YC releases Vibe Coding experience: "How to Get the Most Out of Vibe Programming"

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.artificialintelligent(AITools such asClaude A new way of developing software using Code, Windsurf, and Aqua. Through this method...AITom is able to write full-stack applications, debug code by pasting error messages, and become a true partner in the development process. He provides complete guides to help developers improve their development speed and efficiency through Vibe coding.

Planning process

Create a comprehensive planFirstly withAICollaborate to write a detailed implementation plan and save it in a Markdown file.
Review and ImproveRemove unnecessary items and mark overly complex features for later processing.
Maintain range controlReserve a separate section for subsequent ideas in order to maintain focus on the current project.
Incremental implementationThe plan will be implemented in stages, rather than building all the content at once.
Track progress:letAIOnce successfully implemented, mark the section as complete.
Submit regularly: Ensure that each workpiece is committed to Git so that version control can be implemented before moving to the next part.

Version control strategy

Strictly use GitDon't rely on it completely.AIThe tool's rollback function always starts each new feature from a clean Git state.
Start with cleanlinessEach new feature starts with a clean Git state, so that...AIIt can roll back if it deviates from the track.
Reset when encountering problems:ifAIStuck on a task? Usegit reset --hard HEADTo reset.
Avoiding cumulative problemsRepeated failed attempts can accumulate bad code, so they need to be avoided.
Cleaning implementationWhen you finally find a solution, reset and implement it in clean code.

testing framework

Prioritize advanced testingFocus on end-to-end integration testing, not unit testing.
Simulate user behaviorTest features by simulating user clicks to ensure they work as expected.
Capture the return:LLMUnnecessary changes may be made to unrelated logic, and tests are needed to capture these regressions.
Before testingMake sure the tests pass before moving on to the next feature.
Using tests as guardrailsProvide clear boundaries starting with test cases to ensure the correctness of functionality.

Effective bug fixes

Using error messagesCopy and paste the error message directly toLLMThis is usually sufficient to identify and fix the problem.
Pre-analysis encoding:letAIConsider multiple possible reasons before writing the code.
Reset after failureAfter each failed attempt, start from a clean state to avoid accumulating bad code.
Implementation log recordingAdding strategic logs can help you better understand what's happening.
Switching modelsTry differentAIUse different models to see which one is more suitable for the current task.
Cleaning implementationFix precise errors on a clean basis to avoid accumulating unnecessary code.

AITool optimization

Create instruction file:forAIWrite detailed instructions so that it better understands your intentions.
Local DocumentsDownload the API documentation to your project folder.LLMIt allows for more accurate access and use.
Use multiple toolsRunning multipleAITools, such as Cursor and Windsurf, to improveHigh efficiencyRate.
Tool specializationChoose the tool based on its strengths. For example, Cursor is suitable for front-end work, while Windsurf is suitable for longer periods of thinking.
Compare output: Generate multiple solutions and select the best one to ensure code quality.

Complex Function Development

Create an independent prototypeBuild complex features in a clean codebase for better control and testing.
Using the reference implementation:point toAIProvide a working example so that it has a clear direction for implementation.
Clear boundariesMaintain consistency with external APIs, allow internal changes, and preserve code flexibility.
Modular architectureThe service infrastructure has clear boundaries and is more suitable for large-scale projects.

Technology stack considerations

Mature frameworkRuby on Rails performs well due to its consistent conventions and is suitable for...AIDevelopment.
Training data is importantNew languages may have less training data, so it may be more advantageous to choose languages with a large amount of training data.
Modularization is keySmall and modular files make working easier and help keep the code clear and maintainable.
Avoid large filesAvoid files with thousands of lines of code, improving code readability and maintainability.

Beyond Coding

DevOpsautomaticchange:useAIConfigure servers, DNS, and hosting to improve deployment efficiency.
Design assistanceGenerate design elements, such as favicons and other design components.
Content creationDrafting documents and marketing materials to support the promotion of the project.
Educational toolsThe implementation is explained line by line to help team members better understand the code.
Use screenshotsShare UI bugs or design inspiration to facilitate better communication and collaboration.
Voice inputUse tools like Aqua for voice input to improve input efficiency.

Continuous improvement

Regular refactoringOnce testing is complete, refactor the code frequently to maintain its clarity and maintainability.
Identify opportunities:letAIIdentify refactoring candidates to improve code quality.
Keepup to dateTry each new model release to take advantage ofup to dateThe technology.
Identify advantagesDifferent models perform well on different tasks, and it is necessary to identify and utilize these advantages.

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

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