Best AI tools for< Input Test Scores >

MacWhisper is a native macOS application that utilizes OpenAI's Whisper technology for transcribing audio files into text. It offers a user-friendly interface for recording, transcribing, and editing audio, making it suitable for various use cases such as transcribing meetings, lectures, interviews, and podcasts. The application is designed to protect user privacy by performing all transcriptions locally on the device, ensuring that no data leaves the user's machine.

PrepAI is an advanced test generator that uses AI to help educators create high-quality assessments quickly and easily. With PrepAI, teachers can save time, engage students with unique questions, and prepare them for success. PrepAI offers a variety of features to make test creation easy, including multiple content input options, various question formats, and an easy-to-use dashboard. PrepAI also offers a variety of advantages for educators, including the ability to analyze higher-order thinking skills, conduct tests effortlessly, and access unlimited question sets.

LLM Clash is a web-based application that allows users to compare the outputs of different large language models (LLMs) on a given task. Users can input a prompt and select which LLMs they want to compare. The application will then display the outputs of the LLMs side-by-side, allowing users to compare their strengths and weaknesses.

Enhans AI Model Generator is an advanced AI tool designed to help users generate AI models efficiently. It utilizes cutting-edge algorithms and machine learning techniques to streamline the model creation process. With Enhans AI Model Generator, users can easily input their data, select the desired parameters, and obtain a customized AI model tailored to their specific needs. The tool is user-friendly and does not require extensive programming knowledge, making it accessible to a wide range of users, from beginners to experts in the field of AI.

Lisapet.AI is an AI prompt testing suite designed for product teams to streamline the process of designing, prototyping, testing, and shipping AI features. It offers a comprehensive platform with features like best-in-class AI playground, variables for dynamic data inputs, structured outputs, side-by-side editing, function calling, image inputs, assertions & metrics, performance comparison, data sets organization, shareable reports, comments & feedback, token & cost stats, and more. The application aims to help teams save time, improve efficiency, and ensure the reliability of AI features through automated prompt testing.

Autoblocks AI is an AI application designed to help users build safe AI apps efficiently. It allows users to ship AI agents in minutes, speeding up the development process significantly. With Autoblocks AI, users can prototype quickly, test at a faster rate, and deploy with confidence. The application is trusted by leading AI teams and focuses on making AI agent development more predictable by addressing the unpredictability of user inputs and non-deterministic models.

LuckyRobots is an AI tool designed to make robotics accessible to software engineers by providing a simulation platform for deploying end-to-end AI models. The platform allows users to interact with robots using natural language commands, explore virtual environments, test robot models in realistic scenarios, and receive camera feeds for monitoring. LuckyRobots aims to train AI models on real-world simulations and respond to natural language inputs, offering a user-friendly and innovative approach to robotics development.

Firebay Studios is an AI-powered platform that enables users to create high-quality radio ads in seconds. The tool helps companies and organizations of all sizes to automate production processes, streamline ad creation, and ultimately boost revenue. With features like AI & Cloned Voices, Editing & Production, Script Writing, SFX & Music, and support for 29 languages, Firebay Studios offers a comprehensive solution for creating captivating audio-based advertisements effortlessly.

Supermeme.ai is an AI meme generator that allows users to turn text into memes using advanced AI technology. Users can create high-quality memes in over 110 languages without the need for design skills. The platform offers a curated database of meme templates, customizable watermarks, and API access for meme generation. Supermeme.ai is designed to streamline the meme-making process and help users engage their audience with creative and viral-worthy content.

MetaGPT is an advanced AI tool that leverages the power of natural language processing to generate human-like text. It uses the latest deep learning models to understand and produce text in various styles and tones. With MetaGPT, users can easily create content for articles, essays, emails, and more with just a few clicks. The tool is designed to assist writers, marketers, students, and anyone looking to enhance their writing productivity and creativity.

PromptifyPRO is an AI-powered writing assistant that helps users generate creative writing prompts. It offers a variety of features to help users overcome writer's block and produce high-quality content. PromptifyPRO is designed for writers of all levels, from beginners to professionals. It is easy to use and can be integrated with other writing tools. PromptifyPRO is a valuable tool for anyone who wants to improve their writing skills and produce more creative content.

Image Variations is an AI image generator tool that allows users to create multiple variations from a single image using stable diffusion. It helps users generate unique and copyright-free images for their projects by adding noise to replicate the style of the original image. The tool is designed to be user-friendly and efficient, providing endless design possibilities for creative projects.

Token Counter is an AI tool designed to convert text input into tokens for various AI models. It helps users accurately determine the token count and associated costs when working with AI models. By providing insights into tokenization strategies and cost structures, Token Counter streamlines the process of utilizing advanced technologies.

Anime AI is a user-friendly website that utilizes advanced AI technology to transform photos into stunning anime-style images or generate anime art from text prompts. With Anime AI, users can effortlessly unleash their creativity and share breathtaking anime creations without the need for drawing skills or AI knowledge. The platform offers a seamless experience for beginners and experienced users alike, allowing them to turn their imagination into reality in just a few seconds.

Janus Pro AI is a cutting-edge multimodal image generation and understanding platform that empowers users to create high-quality images for various projects. It offers powerful features such as multiple art styles, smart editing, lightning-fast image generation, high resolution output, commercial rights, and 24/7 generation service. The platform is built on DeepSeek's advanced architecture, providing users with a seamless experience in generating images in different styles and settings.

QuestionWell is an AI question generator designed for teachers. It is a mom-&-pop AI start-up that allows users to generate an endless supply of standards-aligned questions and learning resources. Users can then export these questions to their favorite platforms such as Blackboard, Microsoft Word, and Microsoft Forms. The tool aims to streamline the process of creating educational content and assessments for teachers, ultimately saving them time and effort. QuestionWell is a user-friendly and efficient AI tool that enhances the teaching experience.

Sourcely is an AI-powered academic search assistant designed to help students and researchers find, summarize, and add credible academic sources efficiently. With access to over 200 million research papers and advanced search filters, Sourcely streamlines the research process by simplifying source discovery, providing targeted citations, offering free PDF downloads, organizing citations in a personal library, generating credible summaries, and exporting references in various formats. The tool aims to save time, enhance the quality of work, and improve academic productivity.

Pump is an AI-powered platform that utilizes group buying and artificial intelligence to help startups save up to 60% on cloud computing costs. It offers discounts previously only available to large companies, alongside 24/7 automated AWS savings. Pump promises to slash runaway cloud computing costs and provides a seamless and efficient experience for its users. The platform is trusted by over 1000 startups across 22 countries and has been recognized as the 'Costco of Cloud' by Forbes.

Uwear.ai is an AI tool that allows users to easily create stunningly complete photos of AI-generated fashion models in just a few clicks. It turns flat lay photos into studio-quality model visuals, eliminating the need for 3D designs, mannequins, or photos of the product already worn. The platform uses its proprietary AI model to keep the generated clothes as close as possible to the input, helping users sell more by generating realistic human models of different sizes and ethnicities.

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Detoxify is a library that provides trained models and code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification. It includes models like 'original', 'unbiased', and 'multilingual' trained on different datasets to detect toxicity and minimize bias. The library aims to help in stopping harmful content online by interpreting visual content in context. Users can fine-tune the models on carefully constructed datasets for research purposes or to aid content moderators in flagging out harmful content quicker. The library is built to be user-friendly and straightforward to use.

Ax is a Typescript library that allows users to build intelligent agents inspired by agentic workflows and the Stanford DSP paper. It seamlessly integrates with multiple Large Language Models (LLMs) and VectorDBs to create RAG pipelines or collaborative agents capable of solving complex problems. The library offers advanced features such as streaming validation, multi-modal DSP, and automatic prompt tuning using optimizers. Users can easily convert documents of any format to text, perform smart chunking, embedding, and querying, and ensure output validation while streaming. Ax is production-ready, written in Typescript, and has zero dependencies.

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)

Contoso Chat is a Python sample demonstrating how to build, evaluate, and deploy a retail copilot application with Azure AI Studio using Promptflow with Prompty assets. The sample implements a Retrieval Augmented Generation approach to answer customer queries based on the company's product catalog and customer purchase history. It utilizes Azure AI Search, Azure Cosmos DB, Azure OpenAI, text-embeddings-ada-002, and GPT models for vectorizing user queries, AI-assisted evaluation, and generating chat responses. By exploring this sample, users can learn to build a retail copilot application, define prompts using Prompty, design, run & evaluate a copilot using Promptflow, provision and deploy the solution to Azure using the Azure Developer CLI, and understand Responsible AI practices for evaluation and content safety.

This repository provides a collection of tools and techniques for defending against injection attacks in software applications. It includes code samples, best practices, and guidelines for implementing secure coding practices to prevent common injection vulnerabilities such as SQL injection, XSS, and command injection. The tools and resources in this repository aim to help developers build more secure and resilient applications by addressing one of the most common and critical security threats in modern software development.

AppWorld is a high-fidelity execution environment of 9 day-to-day apps, operable via 457 APIs, populated with digital activities of ~100 people living in a simulated world. It provides a benchmark of natural, diverse, and challenging autonomous agent tasks requiring rich and interactive coding. The repository includes implementations of AppWorld apps and APIs, along with tests. It also introduces safety features for code execution and provides guides for building agents and extending the benchmark.

Bench is a tool for evaluating LLMs for production use cases. It provides a standardized workflow for LLM evaluation with a common interface across tasks and use cases. Bench can be used to test whether open source LLMs can do as well as the top closed-source LLM API providers on specific data, and to translate the rankings on LLM leaderboards and benchmarks into scores that are relevant for actual use cases.

imodelsX is a Scikit-learn friendly library that provides tools for explaining, predicting, and steering text models/data. It also includes a collection of utilities for getting started with text data. **Explainable modeling/steering** | Model | Reference | Output | Description | |---|---|---|---| | Tree-Prompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/tree_prompt) | Explanation + Steering | Generates a tree of prompts to steer an LLM (_Official_) | | iPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/iprompt) | Explanation + Steering | Generates a prompt that explains patterns in data (_Official_) | | AutoPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/autoprompt) | Explanation + Steering | Find a natural-language prompt using input-gradients (⌛ In progress)| | D3 | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/d3) | Explanation | Explain the difference between two distributions | | SASC | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/sasc) | Explanation | Explain a black-box text module using an LLM (_Official_) | | Aug-Linear | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_linear) | Linear model | Fit better linear model using an LLM to extract embeddings (_Official_) | | Aug-Tree | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_tree) | Decision tree | Fit better decision tree using an LLM to expand features (_Official_) | **General utilities** | Model | Reference | |---|---| | LLM wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/llm) | Easily call different LLMs | | | Dataset wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/data) | Download minimially processed huggingface datasets | | | Bag of Ngrams | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/bag_of_ngrams) | Learn a linear model of ngrams | | | Linear Finetune | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/linear_finetune) | Finetune a single linear layer on top of LLM embeddings | | **Related work** * [imodels package](https://github.com/microsoft/interpretml/tree/main/imodels) (JOSS 2021) - interpretable ML package for concise, transparent, and accurate predictive modeling (sklearn-compatible). * [Adaptive wavelet distillation](https://arxiv.org/abs/2111.06185) (NeurIPS 2021) - distilling a neural network into a concise wavelet model * [Transformation importance](https://arxiv.org/abs/1912.04938) (ICLR 2020 workshop) - using simple reparameterizations, allows for calculating disentangled importances to transformations of the input (e.g. assigning importances to different frequencies) * [Hierarchical interpretations](https://arxiv.org/abs/1807.03343) (ICLR 2019) - extends CD to CNNs / arbitrary DNNs, and aggregates explanations into a hierarchy * [Interpretation regularization](https://arxiv.org/abs/2006.14340) (ICML 2020) - penalizes CD / ACD scores during training to make models generalize better * [PDR interpretability framework](https://www.pnas.org/doi/10.1073/pnas.1814225116) (PNAS 2019) - an overarching framewwork for guiding and framing interpretable machine learning

LLM-jp-eval is a tool designed to automatically evaluate Japanese large language models across multiple datasets. It provides functionalities such as converting existing Japanese evaluation data to text generation task evaluation datasets, executing evaluations of large language models across multiple datasets, and generating instruction data (jaster) in the format of evaluation data prompts. Users can manage the evaluation settings through a config file and use Hydra to load them. The tool supports saving evaluation results and logs using wandb. Users can add new evaluation datasets by following specific steps and guidelines provided in the tool's documentation. It is important to note that using jaster for instruction tuning can lead to artificially high evaluation scores, so caution is advised when interpreting the results.

VSP-LLM (Visual Speech Processing incorporated with LLMs) is a novel framework that maximizes context modeling ability by leveraging the power of LLMs. It performs multi-tasks of visual speech recognition and translation, where given instructions control the task type. The input video is mapped to the input latent space of a LLM using a self-supervised visual speech model. To address redundant information in input frames, a deduplication method is employed using visual speech units. VSP-LLM utilizes Low Rank Adaptors (LoRA) for computationally efficient training.

OlympicArena is a comprehensive benchmark designed to evaluate advanced AI capabilities across various disciplines. It aims to push AI towards superintelligence by tackling complex challenges in science and beyond. The repository provides detailed data for different disciplines, allows users to run inference and evaluation locally, and offers a submission platform for testing models on the test set. Additionally, it includes an annotation interface and encourages users to cite their paper if they find the code or dataset helpful.

PyCM is a Python library for multi-class confusion matrices, providing support for input data vectors and direct matrices. It is a comprehensive tool for post-classification model evaluation, offering a wide range of metrics for predictive models and accurate evaluation of various classifiers. PyCM is designed for data scientists who require diverse metrics for their models.

REINVENT is a molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design, molecule optimization, and other small molecule design tasks. It uses a Reinforcement Learning (RL) algorithm to generate optimized molecules compliant with a user-defined property profile defined as a multi-component score. Transfer Learning (TL) can be used to create or pre-train a model that generates molecules closer to a set of input molecules.

MathCoder is a repository focused on enhancing mathematical reasoning by fine-tuning open-source language models to use code for modeling and deriving math equations. It introduces MathCodeInstruct dataset with solutions interleaving natural language, code, and execution results. The repository provides MathCoder models capable of generating code-based solutions for challenging math problems, achieving state-of-the-art scores on MATH and GSM8K datasets. It offers tools for model deployment, inference, and evaluation, along with a citation for referencing the work.

Prometheus-Eval is a repository dedicated to evaluating large language models (LLMs) in generation tasks. It provides state-of-the-art language models like Prometheus 2 (7B & 8x7B) for assessing in pairwise ranking formats and achieving high correlation scores with benchmarks. The repository includes tools for training, evaluating, and using these models, along with scripts for fine-tuning on custom datasets. Prometheus aims to address issues like fairness, controllability, and affordability in evaluations by simulating human judgments and proprietary LM-based assessments.

MathVerse is an all-around visual math benchmark designed to evaluate the capabilities of Multi-modal Large Language Models (MLLMs) in visual math problem-solving. It collects high-quality math problems with diagrams to assess how well MLLMs can understand visual diagrams for mathematical reasoning. The benchmark includes 2,612 problems transformed into six versions each, contributing to 15K test samples. It also introduces a Chain-of-Thought (CoT) Evaluation strategy for fine-grained assessment of output answers.

Alice is an agentic workflow framework that integrates task execution and intelligent chat capabilities. It provides a flexible environment for creating, managing, and deploying AI agents for various purposes, leveraging a microservices architecture with MongoDB for data persistence. The framework consists of components like APIs, agents, tasks, and chats that interact to produce outputs through files, messages, task results, and URL references. Users can create, test, and deploy agentic solutions in a human-language framework, making it easy to engage with by both users and agents. The tool offers an open-source option, user management, flexible model deployment, and programmatic access to tasks and chats.

The 'ailab' project is an experimental ground for code generation combining AI (especially coding agents) and Deno. It aims to manage configuration files defining coding rules and modes in Deno projects, enhancing the quality and efficiency of code generation by AI. The project focuses on defining clear rules and modes for AI coding agents, establishing best practices in Deno projects, providing mechanisms for type-safe code generation and validation, applying test-driven development (TDD) workflow to AI coding, and offering implementation examples utilizing design patterns like adapter pattern.

ProbSem is a repository that provides a framework to leverage large language models (LLMs) for assigning context-conditional probability distributions over queried strings. It supports OpenAI engines and HuggingFace CausalLM models, and is flexible for research applications in linguistics, cognitive science, program synthesis, and NLP. Users can define prompts, contexts, and queries to derive probability distributions over possible completions, enabling tasks like cloze completion, multiple-choice QA, semantic parsing, and code completion. The repository offers CLI and API interfaces for evaluation, with options to customize models, normalize scores, and adjust temperature for probability distributions.

Expert Aid in Behavior Intervention Plans and MTSS, crafting educational and practical client handouts, inputting test scores, and generating reports.

Interactive tech quiz host with user input and vetted updates.

Creates diverse English-Japanese flashcards from various inputs.

입력하신 문장의 맞춤법을 검사하고 올바른 사용법을 알려드립니다 | Checks the user's Korean input and provides corrections.

Input your work and harvard referencing will be automated for you

input text and outputs scrambled readable text

Input song name to get matching song w same BPM and Key for DJs

Input a word, and GPTs returns SF Symbol names that work best for the input word.

1) Input a URL, attachment, or copy/paste a bunch of info about your biz. 2) I will return a summary of what's important. 3) Use what I give you for other prompts, e.g.: marketing strategy, content ideas, competitive analysis, etc

Drop your requirements here and we'll Simply input, analyze, and refine for corner case detection!

Script Examples and Resource Center for Helping with LAMMPS Input Generation and High-quality Tutorials (SERCHLIGHT)

Takes all input into consideration and creates a JSON-appropriate response. Also useful for creating templates.

Feed me any input and i'll match it with the relevant MITRE ATT&CK techniques and tactics (@mthcht)

Turn any input into a viral tweet in your tone. Publish instantly with just a click.

🇬🇧🇪🇸🇫🇷🇩🇪🇷🇺🇯🇵→🇨🇳 Just input your original text.只需要直接输入你需要翻译的文字。

Looks at every input as on a gird

Generates story beginnings based on user input, tailored for various ages.

Generates PRDs matching the user's input language.

Creates Notion icons from any input

Makes greentexts of whatever you input