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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.

As LLM costs keep falling, how can Java developers seize the AI opportunity? This article explores LangChain4J's core capabilities, supported models and vector databases, and compares LangChain4J vs. Spring AI to help you build local knowledge bases and intelligent customer service systems.

A detailed guide to Coze's core features: cross-platform interoperability, the Skills system, multi-agent collaboration, and workflow building. Compare Coze and Dify to build practical AI apps with zero coding.

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.

An in-depth look at Claude Code's development and design philosophy: why the CLI form, how the "minimal scaffolding" architecture works, and how it balances safety with autonomy in AI programming.

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.

Kontext is a context migration tool for multi-AI users, enabling one-click transfer of full conversation history between ChatGPT, Claude, and Gemini to solve the context silo problem.

Deep dive into NVIDIA AI-Q Blueprint production deployment on Oracle Cloud Infrastructure, covering NIM microservices, RAG architecture, multi-agent orchestration, and OCI GPU selection for enterprise AI agents.

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.

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.

Manticore Search restructured its ONNX inference path to achieve 14x faster text embeddings. Deep dive into batching, session reuse, zero-copy memory, and thread tuning for vector search systems.

A complete guide to ByteDance's Coze platform: agents, AI apps, workflows, nodes, and plugins explained. Build AI applications with no coding required.

Learn RAG fundamentals and build an enterprise knowledge base chatbot with Dify in 4 steps: data prep, model config, knowledge base import, and workflow orchestration.

Master OpenAI Codex CLI from setup to enterprise use: slash commands, AGENTS.md, MCP protocol, multi-agent coordination, plugin development, and RAG project implementation.

Why did Claude Code abandon RAG for Grep? Breaking down the three root causes — undiagnosability, the multiplication effect, and index staleness — behind the shift to Agentic Search.

Master full-stack AI development with Vercel: from LLM, RAG, and vector embeddings to AI SDK, AI Gateway, and v0 — build production-ready AI web apps end to end.

Coding alone isn't enough anymore. Learn the 5 key steps to commanding AI Agents—define outcomes, split tasks, provide context, iterate small, and keep humans in the loop.

Deep dive into Dify, the open-source LLM app development platform featuring visual workflow engine, RAG knowledge base, Agent capabilities, hundreds of model integrations, Docker deployment, and comparisons with LangChain, Coze, and FastGPT.

Complete guide to Dify's four core modules (Explore, Studio, Knowledge Base, Tools) with step-by-step Docker self-hosted deployment instructions for building production-grade AI apps.

Step-by-step guide to deploying Dify locally using BT Panel, covering VM setup, Ubuntu configuration, and Docker deployment for a private AI dev platform.