D
DanXiao
Guest
Exploring AI Programming: An Unexpected Success
After a month of using Cursor, I attempted to develop a small game called Puzzlepave through conversational AI programming for the first time. The game is based on the p5.js framework, about which I had no prior knowledge. With the help of Cursor's AI programming capabilities, I not only successfully completed the game development but also deeply experienced the immense potential of AI tools in programming. This article will share the surprises and challenges encountered during this development process and discuss how to address new issues brought by AI programming.
The Surprises of AI Programming: Cursor's Powerful Advantages
Using Cursor to develop Puzzlepave allowed me to experience the unique charm of AI programming tools. Here are some notable advantages:
- Quick Start with Zero Foundation: Despite having no experience with p5.js, Cursor's conversational guidance and code generation helped me quickly grasp the core concepts of the framework and directly produce runnable code. This allowed me to focus on game logic and creativity without spending weeks learning the framework.
- Rapid Problem Solving and Bug Fixing: During development, whenever I encountered errors, I could simply copy the error messages to Cursor, which would quickly identify the issue and provide solutions. For example, when implementing the grid logic for Puzzlepave, the AI swiftly fixed a bug caused by incorrect coordinate calculations, greatly improving development efficiency.
Challenges and Concerns: The Double-Edged Sword of AI Programming
While Cursor brought surprises, using AI for programming also presented challenges, especially since I wasnβt fully familiar with p5.js or the generated code. Below are some key issues:
- Code Readability Challenges: Although AI-generated code was functional, it was sometimes structurally complex with deeply nested logic. Even with my experience in other frameworks and attempts to request modular designs from the AI, the code was still not perfect, making it difficult to read and understand, particularly with advanced p5.js features.
- Iterative Maintenance Risks: Due to my limited understanding of the code, adding new features or optimizing existing logic in the future could be challenging. For instance, modifying the gameβs core mechanics might require refactoring AI-generated code, which poses a challenge for me.
- Debugging Complexity: If the game encounters bugs, especially those related to the underlying mechanisms of p5.js, I may struggle to identify the root cause quickly due to my lack of deep framework knowledge.
- Project Control Risks: The most concerning issue is that AI-generated code might one day become βonly understandable by AI,β leading to a situation where the project cannot be iterated or critical bugs cannot be fixed, ultimately hitting a development bottleneck.
These challenges made me realize that while AI programming is efficient, it requires new development strategies to ensure project sustainability.
Strategies to Address AI Programming Challenges
To tackle the challenges of AI programming, I summarized two core strategies to reduce risks and improve project maintainability:
1. Modular Design: Encapsulating Risks, Enhancing Control
Modular design involves breaking down complex systems into independent, reusable modules. In AI programming, modularity can effectively reduce code complexity and encapsulate potential risks, similar to how clear module designs improve maintainability when using open-source frameworks like React or Django.
- Specific Approach: While developing Puzzlepave, I requested Cursor to split the game logic into multiple modules, such as βgame configuration,β βlevel configuration,β and βgrid logic.β Each moduleβs code was stored independently, minimizing interference and reducing overall code complexity.
- Advantages: Modularity makes code easier to understand and maintain. If an issue arises in a specific module, I can debug or rewrite it independently without affecting the entire codebase. Like trusting the modular design of jQuery or TensorFlow, modular AI code facilitates future replacements or feature upgrades.
2. Comprehensive Documentation: Safeguarding AI Programming
Complete product and technical documentation are the βlifelineβ of AI programming projects. Detailed documentation provides traceable context for AI-generated code.
- Product Documentation: Documenting Puzzlepaveβs functional requirements, user interaction flows, and design goals. For example, I recorded the gameβs core mechanics (e.g., puzzle movement rules) and user interface designs to ensure quick recollection of the project context during future iterations.
- Technical Documentation: Detailed records of the codeβs directory structure, routing logic, and module divisions. For instance, I created clear directory descriptions for Puzzlepaveβs code structure, outlining the responsibilities and interactions of modules like game configuration, level configuration, and grid logic, making it easy to locate and understand code.
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