deanlu.ai explores enterprise AI application practices, covering LLM architecture, RAG systems, AI agents, knowledge management, and business workflow automation.
This site is my public AI knowledge base for turning learning notes into reusable decision context. The current notes connect LLM foundations, Claude/Cowork operating practices, and NVIDIA AI platform research across agents, AI infrastructure, physical AI, and industry-specific models. The goal is to make enterprise AI strategy more inspectable, practical, and ready for manufacturing-grade systems.
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- Agentic AI in Engineering and Manufacturing: The near-term value of AI in engineering and manufacturing sits in structured, repetitive, data-heavy, and tool-orchestration work. Manufacturing demand, labor pressure, supply-chain regionalization, and increasingly complex engineering systems are forcing organizations to improve productivity without weakening quality control. The paper argues that adoption is constrained less by model intelligence than…