Open Source ChromeOS Libraries - Page 36
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Libraries for ChromeOS
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DOMtastic
Small, fast, and modular DOM and event library for modern browsers
Small, fast, and modular DOM and event library for modern browsers. Same familiar API as jQuery (but without the extra "weight" of modules like $.ajax, $.animate, and $.Deferred). Dependency-free. Weighs in at only 1KB to 12KB (minified), depending on the included modules. The full bundle is about 4KB gzipped. DOMtastic also serves as a starting point for your own application-specific DOM API (read more). Rollup and babel are used to create a UMD bundle (supporting AMD, CommonJS, and fallback to browser global). -
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DSL-JSON library
High performance JVM JSON library
Fastest JVM (Java/Android/Scala/Kotlin) JSON library with advanced compile-time data binding support. Java JSON library designed for performance. Originally built for invasive software composition with DSL Platform compiler. v1 core library was targeting Java6, while v2 targets Java8. So there is no need anymore for dsl-json-java8 project and instead, just dsl-json project should be referenced. From v2 only a single annotation processor is supported, which was previously in the java8 project. Joda and some other specific libraries were removed. If you still need converters for those types, you need to include them in your project. -
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Darkmode.js
Add a dark-mode / night-mode to your website in a few seconds
Darkmode.js is a lightweight JavaScript library that enables websites to easily implement a dark mode toggle. It provides a floating switch for users to switch between light and dark themes without requiring major modifications to the website’s codebase. -
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Dart Sass
The reference implementation of Sass, written in Dart
Dart Sass is the canonical, fully‑featured official implementation of the Sass stylesheet preprocessor, superseding Ruby Sass. Written in Dart, it emphasizes speed, portability and ease of maintenance. Available as standalone CLI, Dart library, and npm JS distribution, it supports semantic versioning, CSS compatibility, and an embedded API for host integration. -
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DarwinKit
Native Mac APIs for Go. Previously known as MacDriver
DarwinKit is a lightweight framework that provides system-level utilities and APIs for macOS and iOS development, allowing deeper interaction with the Darwin operating system. -
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DASEIN CLOUD HAS MOVED TO GITHUB. This SourceForge presence represents Dasein Cloud through Dasein Cloud 2012.04 and point releases for that major release. We are still updating 2012.04 with bug fixes for all clouds, so there is no immediate need to move to the latest release, 2012.09. All versions starting with 2012.09 are available at: http://github.org/greese/dasein-cloud
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Data science blogs
A curated list of data science blogs
Data Science Blogs is a curated repository that aggregates a wide range of high-quality blogs and resources related to data science, machine learning, and analytics into a single organized collection. It serves as a discovery platform for practitioners, researchers, and learners who want to stay updated with industry trends, techniques, and insights without manually searching for reliable sources. The repository includes links to personal blogs, professional publications, and educational resources, often accompanied by RSS feeds for easy subscription and content tracking. By organizing these resources in a centralized and structured format, it reduces the friction associated with finding relevant and trustworthy information in a rapidly evolving field. The project is community-driven, allowing contributors to expand and maintain the list as new blogs emerge and existing ones evolve. -
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This is an ALPHA version - the RELEASE version is now on github - https://github.com/fimtra/datafission.
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DataScienceR
a curated list of R tutorials for Data Science, NLP
The DataScienceR repository is a curated collection of tutorials, sample code, and project templates for learning data science using the R programming language. It includes an assortment of exercises, sample datasets, and instructional code that cover the core steps of a data science project: data ingestion, cleaning, exploratory analysis, modeling, evaluation, and visualization. Many of the modules demonstrate best practices in R, such as using the tidyverse, R Markdown, modular scripting, and reproducible workflows. The repository also shows examples of linking R with external resources — APIs, databases, and file formats — and integrating into larger pipelines. It acts as a learning scaffold for students or beginners transitioning to more advanced data science work in R, offering a hands-on, example-driven approach. The structure encourages modularity, readability, and reproducible practices, making it a useful reference repository for learners and educators alike. -
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DataTables
Tables plug-in for jQuery
DataTables is a jQuery plugin that enhances HTML tables with powerful dynamic features such as searching, sorting, and pagination. It transforms plain static tables into interactive data explorers with minimal code changes. Developers can customize its behavior through an extensive API, adding export buttons, column visibility toggles, custom filters, and integration with remote data sources via Ajax. The plugin is modular, with a core that provides essentials and a wide range of extensions that cover advanced functionality like fixed headers, responsive layouts, and inline editing. Styling can be adapted to frameworks like Bootstrap or Foundation, making it blend seamlessly into modern UIs. Because it handles large datasets efficiently, DataTables has become a go-to component for dashboards, reporting tools, and admin interfaces. -
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Databene Commons
databene commons provides extensions to the Java core library
'databene commons' is an open source Java library that provides extensions to the Java core library by utility classes, abstract concepts and concrete implementations. -
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DateTimePicker
Responsive jQuery DateTime Picker plugin for Web & Mobile
DateTime Picker is a quick highly customizable jQuery plugin. The design is clutter-free & well-suited for embedding it into mobile application development. Users can change values using +/- buttons or type values directly into the textbox by selecting each value field. For the web, the picker can be bound relative to the reference element, were it will appear at the bottom of the element. For mobile, the picker can appear as a dialog box covering the entire window. -
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DeckTransition
A library to recreate the iOS Apple Music now playing transition
DeckTransition is an attempt to recreate the card-like transition found in the iOS 10 Apple Music and iMessage apps. The transition can be called from code or using a storyboard. To use via storyboards, just setup a custom segue (kind set to custom), and set the class to DeckSegue. Set modalPresentationCapturesStatusBarAppearance to true in your modal view controller, and override the preferredStatusBarStyle variable to return .lightContent. By default, DeckTransition has a swipe-to-dismiss gesture which is automatically enabled when your modalʼs main UIScrollView is scrolled to the top. DeckTransition has an internal heuristic to determine which UIScrollView should be tracked for the swipe-to-dismiss gesture. In general, this should be sufficient for and cover most use cases. -
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Decompose
Kotlin Multiplatform lifecycle-aware business logic components
Decompose is a Kotlin Multiplatform library for breaking down your code into tree-structured lifecycle-aware business logic components (aka BLoC), with routing functionality and pluggable UI (Jetpack/Multiplatform Compose, Android Views, SwiftUI, Kotlin/React, etc.). -
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Deep Learning Essay Reading
Read classic and new deep learning papers paragraph by paragraph
Deep Learning Essay Reading repository is a comprehensive collection of machine learning and deep learning research summaries designed to make cutting-edge academic work more accessible. Instead of reading entire dense academic papers, contributors provide structured breakdowns and insights into the most influential research from the past decade, often including explanation highlights and key takeaways. The content spans foundational models, architectures, and training methodologies across computer vision, natural language processing, generative models, and other machine learning domains. These summaries help students, researchers, and engineers stay up to date with breakthroughs in the field without needing to sift through full academic documents. With thousands of stars and forks, this repository has become a widely referenced learning resource for anyone interested in understanding the technical ideas behind major advancements. -
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DeepDream
This repository contains IPython Notebook with sample code
DeepDream is a small, educational repository that accompanies Google’s original “Inceptionism” blog post by providing a runnable IPython/Jupyter notebook that demonstrates how to “dream” through a convolutional neural network. The notebook shows how to take a trained vision model and iteratively amplify patterns the network detects, producing the hallmark surreal, hallucinatory visuals. It walks through loading a pretrained network, selecting layers and channels to maximize, computing gradients with respect to the input image, and applying multi-scale “octave” processing to reveal fine and coarse patterns. The code is intentionally compact and exploratory, encouraging users to tweak layers, step sizes, and scales to influence the aesthetic. Although minimal, it illustrates important concepts like feature visualization, activation maximization, and the effect of different receptive fields on the final image. -
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DeepEP
DeepEP: an efficient expert-parallel communication library
DeepEP is a communication library designed specifically to support Mixture-of-Experts (MoE) and expert parallelism (EP) deployments. Its core role is to implement high-throughput, low-latency all-to-all GPU communication kernels, which handle the dispatching of tokens to different experts (or shards) and then combining expert outputs back into the main data flow. Because MoE architectures require routing inputs to different experts, communication overhead can become a bottleneck — DeepEP addresses that by providing optimized GPU kernels and efficient dispatch/combining logic. The library also supports low-precision operations (such as FP8) to reduce memory and bandwidth usage during communication. DeepEP is aimed at large-scale model inference or training systems where expert parallelism is used to scale model capacity without replicating entire networks. -
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DeepMind Research
Implementations and code to accompany DeepMind publications
This repository collects reference implementations and illustrative code accompanying a wide range of DeepMind publications, making it easier for the research community to reproduce results, inspect algorithms, and build on prior work. The top level organizes many paper-specific directories across domains such as deep reinforcement learning, self-supervised vision, generative modeling, scientific ML, and program synthesis—for example BYOL, Perceiver/Perceiver IO, Enformer for genomics, MeshGraphNets for physics, RL Unplugged, Nowcasting for weather, and more. Each project folder typically includes its own README, scripts, and notebooks so you can run experiments or explore models in isolation, and many link to associated datasets or external environments like DeepMind Lab and StarCraft II. The codebase is primarily Jupyter Notebooks and Python, reflecting an emphasis on experimentation and pedagogy rather than production packaging. -
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Deeplearning-papernotes
Summaries and notes on Deep Learning research papers
Deeplearning-papernotes is an implementation of Convolutional Neural Networks for sentence and text classification in TensorFlow, based on a well-known research paper that applies CNN architectures to natural language processing tasks with strong performance in sentiment analysis and similar classification problems. The repository provides the complete network definition, including an embedding layer to convert words into dense representations, convolution and max-pooling layers to extract informative features, and a final softmax classifier to distinguish between target classes. It includes data preprocessing helpers, training scripts, and configuration options so developers can experiment with different filter sizes, dropout rates, and hyperparameters to optimize performance for their dataset. -
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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. -
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An Error Handler for C/C++ funtions, focused on code readability. It allows a clear control flow, adding Ignore, Retry, Jump, and Back functionality when a function returns error. It's macro based, written in C and targeting embedded systems.
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Design Patterns Written in Unity3D
All Gang of Four Design Patterns written in Unity C#
The Design Patterns Written in Unity3D project is a guide to implementing software design patterns specifically within the Unity game development environment. It demonstrates how common patterns such as singleton, observer, factory, and state can be adapted to Unity’s architecture. The repository includes practical examples and code snippets that show how to structure game systems for scalability and maintainability. It emphasizes clean code practices and modular design, helping developers build more robust and reusable components. The project also explains the reasoning behind each pattern, making it educational as well as practical. By focusing on Unity-specific use cases, it addresses challenges unique to game development workflows. Overall, it provides a valuable reference for improving code quality in Unity projects.
