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In-depth review of the Xiaodu Health Screen: a 10.1-inch large display with an AI large model, supporting remote care, emergency calling, and smart companionship, designed for the elderly. Final price as low as ~598 yuan with national subsidies.

What is an AI Agent? This article systematically explains the core architecture of AI agents (LLM + Planning + Memory + Tools), how they differ from ChatGPT, their combination with robots, and why developers must master Agent development skills.

A tailored large-model learning path for ordinary programmers: from prompt engineering, API calls, and LangChain, to RAG, Agents, fine-tuning, and enterprise deployment—six steps to build AI application skills fast.

Deep dive into NVFP4 quantization: using NVIDIA Model Optimizer to compress Nemotron 3 Ultra to FP4 checkpoints, reducing memory by 75% and boosting inference throughput on Blackwell GPUs.

Top LLMs are pushing beyond existing human vocabulary, producing neologisms and expressive distortion. This article analyzes the tension between LLM high-dimensional semantic spaces and natural language symbol systems.

Confused about breaking into AI LLMs? This guide breaks down the two core career tracks — Engineering & Deployment vs. Algorithm Research — covering RAG, Agents, and more.

Why do banks and hospitals build Local AI instead of using cloud services? This guide covers the full tech stack — Ollama, RAG, vector databases — and real-world enterprise deployment use cases.

A complete learning roadmap for AI large model development — covering Transformer, Prompt Engineering, RAG, LangChain, Agent development, fine-tuning, and deployment.

A systematic AI LLM learning roadmap from scratch, covering Python basics, Prompt Engineering, RAG, Agent development, and enterprise-level projects.

A comprehensive 748-episode AI LLM tutorial covering Transformer architecture, Prompt Engineering, RAG, Agent, fine-tuning, and enterprise projects like AI customer service and knowledge bases.

A systematic three-phase AI LLM career transition roadmap: from Transformer fundamentals to RAG, Agent & LangChain development, to LoRA fine-tuning. Build enterprise-ready skills in two months.

Analysis of a 748-episode, 198-hour AI LLM development tutorial covering API integration, prompt engineering, RAG, AI Agents, fine-tuning, multimodal development, and deployment.

In-depth analysis of Bilibili's 748-episode AI LLM tutorial covering RAG, Agent, and fine-tuning. Includes content structure breakdown and practical study tips for beginners.

Learn how to integrate Spring AI with Ollama to run open-source LLMs like Llama and Gemma locally for free. Covers setup, configuration, and code — switch from OpenAI by just changing dependencies.

A detailed AI LLM learning roadmap covering Transformer architecture, Prompt Engineering, RAG, Agent development, model fine-tuning & deployment, with enterprise project guides.

Deep breakdown of a popular AI large model learning roadmap covering LangChain, RAG, Agent, and LoRA fine-tuning across three stages, with analysis of its strengths and limitations for career changers.

Deep dive into AI large model principles, from Transformer architecture to probabilistic inference, with practical guidance on LLM applications in testing and AI testing strategies.

A systematic guide to learning AI large language models, covering Transformer architecture, prompt engineering, RAG, AI Agents, fine-tuning, and enterprise projects from beginner to production-ready.

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

A deep dive into LLM observability, evaluation systems, and experimentation loops for production AI. Covers OpenTelemetry, trace monitoring, five eval signal types, four scope levels, and automated improvement flywheels.