42 related articles

A systematic four-stage roadmap for AI Agent development: fundamentals, core principles, enhancement, and real-world deployment. Build complete Agent skills.

Struggling with math and Python when learning AI from scratch? This article lays out a five-step entry path: grasp the concepts, learn Python lightly, master ML and deep learning principles, get hands-on with PyTorch, then deepen understanding through real projects.

A detailed guide to Coze's core features: cross-platform interoperability, the Skills system, multi-agent collaboration, and workflow building. Compare Coze and Dify to build practical AI apps with zero coding.

A systematic YOLO learning roadmap: from understanding V1/V3/V4 version evolution, to building knowledge via video, to mastering implementation by debugging source code.

Silicon Valley tech giants are quietly abandoning the doomsday 'AI destroys jobs' narrative in favor of AI augmentation and human-AI collaboration. A deep analysis of the reality gap, regulatory pressure, and business logic behind this reversal.

Generative AI is reshaping software development, shrinking demand for junior developer roles. This article analyzes why entry-level positions are most at risk, the talent pipeline implications, and how junior developers can leverage AI tools to stay competitive.

A detailed four-stage competency model for AI Agent development: from Python/RAG basics (15K) to workflow orchestration (20K), inference optimization (30K), and Agent cluster governance (40K RMB).
AI's Impact on the Job Market: Billion…
AI is rapidly disrupting the global job market, threatening billions of roles from content creation to programming. Explore the risks, opportunities, and strategies for navigating this transformation.

Anthropic's latest report reveals over 80% of its codebase is AI-written and engineer output has grown 8x. A deep analysis of AI's impact on software development, the taste moat, AI bubble stages, and loop engineering.

Enterprise AI shouldn't be a zero-sum game. Learn how a positive-sum approach creates shared value for businesses, employees, and customers alike.

A complete roadmap for learning AI Agent development from scratch, covering Python & LLM basics, five core skills, and hands-on RAG projects in 1-2 months.

Deep dive into why industrial AI Agents fail: 4 critical strategies covering edge deployment, closed-loop control, rule verification, and safety mechanisms for reliable factory AI systems.

A systematic AI Agent development roadmap covering core concepts, ReAct paradigm principles, multi-agent collaboration, and hands-on projects across four stages to master agent development in 2-3 months.

Open-source AI Agent tutorial project with 2600+ GitHub Stars covering multi-agent systems, memory, planning, and reasoning loops via Jupyter Notebooks for hands-on learning.

Deep dive into LSEG's AI transformation strategy — from integrating 33PB of data to MCP-powered ChatGPT access — revealing how financial institutions scale AI through evaluation frameworks, governance, and cultural change.
Deep DivesDeep dive into Claude Code's core mechanisms including tool calling, agentic search, memory systems, and MCP protocol extensions. Learn how this CLI agent tool autonomously plans and executes complex development tasks.
Expert OpinionsAI can replace thinking but not understanding. This article explores the key difference, reveals three cognitive traps for AI users, and offers practical ways to preserve your understanding.
TutorialsDeep analysis of RAG technology's core principles, three key values, enterprise implementation cases, common pitfalls, and a systematic learning roadmap covering vector databases, retrieval optimization, and Knowledge Graph fusion.
TutorialsComplete guide to enterprise RAG architecture covering data indexing, vectorization, and retrieval optimization. Practical insights on chunking strategies, hybrid retrieval, and hallucination control for production-grade LLM applications.
TutorialsLearn AI agent architecture (LLM + workflow + knowledge base), compare tools like Dify and LangChain, and use MCP Server to auto-generate test cases.