Research & Education

Minitron is an AI chatbot tool developed by NVIDIA and hosted on Hugging Face Spaces. The current status indicates a runtime error, preventing the application from functioning. The error message suggests an issue with NVIDIA driver detection, indicating that the application requires an NVIDIA GPU and a properly installed driver to run. While the intended functionality is an AI chatbot, the current state of the application on Hugging Face Spaces is non-operational due to this technical issue. Users attempting to access the tool will encounter this error, preventing any interaction with the chatbot features.

Edvice is an AI-driven platform designed to empower students in their career exploration and decision-making process. It offers expert guidance and personalized assessments to help individuals discover their ideal career path. The platform assists students in making informed choices by providing tailored career paths, recommendations for skill development, and insights into both STEM and non-STEM fields. Beyond career guidance, Edvice also supports students with scholarship and admissions assistance, aiming to build a mission-driven community of certified career counselors.

MedTechLabs is a long-term investment initiated in 2018, managed by KTH, Karolinska Institutet, and Region Stockholm, dedicated to advancing medical technology. The center focuses on needs-motivated, clinically-based research and education, aiming to contribute to the development of medical technology from both national and international perspectives. Their research spans various areas of advanced medical technology, including prevention, diagnosis, and therapy, with specific programs like Spectral CT and Endovascular techniques. MedTechLabs also fosters a community of over 20 researchers from KTH and KI, actively publishing research results and engaging in seminars and interviews to share their findings and impact on healthcare.

cornell-cs5785-2025-applied-ml is a GitHub repository containing comprehensive lecture materials for Cornell's CS5785 Applied Machine Learning course, specifically for the Fall 2025 edition. This resource includes executable course notes and slides, making it a valuable asset for both students enrolled in the course and educators looking for high-quality machine learning content. The repository is organized with dedicated folders for slides and lecture notes, along with a `requirements.txt` file to ensure users can easily set up their virtual environments and run the provided content. It serves as a practical, hands-on learning resource for applied machine learning concepts.

DeepScan: AI Search Assistant is an iOS mobile application developed by Plus Apps, designed to provide efficient information discovery through AI-powered deep search technology. The tool explores, analyzes, and summarizes publicly available data from reliable sources, presenting insights in a clear and structured format. It is ideal for quick research, fact verification, and uncovering meaningful insights on the go. Plus Apps, the creator, focuses on building highly scalable mobile apps in Lifestyle, Entertainment, Utilities, and Productivity categories, with products reaching over 1 million users worldwide.

Tapotons is a French online platform dedicated to teaching dactylography, enabling users to type efficiently and without errors. It provides comprehensive courses and varied exercises designed to help individuals master ten-finger typing, primarily for AZERTY keyboards. The platform includes features like typing speed tests (WPM) and detailed statistics to track progress. A unique "Cibler vos erreurs" (Target your errors) option uses AI to generate personalized exercises based on a user's most frequent typing mistakes, ensuring targeted improvement. Tapotons aims to boost daily productivity for students, developers, secretaries, and anyone who frequently uses a computer.

Savantic AI Lab operates as a full-stack AI lab, combining deep scientific expertise with real-world application to develop scalable, sustainable, and transformative AI solutions. With over two decades of innovation, they focus on "Meaningful AI" to drive sustainable growth, measurable impact, and long-term value across various industries. Their services range from research to real-world implementation, helping organizations turn AI potential into business impact. Savantic emphasizes ethical and responsible AI, ensuring solutions prioritize sustainability and deliver tangible results. They work with diverse sectors including Retail & Logistics, Medtech & Life Sciences, Industry & Energy, and Public Transportation & Municipalities.

Solohustller is an online learning platform dedicated to empowering ambitious individuals to master new skills and advance their careers. The platform is built to support learners in transforming their professional trajectories through educational resources. It focuses on providing a structured environment for skill development, aiming to help users achieve their learning and career goals. While specific features are not detailed on the public-facing login page, the overarching mission is to facilitate growth and achievement for its user base.

geospatial-machine-learning is an open-source GitHub repository offering a comprehensive, curated list of resources for Machine Learning in Geospatial Data Science. This valuable resource is organized into several key categories, including code projects and workflows, datasets, academic papers, relevant books, educational courses, and a directory of companies active in the geospatial machine learning space. It serves as a central hub for anyone looking to explore or deepen their understanding of this interdisciplinary field, providing direct links to a wide array of materials for learning and practical application.

Calculator Star is an intelligent calculator app designed for iOS that combines traditional calculator functionality with advanced voice-powered AI assistance. Users can ask math questions in plain English, such as "If I work 8 hours a day at $25 per hour, how much will I earn in a week?", and receive answers with step-by-step explanations. The app handles a wide range of calculations including basic arithmetic, percentages, time calculations, currency conversions, unit conversions, and word problems. It also features a calculation history, allowing users to review previous problems and their logic. While basic functions work offline, the voice AI features require an internet connection for processing. Privacy is a priority, with voice processing done securely and recordings never stored.

Awesome-RL-VLA is a comprehensive GitHub repository dedicated to Reinforcement Learning of Vision-Language-Action (RL-VLA) models for robotic manipulation. It serves as a curated list of academic papers and resources, offering a detailed overview of various training paradigms, methodologies, and state-of-the-art approaches in the field. The repository categorizes RL-VLA research into Offline, Online, and Test-time RL-VLA, detailing key research directions and adaptation mechanisms for each. It also includes a substantial paper collection with a legend for easy navigation, useful resources covering action optimization, base VLA models, datasets, benchmarks, frameworks, and tools. This resource is invaluable for researchers and academics looking to explore or contribute to the rapidly evolving domain of AI-powered robotic manipulation.

Awesome-RGBT-Fusion is a comprehensive, open-source collection dedicated to deep learning-based RGB-T fusion methods, codes, and datasets. This resource is invaluable for researchers and developers working in computer vision, particularly those interested in multispectral data. The collection covers key areas such as Multispectral Pedestrian Detection, RGB-T Aerial Object Detection, RGB-T Semantic Segmentation, RGB-T Crowd Counting, and RGB-T Fusion Tracking. It provides access to various datasets, tools, and a curated list of academic papers with links to PDFs and code repositories. The project actively encourages contributions, making it a dynamic and evolving hub for advancements in RGB-T fusion.

Vespa is an open-source platform designed for AI and data processing, specializing in online search, recommendation, and personalization use cases. It allows users to search, make inferences, and organize various data types including vectors, tensors, text, and structured data, all at serving time and at any scale. The platform is built for high availability and performance, capable of handling hundreds of thousands of queries per second. Vespa has been in development for many years and is used in large internet services. It provides APIs for interfacing and supports the development of custom Java components. Users can deploy applications to the Vespa cloud service or run their own instances, with comprehensive documentation and a community Slack channel available for support.

Coursera-Machine-Learning-Stanford is an open-source repository offering solutions to all programming assignments and quizzes from Andrew Ng's renowned Machine Learning course on Coursera. This resource is designed to give users a broad understanding of machine learning algorithms. It encourages students to attempt assignments independently first, providing the solutions as a reference when they encounter difficulties. The repository includes content for all weeks of the course, from Week 1 to Week 11, covering various machine learning concepts. It's a valuable aid for students looking to deepen their knowledge and practice in the field of machine learning.

Janus Pro WebGPU is an innovative in-browser AI tool designed for unified multimodal understanding and generation. Hosted on Hugging Face Spaces, it offers a unique capability to render LaTeX math expressions as crisp, high-quality graphics directly within your web browser using WebGPU technology. This eliminates the need for external rendering tools or complex setups, providing an instant visual feedback loop for mathematical notation. The tool is part of the WebML Community's efforts to bring advanced AI capabilities to the web, making it accessible for experimentation and learning. Its focus on in-browser processing highlights a commitment to efficient and client-side AI applications.

slime is an advanced post-training framework designed for Reinforcement Learning (RL) scaling, specifically tailored for large language models. It achieves high-performance training by seamlessly integrating Megatron with SGLang, enabling efficient and scalable operations. The framework supports flexible data generation through custom data workflows, allowing users to adapt to various training requirements. slime facilitates efficient training across different modes, making it a versatile solution for developers and researchers working with large language models and RL applications. Its focus on performance and flexibility makes it suitable for complex AI development tasks.

awesome-segment-anything is a comprehensive repository dedicated to tracking and summarizing research progress related to Segment Anything in the field of Computer Vision. It provides a curated list of papers and projects, covering various applications such as medical image segmentation, inpainting, camouflaged object detection, video frame interpolation, and robotics. The repository is continuously updated with the latest breakthroughs, including new models like SAM 3 and EfficientSAM. It serves as a valuable resource for researchers and academics looking to stay informed about developments and applications of Segment Anything.

Falcon-H1-Tiny offers a series of compact language models designed to push the boundaries of AI capabilities at a small scale. These models are available on Hugging Face Spaces and are ideal for research and experimentation. Users can input prompts and receive generated responses from these lightweight but capable AI models, making them suitable for various applications including research paper analysis, data visualization, and the development of small-scale AI applications. The focus on models with 100 million parameters or less makes them particularly efficient and accessible for developers and researchers working with limited resources.

Python-Machine-Learning-Second-Edition is a comprehensive code repository accompanying the second edition of the book published by Packt. This resource is designed to support readers in their journey to learn and implement machine learning models. It includes all the necessary project files, allowing users to follow along with the book's examples and exercises. The content specifically focuses on practical applications of machine learning using popular libraries such as TensorFlow and scikit-learn, making it an invaluable asset for those looking to gain hands-on experience in the field. It serves as a practical companion for understanding and applying machine learning concepts.

AI Huberman is an AI-powered tool designed to simplify and provide insights from the extensive Huberman Lab podcast content. Users can ask science and health-related questions and receive AI-driven answers derived directly from podcast transcripts. The platform also offers timestamped links to relevant YouTube videos, allowing users to delve deeper into specific topics. Additionally, it provides personalized episode recommendations based on user queries and interests. This tool is ideal for anyone looking to quickly access and understand complex neuroscience and health information discussed in the Huberman Lab podcast, presented in clear, jargon-free language.

RLzoo is a comprehensive open-source reinforcement learning zoo designed for simple usage, implemented with TensorFlow 2.0 and leveraging the neural network layer APIs of TensorLayer2.0+. It offers a hands-on approach for reinforcement learning practices and benchmarks, supporting basic toy-tests like OpenAI Gym and DeepMind Control Suite with minimal configuration. Additionally, RLzoo supports robot learning environments such as RLBench. The platform provides both implicit and explicit configuration interfaces for running learning algorithms, making it flexible and convenient for users. It also supports distributed training across multiple computational nodes using the Kungfu package, catering to more realistic and large-scale scenarios.

Learna is an AI-powered English tutor developed by Codeway, designed to make language learning personal, fun, and effective. It speaks, listens, and adapts like a human teacher, utilizing advanced technologies such as talking head tech, Gaussian image generation, and natural language understanding. With over 15 million downloads and a 4.5 rating, Learna offers an engaging and interactive way for users to improve their English proficiency through AI-driven conversations and adaptive learning experiences. It is available on both Google Play and the App Store.

DeepFAS serves as an official repository for "Deep Learning for Face Anti-Spoofing: A Survey," offering a comprehensive review of recent advancements in deep learning techniques for face anti-spoofing (FAS). The resource covers various methodologies, including hybrid, pure deep learning, and generalized learning approaches for monocular RGB FAS, as well as multi-modal and specialized sensor-based FAS. It meticulously details publicly available datasets, outlining their characteristics, setup, and attack types, alongside classical evaluation protocols. Researchers and developers can leverage this tool to understand the landscape of FAS, compare different methods, and identify suitable datasets for their work, making it an invaluable academic resource.

Paloma is an innovative education tool designed to empower Title I districts and Charter Management Organizations (CMOs) by transforming parents into effective teaching partners. The platform offers a mobile web-app that facilitates daily, short-burst tutoring sessions in reading and math, personalized to each student's learning needs and interests. Paloma texts families with daily sessions, structured with 'I do, we do, you do' building blocks, and creates decodable books and math story problems where kids are protagonists. This approach aims to unlock up to 1,000 extra hours of one-on-one instructional time per classroom annually, significantly boosting student academic success and attendance while saving teachers valuable time.