Open Source Unix Shell Software Development Software for Linux

EDE is fast desktop environment with familiar look and feel. It should work fine on slower computers too.

gVisor is an application kernel developed by Google that provides a strong layer of isolation between applications and the host operating system. Written in Go, it implements a Linux-compatible system call interface that runs entirely in user space, creating a secure sandboxed environment for containers. Unlike traditional virtual machines or lightweight syscall filters, gVisor follows a third approach that offers many of the security benefits of virtualization while maintaining the speed, resource efficiency, and flexibility of containers. Its key runtime, runsc, integrates seamlessly with container ecosystems such as Docker and Kubernetes, making it easy to deploy sandboxed workloads using familiar tools. By intercepting and safely handling syscalls from applications, gVisor reduces the attack surface of the host kernel, mitigating risks associated with running untrusted or potentially malicious code in containerized environments.

This project provides a step-by-step educational guide on building a simple operating system kernel entirely from scratch, specifically targeting the Raspberry Pi 3. The operating system, referred to as RPi OS, is inspired by the Linux kernel but remains intentionally minimal to focus on clarity and teaching fundamental OS concepts. Each lesson introduces a kernel feature and demonstrates its implementation within RPi OS, followed by a comparison to how the same concept is realized in the Linux kernel. The repository is structured so that every lesson corresponds to a snapshot of the source code at that stage, making it easier for learners to follow the progression of kernel development. The design of the lessons emphasizes gradual learning, allowing readers without prior OS development experience to understand kernel internals. The project is still under active development and encourages community contributions to expand its lessons and improve its content.

Graphical OS Kernel compiled with gcc 4.6.1 and nasm 2.09.08

Forth Compiler/Interpreter, fig-Typ (1.0), 'indirectly threaded code', für ia32-pc-linux/ELF-Rechner; unabhängig von externen programmen, libc u.dgl.

A collection of development tools and build environments for Interix using the Microsoft compiler. Aims to facilitate interaction between Visual Studio, Microsoft build and GNU tools. Ex Visual Studio Addins, shell scripts, makefiles and library patches.

This project aims to make GNU/Linux really usable in the LGPL SPARCv8 processor called LEON.

MDF is microkernel operating system. Module architecture minimalistic design.

nsync is a portable C library that provides a collection of advanced synchronization primitives designed to facilitate safe and efficient multithreaded programming. It offers reader-writer locks, condition variables, run-once initialization, waitable counters, and waitable bits for coordination and cancellation between threads. Unlike traditional pthreads-based synchronization, nsync introduces conditional critical sections, allowing developers to wait for arbitrary conditions without explicit signaling or complex loop-based logic. This approach simplifies concurrency management and often improves readability and maintainability of multithreaded code. The library emphasizes efficiency, with locks and condition variables occupying minimal memory and supporting cancellation mechanisms through nsync_note objects rather than thread-level cancellation. Designed with portability and performance in mind, nsync can be compiled on Unix-like systems and Windows using a C90 compiler.

The NT POSIX C Runtime Library is an IEEE standard runtime library for the NT POSIX subsystem. It can be used as a complete replacement or a side by side alternative for what is shipped with the Interix SDK.

VSPMS is the "Very Simple Project Management System", which is a cross-platform set of shell tools and aliases to establish and share project environment variables, aliases, documentation, and setup procedures.

XNNPACK is a highly optimized, low-level neural network inference library developed by Google for accelerating deep learning workloads across a variety of hardware architectures, including ARM, x86, WebAssembly, and RISC-V. Rather than serving as a standalone ML framework, XNNPACK provides high-performance computational primitives—such as convolutions, pooling, activation functions, and arithmetic operations—that are integrated into higher-level frameworks like TensorFlow Lite, PyTorch Mobile, ONNX Runtime, TensorFlow.js, and MediaPipe. The library is written in C/C++ and designed for maximum portability, efficiency, and performance, leveraging platform-specific instruction sets (e.g., NEON, AVX, SIMD) for optimized execution. It supports NHWC tensor layouts and allows flexible striding along the channel dimension to efficiently handle channel-split and concatenation operations without additional cost.

a performance measurement framework and sample tools