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
-
Multiple AI Personalities
- Predictive AI that learns player patterns
- Random AI for unpredictable gameplay
- Aggressive AI that counters player strategies
-
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:
- Breaking down complex features into smaller, manageable prompts
- Using clear, specific requests for AI behavior implementation
- Iterative refinement of generated code
- 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
-
Implementing AI Strategy
- How Amazon Q CLI helped design different AI personalities
- Balancing difficulty levels
-
UI Development
- Creating an intuitive interface
- Handling real-time updates
** Learning Outcomes**
- Mastered Amazon Q CLI's capabilities for game development
- Learned effective AI prompting techniques
- Gained experience in Python game development
- 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
Resources
- GitHub Repository:https://github.com/gagan-1211/Amazon-Q-CLI.git
- Amazon Q CLI Documentation
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