#Embedding model
70 related articles
Deep Dives·4 min
AI Hallucinations: Why Large Language Models Inevitably "Make Things Up" and How to Deal With It
Deep dive into AI hallucination's three root causes: training objective flaws, exposure bias, and probabilistic generation. Covers classification and practical mitigation strategies including RAG.
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Tutorials·2 min
Two MCP Plugins Combined: Solving Claude Code's Memory Loss Problem in Large Projects
Claude Code loses memory on large projects? Learn how Cloud Context and Context Mode MCP plugins combine vector-indexed retrieval with 98% compression to solve context window overflow.
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Expert Opinions·2 min
Cross-Agent State Sharing: A More Practical Context Solution Than Memory Management
Analysis of context fragmentation in multi-Agent collaboration, comparing memory vs. state management approaches, and how tools like Opal Bridge enable seamless switching between Claude Code, Codex, and other Agents.
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Tutorials·3 min
Building an AI Agent Mobile App with Zero Code: From Prompt to App Store — A Complete Walkthrough
Learn how to use VibeCodeApp to build a D2C e-commerce AI Agent mobile app with RAG capabilities using natural language prompts — from writing prompts to App Store deployment.
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Tutorials·2 min
LangChain 1.0 Complete Guide: Architecture, Core Modules, and Learning Path
Deep dive into LangChain 1.0's three-layer architecture (LangChain, LangGraph, Deep Agents), core components like Models, Tools, and Memory, plus a complete learning path from semantic search to multi-agent collaboration.
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Tutorials·4 min
Zion No-Code Platform: A Complete Hands-On Tutorial for Building AI Agents with Drag-and-Drop
Learn how Zion's no-code platform lets you build AI agents via drag-and-drop, with a hands-on prompt optimization assistant tutorial covering knowledge base integration, UI building, and API access.
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Industry Insights·4 min
In-Depth Analysis of the AI Large Model Job Market: Two Core Directions and Future Trends
In-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.
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Industry Insights·3 min
Career Switching to AI with Zero Background: Is a Three-Month Fast Track Really Feasible?
In-depth analysis of switching to AI with zero background: insights from 300+ job descriptions, tailored advice for different backgrounds, and realistic expectations for the three-month timeline.
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Tutorials·4 min
Practical Guide to Building a Local AI Knowledge Base with Qwen3.5 + RAGFlow + Ollama
Step-by-step guide to building a local RAG knowledge base using RAGFlow, Ollama, and LM Studio with Docker, covering Embedding model deployment and network troubleshooting for private AI Q&A.
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Tutorials·3 min
Java Developers Transitioning to AI Engineers: New Interview Trends and Learning Roadmap
Deep analysis of interview trends for Java developers transitioning to AI engineers, covering LLM integration, RAG, Spring AI framework practice, with a complete learning roadmap.
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Tutorials·3 min
AnythingLLM Installation & Configuration Guide: Building a Local Knowledge Base with API Integration
Complete guide to AnythingLLM local knowledge base setup: installation tips, Ollama model configuration, document vectorization, recall optimization, and API integration.
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Tutorials·3 min
Learn AI Agents in 30 Days: A Four-Stage Learning Roadmap from Zero to Production
A systematic breakdown of the AI Agent learning roadmap covering core architecture, ReAct/CoT paradigms, multi-agent collaboration, and Prompt optimization across four stages with quality resource recommendations.
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Tutorials·3 min
Complete Learning Roadmap for Java Developers Transitioning to AI Engineering
A detailed five-phase learning roadmap for Java developers transitioning to AI engineering, covering Spring AI, LangChain4j, RAG core technology, and Agent development.
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Product Reviews·3 min
Deep Dive into Tencent Marvis: How a System-Level AI Assistant Redefines Human-Computer Interaction
Deep dive into Tencent Marvis system-level AI assistant, analyzing its local knowledge base, semantic search, privacy mode, and how Agents evolve from tools to OS integration.
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Deep Dives·4 min
AI Agents 101: Core Concepts, Technical Architecture, and How They Work
A beginner's guide to AI Agents: understand core concepts, the perception-decision-action loop, LLM, tool calling, memory systems, and RAG architecture explained from scratch.
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Tutorials·4 min
Complete Guide to Building a Local AI Knowledge Base with Qwen3.5 + RAGFlow + Ollama
Complete guide to building a local AI knowledge base with Qwen3.5, RAGFlow, and Ollama, covering Docker deployment, Embedding model configuration, knowledge base creation, and RAG system setup.
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Tutorials·3 min
Getting Started with Spring AI Alibaba: The Go-To Framework for Java Developers Integrating LLMs
A deep dive into Spring AI Alibaba's core positioning and advantages, helping Java developers quickly understand how to integrate LLMs through this framework.
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Tutorials·3 min
Can You Really Transition to AI/LLM in Three Months? A Deep Dive into the Learning Roadmap
Deep dive into a popular 3-month AI/LLM transition roadmap: from Python basics and Prompt engineering to LangChain, RAG, Agents, and hands-on projects, with realistic time estimates and pitfall warnings.
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Tutorials·3 min
OpenClaw Advanced Guide: Model Selection, Memory Restructuring & Gateway Auto-Repair
Deep dive into OpenClaw advanced techniques: Claude Opus 4.6 vs GPT-5.2 model selection, topic-based memory splitting with LanceDB vectorization, Codex deep search integration, and systemd + Claude Code Gateway auto-repair.
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Deep Dives
CodeRAG Technical Deep Dive: Four Core…
·3 min
CodeRAG Technical Deep Dive: Four Core Components That Help AI Truly Understand Your Codebase
Deep dive into CodeRAG's four core technologies: vector similarity search, file system tools, Code Knowledge Graph (CKG), and DeepWiki — how they work together to help AI coding assistants truly understand enterprise codebases and eliminate hallucinations.
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