Author of the rpcx microservice framework, original book author, and chair of Baidu's Go CMC committee.
GoSkills Agent is a lightweight, framework‑less Go implementation of a Deep Research AI assistant that combines autonomous planning, real‑time web search, intelligent analysis, and markdown reporting, offering a fully native CLI experience with clear architecture and extensible components.
This article walks through every stage a packet undergoes inside the Linux kernel—from the moment an application calls write() or send() to the final recv() call—covering socket handling, routing, ARP/NDP resolution, queuing, NIC offloads, and reassembly with concrete commands and code examples.
This article compiles the entry points, documentation links, pricing details, and hands‑on API examples for several leading large‑language‑model providers—including DeepSeek, Alibaba Cloud, Baidu Qianfan, ByteDance Volcengine, OpenRouter, and Google Gemini—while comparing their usability, free‑tier offers, and developer experience.
This article explains the concept of Claude Skills, walks through a Go library that parses skill packages, demonstrates a CLI inspector, shows how to run skills with Deepseek‑v3 via an OpenAI‑compatible API, and outlines future security enhancements.
This article walks through setting up MCP servers in both stdio and SSE modes, shows how to load their tools into LangChain agents using the new create_agent API, compares the two communication methods, and provides complete runnable Python examples.
This guide explains LangChainGo’s Chain abstraction, detailing the core Chain interface, the LLMChain implementation, various chain types such as SequentialChain and RetrievalQA, and provides a complete walkthrough of setting up prompts, creating chains, and executing them with example Go code.
The article examines Go 1.25’s experimental Green Tea garbage collector, explains why the traditional mark‑sweep approach hurts modern CPUs, details the page‑oriented redesign and its AVX‑512 vector acceleration, and shows how these changes can cut GC‑related CPU usage by 10‑40%.
LangChain’s stateless LLMs require a memory component to retain conversation context, and this article explains the importance of memory, compares built‑in memory types, and walks through two practical examples—custom buffer memory and basic message history—showing how to implement and use them with code.
LangGraph extends LangChain by letting developers define stateful, multi‑agent LLM workflows as graphs with nodes, edges, and shared state, and the article walks through core concepts, typical use cases, and a detailed example that shows how to define state, nodes, edges, compile and run the graph.
This guide explains what LangChain toolkits are, why they simplify building domain‑specific AI agents, lists common built‑in toolkits, and walks through the step‑by‑step process of instantiating a toolkit, retrieving its tools, and creating an OpenAI‑powered agent, illustrated with a SQL database example.
