Research project. A Memory solution for users, teams, and applications.

The world's fastest CRDT. WIP.

An open source cybersecurity protocol for syncing decentralized graph data.

Pure Rust multimedia format demuxing, tag reading, and audio decoding library

Declare your digital independence

Tooling for creating your own distributed systems.

2D and 3D physics engines focused on performance.

Integrate wgpu into existing iOS | Android apps.

Ray tracer written using compute shaders in WebGPU via wgpu-rs with SDL2 for windowing

A raytracer written with webgpu (wgpu-rs).

3D fluid simulation experiments in Rust, using WebGPU-rs (WIP)

A WebGPU-accelerated ONNX inference run-time written 100% in Rust, ready for native and the web

Implementation of the RWKV language model in pure WebGPU/Rust.

RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RN…

A cross-platform, safe, pure-Rust graphics API.

The write-once-run-anywhere GPGPU library for Rust

Async Mesh Network Protocol for Extreme Communication -- Innovative, Resilient, and Decentralized

UppScale is a free image processing application that leverages AI to enhance and upscale images. It provides powerful tools for improving image quality, enhancing lighting, and increasing resolution.

A cargo plugin to easily build Swift packages from Rust code

The current, performant & industrial strength version of Holochain on Rust.

Lurk is a Turing-complete programming language for recursive zk-SNARKs. This is the prior, elliptic curve based variant of Lurk (contact: @porcuquine)

Nova: High-speed recursive zero-knowledge arguments from folding schemes

Creating a group coherence with holochain apps

Infinite Photorealistic Worlds using Procedural Generation

Multi-view-AE: An extensive collection of multi-modal autoencoders implemented in a modular, scikit-learn style framework.

HNSW tutorial

⬆️ Media Capture in Swift

This is Pytorch re-implementation of our CVPR 2020 paper "Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation" (https://arxiv.org/abs/1911.10194)