Best AI tools for< Duplicate Campaigns >

Quetext is a plagiarism checker and AI content detector that helps students, teachers, and professionals identify potential plagiarism and AI in their work. With its deep search technology, contextual analysis, and smart algorithms, Quetext makes checking writing easier and more accurate. Quetext also offers a variety of features such as bulk uploads, source exclusion, enhanced citation generator, grammar & spell check, and Deep Search. With its rich and intuitive feedback, Quetext helps users find plagiarism and AI with less stress.

Nero Platinum Suite is a comprehensive software collection for Windows PCs that provides a wide range of multimedia capabilities, including burning, managing, optimizing, and editing photos, videos, and music files. It includes various AI-powered features such as the Nero AI Image Upscaler, Nero AI Video Upscaler, and Nero AI Photo Tagger, which enhance and simplify multimedia tasks.

Goodlookup is a smart function for spreadsheet users that gets very close to semantic understanding. It’s a pre-trained model that has the intuition of GPT-3 and the join capabilities of fuzzy matching. Use it like vlookup or index match to speed up your topic clustering work in google sheets!

Duplikate is a next-generation AI-powered Community Management tool designed to assist users in managing their social media accounts more efficiently. It helps users save time by retrieving relevant social media posts, categorizing them, and duplicating them with modifications to better suit their audience. The tool is powered by OpenAI and offers features such as post scraping, filtering, and copying, with upcoming features including image generation. Users have praised Duplikate for its ability to streamline content creation, improve engagement, and save time in managing social media accounts.

Roundtable is an AI-assisted data cleaning tool that improves market research data quality by leveraging human-in-the-loop AI technology. It offers an easy-to-integrate API for cleaning open-ended survey responses, saving users up to 70% of their time. The tool helps in detecting unnatural typing, programmatic entries, and bots, while also providing multilingual functionality for global market deployment. Roundtable is trusted by leaders in over 25 global markets for its efficiency in improving data quality and reducing manual work.

Dart is an AI project management software designed to streamline task execution, subtask generation, project planning, and duplicate detection. It offers features like intelligent task management, sprints, roadmaps, calendar views, document storage, meeting notes, and seamless integrations with other workplace tools. Dart is used by engineering teams, product managers, business leaders, designers, and sales professionals to enhance productivity and project outcomes.

Trust Stamp is a global provider of AI-powered identity services offering a full suite of identity tools, including biometric multi-factor authentication, document validation, identity validation, duplicate detection, and geolocation services. The application is designed to empower organizations across various sectors with advanced biometric identity solutions to reduce fraud, protect personal data privacy, increase operational efficiency, and reach a broader user base worldwide through unique data transformation and comparison capabilities. Founded in 2016, Trust Stamp has achieved significant milestones in net sales, gross profit, and strategic partnerships, positioning itself as a leader in the identity verification industry.

AppZen is an AI-powered application designed for modern finance teams to streamline accounts payable processes, automate invoice and expense auditing, and improve compliance. It offers features such as Autonomous AP for invoice automation, Expense Audit for T&E spend management, and Card Audit for analyzing card spend. AppZen's AI learns and understands business practices, ensures compliance, and integrates with existing systems easily. The application helps prevent duplicate spend, fraud, and FCPA violations, making it a valuable tool for finance professionals.

Snapy is an AI-powered video editing and generation tool that helps content creators create short videos, edit podcasts, and remove silent parts from videos. It offers a range of features such as turning text prompts into short videos, condensing long videos into engaging short clips, automatically removing silent parts from audio files, and auto-trimming, removing duplicate sentences and filler words, and adding subtitles to short videos. Snapy is designed to save time and effort for content creators, allowing them to publish more content, create more engaging videos, and improve the quality of their audio and video content.

The AI photo organizer is a user-friendly web application that utilizes neural networks to help users securely and efficiently organize their photo collections. Users can create custom classes, sort photos, eliminate duplicates, and manage their data locally on their own computer. The application offers a seamless photo management experience, emphasizing simplicity and privacy.

DVC is an open-source platform for managing machine learning data and experiments. It provides a unified interface for working with data from various sources, including local files, cloud storage, and databases. DVC also includes tools for versioning data and experiments, tracking metrics, and automating compute resources. DVC is designed to make it easy for data scientists and machine learning engineers to collaborate on projects and share their work with others.

Lenso.ai is an AI-powered reverse image search tool that allows users to explore billions of images from the web with advanced AI technology. It offers a more accurate and efficient process of reverse image search compared to traditional methods. Users can search for places, people, duplicates, and related images effortlessly. The tool is designed to cater to diverse needs, from professional photographers to marketers and enthusiasts, providing a faster, easier, and more accurate image search experience.

Hints is a sales AI assistant that helps sales reps to get more hours in a day while keeping CRM data accurate automatically. It works with Salesforce, Hubspot, and Pipedrive. With Hints, sales reps can log and retrieve CRM data on any device with chat and voice, get guidance on their next steps, and reminders of what's missing. Hints can also help sales reps to create complex CRM updates in seconds, find duplicates, suggest actions, automatically create associations, and look up sales data through chat and voice commands. Hints can assist sales reps in building the perfect sales process for their team and provides fast onboarding for new sales reps.

Bricks is an AI-first spreadsheet application that simplifies the process of creating and sharing reports, presentations, charts, and visuals using your data. It eliminates the need for advanced spreadsheet expertise, allowing users to effortlessly generate various types of content. Bricks offers a wide range of pre-built templates and tools to enhance productivity and creativity in data analysis and visualization.

Restb.ai is a leading provider of visual insights for real estate companies, utilizing computer vision and AI to analyze property images. The application offers solutions for AVMs, iBuyers, investors, appraisals, inspections, property search, marketing, insurance companies, and more. By providing actionable and unique data at scale, Restb.ai helps improve valuation accuracy, automate manual processes, and enhance property interactions. The platform enables users to leverage visual insights to optimize valuations, automate report quality checks, enhance listings, improve data collection, and more.

The website offers content moderation and image analysis products using powerful APIs to automatically assess, filter, and moderate images, videos, and text. It provides features such as image moderation, video moderation, text moderation, AI image detection, and video anonymization. The application helps in detecting unwanted content, AI-generated images, and personal information in videos. It also offers tools to identify near-duplicates, spam, and abusive links, and prevent phishing and circumvention attempts. The platform is fast, scalable, accurate, easy to integrate, and privacy compliant, making it suitable for various industries like marketplaces, dating apps, and news platforms.

Anea is a unique marketplace for custom-made jewelry, where users can turn their dreams into reality with the help of AI technology. The platform connects customers with Danish goldsmiths to handcraft their personalized jewelry pieces. Anea offers a wide range of jewelry options, from delicate and stackable sets to statement pieces and wedding collections. With a focus on sustainability and ethical sourcing, Anea ensures a secure shopping experience for its users.

QRev is an open-source alternative to Salesforce, offering AI agents to scale sales organizations infinitely. It aims to provide digital workers for various sales roles or a superagent named Qai. The tech stack includes TypeScript for frontend, NodeJS for backend, MongoDB for app server database, ChromaDB for vector database, SQLite for AI server SQL relational database, and Langchain for LLM tooling. The tool allows users to run client app, app server, and AI server components. It requires Node.js and MongoDB to be installed, and provides detailed setup instructions in the README file.

FeedGen is an open-source tool that uses Google Cloud's state-of-the-art Large Language Models (LLMs) to improve product titles, generate more comprehensive descriptions, and fill missing attributes in product feeds. It helps merchants and advertisers surface and fix quality issues in their feeds using Generative AI in a simple and configurable way. The tool relies on GCP's Vertex AI API to provide both zero-shot and few-shot inference capabilities on GCP's foundational LLMs. With few-shot prompting, users can customize the model's responses towards their own data, achieving higher quality and more consistent output. FeedGen is an Apps Script based application that runs as an HTML sidebar in Google Sheets, allowing users to optimize their feeds with ease.

Iceburg CRM is a metadata driven CRM with AI abilities that allows users to quickly prototype any CRM. It offers features like metadata creations, import/export in multiple formats, field validation, themes, role permissions, calendar, audit logs, API, workflow, field level relationships, module level relationships, and more. Created with Vue 3 for the frontend, Laravel 10 for the backend, Tailwinds with DaisyUI plugin, and Inertia for routing. Users can install default, admin panel, core, custom, or AI versions. The tool supports AI Assist for module data suggestions and provides API endpoints for CRM modules, search, specific module data, record updates, and deletions. Iceburg CRM also includes themes, custom field types, calendar, datalets, workflow, roles and permissions, import/export functionality, and custom seeding options.

The repository 'Awesome-LLM4Cybersecurity' provides a comprehensive overview of the applications of Large Language Models (LLMs) in cybersecurity. It includes a systematic literature review covering topics such as constructing cybersecurity-oriented domain LLMs, potential applications of LLMs in cybersecurity, and research directions in the field. The repository analyzes various benchmarks, datasets, and applications of LLMs in cybersecurity tasks like threat intelligence, fuzzing, vulnerabilities detection, insecure code generation, program repair, anomaly detection, and LLM-assisted attacks.

WrenAI is a data assistant tool that helps users get results and insights faster by asking questions in natural language, without writing SQL. It leverages Large Language Models (LLM) with Retrieval-Augmented Generation (RAG) technology to enhance comprehension of internal data. Key benefits include fast onboarding, secure design, and open-source availability. WrenAI consists of three core services: Wren UI (intuitive user interface), Wren AI Service (processes queries using a vector database), and Wren Engine (platform backbone). It is currently in alpha version, with new releases planned biweekly.

AIL framework is a modular framework to analyze potential information leaks from unstructured data sources like pastes from Pastebin or similar services or unstructured data streams. AIL framework is flexible and can be extended to support other functionalities to mine or process sensitive information (e.g. data leak prevention).

AIL framework is a modular framework to analyze potential information leaks from unstructured data sources like pastes from Pastebin or similar services or unstructured data streams. AIL framework is flexible and can be extended to support other functionalities to mine or process sensitive information (e.g. data leak prevention).

Upgini is an intelligent data search engine with a Python library that helps users find and add relevant features to their ML pipeline from various public, community, and premium external data sources. It automates the optimization of connected data sources by generating an optimal set of machine learning features using large language models, GraphNNs, and recurrent neural networks. The tool aims to simplify feature search and enrichment for external data to make it a standard approach in machine learning pipelines. It democratizes access to data sources for the data science community.

Advanced RAG Techniques is a comprehensive collection of cutting-edge Retrieval-Augmented Generation (RAG) tutorials aimed at enhancing the accuracy, efficiency, and contextual richness of RAG systems. The repository serves as a hub for state-of-the-art RAG enhancements, comprehensive documentation, practical implementation guidelines, and regular updates with the latest advancements. It covers a wide range of techniques from foundational RAG methods to advanced retrieval methods, iterative and adaptive techniques, evaluation processes, explainability and transparency features, and advanced architectures integrating knowledge graphs and recursive processing.

Pax is a framework to configure and run machine learning experiments on top of Jax.

The ChatGPT Telegram Bot is a powerful Telegram bot that utilizes various GPT models, including GPT3.5, GPT4, GPT4 Turbo, GPT4 Vision, DALL·E 3, Groq Mixtral-8x7b/LLaMA2-70b, and Claude2.1/Claude3 opus/sonnet API. It enables users to engage in efficient conversations and information searches on Telegram. The bot supports multiple AI models, online search with DuckDuckGo and Google, user-friendly interface, efficient message processing, document interaction, Markdown rendering, and convenient deployment options like Zeabur, Replit, and Docker. Users can set environment variables for configuration and deployment. The bot also provides Q&A functionality, supports model switching, and can be deployed in group chats with whitelisting. The project is open source under GPLv3 license.

Home-Gallery.org is a self-hosted open-source web gallery for browsing personal photos and videos with tagging, mobile-friendly interface, and AI-powered image and face discovery. It aims to provide a fast user experience on mobile phones and help users browse and rediscover memories from their media archive. The tool allows users to serve their local data without relying on cloud services, view photos and videos from mobile phones, and manage images from multiple media source directories. Features include endless photo stream, video transcoding, reverse image lookup, face detection, GEO location reverse lookups, tagging, and more. The tool runs on NodeJS and supports various platforms like Linux, Mac, and Windows.

unsight.dev is a tool built on Nuxt that helps detect duplicate GitHub issues and areas of concern across related repositories. It utilizes Nitro server API routes, GitHub API, and a GitHub App, along with UnoCSS. The tool is deployed on Cloudflare with NuxtHub, using Workers AI, Workers KV, and Vectorize. It also offers a browser extension soon to be released. Users can try the app locally for tweaking the UI and setting up a full development environment as a GitHub App.

Trieve is an advanced relevance API for hybrid search, recommendations, and RAG. It offers a range of features including self-hosting, semantic dense vector search, typo tolerant full-text/neural search, sub-sentence highlighting, recommendations, convenient RAG API routes, the ability to bring your own models, hybrid search with cross-encoder re-ranking, recency biasing, tunable popularity-based ranking, filtering, duplicate detection, and grouping. Trieve is designed to be flexible and customizable, allowing users to tailor it to their specific needs. It is also easy to use, with a simple API and well-documented features.

The LLM-Aided OCR Project is an advanced system that enhances Optical Character Recognition (OCR) output by leveraging natural language processing techniques and large language models. It offers features like PDF to image conversion, OCR using Tesseract, error correction using LLMs, smart text chunking, markdown formatting, duplicate content removal, quality assessment, support for local and cloud-based LLMs, asynchronous processing, detailed logging, and GPU acceleration. The project provides detailed technical overview, text processing pipeline, LLM integration, token management, quality assessment, logging, configuration, and customization. It requires Python 3.12+, Tesseract OCR engine, PDF2Image library, PyTesseract, and optional OpenAI or Anthropic API support for cloud-based LLMs. The installation process involves setting up the project, installing dependencies, and configuring environment variables. Users can place a PDF file in the project directory, update input file path, and run the script to generate post-processed text. The project optimizes processing with concurrent processing, context preservation, and adaptive token management. Configuration settings include choosing between local or API-based LLMs, selecting API provider, specifying models, and setting context size for local LLMs. Output files include raw OCR output and LLM-corrected text. Limitations include performance dependency on LLM quality and time-consuming processing for large documents.

**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.

Swirl is an open-source software that allows users to simultaneously search multiple content sources and receive AI-ranked results. It connects to various data sources, including databases, public data services, and enterprise sources, and utilizes AI and LLMs to generate insights and answers based on the user's data. Swirl is easy to use, requiring only the download of a YML file, starting in Docker, and searching with Swirl. Users can add credentials to preloaded SearchProviders to access more sources. Swirl also offers integration with ChatGPT as a configured AI model. It adapts and distributes user queries to anything with a search API, re-ranking the unified results using Large Language Models without extracting or indexing anything. Swirl includes five Google Programmable Search Engines (PSEs) to get users up and running quickly. Key features of Swirl include Microsoft 365 integration, SearchProvider configurations, query adaptation, synchronous or asynchronous search federation, optional subscribe feature, pipelining of Processor stages, results stored in SQLite3 or PostgreSQL, built-in Query Transformation support, matching on word stems and handling of stopwords, duplicate detection, re-ranking of unified results using Cosine Vector Similarity, result mixers, page through all results requested, sample data sets, optional spell correction, optional search/result expiration service, easily extensible Connector and Mixer objects, and a welcoming community for collaboration and support.

This repository contains the training and inference code needed to replicate the winning solution to the AI Mathematical Olympiad - Progress Prize 1. It consists of fine-tuning DeepSeekMath-Base 7B, high-quality training datasets, a self-consistency decoding algorithm, and carefully chosen validation sets. The training methodology involves Chain of Thought (CoT) and Tool Integrated Reasoning (TIR) training stages. Two datasets, NuminaMath-CoT and NuminaMath-TIR, were used to fine-tune the models. The models were trained using open-source libraries like TRL, PyTorch, vLLM, and DeepSpeed. Post-training quantization to 8-bit precision was done to improve performance on Kaggle's T4 GPUs. The project structure includes scripts for training, quantization, and inference, along with necessary installation instructions and hardware/software specifications.

Create, analyze & duplicate customized automated message campaigns to boost retention & drive revenue for your website or app

Type “Start” and Get Exact Details on Image Generation and/or Duplication

Maintain the originality of your work with our Plagiarism Checker. This plagiarism checker identifies duplicate content, ensuring your work's uniqueness and integrity.

完美复制输入的文字

I guide users on obtaining receipts from Web services.

Confirms image uploads without analysis or detail.

Checks blogs for Google plugin plagiarism.

Active, formal analyst of potential name duplicates.

Fix mismatched entries in your data

Specializes in organizing business card data into various formats

Provide the information about each receipt from your shopping.