This repository provides a comprehensive guide for developers looking to leverage AI for coding, covering tools, techniques, and best practices for AI-assisted code generation and "vibe coding." It aims to demystify the rapidly evolving landscape of AI coding tools and methodologies for both novice and experienced programmers.

How It Works

The guide advocates for a structured approach to AI coding, emphasizing the creation of Product Requirements Documents (PRDs) and detailed task lists to guide AI models. It promotes using AI as a "copilot" for augmenting developer productivity and as an "agent" for autonomous code generation, while cautioning against over-reliance on the latter for complex projects. Key practices include breaking down tasks, using specific LLMs for different stages (e.g., brainstorming, coding), and employing prompt engineering techniques.

Quick Start & Requirements

• Tools: Recommends tools like Cursor, Windsurf, Aider, Claude Code, and web-based platforms like Bolt, Replit, v0, or Lovable.
• Setup: Requires API keys for various LLM providers (OpenAI, Anthropic, Gemini via OpenRouter). Some tools may require specific Python versions or Docker.
• Resources: Links to numerous external resources, tutorials, and tool documentation are provided throughout the README.

Highlighted Details

• Detailed comparison of LLMs for various coding tasks (brainstorming, PRD creation, coding).
• Guidance on prompt engineering, including structuring prompts and using tools like RepoPrompt.
• Strategies for managing AI-generated code, handling errors, and ensuring code quality through testing and review.
• Explanation of concepts like Model Context Protocol (MCP), Simple Language Open Protocol (SLOP), and Agent-to-Agent (A2A) communication.

Maintenance & Community

The project is maintained by Vilson Vieira and Eric S. Raymond, with contributions from a wider community. Links to community resources or direct contact information for contributors are not explicitly detailed, but the project encourages contributions via PRs or issues.

Licensing & Compatibility

The repository is licensed under the MIT license, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

The rapid evolution of AI models means that specific tool recommendations and LLM performance comparisons can become outdated quickly. The guide stresses the importance of critical review of AI-generated code due to potential hallucinations and misinterpretations of specifications. Some tools, like Claude Code, can be expensive to use without careful monitoring.