Coding & Development

MCP Showcase provides a platform for auto-generating live, interactive MCP playgrounds for your MCP server, enabling developers and decision-makers to explore, chat with, and integrate APIs quickly. It aims to accelerate developer onboarding by offering real-time feedback and interactive documentation, making it easier to understand MCP APIs than with static documents. The tool also helps bridge the buyer-developer gap by allowing non-technical stakeholders to "see it work," thereby shrinking the sales funnel. Product teams can gain real-time insights into how prospects use the playground, facilitating faster feature refinement and quality improvements. Key features include a launch-ready MCP sandbox with mocked data, SSE and streamable HTTP support, and automatic MCP introspection. It also offers interactive documentation and an MCP chat connected to the tools, along with sample chat history for better understanding.

robomimic is a comprehensive, modular framework designed for robot learning from demonstration. It offers a wide array of demonstration datasets specifically collected for robot manipulation domains, alongside robust offline learning algorithms to effectively learn from these datasets. The primary goal of robomimic is to enhance the accessibility and reproducibility of robot learning research, enabling researchers and practitioners to benchmark tasks and algorithms consistently. This framework facilitates the development of the next generation of robot learning algorithms, supporting features like Diffusion Policy, multi-dataset training, language-conditioned policies, and integration with robosuite and DeepMind MuJoCo bindings. It also supports various observation modalities, pre-trained image representations, and logging with wandb.

SimpleVLA-RL is an open-source reinforcement learning (RL) framework designed to efficiently scale the training of Vision-Language-Action (VLA) models. It provides an end-to-end RL pipeline built on veRL, incorporating VLA-specific optimizations such as multi-environment parallel rendering for accelerated trajectory sampling. The framework leverages state-of-the-art infrastructure for efficient distributed training, hybrid communication patterns, and optimized memory management. SimpleVLA-RL supports various VLA models like OpenVLA and OpenVLA-OFT, and benchmarks including LIBERO and RoboTwin 1.0/2.0. It emphasizes minimal reward engineering with binary outcome rewards and includes exploration strategies like dynamic sampling and adaptive clipping. The modular architecture allows for easy integration of new VLA models, benchmarks, and RL algorithms, making it a powerful tool for researchers and developers in the field.

Semantic Segmentation Editor is an open-source, web-based labeling tool designed for creating AI training datasets from both 2D bitmap images and 3D point clouds. Developed by Hitachi Automotive And Industry Lab, it is particularly useful for autonomous driving research. The tool supports various image formats like JPG and PNG, and point cloud formats including ASCII, Binary, and Binary compressed. It offers a comprehensive set of tools for polygon drawing, magic tool for contrast detection, manipulation, cutting/expanding, and contiguous polygon creation for bitmap images. For point clouds, it provides functionalities for rotation, zooming, and point selection. The editor is built using Meteor, React, Paper.js, and three.js, and can be run via Docker Compose or from source.

SensorsCalibration, also known as OpenCalib, is a comprehensive open-source toolbox designed for multi-sensor calibration in autonomous driving applications. Accurate sensor calibration is a foundational requirement for any autonomous system, enabling precise sensor fusion and subsequent processing steps like obstacle detection, localization, mapping, and control. This toolbox addresses the critical need for reliable calibration of various sensors, including IMU, LiDAR, Camera, and Radar. It offers both road scene-based calibration tools for parameters like camera intrinsics, lidar2imu, and surround-camera, as well as factory calibration tools supporting different board types such as chessboard, circle board, and Apriltag board. Additionally, it includes SensorX2car for online calibration of sensor-to-car coordinate systems.

SelfExSR is a research code implementation for single image super-resolution, based on the paper "Single Image Super-Resolution from Transformed Self-Exemplars" (CVPR 2015). This algorithm stands out by achieving state-of-the-art performance in image super-resolution without requiring any external training dataset, complex feature extraction, or complicated learning algorithms. It operates by learning from transformed self-exemplars within the image itself. The repository provides the MATLAB source code, testing images for various datasets (Set5, Set14, Urban 100, BSD 100, Sun-Hays 80), and precomputed results for comparison with other state-of-the-art methods. While designed as educational code and not optimized for speed, users can adjust iteration numbers for a trade-off between speed and visual quality.

SuGaR (Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering) is a PyTorch implementation designed to extract precise and extremely fast meshes from 3D Gaussian Splatting reconstructions. It introduces a regularization term that aligns 3D Gaussians with the scene's surface, allowing for efficient point sampling and mesh extraction using Poisson reconstruction. This method preserves details and is significantly faster than traditional Neural SDFs. SuGaR also offers an optional refinement strategy that binds Gaussians to the mesh surface, enabling joint optimization for easy editing, sculpting, rigging, and animation in traditional software like Blender, Unity, or Unreal Engine. This allows users to retrieve an editable mesh for realistic rendering within minutes, offering superior rendering quality compared to state-of-the-art methods.

spring-boot-rest-example is a sample Java/Maven/Spring Boot application designed to serve as a starter for building microservices. It implements REST APIs using Spring Boot, an in-memory H2 database, and an embedded Tomcat server. The project demonstrates full integration with the Spring Framework, including inversion of control and dependency injection. It comes with built-in health checks, metrics, and other operational endpoints via the Actuator module. The application also showcases Swagger2 for API documentation, Spring Data JPA/Hibernate for data persistence, and MockMVC for testing. It's easily configurable to work with other relational databases like MySQL or PostgreSQL.

This open-source project provides a Tensorflow 2.x based implementation of state-of-the-art models for single image super-resolution, including Enhanced Deep Residual Networks (EDSR), Wide Activation for Efficient and Accurate Image Super-Resolution (WDSR), and Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (SRGAN). It offers a high-level training API, enabling users to train models as described in the respective papers and fine-tune EDSR and WDSR models within an SRGAN context. The tool includes a DIV2K data provider for automatic dataset downloads and offers pre-trained weights for quick setup. It's ideal for developers and researchers working on image processing and computer vision tasks.

SphereFace offers a comprehensive open-source implementation of the SphereFace algorithm, a deep hypersphere embedding method for face recognition. This tool provides a full pipeline covering face detection, alignment, and recognition, making it valuable for researchers and developers in computer vision. It includes detailed instructions for installation and usage, demonstrating how to train models on datasets like CASIA-WebFace and evaluate performance on LFW. The repository also features various network architectures, including SphereFace-20, and highlights its state-of-the-art verification performance in challenges like MegaFace. Additionally, it provides insights into the underlying mathematical concepts and practical considerations for training, such as gradient normalization and convergence difficulties, along with links to third-party re-implementations and related angular margin learning resources.

SSL4MIS (Semi Supervised Learning for Medical Image Segmentation) is a comprehensive resource for researchers and developers focusing on medical image analysis. It offers a curated collection of literature reviews and practical code implementations for semi-supervised learning techniques. The repository includes re-implementations of various semi-supervised methods such as Mean Teacher, Entropy Minimization, and FixMatch, adapted for medical image segmentation. Additionally, it supports a range of 2D and 3D backbone networks like UNet, nnUNet, and Swin-UNet. This project aims to establish a benchmark for semi-supervised medical image segmentation, fostering easier evaluation and fair comparison within the medical image computing community. It also covers active learning and source-free domain adaptation for medical image analysis.

synthetic-computer-vision is a GitHub repository dedicated to tracking and organizing resources related to the use of synthetic images in computer vision research. It serves as a valuable hub for researchers, offering a curated list of synthetic datasets such as SunCG, Minos, and Synthia, alongside various tools like AirSim, CARLA, and UnrealCV. The repository also includes a collection of relevant academic publications, categorized by year, with links to papers, code, and project pages. Users are encouraged to contribute by adding missing works or updating existing information through pull requests, making it a collaborative and up-to-date resource for the computer vision community.

tensorflow-yolo offers a TensorFlow-based implementation of the YOLO (You Only Look Once) real-time object detection system. This open-source project allows developers and researchers to train and test their own object detection models using TensorFlow 1.0. The repository includes instructions for downloading pre-trained models, setting up training data using Pascal-VOC2007, and converting custom data to the required text_record format. It provides the necessary tools and scripts for preprocessing data, configuring training parameters, and running demonstrations, making it a valuable resource for those working with real-time object detection.

tmrl is a comprehensive open-source Python framework for training Deep Reinforcement Learning (RL) AIs in real-time applications, such as robotics, video games, and high-frequency control. It features a distributed architecture, enabling secure remote training and fine-grained customizability. The framework comes with a readily implemented example pipeline for the TrackMania 2020 racing video game, allowing users to train policies with state-of-the-art algorithms like Soft Actor-Critic (SAC) and Randomized Ensembled Double Q-Learning (REDQ). tmrl also provides a Gymnasium environment for TrackMania, making it easy to integrate into existing training frameworks. It supports both vision-based (CNN for raw images) and simpler rangefinder (MLP for LIDAR) observations, and offers analog control via a virtual gamepad.

YOLOv13 is an open-source implementation for real-time object detection, leveraging hypergraph-enhanced adaptive visual perception. It introduces HyperACE for exploring high-order correlations between pixels in multi-scale feature maps and FullPAD for fine-grained information flow and representational synergy across the entire detection pipeline. The tool also incorporates model lightweighting via DS-based Blocks, replacing large-kernel convolutions with depthwise separable convolutions for faster inference without sacrificing accuracy. YOLOv13 is available in Nano, Small, Large, and X-Large variants, offering cutting-edge performance and efficiency for various object detection tasks. It supports deployment on platforms like Huawei Ascend and Rockchip, and includes a FastAPI REST API.

MessageDisplayKit is an open-source framework designed to help developers build instant messaging (IM) applications with features akin to WeChat. It supports a wide range of messaging capabilities, including sending text, pictures, audio, video, and location messages. Beyond core messaging, the kit also includes functionalities for managing local address books, sharing moments with friends, and other interactive social features like 'drift bottle' and 'shake for new friends'. The project is highly customizable, supports arbitrary message sizes, and includes data detectors for recognizing phone numbers, links, and dates. It is compatible with iPhone and iPad, Xcode6 or later, and iOS 6.0+, making it a valuable learning resource and a foundation for IM app development.

VTIL-Core, standing for Virtual-machine Translation Intermediate Language, is a set of tools built around an optimizing compiler. Its primary purpose is binary de-obfuscation and de-virtualization, making it a valuable asset for reverse engineering and security research. Unlike other optimizing compilers such as LLVM, VTIL features an extremely versatile Intermediate Language (IL) that simplifies lifting from various architectures, including stack machines. It maintains the native ISA's concepts like the stack, physical registers, and non-SSA architecture of a general-purpose CPU, allowing native instructions to be embedded within the IL stream and physical registers to be addressed freely. VTIL also facilitates code emission back into native formats at any virtual address without file format constraints. This repository contains the core components of the VTIL Project, with further documentation and an organization website planned for its initial release.

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.

awesome-offline-rl is a comprehensive, open-source collection of research and review papers specifically focused on offline reinforcement learning (offline-rl) algorithms. Maintained by researchers from Cornell University and Hanjuku-kaso Co., Ltd., this repository serves as a valuable index for anyone delving into the field. It organizes papers into categories such as Review/Survey/Position Papers, Offline RL: Theory/Methods, Benchmarks/Experiments, and Applications, as well as Off-Policy Evaluation and Learning. The resource also lists open-source software, implementations, blogs, podcasts, workshops, tutorials, and talks, making it a central hub for academic and practical insights into offline RL. Contributions are welcomed to expand and maintain this growing index.

TIDE (A General Toolbox for Identifying Object Detection Errors) is an easy-to-use, open-source Python package designed to compute and evaluate the impact of object detection and instance segmentation errors on overall model performance. It serves as a drop-in replacement for the COCO Evaluation toolkit, offering functionalities to summarize results in console tables and generate summary plots for error analysis. TIDE supports various datasets including COCO, LVIS, Pascal, and Cityscapes, with plans for more detailed documentation on custom database drivers. The tool is ideal for researchers and developers working on computer vision tasks who need to deeply understand and improve their object detection and segmentation models.

talking-head-anime-2-demo provides demo programs for the "Talking Head Anime from a Single Image 2: More Expressive" project. It features a manual poser for manipulating facial expressions and head rotation of anime characters via a graphical user interface or Jupyter notebook. Additionally, an iFacialMocap puppeteer allows users to transfer their own facial motion, captured by an iOS device, to an anime character image. The tool requires a powerful Nvidia GPU and specific software environments, including Python and PyTorch. It's designed for those interested in AI-driven animation and character manipulation, offering a hands-on approach to exploring expressive anime head movements.

Opus-MT is an open-source project offering neural machine translation models and web services, built upon Marian-NMT and trained using OPUS data. It features SentencePiece-based segmentation and guided alignment for its models. The platform provides pre-trained, downloadable translation models under a CC-BY 4.0 license, including those from the Tatoeba translation challenge. Users can set up a Tornado-based web application with a UI and API for multiple language pairs, or a simpler websocket service. While it includes scripts for training models, these are currently optimized for the University of Helsinki and CSC computing environments. Opus-MT is ideal for researchers and developers looking to integrate or build upon open translation services.

awesome-NeRF-and-3DGS-SLAM is a curated, open-source repository offering a comprehensive list of resources focused on Implicit Representations, Neural Radiance Fields (NeRF), and 3D Gaussian Splatting papers within the SLAM (Simultaneous Localization and Mapping) and Robotics domains. This valuable resource includes direct links to papers, videos, code repositories, and related websites, making it an essential reference for researchers and academics. It covers general NeRF models, survey papers, benchmarks, tutorials, and specific applications in Visual-SLAM, Lidar-SLAM, and Multimodal-SLAM for both NeRF and 3D Gaussian Splatting. The repository also delves into robotics applications such as manipulation, reinforcement learning, planning, navigation, localization, and re-localization, providing a centralized hub for cutting-edge research in these fields.

Nerfies is an open-source project that hosts the source code for the Nerfies website, which is dedicated to Deformable Neural Radiance Fields. This repository serves as a valuable resource for researchers and developers working with neural radiance fields, particularly those interested in creating dynamic and deformable 3D scenes from 2D images. The project is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License, encouraging collaboration and further development within the AI community. It provides the foundational code for understanding and implementing Nerfies, making it an essential reference for advancing research in computer vision and graphics.