Best AI tools for< Sat Tutor >

The SAT Reading & Writing Question Generator is an AI-powered tool designed to help students practice for the SAT exam. It generates a wide variety of reading and writing questions to improve students' skills and boost their confidence. With its smart algorithms, the tool provides personalized practice questions tailored to each student's needs, making exam preparation more effective and efficient. The tool is user-friendly and accessible, offering a seamless experience for students to enhance their test-taking abilities.

The website offers a comprehensive digital SAT preparation tool that includes full-length tests, AI-powered study plans, and proven learning methods to help students maximize their SAT scores. It provides adaptive testing structure, scoring system, test duration & format, strategic study points, personalized study plans, expert-curated SAT question bank, adaptive full-length practice tests, instant score calculator, and SAT flashcards. The platform aims to enhance accessibility, efficiency, and effectiveness in SAT preparation through a systematic approach designed to boost students' scores by 200+ points.

EducUp Studio is an online platform designed to help educators create engaging and interactive learning experiences for their students. The platform offers a variety of tools and resources that allow teachers to easily design and customize educational content, such as quizzes, presentations, and interactive lessons. EducUp Studio aims to enhance the teaching and learning process by providing a user-friendly interface and a range of features to support educators in creating dynamic and effective educational materials.

MentoMind is a revolutionary digital SAT prep platform that utilizes AI tools to provide a comprehensive learning experience for students. The platform offers interactive, personalized, and insight-driven practice tests that replicate the real exam setting and difficulty. With detailed performance analysis reports and a personalized learning pathway, MentoMind helps students gain mastery in all topics of the SAT syllabus. The AI-powered chatbot provides 24/7 assistance, instant answers, tips, and tricks to support the learning journey. Students can compete in fun challenges to reinforce knowledge and stay motivated while preparing for the SAT.

PrepGenius.ai is an AI-driven test preparation platform designed to revolutionize the way students prepare for AP courses, college admission tests, and more. The platform offers personalized study plans, real-time feedback, interactive learning tools, and comprehensive resources to help students understand their strengths and weaknesses. With PrepGenius.ai, students can study smarter, receive tailored feedback, and track their progress to improve their test scores effectively.

Quizbot.ai is an advanced AI question generator designed to revolutionize the process of question and exam development. It offers a cutting-edge artificial intelligence system that can generate various types of questions from different sources like PDFs, Word documents, videos, images, and more. Quizbot.ai is a versatile tool that caters to multiple languages and question types, providing a personalized and engaging learning experience for users across various industries. The platform ensures scalability, flexibility, and personalized assessments, along with detailed analytics and insights to track learner performance. Quizbot.ai is secure, user-friendly, and offers a range of subscription plans to suit different needs.

The Princeton Review is an AI-based test preparation and tutoring platform offering personalized academic support, test prep courses, and college admissions counseling. With over 43 years of industry experience and a track record of helping millions of students, The Princeton Review uses sophisticated AI technology to provide students with tailored learning experiences, expert-led videos, interactive reports, and feedback tools for essays and homework. The platform covers a wide range of subjects and exams, from K-12 academics to graduate and professional tests like SAT, ACT, MCAT, LSAT, GRE, GMAT, and more. Additionally, it offers services for college admissions counseling, school partnerships, and international licensing.

This repository provides a comprehensive guide to large language models (LLMs), covering various aspects such as training, fine-tuning, compression, and applications. It includes detailed tutorials, code examples, and explanations of key concepts and techniques. The repository is maintained by Liguo Dong, an AI researcher and engineer with expertise in LLM research and development.

LLM4Opt is a collection of references and papers focusing on applying Large Language Models (LLMs) for diverse optimization tasks. The repository includes research papers, tutorials, workshops, competitions, and related collections related to LLMs in optimization. It covers a wide range of topics such as algorithm search, code generation, machine learning, science, industry, and more. The goal is to provide a comprehensive resource for researchers and practitioners interested in leveraging LLMs for optimization tasks.

Petals is a tool that allows users to run large language models at home in a BitTorrent-style manner. It enables fine-tuning and inference up to 10x faster than offloading. Users can generate text with distributed models like Llama 2, Falcon, and BLOOM, and fine-tune them for specific tasks directly from their desktop computer or Google Colab. Petals is a community-run system that relies on people sharing their GPUs to increase its capacity and offer a distributed network for hosting model layers.

llm_benchmarks is a collection of benchmarks and datasets for evaluating Large Language Models (LLMs). It includes various tasks and datasets to assess LLMs' knowledge, reasoning, language understanding, and conversational abilities. The repository aims to provide comprehensive evaluation resources for LLMs across different domains and applications, such as education, healthcare, content moderation, coding, and conversational AI. Researchers and developers can leverage these benchmarks to test and improve the performance of LLMs in various real-world scenarios.

Flyte is an open-source orchestrator that facilitates building production-grade data and ML pipelines. It is built for scalability and reproducibility, leveraging Kubernetes as its underlying platform. With Flyte, user teams can construct pipelines using the Python SDK, and seamlessly deploy them on both cloud and on-premises environments, enabling distributed processing and efficient resource utilization.

CogVideo is an open-source repository that provides pretrained text-to-video models for generating videos based on input text. It includes models like CogVideoX-2B and CogVideo, offering powerful video generation capabilities. The repository offers tools for inference, fine-tuning, and model conversion, along with demos showcasing the model's capabilities through CLI, web UI, and online experiences. CogVideo aims to facilitate the creation of high-quality videos from textual descriptions, catering to a wide range of applications.

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)

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, ,'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in less than 3 words,Verb + noun form,in daily spoken language,in lowercase letters).Answer in english languagesname:Awesome-Chinese-LLM readme:# Awesome Chinese LLM   An Awesome Collection for LLM in Chinese 收集和梳理中文LLM相关    自ChatGPT为代表的大语言模型（Large Language Model, LLM）出现以后，由于其惊人的类通用人工智能（AGI）的能力，掀起了新一轮自然语言处理领域的研究和应用的浪潮。尤其是以ChatGLM、LLaMA等平民玩家都能跑起来的较小规模的LLM开源之后，业界涌现了非常多基于LLM的二次微调或应用的案例。本项目旨在收集和梳理中文LLM相关的开源模型、应用、数据集及教程等资料，目前收录的资源已达100+个！ 如果本项目能给您带来一点点帮助，麻烦点个⭐️吧～ 同时也欢迎大家贡献本项目未收录的开源模型、应用、数据集等。提供新的仓库信息请发起PR，并按照本项目的格式提供仓库链接、star数，简介等相关信息，感谢~

The Korean SAT LLM Leaderboard is a benchmarking project that allows users to test their fine-tuned Korean language models on a 10-year dataset of the Korean College Scholastic Ability Test (CSAT). The project provides a platform to compare human academic ability with the performance of large language models (LLMs) on various question types to assess reading comprehension, critical thinking, and sentence interpretation skills. It aims to share benchmark data, utilize a reliable evaluation dataset curated by the Korea Institute for Curriculum and Evaluation, provide annual updates to prevent data leakage, and promote open-source LLM advancement for achieving top-tier performance on the Korean CSAT.

The 'unilm' repository is a collection of tools, models, and architectures for Foundation Models and General AI, focusing on tasks such as NLP, MT, Speech, Document AI, and Multimodal AI. It includes various pre-trained models, such as UniLM, InfoXLM, DeltaLM, MiniLM, AdaLM, BEiT, LayoutLM, WavLM, VALL-E, and more, designed for tasks like language understanding, generation, translation, vision, speech, and multimodal processing. The repository also features toolkits like s2s-ft for sequence-to-sequence fine-tuning and Aggressive Decoding for efficient sequence-to-sequence decoding. Additionally, it offers applications like TrOCR for OCR, LayoutReader for reading order detection, and XLM-T for multilingual NMT.

Trapster Community is a low-interaction honeypot designed for internal networks or credential capture. It monitors and detects suspicious activities, providing deceptive security layer. Features include mimicking network services, asynchronous framework, easy configuration, expandable services, and HTTP honeypot engine with AI capabilities. Supported protocols include DNS, HTTP/HTTPS, FTP, LDAP, MSSQL, POSTGRES, RDP, SNMP, SSH, TELNET, VNC, and RSYNC. The tool generates various types of logs and offers HTTP engine with AI capabilities to emulate websites using YAML configuration. Contributions are welcome under AGPLv3+ license.

Awesome-LLM-Eval: a curated list of tools, benchmarks, demos, papers for Large Language Models (like ChatGPT, LLaMA, GLM, Baichuan, etc) Evaluation on Language capabilities, Knowledge, Reasoning, Fairness and Safety.

**Curated collection of papers and resources on how to unlock the reasoning ability of LLMs and MLLMs.** **Description in less than 400 words, no line breaks and quotation marks.** Large Language Models (LLMs) have revolutionized the NLP landscape, showing improved performance and sample efficiency over smaller models. However, increasing model size alone has not proved sufficient for high performance on challenging reasoning tasks, such as solving arithmetic or commonsense problems. This curated collection of papers and resources presents the latest advancements in unlocking the reasoning abilities of LLMs and Multimodal LLMs (MLLMs). It covers various techniques, benchmarks, and applications, providing a comprehensive overview of the field. **5 jobs suitable for this tool, in lowercase letters.** - content writer - researcher - data analyst - software engineer - product manager **Keywords of the tool, in lowercase letters.** - llm - reasoning - multimodal - chain-of-thought - prompt engineering **5 specific tasks user can use this tool to do, in less than 3 words, Verb + noun form, in daily spoken language.** - write a story - answer a question - translate a language - generate code - summarize a document

ScreenAgent is a project focused on creating an environment for Visual Language Model agents (VLM Agent) to interact with real computer screens. The project includes designing an automatic control process for agents to interact with the environment and complete multi-step tasks. It also involves building the ScreenAgent dataset, which collects screenshots and action sequences for various daily computer tasks. The project provides a controller client code, configuration files, and model training code to enable users to control a desktop with a large model.

MathEval is a benchmark designed for evaluating the mathematical capabilities of large models. It includes over 20 evaluation datasets covering various mathematical domains with more than 30,000 math problems. The goal is to assess the performance of large models across different difficulty levels and mathematical subfields. MathEval serves as a reliable reference for comparing mathematical abilities among large models and offers guidance on enhancing their mathematical capabilities in the future.

Upload your SAT score PDF to our calculator and analyze how you did and how to preform better

SAT test preparation assistant, offering strategies and tips.

Asistente experto para despejar dudas del SAT con respuestas rápidas y en lenguaje sencillo. ¡Pregunta y simplifica tus trámites tributarios!

Knows the docs and can query official ordinal endpoints—Sat Numbers, Inscription IDs, and more.

Binary tools & Z3 CLI