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 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.
The article explains how AI agents can move beyond single‑turn responses by using two enhanced reflection strategies—external tool validation and multi‑path optimization (LATS)—to iteratively improve output quality, reliability, and applicability in complex, high‑stakes tasks.
This article explains how a simple neural network trained for classification can be adapted to generate natural language by expanding its output layer, encoding characters as numbers, using a sliding‑window context, and recursively predicting the next token, illustrating each step with diagrams and concrete examples.
This article walks through the complete training cycle of a simple neural network—from random weight initialization and forward propagation with labeled data, through loss calculation and gradient‑based weight updates, to iterative epochs, average loss, and practical issues like gradient explosion and vanishing.
This article explains the Evaluator‑Optimizer reflection pattern for LLM agents, shows how it can improve output quality in single‑ or multi‑agent tasks, and provides a step‑by‑step PydanticAI implementation with code examples and practical usage tips.
This article explains the Orchestrator‑Workers pattern from Anthropic’s “Build effective agents”, compares it with routing and parallel modes, distinguishes it from Supervisor agents, and provides a step‑by‑step Python implementation using Pydantic AI, including model definitions, prompts, orchestration logic, worker execution, and a test example.
This tutorial walks through building semantic routing and parallel execution patterns for LLM agents using the lightweight PydanticAI framework, providing step‑by‑step code, example configurations, and practical observations to help developers create flexible AI‑driven workflows.
This article introduces LlamaIndex Workflows, explains its event‑driven design, walks through a multi‑agent demo that combines weather search and email sending, provides complete Python code for defining events, steps, and the orchestrator, and compares its strengths and limitations against similar frameworks.
