AI-Powered Game Development: Challenges and Insights In this second part of the Online Web Game...
Vibe Coding -- Example in Cursor -- Making a Word Game
Collaborating with AI to Complete an Unfinished Word Game
Vibe coding is a unique approach to programming where developers work alongside AI agents to iteratively improve their code. In this session, the goal is to complete a word game that was originally designed by a friend but left unfinished by a hired developer. The process involves debugging and refining AI-generated code to ensure functionality and reliability.
Setting Up the Word Game
- The first attempt at generating the game using an AI agent resulted in numerous bugs and inconsistencies.
- A more structured and step-by-step approach was taken to provide clearer instructions.
- The game was initially developed with a text-only interface to prioritize logic over visuals.
- Python 3.5 Sonet was used due to stability concerns with version 3.7.
Testing the Basic Game Structure
- The AI successfully created a working base structure for the game.
- The game required installing dependencies and setting up the terminal-based interface.
- Initial tests verified that the board, turns, and hand mechanics were functioning.
Debugging Gameplay Issues
- The game allows players to enter letters and positions to play words.
- Some unexpected issues arose, such as:
- Words that shouldn't be valid were being accepted.
- The game sometimes failed to recognize legitimate words.
- Errors in turn-based mechanics, like improper handling of card replacements.
- Continuous debugging and refining were necessary to fix these errors, following principles of how to build AI agents the right way.
Stealing Mechanic and Scoring System
- The word-stealing feature had a bug where it mistakenly stole letters from the player instead of the opponent.
- The AI was tasked with fixing the bug by adjusting the logic behind how cards are transferred between players.
- Additional testing revealed that some valid words weren't being recognized due to case sensitivity and incorrect API responses.
Fixing API and Dictionary Lookups
- The game initially used an external dictionary API to validate words, but it was unreliable and frequently returned errors.
- A switch was made to using GPT-3.5 Turbo for word validation, providing a more reliable and scalable solution similar to AI-augmented document processing techniques.
- Adjustments were made to ensure word validation was case-insensitive and properly formatted.
Final Adjustments and Performance Review
- Over 300 lines of code were generated and debugged within 45 minutes, demonstrating the efficiency of AI-assisted coding. This kind of productivity raises interesting questions about the future of programming.
- The AI's ability to find and fix its own errors helped speed up development significantly, showcasing effective task automation in action.
- The iterative process involved constant testing, debugging, and refining, leading to a more polished final product.
Conclusion
In conclusion, Vibe coding with AI proves to be a powerful approach for rapid prototyping and iterative development. While AI agents can generate functional code quickly, human oversight remains crucial to refining logic and fixing nuanced issues - something an experienced AI implementation consultant understands well. This session showcased how custom AI solutions can accelerate the development process, especially when working on rule-based games like this word game. The insights gained through this process demonstrate the value of AI for business intelligence by revealing more efficient development workflows. Looking ahead, the next steps involve implementing a graphical interface to enhance the user experience.
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