94 related articles

Why has AI engineering methodology evolved from prompts to context engineering and now Harness engineering? This article examines three paradigms, key bottlenecks, and the Agent = Model + Harness formula.

Master LangChain from scratch: the three limitations of LLMs, init_chat_model unified interface config, the Message type system, and the path from LLM calls to Agent development.

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.

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.

An in-depth analysis of LangGraph's core concepts: short-term and long-term storage mechanisms, its differences from LangChain, the MIT open-source license, and private deployment solutions for enterprise Agent development.

An in-depth guide to installing, configuring, and extending OpenCode, the terminal AI coding assistant. Covers desktop and WSL installation, model config, MCP integration, and custom Agents.

Pure frontend roles are shrinking fast. Learn how mastering NestJS and LangChain AI agent development can unlock a 20–30% salary boost on your full-stack AI transition path.

A deep dive into Harness Engineering architecture: building an AI procurement assistant on ERP systems, covering multi-agent orchestration, MCP protocol, ASGI deployment, and sandbox isolation.

How to learn LLMs from scratch? This guide covers personalized learning paths for 3 types of learners, hardware tips (16GB RAM is enough), Python prep, and cloud GPU options.

Why do banks and hospitals build Local AI instead of using cloud services? This guide covers the full tech stack — Ollama, RAG, vector databases — and real-world enterprise deployment use cases.

A complete MCP practical guide using the official Python SDK — covering environment setup, FastMCP server development, Inspector debugging, and multi-client integration with Cursor and Cline.

How can ordinary programmers break into AI? This guide breaks down the gap between algorithm engineers and AI app developers, covering Agent development, model fine-tuning, salary trends, and the three hidden risks behind the current opportunity window.

Learn how to build a full-stack AI e-commerce system with RAG-powered customer service using Cursor AI, LangChain, FastAPI, Vue3, and WeChat Mini Program.

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.

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

A complete guide to Vibe Coding's three stages, Cursor tool selection, and hands-on workflow—helping non-programmers and professionals master AI programming for multiplied productivity.

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

Deep dive into GitHub Copilot SDK: Agent Loop, Hooks lifecycle control, Custom Agents orchestration, Skills modules, and local model integration for AI app development.

Deep dive into Agent Skill's core design—Progressive Disclosure—with detailed middleware and dynamic tool implementation, Multi-Agent comparison, and practical tips.

Full walkthrough of building a FastAPI + Vue3 library management system in 15 minutes with Cursor AI, covering structured prompts, Plan & Build strategy, and bug fixes.