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A systematic guide to AI Agent development from beginner to deployment, covering task planning, tool calling, memory management, learning paths, and realistic commercial monetization considerations.

How to learn AI Agent development from scratch? This article outlines a clear 3-step path: Python crash course, LLM theory & practice, and LangChain framework project implementation.

Complete guide for backend developers transitioning to AI/LLM engineering. Covers the 4 core skills—Python, RAG, Fine-tuning, and Agents—with a phased learning roadmap and practical project advice.

Deep dive into Spring AI framework's core features including provider-agnostic unified API abstraction, RAG retrieval-augmented generation, and structured output to help Java developers build enterprise AI apps.

Deep analysis of the EYG programming language's core design, including algebraic effects, program state persistence, and cross-platform portability, exploring how it addresses modern software fragmentation.

How to learn AI Agents from scratch? This guide covers two clear paths: developers go from Python to LLMs to open-source framework source code; practitioners use Claude Code or similar tools to get results fast.

SlopCodeBench sparks deep reflection on AI code evaluation. From benchmark contamination to pass-rate pitfalls, exploring why current benchmarks fail to measure real code quality.

Learn AI Agent core principles from scratch: understand how Agents differ from LLMs, their execution mechanisms, why rule design matters, and find the right learning path for your goals.

Senior data scientist interviews are broad and multi-round. Learn an efficient evergreen fundamentals + targeted sprint strategy covering ML, SQL, system design, and mindset tips.

A detailed comparison of OpenAI Codex and Claude Code with hands-on testing. From AI agent concepts to account setup, helping developers quickly master AI coding agents.

A complete guide to learning AI Agents: from large model fundamentals and core technologies to hands-on projects. Systematically outlines beginner methods and exposes crash-course marketing traps.

A complete guide to Dify, the open-source AI application platform: its core positioning, key differences from Coze, workflow-building capabilities, and enterprise private deployment advantages.

Vibe Coding is the new AI-era programming paradigm. Describe what you want in plain language; let AI generate the code. Learn the 3-stage path: mindset, quality, and real projects.

A systematic roadmap from LangChain and LangGraph to multi-agent development, covering RAG, Tool Calling, MCP, and more, helping developers break into AI app development.

A 3-month structured roadmap for developers transitioning into AI/LLM engineering: Python & API basics, LangChain/FastAPI stack, and RAG/Agent projects.

A structured 3-phase roadmap for frontend developers transitioning to AI: master Transformer fundamentals, build RAG & Agent skills, then advance to model fine-tuning.

A complete Python learning path for beginners covering three modules: Fundamentals, Intermediate, and Hands-On Practice — including web scraping, office automation, and data analysis.

Learn how to build full-stack WeChat Mini Programs using only JavaScript. This guide covers cloud databases, cloud functions, and cloud storage with a real-world project.

A complete guide to Claude Code: environment setup, switching to domestic LLMs, CLI commands, Git workflows, MCP, Subagents, and enterprise project walkthroughs.