Best AI tools for< Multitask >

SigmaOS is a revolutionary AI-powered browser application that offers a fresh and organized browsing experience. It features innovative tools such as Workspaces for tab organization, Vertical tabs for task management, Split Screen for multitasking, Lazy Search for quick searches, and Ask Anything for contextual information retrieval. With advantages like ad-free browsing, focus mode, easy migration, and community support, SigmaOS aims to simplify and enhance users' internet interactions. However, it has limitations such as being available only on macOS and lacking support for Windows. SigmaOS leverages AI technology, including the A1Kit browser engine, to provide intelligent assistance and streamline daily tasks.

Recast is a platform that transforms articles into rich audio summaries, making it easier for users to consume content on the go, while working out, or simply looking for a more convenient way to stay informed. It provides entertaining, informative, and easy-to-understand audio conversations, helping users save time, reduce screen-time, understand content more deeply, discover interesting stories, and clear their reading list. Recast aims to enhance the reading experience by converting long articles into engaging podcasts, enabling users to enjoy content in a conversational format.

Article2audio is a text-to-speech application that focuses on web content. It uses AI to understand and enhance English articles and blog posts before converting them to audio, making listening easier and more natural. Some of its key features include descriptive imagery, table summaries, complex text interpretation, and meaningful voice-overs.

Listen2.AI is a mobile application that provides real-time news in a podcast format. It offers hands-free news consumption, multilingual support, and diverse perspectives. The app is designed to keep users informed and engaged with the world around them, even when they are on the move or multitasking.

AudioBook Bot is an AI-powered application that converts text into spoken audio, providing users with the convenience of listening to books and other text-based content. The tool utilizes advanced natural language processing and speech synthesis technologies to create high-quality audio renditions. Users can simply input text, and the bot will generate an audio version that can be played on various devices. With its user-friendly interface and efficient processing capabilities, AudioBook Bot offers a seamless experience for those who prefer listening over reading.

Sead is an AI-powered application that transforms articles into podcasts, offering users the flexibility to read or listen to content at their convenience. By leveraging AI technology, Sead enhances the reading experience by providing audio narration, summarizing key points, and enabling translation into multiple languages. Users can save time, improve understanding, and multitask efficiently with Sead's intelligent features. The app aims to streamline the consumption of information and promote a smarter way of reading and listening.

Lettercast is an AI-powered application that transforms your newsletters into personalized audio and text summaries, allowing you to stay up-to-date with all the news on the go. It converts your newsletters into bite-sized podcasts using the latest AI technology, providing a convenient way to consume information while commuting, working out, or cooking. With no limit on the number of newsletters you can convert, Lettercast ensures that you can listen to your daily podcasts at your convenience, all while prioritizing data security and user privacy.

Auphonic is an AI-powered audio post-production web tool designed to help users achieve professional-quality audio results effortlessly. It offers a range of features such as Intelligent Leveler, Noise & Reverb Reduction, Filtering & AutoEQ, Cut Filler Words and Silence, Multitrack Algorithms, Loudness Specifications, Speech2Text & Automatic Shownotes, Video Support, Metadata & Chapters, and more. Auphonic is widely used by podcasters, educators, content creators, and audiobook producers to enhance their audio content and streamline their workflows. With its intuitive interface and advanced algorithms, Auphonic simplifies the audio editing process and ensures consistent audio quality across different platforms.

Descript is an AI-powered editing assistant that allows users to edit videos and podcasts with ease. It offers features such as video editing, multitrack audio editing, clip selection, remote recording, captions, screen recording, transcription, AI speech generation, and more. Descript's AI capabilities help users create high-quality content effortlessly, making it a valuable tool for creators and teams. With a user-friendly interface and advanced AI features, Descript simplifies the video editing process and enhances productivity.

Speechki is an AI Realistic Voice Generator and Text-to-Speech Solution offering over 1,100 voices in 80+ languages. It provides a user-friendly platform for converting text into engaging audio with AI-powered voices. The application is designed to cater to various needs such as audiobook production, content creation, podcasting, and more. With features like real-time proof-listening, chapter-like formatting, streamlined role management, precision pause control, and nuanced speech control, Speechki aims to enhance the user experience and deliver lifelike audio output. The tool also offers global reach with multicast and multilanguage support, making it suitable for a diverse audience.

Push Lap Wholesale is a powerful Amazon wholesale sourcing tool that helps you find profitable products, save time, and hit your targets. With 97% matching accuracy, you can quickly scan your bulk price lists and never miss a potential profitable product. Our world-class platform provides you with detailed product information, including Keepa charts, historical averages, profit calculations, variation data, competition analysis, and multipack adjustments. You can also create and manage suppliers' purchase lists, export them as CSV files, and get the total cost and profit of each list. Additionally, we provide you with 500+ auto-ungate ASINs for free and access to a list of over 4,000 vetted and verified distributors with contact details.

LLM-Zero-to-Hundred is a repository showcasing various applications of LLM chatbots and providing insights into training and fine-tuning Language Models. It includes projects like WebGPT, RAG-GPT, WebRAGQuery, LLM Full Finetuning, RAG-Master LLamaindex vs Langchain, open-source-RAG-GEMMA, and HUMAIN: Advanced Multimodal, Multitask Chatbot. The projects cover features like ChatGPT-like interaction, RAG capabilities, image generation and understanding, DuckDuckGo integration, summarization, text and voice interaction, and memory access. Tutorials include LLM Function Calling and Visualizing Text Vectorization. The projects have a general structure with folders for README, HELPER, .env, configs, data, src, images, and utils.

AudioLLMs is a curated collection of research papers focusing on developing, implementing, and evaluating language models for audio data. The repository aims to provide researchers and practitioners with a comprehensive resource to explore the latest advancements in AudioLLMs. It includes models for speech interaction, speech recognition, speech translation, audio generation, and more. Additionally, it covers methodologies like multitask audioLLMs and segment-level Q-Former, as well as evaluation benchmarks like AudioBench and AIR-Bench. Adversarial attacks such as VoiceJailbreak are also discussed.

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.

This repository contains a minimal, clean code implementation of the Byte Pair Encoding (BPE) algorithm, commonly used in LLM tokenization. The BPE algorithm is "byte-level" because it runs on UTF-8 encoded strings. This algorithm was popularized for LLMs by the GPT-2 paper and the associated GPT-2 code release from OpenAI. Sennrich et al. 2015 is cited as the original reference for the use of BPE in NLP applications. Today, all modern LLMs (e.g. GPT, Llama, Mistral) use this algorithm to train their tokenizers. There are two Tokenizers in this repository, both of which can perform the 3 primary functions of a Tokenizer: 1) train the tokenizer vocabulary and merges on a given text, 2) encode from text to tokens, 3) decode from tokens to text. The files of the repo are as follows: 1. minbpe/base.py: Implements the `Tokenizer` class, which is the base class. It contains the `train`, `encode`, and `decode` stubs, save/load functionality, and there are also a few common utility functions. This class is not meant to be used directly, but rather to be inherited from. 2. minbpe/basic.py: Implements the `BasicTokenizer`, the simplest implementation of the BPE algorithm that runs directly on text. 3. minbpe/regex.py: Implements the `RegexTokenizer` that further splits the input text by a regex pattern, which is a preprocessing stage that splits up the input text by categories (think: letters, numbers, punctuation) before tokenization. This ensures that no merges will happen across category boundaries. This was introduced in the GPT-2 paper and continues to be in use as of GPT-4. This class also handles special tokens, if any. 4. minbpe/gpt4.py: Implements the `GPT4Tokenizer`. This class is a light wrapper around the `RegexTokenizer` (2, above) that exactly reproduces the tokenization of GPT-4 in the tiktoken library. The wrapping handles some details around recovering the exact merges in the tokenizer, and the handling of some unfortunate (and likely historical?) 1-byte token permutations. Finally, the script train.py trains the two major tokenizers on the input text tests/taylorswift.txt (this is the Wikipedia entry for her kek) and saves the vocab to disk for visualization. This script runs in about 25 seconds on my (M1) MacBook. All of the files above are very short and thoroughly commented, and also contain a usage example on the bottom of the file.

Awesome-LLM-Large-Language-Models-Notes is a repository that provides a comprehensive collection of information on various Large Language Models (LLMs) classified by year, size, and name. It includes details on known LLM models, their papers, implementations, and specific characteristics. The repository also covers LLM models classified by architecture, must-read papers, blog articles, tutorials, and implementations from scratch. It serves as a valuable resource for individuals interested in understanding and working with LLMs in the field of Natural Language Processing (NLP).

This repository serves as a collection of Google Colaboratory Notebooks for various tasks in Natural Language Processing (NLP), Natural Language Generation (NLG), Computer Vision, Generative Adversarial Networks (GANs), Streamlit applications, tutorials, UI/UX experiments, and other miscellaneous projects. It includes a wide range of pre-trained models, fine-tuning examples, and demos for tasks such as text generation, image processing, and more. The notebooks cover topics like self-attention, language model finetuning, emotion detection, image inpainting, and streamlit app creation. Users can explore different models, datasets, and techniques through these shared notebooks.

Decipher is a tool that utilizes AI-generated transcription subtitles to automatically add subtitles to videos. It eliminates the need for manual transcription, making videos more accessible. The tool uses OpenAI's Whisper, a State-of-the-Art speech recognition system trained on a large dataset for improved robustness to accents, background noise, and technical language.

This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.

SiLLM is a toolkit that simplifies the process of training and running Large Language Models (LLMs) on Apple Silicon by leveraging the MLX framework. It provides features such as LLM loading, LoRA training, DPO training, a web app for a seamless chat experience, an API server with OpenAI compatible chat endpoints, and command-line interface (CLI) scripts for chat, server, LoRA fine-tuning, DPO fine-tuning, conversion, and quantization.

Awesome-LLM is a curated list of resources related to large language models, focusing on papers, projects, frameworks, tools, tutorials, courses, opinions, and other useful resources in the field. It covers trending LLM projects, milestone papers, other papers, open LLM projects, LLM training frameworks, LLM evaluation frameworks, tools for deploying LLM, prompting libraries & tools, tutorials, courses, books, and opinions. The repository provides a comprehensive overview of the latest advancements and resources in the field of large language models.

The Open MatSci ML Toolkit is a flexible framework for machine learning in materials science. It provides a unified interface to a variety of materials science datasets, as well as a set of tools for data preprocessing, model training, and evaluation. The toolkit is designed to be easy to use for both beginners and experienced researchers, and it can be used to train models for a wide range of tasks, including property prediction, materials discovery, and materials design.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

This repository provides a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from **model-centric** , **data-centric** , and **framework-centric** perspective, respectively. We hope our survey and this GitHub repository can serve as valuable resources to help researchers and practitioners gain a systematic understanding of the research developments in efficient LLMs and inspire them to contribute to this important and exciting field.

Awesome-LLM-Eval: a curated list of tools, benchmarks, demos, papers for Large Language Models (like ChatGPT, LLaMA, GLM, Baichuan, etc) Evaluation on Language capabilities, Knowledge, Reasoning, Fairness and Safety.

Advanced multi-tasking GPT with real-time data management, image generation, and document editing.