Open Source Linux Libraries - Page 42
Sort By:
Libraries for Linux
View 52 business solutions-
Earn up to 16% annual interest with Nexo.Generate interest, access liquidity without selling, and execute trades seamlessly. All in one platform. Geographic restrictions, eligibility, and terms apply.
-
Compliant and Reliable File Transfers Backed by Top Security CertificationsStop relying on non-certified, legacy file transfer tools that creak under the weight of modern security demands. Get full audit trails, advanced access controls and more supported by an award-winning team of experts. Start your free 25-day trial today.
-
1
Cosmopolitan
Build-once run-anywhere c library
Cosmopolitan Libc makes C a build-once run-anywhere language, like Java, except it doesn't need an interpreter or virtual machine. Instead, it reconfigures stock GCC and Clang to output a POSIX-approved polyglot format that runs natively on Linux + Mac + Windows + FreeBSD + OpenBSD + NetBSD + BIOS with the best possible performance and the tiniest footprint imaginable. Cosmopolitan can be compiled from source on any Linux distro. GNU make needs to be installed beforehand. This is a freestanding hermetic repository that bootstraps using a vendored static gcc9 executable. No further dependencies are required. -
2
Day.js
Fast, immutable 2kB alternative to Moment.js with the same modern API
Day.js is a fast, immutable, minimalist JavaScript library for parsing, validating, manipulating, and displaying dates and times for modern browsers. Its API is largely compatible with that of Moment.js, so if you know how to use Moment.js you’ll know how to use Day.js. Why opt for Day.js? Because it’s at just 2kB it involves less JavaScript to download, parse and execute, and more time for your code. Day.js is also immutable, chainable and has great support for internationalization. It currently supports all browsers. -
3
DeepLearning
Deep Learning (Flower Book) mathematical derivation
" Deep Learning " is the only comprehensive book in the field of deep learning. The full name is also called the Deep Learning AI Bible (Deep Learning) . It is edited by three world-renowned experts, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Includes linear algebra, probability theory, information theory, numerical optimization, and related content in machine learning. At the same time, it also introduces deep learning techniques used by practitioners in the industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling and practical methods, and investigates topics such as natural language processing, Applications in speech recognition, computer vision, online recommender systems, bioinformatics, and video games. Finally, the Deep Learning book provides research directions covering theoretical topics including linear factor models, autoencoders, representation learning, structured probabilistic models, etc. -
4
Deequ
Deequ is a library built on top of Apache Spark
Deequ is a library built atop Apache Spark that enables defining “unit tests for data” — that is, formal constraints or checks on datasets to ensure data quality along dimensions such as completeness, uniqueness, value ranges, correlations, etc. It can scale to large datasets (billions of rows) by translating those data checks into Spark jobs. Deequ supports advanced features like a metrics repository for storing computed statistics over time, anomaly detection of data quality metrics, and the suggestion of likely constraints automatically for new datasets. It also includes a little domain-specific language called DQDL (Data Quality Definition Language) which allows declarative specification of quality rules. Users typically run Deequ before feeding data downstream (to ML pipelines, analytics, or production systems), enabling early detection and isolation of data errors. There is also a Python wrapper, PyDeequ, for users who prefer working from Python environments. -
Streamline Azure Security with Palo Alto Networks VM-SeriesImprove your security posture and reduce incident response time. Use the VM-Series to natively analyze Azure traffic and dynamically drive policy updates based on workload changes.
-
5
DevExtreme
HTML5 JavaScript Component Suite for Responsive Web Development
DevExtreme is a comprehensive, enterprise-grade JavaScript UI component suite developed by DevExpress, designed to help developers build highly interactive, data-driven web applications across multiple frontend frameworks. It supports major ecosystems such as Angular, React, Vue, and jQuery, allowing teams to maintain consistency while working across different stacks. The library includes more than 80 components covering data grids, charts, schedulers, forms, editors, and navigation elements, making it suitable for complex business applications like dashboards, CRM systems, and SaaS platforms. A key strength of DevExtreme is its built-in data layer, which enables efficient handling of large datasets, real-time updates, and server-side operations such as filtering and sorting. The components are responsive and accessible, ensuring compatibility across devices, screen sizes, and assistive technologies. -
6
Discordeno
Discord API library for Node and Deno
Discord API library for Node.JS, Deno & Bun. Discordeno is actively maintained to guarantee excellent performance, latest features, and ease of use. The functional API eliminates the challenges of extending built-in classes and inheritance while ensuring overall simple but performant code. Discordeno offers the option to have practically any component of a bot as a separate piece, including standalone REST, gateways, custom caches, and more. Remove any properties, if your bot doesn't need them. For instance, remove Channel.topic if your bot doesn't require it. You may save GBs of RAM in this way. A few lines of code are all that are needed to accomplish this for any property on any object. -
7
Distributions
NodeSource Node.js Binary Distributions
distributions is NodeSource’s repository for maintaining installation scripts and binary distributions of Node.js for Linux systems. It provides a reliable way to install and manage Node.js across multiple distributions and versions, including Debian, Ubuntu, Fedora, CentOS, and Enterprise Linux derivatives. The repository contains setup scripts that configure system repositories and allow users to install Node.js using their package manager, ensuring they always receive secure and up-to-date builds. This project is especially useful for developers and system administrators who need a consistent, trusted source of Node.js binaries beyond what is included in default OS repositories. By streamlining the installation process, it saves time and reduces the complexity of managing different Node.js versions across environments. The repository is actively updated to track new Node.js releases and long-term support (LTS) schedules. -
8
Dragonboat
A feature complete and high performance multi-group Raft library in Go
Dragonboat is a high-performance multi-group Raft consensus library in pure Go. Consensus algorithms such as Raft provides fault tolerance by allowing a system to continue to operate as long as the majority of member servers are available. For example, a Raft shard of 5 servers can make progress even if 2 servers fail. It also appears to clients as a single entity with strong data consistency always provided. All Raft replicas can be used to handle read requests for aggregated read throughput. Dragonboat handles all technical difficulties associated with Raft to allow users to just focus on their application domains. It is also very easy to use, our step-by-step examples can help new users to master it in half an hour. Easy to use pure-Go APIs for building Raft based applications. Feature complete and scalable multi-group Raft implementation. Disk based and memory based state machine support. Fully pipelined and TLS mutual authentication support. -
9
Dragonbox
Reference implementation of Dragonbox in C++
This library is a reference implementation of Dragonbox in C++. Dragonbox is a float-to-string conversion algorithm based on a beautiful algorithm Schubfach, developed by Raffaello Giulietti in 2017-2018. Dragonbox is further inspired by Grisu and Grisu-Exact. Dragonbox generates a pair of integers from a floating-point number: the decimal significand and the decimal exponent of the input floating-point number. These integers can then be used for string generation of the decimal representation of the input floating-point number, the procedure commonly called ftoa or dtoa. -
Gemini 3 and 200+ AI Models on One PlatformBuild, govern, and optimize agents and models with Gemini Enterprise Agent Platform.
-
10
Einsen
Einsen is a prioritization app that uses Eisenhower matrix technique
Einsen is a prioritization app that uses Eisenhower matrix technique as a workflow to prioritize a list of tasks & built to Demonstrate the use of Jetpack Compose with Modern Android Architecture Components & MVVM Architecture. -
11
Emoji Mart
One component to pick them all
Data required for the picker to work has been completely decoupled from the library. That gives developers the flexibility to better control their app bundle size and let them choose how and when this data is loaded. Callback when the Add custom emoji button is clicked. The button will only be displayed if this callback is provided. It is displayed when search returns no results. You can use custom emojis by providing an array of categories and their emojis. Emojis also support multiple skin tones and can be GIFs or SVGs. You can use custom category icons by providing an object with the category name as key and the icon as value. Currently supported formats are svg string and src. First, you need to make sure data has been initialized. You need to call this only once per page load. Note that if you call init like this, you don’t necessarily need to include data in your Picker props. It doesn’t hurt either, it will noop. -
12
Express ES2017 REST API Boilerplate
Express starter for building RESTful APIs
A boilerplate for building RESTful APIs with Node.js and Express, providing a pre-configured structure for efficient backend development. -
13
Flow Typed
A central repository for Flow library definitions
High-quality library definitions for Flow. flow-typed is a repository of third-party library interface definitions for use with Flow. When you start a project with Flow, you likely want to use some third-party libraries that were not written with Flow. By default, Flow will just ignore these libraries leaving them untyped. As a result, Flow can't give errors if you accidentally misuse the library (nor will it be able to auto-complete the library). To address this, Flow supports library definitions which allow you to describe the interface of a module or library separate from the implementation of that module/library. The flow-typed repo is a collection of high-quality library definitions, tests to ensure that definitions remain high quality, and tools to make it as easy as possible to import them into your project. All you have to do when you add one or more new dependencies to your project is run flow-typed install. -
14
FlowRedux
Kotlin Multiplatform Statemachine library with nice DSL based on Flow
Building async. running Kotlin Multiplatform state machine made easy with a DSL and coroutines. -
15
Flutter Dojo
A beautiful design and useful project
Flutter Dojo is an educational and practical Flutter UI project that provides a large collection of demo screens, layouts, and component examples designed to help developers learn and experiment with Flutter’s capabilities. Rather than being a traditional component library, it acts as a showcase and playground where developers can explore different UI patterns, animations, and design implementations within a single project. The repository includes numerous real-world interface examples, allowing developers to study how complex layouts and interactions are structured in Flutter applications. It is particularly useful for beginners and intermediate developers who want to improve their understanding of Flutter widgets, layout systems, and state management concepts. The project emphasizes hands-on learning by presenting working examples that can be modified and extended directly. It also demonstrates best practices for organizing Flutter code and structuring scalable UI projects. -
16
Flutter EasyRefresh
A flutter widget that provides pull-down refresh and pull-up load
flutter_easy_refresh is a refresh widget library for Flutter that supports pull-to-refresh and load-more gestures with highly customizable behavior. It provides multiple built-in refresh headers and footers, including classic, material, ball-beat, and custom animated styles. Developers can easily implement infinite scrolling, lazy loading, and paginated content with fine-grained control over triggers and states. The library emphasizes smooth, adaptive animations and compatibility with slivers, lists, and grids. Because it is extensible, users can design their own refresh indicators to match branding or app style. Its focus is on delivering a responsive, modern UX for content-heavy apps such as feeds, shopping apps, and news readers. -
17
Flux3D.jl
3D computer vision library in Julia
Flux3D.jl is a 3D vision library, written completely in Julia. This package utilizes Flux.jl and Zygote.jl as its building blocks for training 3D vision models and for supporting differentiation. This package also have support of CUDA GPU acceleration with CUDA.jl. -
18
Form-Data
A module to create readable `"multipart/form-data"` streams
A library to create readable "multipart/form-data" streams. Can be used to submit forms and file uploads to other web applications. -
19
FunctionalPlus
Functional Programming Library for C++
Great code should mostly be self-documenting, but while using C++ in reality you can find yourself dealing with low-level stuff like iterators or hand-written loops that distract from the actual essence of your code. FunctionalPlus is a small header-only library supporting you in reducing code noise and in dealing with only one single level of abstraction at a time. By increasing brevity and maintainability of your code it can improve productivity (and fun!) in the long run. It pursues these goals by providing pure and easy-to-use functions that free you from implementing commonly used flows of control over and over again. -
20
GLM.jl
Generalized linear models in Julia
GLM.jl is a Julia package for fitting linear and generalized linear models (GLMs) with a syntax and functionality familiar to users of R or other statistical environments. It is part of the JuliaStats ecosystem and is tightly integrated with StatsModels.jl for formula handling, and Distributions.jl for specifying error families. The package supports modeling through both formula-based (e.g. @formula) and matrix-based interfaces, allowing both high-level convenience and low-level control. Under the hood, GLM.jl separates the linear predictor and response objects, allowing flexible combinations of link functions, variance structures, and fitting methods. -
21
Gen.jl
A general-purpose probabilistic programming system
An open-source stack for generative modeling and probabilistic inference. Gen’s inference library gives users building blocks for writing efficient probabilistic inference algorithms that are tailored to their models, while automating the tricky math and the low-level implementation details. Gen helps users write hybrid algorithms that combine neural networks, variational inference, sequential Monte Carlo samplers, and Markov chain Monte Carlo. Gen features an easy-to-use modeling language for writing down generative models, inference models, variational families, and proposal distributions using ordinary code. But it also lets users migrate parts of their model or inference algorithm to specialized modeling languages for which it can generate especially fast code. Users can also hand-code parts of their models that demand better performance. Neural network inference is fast, but can be inaccurate on out-of-distribution data, and requires expensive training. -
22
Google Cloud Platform Go Samples
Sample apps and code written for Google Cloud
Google Cloud Platform Go Samples repository is a comprehensive collection of Go-based code examples that demonstrate how to build applications and services using Google Cloud Platform. It provides developers with practical implementations that cover a wide spectrum of cloud functionalities, including storage, compute, networking, and machine learning services. Each sample is designed to be easily reusable, allowing developers to copy code directly into their own projects as a starting point for development. The repository includes both simple quickstart examples and more advanced application patterns, often accompanied by documentation guides that explain how to deploy and run them in different environments. It also showcases integrations with services such as Pub/Sub, BigQuery, Cloud Functions, and Vertex AI, helping developers understand how to orchestrate distributed systems in Go. -
23
Gradle Advanced Build Version Plugin
A plugin to generate the Android version code and version name
A plugin to generate the Android version code and version name automatically based on git commits number, date and Semantic Versioning. -
24
Gval
Expression evaluation in golang
gval is a versatile expression evaluation library for Go, enabling developers to parse and evaluate expressions dynamically. It supports a variety of expression types, including arithmetic, logical, and text-based, and allows for the composition of custom languages by replacing functions and operators. -
25
HaishinKit
Camera and Microphone streaming library via RTMP and SRT for iOS, Mac
Camera and Microphone streaming library via RTMP and SRT for iOS, macOS, tvOS and visionOS.
