Skip to main content

What & Why

Why I Decided to Write a Webbook on AI-Assisted Coding​

With AI-powered tools like Cursor, Bolt, and Vercel v0, anyone can code. That’s incredible—but without a solid foundation in software engineering, AI-generated projects quickly hit a wall.

I recently worked with a team of founders who had no prior programming experience but successfully built an MVP using AI-assisted coding tools. When I joined to review their work and identify bottlenecks for scaling, I noticed:

  • No testing framework.
  • No development workflow or documentation.
  • No scalability considerations—limited to fewer than 100 concurrent users.
  • No proper exception handling.
  • No monitoring solution.

The fixes were simple, and the founders quickly improved their project once they knew what to focus on. Seeing this, I realized many AI-first developers need a practical guide to help them transition from MVP to a robust, maintainable, and scalable product.

That’s why I’m writing this mini-book on AI-assisted coding—to help AI-driven programmers adopt essential software engineering principles without unnecessary complexity. This guide will be short, actionable, and immediately useful for anyone building with AI.

Who is This For?​

  • AI-first developers who generate code but struggle with best practices.
  • Non-traditional engineers (startup founders, domain experts) building products with AI.
  • Experienced engineers who want to integrate AI into workflows without sacrificing quality.

Functional Requirements (MVP)​

  • Accessible: Must be readable on any browser via GitHub Pages.
  • Relevant: Includes real-world examples, best practices, and actionable takeaways.
  • Continuously Updated: Built in public, with frequent improvements and refinements.
  • Deployed Automatically: Uses CI/CD for seamless publishing.

Non-Functional requirements​

  • Engaging: Simple language, no unnecessary jargon, yet introduces key terms for communication.
  • Maintainable: Source code and content should be structured for long-term contributions.
  • Scalable: Supports extensions—future topics, translations, and external contributions.