An engineering notebook on AI, systems, and everyday experiments.

闭环学习强调持续反馈与调整而非一次成功，持续校准方向的能力比初始瞄准的精度更为重要。

Closed-loop learning emphasizes continuous feedback and adjustment over one-shot perfection—the ability to calibrate direction matters more than initial aim.

对比尊界S800与迈巴赫的品牌策略：清单式技术对标vs体验驱动型叙事，科技参数堆砌vs从容克制的品牌调性，探讨技术豪华与故事豪华的不同路径。

Comparing Zunjie S800's tech-focused marketing with Maybach's experience-driven branding - checklist-driven features vs. cultivated prestige, and what this reveals about different paths to defining luxury.

分析为什么模型越来越强但用户感受不到：App设计滞后于模型能力。对比Claude、Gemini和ChatGPT的产品体验，发现OpenAI是唯一同等重视产品设计和模型能力的公司。

Analyzes why users don't feel AI getting smarter despite model advances: apps lag behind capabilities. Compares Claude, Gemini, and ChatGPT products, finding OpenAI the only company equally prioritizing product design and model capability.

在AI指导下探索水质对水培白菜、泡泡水和意式咖啡的影响，通过KH/GH测量发现跨领域知识迁移的复利效应，体会量化与好奇心的力量。

Using AI to explore water quality across hydroponics, bubble solutions, and espresso. How KH/GH measurements and AI guidance transformed my understanding of everyday water chemistry.

AI帮忙写的文章空中楼阁，不是AI写得不好，是我没喂够context。作为Manager要审核输出，作为Enabler要提供长期积累。判断界限才是协作的开始。

An AI-assisted article failed not because AI wrote poorly, but because I didn't feed it enough context. As Manager, verify output; as Enabler, provide your accumulated materials. Defining boundaries is where collaboration begins.