Open Source Libraries - Page 8
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env
A simple and zero-dependencies library to parse environment variables
Parse environment variables to structs in Go. In Go, it’s dead simple to get the value from an environment variable. If you have a type that is not supported out of the box by the lib, you are able to use (or define) and pass custom parsers (and their associated reflect.Type) to the env.ParseWithFuncs() function. Env supports by default anything that implements the TextUnmarshaler interface. That includes things like time.Time for example. The upside is that depending on the format you need, you don't need to change anything. The downside is that if you do need time in another format, you'll need to create your own type. By default, slice types will split the environment value on ,; you can change this behavior by setting the envSeparator tag. If you set the envExpand tag, environment variables (either in ${var} or $var format) in the string will be replaced according with the actual value of the variable. -
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nuwa-skill
Mental models, decision heuristics, expressing DNA
nuwa-skill is an AI-oriented project focused on defining, managing, and executing modular “skills” that can be used by intelligent agents or automation systems. It provides a framework for organizing capabilities into reusable units that can be invoked dynamically depending on context or user input. The project is designed to integrate with AI systems, enabling them to perform structured tasks such as data retrieval, processing, or interaction with external services. It emphasizes modularity and extensibility, allowing developers to create new skills and plug them into the system without disrupting existing functionality. Nuwa Skill also supports orchestration, enabling multiple skills to work together to accomplish more complex objectives. The architecture is typically designed for flexibility, making it suitable for applications in conversational AI, automation, or intelligent assistants. Overall, it serves as a foundation for building scalable and extensible AI-driven systems. -
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spaCy
Industrial-strength Natural Language Processing (NLP)
spaCy is a library built on the very latest research for advanced Natural Language Processing (NLP) in Python and Cython. Since its inception it was designed to be used for real world applications-- for building real products and gathering real insights. It comes with pretrained statistical models and word vectors, convolutional neural network models, easy deep learning integration and so much more. spaCy is the fastest syntactic parser in the world according to independent benchmarks, with an accuracy within 1% of the best available. It's blazing fast, easy to install and comes with a simple and productive API. -
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AWS Lambda for .NET
Libraries, samples and tools to help .NET Core developers
AWS Lambda for .NET is the repository that houses tools, libraries and templates to build and deploy AWS Lambda functions using the .NET ecosystem (including .NET Core and .NET 7+). It provides NuGet packages for event models (e.g., API Gateway, DynamoDB, S3), utilities such as Amazon.Lambda.Tools for CLI deployment, and Blueprints/templates to scaffold serverless .NET projects quickly. The framework supports writing functions in C# (and VB/F# where applicable), integrates with the .NET CLI and Visual Studio tooling, and allows developers to treat Lambda functions like any other managed .NET application while handling the deployment, packaging and runtime glue. Its goal is to make serverless in AWS feel natural for .NET developers—abstracting away boilerplate and letting you focus on business logic rather than deployment artifacts. It also supports advanced use cases such as .NET Core integration, source-generator annotations, and custom middleware within a function context. -
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ArduinoJson
JSON library for Arduino and embedded C++. Simple and efficient
ArduinoJson is a C++ JSON library for Arduino and IoT (Internet Of Things). ArduinoJson has a simple and intuitive syntax to handle objects and arrays. ArduinoJson supports both JSON serialization and deserialization. ArduinoJson uses a fixed memory allocation, allowing to work on devices with very little RAM. ArduinoJson can filter large inputs to keep only fields that are relevant to your application, thereby saving a lot of memory. ArduinoJson can parse directly from an input Stream or std::istream. ArduinoJson can produce compact or prettified documents. ArduinoJson works directly with strings stored in program memory. ArduinoJson supports two coding styles, with implicit or explicit casts. ArduinoJson deduplicates strings in the JSON document. When you have several identical keys or values, the JsonDocument only stores one of each. In practice, this feature reduces memory consumption by 20-30%. -
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CoreUI
Open Source UI Kit built on top of Bootstrap 5 and plain JavaScript
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IRremote Arduino Library
Infrared remote library for Arduino, send and receive infrared signals
This library enables you to send and receive using infra-red signals on an Arduino. You can use any pin for sending now like you are used to receiving. Simultaneous sending and receiving. See the SendAndReceive example. No more need to use 32-bit hex values in your code. Instead, a (8 bit) command value is provided for decoding (as well as a 16-bit address and a protocol number). Protocol values comply to protocol standards, i.e. NEC, Panasonic, Sony, Samsung, and JVC decode and send LSB first. Supports more protocols, since adding a protocol is quite easy now. Better documentation and more examples. Compatible with tone() library, see ReceiveDemo. Supports more platforms, since the new structure allows to easily add a new platform. Feedback LED also for sending. Ability to generate a non-PWM signal to just simulate an active low receiver signal for direct connection to existent receiving devices without using IR. -
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Perfetto
Production-grade client-side tracing, profiling, and analysis
Perfetto is a production-grade tracing platform for Android, Linux, and Chrome that captures extremely detailed information about what a system is doing over time. It’s designed around a low-overhead producer/consumer model: instrumented components (“producers”) write binary events into shared memory buffers and a collector (“service”) reliably streams them to storage. The data model spans kernel and userspace, so you can stitch together CPU scheduling, app lifecycles, binder/IPC hops, GPU work, power and thermal signals, file I/O, heap samples, and more into a single coherent timeline. Perfetto’s ecosystem includes a web-based UI that can load multi-GB traces directly in the browser and an offline “trace processor” that exposes the trace as a queryable SQL-like table schema for deep analysis and automation. It integrates natively with Android (replacing older atrace/Systrace paths) and can sit alongside custom instrumentation in apps or services through its SDKs. -
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QR Code generator library
High-quality QR Code generator library in Java, TypeScript/JavaScript
This project aims to be the best, clearest library for generating QR Codes. My primary goals are flexible options and absolute correctness. The secondary goals are compact implementation size and good documentation comments. This work is an independent implementation based on reading the official ISO specification documents. I believe that my library has a more intuitive API and shorter code length than competing libraries out there. The library is designed first in Java and then ported to TypeScript, Python, Rust, C++, and C. It is open source under the MIT License. For each language, the codebase is roughly 1000 lines of code and has no dependencies other than the respective language’s standard library. -
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The Arcade Library
Easy to use Python library for creating 2D arcade games
Arcade is an easy-to-use Python library for creating 2D video games. It provides a modern and straightforward API, enabling developers to craft engaging games and graphical applications efficiently. Arcade supports rendering shapes, handling user input, and managing game physics, making it suitable for both beginners and experienced developers. -
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XNNPACK
High-efficiency floating-point neural network inference operators
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. -
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anime.js
JavaScript animation engine
anime.js is a lightweight JavaScript animation library that allows you to create complex animations with a single, powerful API. It works with just about anything web-based: CSS properties, SVG, DOM attributes and JavaScript Objects. anime.js makes it easy to create layered and complex animations, and gives you full control of timing and triggering events so they move exactly how and when you want them. It supports some of the latest browsers, and offers plenty of examples and demonstrations to get you animating in no time. Animate everything you want simply and easily with anime.js. -
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libdatachannel
C/C++ WebRTC network library featuring Data Channels, Media Transport
libdatachannel is a C++ library that implements WebRTC DataChannels and ICE transports, enabling peer-to-peer communication without relying on media components. It is ideal for developers who want efficient, low-latency P2P communication for data transmission in IoT, multiplayer games, decentralized apps, or custom signaling architectures. Built for performance and minimalism, it allows fine-grained control over STUN, TURN, DTLS, and SCTP layers, with a clean and modern C++ interface. -
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libplacebo
Official mirror of libplacebo
libplacebo is a flexible, high-performance graphics library built on top of Vulkan, designed to provide reusable GPU-accelerated components for media applications. It originated as a core part of the rendering pipeline for the mpv media player and has since grown into a standalone library used for tone mapping, dithering, color space conversion, and more. libplacebo is ideal for developers looking to integrate sophisticated video rendering and post-processing into their own applications with full control over shaders and rendering stages. -
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Neuroph is lightweight Java Neural Network Framework which can be used to develop common neural network architectures. Small number of basic classes which correspond to basic NN concepts, and GUI editor makes it easy to learn and use.
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Alpa
Training and serving large-scale neural networks
Alpa is a system for training and serving large-scale neural networks. Scaling neural networks to hundreds of billions of parameters has enabled dramatic breakthroughs such as GPT-3, but training and serving these large-scale neural networks require complicated distributed system techniques. Alpa aims to automate large-scale distributed training and serving with just a few lines of code. -
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DeepSeed
Deep learning optimization library making distributed training easy
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. DeepSpeed delivers extreme-scale model training for everyone, from data scientists training on massive supercomputers to those training on low-end clusters or even on a single GPU. Using current generation of GPU clusters with hundreds of devices, 3D parallelism of DeepSpeed can efficiently train deep learning models with trillions of parameters. With just a single GPU, ZeRO-Offload of DeepSpeed can train models with over 10B parameters, 10x bigger than the state of arts, democratizing multi-billion-parameter model training such that many deep learning scientists can explore bigger and better models. Sparse attention of DeepSpeed powers an order-of-magnitude longer input sequence and obtains up to 6x faster execution comparing with dense transformers. -
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Elixir LangChain
Elixir implementation of a LangChain style framework
Elixir LangChain is an Elixir-native implementation of the LangChain concept, designed to help developers build applications powered by large language models using the Elixir ecosystem. The library focuses on enabling composable workflows where different processes, APIs, and services can be connected into structured pipelines driven by LLMs. It mirrors the architecture of traditional LangChain frameworks by providing abstractions for chains, tools, agents, and model integrations while adapting them to Elixir’s concurrency model. This makes it particularly well-suited for building scalable, real-time AI systems that benefit from Elixir’s fault tolerance and lightweight process management. The framework supports multiple providers and includes features such as streaming responses, tool calling, and structured outputs, allowing developers to create complex AI-driven workflows. -
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Flutter Rust Bridge
Rust binding generator, feature-rich, but seamless and simple
flutter_rust_bridge is a toolchain that generates ergonomic bindings between Rust and Dart/Flutter so you can write performance-critical logic in Rust while building UI in Flutter. It abstracts away the low-level FFI details, letting developers define Rust functions and data structures that are safely callable from Dart. The project supports passing complex types, handling async operations and streams, and integrating with Flutter across mobile and desktop targets. By leaning on Rust’s memory safety and zero-cost abstractions, it enables compute-heavy tasks—parsing, crypto, image/audio processing, and more—without sacrificing Flutter’s developer experience. Build scripts and templates streamline packaging and distribution so the Rust side fits cleanly into CI and multi-platform releases. In practice, teams gain a maintainable way to share one performant Rust core across multiple Flutter apps while keeping the UI reactive and fast. -
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Go Jsonnet
This an implementation of Jsonnet in pure Go
go-jsonnet is a pure Go implementation of the Jsonnet data templating language, which extends JSON with variables, functions, imports, and a standard library so you can generate complex configuration safely. Instead of hand-maintaining massive JSON files, you write concise, reusable templates that evaluate to JSON or YAML, with deterministic semantics and rich error messages. The repository ships both an embeddable VM for Go programs and a command-line interpreter, making it easy to integrate templating into build systems, CLIs, and services. Import paths and custom resolvers let you structure large configuration trees across directories or remote sources, while top-level arguments and external variables parameterize builds per environment. Native functions can be registered from Go to expose domain-specific helpers without forking the language. Tooling such as a formatter and linter-friendly errors encourages clean, consistent templates that scale across teams. -
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Google Cloud Node.js Client
Google Cloud Client Library for Node.js
The Google Cloud Node.js Client is an official library that provides idiomatic Node.js interfaces for Google Cloud services, simplifying the integration of these services into Node.js applications. -
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Infinity For Reddit
A Reddit client for Android
A Reddit client on Android written in Java. It does not have any ads and it features a clean UI and smooth browsing experience. Infinity is a beautiful, feature-rich app that offers a smooth Reddit browsing experience. It is completely free and ad-less; you can browse Reddit without interruptions or distractions. Built by a passionate university student, Infinity is open source. -
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Intent UI
Intent UI is a chill set of React components
Intent UI is a modern React component library focused on accessibility, customization, and developer ownership, built on top of React Aria Components and styled with Tailwind CSS. It follows a copy-and-paste philosophy similar to shadcn/ui, allowing developers to directly integrate and modify components within their own codebases instead of relying on rigid dependencies. The library emphasizes accessible design by leveraging ARIA standards and best practices, ensuring that components work well with keyboard navigation and assistive technologies. It includes a wide range of components such as overlays, forms, visualizations, and layout elements, making it suitable for building full web applications. Intent UI also provides templates, blocks, and starter kits that accelerate development and reduce setup time for new projects. Its integration with modern frameworks like Next.js, Vite, and TanStack Router further enhances its flexibility and ease of adoption. -
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JsBarcode
Barcode generation library written in JavaScript
JsBarcode is a barcode generator written in JavaScript. It supports multiple barcode formats and works in browsers and with Node.js. It has no dependencies when it is used for the web but works with jQuery if you are into that. Barcode generation library written in JavaScript that works in both the browser and on Node.js. Easy to use, yet powerful barcode generator for the web and Node.js. Download the library and include the script or use the CDN. Customize the barcode with the 17 different options.
