SuperAPI AI Directory
Partner with us中文AI newsFavorites
DirectoryNews and tutorials

YC Introduces Vibe Coding Experience 'How to Get the Most Out of Vibe Coding'

Author: AI小集 · Original source: AI工具集

YC's Vibe Coding experience is compiled from YC's video 'How To Get The Most Out Of Vibe Coding | Startup School', shared by YC partner Tom Blomfield, and is a new way of developing software based on artificial intelligence (AI) tools such asClaude Code、Windsurfand Aqua. Through this approach, AI can write full-stack applications, debug code by pasting error messages, and become a true partner in the development process. Tom provides a complete guide to help developers throughvibe codingto improve development speed and efficiency.

Core Content

Planning Process

  • Create a Comprehensive Plan: First, collaborate with AI to write a detailed implementation plan, saved in a Markdown file.
  • Review and Refine: Remove unnecessary items, flag overly complex features for later handling.
  • Maintain Scope Control: Keep a separate section for future ideas to maintain focus on the current project.
  • Incremental Implementation: Implement the plan step by step in parts, not building everything at once.
  • Track Progress: Have AI mark sections as complete after successful implementation.
  • Commit Regularly: Ensure each working part is committed to Git so that version control can be done before moving to the next part.

Version Control Strategy

  • Strictly Use Git: Do not rely entirely on AI tools' rollback features, always start each new feature from a clean Git state.
  • Start Clean: Start each new feature from a clean Git state so that you can roll back if AI goes off track.
  • Reset When Problems Arise: If AI gets stuck on a task, usegit reset --hard HEADto reset.
  • Avoid Accumulating Problems: Multiple failed attempts will accumulate bad code, so this needs to be avoided.
  • Clean Implementation: When you finally find a solution, reset and implement it in pure code.

Testing Framework

  • Prioritize High-Level Testing: Focus on end-to-end integration tests, not unit tests.
  • Simulate User Behavior: Test features by simulating user clicks to ensure they work as expected.
  • Catch Regressions: LLMs may make unnecessary changes to unrelated logic, and tests are needed to catch these regressions.
  • Test Before Proceeding: Ensure tests pass before moving to the next feature.
  • Use Tests as Guardrails: Start with test cases to provide clear boundaries and ensure feature correctness.

Effectively Fix Bugs

  • Utilize Error Messages: Copy and paste error messages directly into the LLM, usually enough to identify and fix the problem.
  • Code After Analysis: Have AI consider multiple possible causes before writing code.
  • Reset After Failure: Start from a clean state after each failed attempt to avoid accumulating bad code.
  • Implement Logging: Adding strategic logging can better understand what is happening.
  • Switch Models: Try different AI models to see which one is more suitable for the current task.
  • Clean implementation: Fix precise errors on a clean foundation to avoid accumulating unnecessary code.

AI tool optimization

  • Create instruction files: Write detailed instructions for AI so it better understands your intent.
  • Local documentation: Download API documentation to the project folder so the LLM can access and use it more accurately.
  • Use multiple tools: Run multiple AI tools at the same time, such as Cursor and Windsurf, to improve efficiency.
  • Tool specialization: Choose tools based on their strengths, for example Cursor is suitable for front-end work, and Windsurf is suitable for longer thinking.
  • Compare outputs: Generate multiple solutions and choose the best one to ensure code quality.

Complex feature development

  • Create independent prototypes: Build complex features in a clean codebase for better control and testing.
  • Use reference implementations: Point AI to working examples so it has a clear implementation direction.
  • Clear boundaries: Keep external APIs consistent, allow internal changes, and maintain code flexibility.
  • Modular architecture: Service infrastructure with clear boundaries is better suited for large projects.

Technology stack considerations

  • Mature frameworks: For example, Ruby on Rails performs well because of its consistent conventions and is suitable for AI development.
  • Training data matters: New languages may have less training data, so choosing a language with a large amount of training data may be more advantageous.
  • Modularity is key: Small files and modular files are easy to work with and help keep code clear and maintainable.
  • Avoid large files: Avoid files with thousands of lines of code to improve code readability and maintainability.

Beyond coding

  • DevOps automation: Use AI to configure servers, DNS, and hosting to improve deployment efficiency.
  • Design assistance: Generate design elements, such as favicons and other design components.
  • Content creation: Draft documentation and marketing materials to support project promotion.
  • Educational tools: Explain implementations line by line to help team members better understand the code.
  • Use screenshots: Share UI bugs or design inspiration for better communication and collaboration.
  • Voice input: Use tools such as Aqua for voice input to improve input efficiency.

Continuous improvement

  • Regular refactoring: Once tests are in place, refactor code frequently to keep it clear and maintainable.
  • Identify opportunities: Let AI find refactoring candidates to improve code quality.
  • Stay up to date: Try every new model release to take advantage of the latest technology.
  • Identify strengths: Different models perform well on different tasks, and these strengths need to be identified and leveraged.

Video address

  • Official video address：https://www.youtube.com/watch?v=BJjsfNO5JTo