Agentic AI

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.

用两分钟指挥AI给300篇文章添加SEO summary的实战案例，拆解五个关键决策：选对执行环境、先建测试再干活、让agent自己处理corner case、divide and conquer、结果导向的prompt写法。

A real-world case study of directing AI to add SEO summaries to 300 articles in two minutes, breaking down five key decisions: choosing the right execution environment, building tests before work, letting agents handle corner cases, divide and conquer, and outcome-oriented prompt writing.

OpenClaw 爆火的原因和去年 DeepSeek 一模一样——不是技术突破，而是把小众体验推向大众。本文不教配置，而是从产品设计角度拆解它的记忆系统、Skills 机制和聊天界面的根本局限，帮你判断该不该跟，以及怎么把核心思路用到自己的工作流里。

OpenClaw went viral for the same reason DeepSeek did — not a technical breakthrough, but bringing a niche power-user experience to the masses. This post skips setup tutorials and instead dissects its memory system, Skills mechanism, and the fundamental ceiling of chat-based AI interfaces, helping you decide whether to adopt it and how to extract its core ideas into your own workflow.