Su San, former staff at several leading tech companies, is a top creator on Juejin and a premium creator on CSDN, and runs the free coding practice site www.susan.net.cn.
This article compares Kafka, RocketMQ, RabbitMQ, and ActiveMQ, explaining their architectures, core concepts, strengths and weaknesses, and provides practical guidance on selecting the most suitable queue based on throughput, reliability, scalability, and use‑case requirements.
This guide walks you through the essential three‑step setup—creating a project workspace, writing an AGENTS.md specification, and using plan mode—then expands to phone integration, Skills, plugins, MCP, automation, Git workflows, and Worktree isolation, showing how to turn Codex from a chat window into a full‑featured AI‑powered development workstation.
This guide provides a comprehensive walkthrough of Loop Engineering configuration, covering file layouts, CLAUDE.md and AGENTS.md templates, scheduling commands, unique features, cross‑tool migration steps, and reusable Skill files for Claude Code, Codex, and Opencode.
PaddleOCR, an open‑source OCR library from Baidu, offers high‑precision multilingual text extraction, lightweight models, and a modern pipeline, with benchmarks showing superior accuracy and speed over Tesseract and EasyOCR, and provides detailed installation, usage, and Java integration guides for developers.
The article explains how to implement a Loop automation system using three essential Markdown files—AGENTS/CLAUDE, STATE, and SKILL—detailing their roles, safety rules, verification steps, prompt commands (/loop and /goal), and a step‑by‑step setup from zero to a near‑unattended AI‑driven workflow.
The article explains why AI coding tools like Claude Code need a concise CLAUDE.md onboarding file, details what should be included, offers concrete best‑practice tips such as keeping the file under 200 lines, using positive directives, leveraging Hooks and AGENTS.md, and provides troubleshooting steps and team‑collaboration strategies.
ZCode is positioned as the official development environment for GLM‑5.2, offering a desktop AI programming workspace that integrates model connection, task management, Git tracking, remote control, and a Goal mode to streamline long‑running coding tasks across macOS, Windows, and Linux.
The article explains why deep nesting of if…else statements harms readability, maintainability and testability, and shows how the Java Easy Rules engine can extract business logic into reusable rule objects with multiple definition styles, lightweight architecture, and integration options.
The article benchmarks three Chinese multimodal large models—Step 3.7 Flash, MiniMax M3, and Qwen 3.6‑flash—across two real‑world tasks, measuring output quality, API latency, and token cost, and concludes that Step 3.7 Flash consistently offers the best speed‑cost trade‑off for production use.
The article explains why AI tasks suffer from long‑lasting, blocking calls, and shows how Apache RocketMQ 5.5.0’s LiteTopic transforms synchronous multi‑agent workflows into asynchronous, non‑blocking pipelines, boosting throughput, preserving session state, and providing smart GPU scheduling.
