Function Calling 解决的是单次调用的消息格式问题，MCP 解决的是工具生态的标准化管理和复用问题，两者是不同抽象层次的东西。MCP 底层依然靠 Function Calling 驱动，模型根本感知不到 MCP 的存在，所有的工具发现、schema 转换、调用路由都发生在宿主程序层。

完全跑偏的那一半：前端工作的天花板，不是切页面。 前端真正值钱的能力——异步流程设计、流式体验优化、交互状态管理、组件化工程思维——这些 AI 一个都学不会。而这些能力，恰好是 AI Agent 应用开发最核心的竞争力。

随着人工智能技术的飞速演进，智能代理（AI Agent）已从单一任务执行工具向多模态协同系统转变。传统单智能体工具在复杂业务场景中逐渐暴露出局限性，如任务处理能力单一、跨系统协作困难、企业级部署复杂等问题，难以满足现代业务对智能化、自动化的 ...

There’s lots to do in this edition of the Python Report: Do more than one thing with Python’s async. Do the math faster in Python with NumPy. Do Python in Visual Studio Code, and do it the right way ...

In this tutorial, we explore tqdm in depth and demonstrate how we build powerful, real-time progress tracking into modern Python workflows. We begin with nested progress bars and manual progress ...

https://www.riteshmodi.com - Data Scientist, AI and blockchain expert with proven open-source solutions on MLOps, LLMOps and GenAIOps. https://www.riteshmodi.com - Data Scientist, AI and blockchain ...

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