E‑commerce, SaaS, AI architect; DDD enthusiast; SKILL enthusiast
In March 2026, Chinese AI large‑model APIs processed 4.69 trillion tokens per week, overtaking the United States, driven by cheap electricity, aggressive tech optimization, and self‑evolving models like MiniMax M2.7, which together lower AI adoption costs and reshape the global AI landscape.
An API interception revealed that Cursor’s high‑valued AI programming platform relies on Moonshot AI’s Kimi K2.5 model, a trillion‑parameter MoE system, and uses a novel self‑summarization technique to compress context, achieving superior benchmark scores and exposing why Western open‑source models fall short.
The article introduces the SKILL framework, explains its file‑based structure and LLM‑driven entry point, compares it with traditional API‑centric backends, outlines its suitable use cases and limitations, and argues that mastering SKILL will become a core productivity skill for developers.
Over four years, AI shifted from a fragmented skill marketplace to integrated, goal‑oriented functions that automate entire workflows, reshaping ecosystems, supply chains, and the very relationship between humans and machines.
Cursor unveiled Composer 2, a self‑trained code‑only LLM that scores 61.3 on its CursorBench, outperforms Claude Opus 4.6 and rivals GPT‑5.4, while charging just $0.50 per million tokens—about one‑tenth the price of competitors—prompting a new AI pricing battle.
OpenAI plans to merge ChatGPT, Codex, and Atlas into a single super‑app that can write code, browse the web, and control webpages, prompting a split among developers between panic over job security and excitement about effortless coding, while the broader industry follows with similar AI‑driven initiatives.
From AI chatbots to desktop coworkers, the article examines how Claude Cowork, OpenClaw and other emerging tools let AI control screens, keyboards, and files, reshaping workflows, sparking a competitive race among tech giants, and raising security and trust challenges for users and SaaS providers.
The article argues that technical brilliance alone rarely earns promotion, illustrating through real workplace stories that leaders prioritize reliability, compliance and emotional support over pure engineering excellence, and offers practical ways for engineers to align their value with business needs.
The article explains how context engineering—building a dynamic system that supplies AI with user intent, dialogue history, long‑term memory, external knowledge and tool definitions—outperforms traditional prompt engineering, eliminates hallucinations, and enables AI to complete complex, end‑to‑end tasks.
The article explains why manual architecture diagramming wastes architects' time, introduces AI drawing tools, outlines three core prompt principles, provides a detailed e‑commerce platform prompt and layered design rules, and shares three advanced tips that can cut hours of work down to minutes.
