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Gagan Nadiger
Gagan Nadiger

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AI-Powered Rock Paper Scissors Game with Amazon Q CLI: A Journey in AI-Assisted Game Development

Introduction
I'm excited to share my journey of creating an advanced Rock Paper Scissors game using Amazon Q CLI as part of the AWS Community Challenge. This project showcases how AI can assist in game development while creating an engaging gaming experience.

Why Rock Paper Scissors?
I chose Rock Paper Scissors because it offered the perfect balance of:

  • Simple core mechanics with room for complexity
  • Opportunity to implement various AI strategies
  • Potential for interesting player-AI interactions
  • Scope for adding engaging features

Key Features

  1. Multiple AI Personalities

    • Predictive AI that learns player patterns
    • Random AI for unpredictable gameplay
    • Aggressive AI that counters player strategies
  2. Innovative Gameplay Elements

    • Deception Rounds: Every 6th round, AI declares its move (but might be lying!)
    • Time-Limited Gameplay: 5-second decision window
    • Achievement System tracking player statistics
    • Global Leaderboard using JSON persistence

Effective Prompting Techniques
Here are some key prompting strategies I discovered while working with Amazon Q CLI:

  1. Breaking down complex features into smaller, manageable prompts
  2. Using clear, specific requests for AI behavior implementation
  3. Iterative refinement of generated code
  4. Leveraging Amazon Q's context awareness

** Development Automation Wins**
Amazon Q CLI helped automate several aspects:

  • Boilerplate code generation
  • UI component creation
  • Game logic implementation
  • Error handling and input validation

Challenges and Solutions

  1. Implementing AI Strategy

    • How Amazon Q CLI helped design different AI personalities
    • Balancing difficulty levels
  2. UI Development

    • Creating an intuitive interface
    • Handling real-time updates

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** Learning Outcomes**

  1. Mastered Amazon Q CLI's capabilities for game development
  2. Learned effective AI prompting techniques
  3. Gained experience in Python game development
  4. Understanding of AI decision-making implementation

Future Improvements

  • Online multiplayer support
  • More AI personalities
  • Additional game modes
  • Mobile version

Conclusion
This project demonstrates the power of AI-assisted game development using Amazon Q CLI. It shows how AI can help create complex, engaging games while maintaining clean, maintainable code.

code:

class MusicPlayer:
    """Handles background music for the game."""

    def __init__(self):
        self.is_playing = False

        if PYGAME_AVAILABLE:
            try:
                pygame.mixer.init()
                self.available = True
            except:
                self.available = False
        else:
            self.available = False

    def play_background_music(self):
        """Start playing background music if available."""
        if not self.available or self.is_playing:
            return

        try:
            if os.path.exists(MUSIC_FILE):
                pygame.mixer.music.load(MUSIC_FILE)
                pygame.mixer.music.play(-1)  # Loop indefinitely
                self.is_playing = True
        except Exception as e:
            print(f"Error playing music: {e}")
            self.available = False

    def stop_background_music(self):
        """Stop the background music if it's playing."""
        if not self.available or not self.is_playing:
            return

        try:
            pygame.mixer.music.stop()
            self.is_playing = False
        except:
            pass
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Resources

AmazonQCLI #GameDev #AWS #Python

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