Nim
Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
HYSEN LABS DIRECTORY
Language tag · curated repositories and deep analysis.
Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
FastAPI Best Practices and Conventions we used at our startup
A Download Manager that speeds up your downloads
iTerm2 is a terminal emulator for Mac OS X that does amazing things.
Make your diffs human readable for improved code quality and faster defect detection. :tada:
A TypeScript-like language for WebAssembly.
This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."
Examples and guides for using the Gemini API
Emoji for everyone. https://twemoji.twitter.com/
swagger-codegen contains a template-driven engine to generate documentation, API clients and server stubs in different languages by parsing your OpenAPI / Swagger definition.
Sample code and notebooks for Generative AI on Google Cloud, with Gemini Enterprise Agent Platform
The Uber Go Style Guide.
A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
Github Pages template based upon HTML and Markdown for personal, portfolio-based websites.
[WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025)
A collection of various deep learning architectures, models, and tips
:video_game: :pencil: A list of Game Development resources to make magic happen.
A skill file for removing AI tells from prose
🌐 DNSHE Official - Stable & Free Subdomains for Developers. Support 180-day renewal window, Anycast DNS, and REST API. (us.ci, cc.cd, de5.net, ccwu.cc)
This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable, and secure AI workflows from session setup to service orchestration.