#Google multi-agent platform
401 related articles
·4 min
No-Code Guide to Claude Code: Building Your Personal AI Automation OS
No coding needed: master Claude Code workflows with folder structure, sub-agents, third-party connectors, and scheduled routines to build your own AI automation OS.
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·3 min
Complete AI Agent Development Learning Path: From Core Principles to Enterprise Deployment
A structured AI Agent learning path covering core principles, prompt engineering, tool use, multi-agent systems, and frameworks like LangChain, CrewAI, and Dify for enterprise deployment.
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·3 min
The Complete LLM Learning Roadmap: From Beginner to Expert — Courses and Resources Recommended
Confused by scattered LLM resources and unclear learning paths? This guide maps a complete roadmap from basics to advanced, covering Karpathy, Stanford CS224N, DeepLearning.AI, Hugging Face, plus RAG, fine-tuning, and Agent deep dives.
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·2 min
AI Agent Development for Beginners: A Complete Three-Stage Learning Roadmap
A complete AI Agent development learning roadmap covering three stages: Fundamentals (environment setup, tool use, memory), Advanced (multi-agent systems, RAG, ReAct), and Practical Projects (enterprise chatbots, automation tools).
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·2 min
AI Agent Development in Practice: A Complete Guide to Framework Selection, Tool Calling, and Production Deployment
A comprehensive guide to AI Agent development: covering Agent vs. Chatbot differences, framework selection, tool calling design, RAG pipeline setup, and production deployment best practices.
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·4 min
LangChain V1.3 in Practice: Master AI Engineering Thinking and Break Free from Tool Dependency
LangChain V1.3 course deep-dive: why engineering thinking beats tool-chasing. Covers RAG accuracy myths, Token cost control, and LangChain/LangGraph/Deep Agent breakdowns.
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·6 min
OpenAI Merges ChatGPT and Codex: The Super App for the AI Agent Era Is Here
OpenAI merges ChatGPT and Codex into a super app and releases three new GPT-5.6 models: Sol, Terra, and Luna. A deep dive into four hands-on workflows—Computer Use, Loops, and multi-threading—for the AI agent era.
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·3 min
How to Choose AI Courses: A Practical Guide to Avoiding Pitfalls and Leveling Up
How to find AI courses worth paying for amid the flood of beginner content. A guide to evaluating courses on Agentic workflows, RAG, fine-tuning, and more.
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·3 min
AI Agent Beginner's Guide: LLMs vs. Agents, and a Full Breakdown of Development Tools
What is an AI Agent? This guide explains the key differences between LLMs and Agents, breaks down the Agent formula (LLM + Workflow + Knowledge Base), and compares tools like Dify, Coze, LangChain, and LlamaIndex.
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·4 min
Hands-On with GPT-5.6: A Single Prompt Builds a Real-Time Voice Anime App
Hands-on with GPT-5.6 Sol: auto-generate real-time voice anime characters from one prompt, write physics engines from scratch, and build unfamiliar toolchains autonomously. In-depth review of coding, agentic tasks, benchmarks, and its hallucination weakness.
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·3 min
LangChain V1.3 Hands-On Tutorial: A Complete Breakdown of RAG, Multi-Agents, and Engineering Mindset
An in-depth look at LangChain V1.3's core philosophy: from RAG to multi-agent workflows. Master LangGraph, Chain, and DeepAgent, learn token control and Human-in-the-loop, and become a true master of AI app development.
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·4 min
A Deep Dive into Claude Cookbooks: A Guide to the Official 47K-Star Hands-On Code Repository
Anthropic's open-source Claude Cookbooks project offers runnable Jupyter Notebook examples covering RAG, Tool Use, multimodal processing, and more—helping developers master Claude API best practices.
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·5 min
How Can Working IT Professionals Learn AI Efficiently? A Practical Learning Path from Zero to Real-World Projects
Limited time but want to learn AI systematically? This guide maps out a practical learning path for working IT pros—from AI application engineering and prompt engineering to RAG and Agents.
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·4 min
AI + Automated Testing: Building an Engine-Agnostic Quality System with Skill + CLI
An in-depth analysis of the AI-driven software testing paradigm: with Skill and CLI as the core hub, supporting both platformized management and digital employees, helping testing teams transform from script writers into capability builders.
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·5 min
Can a Mixture of Experts Beat a Single Large Model? A Deep Dive into the Hermes 2.0 Architecture
A hands-on analysis of the Hermes 2.0 hybrid multi-agent system: can multi-model collaboration beat a single top-tier LLM? We break down how the Mixture of Experts (MoE) architecture works, AgentOS features, and model-agnostic design.
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·6 min
GPT-5.6 Sol Hands-On: How Multi-Agent Parallelism Is Reshaping AI Coding Workflows
Based on Fireship's review, an in-depth look at GPT-5.6 Sol's Ultra Mode multi-agent parallelism, its 91.9% Terminal Bench score, and how it differs from Claude Fable in cost, speed, and precision.
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·4 min
GPT-5.6 In-Depth Analysis: Model Architecture, Performance Benchmarks, and a Complete Guide to the Agentic Era
A comprehensive analysis of GPT-5.6: the Sol/Terra/Luna product-line positioning, MoE architecture, 1.5M token context, coding agent benchmarks, and the shift from Chatbot to Agent.
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·6 min
AI Agent Development Learning Path: Four Stages from Zero to Hands-On Projects
A systematic breakdown of the AI agent development learning path, covering four stages: fundamentals, RAG knowledge bases, tool use, multi-agent collaboration, and hands-on projects.
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·4 min
The Complete AI Agent Development Learning Path: From Zero to Enterprise-Grade Practice
A systematic AI Agent development learning path covering fundamentals, prompt engineering, tool calling, multi-agent collaboration, and hands-on practice with LangChain, CrewAI, and Dify.
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·7 min
Breaking Down the Core Skills of an AI Application Engineer: From Beginner to Industrial-Grade Deployment
Knowing how to call an API doesn't make you an AI engineer. This article breaks down the complete skill structure of an AI application engineer, covering Python fundamentals, LLM fine-tuning, Agent development, and enterprise projects.
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