Best AI tools for< Mask Sensitive Information >

Syntho is a self-service AI-generated synthetic data platform that offers a comprehensive solution for generating synthetic data for various purposes. It provides tools for de-identification, test data management, rule-based synthetic data generation, data masking, and more. With a focus on privacy and accuracy, Syntho enables users to create synthetic data that mirrors real production data while ensuring compliance with regulations and data privacy standards. The platform offers a range of features and use cases tailored to different industries, including healthcare, finance, and public organizations.

Protecto is an Enterprise AI Data Security & Privacy Guardrails application that offers solutions for protecting sensitive data in AI applications. It helps organizations maintain data security and compliance with regulations like HIPAA, GDPR, and PCI. Protecto identifies and masks sensitive data while retaining context and semantic meaning, ensuring accuracy in AI applications. The application provides custom scans, unmasking controls, and versatile data protection across structured, semi-structured, and unstructured text. It is preferred by leading Gen AI companies for its robust and cost-effective data security solutions.

Undress App is an AI tool that allows users to nudify any person in a photo using AI technology. The application provides a 3-step instruction on how to achieve this, emphasizing the importance of photo quality and proper positioning of the person in the image. It discusses the ethical implications of using AI to undress individuals and highlights the creative and potentially harmful uses of such technology. Undress App aims to offer a platform for creative self-expression and entertainment while acknowledging the potential misuse of the tool.

TUNiB is an AI application that specializes in creating conversational AI that people emotionally engage with. It offers services such as NLP APIs for detecting hate speech and breaches of private information, safety checks for toxicity detection, de-identification for masking personal information, and various analytics services like text, image, news, and video analytics. TUNiB aims to provide cutting-edge technologies while upholding high ethical standards.

DeepMake is a powerful AI tool that empowers users to unleash their creativity by providing control over Open Source AI tools for enhancing visual content. With DeepMake, users can create, edit, and enhance images and videos without any usage limits or reliance on cloud services. The application runs locally on the user's computer, offering a higher level of control over AI-generated output and introducing new AI tools regularly to stay at the forefront of AI capabilities.

Tovie AI is a platform offering Generative AI and on-prem LLM solutions for enterprise applications. It provides a range of AI tools such as GenAI Agents, Data Agent, Data Mask, AI Bots, Voice Bots, Chatbots, and Sector-specific Assistants. Tovie AI aims to enhance customer service, boost employee engagement, and improve business operations through its tailored AI solutions. The platform also offers consulting services to help businesses leverage Generative AI for business optimization and growth.

Virtual Staging is an AI-powered tool that allows you to virtually stage your empty rooms in seconds. Simply take a picture of your empty room, mask it, and get it staged in seconds. Virtual Staging is the only solution you need for virtual staging. It is realistic, lightning fast, and cheap. You can pay $1 instead of $25 for virtual staging.

NeMo Guardrails is an open-source toolkit for easily adding _programmable guardrails_ to LLM-based conversational applications. Guardrails (or "rails" for short) are specific ways of controlling the output of a large language model, such as not talking about politics, responding in a particular way to specific user requests, following a predefined dialog path, using a particular language style, extracting structured data, and more.

LitData is a tool designed for blazingly fast, distributed streaming of training data from any cloud storage. It allows users to transform and optimize data in cloud storage environments efficiently and intuitively, supporting various data types like images, text, video, audio, geo-spatial, and multimodal data. LitData integrates smoothly with frameworks such as LitGPT and PyTorch, enabling seamless streaming of data to multiple machines. Key features include multi-GPU/multi-node support, easy data mixing, pause & resume functionality, support for profiling, memory footprint reduction, cache size configuration, and on-prem optimizations. The tool also provides benchmarks for measuring streaming speed and conversion efficiency, along with runnable templates for different data types. LitData enables infinite cloud data processing by utilizing the Lightning.ai platform to scale data processing with optimized machines.

K8sGPT is a tool for scanning your Kubernetes clusters, diagnosing, and triaging issues in simple English. It has SRE experience codified into its analyzers and helps to pull out the most relevant information to enrich it with AI.

Deep-Live-Cam is a software tool designed to assist artists in tasks such as animating custom characters or using characters as models for clothing. The tool includes built-in checks to prevent unethical applications, such as working on inappropriate media. Users are expected to use the tool responsibly and adhere to local laws, especially when using real faces for deepfake content. The tool supports both CPU and GPU acceleration for faster processing and provides a user-friendly GUI for swapping faces in images or videos.

NextChat is a well-designed cross-platform ChatGPT web UI tool that supports Claude, GPT4, and Gemini Pro. It offers a compact client for Linux, Windows, and MacOS, with features like self-deployed LLMs compatibility, privacy-first data storage, markdown support, responsive design, and fast loading speed. Users can create, share, and debug chat tools with prompt templates, access various prompts, compress chat history, and use multiple languages. The tool also supports enterprise-level privatization and customization deployment, with features like brand customization, resource integration, permission control, knowledge integration, security auditing, private deployment, and continuous updates.

qapyq is an image viewer and AI-assisted editing tool designed to help curate datasets for generative AI models. It offers features such as image viewing, editing, captioning, batch processing, and AI assistance. Users can perform tasks like cropping, scaling, editing masks, tagging, and applying sorting and filtering rules. The tool supports state-of-the-art captioning and masking models, with options for model settings, GPU acceleration, and quantization. qapyq aims to streamline the process of preparing images for training AI models by providing a user-friendly interface and advanced functionalities.

ChatGPT Next Web is a well-designed cross-platform ChatGPT web UI tool that supports Claude, GPT4, and Gemini Pro models. It allows users to deploy their private ChatGPT applications with ease. The tool offers features like one-click deployment, compact client for Linux/Windows/MacOS, compatibility with self-deployed LLMs, privacy-first approach with local data storage, markdown support, responsive design, fast loading speed, prompt templates, awesome prompts, chat history compression, multilingual support, and more.

Awesome-LLM-Quantization is a curated list of resources related to quantization techniques for Large Language Models (LLMs). Quantization is a crucial step in deploying LLMs on resource-constrained devices, such as mobile phones or edge devices, by reducing the model's size and computational requirements.

This repository contains a collection of interview questions related to various topics such as statistics, machine learning, deep learning, Python, networking, operating systems, data structures, and algorithms. The questions cover a wide range of concepts and are suitable for individuals preparing for technical interviews in the field of artificial intelligence and data science.

The Electronic Component Classifier is a project that uses machine learning and artificial intelligence to automate the identification and classification of electrical and electronic components. It features component classification into seven classes, user-friendly design, and integration with Flask for a user-friendly interface. The project aims to reduce human error in component identification, make the process safer and more reliable, and potentially help visually impaired individuals in identifying electronic components.

Awesome LLM compression research papers and tools to accelerate LLM training and inference.

Quantus is a toolkit designed for the evaluation of neural network explanations. It offers more than 30 metrics in 6 categories for eXplainable Artificial Intelligence (XAI) evaluation. The toolkit supports different data types (image, time-series, tabular, NLP) and models (PyTorch, TensorFlow). It provides built-in support for explanation methods like captum, tf-explain, and zennit. Quantus is under active development and aims to provide a comprehensive set of quantitative evaluation metrics for XAI methods.

MFLUX is a line-by-line port of the FLUX implementation in the Huggingface Diffusers library to Apple MLX. It aims to run powerful FLUX models from Black Forest Labs locally on Mac machines. The codebase is minimal and explicit, prioritizing readability over generality and performance. Models are implemented from scratch in MLX, with tokenizers from the Huggingface Transformers library. Dependencies include Numpy and Pillow for image post-processing. Installation can be done using `uv tool` or classic virtual environment setup. Command-line arguments allow for image generation with specified models, prompts, and optional parameters. Quantization options for speed and memory reduction are available. LoRA adapters can be loaded for fine-tuning image generation. Controlnet support provides more control over image generation with reference images. Current limitations include generating images one by one, lack of support for negative prompts, and some LoRA adapters not working.

Cool-GenAI-Fashion-Papers is a curated list of resources related to GenAI-Fashion, including papers, workshops, companies, and products. It covers a wide range of topics such as fashion design synthesis, outfit recommendation, fashion knowledge extraction, trend analysis, and more. The repository provides valuable insights and resources for researchers, industry professionals, and enthusiasts interested in the intersection of AI and fashion.

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