138 related articles

A structured AI Agent learning roadmap covering fundamentals (Agent principles, Prompt engineering), advanced topics (RAG, multi-agent collaboration), and three hands-on projects — ideal for beginners.

A systematic breakdown of the AI LLM learning roadmap covering prompt engineering, AI Agent development, RAG knowledge bases, model fine-tuning, and hands-on projects for beginners.

Choose the right AI Agent platform by evaluating model flexibility, observability, tool integration, security compliance, and total cost. A complete decision framework to help technical leaders avoid vendor lock-in.

Choose an AI Agent platform by evaluating model flexibility, observability, tool integration, security compliance, and total cost. A complete decision framework to avoid vendor lock-in.

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.

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

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.

Want to become an AI Agent engineer? This article breaks down a 4-week roadmap: from core agent architecture and ReAct, to multi-agent collaboration and real projects.

An in-depth look at an intelligent paper writing platform built on FastAPI + Vue 3, combining LLM, RAG, and multi-Agent collaboration for full-process automation—an excellent case study for AI developers.

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 AI coding tools really earn you $3K/month with zero experience? We break down the marketing hype around Codex freelancing, examine the 4-week roadmap, and reveal what AI-assisted coding can realistically offer.

A systematic roadmap from LangChain and LangGraph to multi-agent development, covering RAG, Tool Calling, MCP, and more, helping developers break into AI app development.

Alibaba open-sources a 2.4 trillion parameter Qwen model and launches the Qwen Token Plan. Chinese models surge, Kimi K3 tops global rankings, and China's AI is reshaping the global competitive landscape.

A comprehensive guide to Dify, the open-source AI application platform: who it's for, core advantages, Dify vs. Coze comparison, and a step-by-step learning path.

A structured 3-phase roadmap for frontend developers transitioning to AI: master Transformer fundamentals, build RAG & Agent skills, then advance to model fine-tuning.

Learn how to build a RAG knowledge base with zero code using Dify's visual platform. Compare Dify vs Coze for private deployment, and master the Dify+Qwen+RAG stack.

Traditional Java roles are shrinking while AI demand surges. Learn the three paths into AI for developers, and why RAG knowledge bases are the highest-ROI entry point for Java engineers.

AI code getting messier with edits? The root cause isn't weak model capability but a lack of context and process. A deep dive into Matt Pocock's Skills v1.1: grilling, vertical-slice tickets, TDD, and WebFinder.
GitHub Daily · July 19: The Dual Advan…
GitHub Trending July 19: ktransformers tops the list with heterogeneous inference optimization, while jcode, cua, and AstrBot signal a maturing Agent ecosystem.

A four-stage AI Agent development roadmap: from core theory and ReAct paradigm to multi-agent collaboration and production deployment. Covers DeepSeek, Coze, Dify, and more.