This article explains the ReAct (Reasoning + Acting) design pattern for large language model agents, detailing its thought‑action‑observation loop, concrete examples, prompt engineering tips, full Python implementations, common pitfalls, and references to the original Google research.
The article introduces metacognitive agents that equip AI with a self‑model to evaluate confidence, domain relevance, tool availability, and risk before acting, demonstrating a LangGraph‑based medical triage assistant with code, workflow, safety advantages, and practical test results.
This comprehensive guide walks you through setting up Claude Code, explains its tool‑use architecture, shows how to configure the environment, manage project context, use plan and think modes, control dialogue flow, create custom commands, and extend functionality with MCP servers such as Playwright.
This article introduces the ReAct (Reason + Act) agent pattern, explains its reasoning‑action‑observation loop, shows how to build a basic single‑call agent and a full ReAct agent with LangGraph, compares their performance on a multi‑step query, and provides a quantitative evaluation highlighting ReAct’s advantages and trade‑offs.
The article reviews GPT‑5.4’s release, comparing its code ability, world knowledge, and multimodal understanding to Claude Opus 4.6 and GPT‑5.3‑Codex, presents benchmark scores (GDPval 83%, SWE‑Bench 57.7%, OSWorld 75%, ToolAthon 54.6%), and highlights new features such as a 1‑million‑token context window, native computer usage, and tool‑search optimization, while discussing pricing and practical usage in OpenClaw.
This guide walks through Claude 4.x’s prompt‑engineering principles, from explicit instructions and background context to long‑term reasoning, state tracking, tool usage, response formatting, sub‑agent orchestration, parallel calls, frontend design, and migration considerations, providing concrete examples and code snippets.
This article breaks down the DeepAgent paper, explaining its novel "main model + auxiliary model" architecture, the memory‑folding mechanism that compresses long‑context reasoning, and the ToolPO reinforcement strategy that enables efficient tool discovery and usage.
This guide explores Cursor AI's billing mechanisms, tool integrations, conversation management strategies, rule configurations, and practical pitfalls, offering developers a comprehensive methodology to maximize productivity, avoid common errors, and leverage advanced features like MAX mode and multi‑dialog workflows.
Experts from Dipu, Ant Financial, iKang, and Zhihu discuss practical strategies for improving large model performance, covering RAG, tool‑using, offline knowledge engineering, multimodal training, evaluation metrics, and future trends, while sharing case studies from manufacturing, healthcare, retail, and C‑end applications.
Taobao Group’s ten large‑model challenges target e‑commerce AI by demanding domain‑specific pre‑training, multi‑step reasoning, extended context handling, factual reliability, intelligent tool orchestration, robust retrieval integration, fuzzy‑intent tool selection, scalable multi‑objective RLHF, improved query rewriting, and knowledge‑driven recommendation.
Taobao Group’s ten large‑model e‑commerce challenges call for researchers to build domain‑specific data pipelines, mitigate forgetting, balance expertise with generality, enable multi‑step reasoning, handle long contexts, reduce hallucinations, integrate tool use, improve fuzzy intent detection, apply multi‑objective RLHF, and generate cognitively novel recommendations.
