How To Get The Most Out Of Vibe Coding | Startup School
Source
youtube.com
Author
Y Combinator
Date
Why it matters
Collects tested tactics for getting better results from AI coding tools, such as treating vibe codingBuilding software by describing what you want to an AI in plain language and steering the result, instead of writing every line yourself.Full definition → like software engineering practice and working around stuck agent loops.
Key takeaways · AI-distilled
YC partner Tom recommends drafting a plan with the LLMA large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.Full definition → in a markdown file, cutting or marking features as won't-do, then implementing one section at a time, running tests, committing to Git, and having the AI mark the section complete.
Tom finds repeated prompting piles layers of bad code onto a fix, so once a solution works he resets Git and feeds that solution to the AI on a clean codebase. He does not yet trust the tools' built-in revert features.
Tom prefers high-level, end-to-end integration tests over the low-level unit tests LLMs default to, because they catch the models' habit of changing unrelated logic for no reason.
Instead of pointing agents at online docs or an MCPThe Model Context Protocol — an open standard that lets any AI assistant plug into any tool or data source without custom integration code.Full definition → server, Tom downloads API documentation into a project subfolder and tells the AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → to read it first. For complex features he builds a standalone reference implementation in a clean codebase.
Tom attributes strong AI results on Ruby on Rails to its 20 years of conventions and consistent training data, noting friends had less success with Rust or Elixir. At recording time he favored Gemini for planning and Sonnet 3.7 for implementation.
Terms in this piece · Glossary
vibe coding — Building software by describing what you want to an AI in plain language and steering the result, instead of writing every line yourself.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
MCP — The Model Context Protocol — an open standard that lets any AI assistant plug into any tool or data source without custom integration code.
