Best AI tools for< Loop Custom Texts >

Tangia is an interactive streaming tool designed to enhance the streaming experience for content creators and viewers. It offers custom text-to-speech interactions, alerts, media sharing capabilities, monitor overlays, and charity integration. With a focus on engagement and community interaction, Tangia provides a wide range of features to create dynamic and entertaining streams. Users can personalize their interactions, incorporate memes, soundbites, and AI conversations, and access a vast library of memes and tools. Tangia aims to revolutionize the streaming experience by combining cutting-edge technology with a tight feedback loop to develop next-gen streaming tools.

Cradl AI is an AI-powered tool designed to automate document workflows with no-code AI. It enables users to extract data from any document automatically, integrate with no-code tools, and build custom AI models through an easy-to-use interface. The tool empowers automation teams across industries by extracting data from complex document layouts, regardless of language or structure. Cradl AI offers features such as line item extraction, fine-tuning AI models, human-in-the-loop validation, and seamless integration with automation tools. It is trusted by organizations for business-critical document automation, providing enterprise-level features like encrypted transmission, GDPR compliance, secure data handling, and auto-scaling.

Lyzr AI is a full-stack agent framework designed to build GenAI applications faster. It offers a range of AI agents for various tasks such as chatbots, knowledge search, summarization, content generation, and data analysis. The platform provides features like memory management, human-in-loop interaction, toxicity control, reinforcement learning, and custom RAG prompts. Lyzr AI ensures data privacy by running data locally on cloud servers. Enterprises and developers can easily configure, deploy, and manage AI agents using Lyzr's platform.

Convert AI is an AI-powered B2B lead generation and appointment setting tool that helps businesses scale their growth by providing intelligent prospect data, on-demand human sales talent, and multi-channel campaigns. The tool leverages AI to generate highly targeted lead lists, execute smart campaigns, deploy multi-channel sequences, create a sales feedback loop, and develop custom playbooks to optimize lead quality over time. Convert AI has been rated 5 stars on Clutch and has successfully closed numerous deals for various clients across different industries.

Derwen is an open-source integration platform for production machine learning in enterprise, specializing in natural language processing, graph technologies, and decision support. It offers expertise in developing knowledge graph applications and domain-specific authoring. Derwen collaborates closely with Hugging Face and provides strong data privacy guarantees, low carbon footprint, and no cloud vendor involvement. The platform aims to empower AI engineers and domain experts with quality, time-to-value, and ownership since 2017.

Relay.app is an AI-powered automation platform that sets a new standard for automation by combining AI capabilities with human-in-the-loop functionality. It allows users to create sophisticated workflows with ease, integrating various apps and services to streamline processes and improve efficiency. With features like built-in AI actions, mini agents, and human-in-the-loop automation, Relay.app empowers users to automate tasks intelligently while maintaining control and oversight. The platform is praised for its user-friendly interface, extensive integrations, and exceptional customer support.

HubSpot is a leading provider of customer relationship management (CRM) software and services. Its platform includes a variety of tools for marketing, sales, customer service, and operations. HubSpot's mission is to help businesses grow better by providing them with the tools and resources they need to succeed.

OpenAI Sora is a text-to-video model that can generate realistic and imaginative video scenes from text instructions. It's designed to simulate the physical world in motion, generating videos up to a minute long while maintaining visual quality and adhering to the user's prompt.

The website offers a precision workflow solution for enterprises in the finance industry, combining AI technology with human oversight to empower financial decisions. It provides features such as accurate search citations, multilingual models, and complex human-in-loop automation. The application integrates seamlessly with existing platforms, uses advanced AI models, and offers meaningful time savings. Users can benefit from the application's ability to ingest unstructured data, improve over time, and avoid hallucinations.

The Notion Automation Hub is a website that provides pre-built Notion automations and databases to help users save time and improve their productivity. The website offers a variety of automations for different use cases, including job roles, workflows, and tasks. Users can also find pre-built database templates, Notion expert resources, and automation tools. The website is not affiliated with Notion Labs Inc.

Olvy is an AI-powered platform that helps businesses manage user feedback efficiently. It offers a unified workspace for surveys, interviews, reviews, support tickets, and sales calls, all automated using state-of-the-art AI technology. Olvy turns customer feedback into actionable insights, empowering decision-making with data-driven insights and enabling companies to stay connected to their users' needs. It provides features like automated feedback organization, integration with multiple sources, and AI-generated summaries to enhance productivity and efficiency.

Ziggy is an AI-powered platform that automates 1-on-1 conversations, enabling users to conduct feedback interviews seamlessly with anyone, anytime. It helps businesses gather insightful and more natural responses from users than static forms ever could. Ziggy's commitment to collaboration and creativity helps teams work together to get the best results. Its powerful analytical tools help ensure users understand their users' wants and needs. With Ziggy, users can see the original responses from their users and use them to make better decisions.

BluePond GenAI PaaS is an automation and insights powerhouse tailored for Property and Casualty Insurance. It offers end-to-end execution support from GenAI data scientists, engineers & human-in-the-loop processing. The platform provides automated intake extraction, classification enrichment, validation, complex document analysis, workflow automation, and decisioning. Users benefit from rapid deployment, complete control of data & IP, and pre-trained P&C domain library. BluePond GenAI PaaS aims to energize and expedite GenAI initiatives throughout the insurance value chain.

SwiftieGPT is an AI tool designed to provide detailed information about Taylor Swift based on publicly available data. Users can ask questions in a clear and concise manner and receive responses related to various aspects of Taylor Swift's life and career, such as tour dates, song lyrics, awards, and more. The tool aims to offer Swifties a comprehensive source of information about their favorite artist, Taylor Swift.

Weavel is an AI tool designed to revolutionize prompt engineering for large language models (LLMs). It offers features such as tracing, dataset curation, batch testing, and evaluations to enhance the performance of LLM applications. Weavel enables users to continuously optimize prompts using real-world data, prevent performance regression with CI/CD integration, and engage in human-in-the-loop interactions for scoring and feedback. Ape, the AI prompt engineer, outperforms competitors on benchmark tests and ensures seamless integration and continuous improvement specific to each user's use case. With Weavel, users can effortlessly evaluate LLM applications without the need for pre-existing datasets, streamlining the assessment process and enhancing overall performance.

Toloka AI is a data labeling platform that empowers AI development by combining human insight with machine learning models. It offers adaptive AutoML, human-in-the-loop workflows, large language models, and automated data labeling. The platform supports various AI solutions with human input, such as e-commerce services, content moderation, computer vision, and NLP. Toloka AI aims to accelerate machine learning processes by providing high-quality human-labeled data and leveraging the power of the crowd.

Super.AI provides Intelligent Document Processing (IDP) solutions powered by Large Language Models (LLMs) and human-in-the-loop (HITL) capabilities. It automates document processing tasks such as data extraction, classification, and redaction, enabling businesses to streamline their workflows and improve accuracy. Super.AI's platform leverages cutting-edge AI models from providers like Amazon, Google, and OpenAI to handle complex documents, ensuring high-quality outputs. With its focus on accuracy, flexibility, and scalability, Super.AI caters to various industries, including financial services, insurance, logistics, and healthcare.

Ocrolus is an intelligent document automation software that leverages AI-driven document processing automation with Human-in-the-Loop. It helps in classifying, capturing, detecting, and analyzing various types of documents to streamline processes and make faster and more accurate financial decisions. The software is designed to assist in tasks such as income verification, fraud detection, cash flow analysis, and business process automation across different industries.

HuLoop Automation is an AI-powered platform that offers intelligent automation solutions for businesses. It provides a no-code platform for automating complex business processes, test automation, and productivity discovery. With a focus on human-in-the-loop decisioning, HuLoop aims to simplify automation implementation and make it fast and affordable for companies. The platform integrates with existing technologies, offers data-driven recommendations, and helps organizations optimize their processes for increased efficiency and productivity.

The Human-Centred Artificial Intelligence Lab (Holzinger Group) is a research group focused on developing AI solutions that are explainable, trustworthy, and aligned with human values, ethical principles, and legal requirements. The lab works on projects related to machine learning, digital pathology, interactive machine learning, and more. Their mission is to combine human and computer intelligence to address pressing problems in various domains such as forestry, health informatics, and cyber-physical systems. The lab emphasizes the importance of explainable AI, human-in-the-loop interactions, and the synergy between human and machine intelligence.

Luna AI is a virtual streamer driven by a 'brain' composed of ChatterBot, GPT, Claude, langchain, chatglm, text-generation-webui, 讯飞星火, 智谱AI. It can interact with viewers in real-time during live streams on platforms like Bilibili, Douyin, Kuaishou, Douyu, or chat with you locally. Luna AI uses natural language processing and text-to-speech technologies like Edge-TTS, VITS-Fast, elevenlabs, bark-gui, VALL-E-X to generate responses to viewer questions and can change voice using so-vits-svc, DDSP-SVC. It can also collaborate with Stable Diffusion for drawing displays and loop custom texts. This project is completely free, and any identical copycat selling programs are pirated, please stop them promptly.

ModelFusion is an abstraction layer for integrating AI models into JavaScript and TypeScript applications, unifying the API for common operations such as text streaming, object generation, and tool usage. It provides features to support production environments, including observability hooks, logging, and automatic retries. You can use ModelFusion to build AI applications, chatbots, and agents. ModelFusion is a non-commercial open source project that is community-driven. You can use it with any supported provider. ModelFusion supports a wide range of models including text generation, image generation, vision, text-to-speech, speech-to-text, and embedding models. ModelFusion infers TypeScript types wherever possible and validates model responses. ModelFusion provides an observer framework and logging support. ModelFusion ensures seamless operation through automatic retries, throttling, and error handling mechanisms. ModelFusion is fully tree-shakeable, can be used in serverless environments, and only uses a minimal set of dependencies.

AIlice is a fully autonomous, general-purpose AI agent that aims to create a standalone artificial intelligence assistant, similar to JARVIS, based on the open-source LLM. AIlice achieves this goal by building a "text computer" that uses a Large Language Model (LLM) as its core processor. Currently, AIlice demonstrates proficiency in a range of tasks, including thematic research, coding, system management, literature reviews, and complex hybrid tasks that go beyond these basic capabilities. AIlice has reached near-perfect performance in everyday tasks using GPT-4 and is making strides towards practical application with the latest open-source models. We will ultimately achieve self-evolution of AI agents. That is, AI agents will autonomously build their own feature expansions and new types of agents, unleashing LLM's knowledge and reasoning capabilities into the real world seamlessly.

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.

**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.

Use the OpenAI API with Ruby! 🤖🩵 Stream text with GPT-4, transcribe and translate audio with Whisper, or create images with DALL·E... Hire me | 🎮 Ruby AI Builders Discord | 🐦 Twitter | 🧠 Anthropic Gem | 🚂 Midjourney Gem ## Table of Contents * Ruby OpenAI * Table of Contents * Installation * Bundler * Gem install * Usage * Quickstart * With Config * Custom timeout or base URI * Extra Headers per Client * Logging * Errors * Faraday middleware * Azure * Ollama * Counting Tokens * Models * Examples * Chat * Streaming Chat * Vision * JSON Mode * Functions * Edits * Embeddings * Batches * Files * Finetunes * Assistants * Threads and Messages * Runs * Runs involving function tools * Image Generation * DALL·E 2 * DALL·E 3 * Image Edit * Image Variations * Moderations * Whisper * Translate * Transcribe * Speech * Errors * Development * Release * Contributing * License * Code of Conduct

Phi-3-MLX is a versatile AI framework that leverages both the Phi-3-Vision multimodal model and the Phi-3-Mini-128K language model optimized for Apple Silicon using the MLX framework. It provides an easy-to-use interface for a wide range of AI tasks, from advanced text generation to visual question answering and code execution. The project features support for batched generation, flexible agent system, custom toolchains, model quantization, LoRA fine-tuning capabilities, and API integration for extended functionality.

ONNX Runtime Generative AI is a library that provides the generative AI loop for ONNX models, including inference with ONNX Runtime, logits processing, search and sampling, and KV cache management. Users can call a high level `generate()` method, or run each iteration of the model in a loop. It supports greedy/beam search and TopP, TopK sampling to generate token sequences, has built in logits processing like repetition penalties, and allows for easy custom scoring.

This starter project is designed to process `.mdx` files in the `pages` directory to use as custom context within OpenAI Text Completion prompts. It involves building a custom ChatGPT style doc search powered by Next.js, OpenAI, and Supabase. The project includes steps for pre-processing knowledge base, storing embeddings in Postgres, performing vector similarity search, and injecting content into OpenAI GPT-3 text completion prompt.

dingllm.nvim is a lightweight configuration for Neovim that provides scripts for invoking various AI models for text generation. It offers functionalities to interact with APIs from OpenAI, Groq, and Anthropic for generating text completions. The configuration is designed to be simple and easy to understand, allowing users to quickly set up and use the provided AI models for text generation tasks.

an infinite vermillion loom

I can make your audio clip loop smoothly

Personification of the blog Strange Loop Canon

A helpful and knowledgeable programming assistant for coding queries and guidance.

Knot expert offering instructions and advice