#LLM interoperability
179 related articles
·4 min
Anthropic's Official Breakdown: Three Core Components for Building AI Agents
Anthropic Developer Conference deep dive into three core AI Agent architectures: Build (code execution), Connect (Web Search & MCP), and Optimize, with live demos and multi-tool collaboration examples.
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·4 min
Agent Development Learning Roadmap: A Four-Stage Systematic Guide from Beginner to Practitioner
A systematic four-stage learning roadmap for AI Agent development, covering core concepts, classic paradigms like ReAct, multi-agent collaboration frameworks, and hands-on projects to master Agent development skills in 2-3 months.
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·2 min
Codex Systematic Tutorial: From Beginner to Enterprise-Level AI Engineering
In-depth guide to Codex AI programming tool: environment setup, Rules system, MCP protocol integration, multi-Agent collaboration, and enterprise RAG customer service project for complete AI engineering deployment.
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·3 min
LangGraph Core Explained: Its Relationship with LangChain and Multi-Agent Orchestration Principles
Deep dive into LangGraph's core positioning, its relationship with LangChain, practical code comparisons of Chain vs Graph, understanding Agent essentials, and multi-agent orchestration design.
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Deep Dives·2 min
MCP and A2A Protocols Explained: The USB Era of Agent Interoperability
A deep dive into MCP (Model Context Protocol) and A2A (Agent-to-Agent Protocol) — their architecture, core features, and how they work together to enable plug-and-play agent interoperability.
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Deep Dives·3 min
MCP Protocol Explained: The Unified Standard and Complete Workflow for LLM Tool Calling
Deep dive into MCP (Model Context Protocol): core concepts, role definitions, and complete workflow. Learn how MCP Server, Client, and Host work together to standardize LLM tool calling.
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Tutorials·3 min
Ollama + Gemma 4 Local Codex Setup: Complete Guide to Zero-Cost AI Programming
Learn how to run Codex locally with Ollama and Gemma 4 for zero-cost AI programming. Covers installation, model selection, and real demos as an alternative to $20-200/month paid plans.
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Tutorials·3 min
OpenCode Getting Started Guide: Choosing Your Installation Method & Core Features Explained
A detailed guide to OpenCode's three installation methods (Desktop, CMD, WSL) and core features including Agent architecture, custom commands, MCP integration, and Agent Skills.
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Tutorials·4 min
The Complete Guide to Claude Code's Extension System: A Five-Layer Architecture That Doubles Development Efficiency
Deep dive into Claude Code's five-layer extension architecture: Claude.md memory, Skills, MCP services, Subagents, and Hooks. Master the Rule of Three and golden combinations to systematically boost AI coding efficiency.
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Industry Insights·4 min
Google I/O 2026 Deep Dive: From Super Apps to the Battle for Ecosystem Dominance
Deep analysis of Google I/O 2026: Gemini 3.5 Flash, Omni video tools, Spark personal Agent, and how Google, OpenAI, and Anthropic are competing for AI ecosystem dominance.
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Deep Dives·4 min
Can AI Agents Replace a Development Team? A Practical Guide for Non-Technical People
Can non-technical people use AI Agents to build virtual dev teams? From CEO Agent to Developer Agent, we analyze the theory vs. reality and offer a practical implementation guide.
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Tutorials·3 min
Spring AI Agent Utils: A Java Agent Toolkit Reverse-Engineered from Claude Code's Core Features
Deep dive into Spring AI Agent Utils toolkit covering Skill modules, Ask a User Question, To Do Write, Auto Memory, and multi-Agent orchestration — empowering Java developers to build powerful AI Agents.
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Product Reviews·3 min
Claude Code with MiniMax M2: Testing a Low-Cost AI Coding Solution Across Three Real Projects
Real-world testing of MiniMax M2 as Claude Code's backend model across three projects: framework migration, iOS development, and full-stack MVP — at just 8% of Claude's price.
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Tutorials·2 min
Getting Started with Claude Code: 5 Core Advantages Over Regular AI Coding Tools
Deep dive into the core differences between Claude Code and regular AI chat tools across 5 dimensions: interaction, context understanding, execution, memory, and tool invocation.
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Tutorials·2 min
Dify 1.8.0 Hands-On Tutorial: Complete Guide from Deployment to Building AI Applications
A detailed guide to Dify 1.8.0 Docker deployment, environment setup, and AI app building. Covers five app types, comparisons with Coze, workflow creation, and more for this open-source AI platform.
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Tutorials·3 min
AI Agent Learning Roadmap: From Beginner to Enterprise-Level Development in Three Months
A detailed three-month AI Agent learning roadmap covering LLM basics, ReAct paradigm, LangChain, memory mechanisms, tool calling, and multi-agent collaboration with practical project suggestions.
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Deep Dives·4 min
MCP Apps Deep Dive: AI Tools Move from Text Exchange to Interactive Collaboration
Deep analysis of MCP Apps: how Anthropic and OpenAI's official MCP extension enables AI tools to return interactive UIs, solving the context gap with Human-in-the-Loop collaboration.
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Tutorials·2 min
Five Core Capabilities of GitHub Copilot's MCP Integration in JetBrains
Deep dive into GitHub Copilot's MCP integration in JetBrains IDEs: Agent Mode, Sampling model scheduling, MCP Prompts, Resources context injection, and Elicitation structured dialogue guidance.
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Tutorials·3 min
LangGraph Multi-Agent Architecture: Graph Structure Principles and Enterprise-Level Implementation Guide
Deep dive into LangGraph's core graph structure design, single and multi-agent collaboration patterns, MCP protocol integration, and Time Travel fault-tolerance, with enterprise-level hybrid multi-agent architecture implementation.
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Product Reviews·2 min
Devin 2.0 Deep Dive: What Can the First AI Software Engineer Actually Do?
Deep dive into Devin 2.0 — the latest evolution of the world's first AI software engineer. Exploring autonomous coding, multi-agent parallelism, Ask Devin, Deep Wiki, and how it differs from Copilot and Cursor.
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