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AI job demand is surging but companies can't find qualified candidates. Learn the 3 core skills—advanced RAG, local model deployment, and full-stack monitoring—to leap from demo builder to production engineer.

A three-step guide to LLM app development: from Prompt Engineering and API calls, to RAG knowledge bases, to Agent development and multi-agent collaboration.
TutorialsRAG (Retrieval-Augmented Generation) is the core solution for LLM hallucination. Learn RAG concepts, how it works, three causes of hallucination, and the complete learning path from basics to Knowledge Graph RAG.
TutorialsComplete guide to enterprise RAG projects covering principles, LangChain implementation, data processing, retrieval optimization, evaluation, and cloud deployment for AI knowledge base applications.
TutorialsA hands-on guide to building a local knowledge graph RAG system using Dify, Neo4j, and Docker for multi-hop reasoning and secure local deployment.
Industry InsightsIn-depth analysis of the AI large model job market, breaking down the two core directions—algorithm research and engineering deployment—covering requirements, barriers, and career prospects.
TutorialsDeep analysis of RAG technology's core principles, three key values, enterprise implementation cases, common pitfalls, and a systematic learning roadmap covering vector databases, retrieval optimization, and Knowledge Graph fusion.
TutorialsComplete guide to enterprise RAG architecture covering data indexing, vectorization, and retrieval optimization. Practical insights on chunking strategies, hybrid retrieval, and hallucination control for production-grade LLM applications.
TutorialsHands-on testing of CodeGraph: build GraphRAG code knowledge graphs via AST parsing without AI, cutting Claude Code query time from 6 min to 2 min and slashing token usage by 90%.
TutorialsLearn how to install and use Agent Skills in Google Anti-Gravity IDE, including official frontend design Skills, UI/UX Pro Max demos, and custom Skills creation.
TutorialsA systematic three-step learning path for LLM Agent development: from Prompt Engineering and API calls, to RAG and vector databases, to ReAct and multi-agent systems.
Product Reviews10 curated open-source Claude Code tools for May covering Token optimization, knowledge graphs, frontend design extraction, browser automation, and more to boost AI coding efficiency.
TutorialsA systematic 2025 LLM career transition roadmap covering Python, Transformers, LangChain, LlamaIndex, RAG, Agent development, and fine-tuning across three phases achievable in 2-3 months.
Deep DivesDeep analysis of the oh-my-kimi open-source project, a multi-agent orchestration framework for Kimi Code CLI featuring Worktree Runtime, DAG planning, MCP hooks, Quality Gates, and Local Graph Memory.
Deep DivesDeep analysis of Ruflo open-source multi-agent orchestration platform, covering swarm intelligence, RAG integration, Claude Code native support, and comparisons with LangGraph and CrewAI.