332 related articles

Many CS students use AI to learn programming but later feel they didn't truly learn. This article breaks down the two AI learning traps and offers Socratic questioning, the Feynman Technique, and more to turn AI into a real learning accelerator.

A beginner's guide to Jenkins: its role as the CI/CD and DevOps hub, JDK requirements, and the four plugin categories (source control, build, security scanning, deployment) that power automation pipelines.

No coding required: use AI agents like Codex and Claude Code to complete full ML experiments via natural language. A real case study with a heart disease dataset.

A clear, practical guide to CI/CD: from Waterfall to DevOps, manual vs. automated deployment, and a full Jenkins + RuoYi hands-on learning path for beginners.

Why do AI results vary so dramatically? LangChain V1.3 reveals the answer: engineering mindset. Covers LangGraph, Deep Agent, RAG, Time Travel, and more.

Enterprise guide to Claude Code: CLI setup, switching to DeepSeek and other Chinese AI models, Git workflow automation, and bug fix loops to boost team productivity.

Learn how to combine Cursor AI with Appium in a 4-step workflow—environment check, test planning, project generation, and reporting—to build mobile automation fast.

How can frontend engineers transition into AI development? This guide covers four agent development directions: RAG, workflow agents, vertical agents, and general-purpose agents — with framework picks like LangChain.js.

Want to run open-source LLMs like DeepSeek locally for free? Ollama is the easiest solution. This guide covers what Ollama is, its core advantages, and key use cases.

A complete guide to OpenAI Codex: CLI setup, slash commands, AGENTS.md, MCP integration, multi-agent collaboration, and a RAG customer service project walkthrough.

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.
AI Can Write Ruby But Can't Navigate C…
A benchmark covering 5 major AI models and 13 real Ruby codebases reveals: AI excels at generating code but struggles to navigate existing codebases. A deep dive into findings, Ruby metaprogramming challenges, and practical implications for developers.

A deep dive into a hands-on AI Agent development book covering component architecture, RAG, multi-agent systems, Function Calling, and production observability.

A deep dive into Agent Skills: SKILL.md structure, four core elements (workflow/docs/tools/assets), real-world case studies, and how Skills differ from prompts.

A deep dive into AI-powered testing: Cursor Skills, Coze agents, and LangChain multi-agent systems for automated test case generation, BDD, and review workflows.

A complete Spring AI guide for Java developers covering ChatModel, EmbeddingModel, ChatMemory, Tool Calling, MCP protocol, and RAG with Milvus. Build LLM apps in Spring Boot.

A deep dive into Agent Skills: learn the file structure (SKILL.md, references, scripts, assets), core principles, and how they differ from prompts. Build your own AI skill bundle from scratch.
AI Agent in 100 Lines of Lisp: Minimal…
A fully functional AI Agent in just 100 lines of Lisp reveals the essence of the ReAct loop. Deep dive into Agent architecture, Lisp's unique strengths, and minimalist engineering philosophy.

Claude Code is Anthropic's local AI programming assistant that reads your entire codebase, auto-debugs, and delivers far higher accuracy than Cursor and Trae. Here's why it's the strongest AI coding tool today.

An in-depth look at LangChain's core value: the three limitations of LLMs, unified model interfaces, modular architecture, configuring the DeepSeek API, and understanding the SystemMessage/HumanMessage/AIMessage/ToolMessage system to build a foundation for Agent development.