#multi-agent
362 related articles
·3 min
Hermes Desktop Officially Launched: A Forever-Free, Self-Evolving AI Agent for Your Desktop
Hermes Desktop is now available for Windows, macOS, and Linux. This MIT-licensed AI Agent features persistent memory, self-evolution, skill management, and multi-platform integration — completely free.
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·3 min
Xiaomi MIMO vs. Huawei Pangu AI Strategy Comparison: The Android vs. iOS Battle of the Agent Era
Xiaomi releases open-source MIMO Code while Huawei enters the Agent era with Pangu. Compare their AI strategies: Xiaomi's Android-like open ecosystem vs. Huawei's iOS-like vertical integration.
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·4 min
Superpowers: Installing Work Standards for Your AI Coding Assistant
How the Superpowers methodology constrains AI coding assistants through requirement clarification, task decomposition, TDD, and verification loops — with setup tips for Trae.
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·3 min
Must-Have Claude Code Plugins: 10 Plugins to Build a Complete Development Environment
10 curated Claude Code plugins covering automation, real-time docs, browser testing, design implementation, and security scanning, with installation order and configuration tips.
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·2 min
Replit's AI Loops Workflow Explained: Multi-Agent Collaboration Replaces Prompt Engineering
Deep dive into Replit's AI Loops workflow: how orchestrators, parallel agents, and Computer Use Verifiers build automated closed-loop systems through multi-agent collaboration.
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·4 min
Claude Code + Skills: A Practical Guide to AI-Powered Test Case Generation
Learn how to use Claude Code + Skills to auto-generate enterprise-grade test cases. Covers AI Agent vs LLM differences, the four core capabilities, and the complete workflow from requirements to test cases.
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·2 min
Deep Agents: Key Topics from the LangChain Chicago Meetup Explained
LangChain's Chicago Meetup spotlights Deep Agents — exploring the evolution from simple Agents to multi-layered reasoning and long-chain task execution.
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·3 min
AI Agent Core Architecture Breakdown: From Concept to Enterprise-Grade Intelligent Agent Development
Deep dive into AI Agent architecture: perception, brain, and action modules. Covers RAG memory systems, tool calling mechanisms, Chain of Thought reasoning, and enterprise agent development roadmap.
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·3 min
Headroom: The Open-Source Compression Tool That Cuts AI Agent Token Costs by 10x
Headroom is an open-source token compression tool by a Netflix engineer that achieves 60%-95% token savings for AI coding tools through intelligent category-based compression.
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·3 min
Multi-Agent Communication & Collaboration in Feishu: A Complete Guide to Configuration and Group Chat Orchestration
Learn how to configure Open Cloud for multi-Agent communication, including session_visibility, agent_to_agent toggle, and whitelist setup, plus Feishu group chat orchestration for AI team collaboration.
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·2 min
AI Agent Development Learning Path: A Practical Guide from Zero to Commercial Deployment
A complete AI Agent development learning path covering theory, frameworks, tool integration, and commercial deployment with real enterprise use cases.
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·3 min
What Makes Claude Code So Powerful? A Five-Dimension Comparison with Traditional AI Chat Tools
A deep comparison of Claude Code vs traditional AI chat tools across 5 dimensions: interaction, context, execution, memory, and tool invocation.
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·3 min
Getting Started with AI Agent Development: A Detailed Three-Stage Learning Roadmap
A complete roadmap for learning AI Agent development from scratch, covering Python & LLM basics, five core skills, and hands-on RAG projects in 1-2 months.
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·4 min
Complete AI Large Language Model Learning Roadmap: From Beginner to Production-Ready
A systematic guide to learning AI large language models, covering Transformer architecture, prompt engineering, RAG, AI Agents, fine-tuning, and enterprise projects from beginner to production-ready.
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·4 min
Learning Path for AI Agent Development: A Complete Guide from Core Architecture to Production Deployment
A complete learning path for AI Agent development covering core architecture, ReAct paradigm, multi-agent collaboration, RAG integration, and lightweight deployment to guide developers from basics to production.
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·3 min
Building AI Agents from Scratch: A Complete Beginner's Guide
Build AI Agents with zero coding experience! Learn prompt engineering, RAG knowledge bases, and workflow orchestration using no-code platforms like Coze and Dify, plus real monetization paths.
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·3 min
AI Large Language Model Learning Roadmap for Beginners: A Complete Plan from Basics to Hands-On Practice
A systematic AI LLM learning roadmap for beginners covering prompt engineering, RAG, LangChain, Agents, and more — with timelines and project suggestions.
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·3 min
AI Agent Development Learning Roadmap: A Four-Stage Guide from Zero to Production
A systematic AI Agent development roadmap covering core concepts, ReAct paradigm principles, multi-agent collaboration, and hands-on projects across four stages to master agent development in 2-3 months.
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·2 min
Andrew Ng on AI Agent Development: Evaluation and Error Analysis Are the Core Competitive Advantage
Andrew Ng argues that the core gap in AI Agent development isn't model selection — it's systematic evals and error analysis. A breakdown of his methodology.
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·2 min
Java Backend Interview Prep Roadmap: A 6-Week Sprint Guide Integrating AI Agent Development
A systematic 6-week Java backend interview prep roadmap covering JVM internals, Spring Boot, Redis, microservices, plus Spring AI, LangChain4j, and RAG for AI Agent development.
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