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 analyzes four mainstream low-code AI Agent development platforms—Coze, Dify, n8n, and LangFlow—detailing their features, deployment options, commercial models, strengths, and weaknesses, and provides a step‑by‑step learning path to help developers select the most suitable framework for 2025.
This article categorizes and compares twelve popular large‑model AI Agent development frameworks—low‑code platforms, basic programming paradigms, advanced code libraries, and multi‑agent systems—detailing their core features, typical use cases, and trade‑offs to help developers choose the most suitable solution.
This tutorial explains how LangGraph handles short‑term memory with InMemorySaver and thread‑isolated checkpoints, how to persist it using PostgresSaver, and how to build long‑term memory with InMemoryStore and PostgresStore, including semantic search and custom storage extensions.
The article introduces DeepSeek-V3.2, highlighting its new DeepSeek Sparse Attention (DSA) that boosts training and inference efficiency by up to 50%, cuts model usage costs dramatically, explains the updated API endpoints, and details the four‑stage post‑training pipeline that underpins the model’s performance improvements.
This tutorial shows how to use LangGraph’s low‑level API to create a multi‑turn chatbot, define structured state, add model and tool nodes, implement conditional branching for tool calls, and reproduce a ReACT graph agent with a weather‑assistant example.
The article explains the fundamental principles of PPO and GRPO reinforcement‑learning algorithms, compares their architectures and training workflows, highlights why GRPO is gaining traction in large‑model fine‑tuning, discusses associated risks, and offers practical guidance on group size selection for engineers preparing for interviews.
This tutorial demonstrates how to use LangGraph's low‑level API to create stateful conditional‑branch, loop, and combined graphs for AI agents, showing step‑by‑step definitions of Pydantic state models, node logic, edge configuration, compilation, and test execution with concrete code examples.
This tutorial explains the design principles of LangGraph's low‑level graph API—nodes, edges, and State—demonstrates a simple addition‑subtraction workflow, shows how to visualize and execute the graph, and introduces Pydantic‑based State validation for more robust agent development.
The article explains how to assess fine‑tuned large models using both human judgment and dataset‑driven metrics, outlines common pitfalls, introduces benchmark datasets and evaluation frameworks, and provides concise answers to related interview questions.
This tutorial walks you through building a LangGraph-powered data analysis assistant that converts natural language into SQL, executes queries via NL2SQL and NL2Python tools, visualizes results with a Python interpreter, and deploys the agent using LangGraph CLI and Agent Chat UI for end‑to‑end interaction.
