Coding & Development

Gemini Image to Code is a web application leveraging Google's Gemini Pro Vision model to transform visual designs into functional code. Users can upload an image, and the tool processes it to generate corresponding HTML/CSS code that accurately recreates the design. This capability is particularly useful for developers and designers looking to quickly prototype web pages or convert mockups into code. The platform offers a real-time code preview, allowing for immediate visualization of the generated output and facilitating rapid iterations. While the core functionality is image-to-code conversion, the tool operates within the Hugging Face Spaces ecosystem, which offers various pricing tiers for compute resources and storage, indicating that while the application itself might be free to use, the underlying infrastructure costs can vary.

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.

This open-source project, Autonomous-Driving-in-Carla-using-Deep-Reinforcement-Learning, focuses on training an autonomous driving agent using Deep Reinforcement Learning (DRL) within the CARLA urban simulation environment. It specifically employs the Proximal Policy Optimization (PPO) algorithm for learning complex decision-making tasks in a continuous state and action space. A key feature is the integration of a Variational Autoencoder (VAE) to compress high-dimensional observations into a low-dimensional latent space, potentially accelerating the agent's learning process. The project provides an end-to-end solution for autonomous driving, covering CARLA environment setup, VAE implementation, and PPO agent training. It includes pre-trained PPO agents for different CARLA towns and detailed instructions for setting up the project, installing dependencies, and running or training new agents.

This repository offers a comprehensive collection of implementations for popular Reinforcement Learning algorithms, primarily using Python 3, OpenAI Gym, and Tensorflow. It serves as an invaluable learning resource, complementing theoretical materials from "Reinforcement Learning: An Introduction (2nd Edition)" and David Silver's Reinforcement Learning Course. Each folder is structured to correspond with specific chapters or topics, providing learning goals, concept summaries, relevant readings, and practical exercises with solutions. The project covers a wide range of algorithms from Dynamic Programming and Monte Carlo methods to Deep Q-Learning and Policy Gradient methods, making it suitable for both students and researchers in the field.

Restaurant Simulator 3D Bar is an immersive simulation game that puts players in charge of every aspect of running a restaurant. From the initial stages of ordering supplies and managing inventory to the fast-paced environment of kitchen operations and ensuring top-notch customer service, players must master various skills. Success hinges on strategic ingredient selection, optimizing cooking efficiency, and consistently delivering a satisfying dining experience to customers. The game challenges players to grow their establishment, expand their menu, and ultimately build a thriving culinary business.

Object-Detection-Metrics is an open-source toolkit designed to provide comprehensive metrics for evaluating object detection algorithms. It addresses the lack of consensus and standardized implementations for these metrics, offering a reliable solution for researchers and developers. The tool includes implementations for popular metrics such as Intersection Over Union (IOU), Precision, Recall, Precision x Recall curve, and Average Precision (AP), including both 11-point and all-point interpolation methods. It simplifies the evaluation process by accepting ground truth and detected bounding boxes without requiring complex file conversions. The implementation has been carefully compared against official versions, ensuring accurate and trustworthy results for benchmarking different approaches.

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.

splatviz is a comprehensive, open-source Python-based interactive viewer designed for real-time editing and analysis of 3D Gaussian Splatting scenes. Utilizing the pyimgui GUI library, it enables direct manipulation of Gaussian Python objects just before rendering, offering extensive editing and visualization capabilities. Users can view multiple scenes simultaneously, either side-by-side or in a split-screen view, and evaluate Python expressions on the resulting scene. Key features include an Edit Widget for real-time manipulation of Gaussian parameters, an Eval Widget for debugging and visualizing variables, and a Camera Widget with Orbit and WASD modes for flexible scene navigation. It also supports attaching to running 3DGS training sessions for live inspection and editing.

UNeXt-pytorch is the official PyTorch implementation of UNeXt, an MLP-based network specifically designed for rapid medical image segmentation. This tool is ideal for researchers and developers working on medical imaging tasks, particularly those requiring quick processing for point-of-care applications. Based on a MICCAI 2022 paper, it offers a robust and efficient solution for segmenting medical images. The open-source nature of the project, hosted on GitHub, allows for community contributions and flexible integration into existing workflows, providing a strong foundation for advanced medical image analysis.

UniDet is an open-source object detection tool designed to operate across multiple large-scale datasets with an automatically learned unified label space. It was the winning solution of the ECCV 2020 Robust Vision Challenges. The tool offers state-of-the-art performance on datasets such as COCO, Objects365, OpenImages, and Mapillary. A key feature is its ability to predict class labels within this unified space, allowing it to be directly used for testing on novel datasets not included in its training. The repository also provides state-of-the-art baselines for Objects365 and OpenImages. UniDet is built on detectron2, making its inference API familiar to users of that framework.

TSFpaper is an open-source GitHub repository dedicated to providing a curated reading list of academic papers focused on Time Series Forecasting (TSF) and Spatio-Temporal Forecasting (STF). The repository organizes these papers by their respective model types, making it easier for users to navigate and find relevant research. It serves as a valuable resource for researchers, academics, and practitioners who are interested in staying updated with the latest advancements in these specialized forecasting domains. The collection aims to streamline the process of discovering key literature, fostering knowledge sharing within the scientific community.

algotrading is an open-source algorithmic trading framework specifically designed for cryptocurrencies, written in Python. It provides a comprehensive set of tools for building and running trading bots, backtesting strategies, and assisting with trading decisions, including defining stop losses and trailing stop losses. The framework can operate with data directly from crypto exchange APIs, databases, or CSV files, supporting both data-driven and event-driven systems. It offers three operating modes: Realtime for live trading or simulation, Tick-by-tick for detailed strategy testing, and Backtest for evaluating strategies with historical data. Users can define custom entry and exit functions, plot trading data, and log performance for analysis.

vectordb, hosted on GitHub, offers a range of plans tailored for developers, from individuals to large enterprises. The platform provides essential features like unlimited public and private repositories, Dependabot security updates, and CI/CD minutes for automating software development workflows. Users can also host software packages and manage projects with integrated Issues & Projects. For teams, advanced collaboration tools such as repository rules, multiple reviewers in pull requests, and code owners are available. Enterprise plans further enhance security, compliance, and flexible deployment options, including data residency and enterprise managed users, making it suitable for diverse development needs.

Algorithmic Trading with Python is a GitHub repository containing the complete source code for the 2020 book by Chris Conlan. This resource is invaluable for researchers and developers interested in algorithmic trading, providing practical Python implementations of key concepts. It includes stand-alone scripts for performance metrics to evaluate trading strategies, common technical indicators implemented in pure Pandas, and methods for converting these indicators into ternary signals. The repository also features a generic grid search wrapper for numeric optimization, object-oriented building blocks for portfolio simulation, and a generic wrapper for multi-core repeated K-fold cross-validation. Additionally, it offers free-to-use simulated End-of-Day stock data and alternative data streams, making it a comprehensive toolkit for learning and applying algorithmic trading principles.

tensor-house offers a comprehensive toolkit for rapid readiness assessment, exploratory data analysis, and prototyping diverse modeling approaches within enterprise AI/ML/data science projects. It includes Jupyter notebooks and demo AI/ML applications tailored for specific business needs such as marketing, pricing, supply chain, and smart manufacturing. This resource is designed to help developers and data scientists quickly build and deploy intelligent applications, manage and compare prompts, and integrate external tools. It also provides features for automating workflows, managing code changes, and securing applications, making it a versatile platform for developing and deploying AI solutions.

testzeus-hercules, also known as Hercules, is an open-source testing agent designed to streamline the quality assurance process for modern web applications. It supports a comprehensive range of validations including UI, API, Security, Accessibility, and Visual testing, all without the need for extensive coding or ongoing maintenance. Hercules automates the heavy lifting of testing, allowing developers and QA professionals to focus on building and improving applications. This tool is particularly beneficial for teams looking to integrate robust, automated testing into their development workflow, ensuring high-quality and secure applications with reduced manual effort.

Crossle is an engaging daily solitaire crossword puzzle that challenges players to form a connected crossword using 12 given letters. The goal is to drag all letters onto the board, ensuring every word is at least three letters long and all words connect to each other. Words are validated against the Collins Scrabble Words dictionary (SOWPODS), providing a robust and familiar word-checking system. This browser-based game offers a fresh puzzle daily, making it a perfect brain teaser for word game enthusiasts looking for a quick, stimulating challenge. It combines elements of traditional crosswords with the strategic placement of letters, similar to Scrabble, in a solitaire format.

Daily Unfold is an engaging digital puzzle game centered around the concept of paper folding, offering a unique blend of spatial reasoning and brain-teasing fun. Players predict where holes will appear when a folded and hole-punched piece of paper is unfolded. The game provides three new puzzles daily—easy, medium, and hard—and unlocks an Expert mode, a timed version of the hard puzzle with a public leaderboard, after all three are completed. Daily Unfold is free to play, with streaks and progress stored locally in the browser, and is supported by ads shown at natural breaks. It functions as a Progressive Web App, allowing for offline play once loaded.

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.

tensortrade is an open-source reinforcement learning framework specifically engineered for the development, evaluation, and deployment of sophisticated trading agents. It provides a comprehensive environment where users can design and rigorously test AI-driven trading strategies. The framework supports the creation of robust models by allowing for extensive simulation and backtesting, ensuring that strategies are optimized before real-world application. Its open-source nature fosters community collaboration and continuous improvement, making it a valuable tool for researchers and practitioners in quantitative finance and AI.

AI-900 Pocket Study offers smart, data-driven preparation apps designed to help individuals pass competitive certification and licensing exams, including PMP, NCE, and Azure. Trusted by thousands of learners globally, the platform provides accurate and up-to-date content developed with subject matter experts and structured around official exam outlines. Its efficient content engine allows for the rapid launch of new exam prep apps, making high-quality study experiences affordable and accessible. The content is continuously refined for relevance, accuracy, and clarity, ensuring learners can focus on mastering the material and successfully passing their exams.

UnrealEnginePython is an open-source plugin designed to embed a full Python VM (versions 3.x and 2.7) directly into Unreal Engine 4, supporting both the editor and runtime environments. This integration provides easy access to UE4's internal API and reflection system, allowing developers to write other plugins, automate tasks, create unit tests, and implement gameplay elements using Python. It's particularly useful for development pipelines already utilizing Python (e.g., Maya, Blender) to seamlessly integrate Unreal Engine. The plugin also exposes wrappers for third-party libraries like FbxSdk, enabling low-level interaction with FBX files. A unique feature is the ability to change Python code even after a project has been packaged, offering flexibility for modding or post-release updates. It supports Unreal Engine versions up to 4.23 and includes experimental Editor/IDE features.

Turbo is a robust framework designed for LuaJIT 2, aimed at simplifying the development of fast and scalable network applications. It leverages an event-driven, non-blocking, and no-thread design to achieve excellent performance and a minimal footprint, making it suitable for high-load applications and embedded systems. The framework supports various network applications, including HTTP REST APIs, dynamic web pages via templating, and WebSockets. It provides generic building blocks like an I/O loop and IO Stream classes, along with customizable TCP (with SSL) server classes. Turbo is particularly optimized for the HTTP(S) protocol, catering to web and HTTP API developers, while also offering direct integration with existing C libraries for ultimate memory and CPU performance.

Richkware is a modern C++20 framework designed for building educational malware agents, offering a comprehensive, secure, and modular architecture. It enables users to understand malware mechanics and cybersecurity defense strategies through practical application. The framework features core capabilities like modern C++20 standards, memory safety, type safety, and thread safety. For security, it incorporates AES-256-GCM encryption, TLS 1.3 communications, and secure key derivation. System integration includes multi-method persistence, privilege management, and stealth operations. Advanced modules cover file management, keylogging, screenshot capabilities, process management, anti-analysis techniques, and self-deletion. Richkware is intended strictly for educational and research purposes, emphasizing ethical use and disclaiming responsibility for misuse.