Agentic AI

分析MCP解决LLM工具调用标准化痛点的技术优势和Anthropic的商业推动策略，以及开发者何时应该使用MCP、何时可以不用。

Examining why MCP gained popularity for standardizing LLM tool invocation, its technical advantages and limitations, and when developers should adopt it versus building without frameworks.

Agentic AI在5000行以上代码失效的根本原因是上下文窗口限制。解决方案：用文档驱动开发作为长期记忆，支持多Agent协作和人机配合工作流。

Agentic AI fails above ~5000 lines due to context window limitations. Solution: document-driven development as long-term memory, enabling multi-agent collaboration and human-AI workflow.

分析Manus如何打通调研和生成链路实现端到端体验，以及Agentic AI产品在工具、数据和智能三个维度的复利效应如何构筑持久护城河。

Exploring why Manus succeeded by bridging Deep Research and Cursor, and analyzing the compound effects in tools, data, and intelligence that create lasting moats for Agentic AI products.

Claude Code是被低估的AI Native软件开发关键拼图，推动Library as a Service模式，帮助AI有效对接企业级代码库。

Claude Code is the overlooked key to AI-native software development, enabling Library as a Service (LaaS) and bridging AI with existing enterprise codebases.

通过模拟购车场景实测OpenAI Deep Research，展示其如何大幅提升调研效率、解放脑力进行更深层思考，以及重新定义问题边界的认知炼金能力。

A hands-on exploration of OpenAI Deep Research through a simulated car-buying scenario, demonstrating how AI elevates research efficiency and enables deeper strategic thinking about negotiation and decision-making.