Coding & Development

Investing Algorithm Framework is a comprehensive Python-based framework designed for the entire lifecycle of automated trading algorithms. It enables users to create, backtest, and deploy trading strategies efficiently. Unlike many quant frameworks that only provide backtest results, this tool offers a full loop from strategy creation to deployment, including a unique feature for comparing multiple strategies in a single, interactive HTML dashboard. It supports over 30 metrics, multi-window robustness testing, equity and drawdown charts, monthly heatmaps, and benchmark comparisons. The framework also facilitates live trading via CCXT, portfolio management, cloud deployment to AWS Lambda or Azure Functions, and integration with various market data providers.

StarDist is an open-source Python implementation for object detection and segmentation using star-convex shapes in 2D and 3D images. It is particularly well-suited for applications in microscopy and histopathology, enabling precise cell and nuclei instance segmentation. The tool trains models to predict distances to object boundaries and probabilities, generating candidate polygons that are refined via non-maximum suppression. StarDist supports multi-class prediction, allowing objects to be classified into discrete categories. It also includes a submodule for computing common instance segmentation metrics, facilitating performance evaluation. Installation is straightforward with pip, and pretrained models are available for various image types.

flutter-unity-view-widget enables developers to seamlessly integrate Unity game engine views into their Flutter applications. This open-source tool supports embedding Unity content in both fullscreen and embeddable modes, making it ideal for adding gamified features or complex 3D experiences to Flutter apps. It works across Android, iPad OS, iOS, and Web platforms, with specific setup instructions for each. The widget supports Unity versions from 2019.4.3 up to 2022.3.x, with recommendations for the latest LTS versions. Developers need to export their Unity project for integration, and the tool provides clear guidance for platform-specific configurations on Android and iOS, including NDK setup and activity modifications. It's designed for technical users familiar with Unity Engine.

Syncora Benchmarks offers a lightweight, plug-and-play solution for evaluating the quality of synthetic data. Users can easily compare synthetic data generated by Syncora with outputs from other generators, such as Gretel and MostlyAI, by simply dropping CSV files into the designated folder. The tool automatically computes a suite of fidelity and similarity metrics, providing instant insights into data quality. It also visualizes comparative results, making it easy to understand the performance of different synthetic data generators. Designed for ease of use, it works with any dataset through a simple file naming convention, eliminating the need for heavy setup. This makes it an accessible tool for quickly assessing and improving synthetic data generation processes.

Huge is an open-source, simple user-authentication solution embedded into a small framework, designed for developers needing a straightforward way to manage user authentication. It works out-of-the-box with an auto-installer and uses the official bcrypt password hashing/salting implementation of PHP 5.5+. The project prioritizes simplicity, making it ideal for smaller projects, typical agency work, and quick prototypes, rather than massive corporate applications. While the project has reached "soft End Of Life" for new features, it remains actively maintained for bug fixes and corrections, ensuring a stable and secure core. It includes features like CSRF blocking, encryption of cookie contents, user registration/login/logout, password reset, remember-me functionality, account verification via email, and basic user types.

HydraLab is an open-source framework designed to facilitate intelligent cloud testing, enabling users to easily build and manage their own cloud-testing infrastructure. It supports scalable test device management through a center-agent distributed design and offers robust test task management with result visualization. The platform powers Android Espresso Test and Appium (Java) tests across Windows, iOS, Android, and Browser platforms. Additionally, HydraLab provides case-free test automation capabilities, including Monkey testing and Smart exploratory testing. It offers an out-of-box Docker image for quick setup and supports integration with Azure Blob Storage for file storage. Developers can also build and run HydraLab from source, making it a flexible solution for diverse testing needs.

Object Detection Web is a free, web-based AI tool hosted on Hugging Face Spaces, developed by Xenova. It provides a straightforward way to perform object detection on images. Users can easily upload their own images or select from example images to see the application identify and label various objects present. This tool is particularly useful for individuals interested in learning about object detection technology, exploring its capabilities, or for simple task automation where identifying objects in images is required. Its accessible web interface makes it suitable for educational purposes and fun exploration without requiring any technical setup.

Gauzilla is a 3D Gaussian Splatting (3DGS) renderer developed in Rust for WebAssembly, featuring lock-free multithreading for platform-agnostic web deployment. It leverages WebGL and CPU splat sorting to ensure high compatibility across various web browsers. The tool can securely load .ply or .splat files from local machines using `rfd` and asynchronously loads .splat files from URLs without requiring async Rust code. Additionally, it supports loading .spz files via a WASM module compiled from the official C++ implementation. Gauzilla is designed for real-time photorealistic rendering of scenes reconstructed from images and videos, making it suitable for Novel View Synthesis applications.

Gaussian Splatting Notes is a free, open-source educational resource offering a comprehensive breakdown of the mathematical formulae behind Gaussian Splatting. This guide, presented as a text version of an explanatory stream, delves into the intricacies of the rasterization process, specifically covering the forward and backward passes. It aims to provide as many details as possible, highlighting core algorithmic concepts and referencing original code snippets to aid understanding. The resource also includes important insights marked with '💡' and clarifies complex topics like 3D covariance reparametrization and 2D Gaussian projection, making it an invaluable aid for those studying this advanced 3D rendering technique.

StreamPETR is an official implementation of a research paper accepted by ICCV 2023, focusing on exploring object-centric temporal modeling for efficient multi-view 3D object detection. This open-source tool provides a robust framework for researchers and developers working in the field of computer vision and autonomous driving. Key features include support for StreamPETR, PETR, and Focal-PETR codebases, flash attention, deformable attention (RepDETR3D), and checkpoints. It also offers functionalities like sliding window training, efficient training in streaming video, TensorRT inference, and 3D object tracking. The repository provides detailed documentation for environment setup, data preparation, and training/inference procedures, along with model zoo results on NuScenes validation and test sets.

easyFL, also known as FLGo, is an experimental and open-source platform designed for federated learning research. It offers a robust and reusable environment for conducting diverse federated learning experiments, featuring comprehensive and easy-to-use modules. Researchers can simulate real-world system heterogeneity, utilize over 50 benchmarks across various data types and communication topologies, and generate federated tasks with specific data distributions using flexible partitioners. The platform also includes implementations of more than 50 algorithms from top-tier conferences and journals, supporting flexible combinations of benchmarks, partitioners, algorithms, and simulators. It provides experimental tools for loading results and using checkpoints for training recovery.

FastV is an open-source inference acceleration method specifically designed for large vision-language models (LVLMs). It operates as a plug-and-play solution, significantly reducing computational costs by pruning redundant visual tokens in the deeper layers of these models. This approach allows for a theoretical FLOPs reduction of up to 45% without compromising performance. FastV has been accepted to ECCV 2024 as an Oral Presentation, highlighting its innovative contribution to the field. The project provides code for setup, visualization of inefficient attention over visual tokens, and comprehensive evaluation scripts for latency and performance reproduction. It supports HuggingFace LLaVA models and is compatible with KV Cache for improved efficiency, particularly in video understanding tasks.

Face-Recognition-Attendance-System is an open-source project designed to automate attendance tracking using face detection and recognition. This system aims to reduce manual errors and provide a reliable method for recording attendance. Key features include checking camera feeds, capturing faces, training the system with new faces, recognizing individuals, and automatically recording attendance. It also offers automatic email notifications and screenshot capabilities. Built with Python 3.7, it leverages modules like OpenCV, Pillow, NumPy, Pandas, Shutil, CSV, and yagmail, utilizing Haar Cascade and LBPH algorithms for face recognition. The project is suitable for developers looking to implement or learn about face recognition attendance systems.

Video-XL is an open-source project offering a family of efficient vision-language models (VLMs) specifically designed for understanding extremely long videos, capable of processing content at an hour scale. The project includes models like Video-XL2 and Video-XL-Pro, which have achieved state-of-the-art results on various long video understanding benchmarks. Video-XL-Pro, for instance, can process up to 10,000 frames on an 80G GPU with only 3 billion parameters. The project provides models, training, and evaluation code, making it a valuable resource for researchers and developers working with extensive video data. It builds upon existing codebases like LongVA and LMMs-Eval for its development and evaluation processes.

booking-js by Timekit is an open-source JavaScript library designed to help developers quickly create and embed beautiful booking widgets. It integrates seamlessly with the Timekit API, enabling robust appointment scheduling functionalities. This tool supports the new projects model and uses an App Widget Key for authentication, ensuring secure and efficient operation. While the repository is primarily for community contributions and customizations, all official documentation, guides, and examples are available on the Timekit developer portal. It's an ideal solution for those looking to implement a customizable booking interface without building from scratch, offering flexibility for developers to tailor the widget to their specific needs.

canvas-editor is an open-source rich text editor designed for web applications, leveraging canvas and SVG for rendering. It offers a comprehensive suite of rich text operations, including undo/redo, font styling, alignment, and list management. Developers can easily insert various elements such as tables, images, links, code blocks, page breaks, and mathematical formulas. The editor also supports printing to picture and PDF, controls like select, text, date, radio, and checkbox, and features like context menus, shortcut keys, drag and drop functionality, headers, footers, page numbers, page margins, watermarks, pagination, and comments. It is ideal for creating custom text editing experiences within web applications.

balena-engine is a container engine specifically designed for embedded, IoT, and Edge computing environments, while maintaining compatibility with Docker containers. Built upon Docker’s Moby Project, it offers significant optimizations for resource-constrained devices. Key features include a 3.5x smaller footprint than Docker CE, multi-architecture support for a wide range of chipsets, and highly efficient updates through true container deltas, which are 10-70x smaller than traditional layer pulls. The engine also prioritizes minimal wear-and-tear on storage, failure-resistant atomic pulls, and conservative memory use to ensure application stability in low-memory situations. It omits features primarily needed for cloud deployments, such as Docker Swarm and certain logging/networking drivers, making it a lightweight, drop-in replacement for Docker CE in IoT contexts.

Awesome-BEV-Perception-Multi-Cameras is a valuable resource for researchers and engineers focused on multi-camera 3D object detection and segmentation within the Bird's-Eye-View (BEV) paradigm. This curated list compiles significant academic papers, including influential works like DETR3D, BEVDet, BEVFormer, BEVDepth, and UniAD. It categorizes papers by key themes such as Longterm BEV, BEV + Stereo, End to End BEV Perception, BEV + Distillation, Robust BEV, Fast BEV, HD Map Construction, Multi-sensor fusion, Survey, Occupancy Network, and Pre-training. Each entry typically includes a link to the paper and its corresponding GitHub repository, making it easy for users to access the research and associated codebases. This tool is essential for staying updated with the latest advancements in vision-centric autonomous driving perception.

Awesome-state-space-models is a comprehensive collection of research papers and repositories focused on state-space models and hybrid models. This GitHub repository serves as a centralized resource for academics, researchers, and engineers interested in the latest advancements and implementations in this field. It includes a wide array of topics, from foundational theories to specific applications in areas like language models, vision, reinforcement learning, and biomedical imaging. The collection is regularly updated with new arXiv preprints and conference papers, offering insights into various model architectures, optimization techniques, and practical use cases, including Mamba, RWKV, and other hybrid approaches.

airframe-react is a free and open-source dashboard template designed for building high-quality admin and analytics interfaces. It leverages Bootstrap 4 and React 16, ensuring responsiveness across smartphones, tablets, and desktops. The template is available under an MIT license, making it highly accessible for developers. It features a minimalist design with an innovative Light UI, perfect for large-scale applications. The project includes React Router and customized reactstrap, with dependencies regularly updated. It offers over 10 layout variations, ready-to-use applications, a large collection of UI components, and more than 120 unique pages, making it ideal for CRMs, CMSs, Admin Panels, and Analytics dashboards.

Superalgos is a free, open-source crypto trading bot designed for automated Bitcoin and cryptocurrency trading. Users can visually design their trading bots, leveraging an integrated charting system, data-mining, backtesting, paper trading, and multi-server crypto bot deployments. The platform is community-owned and incentivizes contributors with its native Superalgos (SA) Token. It offers comprehensive interactive tutorials to guide users through data mining, strategy backtesting, and live trading sessions. Installation options include developer setups, Docker deployments, Raspberry Pi, and public cloud, catering to various user needs from learning to production trading.

The 3D Designer Agent is an interactive web application built with Streamlit, hosted on Hugging Face Spaces. This AI tool specializes in transforming text prompts into 3D models, specifically generating printable STL files. It integrates OpenAI for natural language understanding and OpenSCAD for 3D modeling, automating the design process from a simple text description. Users can engage with various functionalities to create custom 3D designs without needing extensive CAD software knowledge. This makes it an accessible solution for individuals looking to quickly visualize and produce physical objects from textual ideas, streamlining the initial stages of 3D design and prototyping.

Swift-YouTube-Player is a Swift library designed to facilitate the embedding and control of YouTube videos directly within iOS applications. Utilizing WKWebView, it provides developers with a straightforward way to integrate YouTube content, offering methods to load videos by ID or URL, and control playback with functions like play, pause, stop, and seek. The library also supports handling YouTube's iFrame player events through a delegate, allowing apps to respond to player readiness, state changes, and quality changes. It is an open-source solution available on GitHub, making it accessible for iOS developers looking to add robust video functionality to their projects.

OCRunner is an open-source tool designed for executing Objective-C code as a script, leveraging an Abstract Syntax Tree (AST) interpreter. It serves as an iOS hotfix SDK, enabling dynamic code patching and execution for applications. Key capabilities include generating binary patch files to increase security, reduce patch size, and optimize startup time. OCRunner supports complete Objective-C syntax, with some limitations regarding pre-compilation and certain C language features. It offers various interaction modes, including interactive, file monitoring, and folder monitoring, allowing real-time code execution and updates on iOS devices. The tool also provides performance testing comparisons against other hotfix libraries like JSPatch and Mango, highlighting its efficiency in patch loading speed.