130 related articles

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.

A complete AI learning workflow: batch download videos, auto-transcribe, generate structured notes with AI, then build intelligent search and Q&A via Dify. Turn scattered videos into a reusable personal knowledge base.

A deep dive into LangChain's positioning and value—why do LLMs need a middle layer? How does LangChain serve as the 'glue' unifying multi-model interfaces and supporting Agent development? Learn its core modules and learning path.

Most Agent projects lack competitiveness in interviews due to missing business value and engineering depth. This article breaks down the 6 core standards of high-value Agent projects.

Want to switch careers into LLM development but don't know where to start? This guide breaks down a four-level skill roadmap — from basics and API calls to RAG, fine-tuning, Agent development, and multimodal — to help you build real AI career value.

Google lets businesses connect their Google Business Profile to Gemini, so the AI can read operating info, reviews, and business data to offer targeted marketing advice—lowering AI barriers for SMBs.

A systematic zero-basis learning path for AI Agent development, covering Python and LLM fundamentals, five core capabilities like task planning and RAG, and LangChain hands-on practice.

A structured 6-week roadmap for enterprise Agent deployment covering LangChain, LangGraph, MCP, and RAG — from planning and memory to multi-agent collaboration and production deployment.

A complete 6-week AI Agent learning roadmap covering core architecture (planning/memory/tool use), the ReAct paradigm, multi-agent collaboration, RAG integration, and production deployment.

An in-depth look at LangChain 1.3's core modules and DeepAgent architecture—covering the Harness philosophy, LangGraph internals, HITL, memory management, and guardrails to master production-grade AI Agent development.

What is an AI Agent? This article systematically explains the core architecture of AI agents (LLM + Planning + Memory + Tools), how they differ from ChatGPT, their combination with robots, and why developers must master Agent development skills.

By capturing and analyzing ChatGPT's real network traffic during web retrieval, this article reveals the underlying logic of AI source selection—covering the layered mechanisms of retrieval recall and content filtering, domain preference patterns, and practical insights for content creators and SEO professionals.

Ternlight is a 7MB WebAssembly-based browser-side text embedding model requiring no server or GPU. Explore its tech, use cases, and tradeoffs for private, offline semantic search.

Silicon Valley tech giants are quietly abandoning the doomsday 'AI destroys jobs' narrative in favor of AI augmentation and human-AI collaboration. A deep analysis of the reality gap, regulatory pressure, and business logic behind this reversal.

Can AI really replace programmers? This article explains Harness Engineering principles and its three evolutionary stages, revealing real pain points of enterprise AI programming.

Deep dive into LangChain 1.0's architecture: LangChain framework, LangGraph multi-Agent orchestration, and LangSmith observability platform, with hands-on RAG and intelligent customer service projects.

Andrew Ng and LangChain CEO Harrison Chase's AI Agents in LangGraph course covers five agent design patterns and LangGraph's graph-based framework for building cyclical AI workflows.

Andrew Ng and LangChain CEO Harrison Chase present AI Agents in LangGraph, covering five core agent design patterns and LangGraph's graph-based framework for building cyclical agentic workflows.

AI customer service is a core tool for digital transformation. This guide covers its value, use cases, and implementation logic, including efficiency gains, cost reduction, and data-driven optimization.

Why do enterprise RAG knowledge bases dazzle in demos but fail in production? This article dissects five critical engineering pitfalls with real-world case studies from million-doc platforms and ops agents.