AI learning, discussion, and hands‑on practice with self‑reflection
Rewriting user prompts before vector similarity retrieval enhances semantic clarity, aligns language with document style, eliminates ambiguity, and guides the embedding model to focus on relevant information, thereby improving retrieval effectiveness.
This article defines the Model Context Protocol (MCP), explains why it is needed to unify LLM and external tool interactions, details its client‑host‑server architecture, compares its communication modes, outlines Java/Spring AI support, and discusses current adoption trends and open challenges.
The article explains MCP's three communication modes—stdio, SSE, and Streamable HTTP—focuses on the Streamable HTTP protocol introduced in 2025, and walks through its four lifecycle phases with detailed JSON‑RPC request and response examples for initialization, runtime operations, updates, and closure.
The article presents a comparative table of variance, covariance, RMSE, MAE, and MSE, detailing their definitions, formulas, units, core functions, and typical application scenarios, and offers guidance on selecting the appropriate metric for single‑variable versus multivariate analysis, robustness needs, and model optimization.
The article explains deepseek-MoE (Mixture of Experts), describing its full English name, Chinese translation, how a gating network selects and weights multiple expert models for each input, and uses an analogy to illustrate load‑balancing and the divide‑and‑conquer design in large AI models.
The article defines Artificial Intelligence, explains how large models enable computers to mimic human intelligence for tasks and learning, and presents a personal view that machines may eventually surpass humans and evolve into a silicon‑based intelligent life with autonomous will.
