#React
352 related articles
Tech Frontiers·2 min
AI Weekly: Kimi K2.6 Tops Open-Source Rankings, Qwen 3.6 and Google TTS Launch Together
Weekly AI roundup: Kimi K2.6 tops open-source rankings, Anthropic launches Opus 4.7 and Claude Design, Alibaba rolls out Qwen 3.6 series, Google releases emotion-controllable TTS model.
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Product Reviews·2 min
Notion MCP Challenge Winners Breakdown: AI Agents Redefine Note-Taking Tools
Deep dive into the three Notion MCP Developer Challenge winners: Note Runway, Deaf Notion, and Relay. See how AI Agents integrate with Notion via MCP to transform note-taking into an AI knowledge hub.
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Deep Dives·3 min
Harness Engineering Explained: Building Stable and Efficient Work Systems for AI
Deep dive into Harness Engineering: how to build execution environments, toolchains, and feedback loops for AI. From Prompt Engineering to system-level engineering for stable AI production.
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Tutorials·3 min
Claude Code Beginner's Guide: Installation, Cost-Saving Solutions & Source Code Architecture Analysis
Claude Code beginner tutorial covering installation, common issues, low-cost alternatives for budget users, and deep analysis of 8 Agent design patterns revealed by the 510K-line source code leak.
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Tutorials·3 min
From Programmer to Agent Developer: A Detailed Four-Stage Learning Roadmap
A systematic four-stage learning roadmap for programmers transitioning to AI Agent development, covering core theory, ReAct and classic paradigms, Prompt engineering, and hands-on projects.
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Tech Frontiers·2 min
GPT-5.2 Released: The Truth and Concerns Behind Its 390x Efficiency Gain
OpenAI releases GPT-5.2 with a 390x efficiency gain on ARC-AGI, beating Claude Opus 4.5. Deep analysis of the efficiency leap, user experience paradox, Disney's $1B deal, and the AI content quality crisis.
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Industry Insights·2 min
The Manus Acquisition Blocked: Where Are the Legal Red Lines for AI Technology Flight?
Analysis of how China's NDRC blocked Meta's $2B Manus acquisition, revealing legal red lines around AI technology export controls and data compliance.
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Tutorials·3 min
Complete Breakdown of Andrew Ng's Agent Course: A Practical Guide to the Five Core Modules of AI Agent Development
Deep dive into Andrew Ng's viral AI Agent course covering five core modules: Reflection, Planning, Tool Use, Multi-Agent Collaboration, and Memory, with practical learning paths for LLM agent development.
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Deep Dives·2 min
Agents Need Control Flow, Not More Prompts
Why do longer Prompts make AI Agents less stable? This article explains the control flow first architecture, replacing natural language control flow with code orchestration to boost multi-step reliability from 40% to over 90%.
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Tutorials·3 min
From Traditional RAG to Agentic RAG: Principles Compared and Enterprise-Grade Implementation Guide
Deep dive into traditional RAG limitations and Agentic RAG upgrades, with ChatBox source code analysis covering core tool design, intelligent decision flows, and LangGraph implementation for enterprise deployment.
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Tutorials·3 min
Complete Installation & Configuration Guide for VectCut AI Editing Agent: Windows & Mac
Complete guide to installing and configuring VectCut AI Editing Agent on Windows and Mac, covering client download, CapCut draft path setup, and Git Bash installation.
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Product Reviews·3 min
Google AI Studio Build 2.0 Deep Dive: A Comprehensive Breakdown of Five Core Upgrades
Deep dive into Google AI Studio Build 2.0's major upgrade: Anti-Gravity agent, Firebase integration, full-stack support, server-side generation, and multi-framework options transforming this free AI coding platform.
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Tutorials·3 min
Frontend Engineers Leveling Up to AI Agents: LangGraph.js Architecture Design & Practical Guide
How can frontend engineers advance into AI Agent development? This guide covers LangGraph.js core architecture (state, nodes, edges), LangChain comparison, and workflow agent design with practical examples.
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Tutorials·2 min
The Three-Layer Pyramid Model for Agent Development: From Beginner to Industrial-Grade Deployment
Deep dive into the Three-Layer Pyramid Model for Agent development, covering autonomous agents, collaborative multi-agent systems, and universal orchestration agents with a complete learning path from beginner to industrial-grade deployment.
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Tutorials·3 min
Enterprise Multi-Agent Development: Low-Code Platforms vs. Hand-Written Code — An In-Depth Comparison
In-depth comparison of two enterprise multi-agent development approaches: low-code platforms like Dify vs. hand-written code with LangGraph. Covers efficiency, flexibility, security, and prompt injection defense strategies.
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Tutorials·3 min
LangChain Core Concepts Explained: A Complete Guide to Components, Chains, and Agents
Deep dive into LangChain's three core concepts—Components, Chains, and Agents. Learn how this open-source framework connects LLMs to the external world and helps developers build enterprise AI apps.
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Tutorials·3 min
Getting Started with LLM Application Development from Scratch: A Complete Guide to Learning Paths and Career Directions
A complete beginner's guide to LLM application development: learn the three key directions (API calling, RAG, Agent), master frameworks like LangChain, and follow a step-by-step learning path to become an AI application developer.
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Tutorials·3 min
Learning LLM Application Development from Scratch: A Complete Roadmap from RAG to Agent
How to start LLM application development from scratch? A complete roadmap covering Python basics, RAG knowledge bases, and Agent development with LangChain.
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Tutorials·3 min
Deep Agents: Enterprise-Grade Agent Engineering and Deep Research Implementation Guide
Learn how the Deep Agents framework solves enterprise AI Agent challenges like tool sprawl and context pollution, with a complete Deep Research implementation guide covering task decomposition, multi-source integration, and structured report generation.
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Product Reviews
AI Coding Appliance vs Cloud LLMs: Can…
·2 min
AI Coding Appliance vs Cloud LLMs: Can ¥480K in Annual Fees Buy 4 Local Deployment Solutions?
A deep cost comparison between AI coding appliances and cloud LLM APIs. A 20-person team spending ¥480K/year on tokens can deploy 4 local OnePanel units at ¥99K each, breaking even in 2.5 months.
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