Research & Education

Seobility is a comprehensive online SEO software designed to help businesses of all sizes improve their search engine rankings and attract more organic traffic. The platform offers a suite of tools including a website audit to identify technical and SEO-related issues, ranking monitoring to track keyword performance and AI overview visibility, and backlink monitoring for off-page SEO. Users can also perform competitor analysis, conduct keyword research, and utilize content optimization tools. Seobility aims to simplify SEO for beginners and professionals alike, providing clear recommendations and an intuitive interface to manage website performance and fix SEO issues effectively. It also includes uptime monitoring and white label reporting for agencies.

Student Leaderboard is an AI education tool hosted on Hugging Face, designed to help educators track student progress and create engaging educational leaderboards. This application allows users to easily view student ranks, usernames, scores, and timestamps for course unit challenges. A unique feature is the ability to click on a username to reveal the student's code, offering deeper insights into their work. It supports gamified learning and student performance analysis, making it a valuable resource for educational purposes. The tool is available for free, promoting accessibility for educators and students alike.

The Assessment Idea Generator from Blueye is a free AI tool designed to help educators create engaging and standards-aligned assignments. It generates creative assessment ideas tailored to specific subjects, grade levels, and learning objectives. Users can brainstorm ideas for various assessment types, including tests, projects, essays, and presentations, ensuring diverse evaluation strategies. The tool stands out by providing subject-specific and standards-based content, allowing educators to input state-specific standards like California State Standards for perfect curriculum alignment. This online idea generator streamlines workflow, saves time, and offers a wealth of inspiration for teachers seeking to diversify their assignments and enhance student learning.

Maml is an open-source code repository for Model-Agnostic Meta-Learning (MAML), a technique designed for the fast adaptation of deep networks. Developed by cbfinn, this repository provides the foundational code accompanying the paper "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks" (Finn et al., ICML 2017). It specifically includes implementations for few-shot supervised learning domain experiments, covering tasks such as sinusoid regression, Omniglot classification, and MiniImagenet classification. The project is built using Python 2.* or 3.* and TensorFlow v1.0+, making it accessible for researchers and developers working in meta-learning and few-shot learning. Users can access data preparation instructions for Omniglot and MiniImagenet, and detailed usage instructions are available within the `main.py` file.

The deep-representation-learning-book repository hosts the complete source code for the academic book 'Learning Deep Representations of Data Distributions'. It is designed for users who wish to compile the book or individual chapters from scratch, access the code used to generate figures within the book, or contribute to its content, including translations or technical additions. The repository provides detailed instructions for building the book using LaTeX, running Python code examples with `uv`, and even building the associated website. While the book itself can be read online, this repository serves as the foundational resource for those looking to engage with its technical underpinnings or contribute to its ongoing development.

MathSolver.top is an AI-powered platform designed to help students solve math problems, understand concepts, and improve their grades. It features a Solver Mode that provides step-by-step solutions with over 95% accuracy for college and Olympia-level math problems in just 10 seconds. The Tutor Mode uses Socratic questioning to guide users, identify weak areas, and deepen understanding, similar to a real tutor. Additionally, the Check Mode verifies answers and pinpoints mistakes. The platform also includes a Knowledge Graph that breaks down curriculum into bite-sized chunks, linking practice questions to each concept, and offers a daily study path that adapts to individual weak areas, generating personalized questions for practice.

Knowing is an AI-powered learning assistant designed to optimize knowledge retention through advanced memory techniques. It leverages spaced repetition and active recall to help users efficiently learn and remember any subject matter. This tool aims to enhance long-term memory and understanding for various educational and professional needs, making it suitable for individuals looking to improve their learning efficiency and recall across different subjects and professional domains.

face.evoLVe is a high-performance, open-source face recognition library designed for comprehensive face-related analytics and applications. It supports both PaddlePaddle and PyTorch frameworks, offering a wide array of features including face alignment (detection, landmark localization, affine transformation), data processing (augmentation, balancing, normalization), and various backbones (ResNet, IR, IR-SE, ResNeXt, DenseNet, MobileNet). The library also incorporates different loss functions like Softmax, Focal, ArcFace, and Triplet, along with performance-enhancing tricks. It addresses challenges in large-scale face recognition by providing an efficient distributed training schema for multi-GPUs, supporting both backbone and head layers. This makes it ideal for researchers and engineers developing deep face recognition models for practical use.

Open Model Evolution is a platform designed for AI model development and experimentation, hosted as a Hugging Face Space. It provides users with the ability to create and explore interactive dashboards, which can include charts, tables, and various form controls. This tool is particularly useful for tracking the evolution of AI models over time, offering a visual and interactive way to monitor progress and changes. Furthermore, it supports researchers and developers in testing model improvements and experimenting with diverse model architectures, facilitating a deeper understanding and optimization of AI systems. The platform aims to streamline the process of AI model development and analysis within an open-source environment.

Deep-Reinforcement-Learning-Algorithms is a comprehensive open-source repository featuring 32 distinct projects focused on deep reinforcement learning methods. Each project is designed to solve specific environments using various algorithms such as Q-learning, DQN, PPO, DDPG, TD3, SAC, and A2C. The collection is structured to demonstrate how different models interact with diverse environments, with some environments being solved by multiple algorithms for comparative study. All projects are presented as Jupyter notebooks, complete with detailed training logs, making it an invaluable resource for learning, experimenting, and understanding the practical application of deep reinforcement learning concepts. It covers topics from Monte-Carlo methods to advanced Actor-Critic approaches.

LegislatureAI is a free tool designed to help users browse bills and meetings across various cities and counties in the Bay Area and Hawaii. It serves as a valuable resource for staying informed about local government activities and legislative developments. The platform provides access to essential legislative information, making it easier for citizens, researchers, and other interested parties to track local policy. By centralizing this data, LegislatureAI aims to enhance transparency and engagement with local governance.

Scenic is an open-source JAX library developed by Google Research, specifically designed for computer vision research with a strong emphasis on attention-based models. It facilitates the development of classification, segmentation, and detection models across multiple modalities, including images, video, audio, and multimodal combinations. The library provides essential boilerplate code for launching experiments, logging, and profiling, alongside optimized training and evaluation loops. Scenic also includes input pipelines for popular vision datasets and a collection of state-of-the-art models and baselines, some developed within Scenic and others reimplemented. Its philosophy prioritizes rapid prototyping and simplicity, encouraging forking and copy-pasting for customization before upstreaming widely useful functionalities.

Kallo, now operating as Motion, is a cloud-based building intelligence platform designed to eliminate data gridlock in the built world. It connects various building systems into a single, secure, mobile-first platform, providing facility management teams with comprehensive visibility, control, and data from anywhere. Motion layers advanced AI-guided analytics and cloud connectivity on top of existing infrastructure, aggregating data from fragmented and siloed systems. This allows for real-time insights, proactive problem prevention, and optimized operations, leading to decreased energy consumption, improved regulatory compliance, and operational excellence. The platform is vendor-agnostic, supports multi-site data normalization, and offers AI-assisted alarm management, freeing teams from repetitive checks and enabling them to focus on strategy.

The Open LMM Subjective Leaderboard is a specialized platform designed for evaluating the subjective performance of Large Multimodal Models (LMMs). It leverages the VLMEvalKit to generate comprehensive benchmark results, offering a clear and comparative view of various AI models. Users can browse and filter leaderboard data, input specific model names, and select different model sizes and types to refine their search. This tool is crucial for researchers and developers who need to assess and compare LMMs based on subjective criteria, helping them identify top-performing models and understand their strengths and weaknesses in real-world applications. The platform aims to provide detailed evaluation results to foster advancements in the field of multimodal AI.

AI Speak, part of the Monkey English suite, provides a fun and engaging platform for children aged 3-11 to master English pronunciation and communication. The tool utilizes proprietary M-Speak technology, which offers real-time speech recognition and syllable-level scoring to help young learners develop native-like pronunciation. Beyond pronunciation, it aims to build confidence in speaking English through interactive courses and activities. AI Speak is designed to be an accessible and effective supplementary learning product, complementing other Monkey English offerings like Monkey Junior and Monkey Stories, to create a comprehensive English learning pathway for children.

Stark Leaderboard offers a platform for evaluating and comparing AI models on the Semi-structured Retrieval Benchmark (STaRK). Users can submit their model's ranked predictions by uploading a CSV file, which must include essential details such as the method name, team, and dataset used. The application then processes this data to calculate and display key retrieval metrics, including Hit@1, Hit@5, and others. This allows researchers and developers to assess their model's performance against a common benchmark and other submissions, fostering competition and advancement in semi-structured retrieval. The leaderboard is hosted on Hugging Face Spaces, making it accessible for the AI community.

Gradio Screen Recorder is a straightforward tool designed for capturing screen activity and saving it as an MP4 video. Hosted on Hugging Face Spaces, it offers a user-friendly interface where users can initiate and terminate screen recordings with dedicated 'Record Screen' and 'Stop Recording' buttons. This tool is particularly useful for quickly creating video demonstrations, tutorials, or capturing specific on-screen actions without the need for complex software installations. It leverages the Gradio framework, making it accessible and easy to integrate for developers working within that ecosystem. Users may need to grant browser permissions for screen and microphone access to utilize its full functionality.

SWE-Wiki, hosted on Hugging Face Spaces, offers a dynamic platform for tracking GitHub community statistics specifically for Software Engineering (SWE) assistants. The tool features a live leaderboard that ranks these assistants based on their contributions, including the number of wiki edits and membership events they generate. Users can also add their own assistants by providing their GitHub username, fostering a collaborative environment for monitoring performance. This tool is designed to provide insights into the activity and impact of SWE assistants within GitHub communities, making it valuable for developers and teams looking to assess and improve their documentation and community engagement efforts.

Dexa is an innovative platform designed to unlock expert knowledge by providing direct answers from trusted professionals featured in various podcasts. Users can ask anything and get insights from neuroscientists, entrepreneurs, urologists, and other specialists. The platform curates content from popular podcasts like Huberman Lab, Impact Theory, and Mind Pump, allowing users to explore topics ranging from health and wellness to business and personal development. Dexa aims to make expert advice instantly accessible, functioning as a personal 'Ask Me Anything' (AMA) session with a diverse range of thought leaders. It also offers features for podcasters to amplify their impact, engage audiences, and gain insights.

MARS (Modular and Realistic Simulator for Autonomous Driving) is an open-source project designed to provide an instance-aware, modular, and realistic simulation environment for autonomous driving research. It allows users to train and test autonomous vehicle algorithms using various datasets like KITTI and vKITTI2. The simulator supports reconstruction and novel view synthesis tasks, offering pre-trained models and the flexibility to train from scratch with custom data. Its modular framework enables combining different architectures for various nodes, such as using Nerfacto for background models. MARS is built upon Nerfstudio and requires an NVIDIA GPU with CUDA for installation and operation.

LazyProgrammer.me provides a comprehensive platform for individuals aiming to build careers in machine learning and data science. The service offers a variety of deep learning and artificial intelligence courses, covering advanced topics such as Generative AI, Transformers for Natural Language Processing (NLP), and time series analysis. It is specifically designed to equip learners with the necessary skills and knowledge to become proficient professionals in these fields. Additionally, LazyProgrammer.me offers free introductory content through its newsletter, allowing prospective students to sample the educational material.

Street Gaussians is an open-source project presented at ECCV 2024, focusing on modeling dynamic urban scenes using Gaussian Splatting. This tool provides a framework for researchers and developers to reconstruct complex, moving urban environments from video data. It includes functionalities for data preparation, such as converting Waymo Open Dataset, generating LiDAR depth, and creating sky masks. Users can configure parameters based on 3D Gaussian Splatting, train models, render scenes, and visualize results. The project offers scripts for training and rendering on example and experimental Waymo scenes, making it a valuable resource for advancing research in dynamic 3D scene reconstruction.

The Open LMM Reasoning Leaderboard is a platform designed to assess and compare the reasoning capabilities of Large Multimodal Models (LMMs). Hosted on Hugging Face Spaces, it provides a comprehensive overview of different LMMs, allowing users to filter and sort models based on criteria such as model name, size, and type. Researchers and developers can customize evaluation dimensions to gain specific insights into model performance metrics. This tool is invaluable for identifying top-performing LMMs and understanding their strengths and weaknesses in various reasoning tasks, contributing to advancements in AI model development and benchmarking.

Depth To 3D Print is a Hugging Face Space that allows users to transform 16-bit PNG depth maps into 3D-printable models. This tool provides a straightforward way to convert digital depth information into physical objects. Users have the option to include an RGB image to add color details to their 3D models, enhancing the visual fidelity of the final print. Key customization features include adjusting the plane depth, controlling the level of embossing, and setting the overall size of the generated 3D model. This flexibility makes it suitable for various applications, from rapid prototyping and educational projects to hobbyist endeavors, enabling the creation of customized physical objects from digital inputs.