Best AI tools for< Identify Areas Where Accessibility Can Be Improved >

IMAGINaiTION is a company that uses artificial intelligence (AI) to create solutions for real-world problems. They believe that AI can be used to make the world a more inclusive and equitable place. One of their projects is a Mobile App Accessibility Audit Tool that uses AI to analyze mobile apps and identify areas where accessibility can be improved. This tool is designed to help developers create more accessible apps that can be used by everyone, regardless of their abilities.

Tute.ai is an AI-powered learning platform that provides personalized learning experiences for students. It uses AI to track student progress, identify areas where they need extra support, and provide them with tailored learning materials. Tute.ai also offers a variety of features to help students learn, such as interactive exercises, quizzes, and games.

Fork.ai is a tool that helps businesses identify technologies used in mobile apps. With Fork.ai, businesses can gain insights into their competitors' tech stacks, identify potential partners, and generate leads. Fork.ai's key features include: - Technology identification: Fork.ai can identify over 1,000 technologies used in mobile apps, including programming languages, frameworks, libraries, and SDKs. - Competitor analysis: Fork.ai provides insights into the technologies used by your competitors, allowing you to identify areas where you can gain a competitive advantage. - Lead generation: Fork.ai can help you generate leads by identifying potential customers who are using specific technologies. - Partnership discovery: Fork.ai can help you identify potential partners who are using complementary technologies.

Learnt.ai is an AI-powered learning platform that provides personalized learning experiences for students. It uses artificial intelligence to analyze student data and identify areas where they need additional support. Learnt.ai then creates personalized learning plans that are tailored to each student's individual needs. The platform also provides students with access to a variety of learning resources, including videos, articles, and interactive exercises.

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.

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.

Hotjar is a website heatmaps and behavior analytics tool that helps businesses understand how users interact with their websites. It provides a suite of tools to track user behavior, including heatmaps, recordings, surveys, and feedback. Hotjar is used by over 1,306,323 websites in 180+ countries.

Amber by inFeedo is an AI-powered employee experience platform that helps organizations collect, analyze, and act on employee feedback. It provides a suite of tools to measure employee engagement, identify areas for improvement, and track progress over time. Amber's AI capabilities enable it to analyze employee feedback in real-time, identify trends and patterns, and provide personalized recommendations to managers. With Amber, organizations can gain a deeper understanding of their employees' needs and create a more positive and productive work environment.

Plerdy is a comprehensive suite of conversion rate optimization tools that helps businesses track, analyze, and convert their website visitors into buyers. With a range of features including website heatmaps, session replay software, pop-up software, website feedback tools, and more, Plerdy provides businesses with the insights they need to improve their website's usability and conversion rates.

We360.ai is an award-winning employee monitoring software that helps businesses track employee productivity, attendance, and time. It offers a range of features such as real-time screenshots, app usage tracking, and detailed reports. We360.ai is designed to help businesses improve efficiency, streamline processes, and make informed decisions.

RightJoin is an AI-powered mock interview platform that helps users prepare for job interviews. It offers personalized interviews tailored to the user's background, role, and company. Users can practice answering common interview questions, receive instant feedback, and identify areas for improvement. RightJoin also provides access to a community of coaches and recruiters for additional support.

Rank Math is an AI-powered SEO tool that helps you optimize your website for search engines. It offers a variety of features to help you improve your website's ranking, including keyword research, on-page optimization, and link building. Rank Math also provides detailed analytics to help you track your progress and identify areas for improvement.

InterviewBoss is an AI-powered interview preparation platform that helps you practice your interviewing skills and get feedback from real interviewers. With InterviewBoss, you can access a library of over 1,000 interview questions, practice your answers with our AI-powered chatbot, and get feedback from real interviewers on your performance. InterviewBoss is the perfect way to prepare for your next job interview and land your dream job.

GymBuddy is an AI-powered workout planner that helps users create personalized and effective workout routines. It uses AI technology to analyze user data and provide tailored exercise recommendations based on their fitness goals and body composition. The app also includes features for tracking progress, monitoring weight, and identifying areas for improvement. With its user-friendly interface and advanced analytics, GymBuddy aims to make fitness accessible and enjoyable for everyone.

Confident AI is an open-source evaluation infrastructure for Large Language Models (LLMs). It provides a centralized platform to judge LLM applications, ensuring substantial benefits and addressing any weaknesses in LLM implementation. With Confident AI, companies can define ground truths to ensure their LLM is behaving as expected, evaluate performance against expected outputs to pinpoint areas for iterations, and utilize advanced diff tracking to guide towards the optimal LLM stack. The platform offers comprehensive analytics to identify areas of focus and features such as A/B testing, evaluation, output classification, reporting dashboard, dataset generation, and detailed monitoring to help productionize LLMs with confidence.

Glean.ai is an AI-powered software designed to enhance accounts payable (AP) processes, making them faster, easier, and smarter. It offers a range of features to streamline AP tasks, including automated data extraction, GL coding, bill approvals and payments, accruals, prepaid amortizations, and more. Glean.ai also provides valuable insights into spending patterns, helping businesses identify areas of overspending and uncover opportunities for cost savings. With its user-friendly interface and robust data benchmarking capabilities, Glean.ai empowers accounting and FP&A teams to collaborate seamlessly, plan effectively, and make informed decisions regarding vendor spend.

VisualHUB is an AI-powered design analysis tool that provides instant insights on UI, UX, readability, and more. It offers features like A/B Testing, UI Analysis, UX Analysis, Readability Analysis, Margin and Hierarchy Analysis, and Competition Analysis. Users can upload product images to receive detailed reports with actionable insights and scores. Trusted by founders and designers, VisualHUB helps optimize design variations and identify areas for improvement in products.

VisualEyes is a user experience (UX) optimization tool that uses attention heatmaps and clarity scores to help businesses improve the effectiveness of their digital products. It provides insights into how users interact with websites and applications, allowing businesses to identify areas for improvement and make data-driven decisions about their designs. VisualEyes is part of Neurons, a leading neuroscience company that specializes in providing AI-powered solutions for businesses.

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.

Salesify is an AI-driven sales coaching tool designed to help sales teams improve their win rates and revenue by providing actionable insights and personalized coaching. The tool leverages AI technology to analyze sales calls, meetings, and customer interactions to identify areas for improvement and optimize the sales process. With features such as speech and language analysis, engagement tracking, and action item identification, Salesify aims to revolutionize sales coaching and drive growth for businesses.

Zshot is a highly customizable framework for performing Zero and Few shot named entity and relationships recognition. It can be used for mentions extraction, wikification, zero and few shot named entity recognition, zero and few shot named relationship recognition, and visualization of zero-shot NER and RE extraction. The framework consists of two main components: the mentions extractor and the linker. There are multiple mentions extractors and linkers available, each serving a specific purpose. Zshot also includes a relations extractor and a knowledge extractor for extracting relations among entities and performing entity classification. The tool requires Python 3.6+ and dependencies like spacy, torch, transformers, evaluate, and datasets for evaluation over datasets like OntoNotes. Optional dependencies include flair and blink for additional functionalities. Zshot provides examples, tutorials, and evaluation methods to assess the performance of the components.

PaperQA is a minimal package for question and answering from PDFs or text files, providing very good answers with in-text citations. It uses OpenAI Embeddings to embed and search documents, and includes a process of embedding docs, queries, searching for top passages, creating summaries, using an LLM to re-score and select relevant summaries, putting summaries into prompt, and generating answers. The tool can be used to answer specific questions related to scientific research by leveraging citations and relevant passages from documents.

Repopack is a powerful tool that packs your entire repository into a single, AI-friendly file. It optimizes your codebase for AI comprehension, is simple to use with customizable options, and respects Gitignore files for security. The tool generates a packed file with clear separators and AI-oriented explanations, making it ideal for use with Generative AI tools like Claude or ChatGPT. Repopack offers command line options, configuration settings, and multiple methods for setting ignore patterns to exclude specific files or directories during the packing process. It includes features like comment removal for supported file types and a security check using Secretlint to detect sensitive information in files.

Parrot.nvim is a Neovim plugin that prioritizes a seamless out-of-the-box experience for text generation. It simplifies functionality and focuses solely on text generation, excluding integration of DALLE and Whisper. It supports persistent conversations as markdown files, custom hooks for inline text editing, multiple providers like Anthropic API, perplexity.ai API, OpenAI API, Mistral API, and local/offline serving via ollama. It allows custom agent definitions, flexible API credential support, and repository-specific instructions with a `.parrot.md` file. It does not have autocompletion or hidden requests in the background to analyze files.

This solution accelerator is built on Azure Cognitive Search Service and Azure OpenAI Service to synthesize post-contact center transcripts for intelligent contact center scenarios. It converts raw transcripts into customer call summaries to extract insights around product and service performance. Key features include conversation summarization, key phrase extraction, speech-to-text transcription, sensitive information extraction, sentiment analysis, and opinion mining. The tool enables data professionals to quickly analyze call logs for improvement in contact center operations.

OpenLIT is an OpenTelemetry-native GenAI and LLM Application Observability tool. It's designed to make the integration process of observability into GenAI projects as easy as pie – literally, with just **a single line of code**. Whether you're working with popular LLM Libraries such as OpenAI and HuggingFace or leveraging vector databases like ChromaDB, OpenLIT ensures your applications are monitored seamlessly, providing critical insights to improve performance and reliability.

**deep** doctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated framework for fine-tuning, evaluating and running models. For more specific text processing tasks use one of the many other great NLP libraries. **deep** doctection focuses on applications and is made for those who want to solve real world problems related to document extraction from PDFs or scans in various image formats. **deep** doctection provides model wrappers of supported libraries for various tasks to be integrated into pipelines. Its core function does not depend on any specific deep learning library. Selected models for the following tasks are currently supported: * Document layout analysis including table recognition in Tensorflow with **Tensorpack**, or PyTorch with **Detectron2**, * OCR with support of **Tesseract**, **DocTr** (Tensorflow and PyTorch implementations available) and a wrapper to an API for a commercial solution, * Text mining for native PDFs with **pdfplumber**, * Language detection with **fastText**, * Deskewing and rotating images with **jdeskew**. * Document and token classification with all LayoutLM models provided by the **Transformer library**. (Yes, you can use any LayoutLM-model with any of the provided OCR-or pdfplumber tools straight away!). * Table detection and table structure recognition with **table-transformer**. * There is a small dataset for token classification available and a lot of new tutorials to show, how to train and evaluate this dataset using LayoutLMv1, LayoutLMv2, LayoutXLM and LayoutLMv3. * Comprehensive configuration of **analyzer** like choosing different models, output parsing, OCR selection. Check this notebook or the docs for more infos. * Document layout analysis and table recognition now runs with **Torchscript** (CPU) as well and **Detectron2** is not required anymore for basic inference. * [**new**] More angle predictors for determining the rotation of a document based on **Tesseract** and **DocTr** (not contained in the built-in Analyzer). * [**new**] Token classification with **LiLT** via **transformers**. We have added a model wrapper for token classification with LiLT and added a some LiLT models to the model catalog that seem to look promising, especially if you want to train a model on non-english data. The training script for LayoutLM can be used for LiLT as well and we will be providing a notebook on how to train a model on a custom dataset soon. **deep** doctection provides on top of that methods for pre-processing inputs to models like cropping or resizing and to post-process results, like validating duplicate outputs, relating words to detected layout segments or ordering words into contiguous text. You will get an output in JSON format that you can customize even further by yourself. Have a look at the **introduction notebook** in the notebook repo for an easy start. Check the **release notes** for recent updates. **deep** doctection or its support libraries provide pre-trained models that are in most of the cases available at the **Hugging Face Model Hub** or that will be automatically downloaded once requested. For instance, you can find pre-trained object detection models from the Tensorpack or Detectron2 framework for coarse layout analysis, table cell detection and table recognition. Training is a substantial part to get pipelines ready on some specific domain, let it be document layout analysis, document classification or NER. **deep** doctection provides training scripts for models that are based on trainers developed from the library that hosts the model code. Moreover, **deep** doctection hosts code to some well established datasets like **Publaynet** that makes it easy to experiment. It also contains mappings from widely used data formats like COCO and it has a dataset framework (akin to **datasets** so that setting up training on a custom dataset becomes very easy. **This notebook** shows you how to do this. **deep** doctection comes equipped with a framework that allows you to evaluate predictions of a single or multiple models in a pipeline against some ground truth. Check again **here** how it is done. Having set up a pipeline it takes you a few lines of code to instantiate the pipeline and after a for loop all pages will be processed through the pipeline.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

Do-Not-Answer is an open-source dataset curated to evaluate Large Language Models' safety mechanisms at a low cost. It consists of prompts to which responsible language models do not answer. The dataset includes human annotations and model-based evaluation using a fine-tuned BERT-like evaluator. The dataset covers 61 specific harms and collects 939 instructions across five risk areas and 12 harm types. Response assessment is done for six models, categorizing responses into harmfulness and action categories. Both human and automatic evaluations show the safety of models across different risk areas. The dataset also includes a Chinese version with 1,014 questions for evaluating Chinese LLMs' risk perception and sensitivity to specific words and phrases.

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.

The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.

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.

Algolisted is a pioneering platform dedicated to algorithmic problem-solving, offering a centralized hub for a diverse array of algorithmic challenges. It provides an immersive online environment for programmers to enhance their skills through Data Structures and Algorithms (DSA) sheets, academic progress tracking, resume refinement with OpenAI integration, adaptive testing, and job opportunity listings. The project is built on the MERN stack, Flask, Beautiful Soup, and Selenium,GEN AI, and deployed on Firebase. Algolisted aims to be a reliable companion in the pursuit of coding knowledge and proficiency.

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.

I'm BizFix, your guide to business optimization using BPI, 5s methods and AI powered Automations.

Optimizes project outcomes through comprehensive post-project evaluations.

Azeroth Data Sage: A detailed Warcraft Log analysis with direct API access. Give the Sage link to a log, ask a question, and the Data Sage will provide!

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I provide Java interview prep and conduct mock interviews.

Evaluates essays, highlights strengths and improvement areas, and justifies scores.

Upload disclosure documents, and I will summarize what's going on, identify red flag areas to look closer at, and answer all Q&A!

Your go-to guide for mastering geometry with ease and fun!

Just send us an image of a scene from a video work and i will guess the name of the work!

Identify and explain mental models for various scenarios.

Analyzes images to identify landmarks and shares historical insights and captivating facts.

Identify and cherish your vintage

Food diary to help you identify food intolerance

Identify and clarify personal values to cultivate a strong sense of purpose and self-confidence

Identify key claims and sniff past the BS with your personal AI Logic Checker and Fallacy Expert.

I confidently identify plants from photos, diagnose issues, and offer advice.

Enables sales & marketing leadership to identify high-value AI use cases

I identify various rocks from images and advise consulting a geologist for certainty.

This interactive inquiry will help identify your relationship attachment style.

Identify your sources of fear and anger and convert those emotions into concrete next steps. Tested and approved by the real Matt Mochary!