"OpenHarness: Open Agent Harness with a Built-in Personal Agent--Ohmo!"

An agent-managed museum exhibit, built in Rust with Gajae-Code / LazyCodex — developed and maintained with no human intervention.

A Claude Code plugin that shows what's happening - context usage, active tools, running agents, and todo progress

👻 Ghostty Sidegeist: a Ghostty fork with vertical tabs in a sidebar and a git panel.

🚀 Efficient implementations for emerging model architectures

Virtualized Elastic KV Cache for Dynamic GPU Sharing and Beyond

The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.

oneAPI Math Library (oneMath)

Some common CUDA kernel implementations (Not the fastest).

Issues related to MLPerf® Inference policies, including rules and suggested changes

Get up and running with Llama 2, Mistral, and other large language models locally.

Get up and running with Kimi-K2.6, GLM-5.2, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.

TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. Tensor…

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

⚡ Build your chatbot within minutes on your favorite device; offer SOTA compression techniques for LLMs; run LLMs efficiently on Intel Platforms⚡

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

The official PyTorch implementation of Google's Gemma models

ggml implementation of BERT

Python bindings for llama.cpp

LLM inference in C/C++

An Open-Source Distributed Deep Learning Framework

A public repo of datasets

Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, DeepSeek, Mixtral, Gemma, Phi, MiniCPM, Qwen-VL, MiniCPM-V, etc.) on Intel XPU (e.g., local PC with iGPU and NPU, discr…

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Distributed Tensorflow, Keras and PyTorch on Apache Spark/Flink & Ray

Java 面试 & 后端通用面试指南，覆盖计算机基础、数据库、分布式、高并发、系统设计与 AI 应用开发

关于Python的面试题

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。

🚀 fullstack tutorial 2022，后台技术栈/架构师之路/全栈开发社区，春招/秋招/校招/面试