Best AI tools for< Study For Exam >

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.

PrepSup is a powerful AI-powered learning platform that provides students with personalized study materials, an AI tutor, and a PDF analyzer to help them excel in their studies. With PrepSup, students can create and share flashcards, access a vast library of pre-made flashcards, and get instant feedback on their progress. The AI tutor provides personalized recommendations and guidance, helping students identify areas for improvement and develop effective study strategies. The PDF analyzer extracts key concepts and insights from PDFs, making it easier for students to understand and retain information. Whether you're preparing for a test, writing a paper, or simply trying to learn a new subject, PrepSup is the perfect tool to help you succeed.

Gauth is an AI-powered homework helper that provides step-by-step solutions to STEM problems. It utilizes advanced algorithms and AI technology to solve complex math, statistics, calculus, physics, chemistry, biology, and history questions. Gauth also offers live expert support, with thousands of real experts available 24/7 to provide detailed explanations and guidance. The app is designed to help students of all grades and levels conquer challenging homework problems and improve their understanding of STEM subjects.

A-Levels.ai is an online learning platform that provides students with personalized learning experiences. The platform uses artificial intelligence to track student progress and identify areas where they need additional support. A-Levels.ai also provides students with access to a library of resources, including video lessons, practice questions, and past papers.

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.

Bard PDF is an AI tool that allows users to engage in conversations with PDF files using powerful AI capabilities. Users can interact with Google Bard or Gemini to chat with PDFs, summarize content, answer questions, and get insights from PDF documents. The application offers extensions based on Google Workspace for seamless integration with Google Docs and Drive. Bard PDF is designed to assist students, researchers, and professionals in studying, conducting research, and navigating various types of documents. With multi-file chats, cited sources, and support for multiple languages, Bard PDF provides a user-friendly experience for interacting with PDF files.

Cephadex is an AI-powered educational platform that provides personalized learning experiences for teachers, students, content creators, and parents. It offers a range of features to transform educational content into interactive and engaging formats, including the ability to create custom flashcards, worksheets, and quizzes from various input sources such as PDFs, videos, and web pages. Cephadex also utilizes spaced repetition and gamification techniques to enhance learning retention and engagement.

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.

HomeworkAI is an AI-powered homework helper that provides step-by-step solutions to students and educational professionals of all levels. With HomeworkAI, users can upload their homework assignments, practice questions, or type in their questions to receive instant and accurate answers. HomeworkAI covers a wide range of subjects, including mathematics, physics, biology, chemistry, literature, and history. The platform is easy to use and provides clear and concise solutions, making it a valuable tool for students looking to improve their grades and understanding of the subject matter.

Knowt is a free online learning platform that offers a variety of study tools, including flashcards, notes, and practice tests. It is designed to be a comprehensive alternative to Quizlet, and it offers a number of features that Quizlet does not, such as the ability to import flashcards from Quizlet, create flashcards from text or images, and use spaced repetition to optimize learning. Knowt also has a large community of users who share flashcards and notes, making it a great resource for students of all levels.

AnkiDecks is an online tool that helps users generate flashcards from files or text using AI. It supports various file formats, including PDFs, Word documents, PowerPoint presentations, and text files. The generated flashcards can be used for studying purposes, particularly for language learning. AnkiDecks also offers features such as cloze (fill-in-the-blank) flashcards and text-to-speech functionality for language flashcards. Users can import the generated flashcards into the popular flashcard software Anki for further review and practice.

College Tools is an AI-powered homework solver that provides instant, expert help to students. It can answer questions from any website, including those without specialized support, and is fully integrated with Learning Management Systems (LMS) such as McGraw Hill Connect, Blackboard, Canvas, Smartbook, Moodle, and many others. College Tools also offers advanced recognition features that allow users to capture and analyze graphs and image-based questions, and provides detailed step-by-step guidance for each question. The tool is designed to help students improve their understanding and academic results.

SelfLearner is an AI-powered learning platform that provides personalized learning experiences for students of all ages. The platform uses artificial intelligence to track student progress, identify areas where they need additional support, and provide them with tailored learning materials. SelfLearner also offers a variety of interactive learning activities, such as games, simulations, and quizzes, to help students learn in a fun and engaging way.

Memrizz is an AI-powered flashcard generator that helps you create personalized flashcards for effective learning. With Memrizz, you can easily create flashcards from text, images, or audio files, and customize them to fit your learning style. Memrizz also provides spaced repetition and gamification features to make learning more engaging and efficient.

Wisdolia is an AI-powered flashcard maker that helps you create flashcards in seconds, not hours. With Wisdolia, you can easily create beautiful flashcards with images, audio, and text. You can also share your flashcards with others and collaborate on projects.

Paperclips is an AI-powered tool that helps users create flashcards from their course notes or any website. It automatically generates flashcards in the same language as the notes, making it easy for users to learn and review material. Paperclips also works with digital notes and can export flashcards to popular platforms like Anki and Quizlet.

Saya is an AI-powered study assistant that helps users learn and work faster. It can summarize PDF documents, YouTube videos, audio files, and Word documents, and it can also be used to search for information on the web. Saya is designed to be user-friendly and easy to use, and it can be accessed from any device with an internet connection.

Quiz Solver is an AI-powered study companion that helps users crack their questions and master their quizzes. It is a Chrome extension that can be used to solve multiple-choice questions from any quiz or assignment from a supported website or LMS. Quiz Solver uses GPT-4, a powerful AI language model, to provide accurate and instant answers. It is privacy-centric and universally compatible, making it a valuable tool for students and professionals alike.

DoryAI is an AI-powered flashcard generator that helps users create personalized flashcards and quizzes in seconds. It offers real-time feedback to enhance learning effectiveness and save time. With DoryAI, users can generate flashcards from various sources, including YouTube videos, PDFs, documents, and custom prompts. They can also export flashcards to popular platforms like Kahoot, Anki, and Quizlet for further learning. DoryAI's mission is to break down learning barriers and make education more accessible and personalized.

DoryAI is an instant flashcard and quiz generator with personalized feedback. It helps users create flashcards and quizzes from various sources, including YouTube videos, PDFs, documents, and custom prompts. DoryAI also provides real-time feedback on answers to enhance learning effectiveness. It is trusted by over 1,500 users, including learners, educators, and organizations.

ChatGPT Queue Prompts is a collection of prompt chains designed to enhance interactions with large language models like ChatGPT. These prompt chains help build context for the AI before performing specific tasks, improving performance. Users can copy and paste prompt chains into the ChatGPT Queue extension to process prompts in sequence. The repository includes example prompt chains for tasks like conducting AI company research, building SEO optimized blog posts, creating courses, revising resumes, enriching leads for CRM, personal finance document creation, workout and nutrition plans, marketing plans, and more.

MiniCheck is an efficient fact-checking tool designed to verify claims against grounding documents using large language models. It provides a sentence-level fact-checking model that can be used to evaluate the consistency of claims with the provided documents. MiniCheck offers different models, including Bespoke-MiniCheck-7B, which is the state-of-the-art and commercially usable. The tool enables users to fact-check multi-sentence claims by breaking them down into individual sentences for optimal performance. It also supports automatic prefix caching for faster inference when repeatedly fact-checking the same document with different claims.

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.

**OpenAI Chat API Workflow for Alfred** An Alfred 5 Workflow for using OpenAI Chat API to interact with GPT-3.5/GPT-4 🤖💬 It also allows image generation 🖼️, image understanding 👀, speech-to-text conversion 🎤, and text-to-speech synthesis 🔈 **Features:** * Execute all features using Alfred UI, selected text, or a dedicated web UI * Web UI is constructed by the workflow and runs locally on your Mac 💻 * API call is made directly between the workflow and OpenAI, ensuring your chat messages are not shared online with anyone other than OpenAI 🔒 * OpenAI does not use the data from the API Platform for training 🚫 * Export chat data to a simple JSON format external file 📄 * Continue the chat by importing the exported data later 🔄

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

LLM-for-misinformation-research is a curated paper list of misinformation research using large language models (LLMs). The repository covers methods for detection and verification, tools for fact-checking complex claims, decision-making and explanation, claim matching, post-hoc explanation generation, and other tasks related to combating misinformation. It includes papers on fake news detection, rumor detection, fact verification, and more, showcasing the application of LLMs in various aspects of misinformation research.

The AI Scientist is a comprehensive system for fully automatic scientific discovery, enabling Foundation Models to perform research independently. It aims to tackle the grand challenge of developing agents capable of conducting scientific research and discovering new knowledge. The tool generates papers on various topics using Large Language Models (LLMs) and provides a platform for exploring new research ideas. Users can create their own templates for specific areas of study and run experiments to generate papers. However, caution is advised as the codebase executes LLM-written code, which may pose risks such as the use of potentially dangerous packages and web access.

ReproModel is an open-source toolbox designed to boost AI research efficiency by enabling researchers to reproduce, compare, train, and test AI models faster. It provides standardized models, dataloaders, and processing procedures, allowing researchers to focus on new datasets and model development. With a no-code solution, users can access benchmark and SOTA models and datasets, utilize training visualizations, extract code for publication, and leverage an LLM-powered automated methodology description writer. The toolbox helps researchers modularize development, compare pipeline performance reproducibly, and reduce time for model development, computation, and writing. Future versions aim to facilitate building upon state-of-the-art research by loading previously published study IDs with verified code, experiments, and results stored in the system.

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.

This repository contains a minimal, clean code implementation of the Byte Pair Encoding (BPE) algorithm, commonly used in LLM tokenization. The BPE algorithm is "byte-level" because it runs on UTF-8 encoded strings. This algorithm was popularized for LLMs by the GPT-2 paper and the associated GPT-2 code release from OpenAI. Sennrich et al. 2015 is cited as the original reference for the use of BPE in NLP applications. Today, all modern LLMs (e.g. GPT, Llama, Mistral) use this algorithm to train their tokenizers. There are two Tokenizers in this repository, both of which can perform the 3 primary functions of a Tokenizer: 1) train the tokenizer vocabulary and merges on a given text, 2) encode from text to tokens, 3) decode from tokens to text. The files of the repo are as follows: 1. minbpe/base.py: Implements the `Tokenizer` class, which is the base class. It contains the `train`, `encode`, and `decode` stubs, save/load functionality, and there are also a few common utility functions. This class is not meant to be used directly, but rather to be inherited from. 2. minbpe/basic.py: Implements the `BasicTokenizer`, the simplest implementation of the BPE algorithm that runs directly on text. 3. minbpe/regex.py: Implements the `RegexTokenizer` that further splits the input text by a regex pattern, which is a preprocessing stage that splits up the input text by categories (think: letters, numbers, punctuation) before tokenization. This ensures that no merges will happen across category boundaries. This was introduced in the GPT-2 paper and continues to be in use as of GPT-4. This class also handles special tokens, if any. 4. minbpe/gpt4.py: Implements the `GPT4Tokenizer`. This class is a light wrapper around the `RegexTokenizer` (2, above) that exactly reproduces the tokenization of GPT-4 in the tiktoken library. The wrapping handles some details around recovering the exact merges in the tokenizer, and the handling of some unfortunate (and likely historical?) 1-byte token permutations. Finally, the script train.py trains the two major tokenizers on the input text tests/taylorswift.txt (this is the Wikipedia entry for her kek) and saves the vocab to disk for visualization. This script runs in about 25 seconds on my (M1) MacBook. All of the files above are very short and thoroughly commented, and also contain a usage example on the bottom of the file.

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.

ChatAFL is a protocol fuzzer guided by large language models (LLMs) that extracts machine-readable grammar for protocol mutation, increases message diversity, and breaks coverage plateaus. It integrates with ProfuzzBench for stateful fuzzing of network protocols, providing smooth integration. The artifact includes modified versions of AFLNet and ProfuzzBench, source code for ChatAFL with proposed strategies, and scripts for setup, execution, analysis, and cleanup. Users can analyze data, construct plots, examine LLM-generated grammars, enriched seeds, and state-stall responses, and reproduce results with downsized experiments. Customization options include modifying fuzzers, tuning parameters, adding new subjects, troubleshooting, and working on GPT-4. Limitations include interaction with OpenAI's Large Language Models and a hard limit of 150,000 tokens per minute.

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.

ShortcutsBench is a project focused on collecting and analyzing workflows created in the Shortcuts app, providing a dataset of shortcut metadata, source files, and API information. It aims to study the integration of large language models with Apple devices, particularly focusing on the role of shortcuts in enhancing user experience. The project offers insights for Shortcuts users, enthusiasts, and researchers to explore, customize workflows, and study automated workflows, low-code programming, and API-based agents.

GOLEM is an open-source AI framework focused on optimization and learning of structured graph-based models using meta-heuristic methods. It emphasizes the potential of meta-heuristics in complex problem spaces where gradient-based methods are not suitable, and the importance of structured models in various problem domains. The framework offers features like structured model optimization, metaheuristic methods, multi-objective optimization, constrained optimization, extensibility, interpretability, and reproducibility. It can be applied to optimization problems represented as directed graphs with defined fitness functions. GOLEM has applications in areas like AutoML, Bayesian network structure search, differential equation discovery, geometric design, and neural architecture search. The project structure includes packages for core functionalities, adapters, graph representation, optimizers, genetic algorithms, utilities, serialization, visualization, examples, and testing. Contributions are welcome, and the project is supported by ITMO University's Research Center Strong Artificial Intelligence in Industry.

PromptAgent is a repository for a novel automatic prompt optimization method that crafts expert-level prompts using language models. It provides a principled framework for prompt optimization by unifying prompt sampling and rewarding using MCTS algorithm. The tool supports different models like openai, palm, and huggingface models. Users can run PromptAgent to optimize prompts for specific tasks by strategically sampling model errors, generating error feedbacks, simulating future rewards, and searching for high-reward paths leading to expert prompts.

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