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 是什么|AI Agent 聊天工具的原理、价值与局限
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 Deep Dive: Why It Went Viral and What It Means for You
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.
从过程确定性到结果确定性:AI 时代的另一种安全感
用Claude Code替代API调用做翻译任务:利用agentic loop实现自我纠错,用evaluation-first定义验收标准,从过程确定性转向结果确定性获得新的安全感。
From Process Certainty to Outcome Certainty: A Different Kind of Confidence in the Age of AI
Why handing translation to Claude Code works better than calling APIs directly - leveraging the agentic loop, evaluation-first mindset, and the ecosystem's runtime layer to achieve outcome certainty over process certainty.