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 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.
This article outlines eight multi‑agent collaboration patterns—Reflection, Sequential, Hierarchical, Transfer, Neural‑Network, Debate, Nested, and Custom—explaining their structures, typical workflows, and concrete examples such as code generation, marketing copy creation, and customer‑service routing, helping AI developers choose the right model for complex tasks.
AutoGen, Microsoft’s advanced multi‑agent framework, lets developers quickly assemble collaborative LLM agents—supporting chat, tool use, and hierarchical group chats—through concise Python code, with examples ranging from simple two‑agent dialogues to complex three‑agent reporting pipelines, while outlining its strengths, limitations, and upcoming v0.4 enhancements.
This article explains vectors as numeric representations of data, how they enable similarity comparison, the role of embedding models and vector databases, their use in semantic search and RAG applications, and discusses their advantages and limitations in modern AI systems.
This article explains why GUI agents are needed, defines their multimodal capabilities, walks through a high‑level automation scenario, details the architecture of large‑model‑driven GUI agents, highlights recent open‑source projects, and compares them with traditional RPA solutions.
This article introduces CrewAI, a high‑level Python framework for constructing multi‑agent systems, explains its core concepts such as Crew, Agent, Tool, Task and Process, walks through a complete demo with code, evaluates its strengths and limitations, and showcases the new Flows feature for more flexible workflow orchestration.
This article explains the hallucination problem of large language models, introduces Retrieval‑Augmented Generation (RAG) as a solution, compares RAG with model fine‑tuning, and outlines basic RAG architecture and workflow for practical applications.
This article explores how compact multimodal models like OmniVision-968M enable efficient generative AI on edge devices, detailing their architectural advantages, benchmark superiority over larger models, and step‑by‑step instructions for local installation and visual inference using NexaSDK.
This article walks through implementing a multi‑agent workflow using LangGraph, comparing it with the lightweight Swam framework, and detailing the code for defining models, tools, agents, graph structures, testing, and evaluating the framework's strengths, limitations, and suitable use cases.
