Coding & Development

vscode-browse-lite is an embedded browser extension designed for Visual Studio Code, offering developers a seamless way to preview web pages directly within their IDE. This tool enhances the development workflow with features like faster page refreshing, ensuring immediate feedback on changes. It is dark mode aware and theme-aware, integrating smoothly with the user's VS Code environment. Crucially, it includes built-in devtools support, allowing for direct debugging and inspection of web content. The extension also boasts extendable actions and the ability to re-open pages in a system browser. Notably, vscode-browse-lite is lightweight, significantly smaller than its predecessor, and does not collect telemetry, prioritizing user privacy and performance.

embedded-resources is an open-source GitHub repository maintained by Embedded Artistry, offering a comprehensive collection of templates, documents, and source code examples specifically tailored for embedded systems development. This resource is designed to assist engineers in designing and building embedded systems and firmware, providing practical, real-world examples. The repository includes various sections such as C and C++ examples, libc and libcpp implementations, interview questions, and manufacturing-related documents. It leverages tools like git-lfs and meson for efficient management and building of projects, making it a valuable asset for developers looking to enhance their embedded artistry skills and streamline their development workflows.

embedded-scripting-languages is a comprehensive, open-source resource offering a curated list of embedded scripting languages. This tool is designed to assist developers in selecting the most appropriate language for their specific application needs. The list includes a wide array of options, from reasonably mature to actively developed languages, and even extends to Datalog implementations. Each entry provides details such as the language's project name/link, implementation language, garbage collection method, and license, along with specific notes. The resource emphasizes languages with strong copyleft licenses as a warning, ensuring developers are aware of potential licensing implications. It's an invaluable reference for anyone looking to integrate scripting capabilities into their projects.

awesome-object-detection is a comprehensive GitHub repository dedicated to curating a vast collection of resources related to object detection. It serves as an invaluable reference for researchers and developers interested in the field, offering a structured list of academic papers and their corresponding implementations for various object detection algorithms. The repository covers a wide range of methods, from foundational models like R-CNN, Fast R-CNN, and Faster R-CNN to more recent advancements such as YOLO, SSD, and Mask R-CNN. Each entry typically includes links to the arXiv paper, official GitHub repositories, and sometimes additional resources like slides or notes. This makes it an excellent starting point for anyone looking to understand the evolution, theory, and practical application of object detection techniques.

Graphify is a powerful data visualization tool that allows users to instantly generate a wide range of diagram types directly from JSON text input. It supports diverse diagramming needs, including class diagrams for system design, entity-relationship (ER) diagrams for database schema visualization, radial diagrams, concept maps, timeline diagrams, and network diagrams. Users can select their preferred output format between PNG and SVG, ensuring high-quality, styled images. This tool simplifies the process of visualizing complex data structures and relationships, making it accessible for various analytical and design tasks without requiring manual drawing or complex software.

Bootlin's training-materials is an open-source repository offering extensive resources for embedded Linux and kernel development. It provides detailed guides and examples for compiling and understanding various system components, including bootloaders, kernel modules, and device drivers. The materials are designed to be highly practical, with instructions for setting up development environments, compiling code, and performing hands-on labs. It includes formatting guidelines for labs and slides, syntax highlighting with `minted` and `pygments`, and recommendations for diagram creation using Dia. This repository is ideal for individuals and organizations looking to enhance their knowledge and skills in embedded systems programming and Linux kernel development.

Repo.js is a jQuery plugin designed to easily embed GitHub repositories directly onto any website. This functionality is particularly beneficial for plugin and library authors who wish to display the contents of their repositories on their project pages, providing visitors with immediate access to code examples and file structures. The plugin integrates seamlessly with jQuery and leverages Markus Ekwall's jQuery Vangogh plugin for sophisticated styling of file contents. Furthermore, it utilizes Ivan Sagalaev's highlight.js for robust syntax highlighting, ensuring that embedded code is presented clearly and professionally. Repo.js simplifies the process of showcasing GitHub content, making it an invaluable tool for developers looking to enhance their online presence.

OpenAI-cookbook is an open-source repository offering a collection of examples and guides designed to help developers effectively use the OpenAI API. It provides practical code samples, primarily in Python, along with clear instructions for accomplishing common tasks and integrating OpenAI's powerful AI models into various applications. The cookbook serves as a valuable resource for understanding API functionalities, exploring different use cases, and accelerating development with OpenAI's technologies. Users need an OpenAI account and API key to run the examples, which can be set via an environment variable or an .env file.

nerf_and_beyond_docs is a curated collection of documents and topics focused on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). This GitHub repository serves as a central hub for papers, discussions, and related technologies accumulated by the NeRF/3DGS & Beyond community. It is designed to help researchers, students, and engineers stay up-to-date with the latest advancements in 3D vision. The collection is actively maintained, with new resources added daily, and includes notes on various works to facilitate understanding. Users can join discussions, contribute, and access a companion book on NeRF/3DGS, making it an invaluable resource for both beginners and experienced professionals in the field.

awesome-cs-cloudnative-blockchain is an extensive open-source repository designed as a growth handbook for individuals interested in computer science, cloud-native technologies, blockchain, web3, and Golang. It offers a curated collection of learning materials, including detailed guides on Go language, Docker, Kubernetes, and various CS fundamentals like operating systems, algorithms, and data structures. The resource also delves into blockchain technology, covering Ethereum, Bitcoin, and Hyperledger Fabric, alongside cryptography and consensus algorithms. It aims to provide a structured learning path for aspiring engineers and those looking to deepen their knowledge in these rapidly evolving fields, with content ranging from beginner to advanced topics and practical project examples.

textlint is an open-source, pluggable linter specifically designed for natural language text, functioning much like ESLint does for code. Unlike many linters, textlint does not come bundled with any rules; instead, users install rules via npm, allowing for highly customized linting environments. This flexibility enables developers and writers to enforce specific style guides, grammar rules, and consistency checks across their documentation, articles, or any text-based content. It's an essential tool for maintaining high-quality written communication in projects, ensuring that text adheres to predefined standards and best practices.

vim-grammarous is a robust grammar checker designed specifically for the Vim text editor, integrating with LanguageTool for comprehensive grammar and style analysis. This plugin automatically handles the download and setup of LanguageTool, requiring Java 8 or later to function. A key feature is its asynchronous command execution, which ensures that grammar checks do not block your workflow, especially beneficial for users on Vim 8.0.27+ or Neovim. It allows users to check grammar for entire buffers or specific text ranges, highlighting errors directly within Vim. The tool also provides an interactive information window for error details, offering options to fix, remove, or disable rules. For advanced users, it offers global mappings for quick actions and integration with unite.vim and denite.nvim for managing error lists.

Heidi is an AI care partner designed to support clinicians throughout their entire workday, from initial documentation to evidence-based decision-making and follow-up. It began by streamlining note-taking and has expanded to support all surrounding clinical tasks. The tool offers features like AI medical scribing, which transcribes consultations and generates structured notes, and an evidence tool for looking up research and treatment information. Heidi aims to reduce administrative burden, save clinicians time, and improve work-life balance, allowing them to focus more on patient care. It supports over 1.5 million consultations weekly and is used by clinicians across more than 10 clinical specialties globally.

Datasets Tagging is a Hugging Face Space application designed to streamline the process of creating and validating structured tags for datasets within the Hugging Face library. Users can input various details such as the dataset name, associated tasks, supported languages, creators, license information, and size. This functionality enables the generation of comprehensive and up-to-date metadata files, significantly improving dataset organization and documentation. The tool is particularly useful for maintaining consistency and discoverability across a large collection of datasets, making it an essential resource for data scientists and developers working with the Hugging Face ecosystem.

embedmd is an open-source tool designed to streamline the process of embedding code snippets into Markdown documentation, ensuring that the code examples remain synchronized with their source files. This eliminates the common problem of outdated or non-compiling code in READMEs and other documentation. It works by interpreting special Markdown comments that act as commands, allowing users to embed entire files or specific sections defined by regular expressions. The tool supports both local file paths and URLs, and can automatically infer the language for syntax highlighting from file extensions. embedmd offers options to either modify Markdown files in place or display the differences, making it a valuable utility for developers and technical writers who need to maintain accurate and up-to-date code documentation.

The Datasets API Playground is a Hugging Face Space designed for exploring and interacting with various API endpoints. This application provides a direct interface to test API calls and understand how different services and functionalities can be integrated and utilized. It serves as a practical environment for developers and data scientists to experiment with datasets and API interactions, facilitating the integration of diverse services. The tool is hosted on Hugging Face, indicating its potential for community-driven development and accessibility within the AI/ML ecosystem.

Docker Examples offers a collection of templates specifically designed for Hugging Face Spaces, enabling users to quickly deploy and configure development environments. This tool simplifies the process of setting up JupyterLab or VSCode instances, providing custom configurations to streamline workflows. It serves as a valuable resource for developers and software engineers looking to understand and implement containerization within the Hugging Face ecosystem. By offering practical examples, Docker Examples helps users grasp the fundamentals of Docker and its application in AI development environments.

CypherScribe is a no-code platform designed to rapidly create and launch interactive, SEO-optimized developer documentation. Users can connect their data sources, customize the appearance with themes, colors, and logos, and claim a custom subdomain for their documentation app, all without writing a single line of code. The platform supports a wide range of content types including headings, paragraphs, tables, code blocks, media, lists, and accordions, and even allows PDF/CSV uploads. It features a rich editor with Markdown support and custom blocks for multilingual code snippets, banners, and toasts. CypherScribe also includes built-in SEO optimization, a search algorithm, and an AI bot trained on your data to assist users. It aims to offload documentation burdens from development teams by providing a fast, customizable, and performant solution.

SWE-Wiki, hosted on Hugging Face Spaces, offers a dynamic platform for tracking GitHub community statistics specifically for Software Engineering (SWE) assistants. The tool features a live leaderboard that ranks these assistants based on their contributions, including the number of wiki edits and membership events they generate. Users can also add their own assistants by providing their GitHub username, fostering a collaborative environment for monitoring performance. This tool is designed to provide insights into the activity and impact of SWE assistants within GitHub communities, making it valuable for developers and teams looking to assess and improve their documentation and community engagement efforts.

awesome-robotics-datasets is a comprehensive, open-source collection of datasets specifically curated for robotics and computer vision research and development. This GitHub repository serves as a valuable resource, offering links to numerous dataset collections, including those for SLAM (Simultaneous Localization and Mapping), computer vision tasks, and various place-specific datasets like driving, flying, underwater, and indoor environments. Researchers and developers can explore datasets from prominent institutions and projects such as KITTI, Waymo, nuScenes, and more. The collection is organized by topic and location, making it easy to find relevant data for specific applications in areas like localization, mapping, object tracking, and 3D reconstruction.

Repo Graph is an interactive visualization tool hosted on Hugging Face Spaces, designed to help users understand the structure of software repositories. By providing a repository name or URL, the application generates a visual graph that maps out the repository’s files, folders, and their interconnections. This byte-level map allows for quick exploration and comprehension of a project's architecture, making it easier to analyze code dependencies, identify key components, and understand the overall organization of AI models or other software projects. It's particularly useful for those working with the Hugging Face Hub, offering a unique perspective on its vast collection of models and datasets.

TensorFlow-Object-Detection-on-the-Raspberry-Pi provides a comprehensive, step-by-step tutorial for implementing TensorFlow's Object Detection API on a Raspberry Pi. This guide enables users to perform real-time object detection on live video feeds from a Picamera or USB webcam. It includes updated instructions for easily installing TensorFlow and the protobuf compiler, making the setup process more accessible. The repository also features a 'Pet Detector' program as an example application, demonstrating how to use object detection to send text alerts when specific objects are detected. This tutorial is ideal for developers looking to create unique detection applications on the Raspberry Pi.

Great Expectations (GX Core) is an open-source data quality tool designed to help data teams ensure the reliability and integrity of their data. It allows users to define, document, and test 'Expectations' – essentially unit tests for data – to always know what to expect from their datasets. GX Core combines community wisdom with a super-simple package, making it easy to implement data quality checks. It supports Python 3.10 through 3.13, with experimental support for Python 3.14 and later. The tool fosters collaboration by providing a common language for data quality tests and automatically generating documentation for validation results, simplifying data quality processes and preserving institutional knowledge about data.

This GitHub repository offers a comprehensive tutorial for training a TensorFlow object detection classifier to detect multiple objects on Windows 10, 8, or 7. It covers the entire process, from installing necessary software like Anaconda, CUDA, and cuDNN, to setting up the TensorFlow Object Detection API directory structure. The tutorial details how to gather and label pictures, generate training data, create a label map, configure and train the model, and finally, export and test the inference graph. It also includes Python scripts for testing the classifier on images, videos, or webcam feeds, and provides files for training a "Pinochle Deck" playing card detector as an example.