28 related articles

Is a linguistics-to-computational-linguistics master's worth it? This article analyzes career paths in computational linguistics in the AI era, the competitive advantages of a hybrid background, and practical advice for transitioning from humanities to NLP.

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.

A beginner's guide to AI Agents: understand core principles, how Agents differ from LLMs, their execution mechanisms, and get tailored learning path recommendations.

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

A beginner's guide to AI large models: clarify the relationships between AI, ML, deep learning, and LLMs, trace the journey from Deep Blue to ChatGPT and DeepSeek, and explore China's model landscape.

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 Python self-study roadmap in three phases: fundamentals, OOP & intermediate skills, and hands-on projects including web scraping and office automation.

A systematic AI Agent learning roadmap for beginners covering core theory, the ReAct paradigm, and multi-agent collaboration, with hands-on project suggestions.

A systematic three-stage AI Agent development roadmap: from Python basics and LLM fundamentals, through five core capabilities like planning and tool use, to hands-on RAG projects for real-world deployment.

Can AI Q&A tools help students and parents resolve major-selection anxiety during college application season? This article analyzes AI consulting quality, use cases, and limitations.

A systematic six-week learning roadmap for AI Agent development covering core architecture, ReAct paradigm, multi-agent collaboration, RAG integration, deployment, and hands-on projects.

A complete guide to building AI agents on Coze, covering core features, Dify comparison, skill store usage, workflow building, and multi-Agent collaboration — no coding required.

In-depth analysis of a UniApp zero-to-hero APP development course covering HTML, CSS, JavaScript, and UniApp, with project-driven teaching and learning path recommendations.

Complete ROS2 beginner's guide covering core concepts, version selection, Ubuntu VM environment setup, and robotics developer career prospects and salaries.

A systematic AI LLM learning roadmap for beginners covering prompt engineering, RAG, LangChain, Agents, and more — with timelines 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.

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.

5 proven paths to making money independently with Python: automation scripts, AI app development, quantitative trading, tool/course sales, and full-stack web services, with pricing references and practical tips.
Expert OpinionsA developer spent $140 in Tokens probing AI's limits and identified three hidden pitfalls: using cheap models, building spaghetti projects, and creating vanity products without demand validation.