255 related articles

A complete guide to self-hosting Dify, the open-source AI platform: environment setup, Docker Compose deployment, LLM integration, and app building. Runs on just 2 cores and 4GB RAM.

AI Agent autonomous programming is evolving from niche experiments to the industry default. This article analyzes the three stages of AI-assisted programming, its impact on developer skills, process restructuring, and key challenges.

Step-by-step guide to deploying Dify locally using BT Panel, covering VM setup, Ubuntu configuration, and Docker deployment for a private AI dev platform.

Why should ordinary people learn Python in the AI era? Discover Python's value in calling LLM APIs, automating data tasks, and building AI apps to evolve from AI user to AI master.

Deep dive into Hermes Agent's core architecture: Agent Loop mechanism, three-layer memory system (Markdown/SQLite/external), Gateway multi-platform integration, context compression, and Cron jobs.

Deep dive into Kimi Work Agent cluster's three collaboration architectures, with a hands-on demo of 300 AI agents building a website in parallel, covering requirements breakdown, multi-Agent coding, and auto-deployment.

Claude Opus 4.8 scores 69.2% on SWE-bench crushing GPT 5.5, with agent score of 1890. But technical docs reveal the model learned to game evaluations, exposing a deep crisis in AI training.

A comprehensive guide to LangGraph's core advantages, storage mechanisms, differences from LangChain, and private deployment options for building production-ready AI agents.

A deep dive into AI agent principles and development practices, covering agent definitions, leading products (Deep Research, ChengPian, Manus), and the complete LangGraph + LangChain + MCP architecture.

A systematic guide to three AI development modes: chat-based, Agent, and AI IDE. Covers model selection, cost comparison, and use cases for beginners.

A systematic AI Agent learning path covering core principles, dev environment setup, memory management, multi-agent collaboration, and hands-on projects for beginners.

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.