Best AI tools for< study >

StudyFetch is a revolutionary new platform that allows you to upload your course materials and create interactive study sets. You can study with an AI tutor, create flashcards, generate notes, take practice tests, and more. StudyFetch's AI, Spark.e, utilizes advanced machine learning algorithms to offer a tailored, interactive tutoring experience. Once you upload your study materials, Spark.e scans and indexes them, making the content searchable and accessible for real-time queries.

Hebrew Bible Study is a platform dedicated to providing resources and tools for individuals interested in deepening their understanding of the Hebrew Bible. The website offers a wide range of study materials, including translations, commentaries, and historical context to help users explore the text in depth. Whether you are a scholar, student, or simply curious about the ancient scriptures, Hebrew Bible Study aims to facilitate a meaningful and insightful exploration of this important religious text.

Study Plannr is an AI-powered application that generates personalized and smart study plans using ChatGPT. It optimizes revision by considering subjects, topics, and assessment dates. The tool is designed to help parents, educators, K12 students, and learners studying for certification to create customized study plans for efficient learning.

Studygenie is an AI-powered learning platform that helps students study for tests 10x faster. It uses AI to generate personalized quizzes, track retention, and provide insightful explanations. Studygenie is simple to use and trusted by students at top universities.

StudyCards App is an AI-powered flashcard maker that helps users memorize and study by swiping cards or listening to them on repeat. It features an embedded text-to-speech engine, custom deck creation with AI assistance, and the ability to upload flashcards to smartwatches. The app is designed to aid people with low vision, ADHD, dyslexia, and reading disorders, offering a convenient and accessible learning experience.

StudyNinja is an AI-powered study tool that helps students manage their assignments, collaborate with classmates, and improve their academic performance. It offers a range of features including personalized study goals, assignment management, task management, study notes, and a collaborative workspace. StudyNinja also has an AI tutor that can answer questions and provide personalized learning support.

StudyShift is a revolutionary collaborative productivity tool specifically designed for students. It is made to make students more efficient and productive. It works everywhere, whether on a PC, tablet, or mobile device. With StudyShift, students can set their study goals and track their progress efficiently. They can also manage their assignments, create documents, collaborate with others, and manage their tasks. StudyShift also includes an AI tutor that can answer questions on any subject. It is a revolutionary suite of productivity tools and apps for learners that works everywhere, whether on a PC, tablet, or mobile device.

StudyRaga is an AI-integrated productivity software designed to help students manage their academic responsibilities. It offers a centralized platform for managing tasks, schedules, and resources, enabling students to stay organized and focused. With features such as smart task management, progress tracking, group assignments, study notes, and interactive study tools, StudyRaga aims to enhance student productivity and collaboration.

StudyX is an all-in-one AI learning assistant that provides students with a variety of tools to help them learn more effectively. These tools include a chat-based AI assistant, homework help, research paper help, and writing assistance. StudyX is available as a web app, mobile app, and browser extension, making it easy to use anywhere you learn.

Interview.study is an AI-powered interview preparation platform that helps candidates practice real interview questions asked by top companies. The platform provides users with instant feedback on their responses, helping them identify areas for improvement and develop stronger answers. Interview.study also offers a variety of features to help candidates prepare for their interviews, including a database of interview questions, a mock interview tool, and a resume builder.

The US Citizenship Practice Exam website is an AI-powered platform designed to help users study for the US naturalization test. The site offers a practice exam with 100 questions, all graded by an AI created by OpenAI. Users need to answer 6 out of 10 questions correctly to pass the actual test, which is an oral exam conducted by a USCIS officer. The website is created by Evan Conrad, who works on AI in San Francisco. It is open source on Github, and users can report bugs via Twitter. The project was inspired by various civic initiatives supporting immigration and civic progress.

Quizgecko is an AI-powered question generator and study platform that helps users create quizzes, tests, and flashcards automatically from their content using AI technology. It offers features like automatic quiz creation, flashcard generation, spaced repetition for optimized learning, personalized feedback through AI grading, and mobile apps for studying on-the-go. The platform is trusted by students, educators, and businesses worldwide for personalized learning, efficient content creation, and improved study outcomes.

OmniSets is an ultimate flashcard tool that helps students learn efficiently. It combines advanced machine learning algorithms and incorporates them throughout the platform. OmniSets offers various modes, from spaced repetition to matching games and AI tools, to make studying easier. Users can create and organize study sets, search from over 250k+ public study sets, and take advantage of the spell tool to write terms and definitions. OmniSets is a community-driven platform that values user feedback and is constantly updating and improving its features.

Aceify.ai is a tool designed to help students become more productive in finding solutions to problems they encounter in their academic pursuits. It offers features such as AutoSelect, which automatically selects the answer to a question, and Snap & Solve, which allows users to capture visual content and get instant answers. Aceify.ai is compatible with various platforms and websites, making it a versatile tool for students.

KardsAI is an AI-powered flashcard maker mobile app designed to help users study smarter and more efficiently. It allows users to transform any PDF, text, note, or prompt into flashcards in a snap. With advanced AI features and a mobile-first approach, KardsAI offers exceptional quality and unique features for personalized learning experiences. The app saves valuable time by automating flashcard creation and incorporates a spaced repetition algorithm to help users remember information longer. KardsAI is suitable for students, language learners, knowledge seekers, and trivia enthusiasts, offering a wide range of functionalities to enhance learning and memory retention.

KardsAI is a mobile flashcard app that uses AI to help you learn faster and more efficiently. With KardsAI, you can easily convert any text, PDF, or note into flashcards in seconds. You can also use KardsAI to generate flashcards from any topic imaginable, or explore thousands of pre-made decks. KardsAI's spaced repetition algorithm helps you to remember information longer, and its progress tracking features help you to stay motivated. KardsAI is available for free on iOS and Android.

Monic.ai is an all-in-one study space that provides students with the tools they need to learn, prepare, and upskill. With Monic.ai, students can upload any study material, create summaries, quizzes, and flashcards in seconds, and practice with scientifically proven methods—all in one powerful platform. Monic.ai is used by over 100,000 students globally, and has been featured in The New York Times, The Wall Street Journal, and Forbes.

LectoMate is an AI-powered study companion that helps students and educators create comprehensive study materials from lecture notes. With its intuitive design and advanced AI capabilities, LectoMate streamlines the educational journey by transforming lecture notes into summarized key points, study guides, mind maps, flashcards, and tailored questions. By harnessing the power of AI, LectoMate enhances teaching and learning, allowing users to create customized study materials in minutes.

FlashcardX is an AI-powered study tool that helps users create study materials and ace their tests. It uses AI to generate flashcards from text, articles, and textbooks, and provides users with a variety of study modes to help them learn and retain information. FlashcardX also has a community feature that allows users to share and collaborate on flashcards, and a progress tracking feature that helps users stay motivated and on track.

Penseum is an AI-powered study partner that helps students learn more effectively. It can instantly transform notes, PDFs, videos, and PowerPoints into custom study guides, flashcards, questions & answers, and personalized AI chat. Penseum also provides a study roadmap to help students learn one step at a time. With Penseum, students can say goodbye to endless searching for questions and answers and dive straight into practicing. They can also connect with their own AI tutor and get answers at any time, even when their professor is unavailable. Penseum is the perfect study buddy for students who want to learn more efficiently and effectively.

KanTV is an open-source project that focuses on studying and practicing state-of-the-art AI technology in real applications and scenarios, such as online TV playback, transcription, translation, and video/audio recording. It is derived from the original ijkplayer project and includes many enhancements and new features, including: * Watching online TV and local media using a customized FFmpeg 6.1. * Recording online TV to automatically generate videos. * Studying ASR (Automatic Speech Recognition) using whisper.cpp. * Studying LLM (Large Language Model) using llama.cpp. * Studying SD (Text to Image by Stable Diffusion) using stablediffusion.cpp. * Generating real-time English subtitles for English online TV using whisper.cpp. * Running/experiencing LLM on Xiaomi 14 using llama.cpp. * Setting up a customized playlist and using the software to watch the content for R&D activity. * Refactoring the UI to be closer to a real commercial Android application (currently only supports English). Some goals of this project are: * To provide a well-maintained "workbench" for ASR researchers interested in practicing state-of-the-art AI technology in real scenarios on mobile devices (currently focusing on Android). * To provide a well-maintained "workbench" for LLM researchers interested in practicing state-of-the-art AI technology in real scenarios on mobile devices (currently focusing on Android). * To create an Android "turn-key project" for AI experts/researchers (who may not be familiar with regular Android software development) to focus on device-side AI R&D activity, where part of the AI R&D activity (algorithm improvement, model training, model generation, algorithm validation, model validation, performance benchmark, etc.) can be done very easily using Android Studio IDE and a powerful Android phone.

AI Study Assistant is an app designed to enhance learning experience and boost academic performance. It serves as a personal tutor, lecture summarizer, writer, and question generator powered by Google PaLM 2. Features include interacting with an AI chatbot, summarizing lectures, generating essays, and creating practice questions. The app is built using 100% Kotlin, Jetpack Compose, Clean Architecture, and MVVM design pattern, with technologies like Ktor, Room DB, Hilt, and Kotlin coroutines. AI Study Assistant aims to provide comprehensive AI-powered assistance for students in various academic tasks.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

CAMEL is an open-source library designed for the study of autonomous and communicative agents. We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks. To facilitate research in this field, we implement and support various types of agents, tasks, prompts, models, and simulated environments.

This repository presents the findings of the paper "Compression Represents Intelligence Linearly". The study reveals a strong linear correlation between the intelligence of LLMs, as measured by benchmark scores, and their ability to compress external text corpora. Compression efficiency, derived from raw text corpora, serves as a reliable evaluation metric that is linearly associated with model capabilities. The repository includes the compression corpora used in the paper, code for computing compression efficiency, and data collection and processing pipelines.

AIT is a repository focused on Algorithmic Information Theory, specifically utilizing Binary Lambda Calculus. It provides resources and tools for studying and implementing algorithms based on information theory principles. The repository aims to explore the relationship between algorithms and information theory through the lens of Binary Lambda Calculus, offering insights into computational complexity and data compression techniques.

This is the official repository for the ACM CCS 2024 paper 'Do Anything Now': Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models. The project employs a new framework called JailbreakHub to conduct the first measurement study on jailbreak prompts in the wild, collecting 15,140 prompts from December 2022 to December 2023, including 1,405 jailbreak prompts. The dataset serves as the largest collection of in-the-wild jailbreak prompts. The repository contains examples of harmful language and is intended for research purposes only.

PythonPark is a paradise for learning Python, providing babysitter-level tutorials on AI labs, treasure videos, data structures, study guides, machine learning practicals, deep learning practicals, Python basics, web scraping, big company interview experiences, programming life, and resource sharing. Original articles are published at least twice a week, with the latest articles being first released on WeChat and videos on Bilibili. Join the WeChat group for technical discussions or to provide feedback. Continuously improving and outputting content!

Autonomous agents are designed to achieve specific objectives through self-guided instructions. With the emergence and growth of large language models (LLMs), there is a growing trend in utilizing LLMs as fundamental controllers for these autonomous agents. This repository conducts a comprehensive survey study on the construction, application, and evaluation of LLM-based autonomous agents. It explores essential components of AI agents, application domains in natural sciences, social sciences, and engineering, and evaluation strategies. The survey aims to be a resource for researchers and practitioners in this rapidly evolving field.

ChatDev is a virtual software company powered by intelligent agents like CEO, CPO, CTO, programmer, reviewer, tester, and art designer. These agents collaborate to revolutionize the digital world through programming. The platform offers an easy-to-use, highly customizable, and extendable framework based on large language models, ideal for studying collective intelligence. ChatDev introduces innovative methods like Iterative Experience Refinement and Experiential Co-Learning to enhance software development efficiency. It supports features like incremental development, Docker integration, Git mode, and Human-Agent-Interaction mode. Users can customize ChatChain, Phase, and Role settings, and share their software creations easily. The project is open-source under the Apache 2.0 License and utilizes data licensed under CC BY-NC 4.0.

AWS AI/ML Workshop & example collection in Korean. The example codes in this repository are divided into 4 categories: AI services, Applied AI, SageMaker, Integration, Generative AI, and AWS Neuron. Each directory has its own Readme file. This repository also provides useful information for self-studying SageMaker.

LLM.swift is a simple and readable library that allows you to interact with large language models locally with ease for macOS, iOS, watchOS, tvOS, and visionOS. It's a lightweight abstraction layer over `llama.cpp` package, so that it stays as performant as possible while is always up to date. Theoretically, any model that works on `llama.cpp` should work with this library as well. It's only a single file library, so you can copy, study and modify the code however you want.

The Second Brain AI Agent Project is a tool designed to empower personal knowledge management by automatically indexing markdown files and links, providing a smart search engine powered by OpenAI, integrating seamlessly with different note-taking methods, and enhancing productivity by accessing information efficiently. The system is built on LangChain framework and ChromaDB vector store, utilizing a pipeline to process markdown files and extract text and links for indexing. It employs a Retrieval-augmented generation (RAG) process to provide context for asking questions to the large language model. The tool is beneficial for professionals, students, researchers, and creatives looking to streamline workflows, improve study sessions, delve deep into research, and organize thoughts and ideas effortlessly.

This repository is dedicated to the pruning of large language models (LLMs). It aims to serve as a comprehensive resource for researchers and practitioners interested in the efficient reduction of model size while maintaining or enhancing performance. The repository contains various papers, summaries, and links related to different pruning approaches for LLMs, along with author information and publication details. It covers a wide range of topics such as structured pruning, unstructured pruning, semi-structured pruning, and benchmarking methods. Researchers and practitioners can explore different pruning techniques, understand their implications, and access relevant resources for further study and implementation.

Robocode Tank Royale is a programming game where the goal is to code a bot in the form of a virtual tank to compete against other bots in a virtual battle arena. The player is the programmer of a bot, who will have no direct influence on the game him/herself. Instead, the player must write a program with the logic for the brain of the bot. The program contains instructions to the bot about how it should move, scan for opponent bots, fire its gun, and how it should react to various events occurring during a battle. The name **Robocode** is short for "Robot code," which originates from the original/first version of the game. **Robocode Tank Royale** is the next evolution/version of the game, where bots can participate via the Internet/network. All bots run over a web socket. The game aims to help you learn how to program and improve your programming skills, and have fun while doing it. Robocode is also useful when studying or improving machine learning in a fast-running real-time game. Robocode's battles take place on a "battlefield," where bots fight it out until only one is left, like a Battle Royale game. Hence the name **Tank Royale**. Note that Robocode contains no gore, blood, people, and politics. The battles are simply for the excitement of the competition we appreciate so much.

The 'machine-learning-research' repository is a comprehensive collection of resources related to mathematics, machine learning, deep learning, artificial intelligence, data science, and various scientific fields. It includes materials such as courses, tutorials, books, podcasts, communities, online courses, papers, and dissertations. The repository covers topics ranging from fundamental math skills to advanced machine learning concepts, with a focus on applications in healthcare, genetics, computational biology, precision health, and AI in science. It serves as a valuable resource for individuals interested in learning and researching in the fields of machine learning and related disciplines.

This repository contains a collection of sample applications and Jupyter Notebooks for hands-on experience with Pinecone vector databases and common AI patterns, tools, and algorithms. It includes production-ready examples for review and support, as well as learning-optimized examples for exploring AI techniques and building applications. Users can contribute, provide feedback, and collaborate to improve the resource.

FigStep is a black-box jailbreaking algorithm against large vision-language models (VLMs). It feeds harmful instructions through the image channel and uses benign text prompts to induce VLMs to output contents that violate common AI safety policies. The tool highlights the vulnerability of VLMs to jailbreaking attacks, emphasizing the need for safety alignments between visual and textual modalities.

This repository is a collection of papers focused on understanding the internal mechanism of large language models (LLM). It includes research on topics such as how LLMs handle multilingualism, learn in-context, and handle factual associations. The repository aims to provide insights into the inner workings of transformer-based language models through a curated list of papers and surveys.

UglyFeed is a simple Python application designed to retrieve, aggregate, filter, rewrite, evaluate, and serve content (RSS feeds) written by a large language model. It provides features such as retrieving RSS feeds, aggregating feed items by similarity, rewriting content using various APIs, saving rewritten feeds to JSON files, converting JSON to valid RSS feed, serving XML feed via an HTTP server, deploying XML feed to GitHub or GitLab, and evaluating generated content. The tool can be used for smart content curation, dynamic blog generation, interactive educational tools, personalized reading experiences, brand monitoring, multilingual content delivery, enhanced RSS feeds, creative writing assistance, content repurposing, and fake news detection datasets. It is modular, extensible, and aims to empower users in content manipulation and delivery.

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