Best AI tools for< Check Format >

Bibit AI is a real estate marketing AI designed to enhance the efficiency and effectiveness of real estate marketing and sales. It can help create listings, descriptions, and property content, and offers a host of other features. Bibit AI is the world's first AI for Real Estate. We are transforming the real estate industry by boosting efficiency and simplifying tasks like listing creation and content generation.

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.

Duplichecker is an AI-based plagiarism checker tool that offers accurate detection of plagiarism in text content. It provides multiple file format support, privacy guarantee, AI-based technology, multilingual support, writing enhancements, fast and deep scanning, and highlights duplication. The tool is developed using AI technology to detect minor traces of plagiarism and identify paraphrased content. It is widely used by writers, teachers, students, bloggers, and webmasters to ensure content originality and avoid copyright infringement.

AI Powered Resume Checker is an AI-driven resume review tool that helps job seekers create impactful resumes that stand out in the job market. With detailed analysis, tailored suggestions, keyword optimization, and formatting tips, the tool empowers users to elevate their resumes and increase their chances of getting noticed by potential employers. The resume review is crafted with insights from industry experts, career coaches, and hiring managers, providing valuable guidance to job seekers.

Talkatoo is a dictation software that uses AI to help veterinarians save time and increase productivity. It offers three levels of control, so you can choose how hands-off you want to be. With Verified, you can simply record your notes and our scribes will verify the accuracy and place them in your PMS for you. With Auto-SOAP Records, you can record an entire exam or dictate your notes after and have Talkatoo auto-magically format the recording into a SOAP note, or other template. With Desktop Dictation, you can dictate in any field, in any app, on Mac or Windows. You can even connect your mobile device as a secure microphone to make the process easier.

Plag is an AI-powered platform that focuses on academic integrity, studies, and artificial intelligence. It offers solutions for students, educators, universities, and businesses in the areas of plagiarism detection, plagiarism removal, text formatting, and proofreading. The platform utilizes multilingual artificial intelligence technology to provide users with advanced tools to enhance their academic work and ensure originality.

Numberly is a free online math assistant that helps you solve equations, perform conversions, and check your calculations as you type. It integrates with your favorite websites and apps, so you can use it anywhere you need to do math. Numberly is perfect for students, professionals, and anyone who wants to make math easier.

Resumecheck.net is an AI-powered resume improvement platform that helps users create error-free, professional resumes that stand out to recruiters. The platform uses GPT4 technology to provide personalized feedback and suggestions, including grammar corrections, formatting adjustments, and industry-specific keyword optimization. Additionally, Resumecheck.net offers an AI Cover Letter Writer that generates tailored cover letters based on the user's resume and the specific job position they are applying for.

Rezi is the leading AI resume builder trusted by over 2 million users. It automates the process of creating a professional resume by utilizing artificial intelligence to write, edit, format, and optimize resumes. Rezi offers features such as AI resume editing, summary generation, keyword scanning, ATS resume checking, cover letter writing, interview practice, and resignation letter creation. The platform aims to help job seekers improve their chances of landing interviews by tailoring resumes with targeted keywords and ensuring content quality. With a user-friendly interface and a range of templates, Rezi simplifies the resume-building process and provides valuable resources for job seekers.

PaletteMaker is a unique tool for creative professionals and color lovers that allows you to create color palettes and test their behavior in pre-made design examples from the most common creative fields such as Logo design, UI/UX, Patterns, Posters and more. Check Color Behavior See how color works together in various of situations in graphic design. AI Color Palettes Filter palettes of different color tone and number of colors. Diverse Creative Fields Check your colors on logo, ui design, posters, illustrations and more. Create Palettes On-The-Go Instantly see the magic of creating color palettes. Totally Free PaletteMaker is created by professional designers, it’s completely free to use and forever will be. Powerful Export Export your palette in various formats, such as Procreate, Adobe ASE, Image, and even Code.

Trinka is an AI-powered English grammar checker and language enhancement writing assistant designed for academic and technical writing. It corrects contextual spelling mistakes and advanced grammar errors by providing writing suggestions in real-time. Trinka helps professionals and academics ensure formal, concise, and engaging writing.

Trinka is an AI-powered English grammar checker and language enhancement writing assistant designed for academic and technical writing. It corrects contextual spelling mistakes and advanced grammar errors by providing writing suggestions in real-time. Trinka helps professionals and academics ensure formal, concise, and engaging writing. Trinka's Enterprise solutions come with unlimited access and great customization options to all of Trinka's powerful capabilities.

Edde.ai is an AI-powered academic writing assistant designed to streamline the academic journey of students and researchers. It offers a range of tools such as real-time suggestions, grammar checks, and stylistic improvements to enhance writing quality. Additionally, Edde.ai provides features like title and outline generation, research helper, citation manager, subject guides, and question solver to simplify the research process. With transparent pricing and a user-friendly interface, Edde.ai aims to empower users in creating high-quality academic content.

The website offers an AI-powered binary vulnerability scanner that allows users to upload a binary file for analysis. The tool decompiles the executable, removes filler, formats the code, and checks for vulnerabilities by comparing against a database of historical vulnerabilities. It utilizes a finetuned CodeT5+ Embedding model to generate function-wise embeddings and checks for similarities against the DiverseVul Dataset. The tool also uses SemGrep to identify vulnerabilities in the code.

The AI Story Generator Tool, VED, is a powerful script and screenplay writing software designed to help users create captivating stories, iconic characters, and legendary universes. It offers features such as smart research, precision quality checks, easy import/export, mentor-guided feedback, and industry-standard formatting. VED acts as a co-pilot, assisting users in transforming their imagination into pitch-perfect narratives with ease and efficiency. The tool aims to streamline the writing process, enhance collaboration, and provide valuable insights to writers of all levels.

Essay Check is a free AI-powered tool that helps students, teachers, content creators, SEO specialists, and legal experts refine their writing, detect plagiarism, and identify AI-generated content. With its user-friendly interface and advanced algorithms, Essay Check analyzes text to identify grammatical errors, spelling mistakes, instances of plagiarism, and the likelihood that content was written using AI. The tool provides detailed feedback and suggestions to help users improve their writing and ensure its originality and authenticity.

Check Typo is an AI-powered spell-checker tool designed to assist users in eliminating typos and grammatical errors from their writing. It seamlessly integrates within various websites, supports multiple languages, and preserves the original text's style and tone. Ideal for students, professionals, and writers, Check Typo enhances the writing experience with AI-driven precision, making it perfect for error-free emails, professional networking on platforms like LinkedIn, and enhancing social media posts across different platforms.

Copyright Check AI is a service that helps protect brands from legal disputes related to copyright violations on social media. The software automatically detects copyright infringements on social profiles, reducing the risk of costly legal action. It is used by Heads of Marketing and In-House Counsel at top brands to avoid lawsuits and potential damages. The service offers a done-for-you audit to highlight violations, deliver reports, and provide ongoing monitoring to ensure brand protection.

Fact Check Anything (FCA) is a browser extension that allows users to fact-check information on the internet. It uses AI to verify statements and provide users with reliable sources. FCA is available for all browsers using the Chromium engine on Windows or MacOS. It is easy to use and can be used on any website. FCA is a valuable tool for anyone who wants to stay informed and fight against misinformation.

Rizz Check is a swipe game where users can befriend AI celebrities and ask them on dates. The game is built with Rizz, a library created by boredhead00.

**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.

A frontend for Trading Strategy protocol.

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. * **Highly performant** : web layer and task coordination built with Rust 🦀, which offers blazing speed in addition to efficient CPU utilization powered by async I/O * **Ease of use** : user interface purely in Python 🐍, by which users can serve their models in an ML framework-agnostic manner using the same code as they do for offline testing * **Dynamic batching** : aggregate requests from different users for batched inference and distribute results back * **Pipelined stages** : spawn multiple processes for pipelined stages to handle CPU/GPU/IO mixed workloads * **Cloud friendly** : designed to run in the cloud, with the model warmup, graceful shutdown, and Prometheus monitoring metrics, easily managed by Kubernetes or any container orchestration systems * **Do one thing well** : focus on the online serving part, users can pay attention to the model optimization and business logic

AirCasting is a platform for gathering, visualizing, and sharing environmental data. It aims to provide a central hub for environmental data, making it easier for people to access and use this information to make informed decisions about their environment.

ManipVQA is a framework that enhances Multimodal Large Language Models (MLLMs) with manipulation-centric knowledge through a Visual Question-Answering (VQA) format. It addresses the deficiency of conventional MLLMs in understanding affordances and physical concepts crucial for manipulation tasks. By infusing robotics-specific knowledge, including tool detection, affordance recognition, and physical concept comprehension, ManipVQA improves the performance of robots in manipulation tasks. The framework involves fine-tuning MLLMs with a curated dataset of interactive objects, enabling robots to understand and execute natural language instructions more effectively.

AI for the command line, built for pipelines. LLM based AI is really good at interpreting the output of commands and returning the results in CLI friendly text formats like Markdown. Mods is a simple tool that makes it super easy to use AI on the command line and in your pipelines. Mods works with OpenAI, Groq, Azure OpenAI, and LocalAI To get started, install Mods and check out some of the examples below. Since Mods has built-in Markdown formatting, you may also want to grab Glow to give the output some _pizzazz_.

This simple package can be used to fix an invalid json string. To know all cases in which this package will work, check out the unit test. Inspired by https://github.com/josdejong/jsonrepair Motivation Some LLMs are a bit iffy when it comes to returning well formed JSON data, sometimes they skip a parentheses and sometimes they add some words in it, because that's what an LLM does. Luckily, the mistakes LLMs make are simple enough to be fixed without destroying the content. I searched for a lightweight python package that was able to reliably fix this problem but couldn't find any. So I wrote one How to use from json_repair import repair_json good_json_string = repair_json(bad_json_string) # If the string was super broken this will return an empty string You can use this library to completely replace `json.loads()`: import json_repair decoded_object = json_repair.loads(json_string) or just import json_repair decoded_object = json_repair.repair_json(json_string, return_objects=True) Read json from a file or file descriptor JSON repair provides also a drop-in replacement for `json.load()`: import json_repair try: file_descriptor = open(fname, 'rb') except OSError: ... with file_descriptor: decoded_object = json_repair.load(file_descriptor) and another method to read from a file: import json_repair try: decoded_object = json_repair.from_file(json_file) except OSError: ... except IOError: ... Keep in mind that the library will not catch any IO-related exception and those will need to be managed by you Performance considerations If you find this library too slow because is using `json.loads()` you can skip that by passing `skip_json_loads=True` to `repair_json`. Like: from json_repair import repair_json good_json_string = repair_json(bad_json_string, skip_json_loads=True) I made a choice of not using any fast json library to avoid having any external dependency, so that anybody can use it regardless of their stack. Some rules of thumb to use: - Setting `return_objects=True` will always be faster because the parser returns an object already and it doesn't have serialize that object to JSON - `skip_json_loads` is faster only if you 100% know that the string is not a valid JSON - If you are having issues with escaping pass the string as **raw** string like: `r"string with escaping\"" Adding to requirements Please pin this library only on the major version! We use TDD and strict semantic versioning, there will be frequent updates and no breaking changes in minor and patch versions. To ensure that you only pin the major version of this library in your `requirements.txt`, specify the package name followed by the major version and a wildcard for minor and patch versions. For example: json_repair==0.* In this example, any version that starts with `0.` will be acceptable, allowing for updates on minor and patch versions. How it works This module will parse the JSON file following the BNF definition: ::= | ::= | | ; Where: ; is a valid real number expressed in one of a number of given formats ; is a string of valid characters enclosed in quotes ; is one of the literal strings 'true', 'false', or 'null' (unquoted) ::=