#object-oriented graph database
27 related articles
YouTrackDB Deep Dive: Core Features an…
·3 min
YouTrackDB Deep Dive: Core Features and Use Cases of an Object-Oriented Graph Database
YouTrackDB is a general-purpose object-oriented graph database combining OOP paradigms with graph data models, supporting classes, inheritance, and polymorphism for knowledge graphs, social networks, and recommendation systems.
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·5 min
Learn AI Agent Development from Scratch: A 3-Step Practical Path to Get Started Quickly
How to learn AI Agent development from scratch? This article outlines a clear 3-step path: Python crash course, LLM theory & practice, and LangChain framework project implementation.
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·8 min
Quick Start with SpringBoot from Scratch: From Java Projects to Enterprise Application Practice
Discover why beginners study SpringBoot for six months without getting started. Learn the 'focus on the big picture' methodology and evolve a simple Java project into a runnable enterprise-level SpringBoot application step by step.
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·7 min
Harness Engineering: Why a Powerful Model Isn't Enough — Here's What Really Matters
A data-deletion disaster reveals the biggest AI Agent risk: the problem isn't the model, it's Harness design. Learn context management, process standards, and permission isolation.
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·7 min
From Chat to Agent Platform: A Practical Guide to Engineering AI Applications
A deep dive into engineering AI applications: from a simple chat page to a multi-layer Agent platform, covering RAG knowledge bases, Workflow scheduling, multi-model management, and run tracing.
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·3 min
Cosmonapse: A Decentralized Multi-Agent Framework Built on an Event Bus
Cosmonapse is an open-source multi-agent framework that replaces central orchestrators with peer nodes, using typed signals and an event bus to fully decouple tool calls, memory, and HITL. Supports Python and TypeScript under Apache 2.0.
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·2 min
AI Large Model Learning Roadmap: A Complete Five-Stage Guide from Zero to Independent Deployment
A complete 5-stage AI large model learning roadmap — from Python basics and prompt engineering to RAG pipelines, Agent development, and private model deployment.
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AI Agent or Workflow? Don't Let the Hy…
·2 min
AI Agent or Workflow? Don't Let the Hype Drive Your Tech Stack Decisions
Should you use AI Agents or deterministic workflows? This deep dive breaks down the real differences, offers clear decision criteria, and helps developers avoid the over-agentification trap.
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·4 min
Getting Started with LangChain: LLM Limitations, Agent Principles, and Environment Setup in Practice
An in-depth look at LangChain's core value: the three limitations of LLMs, unified model interfaces, modular architecture, configuring the DeepSeek API, and understanding the SystemMessage/HumanMessage/AIMessage/ToolMessage system to build a foundation for Agent development.
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·4 min
Superpowers: Enabling AI Agents to Truly Master Software Development Methodology
Superpowers is a trending GitHub project that builds an agentic skills framework with Shell scripts, injecting software engineering methodology into AI coding workflows. A deep dive into its architecture and value.
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·7 min
Getting Started with LangChain 1.3: Core Concepts of LLMs and Agents
An in-depth breakdown of LangChain 1.3's core concepts, covering the three major limitations of LLMs, Agent architecture, memory management, and a complete learning path. Master LangChain and LangGraph to quickly build AI development skills.
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·7 min
Spec-Driven Development: Andrew Ng Teaches You to Master AI Coding Agents Efficiently
Andrew Ng partners with JetBrains on a new course systematically teaching Spec-Driven Development. By writing high-quality specs, developers can precisely control AI coding agents, eliminate context decay, and boost intent fidelity.
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·7 min
Free Mermaid Diagram Editor: An Essential Code-Based Diagramming Tool for Developers
Explore the core features and use cases of the free Mermaid Diagram Editor. Supporting flowcharts, sequence diagrams, Gantt charts and more, it follows the 'diagrams as code' philosophy to enable version-controlled technical documentation for developers and architects.
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·7 min
Learning Python from Scratch: A Complete Guide to the Three-Stage Learning Path
Want to learn Python from scratch but don't know where to begin? This article breaks down three stages—basic syntax, advanced mastery, and hands-on practice—with real projects in crawling, automation, and data analysis to help you build programming thinking.
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·6 min
video-use: The Open-Source Tool That Lets AI Coding Agents Auto-Edit Videos
video-use from the browser-use team lets AI coding Agents auto-edit videos via natural language. 13K+ GitHub stars, batch processing, silence removal, FFmpeg integration.
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·7 min
The C Readability Crisis: Engineering Reflections Behind Clever Tricks
Why do C programmers keep creating readability disasters? An in-depth look at macro abuse, fancy pointer tricks, and over-nesting—and how coding standards, static analysis, and code review protect maintainability.
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·6 min
LangChain 1.0 Comprehensive Guide: Three Core Frameworks and Hands-On AI Application Development
Deep dive into LangChain 1.0's architecture: LangChain framework, LangGraph multi-Agent orchestration, and LangSmith observability platform, with hands-on RAG and intelligent customer service projects.
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·5 min
UE5.8 + Claude Code: A Complete Guide to AI Agents Directly Controlling Unreal Engine
UE5.8's official MCP plugin enables Claude Code and other AI Agents to directly control Blueprints, PCG graphs, materials, and level design. Complete setup and testing guide included.
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·4 min
Getting Started with Software Testing: A Complete Guide to Core Concepts and Classification Systems
A comprehensive guide to software testing fundamentals covering definitions, purposes, classification by phase, technique, and method, plus core concepts like smoke testing and regression testing.
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·3 min
AI Agent Development: A Three-Stage Learning Roadmap from Zero to One
A systematic three-stage AI Agent development roadmap: from Python basics and LLM fundamentals, through five core capabilities like planning and tool use, to hands-on RAG projects for real-world deployment.
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