230 related articles

An in-depth breakdown of LangChain 1.3's core concepts, covering the three major limitations of LLMs, Agent architecture, memory management, and a complete learning path. Master LangChain and LangGraph to quickly build AI development skills.

A detailed guide to Dify, the open-source LLM app development platform, covering its core features and full local deployment via VMware + Ubuntu + aaPanel + Docker. Supports 100+ models like DeepSeek and ChatGPT to build enterprise AI apps fast.

A systematic guide to Dify's three deployment methods (Docker/source/online), five application types, and hands-on workflow nodes—covering LLM integration, MySQL config, and app publishing.

Breaking down a 10-hour Python course for absolute beginners — covering syntax, OOP, functional programming, web scraping, and automation, with mind maps and exercises.

Learning Python from scratch? This article breaks down the three learning stages—Fundamentals, Intermediate, and Practice—covering variables, OOP, scraping, and data analysis to help you plan a systematic Python path.

How can beginners learn Python without getting lost? This guide outlines a 3-stage learning path covering basics, advanced topics, and hands-on practice in web scraping, data analysis, and office automation.

Frontend hiring now treats AI capabilities as a core assessment, covering RAG knowledge bases, AI Agent development, and LangChain.js engineering. Learn how LangChain.js + Nuxt.js helps frontend developers build memory- and retrieval-capable AI full-stack apps.

A hands-on guide to building an enterprise-grade AI Agent workflow orchestration app with Electron Forge and LangGraph, covering local LLM deployment (Qwen3-0.6B), node-based visual canvas design, and full Function Calling integration.

A plain-language guide to how Python web scrapers work: from HTTP requests and responses, HTML tag parsing, to the complete three-step data scraping workflow, with a focus on the three legal red lines and compliance advice.

Want to learn Python from scratch but don't know where to begin? This article breaks down three stages—basic syntax, advanced mastery, and hands-on practice—with real projects in crawling, automation, and data analysis to help you build programming thinking.

A systematic Claude Code learning guide built for Chinese developers, covering ten core modules including Slash Commands, Memory, MCP, and Hooks, with a three-tier path to build an AI coding workflow in 11–13 hours.

Master OpenAI Codex fast, even from scratch! Learn Codex vs ChatGPT differences, four versions, interface tips, plugins & skills, browser automation, plus six best practices.

A beginner's guide to the LangChain open-source framework: explaining how to use the init_chat_model unified interface, tips for disabling DeepSeek's thinking mode, and core essentials of Agent development.

Matt Pocock's 4-step framework for AI Agent Skill design: Trigger, Structure, Steering, Pruning. Escape skill hell, tell good skills from bad, and master leading words to make Agents follow your intent.

Matt Pocock's 4-step framework for AI Agent Skill design: Trigger, Structure, Steering, Pruning. Escape skill hell and learn to write high-quality skills.

A complete Spring AI 2.0 guide for Java developers covering unified API abstraction, RAG, tool calling, MCP protocol, and enterprise projects to build AI Agents.

A practical guide for Java developers to build AI apps without switching to Python. Learn LangChain4j, RAG, Function Calling, and MCP through an airline customer service project.

Two methods for connecting external models to Codex: manually configure keys via relay services, or use the CC Tool to auto-bridge GPT, DeepSeek, and more. Covers auth/config files, CC Tool usage, and multi-model switching.

In-depth analysis of OpenAI Codex's four usage forms, comparing Codex, Claude Code, and Cursor across price, stability, and frontend/backend fit to help developers choose the right AI programming tool.

Taste-Skill is a viral open-source JavaScript project with 56K+ GitHub Stars. It uses prompt engineering and anti-slop rules to help AI generate higher-quality, more distinctive content.