YC Launches Vibe Coding Guide: “How to Get the Most Out of Vibe Coding”

YC’s Vibe Coding Experience

The Vibe Coding experience from YC is based on the video “How To Get The Most Out Of Vibe Coding | Startup School”, shared by YC partner Tom Blomfield. It introduces a new way of software development using AI tools such as Claude Code, Windsurf, and Aqua. With this approach, AI can write full-stack applications, debug code by pasting error messages, and act as a true collaborator throughout the development process. Tom provides a comprehensive guide to help developers increase speed and efficiency through Vibe Coding.

YC Launches Vibe Coding Guide:

Core Content

Planning Process

Create a Comprehensive Plan: Collaborate with AI to draft a detailed implementation plan, saved in a Markdown file.
Review and Refine: Remove unnecessary items and mark overly complex features for later handling.
Maintain Scope Control: Reserve separate sections for future ideas to keep the current project focused.
Incremental Implementation: Implement the plan in parts, rather than building everything at once.
Track Progress: Let AI mark sections as complete once successfully implemented.
Regular Commits: Ensure each work segment is committed to Git for version control before moving to the next.

Version Control Strategy

Strict Git Usage: Don’t rely solely on AI rollback; always start new features from a clean Git state.
Start Clean: Begin each new feature from a clean Git state to allow easy rollback if AI goes off track.
Reset on Problems: Use git reset --hard HEAD if AI gets stuck on a task.
Avoid Accumulating Bad Code: Multiple failed attempts create messy code, which should be avoided.
Clean Implementation: Implement final solutions on a reset, clean codebase.

Testing Framework

Prioritize End-to-End Tests: Focus on integration testing rather than unit testing.
Simulate User Behavior: Test features by simulating user interactions to ensure proper functionality.
Catch Regressions: LLMs may unintentionally modify unrelated logic; tests help catch regressions.
Test Before Moving On: Ensure tests pass before implementing the next feature.
Use Tests as Guardrails: Start with clear test cases to define boundaries and ensure correctness.

Effective Bug Fixing

Leverage Error Messages: Paste error messages directly into the LLM to identify and fix issues.
Analyze Before Coding: Let AI consider multiple potential causes before generating code.
Reset After Failures: Start from a clean state after every failed attempt to avoid accumulating bad code.
Log Strategically: Add logs to better understand what is happening.
Switch Models: Try different AI models to find the best fit for the task.
Clean Implementation: Fix issues on a clean base, avoiding unnecessary accumulated code.

AI Tool Optimization

Create Instruction Files: Provide detailed instructions to AI for better understanding of your intent.
Local Documentation: Download API docs into the project folder so the LLM can access them accurately.
Use Multiple Tools: Run multiple AI tools (e.g., Cursor and Windsurf) simultaneously to increase efficiency.
Tool Specialization: Choose tools based on their strengths—Cursor for frontend, Windsurf for longer reasoning tasks.
Compare Outputs: Generate multiple solutions and select the best to ensure code quality.

Developing Complex Features

Create Independent Prototypes: Build complex features in a clean codebase for better control and testing.
Use Reference Implementations: Point AI to working examples for clear guidance.
Clear Boundaries: Keep external APIs consistent while allowing internal changes to maintain flexibility.
Modular Architecture: Service infrastructure with clear boundaries suits large projects.

Tech Stack Considerations

Mature Frameworks: Frameworks like Ruby on Rails work well due to consistent conventions, suitable for AI development.
Training Data Matters: Languages with more training data may be more effective for AI coding.
Modularity is Key: Small, modular files help maintain clear and manageable code.
Avoid Large Files: Files with thousands of lines of code reduce readability and maintainability.

Beyond Coding

DevOps Automation: Use AI to configure servers, DNS, and hosting for efficient deployment.
Design Assistance: Generate design elements such as favicons and other UI components.
Content Creation: Draft documentation and marketing materials to support project promotion.
Educational Tools: Explain implementations line by line to help team members understand code.
Use Screenshots: Share UI bugs or design inspiration for better collaboration.
Voice Input: Use tools like Aqua for voice input to speed up interaction.

Continuous Improvement

Regular Refactoring: Frequently refactor code after tests to maintain clarity and maintainability.
Identify Opportunities: Let AI suggest refactoring candidates to improve code quality.
Stay Updated: Try each new model release to leverage the latest technology.
Identify Strengths: Different models excel at different tasks; recognize and utilize these advantages.