Coding & Development

Smart-money-concepts is a Python package designed for algorithmic trading, integrating Inner Circle Trader (ICT) concepts into Python. It provides a suite of indicators such as Fair Value Gap (FVG), Swing Highs and Lows, Break of Structure (BOS) & Change of Character (CHoCH), Order Blocks (OB), and Liquidity. The package also includes functionalities to identify previous highs and lows across different timeframes and to analyze session-specific market activity and retracements. This tool is intended for traders and investors seeking to gain deeper insights into market sentiment, trends, and potential reversals through programmatic analysis.

Ground News is a platform designed to combat media bias by aggregating news from a vast array of sources globally. It allows users to compare headlines and coverage of the same news story across the political spectrum, providing media bias ratings, factuality ratings, and ownership information for each source. The tool aims to help users identify their own blindspots in news consumption and escape algorithmic echo chambers, fostering a more nuanced understanding of current events. Key features include a daily briefing, trending topics, local news, and a 'Blindspot' feed that highlights stories disproportionately covered by one side of the political spectrum.

ngrok-go is an open-source and idiomatic Go package designed to embed ngrok networking directly into Go applications. It functions as an ngrok agent packaged as a Go library, allowing developers to serve Go apps on the internet with a single line of code. This eliminates the need for manual setup of low-level network primitives such as IPs, certificates, load balancers, and even ports. Applications using ngrok-go listen on ngrok's global cloud service but receive connections via the standard net.Listener interface. It also supports ngrok's Traffic Policy engine for applying API Gateway behaviors like authentication, rate-limiting, and routing at the cloud service level.

Unique3D is an open-source project designed for high-quality and efficient 3D mesh generation from a single image. Developed by AiuniAI, this tool leverages AI to create detailed 3D models, making it suitable for various 3D content creation tasks. It supports 3D reconstruction from single-view wild images, producing textured meshes in approximately 30 seconds. The project is continuously under construction, with plans for further features like ComfyUI and Docker support, as well as training code release. Users can run a local Gradio demo for interactive inference and benefit from detailed installation guides for both Linux and Windows systems. Unique3D is particularly sensitive to input image characteristics, performing best with orthographic front-facing images to avoid squashed or incomplete reconstructions.

Unsupervised-Classification is a GitHub repository offering a PyTorch implementation of the paper "SCAN: Learning to Classify Images without Labels." This tool addresses the challenge of automatically grouping images into semantically meaningful clusters when ground-truth annotations are absent. It deviates from recent end-to-end approaches by advocating a two-step method where feature learning and clustering are decoupled. The project demonstrates significant performance improvements over state-of-the-art methods on various benchmarks, including CIFAR10, CIFAR100-20, STL10, and ImageNet. It provides code for pretext tasks (like SimCLR), clustering (SCAN), and self-labeling steps, along with pretrained models and evaluation scripts, making it a valuable resource for researchers in computer vision and unsupervised learning.

tracking.js is an open-source JavaScript library designed to integrate various computer vision algorithms and techniques directly into web browsers. Leveraging modern HTML5 specifications, it allows developers to implement real-time functionalities such as color tracking and face detection with a lightweight core, approximately 7 KB. The library provides an intuitive interface for tasks like object tracking, feature detection, and image processing (convolution, grayscale, blur, integral image, Sobel). It supports integration with HTML elements like `<canvas>`, `<video>`, and `<img>`, making it versatile for web-based computer vision applications. While browser support is broad, camera access relies on the getUserMedia API, which may have varying compatibility.

AliceVision is an open-source photogrammetric computer vision framework designed for 3D reconstruction and camera tracking. It provides a robust software foundation with state-of-the-art computer vision algorithms that can be tested, analyzed, and reused. The project is a collaborative effort between academia and industry, ensuring cutting-edge algorithms meet the quality and robustness required for production use. It allows users to infer the geometry of a scene from a set of unordered photographs or videos, effectively reversing the 3D scene to 2D projection process. The framework is primarily used through Meshroom, which offers both a user interface and a command-line tool for launching the AliceVision pipeline and customizing workflows with Python scripting.

awesome-embedded-rust is a comprehensive, curated list of resources specifically designed for embedded and low-level development using the Rust programming language. This project is maintained by the Rust Embedded Resources team and serves as a central hub for developers. It features an extensive collection of useful crates, including peripheral access crates for various microcontrollers like Microchip, Nordic, NXP, Raspberry Pi, and STMicroelectronics, as well as HAL implementation and architecture support crates. The list also provides information on real-time operating systems (RTOS) like Drone OS, FreeRTOS.rs, and Tock, alongside a wide array of development tools such as `svd2rust` for generating Rust structs from SVD files, `cargo-flash` for binary downloads, and the `Knurling Tools` suite for building, debugging, and testing embedded Rust systems. Additionally, it offers a rich selection of free and paid books, blogs, and training materials, covering topics from introductory embedded Rust to advanced DSP on Cortex-M microcontrollers.

Revise.js is a foundational JavaScript library designed to simplify the development of contenteditable-based web text editors. It addresses the complexities of working with the native `contenteditable` attribute by offering key building blocks: a `<content-area>` web component that reads the DOM as a clean string value, an algebraic `Edit` data structure for describing and manipulating text changes, and a declarative component model. This library enables developers to build highly customized rich-text editing experiences without the overhead of full-fledged editor frameworks. It supports features like undo/redo history, collaborative editing, and stable keys for line-based rendering, making it ideal for creating robust and flexible text editing solutions.

UAParser.js is a robust open-source JavaScript library designed for comprehensive user-agent string parsing. It accurately identifies various components of a user's environment, including the browser type and version, operating system, device type (e.g., mobile, tablet, desktop), CPU architecture, and even specific bots or AI crawlers. This versatility makes it suitable for both client-side applications running in web browsers and server-side operations using Node.js. Developers can leverage UAParser.js to tailor content, optimize user experiences, or gather analytics based on detailed user-agent information, ensuring compatibility and performance across diverse platforms. Its open-source nature fosters community contributions and transparency, making it a reliable choice for user-agent detection needs.

Loom is an innovative open-source component framework designed for the Go programming language, enabling developers to construct user interfaces across diverse platforms, including web and terminal environments. Unlike traditional frameworks that rely on HTML or JSX, Loom leverages pure Go functions for markup, allowing for flexible and idiomatic UI construction. Its core differentiator is a robust signal-based reactive model that supports concurrency, allowing updates from hundreds of concurrent tasks and managing effects across multiple goroutines without risk of pollution. Loom provides the reactive model and basic components, while platform-specific renderers like LOOM-TERM and LOOM-WEB extend its capabilities for particular environments. The framework emphasizes explicit reactivity, giving developers fine-grained control over UI updates. It is currently in early development, with plans for enhanced stability, documentation, and advanced features like async memos for improved asynchronous operations.

AvatarCLIP is an open-source research project from SIGGRAPH 2022 that provides a powerful solution for zero-shot text-driven generation and animation of 3D avatars. Users can generate detailed 3D avatar shapes and textures from simple text descriptions, such as "a tall and skinny female soldier." The tool also supports motion generation, allowing users to animate their creations with text prompts like "arguing" or "running." A key feature is the ability to convert generated avatars into animatable FBX format, making them compatible with popular 3D software like Blender and Unity3D, or for upload to platforms like Mixamo for access to extensive motion libraries. The project offers a Colab Demo for avatar generation and provides comprehensive instructions for installation and data preparation.

avod is an open-source implementation of the Aggregate View Object Detection (AVOD) network, specifically designed for 3D object detection in autonomous driving scenarios. This repository offers a Python-based solution for researchers and developers to implement and experiment with advanced 3D object detection algorithms. It leverages view aggregation techniques to enhance detection accuracy. The project includes detailed instructions for setting up the environment, installing dependencies, configuring training parameters, and running evaluations on datasets like KITTI. It also provides pre-trained models and scripts for visualizing results, making it a comprehensive resource for those working in the field of autonomous vehicle perception.

avr-hal is an open-source Hardware Abstraction Layer (HAL) designed for AVR microcontrollers and common boards like Arduino. It provides a set of abstractions that simplify interaction with AVR hardware, making embedded systems development with Rust more accessible. The project is built on the `avr-device` crate and supports various AVR microcontrollers. It includes `arduino-hal` for Arduino boards, `mcu/atmega-hal` and `mcu/attiny-hal` for specific MCU families, and `avr-hal-generic` for writing drivers compatible with any AVR chip. The repository also features `ravedude`, a utility for integrating flashing and serial console output into the `cargo` workflow, streamlining the development process.

Awesome-Implicit-NeRF-Robotics is a curated repository offering a comprehensive list of research papers, code implementations, and related websites focused on Implicit Representations and Neural Radiance Fields (NeRF) within the Robotics and Reinforcement Learning (RL) domains. This resource is largely based on the survey paper "Neural Fields in Robotics: A Survey." It categorizes papers into key areas such as Object Pose Estimation, SLAM, Manipulation/RL, Object Reconstruction, Physics, and Planning/Navigation, making it an invaluable resource for academics and practitioners exploring these advanced topics. The repository is actively maintained, with regular updates on new research and workshops in the field.

awesome-self-driving-car is a comprehensive, open-source curated list of resources dedicated to self-driving car technology. It serves as a valuable hub for developers, researchers, and students interested in autonomous vehicles, offering links to full-stack open-source projects like Apollo and Autoware, as well as essential libraries such as ROS, OpenCV, and TensorFlow. The list also includes academic courses from institutions like Udacity and MIT, alongside a vast collection of papers and blogs covering topics from HD mapping and simulation to localization, perception, planning, and control. Furthermore, it details various systems, hardware components, datasets, and benchmarks crucial for autonomous driving research and development.

PyTorch-RL offers a comprehensive PyTorch implementation of various deep reinforcement learning algorithms. This repository is designed for researchers and developers working with reinforcement learning, providing ready-to-use implementations of popular policy gradient methods such as Trust Region Policy Optimization (TRPO), Proximal Policy Optimization (PPO), and Synchronous A3C (A2C). Additionally, it includes Generative Adversarial Imitation Learning (GAIL). A key feature is its fast Fisher vector product calculation and support for multiprocessing, enabling agents to collect samples from multiple environments simultaneously for improved performance. It supports both discrete and continuous action spaces, making it versatile for different reinforcement learning tasks.

goexif is an open-source Go library designed for decoding embedded EXIF metadata from image files. It offers functionality for handling both basic EXIF and TIFF encoded data, with its capabilities divided into two separate packages: 'exif' and 'tiff'. The 'exif' package depends on the 'tiff' package for its operations. Currently in an alpha stage, the project welcomes suggestions and pull requests from the community to enhance its features and stability. Developers can easily integrate goexif into their Go projects to extract valuable information such as camera model, focal length, date/time taken, and GPS coordinates from image files.

Now House offers a specialized ledger API built for brokerages, focusing on post-trade operations for the 21st century. It provides a highly available and blazing-fast platform for managing financial ledgers, ensuring high throughput and low latency for reads and writes through multi-region cloud hosting. The system incorporates safeguards and security features like double-entry accounting and immutable backups to ensure every share is accounted for. Users can maintain schema flexibility by creating transaction templates, defining asset classes, and adding them to custom ledgers. This allows firms to reduce reconciliations against custodian daily snapshot files and create separate ledgers for various needs, such as IRAs, equities, or emerging markets strategies. It also enables real-time tracking of trade and funding progress and provides audit-ready transaction records.

Voxelpose-pytorch is the official PyTorch implementation of the research paper "VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment." This open-source tool enables researchers and developers to perform 3D human pose estimation using data from multiple cameras in uncontrolled environments. It includes detailed instructions for installation, data preparation using datasets like Shelf/Campus and CMU Panoptic, and guidance for training and evaluating models. The repository also provides pre-trained backbone models and camera parameters to facilitate immediate use and experimentation. It's a valuable resource for those working on advanced computer vision and human motion analysis.

GauStudio is a modular framework designed to support and accelerate research and development in the rapidly advancing field of 3D Gaussian Splatting (3DGS) and its diverse applications. It offers functionalities like mesh extraction and rendering, and supports various 3DGS methods. The framework includes curated datasets for evaluating 3DGS methods under diverse conditions, including synthetic datasets and real-world scenes with high-quality normal annotations. GauStudio also provides LoFTR-based initial point clouds for better initialization and plans to release more 3DGS-based methods, dataset loaders, and visualization tools in the near future. It is released under the MIT License, with commercial cooperation welcomed.

jsfeat is an open-source JavaScript Computer Vision library designed for developers to explore and implement modern computer vision algorithms using JS/HTML5. The library provides a comprehensive set of features, including custom data structures and essential image processing methods such as grayscale conversion, box blur, Gaussian blur, histogram equalization, Canny edges, and various derivative calculations. It also incorporates a Linear Algebra module for LU, Cholesky, and SVD solvers, along with Eigen Vectors and Values. For advanced applications, jsfeat offers a Multiview module with Affine2D and Homography2D motion kernels, and RANSAC/LMEDS motion estimators. Additionally, it includes feature detectors like Fast Corners, YAPE06, YAPE, and ORB, as well as Lucas-Kanade optical flow and HAAR/BBF object detectors, making it a versatile tool for computer vision development.

SegLossOdyssey is an open-source repository offering a comprehensive collection of loss functions specifically designed for medical image segmentation. This tool is invaluable for researchers and practitioners aiming to enhance the accuracy and robustness of their segmentation models, particularly in tasks involving highly imbalanced data. The collection includes implementations in PyTorch and Keras, covering a wide array of loss functions from various research papers and challenges. It highlights the effectiveness of compound loss functions for challenging segmentation tasks and provides a valuable resource for exploring and applying state-of-the-art loss functions in medical imaging.

Lite YouTube Embed is an open-source custom element designed to significantly improve the performance of embedded YouTube videos on websites. It renders videos approximately 224 times faster than a traditional YouTube iframe, focusing on visual performance and quicker loading times. The tool uses `youtube-nocookie.com` for enhanced user privacy and supports progressive enhancement for deferred loading with JavaScript. Developers can customize poster images, access the YouTube Iframe Player API, add video titles, and apply custom player parameters to control video behavior and appearance. It is available as an npm package and can be easily integrated by including its CSS and JavaScript files.