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A systematic three-stage AI Agent development roadmap: from Python basics and LLM fundamentals, through five core capabilities like planning and tool use, to hands-on RAG projects for real-world deployment.

A systematic six-week learning roadmap for AI Agent development covering core architecture, ReAct paradigm, multi-agent collaboration, RAG integration, deployment, and hands-on projects.

A comprehensive guide to Claude Code Skills and MCP resources, covering international platforms like Skills.mp and Smithery plus Chinese alternatives, with a quick selection guide to boost AI coding productivity.

An in-depth look at Cursor, the AI-native programming IDE, covering intelligent code generation, multi-model support, context awareness, and how it compares to traditional IDEs across six key dimensions.

Complete guide to Coze workflow development covering Agent building, node orchestration, plugin systems, API integration, and a Coze vs Dify comparison.

A comprehensive guide to LangGraph's three core advantages, its relationship with LangChain, short-term and long-term storage mechanisms, and deployment strategies for development and production environments.

Deep dive into OpenAI Codex desktop's three core capabilities: AI coding & debugging, browser automation, and computer/iPhone control. Includes ChatGPT comparison and membership tips.

Step-by-step guide to installing Claude Code CLI in China using Node.js, Git, CC Switch, and an API relay service to bypass Anthropic's access restrictions.

A systematic AI Agent development learning roadmap covering prompt engineering, RAG, multi-Agent collaboration, tool calling, and more—with phased learning advice and 28 hands-on project references.

Master 10 power-user tips for OpenAI Codex: from assigning tasks and phased planning to automated testing and code reviews. Make AI Agents truly work for you.

A deep dive into Agent Skill's core concepts and internal structure, covering skill.md, references, scripts, and assets with a restaurant poster Skill example.

A deep dive into the three-step LLM development learning path: from prompt engineering and RAG knowledge bases to AI Agent development, with realistic timelines for beginners and experienced developers.

A complete guide to 5 local LLM deployment methods: LlamaCPP, Ollama, LM Studio, vLLM/SGLang, and MLX-LM — from personal dev to production environments.

A systematic AI Agent learning path covering core principles, Prompt engineering, RAG, multi-Agent collaboration, and hands-on projects for beginners.

A 6-week systematic learning roadmap for AI Agent development, covering core architecture, ReAct principles, multi-agent collaboration, RAG integration, and deployment.

Build an AI Agent from scratch with 200 lines of Python, covering prompts, memory, tool calling, RAG, and Skills — a practical guide for developers.

A systematic guide to OpenAI Codex and AI LLM learning, covering Transformer basics, dev environment setup, prompt engineering, RAG deployment, LoRA fine-tuning, and AI Agent enterprise projects.

A complete roadmap for learning AI Agent development from scratch, covering Python & LLM basics, five core skills, and hands-on RAG projects in 1-2 months.

A systematic guide to learning AI large language models, covering Transformer architecture, prompt engineering, RAG, AI Agents, fine-tuning, and enterprise projects from beginner to production-ready.

A deep dive into Dify, the open-source AI app development platform — covering core features, Coze comparison, enterprise use cases, and a learning roadmap.