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

分享3分钟语音prompt如何让AI完成资深科学家一整天的工作量。详解招聘、任务委托、入职培训、过程指导和产品验收五大管理环节如何释放AI的全部潜力。

Shares how a 3-minute voice prompt led to AI completing a senior scientist's full-day work. Details five management principles—hiring, task delegation, onboarding, process guidance, and product acceptance—that unlock AI's full potential.

AI"偷懒"的本质是LLM输出长度限制导致的注意力分散。Wide Research通过多轻量模型并行处理子任务、主LLM汇总的方式解决，分享为Codex构建该能力的设计思路。

Why AI slacks off on large tasks: LLM output length limitations cause attention drift. Wide Research solves this by parallelizing with lightweight models, then aggregating results with a primary LLM.

深入解析电车与油车底盘操控之争：油车通过战略自由塑造性格，电车用战术科技对抗物理惯性，两种截然不同的工程哲学。

A deep dive into EV vs ICE car chassis engineering, exploring different philosophies: ICE cars shape character through strategic freedom, while EVs fight physics with tactical technology.

分析OpenAI Apps SDK通过_meta域绕过context window的做法如何违背MCP设计哲学，以及协议分裂成不同dialect的潜在危机。

Analyzing how OpenAI's Apps SDK extension with _meta field violates MCP's design philosophy, creating dialects that may fragment the standard like SQL or CSS.

将常见的AI使用挫折重新定义为管理问题。当把AI当作实习生而非工具时，不可靠、幻觉和代码质量等问题都可以通过建立信任、层级分工等成熟的管理原则来解决。

Addresses common AI frustrations by reframing them as management problems. When we treat AI as an intern rather than a tool, issues like unreliability, hallucinations, and code quality become solvable through established management principles like trust-building and hierarchy.