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Quid is an AI-powered consumer and market intelligence platform that goes beyond simple data collection and analytics. It provides a complete picture of customer context, helping businesses make informed decisions based on future trends and opportunities. With features like Quid Discover for uncovering insights, Quid Monitor for real-time analytics, Quid Predict for future focus, Quid Compete for competitive analysis, and Quid Connect for data integration, the platform empowers organizations with proactive, data-driven decision-making.

DINGR is an AI-powered solution designed to help gamers analyze their performance in League of Legends. The tool uses AI algorithms to provide accurate insights into gameplay, comparing performance metrics with friends and offering suggestions for improvement. DINGR is currently in development, with a focus on enhancing the gaming experience through data-driven analysis and personalized feedback.

The website is a platform that provides comprehensive and unbiased reviews of various AI tools and applications. It aims to help users, especially small businesses, make informed decisions about selecting the right AI tools to enhance productivity and stay ahead of the competition. The site offers detailed comparisons, interviews with AI innovators, expert tips, and insights into the future of artificial intelligence. It also features blog posts on AI-related topics, free resources, and a newsletter for staying updated on the latest AI trends and tools.

Competely is an AI-powered competitive analysis tool that helps users say goodbye to tedious manual research by providing comprehensive and detailed insights into competitors across various industries. Users can save days of manual work by utilizing Competely to generate a side-by-side comparison of competitors, covering aspects such as marketing, product features, pricing, audience, customer sentiment, company info, and SWOT analysis. The tool is suitable for founders, executives, marketers, product managers, agencies, and consultants, offering valuable data-driven decisions and strategies to outsmart the competition.

Product Lab AI is an innovative AI tool designed to assist businesses in optimizing their product development process. The tool utilizes advanced machine learning algorithms to analyze market trends, consumer preferences, and competitor strategies. By leveraging AI technology, Product Lab AI provides valuable insights and recommendations to help companies enhance their product offerings and stay ahead of the competition. With its user-friendly interface and customizable features, Product Lab AI is a powerful solution for streamlining product development workflows and driving business growth.

EarningsCall.ai is an AI-powered platform that provides stock earnings call summaries and insights to help users turn earnings calls into a competitive advantage. The platform allows users to compare competitors, analyze trends, and make smarter business and investment decisions without the need to read full transcripts. EarningsCall.ai offers instant competitive benchmarking, market trend analysis, and use cases for business leaders, financial professionals, consultants, and advisors. The platform is designed to save time and provide valuable insights for corporate strategy, investment decisions, and financial research.

RivalOut is a competitive intelligence platform that leverages AI technology to provide actionable insights for businesses to monitor and analyze their competitors effectively. The platform offers features such as traffic monitoring, SEO analysis, social media tracking, business profile rating, automated reports, and AI-powered competitor analysis. RivalOut aims to simplify competitive analysis by delivering real-time competitor move alerts, identifying new competitors, and offering affordable pricing compared to traditional data-heavy platforms.

Essense is an AI-powered platform that specializes in transforming qualitative customer feedback and competitor reviews into actionable insights. By focusing on the most critical customer needs, the platform helps businesses improve product adoption, engagement, and overall customer satisfaction. Essense enables companies to make informed decisions regarding product development, marketing strategies, and competitive positioning through advanced AI analytics.

iPick.ai is an AI-powered platform designed for smarter investments in the stock market. Users can register for free to access AI insights, follow and rank stocks, and utilize premium features such as news summaries and YouTube videos. The platform allows users to ask questions, explore top industry performers, compare stocks with competitors, and more. iPick.ai aims to streamline stock research by providing valuable information and analysis to help users make informed investment decisions.

Electe is an AI-powered platform that empowers businesses to leverage the potential of artificial intelligence for data analysis and insights. With its intuitive interface and advanced AI algorithms, Electe enables users to extract valuable insights from their data, visualize data through intuitive graphs and customizable dashboards, generate personalized notes based on customer order analysis, monitor and compare competitor performance, and automate data extraction and classification using machine learning techniques. The platform also offers features like Q&A Document interaction, advanced presentations generation, daily email reports, and mobile app access. Electe is designed to cater to businesses of all sizes, providing scalable plans with essential functionalities, advanced analysis tools, and premium support.

Humbi AI is an AI-powered platform offering actuarial services for healthcare organizations, health plans, provider organizations, and drug manufacturers. The platform combines data science and actuarial expertise to provide competitive intelligence analytics, helping clients identify opportunities and manage risks effectively. Humbi AI offers a range of services from data management to actuarial design, incorporating knowledge from various fields such as medical science and technology. The platform includes tools for Medicare Advantage strategy building, provider performance comparison, pharmacy management, and access to medical and pharmacy data for millions of members.

RANKSHIFT is an AI brand visibility tracking tool that helps businesses monitor their visibility in AI search engines and optimize their presence. It provides insights on how AI platforms mention brands, compares visibility against competitors, and offers data-driven insights to improve AI search strategy. The tool tracks brand mentions, identifies sources influencing AI-generated answers, and helps businesses stay ahead in the competitive AI landscape.

Opinly is an AI-powered competitive analysis tool designed to provide businesses with real-time data tracking and comprehensive competitive analysis. It offers services such as competitive price analysis, product comparison, landing page analysis, SEO monitoring, and competitor email monitoring. Opinly helps businesses gain strategic insights, understand market dynamics, make informed decisions, and stay ahead in the competitive market.

Local Falcon is an AI-powered local rank tracking and analysis tool designed to provide businesses with insights into their local search rankings. It offers features such as AI analysis, competitor tracking, trend reports, and campaign monitoring. The tool helps businesses improve their local SEO performance and visibility by offering personalized recommendations based on key local search data. With a user-friendly interface and customizable payment plans, Local Falcon is suitable for professionals in various industries, from marketing agencies to small businesses.

Flux AI Online is a next-generation AI image generation tool that outperforms competitors in speed and quality. It offers lightning-fast image generation, unparalleled quality with the Flux.1 model, and a user-friendly interface perfect for beginners and professionals. Users can create stunning visuals in seconds, explore advanced features like Flux Pro and Flux Schnell, and choose from various pricing plans to suit their needs. Flux AI is open-source, available on GitHub and Hugging Face, and allows for commercial use.

Vendelux is an AI-powered event intelligence platform that provides insights on attendees and sponsors of over 200,000 B2B events. It offers event marketers and organizers data-driven solutions to maximize event ROI, make informed decisions on event sponsorships, and book more qualified meetings.

Octoparse VOC is an AI-native tool that simplifies consumer review analysis for brands and sellers. It leverages Generative AI and NLP models to extract valuable insights from product reviews and haul videos, helping businesses understand customer sentiment and pain points. The tool centralizes reviews, provides on-page analysis, surfaces important Q&As, and enables comparison with competitors. Octoparse VOC offers actionable insights tailored to business needs, empowering users to make informed decisions and improve their market position.

Lilac is an AI tool designed to enhance data quality and exploration for AI applications. It offers features such as data search, quantification, editing, clustering, semantic search, field comparison, and fuzzy-concept search. Lilac enables users to accelerate dataset computations and transformations, making it a valuable asset for data scientists and AI practitioners. The tool is trusted by Alignment Lab and is recommended for working with LLM datasets.

PropHunt.ai is an AI-powered platform designed for real estate professionals and property investors. It utilizes advanced machine learning algorithms to analyze property data and provide valuable insights for property hunting and investment decisions. The platform offers features such as property price prediction, neighborhood analysis, investment risk assessment, property comparison, and market trend forecasting. With PropHunt.ai, users can make informed decisions, optimize their property investments, and stay ahead in the competitive real estate market.

CloneMyVoice is an AI tool that specializes in creating AI audio voiceovers for long-form content such as podcasts, presentations, and social media. Users can save up to 80% compared to competitors and 99% compared to human voice actors. The platform allows users to upload source audio files and text, provide voice samples, and receive processed audio files within one hour. CloneMyVoice offers the ability to create audio presentations, social media content, podcasts, and audio books effortlessly. The AI can generate flawless English voices with British or American accents, capturing the tone and essence of the original voice.

The Self-Iterative Agent System for Complex Problem Solving is a solution developed for the Alibaba Mathematical Competition (AI Challenge). It involves multiple LLMs engaging in multi-round 'self-questioning' to iteratively refine the problem-solving process and select optimal solutions. The system consists of main and evaluation models, with a process that includes detailed problem-solving steps, feedback loops, and iterative improvements. The approach emphasizes communication and reasoning between sub-agents, knowledge extraction, and the importance of Agent-like architectures in complex tasks. While effective, there is room for improvement in model capabilities and error prevention mechanisms.

The Korean SAT LLM Leaderboard is a benchmarking project that allows users to test their fine-tuned Korean language models on a 10-year dataset of the Korean College Scholastic Ability Test (CSAT). The project provides a platform to compare human academic ability with the performance of large language models (LLMs) on various question types to assess reading comprehension, critical thinking, and sentence interpretation skills. It aims to share benchmark data, utilize a reliable evaluation dataset curated by the Korea Institute for Curriculum and Evaluation, provide annual updates to prevent data leakage, and promote open-source LLM advancement for achieving top-tier performance on the Korean CSAT.

QANTA is a system and dataset for question answering tasks. It provides a script to download datasets, preprocesses questions, and matches them with Wikipedia pages. The system includes various datasets, training, dev, and test data in JSON and SQLite formats. Dependencies include Python 3.6, `click`, and NLTK models. Elastic Search 5.6 is needed for the Guesser component. Configuration is managed through environment variables and YAML files. QANTA supports multiple guesser implementations that can be enabled/disabled. Running QANTA involves using `cli.py` and Luigi pipelines. The system accesses raw Wikipedia dumps for data processing. The QANTA ID numbering scheme categorizes datasets based on events and competitions.

This repository contains the code for HOLD, a method that jointly reconstructs hands and objects from monocular videos without assuming a pre-scanned object template. It can reconstruct 3D geometries of novel objects and hands, enabling template-free bimanual hand-object reconstruction, textureless object interaction with hands, and multiple objects interaction with hands. The repository provides instructions to download in-the-wild videos from HOLD, preprocess and train on custom videos, a volumetric rendering framework, a generalized codebase for single and two hand interaction with objects, a viewer to interact with predictions, and code to evaluate and compare with HOLD in HO3D. The repository also includes documentation for setup, training, evaluation, visualization, preprocessing custom sequences, and using HOLD on ARCTIC.

Eval-Scope is a framework for evaluating and improving large language models (LLMs). It provides a set of commonly used test datasets, metrics, and a unified model interface for generating and evaluating LLM responses. Eval-Scope also includes an automatic evaluator that can score objective questions and use expert models to evaluate complex tasks. Additionally, it offers a visual report generator, an arena mode for comparing multiple models, and a variety of other features to support LLM evaluation and development.

Aim is an open-source, self-hosted ML experiment tracking tool designed to handle 10,000s of training runs. Aim provides a performant and beautiful UI for exploring and comparing training runs. Additionally, its SDK enables programmatic access to tracked metadata — perfect for automations and Jupyter Notebook analysis. **Aim's mission is to democratize AI dev tools 🎯**

MathEval is a benchmark designed for evaluating the mathematical capabilities of large models. It includes over 20 evaluation datasets covering various mathematical domains with more than 30,000 math problems. The goal is to assess the performance of large models across different difficulty levels and mathematical subfields. MathEval serves as a reliable reference for comparing mathematical abilities among large models and offers guidance on enhancing their mathematical capabilities in the future.

RAID is the largest and most comprehensive dataset for evaluating AI-generated text detectors. It contains over 10 million documents spanning 11 LLMs, 11 genres, 4 decoding strategies, and 12 adversarial attacks. RAID is designed to be the go-to location for trustworthy third-party evaluation of popular detectors. The dataset covers diverse models, domains, sampling strategies, and attacks, making it a valuable resource for training detectors, evaluating generalization, protecting against adversaries, and comparing to state-of-the-art models from academia and industry.

This repository contains a curated list of cutting-edge works in Simultaneous Localization and Mapping (SLAM). It includes research papers, projects, and tools related to various aspects of SLAM, such as 3D reconstruction, semantic mapping, novel algorithms, large-scale mapping, and more. The repository aims to showcase the latest advancements in SLAM technology and provide resources for researchers and practitioners in the field.

This repository contains code for manipulating Large Language Models (LLMs) to increase the visibility of specific content or products in search engine recommendations. By adding a Strategic Text Sequence (STS) to a product's information page, the target product's rank in the LLM's recommendation can be optimized. The code includes scripts for generating and evaluating the STS, as well as plotting the results. The tool requires NVIDIA A100 GPUs for optimization and can be run in a Conda environment.

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.

Deej-A.I. is an advanced machine learning project that aims to revolutionize music recommendation systems by using artificial intelligence to analyze and recommend songs based on their content and characteristics. The project involves scraping playlists from Spotify, creating embeddings of songs, training neural networks to analyze spectrograms, and generating recommendations based on similarities in music features. Deej-A.I. offers a unique approach to music curation, focusing on the 'what' rather than the 'how' of DJing, and providing users with personalized and creative music suggestions.

Minic is a chess engine developed for learning about chess programming and modern C++. It is compatible with CECP and UCI protocols, making it usable in various software. Minic has evolved from a one-file code to a more classic C++ style, incorporating features like evaluation tuning, perft, tests, and more. It has integrated NNUE frameworks from Stockfish and Seer implementations to enhance its strength. Minic is currently ranked among the top engines with an Elo rating around 3400 at CCRL scale.

This repository contains a collection of papers and resources related to Large Language Models (LLMs), including their applications in various domains such as text generation, translation, question answering, and dialogue systems. The repository also includes discussions on the ethical and societal implications of LLMs. **Description** This repository is a collection of papers and resources related to Large Language Models (LLMs). LLMs are a type of artificial intelligence (AI) that can understand and generate human-like text. They have a wide range of applications, including text generation, translation, question answering, and dialogue systems. **For Jobs** - **Content Writer** - **Copywriter** - **Editor** - **Journalist** - **Marketer** **AI Keywords** - **Large Language Models** - **Natural Language Processing** - **Machine Learning** - **Artificial Intelligence** - **Deep Learning** **For Tasks** - **Generate text** - **Translate text** - **Answer questions** - **Engage in dialogue** - **Summarize text**

This leaderboard evaluates the hallucination rate of various Large Language Models (LLMs) when summarizing documents. It uses a model trained by Vectara to detect hallucinations in LLM outputs. The leaderboard includes models from OpenAI, Anthropic, Google, Microsoft, Amazon, and others. The evaluation is based on 831 documents that were summarized by all the models. The leaderboard shows the hallucination rate, factual consistency rate, answer rate, and average summary length for each model.

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 vscode-pddl extension provides comprehensive support for Planning Domain Description Language (PDDL) in Visual Studio Code. It enables users to model planning domains, validate them, industrialize planning solutions, and run planners. The extension offers features like syntax highlighting, auto-completion, plan visualization, plan validation, plan happenings evaluation, search debugging, and integration with Planning.Domains. Users can create PDDL files, run planners, visualize plans, and debug search algorithms efficiently within VS Code.

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