#Multi-Agent
252 related articles
·3 min
Vibe Coding in Practice: A Junior Student Uses Cursor to Build a Multi-Agent System with 51 AI Officials Based on the Three Departments and Six Ministries Framework
A junior student uses Cursor and Vibe Coding to build a multi-agent system with 51 AI officials modeled on China's Three Departments and Six Ministries, featuring task distribution, approval workflows, and Token cost visualization.
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·2 min
Anthropic Team in Practice: Claude Code Experience with 16 Parallel Agents Developing a Compiler
Deep analysis of Anthropic's real-world Claude Code practices: 16 parallel Agents building a C compiler, three-role architecture for full-stack apps, smart approvals solving 93% blind approval issues, and six official best practices.
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·2 min
AI Super App Showdown: A Comprehensive Comparison of Cursor, Codex, Claude, and Anti-Gravity
In-depth comparison of four AI Super Apps — Cursor, Codex, Claude Desktop, and Anti-Gravity — across 11 dimensions to help you find the best AI dev tool.
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Cursor Internal Data: 75% of Code Generated by AI, Engineers Transforming into Agent Managers
Cursor reveals internal data: enterprise AI-generated code surges from 15% to 75%, Agent requests grow 15x YoY, and 30% of PRs are completed entirely by Agents. Engineers are becoming Agent managers.
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Complete Guide to OpenAI Codex: CLI Installation, VS Code Configuration & Practical Tips
Complete guide to OpenAI Codex setup: CLI installation, VS Code extension config, Agents.md best practices, MCP integration, and programmatic usage for efficient AI coding workflows.
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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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·4 min
The Real Logic of Getting Rich with AI: Toolbox Thinking and the Three-Layer Capability Model
Mastering AI tools doesn't equal making money. This article breaks down the three-layer AI wealth model: LLM prompting, automation workflows, and agent collaboration, plus the MAPS framework and Three R's Rule.
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·4 min
Claude Code Practical Guide: Deep Dive into 7 Core Modules with Learning Path
Deep dive into Claude Code's 7 core modules: project integration, agent construction, multi-agent collaboration, plugin systems, and workflow automation, with learning tips and certification trends.
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·2 min
CreateNow Agent Configuration Tutorial: One-Click Integration with DeepSeek and Other Major LLMs
Step-by-step guide to configuring CreateNow agents with DeepSeek, Kimi, and Xiaomi LLMs. Covers one-click setup, custom model integration, and API Key acquisition for building AI digital employees.
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·2 min
Hermes Agent Seven-Layer Configuration Guide: From VPS Deployment to MCP Server — A Complete Tutorial
A detailed guide to Hermes Agent's seven-layer configuration: VPS deployment, Discord integration, scheduled backups, Kanban management, holographic memory, and MCP server setup.
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·3 min
Claude Code + Codex: The Best Practice of One-Writes-One-Reviews AI Programming
A detailed guide on using Claude Code for writing and Codex for reviewing in AI programming. Includes a five-step closed-loop workflow and cross-validation techniques.
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The Five-Layer Evolution of Scaling La…
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The Five-Layer Evolution of Scaling Law: How Physical AI Opens the Next Growth Curve
Deep analysis of Scaling Law's five-layer evolution from Pre-Training to Multi-Agent, exploring Physical AI's World Models, edge inference, and emotional interaction.
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Claude Code Complete Tutorial: Best Pr…
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Claude Code Complete Tutorial: Best Practices from Architecture to Parallel Development
DeepLearning.ai and Anthropic's joint Claude Code course covers architecture, parallel development, and MCP server integration. From RAG chatbots to Figma-to-code workflows, master AI coding assistant best practices.
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Claude Code Dynamic Workflows Explaine…
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Claude Code Dynamic Workflows Explained: A Hands-On Guide to Sub-Agent Orchestration and Reuse
Learn how Claude Code Dynamic Workflows orchestrate sub-agents for parallel tasks. Compare four collaboration modes, enable workflows, and save them for reuse.
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Claude Code Ultra Code in Practice: On…
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Claude Code Ultra Code in Practice: One Person Orchestrating 100+ Agents in Parallel Development
Learn how to use Claude Code's Ultra Code and Dynamic Workflow to orchestrate 100+ Agents in parallel, with Deep Research demos, token-saving tips, and workflow reuse methods.
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Codex Systematic Tutorial: From Beginn…
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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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Codex and Claude Code Multi-Agent Coll…
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Codex and Claude Code Multi-Agent Collaboration: A Practical Guide to Team-Based Workflows
Learn how to make Codex and Claude Code collaborate like a team. Use a cloud Agent orchestrator, shared project spaces, and clear task division to build a multi-AI Agent team workflow.
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AI Agent Productivity Guide for Develo…
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AI Agent Productivity Guide for Developers: Practical Methods to Automate 84% of Non-Coding Work
Developers spend 84% of their time on non-coding tasks. Learn how to use AI Agents to automate Jira tickets, tech docs, and multi-project management with Claude Code and Tmux workflows.
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AI Agent Core Architecture Explained: …
·4 min
AI Agent Core Architecture Explained: A Deep Dive into the Four Key Modules — Perception, Brain, Action, and Memory
Deep dive into AI Agent architecture: explore the four core modules — Perception, Brain, Action, and Memory — covering RAG, tool calling, Chain of Thought, and more.
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AI Large Language Model Learning Roadm…
·4 min
AI Large Language Model Learning Roadmap: A Complete Guide from Zero to Project Implementation
A systematic AI LLM learning roadmap covering prompt engineering, RAG, AI Agent development, and fine-tuning — with beginner-friendly paths and practical tips.
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