Open Source Unix Shell Software Development Software
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Unix Shell Software Development Software
View 5779 business solutionsBrowse free open source Unix Shell Software Development Software and projects below. Use the toggles on the left to filter open source Unix Shell Software Development Software by OS, license, language, programming language, and project status.
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DocFetcher
Desktop search application
DocFetcher is an Open Source desktop search application: It allows you to search the contents of files on your computer. — You can think of it as Google for your local files. The application runs on Windows, Linux and Mac OS X. -
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Pootle, Virtaal & Translate Toolkit
Localization tools built by localizers for localizers
Tools for localization: - Pootle: web based translation management system. - Virtaal: Computer Aided Translation (CAT) tool. - Translate Toolkit: QA, format conversion and support (PO, Java .properties, OpenOffice, Mozilla, XLIFF, TMX, TBX, CSV, Qt .ts). -
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Crosvm
The Chrome OS Virtual Machine Monitor
crosvm (ChromeOS Virtual Machine Monitor) is a secure, lightweight virtual machine monitor built on top of the Linux KVM hypervisor. Developed for ChromeOS, it is designed to isolate and execute Linux and Android guests efficiently while maintaining strong security boundaries. Unlike general-purpose emulators like QEMU, crosvm avoids full hardware emulation and focuses on modern paravirtualized I/O using the virtio standard, reducing complexity and attack surface. Written in Rust, it emphasizes memory safety and modularity, allowing sandboxed device emulation with fine-grained privilege separation. crosvm underpins several ChromeOS subsystems, including Android Runtime for Chrome (ARCVM) and Crostini Linux containers, enabling rich application compatibility within a tightly controlled environment. -
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Gin Config
Gin provides a lightweight configuration framework for Python
Gin Config is a lightweight and flexible configuration framework for Python built around dependency injection. It enables developers to manage complex parameter hierarchies—particularly common in machine learning experiments—without relying on boilerplate configuration classes or protos. By decorating functions and classes with @gin.configurable, Gin allows their parameters to be overridden using simple configuration files (.gin) or command-line bindings. Users can define default parameter values, scoped configurations, and modular references to functions, classes, or instances, resulting in highly composable and dynamic experiment setups. Gin is particularly popular in TensorFlow and PyTorch projects, where researchers and developers need to tune numerous interdependent parameters across models, datasets, optimizers, and training pipelines. -
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Shaderc
A collection of tools, libraries, and tests for Vulkan shader
Shaderc is a collection of tools and libraries for compiling shaders—small programs that run on GPUs—into SPIR-V, the intermediate representation used by the Vulkan graphics API. It provides both a command-line tool (glslc) and a C/C++ library (libshaderc) that wrap the functionality of glslang (the Khronos reference compiler for GLSL) and SPIRV-Tools to deliver a modern, scriptable, and efficient shader compilation workflow. The glslc compiler offers a GCC/Clang-like interface for building GLSL and HLSL shaders, making it easy to integrate into existing build systems. Meanwhile, libshaderc exposes a stable API that allows developers to programmatically compile shader strings into SPIR-V modules within graphics engines and tools. Shaderc supports advanced features such as file inclusion (#include), concurrency, and cross-platform builds, and it maintains backward compatibility for long-term projects. -
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PrettyTensor
Pretty Tensor: Fluent Networks in TensorFlow
Pretty Tensor is a high-level API built on top of TensorFlow that simplifies the process of creating and managing deep learning models. It wraps TensorFlow tensors in a chainable object syntax, allowing developers to build multi-layer neural networks with concise and readable code. Pretty Tensor preserves full compatibility with TensorFlow’s core functionality while providing syntactic sugar for defining complex architectures such as convolutional and recurrent networks. The library’s design emphasizes flexibility and modularity, supporting advanced features like default scopes, parameter templates, and variable reuse. It also allows easy integration with custom operations and third-party libraries, making it ideal for both research experimentation and production-grade modeling. By combining TensorFlow’s power with an intuitive builder-style API, Pretty Tensor accelerates model development without sacrificing transparency or control. -
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SVoice (Speech Voice Separation)
We provide a PyTorch implementation of the paper Voice Separation
SVoice is a PyTorch-based implementation of Facebook Research’s study on speaker voice separation as described in the paper “Voice Separation with an Unknown Number of Multiple Speakers.” This project presents a deep learning framework capable of separating mixed audio sequences where several people speak simultaneously, without prior knowledge of how many speakers are present. The model employs gated neural networks with recurrent processing blocks that disentangle voices over multiple computational steps, while maintaining speaker consistency across output channels. Separate models are trained for different speaker counts, and the largest-capacity model dynamically determines the actual number of speakers in a mixture. The repository includes all necessary scripts for training, dataset preparation, distributed training, evaluation, and audio separation. -
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Ansible Examples
A few starter examples of ansible playbooks, to show features
This repository collects practical, real-world examples of using Ansible to automate infrastructure, deployments, and configurations. Each directory demonstrates a specific use case—ranging from setting up web servers, load balancers, and databases to orchestrating multi-tier applications in cloud environments. The examples highlight common Ansible practices such as organizing inventories, writing reusable playbooks, using roles, and handling variables and templates. They’re designed to be adapted directly into your own infrastructure or to serve as reference blueprints when learning how to structure automation projects. Whether you’re managing a handful of servers or deploying at scale, this repo provides starting points that illustrate how Ansible can streamline repetitive operational tasks. -
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NGINX Admin’s Handbook
How to improve NGINX performance, security, and other important things
nginx-admins-handbook is a practical, in-depth guide for configuring, securing, and operating NGINX across real-world deployments. It distills years of research, notes, and field experience into a single handbook that complements the official docs with concrete rules, explanations, and curated external references. The handbook spans fundamentals and advanced topics alike, from HTTP and SSL/TLS basics to reverse proxy patterns, performance tuning, debugging workflows, and hardening strategies. A centerpiece is its prioritized checklist of 79 rules, grouped by criticality, helping readers focus on what most impacts security, reliability, and speed. Instead of copy-paste snippets in isolation, it emphasizes understanding trade-offs, avoiding common pitfalls, and balancing security with usability. Designed for system administrators and web application engineers, it aims to be a living companion that encourages experimentation, measurement, and continuous improvement of NGINX configurations -
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What is DracOS GNU/Linux Remastered ? DracOS GNU/Linux Remastered ( https://github.com/dracos-linux ) is the Linux operating system from Indonesia , open source is built based on Debian live project under the protection of the GNU General Public License v3.0. This operating system is one variant of Linux distributions, which is used to perform security testing (penetration testing). Dracos linux in Arm by hundreds hydraulic pentest, forensics and reverse engineering. Use a GUI-based tools-tools the software using the CLI (command line interface) and GUI (graphical user interface) to perform its operations. Now Dracos currently already up to version 3.1.5 with the code name "KUNTILANAK WITH REMASTERED".
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I3D models trained on Kinetics
Convolutional neural network model for video classification
Kinetics-I3D, developed by Google DeepMind, provides trained models and implementation code for the Inflated 3D ConvNet (I3D) architecture introduced in the paper “Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset” (CVPR 2017). The I3D model extends the 2D convolutional structure of Inception-v1 into 3D, allowing it to capture spatial and temporal information from videos for action recognition. This repository includes pretrained I3D models on the Kinetics dataset, with both RGB and optical flow input streams. The models have achieved state-of-the-art results on benchmark datasets such as UCF101 and HMDB51, and also won first place in the CVPR 2017 Charades Challenge. The project provides TensorFlow and Sonnet-based implementations, pretrained checkpoints, and example scripts for evaluating or fine-tuning models. It also offers sample data, including preprocessed video frames and optical flow arrays, to demonstrate how to run inference and visualize outputs. -
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Super-Linter
Combination of multiple linters to install as a GitHub Action
This repository is for the GitHub Action to run a Super-Linter. It is a simple combination of various linters, written in bash, to help validate your source code. The super-linter finds issues and reports them to the console output. Fixes are suggested in the console output but not automatically fixed, and a status check will show up as failed on the pull request. The design of the Super-Linter is currently to allow linting to occur in GitHub Actions as a part of continuous integration occurring on pull requests as the commits get pushed. It works best when commits are being pushed early and often to a branch with an open or draft pull request. There is some desire to move this closer to local development for faster feedback on linting errors but this is not yet supported. There is no need to set the GitHub Secret as it is automatically set by GitHub, it only needs to be passed to the action. -
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macOS Simple KVM
Tools to set up a quick macOS VM in QEMU, accelerated by KVM
macOS-Simple-KVM is a project that provides scripts and configuration files to easily set up and run macOS in a virtual machine using QEMU and KVM. It simplifies what is typically a complex process by offering a straightforward approach to creating a macOS VM on Linux systems with hardware virtualization support. The repository includes tools for preparing installation media, configuring virtual hardware, and managing VM launch scripts. By using KVM acceleration, the virtual machine runs with near-native performance, making it useful for testing, development, or personal experimentation. The project also supports GPU passthrough and other advanced configurations for users who want a more optimized macOS VM environment. While primarily intended for educational and testing purposes, it demonstrates how macOS can be virtualized outside of Apple hardware. -
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Easy control of virtual machines of VirtualBox (virtualization solution) on a Linux headless server. Autostart and autosave on boot/halt. Start, stop, save, backup and show status of sessions in batch mode from command line.
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KaNaPi
Educational Linux Distribution
Main goals: * Prepare operating system based on Linux kernel and free software for use at home from scratch by building sources. Binary packages/images are also available. * Each package is installed in separate directory, so you can use different versions of applications and libraries by design. * There is only one user 'kanapi' with root permissions, so you don't have to login, remember passwords, etc. * Simple configuration * Automatic compilation. -
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This is a group of people (feel free to join us), developing small programs. We're mainly develop the program/script, Syscriptor, that displays information about your hardware and software.
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cnn-for-image-retrieval is a research-oriented project that demonstrates the use of convolutional neural networks (CNNs) for image retrieval tasks. The repository provides implementations of CNN-based methods to extract feature representations from images and use them for similarity-based retrieval. It focuses on applying deep learning techniques to improve upon traditional handcrafted descriptors by learning features directly from data. The code includes training and evaluation scripts that can be adapted for custom datasets, making it useful for experimenting with retrieval systems in computer vision. By leveraging CNN architectures, the project showcases how learned embeddings can capture semantic similarity across varied images. This resource serves as both an educational reference and a foundation for further exploration in image retrieval research.
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Consistent Depth
We estimate dense, flicker-free, geometrically consistent depth
Consistent Depth is a research project developed by Facebook Research that presents an algorithm for reconstructing dense and geometrically consistent depth information for all pixels in a monocular video. The system builds upon traditional structure-from-motion (SfM) techniques to provide geometric constraints while integrating a convolutional neural network trained for single-image depth estimation. During inference, the model fine-tunes itself to align with the geometric constraints of a specific input video, ensuring stable and realistic depth maps even in less-constrained regions. This approach achieves improved geometric consistency and visual stability compared to prior monocular reconstruction methods. The project can process challenging hand-held video footage, including those with moderate dynamic motion, making it practical for real-world usage. -
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RefineNet
RefineNet: Multi-Path Refinement Networks
RefineNet is a MATLAB-based framework for semantic image segmentation and general dense prediction tasks. It implements the architecture presented in the CVPR 2017 paper RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation and its extended version published in TPAMI 2019. The framework uses multi-path refinement and improved residual pooling to achieve high-quality segmentation results across multiple benchmark datasets. It provides trained models for datasets such as PASCAL VOC 2012, Cityscapes, NYUDv2, Person_Parts, PASCAL_Context, SUNRGBD, and ADE20k, with versions based on ResNet-101 and ResNet-152 backbones. The repository supports both single-scale and multi-scale prediction, with scripts for training, testing, and evaluating segmentation performance. While this codebase is specific to MATLAB and MatConvNet, a PyTorch implementation and lighter-weight variants are also available from the community. -
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The Indian Linux Project Goal is to build a Indian language enabled Linux distro & applications with support for Indian Languages
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OpenNode is a open source server virtualization solution providing easy to use bare-metal ISO installer. It's based on CentOS and supports both OpenVZ container-based virtualization and emerging KVM full virtualization technology on the same host.
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OPSI UPDATER
Check and Update Products on OPSI Server
This OPSI SERVER Command line Tool (Opsi Updater) will check for available OPSI Product Updates on official Servers (example Mozilla) and will allow to Download and Update older Product Versions on OPSI Repository and distribute them on clients having older versions. readme: http://svn.code.sourceforge.net/p/opsiupdater/code-0/trunk/readme.txt -
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vEMan - VMware ESX/ESXi Manager
An unofficial VMware® ESX/ESXi Manager for Linux
vEMan - [v]Mware [E]SX [Man]ager provides a GUI for managing ESX servers natively from within your Linux desktop without the need to use Windows vSphere® anymore! Support vEMan development for v2.0 by gofundme! http://www.gofundme.com/vEMan Why I started vEMan? Because I hate it to start my Windows VM to manage ESX(i) servers ;o) vEMan is (maybe) not needed if you use vCenter 5.x: http://kb.vmware.com/kb/2005377 --> vSphere >= v5 has a "web client" BUT keep in mind that there are caveats: Not all features are available in the web client AND it can connect to vCenters only (see: http://kb.vmware.com/kb/1006095) - ESX(i) v3.x or 4.x? - v5.x - but no vCenter? - you want a native Linux vSphere client? Then try vEMan! ------- vEMan is not published or pronounced by VMware Inc. vEMan is an OpenSource project from a private person without any warranty or claim to bother VMware Inc. VMware,vSphere,ESX/ESXi,vCenter are trademarks by VMware Inc. -
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Classic HWUT - rename of previous HWUT
Software Unit Tests (Language Independent Approach)
Automation of Unit and System Tests. Tests can be implemented in any language and on many platforms. The flexible approach enables the inclusion of many types of tests, such as memory leak checks (using valgrind), coding rule checks, complexity checks, etc. Tests are run by a simple call to hwut in a base directory of a project. In particular for C, HWUT supports make file generation using 'sos' and 'sols' modes. Remote control-able function stubs may be generated using the 'stub' mode. Test cases can be generated using the 'gen' mode, and state machine walkers by the 'sm_walker' mode. Over the last decade HWUT has matured towards a full fledged unit test tool for a wide variety of applications. -
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ZED OS
ZED OS the clone of Windows XP
ZED OS is a portuguese Linux based distribution (remaster of Ubuntu Mate). It was designed for users who want an innovative system. With both 3D and 2D interfaces you'll be able to chosse the better one according to your hardware/needs. It's Web Based and inspired in Windows XP from Microsoft
