ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

Public repository for Agent Skills

🦙 MegaLinter analyzes 50 languages, 22 formats, 21 tooling formats, excessive copy-pastes, spelling mistakes and security issues in your repository sources with a GitHub Action, other CI tools or l…

Golden config for golangci-lint

KAI Scheduler is an open source Kubernetes Native scheduler for AI workloads at large scale

GoMock is a mocking framework for the Go programming language.

🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools

Fast key-value DB in Go.

The missing interface filesystem abstraction for Go

Consistent hashing with bounded loads in Golang

List of projects that provide terminal user interfaces

Nano vLLM

Virtual Kubelet is an open source Kubernetes kubelet implementation.

LeaderWorkerSet: An API for deploying a group of pods as a unit of replication

OpenGFW is a flexible, easy-to-use, open source implementation of GFW (Great Firewall of China) on Linux

A community driven registry service for Model Context Protocol (MCP) servers.

Build and run containers leveraging NVIDIA GPUs

Git commit template for better commit messages

VictoriaMetrics: fast, cost-effective monitoring solution and time series database

Continuous Profiling Platform. Debug performance issues down to a single line of code

Tool for generating Clang's JSON Compilation Database files for make-based build systems.

An open-source AI agent that brings the power of Gemini directly into your terminal.

SGLang is a fast serving framework for large language models and vision language models.

Inference server benchmarking tool

Tensors and Dynamic neural networks in Python with strong GPU acceleration

A high-throughput and memory-efficient inference and serving engine for LLMs

DeepEP: an efficient expert-parallel communication library

Mooncake is the serving platform for Kimi, a leading LLM service provided by Moonshot AI.

This is the notes of the way of machine learning study. You may find something useful in it.

Automatically crawl arXiv papers daily and summarize them using AI. Illustrating them using GitHub Pages.