Best AI tools for< study for us citizenship test >

This website provides a practice exam for the US naturalization test. All questions are graded by GPT, an AI created by OpenAI. The actual test is an oral test graded by a USCIS officer. You'll be asked 10 of 100 of these questions and you'll need to get 6 of them right to pass. The website is open source on Github. If you find a bug, please send a DM on Twitter: @evanjconrad.

Tutopa is an AI-driven educational platform that provides personalized tutoring and extensive resources to boost student success and support parents. With multilingual support, instant feedback for faster progress, and 24/7 availability, Tutopa makes learning more accessible and effective for students of all ages.

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.

Synaptiq is an AI-powered learning platform for medical students and doctors. It uses publisher-grade content, cutting-edge algorithms, and personalized review schedules to help learners study less and remember more. Synaptiq's library of flashcards, practice tests, and free-response questions is built from the most reliable sources, like publishers' textbooks and medical school curricula. Its evidence-backed spaced repetition algorithms are optimized for medical education and put learners' personalized review schedules on autopilot. Synaptiq is trusted by medical students and doctors across hundreds of institutions. It is a go-to resource for learning and reviewing the large amount of material to cover in medical school and beyond.

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.

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.

Apex Vision AI is an AI-powered homework helper that provides instant answers and assistance to college students. It utilizes advanced machine learning algorithms to generate accurate answers for multiple-choice homework and quizzes, saving students time and boosting their confidence. The extension seamlessly integrates into the user's browser, offering real-time answers with a click or keyboard shortcut. Its user-friendly interface and intuitive design make it easy for students to use, helping them study smarter and not harder.

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.

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.

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.

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.

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.

StudyCards App is a modern flashcard app with a paper-and-sticky-note interface. It also features embedded iOS/Android text-to-speech engine, which can read the text from the sides of your cards aloud in a natural-sounding voice. Create custom decks with the assistance from Artificial Intelligence, organize your study materials, and track your progress, all in one convenient app. With StudyCards, you can easily create your own flashcard deck with a question on one side and the answer on the other. The app also allows you to choose a language for each side, and will pronounce both the questions and answers for you, even in eyes-free mode. But that's not all - StudyCards is the only app that lets you upload your flashcards to your watch, so you can listen and memorize information even when you're running, biking, or hiking without your phone. With StudyCards, learning is more convenient and accessible than ever before.

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.

Coursable is a personal AI study guide that helps you learn more effectively. It uses artificial intelligence to create personalized study plans, track your progress, and provide feedback. Coursable is designed to help you learn anything, from new languages to complex scientific concepts.

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.

The W3C Cognitive AI Community Group focuses on advancing Cognitive AI through collaboration on defining use cases, open source implementations, and application areas. The group aims to demonstrate the potential of Cognitive AI in various domains such as customer services, healthcare, cybersecurity, online learning, autonomous vehicles, manufacturing, and web search. They work on formal specifications for chunk data and rules, plausible knowledge notation, and neural networks for human-like AI. The group positions Cognitive AI as a combination of symbolic and statistical approaches inspired by human thought processes. They address research challenges including mimicry, emotional intelligence, natural language processing, and common sense reasoning. The long-term goal is to develop cognitive agents that are knowledgeable, creative, collaborative, empathic, and multilingual, capable of continual learning and self-awareness.

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.

MotionLLM is a framework for human behavior understanding that leverages Large Language Models (LLMs) to jointly model videos and motion sequences. It provides a unified training strategy, dataset MoVid, and MoVid-Bench for evaluating human behavior comprehension. The framework excels in captioning, spatial-temporal comprehension, and reasoning abilities.

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.

A repository that lists papers related to Large Language Model (LLM) based agents. The repository covers various topics including survey, planning, feedback & reflection, memory mechanism, role playing, game playing, tool usage & human-agent interaction, benchmark & evaluation, environment & platform, agent framework, multi-agent system, and agent fine-tuning. It provides a comprehensive collection of research papers on LLM-based agents, exploring different aspects of AI agent architectures and applications.

The Microsoft AI for Earth program hosts geospatial data on Azure that is important to environmental sustainability and Earth science. This repo hosts documentation and demonstration notebooks for all the data that is managed by AI for Earth. It also serves as a "staging ground" for the Planetary Computer Data Catalog.

A curated list of Generative AI projects, tools, artworks, and models

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.

hackingBuddyGPT is a framework for testing LLM-based agents for security testing. It aims to create common ground truth by creating common security testbeds and benchmarks, evaluating multiple LLMs and techniques against those, and publishing prototypes and findings as open-source/open-access reports. The initial focus is on evaluating the efficiency of LLMs for Linux privilege escalation attacks, but the framework is being expanded to evaluate the use of LLMs for web penetration-testing and web API testing. hackingBuddyGPT is released as open-source to level the playing field for blue teams against APTs that have access to more sophisticated resources.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

Awesome MLSecOps is a curated list of open-source tools, resources, and tutorials for MLSecOps (Machine Learning Security Operations). It includes a wide range of security tools and libraries for protecting machine learning models against adversarial attacks, as well as resources for AI security, data anonymization, model security, and more. The repository aims to provide a comprehensive collection of tools and information to help users secure their machine learning systems and infrastructure.

This repository provides a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from **model-centric** , **data-centric** , and **framework-centric** perspective, respectively. We hope our survey and this GitHub repository can serve as valuable resources to help researchers and practitioners gain a systematic understanding of the research developments in efficient LLMs and inspire them to contribute to this important and exciting field.

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.

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