Best AI tools for< Find Libraries >

Fork.ai is a tool that helps businesses identify technologies used in mobile apps. With Fork.ai, businesses can gain insights into their competitors' tech stacks, identify potential partners, and generate leads. Fork.ai's key features include: - Technology identification: Fork.ai can identify over 1,000 technologies used in mobile apps, including programming languages, frameworks, libraries, and SDKs. - Competitor analysis: Fork.ai provides insights into the technologies used by your competitors, allowing you to identify areas where you can gain a competitive advantage. - Lead generation: Fork.ai can help you generate leads by identifying potential customers who are using specific technologies. - Partnership discovery: Fork.ai can help you identify potential partners who are using complementary technologies.

The Intel Gaudi AI accelerator developer website provides resources, guidance, tools, and support for building, migrating, and optimizing AI models. It offers software, model references, libraries, containers, and tools for training and deploying Generative AI and Large Language Models. The site focuses on the Intel Gaudi accelerators, including tutorials, documentation, and support for developers to enhance AI model performance.

UpCodes is a searchable platform that provides access to building codes, assemblies, and building products libraries. It offers a comprehensive database of regulations and standards for construction projects, enabling users to easily search and reference relevant information. With UpCodes, architects, engineers, contractors, and other industry professionals can streamline their workflow, ensure compliance with codes, and enhance the quality of their designs. The platform is designed to simplify the process of accessing and interpreting building codes, saving time and reducing errors in construction projects.

Find your next book is an AI-powered librarian that provides personalized book recommendations based on your preferences. It uses advanced algorithms to analyze your reading history, interests, and other factors to suggest books that you're likely to enjoy. The platform offers a wide range of genres and authors to choose from, making it easy to find your next favorite read.

AI Bookstore is a website that uses AI to help users find books that they want to read. Users can ask the AI questions about what they are looking for, and the AI will recommend books that match their criteria. The AI can also generate personalized recommendations based on a user's reading history.

Connected Papers is a search engine for academic papers that uses artificial intelligence to help users find and explore relevant research. It allows users to search for papers by keyword, author, or title, and then explore the connections between them. Connected Papers also provides a variety of tools to help users organize and manage their research, including the ability to create custom collections of papers, add notes and annotations, and share their research with others.

NextThreeBooks.com is an AI-powered book recommendation service that provides personalized suggestions based on your reading preferences. Share your preferences, tell us about yourself, and receive three carefully curated book suggestions with detailed explanations. We use GPT-3 to provide personalized book suggestions tailored to your reading preferences. Find your next favorite read with ease!

Sourcely is an AI-powered academic search assistant designed to help students and researchers find, summarize, and add credible academic sources efficiently. With access to over 200 million research papers and advanced search filters, Sourcely streamlines the research process by simplifying source discovery, providing targeted citations, offering free PDF downloads, organizing citations in a personal library, generating credible summaries, and exporting references in various formats. The tool aims to save time, enhance the quality of work, and improve academic productivity.

BookSurfAi is an AI-powered book recommendation tool that helps users discover their next favorite book. By leveraging artificial intelligence technology, the application provides personalized reading suggestions based on individual preferences and reading habits. BookSurfAi aims to enhance the reading experience by offering tailored recommendations that cater to each user's unique tastes and interests. With a user-friendly interface, BookSurfAi makes it easy for book lovers to explore new literary works and expand their reading horizons.

BooksAI is an AI-powered tool that provides book summaries, recommendations, and more. With over 40 million book summaries available, BooksAI makes it easy to discover new books and learn about your favorites. BooksAI's summaries are concise and easy to understand, making them perfect for busy professionals, students, and anyone who wants to learn more about the world's greatest books.

BookAbout is an AI-powered application that helps users discover their next favorite book. By leveraging artificial intelligence technology, BookAbout provides personalized book recommendations based on users' preferences and reading history. Users can be as specific as they like in their search for a new book, ensuring that they find the perfect read every time. As an Amazon Associate, BookAbout may earn a commission from qualifying purchases made through Amazon store links.

The Movie & Book Recommender is an AI-powered tool that helps users discover their next favorite movie or book by providing personalized recommendations. Users can choose between a Movie Recommender and a Book Recommender, with options to receive 2, 4, 5, 6, 8, or 10 recommendations. The tool was built by Dapo Adedire and utilizes AI technology from OpenAI, with a user-friendly interface for easy navigation and selection.

Midpage is a legal research platform powered by Generative AI, designed to provide comprehensive legal research capabilities to students and professionals. The platform offers advanced features such as grid-based search, case filtering with AI, proactive annotations, and seamless integration of research into documents. Midpage aims to streamline the legal research process by leveraging AI technology to enhance efficiency and accuracy in analyzing legal cases and statutes.

Open Knowledge Maps is the world's largest AI-based search engine for scientific knowledge. It aims to revolutionize discovery by increasing the visibility of research findings for science and society. The platform is open and nonprofit, based on the principles of open science, with a mission to create an inclusive, sustainable, and equitable infrastructure for all users. Users can map research topics with AI, find documents, and identify concepts to enhance their literature search experience.

Talpa Search is a search engine developed by LibraryThing, allowing users to search for books, movies, music, and various other topics. Users can search for a wide range of queries, from specific book quotes to movie genres. The platform aims to provide a user-friendly and efficient search experience for individuals looking to explore and discover new content.

NextThreeBooks.com is an AI-powered book recommendation platform that helps users find their perfect book match based on their reading preferences. Users can share their reading preferences with the AI-powered engine, which then provides personalized book suggestions tailored to their preferences. The platform uses GPT-3 to offer three carefully curated book recommendations with detailed explanations, making it easier for users to discover their next favorite read. NextThreeBooks.com has recommended books to users in over 80 countries and has been featured in various newsletters, building a growing community of readers.

arXiv.org is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv.

Wikidata is a free and open knowledge base that can be read and edited by both humans and machines. It acts as central storage for the structured data of its Wikimedia sister projects including Wikipedia, Wikivoyage, Wiktionary, Wikisource, and others. Wikidata also provides support to many other sites and services beyond just Wikimedia projects!

The StoryGraph is a book tracking and recommendation app that uses machine learning to help users find books they'll enjoy. It offers a variety of features, including personalized recommendations, mood-based browsing, and social features like book clubs and reading buddies. The StoryGraph is a great tool for anyone who loves to read and wants to discover new books.

ResearchRabbit is a research tool that helps researchers discover and organize academic papers. It uses artificial intelligence to recommend papers that are relevant to a researcher's interests and to visualize networks of papers and co-authorships. ResearchRabbit also allows researchers to collaborate on collections of papers and to share their findings with others.

Free-one-api is a tool that allows access to all LLM reverse engineering libraries in a standard OpenAI API format. It supports automatic load balancing, Web UI, stream mode, multiple LLM reverse libraries, heartbeat detection mechanism, automatic disabling of unavailable channels, and runtime log recording. The tool is designed to work with the 'one-api' project and 'songquanpeng/one-api' for accessing official interfaces of various LLMs (paid). Contributors are needed to test adapters, find new reverse engineering libraries, and submit PRs.

Awesome AI Tools is a curated list of popular tools and resources for artificial intelligence enthusiasts. It includes a wide range of tools such as machine learning libraries, deep learning frameworks, data visualization tools, and natural language processing resources. Whether you are a beginner or an experienced AI practitioner, this repository aims to provide you with a comprehensive collection of tools to enhance your AI projects and research. Explore the list to discover new tools, stay updated with the latest advancements in AI technology, and find the right resources to support your AI endeavors.

The oneAPI-samples repository contains a collection of samples for the Intel oneAPI Toolkits. These samples cover various topics such as AI and analytics, end-to-end workloads, features and functionality, getting started samples, Jupyter notebooks, direct programming, C++, Fortran, libraries, publications, rendering toolkit, and tools. Users can find samples based on expertise, programming language, and target device. The repository structure is organized by high-level categories, and platform validation includes Ubuntu 22.04, Windows 11, and macOS. The repository provides instructions for getting samples, including cloning the repository or downloading specific tagged versions. Users can also use integrated development environments (IDEs) like Visual Studio Code. The code samples are licensed under the MIT license.

**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.

Pathway is a Python data processing framework for analytics and AI pipelines over data streams. It's the ideal solution for real-time processing use cases like streaming ETL or RAG pipelines for unstructured data. Pathway comes with an **easy-to-use Python API** , allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: **you can use it in both development and production environments, handling both batch and streaming data effectively**. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing data streams. Pathway is powered by a **scalable Rust engine** based on Differential Dataflow and performs incremental computation. Your Pathway code, despite being written in Python, is run by the Rust engine, enabling multithreading, multiprocessing, and distributed computations. All the pipeline is kept in memory and can be easily deployed with **Docker and Kubernetes**. You can install Pathway with pip: `pip install -U pathway` For any questions, you will find the community and team behind the project on Discord.

Qbot is an open-source project designed to help users quickly build their own QQ chatbot. The bot deployed using this project has various capabilities, including intelligent sentence segmentation, intent recognition, voice and drawing replies, autonomous selection of when to play local music, and decision-making on sending emojis. Qbot leverages other open-source projects and allows users to customize triggers, system prompts, chat models, and more through configuration files. Users can modify the Qbot.py source code to tailor the bot's behavior. The project requires NTQQ and LLonebot's NTQQ plugin for deployment, along with additional configurations for triggers, system prompts, and chat models. Users can start the bot by running Qbot.py after installing necessary libraries and ensuring the NTQQ is running. Qbot also supports features like sending music from the data/smusic folder and emojis based on emotions. Local voice synthesis can be deployed for voice outputs. Qbot provides commands like #reset to clear short-term memory and addresses common issues like program crashes due to encoding format, message sending/receiving failures, voice synthesis failures, and connection issues. Users are encouraged to give the project a star if they find it useful.

The Modular Accelerated Xecution (MAX) platform is an integrated suite of AI libraries, tools, and technologies that unifies commonly fragmented AI deployment workflows. MAX accelerates time to market for the latest innovations by giving AI developers a single toolchain that unlocks full programmability, unparalleled performance, and seamless hardware portability.

The Modular Accelerated Xecution (MAX) platform is an integrated suite of AI libraries, tools, and technologies that unifies commonly fragmented AI deployment workflows. MAX accelerates time to market for the latest innovations by giving AI developers a single toolchain that unlocks full programmability, unparalleled performance, and seamless hardware portability.

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.

Awesome_GPT_Super_Prompting is a repository that provides resources related to Jailbreaks, Leaks, Injections, Libraries, Attack, Defense, and Prompt Engineering. It includes information on ChatGPT Jailbreaks, GPT Assistants Prompt Leaks, GPTs Prompt Injection, LLM Prompt Security, Super Prompts, Prompt Hack, Prompt Security, Ai Prompt Engineering, and Adversarial Machine Learning. The repository contains curated lists of repositories, tools, and resources related to GPTs, prompt engineering, prompt libraries, and secure prompting. It also offers insights into Cyber-Albsecop GPT Agents and Super Prompts for custom GPT usage.

This repository serves as a platform for coordinating ecosystem-wide work related to free-threading topics in Python. It aims to track, understand, and provide documentation for common issues across multiple libraries. Specific project-related issues should be reported in the respective project's issue tracker. For detailed documentation on free-threading topics, visit py-free-threading.github.io.

The LLM Engineer Toolkit is a curated repository containing over 120 LLM libraries categorized for various tasks such as training, application development, inference, serving, data extraction, data generation, agents, evaluation, monitoring, prompts, structured outputs, safety, security, embedding models, and other miscellaneous tools. It includes libraries for fine-tuning LLMs, building applications powered by LLMs, serving LLM models, extracting data, generating synthetic data, creating AI agents, evaluating LLM applications, monitoring LLM performance, optimizing prompts, handling structured outputs, ensuring safety and security, embedding models, and more. The toolkit covers a wide range of tools and frameworks to streamline the development, deployment, and optimization of large language models.

The 'nlp-llms-resources' repository is a comprehensive resource list for Natural Language Processing (NLP) and Large Language Models (LLMs). It covers a wide range of topics including traditional NLP datasets, data acquisition, libraries for NLP, neural networks, sentiment analysis, optical character recognition, information extraction, semantics, topic modeling, multilingual NLP, domain-specific LLMs, vector databases, ethics, costing, books, courses, surveys, aggregators, newsletters, papers, conferences, and societies. The repository provides valuable information and resources for individuals interested in NLP and LLMs.

This practical free hands-on course focuses on Large Language models and their applications, providing a hands-on experience using models from OpenAI and the Hugging Face library. The course is divided into three major sections: Techniques and Libraries, Projects, and Enterprise Solutions. It covers topics such as Chatbots, Code Generation, Vector databases, LangChain, Fine Tuning, PEFT Fine Tuning, Soft Prompt tuning, LoRA, QLoRA, Evaluate Models, Knowledge Distillation, and more. Each section contains chapters with lessons supported by notebooks and articles. The course aims to help users build projects and explore enterprise solutions using Large Language Models.

The KAN-GPT repository is a PyTorch implementation of Generative Pre-trained Transformers (GPTs) using Kolmogorov-Arnold Networks (KANs) for language modeling. It provides a model for generating text based on prompts, with a focus on improving performance compared to traditional MLP-GPT models. The repository includes scripts for training the model, downloading datasets, and evaluating model performance. Development tasks include integrating with other libraries, testing, and documentation.

The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.

This repository provides a hands-on approach to Retrieval-Augmented Generation (RAG) techniques, simplifying advanced concepts into understandable implementations using Python libraries like openai, numpy, and matplotlib. It offers a collection of Jupyter Notebooks with concise explanations, step-by-step implementations, code examples, evaluations, and visualizations for various RAG techniques. The goal is to make RAG more accessible and demystify its workings for educational purposes.

cuVS is a library that contains state-of-the-art implementations of several algorithms for running approximate nearest neighbors and clustering on the GPU. It can be used directly or through the various databases and other libraries that have integrated it. The primary goal of cuVS is to simplify the use of GPUs for vector similarity search and clustering.

LocalAIVoiceChat is an experimental alpha software that enables real-time voice chat with a customizable AI personality and voice on your PC. It integrates Zephyr 7B language model with speech-to-text and text-to-speech libraries. The tool is designed for users interested in state-of-the-art voice solutions and provides an early version of a local real-time chatbot.

Elevate your PHP programming with AI-guided support. Need expert insights, bug resolutions, code optimizations, or upgrades? PHP Mentor delivers custom assistance for developers across all expertise levels, making coding simpler.

Provides friendly, detailed comparisons of coding libraries.

Powerful Rust coding assistant. Trained on a vast array of the best up-to-date Rust resources, libraries and frameworks. Start with a quest! 🥷 (V1.7)

Concise Java library info with source and Maven links

Enthusiastic sci-fi book guide, helps find sci-fi books & avoid spoilers.

This AI tool by Learning Revolution and Hepler Consulting helps you find a good book to read, as well as its corresponding record on WorldCat.org.

I search PubMed and Google Scholar to find academic papers and articles.

A digital librarian who identifies books from photos and provides detailed information.

I translate books into various languages, focusing on specific chapters.

Your personal Librarian. Knows a lot about books and their publishing history!

Public Domain PDF Books Finder GPT offers an expansive library of PDFs for easy search and download. It now specializes in finding public domain books from trusted sources.

This Assistant assists library catalogers in selecting authority records. It advises librarians in creating queries and selecting the most relevant Name and Subject Heading Authority Records.

Votre assistant pour trouver films et séries en streaming et téléchargement gratuit

A helper for finding classical music school applications.

SGF identifies research gaps using scholarly sources. It creates proposals with abstracts, literature reviews, and a reference list tailored for academic research.

ニュース記事からソース情報を調べてくれるbot

A fun game of BOOK versus BOOK. Get the conversation and debates going!

Your personal reading schedule assistant.

Comprehensive LibGuide Assistant

I am a Starfleet library computer.