Tech blogger with over 10 years of experience at leading tech firms, AI efficiency and delivery expert focusing on AI productivity. Covers tech gadgets, AI-driven efficiency, and leisure— AI leisure community. 🛰 szzdzhp001
The author asked six leading AI models (ChatGPT, Gemini, DeepSeek, Wenxin, Doubao, and Qianwen) to rank the top three domestic AI agents, and all models consistently placed Baidu first, a result supported by product breadth, search capability, and full‑stack infrastructure.
Anthropic’s engineering blog details a multi‑agent harness that splits generation and evaluation tasks, tackles Claude’s context‑anxiety and self‑assessment issues, and demonstrates through front‑end design and full‑stack app experiments how the system can run continuously for hours with higher quality output.
Google's ADK team outlines five recurring Agent Skill design patterns—Tool Wrapper, Generator, Reviewer, Inversion, and Pipeline—each solving a concrete pain point, with advantages, suitable scenarios, and ready‑to‑use YAML prompt examples for building more effective AI agents.
Google Labs’ March 18 update to Stitch introduces an AI‑native infinite canvas, a stronger design agent, voice‑driven collaboration, instant interactive prototyping, and the DESIGN.md system, dramatically lowering design barriers, speeding workflows, and sparking a debate on the future role of designers.
Claude Code’s newly released Channels feature lets developers control active Claude Code sessions from Telegram or Discord on their phones, using the Model Context Protocol (MCP) to create a bidirectional server, while the preview highlights both setup steps and its broader strategic impact on AI‑driven development.
Anthropic engineers share nine practical lessons from hundreds of Claude Code Skills, covering folder‑based design, nine skill categories, the importance of Gotchas, description as a trigger, giving Claude code, memory handling, flexible hooks, and team distribution strategies.
The author argues that AI is not a mere tool but a productivity multiplier that can turn a single person into a “super‑individual,” illustrating this with personal workflow automation and a yoga studio’s transformation, and explains why many fail to benefit due to weak models, unclear use‑cases, and lack of integration into daily work.
The Kimi team introduces Attention Residuals, a softmax‑based replacement for the uniform residual connections used in Transformers for a decade, enabling selective aggregation of layer histories, reducing hidden‑state growth, and achieving a 1.25× compute‑efficiency gain on a 48‑billion‑parameter MoE model with less than 2% inference latency increase.
Context Hub, an open‑source tool from Andrew Ng's team, automates real‑time API documentation retrieval and adds persistent annotations and community feedback, enabling AI coding assistants to avoid outdated calls, remember previous mistakes, and continuously improve their knowledge base.
Anthropic’s newly released 33‑page PDF walks developers through every step of creating Claude Skills—covering folder layout, YAML description, three‑layer progressive disclosure, five work modes, testing methods, token savings, and distribution—providing the most comprehensive official reference to date.
