Focused on deep research and development of large-model applications. Authors of "RAG Application Development and Optimization Based on Large Models" and "MCP Principles Unveiled and Development Guide". Primarily B2B, with B2C as a supplement.
This guide walks through building a multi‑user AI agent platform that runs code and browser tasks inside Docker containers, covering tool creation, prompt design, ReAct agent assembly, testing, and future improvements, with full Python examples and a public GitHub repository.
This guide walks through constructing a multi‑user, cloud‑hosted Agent‑as‑a‑Service platform using Docker containers for isolation, detailing the system architecture, required Docker image, container management API, tool implementations such as code execution and web browsing, and provides complete Python code examples for testing and deployment.
This article examines how swapping in DeepSeek‑R1 enhances Retrieval‑Augmented Generation with deeper reasoning, outlines its benefits and pitfalls—including slower inference, higher compute costs, and hallucinations—provides a simple hallucination test, and proposes an Agentic RAG research assistant to balance accuracy and creativity.
This article explains the self‑attention mechanism behind large language models, detailing why static word importance fails, how queries, keys, and values are generated, how attention scores are computed, scaled, softmaxed, and used to produce context‑aware word vectors, while noting computational costs.
This article explains what Web Agents are, their ReAct‑style reasoning loop, key implementation technologies such as observation parsing, multimodal models, and browser control tools like Selenium and Playwright, and demonstrates building a DeepSeek‑powered Web Agent with the Browser‑use framework, including code samples and performance insights.
DeepSeek‑R1 excels at deep reasoning but lacks native structured output; this guide explains why structured output matters, outlines common API‑level techniques, and provides three practical solutions—using an auxiliary model with a LangChain chain, a LangGraph workflow, and a ReAct agent—complete with code snippets and JSON‑mode tips.
This article explains how language models evolved from character‑level embeddings to word‑level and finally to sub‑word tokenizers, highlighting the efficiency, vocabulary coverage, and practical engineering challenges of sub‑word segmentation in modern AI systems.
This guide outlines the essential foundations for becoming a data science professional, debunking common myths, highlighting three core skill areas—mathematics, machine learning, and programming—and detailing key concepts such as linear algebra, probability, calculus, and essential Python libraries.
This article provides a comprehensive, step‑by‑step guide to Microsoft AutoGen 0.4, explaining its layered architecture, core concepts such as Agent, Runtime, and Agent ID, and demonstrating both a simple Hello‑World multi‑agent example and an AI‑enabled agent with full Python code snippets.
This article explains how embeddings convert basic character indices into high‑dimensional vectors, describes their training via gradient descent, introduces the embedding matrix, and shows how these vectors enable modern language models to capture semantic relationships and be reused across tasks.
