is clean, well-structured, and maintainable. Copilot didn’t just dump code - it created organized, readable implementations that follow best practices.

What You Can Configure

Open the config panel and customize:

Field Dimensions: Make it tiny or massive
Number of Areas: 2, 4, 6, 8 - your choice
Ball Size: Bigger balls = faster conquering (but less precise control)
Game Speed: Adjust the physics tick rate

Want to watch 8 tiny balls battle it out on a massive field? Go for it.

Prefer 2 giant balls in a compact arena? That works too.

The game adapts.

The AI-Assisted Development Experience

Let’s talk about working with Copilot on this project.

What Worked Really Well

Complex logic generation: Copilot nailed the physics calculations and collision detection. Angle of incidence = angle of reflection? Perfect implementation on the first try.

Boilerplate elimination: All the canvas setup, game loop structure, and state management? Generated instantly.

Edge case handling: I mentioned that balls should bounce off each other. Copilot automatically added the collision detection and resolution.

What Needed Human Touch

Game balance: The initial loss condition was too aggressive. I adjusted it from 6x ball size to 9x for better gameplay.

Visual polish: Color selection and UI layout benefited from human aesthetic judgment.

Strategic depth: Fine-tuning how territory transfer works required gameplay testing and iteration.

Lessons Learned

After building an entire game with AI assistance, here’s what I took away:

1. AI is great at well-defined problems

The clearer your specifications, the better the output. “Balls should bounce at realistic angles” produced perfect code. “Make it feel good” required human iteration.

2. You still need to understand what’s happening

Copilot generates code fast. But you need to read it, understand it, and validate it. Blind acceptance leads to bugs.

3. Iteration is faster, not eliminated

You’ll still iterate. But instead of writing everything from scratch, you’re reviewing and refining. That’s a huge productivity boost.

4. Complex systems are totally doable

I was surprised how well Copilot handled the multi-ball physics and territory conquest logic. These aren’t trivial systems. But with good prompts, they came together smoothly.