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Vibe Coding isn't a myth: non-developers can build real apps with AI too

· 6 min read
Booker Zhao
AI Full-Stack Engineer / CloudBase AI ToolKit Author

Ever run into this: AI generates your app, then it breaks and you don't even know what question to ask? AI gives you N solutions but you have no idea which one is right?

This is the pain point for many non-technical people using AI to code:

  • Debug hell: page shows something wrong, but no idea which layer is the issue (frontend? backend? deployment?)
  • Can't ask the right question: don't know how to describe the problem so AI understands and gives accurate answers
  • Unclear learning path: know you need some basics, but don't know where to start or what's enough

Today I'll share a systematic approach — from foundational concepts to debugging techniques to backend solutions — to take you from "able to generate apps" to "able to build real, usable apps."

Why does your AI code always need rework? SBE methodology has the answer

· 5 min read
Booker Zhao
AI Full-Stack Engineer / CloudBase AI ToolKit Author

After a few months of coding with the Spec workflow, I kept asking myself one question: why does this "write requirements first, design next, then break down tasks" process actually work?

Then I came across Gojko Adzic's Specification by Example: How Successful Teams Deliver the Right Software, and it clicked. Behind Kiro's Spec workflow is exactly the SBE (Specification by Example) methodology. This article explains the relationship between the two — and why your AI coding keeps ending up in rework.

AI coding without luck: the complete guide to replicating Kiro's Spec workflow

· 7 min read
Booker Zhao
AI Full-Stack Engineer / CloudBase AI ToolKit Author

After more than a year of AI-assisted coding, my biggest lesson is this: vibe coding feels great in the moment, but it falls apart the moment a project gets complex. A one-line requirement, AI starts coding on its own assumptions, and what comes out is always a bit off. You ask it to fix something, it fixes that and breaks something else. A few rounds of this and your codebase has tripled in size while the number of working features barely grew.

Eventually I figured it out: the problem isn't that AI writes bad code. It's that I don't say clearly what I want.

Kiro's Spec workflow is built to solve exactly that. Let's walk through it from scratch.