Master's graduate from Beijing Institute of Technology, published four top‑journal papers, previously worked as a developer at ByteDance and Alibaba. Currently researching large models at a major state‑owned enterprise. Committed to sharing concise, practical AI large‑model development experience, believing that AI large models will become as essential as PCs in the future. Let's start experimenting now!
This article explains LangGraph's multi‑agent design patterns, focusing on the Supervisor Architecture for centralized coordination and the Hierarchical Architecture for scalable team‑based management, and provides step‑by‑step code examples that demonstrate how to implement both patterns.
This article introduces three core LangGraph workflow patterns—Prompt‑Chain, Router, and Parallel—explaining their concepts, advantages, and concrete Python code examples that demonstrate how to decompose tasks, route requests, and run sub‑tasks concurrently for more reliable and efficient AI agents.
This tutorial walks through the complete development of an automatic email‑processing agent using LangGraph 1.0, covering scenario analysis, state design, node implementation, graph assembly, and testing with both high‑priority bug reports and routine greeting emails, while demonstrating state management, conditional routing, and human‑in‑the‑loop controls.
This article walks through three advanced LangGraph 1.0 features—using the Command object for conditional routing, checkpoint‑based memory for state persistence across invocations, and interrupt‑driven human‑in‑the‑loop control—providing concrete code examples, execution traces, and a comparison of design trade‑offs.
This article introduces LangGraph 1.0 as a programming‑language‑style framework for AI agents, explains its core abstractions—State, Node, Edge, Reducer, and Human‑in‑the‑Loop—shows how to define state and node functions, builds simple and parallel graphs with static, conditional, and MapReduce edges, and demonstrates conflict‑resolution using built‑in and custom reducers.
This article walks through using AntV Infographic together with Vibe Coding and Trae Solo to create an AI‑powered chart generator that turns natural‑language descriptions into beautiful infographics, covering configuration basics, prompt engineering, automated code generation, testing, and result analysis.
This article walks through a three‑step Vibe‑Coding workflow—structured prompt creation, prompt optimization with DeepSeek, and precise bug‑fix guidance—to automatically generate, refine, and extend a React + TypeScript front‑end for a multimodal RAG system using Trae Solo, covering architecture, streaming chat, and PDF citation features.
This article provides a detailed, easy‑to‑understand analysis of DeepSeek‑Math‑V2’s self‑verification training method and DeepSeek‑V3.2’s GRPO framework, sparse‑attention DSA mechanism, massive agent data pipeline, and benchmark results that place both models among the world’s top open‑source large language models.
This article demonstrates how to use ByteDance's Trae Solo to automatically generate a FastAPI service that wraps a LangChain/LangGraph weather‑assistant agent, enabling seamless OpenAI‑style access from the Chatbox client, with step‑by‑step code, testing, and deployment details.
This article walks through building a LangChain‑based multimodal RAG system that parses PDFs (both native and scanned), splits them into semantic chunks, stores embeddings in a vector database, and generates answers with precise source citations, complete with code samples and API integration.
