Coding & Development

RPi-Monitor is an open-source application specifically designed for real-time monitoring of embedded devices, with a primary focus on the Raspberry Pi B platform. It offers a robust set of features including an embedded web server for easy access, alert messaging to notify users of critical events, and SNMP integration for broader network management. The tool is engineered to separate information extraction from presentation, enhancing both performance and security. Installation is straightforward with Debian packages available for Raspbian, and documentation covers usage, customization, and development contributions. It's a valuable resource for anyone needing detailed, real-time insights into their embedded systems.

face.evoLVe is a high-performance, open-source face recognition library designed for comprehensive face-related analytics and applications. It supports both PaddlePaddle and PyTorch frameworks, offering a wide array of features including face alignment (detection, landmark localization, affine transformation), data processing (augmentation, balancing, normalization), and various backbones (ResNet, IR, IR-SE, ResNeXt, DenseNet, MobileNet). The library also incorporates different loss functions like Softmax, Focal, ArcFace, and Triplet, along with performance-enhancing tricks. It addresses challenges in large-scale face recognition by providing an efficient distributed training schema for multi-GPUs, supporting both backbone and head layers. This makes it ideal for researchers and engineers developing deep face recognition models for practical use.

RouterSploit is an open-source exploitation framework specifically designed for embedded devices, offering a comprehensive suite of modules to aid in penetration testing and vulnerability assessment. It includes exploits for identified vulnerabilities, credential testing modules for network services, and scanners to check for known weaknesses. Additionally, it provides payload generation capabilities for various architectures and injection points, alongside generic attack modules. The framework is under active development, with new modules frequently added, making it a dynamic tool for security researchers and cybersecurity professionals focused on embedded system security. It supports installation on various Linux distributions and OSX, and can also be run via Docker.

OpenNHP is a lightweight, cryptography-powered, open-source toolkit designed to implement Zero Trust security for infrastructure, applications, and data. It serves as the reference implementation for the Cloud Security Alliance's Network-infrastructure Hiding Protocol (NHP) specification. The toolkit features two core protocols: NHP, which conceals server ports, IP addresses, and domain names to protect against unauthorized access, and Data-content Hiding Protocol (DHP), ensuring data security and privacy through encryption and confidential computing. OpenNHP inverts traditional security models by making resources invisible until trusted, granting access only after cryptographically signed authentication. It integrates with existing IAM, DNS, FIDO, and Zero Trust policy engines, extending current security stacks.

NewsRecommendSystem is an open-source personalized news recommendation system designed to be easily adapted for various applications. It incorporates three core recommendation algorithms: collaborative filtering, content-based recommendation, and hot news recommendation. The collaborative filtering component leverages Mahout's library, while the content-based recommendation features an improved algorithm based on relevant research. Hot news recommendation identifies and suggests recently popular articles. The system requires integration with a news module for regular news collection and supports interaction with MySQL databases, allowing for flexible deployment. Users can configure which algorithms to enable, select target user groups (all, active, or custom), and choose between one-time or scheduled recommendation generation.

Luos-engine is an open-source, lightweight library designed to manage hardware products as a collection of independent software features. It functions as a real-time orchestrator for cyber-physical systems, facilitating the design, testing, and deployment of embedded applications and digital twins. The tool can be utilized on any microcontroller or computer, across various networks, promoting free and fast development of multi-electronic-board connected products. By using Luos-engine, developers can leverage existing work, accelerate time-to-market, and ensure robustness and universality of their applications. It supports development, debugging, validation, monitoring, and management from anywhere, promoting organized and effective development practices for scalability and adaptability.

PFLlib is a comprehensive and beginner-friendly library and benchmark for personalized federated learning (PFL). It enables users to quickly grasp and implement federated learning concepts, even on a personal computer. The platform supports 39 traditional and personalized FL algorithms, 3 scenarios, and 24 datasets, facilitating extensive research and experimentation. Key features include real-machine deployment capabilities with HtFL-OnDevice, privacy evaluation, and systematic research support. PFLlib primarily focuses on data (statistical) heterogeneity, offering tools to generate various non-IID data distributions. It also provides an easy-to-extend framework for adding new data or algorithms, fostering community contributions.

Ocean is Meta's in-house, open-source framework designed for Computer Vision (CV) and Augmented Reality (AR) applications. Implemented mainly in C/C++, it offers a platform-independent solution for developers working on advanced CV and AR projects. The framework provides the foundational tools and libraries necessary for building sophisticated applications in these domains. It is released under the MIT License, with specific components under the Art Attribution License 1.0, making it accessible for a wide range of development needs. Developers can leverage Ocean to create applications for various platforms including Android, iOS, Linux, macOS, Meta Quest, and Windows, requiring Python 3.8+, CMake 3.26+, and C++20 for building from source.

Pelee is an open-source, real-time object detection system specifically optimized for mobile devices. Based on the SSD framework, this system was presented at NeurIPS 2018 and offers a robust solution for mobile vision applications. The repository provides the necessary code for researchers and developers to implement and experiment with the Pelee object detection model. It includes tools for training on VOC and COCO datasets, evaluation scripts, and pre-trained models for various configurations. Pelee demonstrates competitive performance in terms of both accuracy (mAP) and frames per second (FPS) on mobile platforms like iPhone 8 and NVIDIA TX2, making it a valuable resource for developing efficient mobile AI applications.

Video Analyst is an open-source project from Megvii Research that provides a collection of fundamental algorithms for video understanding tasks. It specifically focuses on Single Object Tracking (SOT) and Video Object Segmentation (VOS). The tool includes implementations like SiamFC++ for robust and accurate visual tracking and a State-Aware Tracker for real-time video object segmentation. It is designed for researchers and developers, offering detailed documentation for setup, model usage, training, and testing. The repository structure is well-organized, with separate modules for experiments, data handling, model building, and pipeline construction, making it a valuable resource for those working on advanced computer vision and video analysis projects.

ROS-Academy-for-Beginners is an open-source collection of code examples specifically designed for the 'Robot Operating System Introduction' course on Chinese University MOOC. This repository offers a comprehensive set of ROS packages, including robot simulation programs, various communication examples (topic, service, action, param), and demonstrations of advanced functionalities like navigation and Simultaneous Localization and Mapping (SLAM). It supports both C++ and Python implementations for many examples, making it versatile for different programming preferences. The project is actively maintained and updated, providing a valuable resource for students and developers looking to learn and implement ROS concepts. It also includes instructions for downloading, compiling, and running the examples, with specific recommendations for the operating environment.

Rebol provides the complete source code for the Rebol interpreter, enabling developers to build and deploy the language on a variety of platforms. Primarily targeting non-Windows systems such as Linux, Mac, BSD, and Android, it also supports Windows builds with some manual configuration. The project emphasizes portability, allowing the interpreter to be built with many different compilers and even cross-compiled for embedded systems. It includes a simple, old-fashioned makefile that is itself built by Rebol, facilitating configuration and the creation of necessary C header files. The project encourages community contributions for porting and improvements, with guidelines for maintaining code clarity and style.

Sferal AI is a no-code platform designed for business professionals to build internal systems and tools using natural language. Users can describe their problems or desired functionalities through conversation, and Sferal AI will construct custom solutions such as order trackers, approval workflows, and dashboards. The platform aims to eliminate the need for developers, product managers, or AI consultants, making it accessible for companies to digitalize operations. It offers a secure, private environment for business data, ensuring that customer lists, orders, and other sensitive information are not shared with other companies. Sferal AI builds real software with databases, secure logins, and dashboards, allowing for easy adjustments and scalability as business needs evolve.

Trackers is an open-source project offering clean and modular re-implementations of prominent multi-object tracking algorithms. Released under the permissive Apache 2.0 license, it provides a flexible solution for integrating advanced tracking capabilities with any detection model a user already employs. The tool supports tracking from various sources like videos, webcams, and RTSP streams, and offers both CLI and Python integration for seamless workflow incorporation. It includes algorithms such as SORT, ByteTrack, and OC-SORT, complete with detailed benchmarks and evaluation tools for comparing tracker performance against ground truth data. Additionally, Trackers facilitates the download of benchmark datasets like MOT17 and SportsMOT, making it a comprehensive resource for computer vision researchers and developers.

AI RPG Adventure is an interactive text-based RPG game hosted on Hugging Face Spaces, powered by Pixeltable. Users can choose their genre, character, and actions, and the AI dynamically generates engaging stories and options in response. This creates a personalized and evolving adventure experience, making each playthrough unique. The tool is designed for those who enjoy interactive storytelling and role-playing games, offering a flexible platform to explore various narratives without predefined paths. It leverages AI to provide a responsive and immersive gaming environment.

LongVU is an AI tool hosted on Hugging Face Spaces that enables users to interact with visual content by uploading videos or images and posing questions or comments. The application then processes the visual input and generates detailed text responses, providing insights and information derived from the content. This functionality makes LongVU a valuable resource for researchers and developers focused on video analysis, image understanding, and general visual content interpretation. It leverages advanced AI models to bridge the gap between visual data and textual explanations, facilitating deeper engagement with multimedia.

Bioclip 2 Demo is an interactive application hosted on Hugging Face Spaces, designed for biological research and data exploration. Users can upload images of plants, animals, or other organisms, and the tool will predict their likely taxonomic rank, such as species, genus, or family. This is achieved using a sophisticated large tree-of-life model. The demo also allows users to supply their own taxonomic tree, offering flexibility for specialized research. It serves as a valuable resource for visualization and understanding biodiversity through image analysis, making advanced biological classification accessible.

AstaBench Leaderboard offers a comprehensive platform for viewing and comparing benchmark leaderboards across diverse AI categories. Users can explore performance metrics for models in areas such as literature understanding, code execution, data analysis, and discovery. The tool is hosted on Hugging Face Spaces by AllenAI, providing a centralized location to track and evaluate the advancements in AI model capabilities. It serves as a valuable resource for researchers and developers to assess the effectiveness of different AI systems without requiring any input, simply by browsing the available leaderboards.

Cad Recode is an innovative AI tool designed to reverse engineer 3D CAD models from mesh files. Users can upload a 3D mesh, and the application processes it by sampling 256 points. From these points, it generates corresponding Python CAD code, effectively translating the visual mesh into programmatic instructions. This generated code can then be used to reconstruct the CAD model, offering a unique approach to converting existing 3D designs into editable, code-based formats. The tool is hosted on Hugging Face Spaces, providing an accessible platform for this specialized conversion task.

Small Object Detection with YOLOX is an AI tool hosted on Hugging Face Spaces, designed for identifying small objects within images. It leverages the YOLOX architecture and offers an enhanced SAHI+YOLOX method for improved detection capabilities. Users can upload or select an image, set parameters like slice size and overlap ratio, and then perform predictions to compare the results between standard YOLOX and SAHI+YOLOX. This tool is valuable for researchers, developers, and educators interested in experimenting with advanced object detection techniques and understanding the benefits of SAHI integration for small object detection.

SnapSite is a free and open-source browser extension designed to capture complete web pages and entire websites for offline access. It saves content as offline-ready ZIP files, ensuring that all assets, including full source code, images, fonts, and animations, are perfectly preserved. The tool offers two capture modes: single page snapshot for exact visual preservation, and full site crawl to archive up to 500 pages from a domain. SnapSite is capable of capturing complex elements like Shadow DOM components, CSS animations, and form states, making it a robust solution for web archiving, reference, or offline development. It also strips tracking scripts and ensures zero broken links for a truly offline experience.

pytorch-yolo-v3 offers a PyTorch implementation of the YOLO v3 object detection algorithm, designed for efficient and real-time object recognition. This repository aims to improve upon existing ports by streamlining the code, removing redundant components, and providing clear documentation. It currently supports detection in single images, multiple images, and video streams, with options to adjust resolution and utilize half-precision floats for faster inference. The project serves as a driver code for research, with plans to include a training module in the future. It requires Python 3.5, OpenCV, and PyTorch 0.4.

The Multimodal Hallucination Leaderboard is a Hugging Face Space developed by Typhoon AI, designed for evaluating and comparing the hallucination tendencies of various multimodal AI models. Users can access and explore existing results from established AI hallucination benchmarks such, as POPE/MHaluBench and AVHalluBench. The platform also provides functionality for users to submit their own evaluation results, contributing to a broader understanding of AI model performance. This tool is particularly valuable for researchers and developers focused on understanding, benchmarking, and ultimately mitigating inaccuracies and hallucinations in AI outputs across different modalities.

Segment Anything with CLIP is an AI tool that leverages the power of image segmentation and CLIP-based text prompts to enable users to segment images using natural language descriptions. This tool is designed to provide a flexible and intuitive way to interact with image data, allowing for precise object isolation based on textual input. It is particularly useful for tasks requiring detailed image manipulation and analysis, offering a unique approach to content creation and advanced image processing. The integration of CLIP allows for a deeper understanding of image content through language, making segmentation more accessible and powerful.