Best AI tools for< Find Table >

Archive is an AI-first influencer marketing platform that helps users track campaigns live, find creators, spy on competitors' influencer programs, and manage shoppable user-generated content (UGC) feeds. The platform automates tracking, managing, and repurposing tagged UGC, allowing users to save time and increase conversion rates. Archive is trusted by 50,000 brands and offers features such as Campaign Dashboards, Social Listening, AI search engine, shoppable video UGC, and 1-click reports.

Lateral is an AI-powered research tool that helps academics and students streamline their workflow by seamlessly searching, saving, and organizing findings across their papers. It uses AI to generate an auto-generated table, name concepts, and provide super search capabilities, making it easy to find relevant information quickly. Lateral also allows users to collaborate and share their work, making it a valuable tool for researchers working on collaborative projects.

DocsAI is an AI-powered document companion that helps you organize, search, and chat with your documents. It integrates with various sources, including websites, text files, PDFs, Docx, Notion, and Confluence. You can customize the companion's appearance to match your brand and suggest better answers to improve its accuracy. DocsAI also offers a chat widget that can be embedded on any website, allowing you to chat with your documents and get summaries, insights, and leads. It is mobile and tablet-friendly, and you can export chats and analyze data to identify trends and improve customer satisfaction. DocsAI is open source and offers custom prompts and multi-language support.

Amy is a workplace assistant that uses conversational technology to help users with a variety of tasks, including communication, HR, web management, and recruitment. Amy can be used to send messages, schedule meetings, manage attendance and leaves, update websites, post blogs and jobs, and find talent. Amy is designed to be easy to use and can be accessed through a variety of devices, including smartphones, tablets, and computers.

Zelma is an AI-powered research assistant that enables users to find, graph, and understand U.S. school testing data using plain English. It allows users to search student test data by school district, demographics, grade, and more, and presents the data with graphs, tables, and descriptions. Zelma aims to make education data easily accessible and understandable for everyone.

ProRank is an AI-powered platform that empowers organizations in the healthcare industry to find and hire top talent with precision and efficiency. The platform combines advanced technology with user-friendly features to streamline the recruitment process, ensuring efficient and effective candidate connections. ProRank offers a curated talent database, AI talent CRM, talent analytics, and key integrations with various ATS vendors, enabling organizations to make data-driven decisions and reduce unconscious bias in recruitment.

SeekOut is an AI-powered platform designed to help organizations find the right candidates for open roles, develop their teams, and improve company culture. It offers features such as external talent sourcing, applicant review, pipeline insights, internal talent development, career compass, and talent intelligence. SeekOut is trusted by over 1,000 leading brands to recruit hard-to-find, diverse talent and manage talent acquisition and management in one platform. The platform integrates external data with HR systems to automatically build comprehensive profiles and provides data-driven insights to understand talent needs and prepare for the future.

Jex is an AI-driven platform that revolutionizes global hiring by cutting out middlemen and providing transparency and efficiency in the recruitment process. It empowers both talent and companies to connect directly, eliminating salary markups and hidden fees. Jex offers dynamic candidate profiles, AI-driven insights, compliance management, and payroll services, streamlining the hiring workflow and saving time and money for users.

PyjamaHR is a leading AI-powered Applicant Tracking System (ATS) and recruitment software designed to streamline the hiring process for businesses of all sizes. It offers advanced features such as source management, candidate evaluation, collaboration tools, and AI-powered candidate tests to enhance the efficiency and effectiveness of the recruitment process. With a user-friendly interface and robust security measures, PyjamaHR is a trusted solution for managing talent acquisition and improving hiring outcomes.

Nebula is an Intelligent Talent Acquisition Platform that reimagines recruitment by providing access to a world-class talent pool, powerful matching engine, talent engagement tools, and actionable insights. It leverages AI algorithms to analyze profiles, eliminate bias, and optimize talent planning and acquisition strategies. Nebula aims to help organizations find better talent faster while promoting diversity, equity, and inclusion in the hiring process.

Popfreelance is an AI freelance marketplace that connects employers with elite AI freelancers and offers exciting AI job opportunities. The platform provides cutting-edge tools for AI freelancers, a marketplace for AI digital products, and a revolutionary cryptocurrency payment system. Popfreelance prioritizes quality, affordability, and data protection, ensuring a seamless and secure experience for users.

Savvy is an AI recruitment platform that connects job seekers with companies and recruiters, offering an Intelligent Matching System to find top talent efficiently. It automates tasks like resume screening, scheduling interviews, and eliminates subconscious bias in the hiring process. Savvy aims to streamline the recruitment process, saving time and resources for both job seekers and recruiters.

CareerGPT is an AI tool designed to revolutionize the candidate selection process by utilizing its proprietary Vectorized Rating System (VRS). The platform aims to assist recruiters in finding the best talent, guide career coaches towards excellence, and help members navigate their path to success. With a focus on leveraging AI technology for optimizing career-related decisions, CareerGPT offers a user-friendly interface and advanced algorithms to streamline the recruitment and coaching processes.

Built In LA is an online community for startups and tech companies in Los Angeles. It provides a platform for job seekers to find tech jobs, tech companies to find talent, and tech enthusiasts to stay up-to-date on the latest news and events in the LA tech scene.

Built In Colorado is an online community for startups and tech companies in Colorado. It provides a platform for job seekers to find tech jobs, tech companies to find talent, and tech enthusiasts to stay up-to-date on the latest news and events in the Colorado tech scene.

QSourcer is an AI-powered talent acquisition tool that leverages Boolean and X-ray search techniques to supercharge talent sourcing. It helps users uncover top talent on platforms like LinkedIn, GitHub, and StackOverflow by simplifying the creation of complex Boolean search queries and providing industry-specific synonyms. With features like generating keywords with AI, multi-language support, and user-friendly interface, QSourcer aims to make talent sourcing efficient, fun, and much easier.

Toptal is a platform that connects top freelance software developers, designers, and finance experts with companies in need of their services. It offers a curated network of talent, ensuring high-quality matches for projects of any scale. By providing a seamless hiring process and dedicated support, Toptal helps businesses access elite professionals for their specific needs.

Talentscreener is an AI-powered talent assessment platform that helps businesses find the best candidates for their open positions. The platform uses a variety of AI algorithms to assess candidates' skills, experience, and personality, and then provides businesses with a ranked list of the most qualified candidates. Talentscreener also offers a variety of other features, such as job posting, candidate management, and reporting.

HeroHunt.ai is an AI-powered recruitment tool that automates the entire recruitment process, from screening candidates to engaging with them. It utilizes cutting-edge AI technology to search through 1 billion profiles worldwide, ensuring accurate matches and personalized outreach. With features like full cycle recruitment automation, AI screening, and auto-engagement, HeroHunt.ai revolutionizes the way companies find and hire talent globally.

Scout is an AI-powered platform that focuses on community-centered recruiting. It connects professionals with job opportunities and helps companies find the right talent faster. With over 5 million visitors and 10,000+ recruiters on the platform, Scout offers customized solutions, bias-free AI processes, and support teams to optimize the hiring process. The platform leverages AI, data-driven insights, and adaptive systems to ensure better fits and faster discovery of talent. Scout is trusted by thousands of organizations for its innovative approach to recruiting.

DeepSeek is a new experimental architecture for a large language model (LLM) powered internet-scale retrieval engine. Unlike current research agents designed as answer engines, DeepSeek aims to process a vast amount of sources to collect a comprehensive list of entities and enrich them with additional relevant data. The end result is a table with retrieved entities and enriched columns, providing a comprehensive overview of the topic. DeepSeek utilizes both standard keyword search and neural search to find relevant content, and employs an LLM to extract specific entities and their associated contents. It also includes a smaller answer agent to enrich the retrieved data, ensuring thoroughness. DeepSeek has the potential to revolutionize research and information gathering by providing a comprehensive and structured way to access information from the vastness of the internet.

**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.

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.

AmbuFlow is a mobile application developed using HTML, CSS, JavaScript, and Google API to notify patients of nearby hospitals and provide estimated ambulance arrival times. It offers critical details like patient's location and enhances GPS route management with real-time traffic data for efficient navigation. The app helps users find nearby hospitals, track ambulances in real-time, and manage ambulance routes based on traffic and distance. It ensures quick emergency response, real-time tracking, enhanced communication, resource management, and a user-friendly interface for seamless navigation in high-stress situations.

FLUTE (Flexible Lookup Table Engine for LUT-quantized LLMs) is a tool designed for uniform quantization and lookup table quantization of weights in lower-precision intervals. It offers flexibility in mapping intervals to arbitrary values through a lookup table. FLUTE supports various quantization formats such as int4, int3, int2, fp4, fp3, fp2, nf4, nf3, nf2, and even custom tables. The tool also introduces new quantization algorithms like Learned Normal Float (NFL) for improved performance and calibration data learning. FLUTE provides benchmarks, model zoo, and integration with frameworks like vLLM and HuggingFace for easy deployment and usage.

LOTUS (LLMs Over Tables of Unstructured and Structured Data) is a query engine that provides a declarative programming model and an optimized query engine for reasoning-based query pipelines over structured and unstructured data. It offers a simple and intuitive Pandas-like API with semantic operators for fast and easy LLM-powered data processing. The tool implements a semantic operator programming model, allowing users to write AI-based pipelines with high-level logic and leaving the rest of the work to the query engine. LOTUS supports various semantic operators like sem_map, sem_filter, sem_extract, sem_agg, sem_topk, sem_join, sem_sim_join, and sem_search, enabling users to perform tasks like mapping records, filtering data, aggregating records, and more. The tool also supports different model classes such as LM, RM, and Reranker for language modeling, retrieval, and reranking tasks respectively.

Invariant Analyzer is an open-source scanner designed for LLM-based AI agents to find bugs, vulnerabilities, and security threats. It scans agent execution traces to identify issues like looping behavior, data leaks, prompt injections, and unsafe code execution. The tool offers a library of built-in checkers, an expressive policy language, data flow analysis, real-time monitoring, and extensible architecture for custom checkers. It helps developers debug AI agents, scan for security violations, and prevent security issues and data breaches during runtime. The analyzer leverages deep contextual understanding and a purpose-built rule matching engine for security policy enforcement.

A web application to explore the capabilities of Google Maps Platform Route Optimization (GMPRO) for solving vehicle routing problems. Users can interact with the GMPRO data model through forms, tables, and maps to construct scenarios, tune constraints, and visualize routes. The application is intended for exploration purposes only and should not be deployed in production. Users are responsible for billing related to cloud resources and API usage. It is important to understand the pricing models for Maps Platform and Route Optimization before using the application.

A web application to explore the capabilities of Google Maps Platform Route Optimization (GMPRO). It helps users understand the data model and functions of the API by presenting interactive forms, tables, and maps. The tool is intended for exploratory use only and should not be deployed in production. Users can construct scenarios, tune constraint parameters, and visualize routes before implementing their own solutions for integrating Route Optimization into their business processes. The application incurs charges related to cloud resources and API usage, and users should be cautious about generating high usage volumes, especially for large scenarios.

This repository contains a collection of tools that can be used with vastai. The tools are free to use, modify and distribute. If you find this useful and wish to donate your welcome to send your donations to the following wallets. BTC 15qkQSYXP2BvpqJkbj2qsNFb6nd7FyVcou XMR 897VkA8sG6gh7yvrKrtvWningikPteojfSgGff3JAUs3cu7jxPDjhiAZRdcQSYPE2VGFVHAdirHqRZEpZsWyPiNK6XPQKAg RVN RSgWs9Co8nQeyPqQAAqHkHhc5ykXyoMDUp USDT(ETH ERC20) 0xa5955cf9fe7af53bcaa1d2404e2b17a1f28aac4f Paypal PayPal.Me/cryptolabsZA

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

This repository provides a comprehensive collection of methodologies, tools, and step-by-step instructions for successful training of large language models (LLMs) and multi-modal models. It is a technical resource suitable for LLM/VLM training engineers and operators, containing numerous scripts and copy-n-paste commands to facilitate quick problem-solving. The repository is an ongoing compilation of the author's experiences training BLOOM-176B and IDEFICS-80B models, and currently focuses on the development and training of Retrieval Augmented Generation (RAG) models at Contextual.AI. The content is organized into six parts: Insights, Hardware, Orchestration, Training, Development, and Miscellaneous. It includes key comparison tables for high-end accelerators and networks, as well as shortcuts to frequently needed tools and guides. The repository is open to contributions and discussions, and is licensed under Attribution-ShareAlike 4.0 International.

SheetCopilot is an assistant agent that manipulates spreadsheets by following user commands. It leverages Large Language Models (LLMs) to interact with spreadsheets like a human expert, enabling non-expert users to complete tasks on complex software such as Google Sheets and Excel via a language interface. The tool observes spreadsheet states, polishes generated solutions based on external action documents and error feedback, and aims to improve success rate and efficiency. SheetCopilot offers a dataset with diverse task categories and operations, supporting operations like entry & manipulation, management, formatting, charts, and pivot tables. Users can interact with SheetCopilot in Excel or Google Sheets, executing tasks like calculating revenue, creating pivot tables, and plotting charts. The tool's evaluation includes performance comparisons with leading LLMs and VBA-based methods on specific datasets, showcasing its capabilities in controlling various aspects of a spreadsheet.

T-MAC is a kernel library that directly supports mixed-precision matrix multiplication without the need for dequantization by utilizing lookup tables. It aims to boost low-bit LLM inference on CPUs by offering support for various low-bit models. T-MAC achieves significant speedup compared to SOTA CPU low-bit framework (llama.cpp) and can even perform well on lower-end devices like Raspberry Pi 5. The tool demonstrates superior performance over existing low-bit GEMM kernels on CPU, reduces power consumption, and provides energy savings. It achieves comparable performance to CUDA GPU on certain tasks while delivering considerable power and energy savings. T-MAC's method involves using lookup tables to support mpGEMM and employs key techniques like precomputing partial sums, shift and accumulate operations, and utilizing tbl/pshuf instructions for fast table lookup.

KaTrain is a tool designed for analyzing games and playing go with AI feedback from KataGo. Users can review their games to find costly moves, play against AI with immediate feedback, play against weakened AI versions, and generate focused SGF reviews. The tool provides various features such as previews, tutorials, installation instructions, and configuration options for KataGo. Users can play against AI, receive instant feedback on moves, explore variations, and request in-depth analysis. KaTrain also supports distributed training for contributing to KataGo's strength and training bigger models. The tool offers themes customization, FAQ section, and opportunities for support and contribution through GitHub issues and Discord community.

PPTist is a web-based presentation application that replicates most features of Microsoft Office PowerPoint. It supports various elements like text, images, shapes, charts, tables, videos, audio, and formulas. Users can edit and present slides directly in a web browser. It offers easy development with Vue 3.x and TypeScript, user-friendly experience with context menu and keyboard shortcuts, and feature-rich functionalities including AI-generated PPTs and mobile editing. PPTist aims to provide a desktop application-level experience for creating presentations.

The RAG Experiment Accelerator is a versatile tool that helps you conduct experiments and evaluations using Azure AI Search and RAG pattern. It offers a rich set of features, including experiment setup, integration with Azure AI Search, Azure Machine Learning, MLFlow, and Azure OpenAI, multiple document chunking strategies, query generation, multiple search types, sub-querying, re-ranking, metrics and evaluation, report generation, and multi-lingual support. The tool is designed to make it easier and faster to run experiments and evaluations of search queries and quality of response from OpenAI, and is useful for researchers, data scientists, and developers who want to test the performance of different search and OpenAI related hyperparameters, compare the effectiveness of various search strategies, fine-tune and optimize parameters, find the best combination of hyperparameters, and generate detailed reports and visualizations from experiment results.

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)

MindNLP is an open-source NLP library based on MindSpore. It provides a platform for solving natural language processing tasks, containing many common approaches in NLP. It can help researchers and developers to construct and train models more conveniently and rapidly. Key features of MindNLP include: * Comprehensive data processing: Several classical NLP datasets are packaged into a friendly module for easy use, such as Multi30k, SQuAD, CoNLL, etc. * Friendly NLP model toolset: MindNLP provides various configurable components. It is friendly to customize models using MindNLP. * Easy-to-use engine: MindNLP simplified complicated training process in MindSpore. It supports Trainer and Evaluator interfaces to train and evaluate models easily. MindNLP supports a wide range of NLP tasks, including: * Language modeling * Machine translation * Question answering * Sentiment analysis * Sequence labeling * Summarization MindNLP also supports industry-leading Large Language Models (LLMs), including Llama, GLM, RWKV, etc. For support related to large language models, including pre-training, fine-tuning, and inference demo examples, you can find them in the "llm" directory. To install MindNLP, you can either install it from Pypi, download the daily build wheel, or install it from source. The installation instructions are provided in the documentation. MindNLP is released under the Apache 2.0 license. If you find this project useful in your research, please consider citing the following paper: @misc{mindnlp2022, title={{MindNLP}: a MindSpore NLP library}, author={MindNLP Contributors}, howpublished = {\url{https://github.com/mindlab-ai/mindnlp}}, year={2022} }

This repository maintains a list of research papers on LLM-based Multi-Agents, categorized into five main streams: Multi-Agents Framework, Multi-Agents Orchestration and Efficiency, Multi-Agents for Problem Solving, Multi-Agents for World Simulation, and Multi-Agents Datasets and Benchmarks. The repository also includes a survey paper on LLM-based Multi-Agents and a table summarizing the key findings of the survey.

Expert in tables, materials, and interior design, offering insightful advice.

Tavern is a fantasy styled GPT tailored to make your fantasy snacks and drinks a reality.

I find classical music career opportunities in table format.

Detailed table game rules expert, always offering multiple options.

Expert in tabletop wargames, rules, news, tournaments, and tutorials!

Adds seller and brand info to Amazon ASIN data tables.

Write what you'd like researched. Get information in a table format.

Your go-to source for in-depth US zip code demographics and statistics, with easy-to-download data tables.

Shalom! Ask me about carpentry and design in Israel. Start with 'Find', 'Show', or 'Tell me about'.

Adolescent health guide with comparative tables and tips.

Generates random fantasy names for tabletop RPG games like Dungeons and Dragons

I generate top 10 PLG strategies in a table format

File organization assistant with a flexible table of contents.

A holiday planning assistant offering recipes, table settings, and festive ideas.

A culinary guide for dinner recipes, etiquette, and experiences.

Crafting immersive TTRPG journeys, complete with vivid visuals.

Japan Market & Diverse Source Tech Expert

Find a recruiter in your industry, specialism, and location

Supports organization growth through strategic talent acquisition.

Find the best AI agent for your problem, no bulk export