This article advocates for adopting the principles of Loop Engineering to protect scarce attention resources and enhance AI programming efficiency by using automated workflows to reduce manual prompting.

AI programming tools have evolved into the multi-task management phase. While they improve parallel efficiency, the frequent context switching is leading to fragmented developer attention and cognitive overload.

This article shares practical experience in designing AI-friendly workflows that allow Agents to handle development chores like testing, code reviews, and O&M, helping you escape the fate of "working for the AI."

Addressing the common pitfalls of unreliable AI coding, this article introduces a "self-acceptance criteria" framework using Linting, integration testing, and browser automation to help you build programming agents capable of self-correction and efficient delivery.

This article debunks the myth that AI programming is only for professionals, demonstrating that ordinary people can leverage AI to write useful code once they reach a basic beginner's level.

This article reveals why AI-generated code becomes difficult to maintain over time and provides two practical methods to improve the maintainability of "Vibe Coding" projects.

Using a "Linus Torvalds style" prompt as a case study, this article reveals the underlying logic of AI programming: using structured instructions to guide AI in writing high-quality code.

This article explores how to expand your use of AI programming tools, treating them as general agents to accomplish diverse automation tasks.

Starting with a story about improving efficiency for product managers, this post explores how to unlock the potential of AI coding tools and become a cross-disciplinary enabler.

This article moves beyond feature comparisons to analyze the suitable scenarios for Cursor and Claude Code from a product philosophy perspective, helping you build your own AI programming workflow.