Computing

提出用户生成软件需要新的范式。软件公司不再交付成品，而是交付"生成内核"——包括核心套件、引导知识和杠杆工具集，专为AI使用而设计，最大化表达范围、意图保真和生成效率。

Proposes that User-Generated Software requires a new paradigm. Instead of finished products, companies deliver "Generative Kernels"—core kits, guiding knowledge, and leverage tools designed for AI to use, maximizing expressive range, intent fidelity, and generation efficiency.

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.

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.