Discover What's Being Vibe Coded

Vibe coding is building software by describing what you want to AI coding models in plain language, then guiding them through conversational prompts and iterative refinement instead of writing every line of syntax by hand. The term was coined by AI researcher Andrej Karpathy to describe how human effort is shifting from typing boilerplate syntax to designing systems and directing AI agents.

To get started as a beginner:

1. Start with a focused idea: Build a small, single-purpose tool or personal dashboard rather than a complex multi-tier platform.
2. Choose the right tool: Use all-in-one browser tools like Lovable or Bolt.new for instant setup, or an AI code editor like Cursor if you want more control.
3. Prompt in small iterations: Guide the AI one component at a time, test it immediately, and fix issues before moving to the next feature.

While anyone can build working prototypes without a technical background, having a basic grasp of web fundamentals like databases, APIs, and authentication helps you take your projects much further. You can read our walkthrough in our guide to creating an app with AI.

The best tool depends on how much code you want to manage yourself and where you prefer to build:

• No-Setup Web Builders (Best for Non-Programmers): Platforms like Lovable, Bolt.new, Replit Agent, and Google AI Studio let you build, preview, and deploy full web applications directly in your browser with zero terminal or local setup.
• AI-First Code Editors and Autonomous Agents (Best for Developers): Tools like Cursor, Devin, Gemini CLI, and Google Antigravity work directly with your local files and Git repositories. They give you full control over your tech stack, custom developer rules, and multi-file workflows.

You can browse real-world projects built with each of these tools in our directory to see what is possible with each platform.

AI-generated code is only as secure as the patterns it was trained on and the instructions you give it. Models can easily introduce security vulnerabilities such as missing input validation, hardcoded API secrets, or flawed access controls if you do not explicitly prompt for them. Always keep private keys in environment variables, verify authentication rules, and never push generated code to production without testing.

As an app grows into dozens of files, AI models often run into context drift, where they lose track of the larger architecture and accidentally break or rewrite working features. The industry-standard ways to manage this are:

1. Keep files small and modular: Split your code into distinct components and utilities so you only give the AI the exact context it needs for each task.
2. Commit with Git after every working prompt: Treat Git like a save point. If an AI prompt introduces a regression, you can revert with one click instead of trying to debug broken code manually.
3. Add automated tests: Simple unit tests or automated browser checks alert you the second an AI change breaks an existing feature.