1026 related articles

Step-by-step guide to installing Claude Code Desktop with DeepSeek integration for direct China access, Chinese localization, and custom Skill setup.

A detailed guide to Qoder's Rules feature: creation methods, type selection, and usage tips. Learn how to persistently constrain AI Agents like CLAUDE.md for better controllability.

Compare Claude Code and ByteDance Codex: their positioning, core capabilities, and use cases. Includes Chinese learning resource recommendations and beginner path selection guide for AI programming.

Step-by-step guide to configuring third-party APIs in Claude Code Desktop via Developer Mode, covering both Claude model access and GPT integration through a proxy tool to reduce AI coding costs.

Deep dive into AI Agent architecture: explore the four core modules — Perception, Brain, Action, and Memory — covering RAG, tool calling, Chain of Thought, and more.

Deep dive into Cursor AI programming tool's four core features, six-dimension comparison with traditional IDEs, and target audience analysis. Learn how this AI-native IDE boosts coding efficiency.

A practical guide for Java developers transitioning to AI app development. Includes a 45-day learning plan covering Spring AI, RAG, Agent skills, plus resume and interview strategies.

Deep dive into the MCP protocol's architecture, server types, scope modes, and context window optimization strategies for configuring Claude Code's external tool connections.

A systematic AI LLM learning roadmap covering prompt engineering, RAG, AI Agent development, and fine-tuning — with beginner-friendly paths and practical tips.

A systematic AI Agent development learning roadmap covering LLM API calls, ReAct framework, memory mechanisms, and multi-agent collaboration across four stages with timeline and project suggestions.

A systematic AI Agent development learning roadmap covering core concepts, ReAct/CoT paradigms, multi-agent collaboration, and hands-on projects across four stages.

A comprehensive guide to AI Agent development for beginners, covering low-code platforms, LangChain framework, and monetization strategies for building and deploying intelligent agents.

Deep dive into global variable pool design for AI Agent development, covering three memory types, variable scoping, node execution architecture, and placeholder variable replacement workflows.

A practical self-study roadmap for AI Agent development: covering core skills, common pitfalls, phased learning plans, and interview prep to help developers go from concept collectors to builders.

A comprehensive analysis of why PyTorch became the most mainstream deep learning framework. Covers framework history, comparisons with TensorFlow and Keras, dynamic graphs, Tensors, installation guide, and cloud trends.

Automate JMeter script generation with AI Skills, cutting manual configuration from 1 hour to 5 minutes. Learn the core Skill template elements, real efficiency data, and team standardization value.

A systematic guide to LangChain LLM application development, covering environment setup, core components (RAG, Chain, Memory), and Agent development to help developers master LLM app building.

A detailed zero-to-hero AI large model learning roadmap covering four phases—fundamentals, RAG, Agents, and engineering deployment—with a practical three-month study plan and career advice.

A detailed guide on using Trae AI IDE with PySide6 to build a desktop base converter from scratch — covering prompt writing, code generation, testing, and EXE packaging.

Vibe Coding lets anyone build software without coding skills. Learn what it is, how to get started, and how to use natural language and AI to go from idea to app.