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The article explains that AI coding agents operate as a simple while‑loop driven by context engineering, details their core control flow, compares various tools, and provides a step‑by‑step Python implementation demonstrating how to define tools, system prompts, and the ReAct loop for practical use.
This article explains the fundamentals of AI agents, compares major frameworks, introduces the ReAct, Plan‑and‑Execute, and Reflection paradigms, and provides a step‑by‑step Python implementation of a lightweight agent loop with LLM calls, tool execution, and context engineering, complete with usage examples and references.
This tutorial shows how to quickly deploy OpenClaw, try the built‑in agent‑browser skill, install a new Email skill from Clawhub, and verify the skill’s availability through QQBot, providing concrete prompts, UI screenshots, and practical tips for extending OpenClaw’s capabilities.
This article details the redesign of a core QQ game achievement service that suffered from low resource utilization and heavy CAS contention, describing how moving from a synchronous C++ implementation to an asynchronous Go‑Kafka pipeline eliminated lock conflicts, reduced server count by 82%, and dramatically improved latency and stability.
Clawdbot (now called OpenClaw) is a local‑first AI agent runtime that integrates large language models with system‑level tools, offering a transparent, extensible architecture, privacy‑first memory, multimodal interaction, and a ReAct‑based orchestration engine for complex automation tasks.
This tutorial walks you through deploying the AI‑powered Clawdbot agent on Tencent Cloud Lighthouse, covering server creation, SSH access, one‑click template installation, interactive onboarding configuration, model and Discord integration, gateway startup, pairing, and verification to achieve a continuously running personal AI assistant.
This article traces the evolution of large language models from the 2017 Transformer breakthrough through successive milestones such as BERT, GPT‑3, RL‑HF alignment, multimodal extensions, open‑source alternatives, and the rise of retrieval‑augmented generation, AI agents, and emerging protocols that shape modern AI applications.
This guide explains how to use advanced AI models such as Gemini, Midjourney, and Stable Diffusion to expand brief, informal user descriptions into comprehensive, high‑quality English prompts that include visual style, subject details, environment, lighting, and camera parameters for image or video generation.
Transitioning from simple chatbots to sophisticated agents, this article explains how expanding context becomes a core variable, detailing the evolution from prompt engineering to context engineering, the challenges of managing growing context, and practical solutions like structured context, tool integration, and the MCP framework for reliable AI systems.
This comprehensive guide explains microservice fundamentals, when to adopt them, key design principles, service modeling techniques, integration patterns, versioning, data handling, monolith decomposition, Conway's law, scaling tactics, and the situations where microservices may not be the right choice, providing actionable insights for building resilient backend systems.
