328 related articles

A comprehensive guide to AI Agent architecture and development, covering automated marketing, intelligent customer service, and investment analysis scenarios with single and multi-agent collaboration.

How to learn AI Agents from scratch? This guide covers two clear paths: developers go from Python to LLMs to open-source framework source code; practitioners use Claude Code or similar tools to get results fast.

Learn AI Agent core principles from scratch: understand how Agents differ from LLMs, their execution mechanisms, why rule design matters, and find the right learning path for your goals.

A 6-week systematic learning path for frontend engineers transitioning to AI Agent development, covering core architecture, ReAct, multi-agent collaboration, RAG integration, and deployment.

A deep engineering analysis of Agent internals: how LLMs decompose tasks via tool calling, why context compression and memory are essential, and why solo developers should avoid heavy frameworks.

Moonshot AI launches Kimi K3 reasoning model with performance rivaling Claude and OpenAI's top models at one-third the price. The US-China AI gap narrows from 6-12 months to just 3 months.

A developer added a DAW to their agentic dev environment with Claude, then paired with AI to finish music — experiencing a true AGI moment in creative collaboration.

A systematic guide to AI Agent development covering core modules, framework selection, tool calling, data preparation, and production deployment to help developers build production-ready Agent applications.

A beginner's guide to AI Agents: understand core principles, how Agents differ from LLMs, their execution mechanisms, and get tailored learning path recommendations.

A deep dive into Agent Skills architecture: modular design, progressive disclosure mechanism, and how it differs from Multi-Agent systems for AI capability extension.

A systematic guide to AI Agent development across four stages: LLM fundamentals, ReAct paradigm, memory & tools, and multi-agent collaboration for developers.

A fresh grad interviewing for a GenAI Trainer role faced prime number coding and activation function questions while the interviewer used Gemini to generate questions live — exposing AI hiring chaos.

A 12-person product team shares real-world experiences with Cursor, Codex, Claude Code, and CodeRabbit—exploring efficiency plateaus, scenario matching, and selection criteria for AI coding tools that actually stick.

Coze is ByteDance's homegrown agent-building platform. This article covers getting started with Coze, its comparison with Dify, skill system, workflow orchestration, and multi-agent collaboration.

Coze is ByteDance's homegrown agent-building platform. This article explains getting started with Coze, comparison with Dify, its skill system, workflow orchestration, and multi-agent collaboration.

A systematic guide to the complete learning path for AI Agent development—covering prompt engineering, RAG knowledge bases, LangChain & LangGraph, fine-tuning, and multi-agent collaboration.

A complete guide to Claude Code from beginner to enterprise practice: covering CLI installation, connecting domestic LLMs via CC Switch, basic commands, Git workflow integration, automated bug fixing, and engineering capabilities like MCP and SubAgents.

A complete guide to learning AI Agents: from large model fundamentals and core technologies to hands-on projects. Systematically outlines beginner methods and exposes crash-course marketing traps.

An in-depth analysis of the three-layer GTM Agent architecture—the Signal, Buyer Intelligence, and Action layers—revealing how context graphs identify anonymous visitors and capture purchase intent.

Can beginners really earn over 10,000 yuan in their first month with AI coding gigs? This article breaks down the four-week AI coding learning path week by week and objectively assesses the real monetization barriers.