Notes on AI, agents, and building things that work.
从 Dots、ChatGPT Space、GPT-6.1 Sol、Ultrafast、Decisions API、Agents API、Codex Cloud、Computer Use 到 Plugin Extensions，本文不只列发布内容，而是用一张完整知识关系图解释 OpenAI 如何把 Chatbot 推向 Digital Employee、Multi-Agent Team 与真正的 Agent Platform。 另加入 Artificial Analysis 的第三方 benchmark snapshot，比较 GPT-6.1 Sol、Astra 与 Luna 的 intelligence / cost trade-off，并延伸到 Model、Reasoning Effort 与 Speed Routing。
Continue reading从 Computer Use、Coding、Professional Work、Science、Cybersecurity、Alignment、Long Context 与 ARC‑AGI 全面拆解 GPT‑6 Astra 的公开 benchmark，并区分真正的能力跃升、harness 带来的系统级提升，以及容易被 marketing 放大的数字。
Continue readingA dated, evidence-backed comparison of Google Gemini 3.8 Flash, Anthropic Claude Fable 5.1, and OpenAI GPT-5.6 Sol for reasoning, coding, AI agents, speed, cost, context, tool use, and real-world engineering workflows.
Continue readingWhy AI model comparisons should show Low, Medium, High, XHigh and Max effort separately instead of comparing only maximum scores.
Continue readingA practical comparison of GPT-5.6 Luna, Terra and Sol across reasoning effort, Artificial Analysis Intelligence Index, cost and first-response latency—and what the results mean for AI model routing.
Continue readingA practical architecture review of Codex Harness: context engineering, deferred tools, sandboxing, verification loops, compaction, persistent execution, and what these ideas mean for building reliable AI agents.
Continue reading从 Codex Harness 的 Context Construction、Deferred Tools、Sandbox、Goal Loop、Compaction 与 App Server，理解真正的 Agent Engineering。
Continue readingWhy long-context AI needs externalized state, bounded recursion, better harnesses, and evaluator-driven learning—not just bigger context windows.
Continue reading长上下文不是终点。真正的变化，是把 Context 从“全部塞进 Prompt”改造成可查询、可计算、可递归、可验证的外部工作环境。本文拆解 RLM、Prime Agent、Continual Harness、Evaluator、自我改进与安全边界，并给出一套可落地的 Thinking Architecture。
Continue readingA practical role-by-role guide to GPT-5.6 Sol, Terra, and Luna: current API pricing, where each model fits across business functions, and how to design a cost-efficient AI routing and permission architecture.
Continue reading坚持并不总是美德，放弃也不一定是失败。本文从 Seth Godin 关于 The Dip、沉没成本、阻力、目标受众与 AI 时代职业价值的讨论中，提炼出一套可实际用于工作、项目、创业和职业选择的 Continue → Pivot → Quit 决策框架。
Continue readingA practical guide to Retrieval-Augmented Generation: how RAG connects language models to external knowledge, improves traceability, and supports reliable AI Agents.
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