Author of the rpcx microservice framework, original book author, and chair of Baidu's Go CMC committee.
Claude’s new Agent Skills let developers package domain expertise into organized SKILL.md files and resource folders, enabling progressive disclosure, code execution, and scalable context loading, with detailed guidance on structure, bundling, evaluation, and security considerations for building robust, reusable AI agents.
This guide explains why large documents must be broken into semantic chunks for LLMs, introduces core parameters like chunk_size and chunk_overlap, compares LangChain's various splitters, and walks through a complete Python example that loads a long text, configures a RecursiveCharacterTextSplitter, and inspects the resulting chunks.
This article walks through the DeepSeek‑V3.2‑Exp inference codebase, detailing its MoE architecture, Multi‑Head Latent Attention, FP8 quantization, custom CUDA kernels, and 8‑GPU NCCL‑based distributed execution from initialization through prefill and decode stages.
This article explains the undocumented IPv4 address shorthand supported by Unix-like tools, detailing how different part counts are expanded, the underlying fill‑in logic, practical command‑line examples, security implications, and cross‑platform compatibility.
The article explains how DeepSeek’s Lightning Indexer acts as a memory‑filtering expert that computes index scores, selects the top‑k relevant tokens, and maps a compact formula to FP8 kernel code, reducing attention complexity from 128K to 2048 tokens for massive sequences.
The article uses a whimsical journey to illustrate how token data is dispatched across GPU clusters—detailing functions like get_dispatch_layout, notify_dispatch, and combine_token, showing RDMA and NVLink pathways, performance experiments, and the final verification of token integrity.
Learn how to use LangChain's Document Loaders to import data from files, web pages, databases, and APIs, understand the Document object structure, compare load() versus lazy_load(), and follow a step‑by‑step Python example that demonstrates loading, inspecting, and optionally processing documents with an LLM.
This guide explains why custom output parsers are needed for LangChain when dealing with non‑JSON or XML responses, walks through inheriting BaseOutputParser, implementing parse() and optional format instructions, and provides a complete Python example that converts a simple "Key: Value" string into a dictionary.
Over the past two years, Go’s sync and sync/atomic packages have introduced new APIs like WaitGroup.Go, enhanced Map methods, modernized atomic types, restructured internal implementations, and added developer‑friendly safeguards such as noCopy, all aimed at improving usability, safety, and performance for concurrent Go programs.
This article explains why LLMs output plain text, introduces LangChain output parsers as the bridge to structured data, details their workflow, reviews built‑in parsers, and walks through a complete Python example that builds a prompt‑model‑parser chain to generate a JSON‑based joke.
