> ## Documentation Index
> Fetch the complete documentation index at: https://vibecoderdocs.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> What Vibe Coder Docs is, who made it, and why it exists.

# Welcome to Vibe Coder Docs

You're building software with AI. That's awesome. But AI coding tools are only as good as the decisions you make while using them.

Whether you're using Claude Code, Codex, Antigravity, or any other AI-powered development tool, you now have the power to create real applications, customize and deploy products, and build things that previously required years of engineering experience. That's incredible — but it's also easy to fall into traps. There are things you need to know that your AI assistant might not proactively tell you.

## What This Site Is

Vibe Coder Docs is a collection of engineering wisdom distilled for people who are building with AI coding tools. It covers the concepts, patterns, and pitfalls that traditionally take years of professional experience to learn.

This isn't a "learn to code" tutorial. It's the stuff that comes *after* your code works — the knowledge that separates "it runs on my machine" from "it's production-ready."

## Who This Is For

<CardGroup cols={2}>
  <Card title="Non-Engineers" icon="user">
    Product managers, designers, founders, and hobbyists using AI tools to build software for the first time.
  </Card>

  <Card title="Early-Stage Developers" icon="seedling">
    Junior developers or career-switchers who want to fill gaps in their foundational knowledge.
  </Card>

  <Card title="AI Agents" icon="robot">
    AI coding assistants that need structured reference material to give better guidance.
  </Card>

  <Card title="Experienced Engineers" icon="graduation-cap">
    Senior devs looking for a reference to share with teammates who are new to vibe coding.
  </Card>
</CardGroup>

## Why Trust This

This documentation is written by experienced software engineers who have:

* Shipped production systems at scale
* Mentored junior developers through exactly these knowledge gaps
* Used AI coding tools extensively and seen the common failure modes firsthand

Every recommendation here comes from real-world experience, not theory.

## Team

<CardGroup cols={2}>
  <Card title="Shannon Duncan" icon="user">
    **Maintainer & Primary Contributor**

    Principal Architect of AI & Agentic Applications at LiveRamp. With experience building petabyte scale data systems, collaboration software, and both high scale batch and realtime systems. Four patents in fault-tolerant big data processing, identity resolution, and cloud-native segmentation. Former Snowflake Customer Advisory Board member and Certified Executive Coach with years of experience mentoring engineers and engineering leaders at every level.

    [LinkedIn](https://linkedin.com/in/jsduncan98)
  </Card>
</CardGroup>

<Note>
  Interested in contributing? We welcome engineers who want to help others avoid the pitfalls they've learned the hard way.
</Note>

## How to Use This Site

1. **Start with [Quickstart](/get-started/quickstart)** to learn how to navigate and get the most out of these docs
2. **Browse [Concepts](/get-started/concepts)** for an overview of all topics covered
3. **Dive into specific sections** as you encounter challenges in your projects