Methodology

我用AI提效很成功，产出和rating都是org最高之一，但升职两次都失败了。后来发现一个讽刺的陷阱：正因为手快好用，老板把你当手而非脑，项目零散多变，反而讲不清一年的成果。最擅长用AI的人，反而最容易被AI替代。破解之道是主动设计奖赏系统，把省下的时间用来做判断而非交付更多。

AI made me a top performer. I was denied promotion twice. Speed makes bosses treat you as a hand, not a brain. The best AI users paradoxically become the most replaceable. The fix: design the incentive structure.

用好AI的第二步不是更会写 prompt，而是先外化、再复用。本文讲清 Skill 如何承载工作知识、好 Skill 的三要素，以及如何组织 Skill 文件夹让 Agent 自动找到。

Step two isn't better prompting. It's externalize first, reuse second. This post explains how Skills carry work knowledge, the three parts of a good Skill, and how to organize them so agents find the right one.

LLM的默认输出是consensus：正确但平庸。Deep Research其实是Wide Research。我们找到了一种系统性方法，用个人认知上下文把LLM从consensus里强行扯出来。一年实验，有控制变量证据。

An LLM's default output is consensus: correct but mediocre. Deep Research is really Wide Research. We found a systematic way to pull LLMs out of consensus using personal cognitive context. One year of experimentation, with controlled evidence.

会用AI和用好AI之间差的是10倍。这个差距的根源在于工作方式，而非模型。本文通过一个完整的工作流例子和上中下三策的框架，解释为什么应该从ChatGPT切换到Cursor这类Agentic工具。

The gap between using AI and using AI well is 10x. That gap comes from how you work, not which model you use. This post walks through a complete workflow example and a Three Tiers framework to explain why you should switch from ChatGPT to agentic tools like Cursor.