卡帕西推荐的AI Coding指南：3招教你效率翻倍

Core Insights - The article emphasizes the efficient use of AI coding tools by selecting the right model based on task type, restructuring workflows, and clarifying human-AI collaboration [1][3][18] Group 1: Model Selection - It is crucial to choose the appropriate coding model based on the task type; for large tasks, Codex is recommended, while Opus is better for smaller, fragmented tasks [6][8] - Codex can read through entire projects to understand logic and fix bugs, making it suitable for complex requirements [7] - For advanced users, GPT-5.2-Codex is suggested for its speed and accuracy, eliminating the need to switch between models [10] Group 2: Workflow Restructuring - A customized workflow allows the author to manage multiple projects simultaneously; ideas are directly added to Codex's queue instead of being noted down [14][15] - A key tip is to avoid rolling back changes, as iterative development is normal and time should not be wasted on reconsidering past decisions [16] - Reusing code from previous projects can save time; Codex can adapt existing code for new functionalities [17] Group 3: Human-AI Collaboration - The principle of human-AI collaboration is that AI should handle execution while humans make decisions, such as selecting libraries and designing system architecture [18][19] - The author provides examples of effective collaboration, including allowing AI to write core code while the human focuses on decision-making [20][21] Group 4: Practical Tips - Start development with a CLI tool to validate core logic before expanding to more complex features [23][24] - Maintain a documentation folder for each project to help the AI understand context and reduce repetitive communication [25][26] - For solo developers, directly committing to the main branch is recommended to avoid complications with multiple branches [27][29]