Best AI tools for< Acquire Data >

Rendered.ai is a platform that provides unlimited synthetic data for AI and ML applications, specifically focusing on computer vision. It helps in generating low-cost physically-accurate data to overcome bias and power innovation in AI and ML. The platform allows users to capture rare events and edge cases, acquire data that is difficult to obtain, overcome data labeling challenges, and simulate restricted or high-risk scenarios. Rendered.ai aims to revolutionize the use of synthetic data in AI and data analytics projects, with a vision that by 2030, synthetic data will surpass real data in AI models.

Magneo is an AI-driven communication platform that caters to schools and local businesses, offering automated solutions to streamline processes such as admission, retention, and enrollment. The platform provides 24/7 support, empowering staff to assist students effectively. Magneo leverages AI to enhance customer service, improve online reviews, automate payments, and collect valuable data insights. Additionally, it offers features like robotic process automation, support for diverse learners, and explainable AI models for compliance.

Ponzu.gg is a domain name that may be for sale. The website provides information about the domain and offers the option to inquire about purchasing it. It is a platform where users can explore the availability of the domain name and potentially acquire it for their own use. The site emphasizes copyright and privacy policy to protect intellectual property rights and user data.

Reachy.ai is an AI Agent for LinkedIn outreach and smarter lead generation. It automates outreach efforts on LinkedIn by identifying potential leads based on their social activities, sending personalized messages, and collecting comprehensive data points to help users establish their ideal customer profile. Reachy operates as a desktop app, ensuring privacy and GDPR compliance. The tool is recognized for its ease of use, AI capabilities, and ability to streamline LinkedIn prospecting processes.

Akadimia Ai is an AI-powered platform designed to provide users with a range of educational resources and tools. The platform leverages artificial intelligence to offer personalized learning experiences, interactive tutorials, and assessments. Users can access a variety of courses, quizzes, and study materials tailored to their individual needs and learning preferences. Akadimia Ai aims to enhance the learning process by offering adaptive content recommendations and progress tracking features. Whether you are a student looking to improve your academic performance or a professional seeking to acquire new skills, Akadimia Ai offers a comprehensive learning solution to help you achieve your goals.

Zeta Global is an AI-powered marketing cloud that helps businesses acquire, grow, and retain customers more efficiently. The Zeta Marketing Platform (ZMP) is a cloud-based system that provides tools for data management, messaging, activation, and more. ZMP is powered by proprietary data and AI, which enables businesses to create individualized experiences and drive outcomes throughout the customer lifecycle.

Kumo is an AI-powered platform that helps businesses personalize customer experiences, acquire new customers, understand customer behavior, improve planning and monitoring, resolve data inconsistencies, fight fraud and abuse, detect money laundering, and empower data scientists with advanced techniques. It offers cutting-edge solutions for various AI and machine learning tasks, such as predictive modeling, anomaly detection, entity resolution, and graph embeddings. Kumo's capabilities are designed to enhance customer interactions, optimize marketing campaigns, and provide valuable insights for businesses across different industries.

Voyager is an open-ended embodied agent powered by large language models, designed for lifelong learning in Minecraft. It continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. The agent consists of three key components: an automatic curriculum for exploration, a skill library for storing complex behaviors, and an iterative prompting mechanism for program improvement.

Obviyo is a product-led growth platform that helps e-commerce businesses increase revenue by matching products to shoppers based on real-time behavior. It uses AI to identify buying signals from individual shoppers and link them with products of interest. Obviyo's suite of AI-powered Growth Bots, cross-platform data, and real-time AI algorithms enable businesses to personalize the shopping experience, upsell and cross-sell products, optimize product merchandising, and acquire new customers. Obviyo integrates with Shopify and Amazon, and has a track record of delivering strong customer results for global brands.

Lushair is an AI-powered platform that offers personalized hair and scalp analysis solutions. It aims to create a digital and intelligent ecosystem for dermatology, providing accurate skin and scalp solutions that are accessible and affordable. Lushair offers services such as personal subscriptions, skin & scalp analysis SAAS, and skin & scalp analysis API for hair care specialists and brands. The platform features historical tracking, multi-node analysis, improved management, AI-generated hair care plans, and an easy-to-use interface. Lushair has received positive feedback for its standardization, customization, and intelligent services in the field of dermatology.

ARC Prize is a platform hosting a $1,000,000+ public competition aimed at beating and open-sourcing a solution to the ARC-AGI benchmark. The platform is dedicated to advancing open artificial general intelligence (AGI) for the public benefit. It provides a formal benchmark, ARC-AGI, created by François Chollet, to measure progress towards AGI by testing the ability to efficiently acquire new skills and solve open-ended problems. ARC Prize encourages participants to try solving test puzzles to identify patterns and improve their AGI skills.

Rosetta AI is an e-commerce marketing platform that uses artificial intelligence to help businesses acquire qualified traffic, personalize interactions, and strengthen member repurchases. It offers a range of solutions, including personalized recommendations, AI-powered advertising, interactive experiences, product label generation, and automated marketing. Rosetta AI is used by over 2000 enterprise and SMB websites worldwide and has helped clients increase their click-through rate, average order value, check-out conversion, and revenue.

Vitrina Business Network is a global platform that connects entertainment companies worldwide, facilitating interactions between content acquirers, distributors, production, post-production, and localization services. The platform leverages data, AI, and human expertise to enhance business growth within the entertainment supply chain. Vitrina offers services such as content licensing, specialist services hiring, and networking opportunities for industry professionals. Through digital storefronts, users can showcase projects and services, fostering partnerships and business opportunities. The platform also provides market intelligence reports, case studies, and industry insights to help users make informed decisions and stay updated on the latest trends in the entertainment industry.

Nextiva is a Unified Customer Experience Management Platform that offers a wide range of AI-powered solutions to help businesses acquire, retain, and grow their customers. It provides personalized customer experiences across various channels, real-time customer insight, automation, workforce engagement management, and more. Nextiva aims to enhance customer interactions, reduce operational costs, and maximize technology investments through its innovative platform.

Ulai is a Gen-AI conversational commerce platform that helps businesses connect with their customers, acquire, retain, and engage them where they are most comfortable. It offers a range of features to help businesses increase revenue, including personalized recommendations, automated post-purchase processes, and seamless integration with third-party applications and CRMs.

DealMachine is a real estate investing platform that provides tools and resources to help investors find, analyze, and acquire off-market properties. The platform includes a variety of features such as driving for dollars, list building, unlimited contact info, marketing automation, and a real estate AI assistant. DealMachine is designed to help investors streamline their real estate investing process and close deals faster.

Legalit Tools is a legal technology platform offering AI-powered tools for legal professionals. It enhances legal research, document automation, client acquisition, and funding solutions, empowering lawyers to deliver superior services efficiently. The platform aims to streamline operations for legal professionals by providing tools for research, document creation, client relations, and more, ultimately improving efficiency and service quality. Legalit Tools is dedicated to transforming the legal industry through AI, with a focus on responsible use of tools and resources, user data protection, and maintaining confidentiality.

The AI-based intelligent bidding information service, known as '조달 AI', offers a revolutionary approach to procurement processes by combining AI learning-based search engine and big data analysis technology. It provides fast and accurate bidding information through unique attachment file search functionality, significantly enhancing procurement notice search and bidding productivity. The service aims to deliver real-time tailored bidding information to companies, optimizing search results and reducing the time required to acquire essential bidding details.

Reply.io is a sales engagement platform that helps businesses automate and scale their sales outreach, acquire new customers, and grow revenue faster. It offers a range of features including multichannel sales engagement, email deliverability tools, AI sales assistant, B2B contact database, meeting scheduling, and analytics. Reply.io integrates with popular CRMs and other sales tools, making it a comprehensive solution for sales teams.

watchID is an AI-powered tool that allows users to identify any watch instantly by simply snapping a photo. It leverages the largest watch database to provide comprehensive information about the watch, including its story, reference number, and where to acquire it. watchID also offers a marketplace where users can browse and purchase watches from various sellers. Additionally, it fosters a community of watch enthusiasts where users can share discoveries, get insights, and connect with fellow enthusiasts.

CALF (LLaTA) is a cross-modal fine-tuning framework that bridges the distribution discrepancy between temporal data and the textual nature of LLMs. It introduces three cross-modal fine-tuning techniques: Cross-Modal Match Module, Feature Regularization Loss, and Output Consistency Loss. The framework aligns time series and textual inputs, ensures effective weight updates, and maintains consistent semantic context for time series data. CALF provides scripts for long-term and short-term forecasting, requires Python 3.9, and utilizes word token embeddings for model training.

The 'awesome-air-quality' repository is a curated list of software libraries, tools, and resources related to air quality data acquisition, analysis, and visualization. It includes libraries in various programming languages such as Python, Java, R, and C#, as well as hardware drivers and software for gas sensors and particulate matter sensors. The repository aims to provide a comprehensive collection of tools for working with air quality data from different sources and for different purposes.

AIGT is a repository containing scripts for deep learning in guided medical interventions, focusing on ultrasound imaging. It provides a complete workflow from formatting and annotations to real-time model deployment. Users can set up an Anaconda environment, run Slicer notebooks, acquire tracked ultrasound data, and process exported data for training. The repository includes tools for segmentation, image export, and annotation creation.

Smile (Statistical Machine Intelligence and Learning Engine) is a comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. It covers every aspect of machine learning, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc. Smile implements major machine learning algorithms and provides interactive shells for Java, Scala, and Kotlin. It supports model serialization, data visualization using SmilePlot and declarative approach, and offers a gallery showcasing various algorithms and visualizations.

CritiqueLLM is an official implementation of a model designed for generating informative critiques to evaluate large language model generation. It includes functionalities for data collection, referenced pointwise grading, referenced pairwise comparison, reference-free pairwise comparison, reference-free pointwise grading, inference for pointwise grading and pairwise comparison, and evaluation of the generated results. The model aims to provide a comprehensive framework for evaluating the performance of large language models based on human ratings and comparisons.

MicroLens is a content-driven micro-video recommendation dataset at scale. It provides a large dataset with multimodal data, including raw text, images, audio, video, and video comments, for tasks such as multi-modal recommendation, foundation model building, and fairness recommendation. The dataset is available in two versions: MicroLens-50K and MicroLens-100K, with extracted features for multimodal recommendation tasks. Researchers can access the dataset through provided links and reach out to the corresponding author for the complete dataset. The repository also includes codes for various algorithms like VideoRec, IDRec, and VIDRec, each implementing different video models and baselines.

Awesome-Papers-Autonomous-Agent is a curated collection of recent papers focusing on autonomous agents, specifically interested in RL-based agents and LLM-based agents. The repository aims to provide a comprehensive resource for researchers and practitioners interested in intelligent agents that can achieve goals, acquire knowledge, and continually improve. The collection includes papers on various topics such as instruction following, building agents based on world models, using language as knowledge, leveraging LLMs as a tool, generalization across tasks, continual learning, combining RL and LLM, transformer-based policies, trajectory to language, trajectory prediction, multimodal agents, training LLMs for generalization and adaptation, task-specific designing, multi-agent systems, experimental analysis, benchmarking, applications, algorithm design, and combining with RL.

LLMeBench is a flexible framework designed for accelerating benchmarking of Large Language Models (LLMs) in the field of Natural Language Processing (NLP). It supports evaluation of various NLP tasks using model providers like OpenAI, HuggingFace Inference API, and Petals. The framework is customizable for different NLP tasks, LLM models, and datasets across multiple languages. It features extensive caching capabilities, supports zero- and few-shot learning paradigms, and allows on-the-fly dataset download and caching. LLMeBench is open-source and continuously expanding to support new models accessible through APIs.

STILL is an open-source project exploring slow-thinking reasoning systems, focusing on o1-like reasoning systems. The project has released technical reports on enhancing LLM reasoning with reward-guided tree search algorithms and implementing slow-thinking reasoning systems using an imitate, explore, and self-improve framework. The project aims to replicate the capabilities of industry-level reasoning systems by fine-tuning reasoning models with long-form thought data and iteratively refining training datasets.

ShortcutsBench is a project focused on collecting and analyzing workflows created in the Shortcuts app, providing a dataset of shortcut metadata, source files, and API information. It aims to study the integration of large language models with Apple devices, particularly focusing on the role of shortcuts in enhancing user experience. The project offers insights for Shortcuts users, enthusiasts, and researchers to explore, customize workflows, and study automated workflows, low-code programming, and API-based agents.

Web-LLM Assistant is a simple web search assistant that leverages a large language model (LLM) running via Llama.cpp to provide informative and context-aware responses to user queries. It combines the power of LLMs with real-time web searching capabilities, allowing it to access up-to-date information and synthesize comprehensive answers. The tool performs web searches, collects and scrapes information from search results, refines search queries, and provides answers based on the acquired information. Users can interact with the tool by asking questions or requesting web searches, making it a valuable resource for obtaining information beyond the LLM's training data.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

This repository contains a curated list of resources on generative information retrieval, including research papers, datasets, tools, and applications. Generative information retrieval is a subfield of information retrieval that uses generative models to generate new documents or passages of text that are relevant to a given query. This can be useful for a variety of tasks, such as question answering, summarization, and document generation. The resources in this repository are intended to help researchers and practitioners stay up-to-date on the latest advances in generative information retrieval.

Ninja is a project that serves as a reverse engineered proxy for ChatGPT. It allows users to acquire API keys, authenticate using email/password, proxy ChatGPT-API/OpenAI-API, access ChatGPT WebUI, utilize IP proxy pool, and solve FunCaptcha with Capsolver.com. The project has a very small memory footprint and is designed for ease of use. Please note that the project has ended.

Awesome-LLM-Interpretability is a curated list of materials related to LLM (Large Language Models) interpretability, covering tutorials, code libraries, surveys, videos, papers, and blogs. It includes resources on transformer mechanistic interpretability, visualization, interventions, probing, fine-tuning, feature representation, learning dynamics, knowledge editing, hallucination detection, and redundancy analysis. The repository aims to provide a comprehensive overview of tools, techniques, and methods for understanding and interpreting the inner workings of large language models.

The LandingLens Python library contains the LandingLens development library and examples that show how to integrate your app with LandingLens in a variety of scenarios. The library allows users to acquire images from different sources, run inference on computer vision models deployed in LandingLens, and provides examples in Jupyter Notebooks and Python apps for various tasks such as object detection, home automation, satellite image analysis, license plate detection, and streaming video analysis.

This repository is an updating survey for Continual Learning of Large Language Models (CL-LLMs), providing a comprehensive overview of various aspects related to the continual learning of large language models. It covers topics such as continual pre-training, domain-adaptive pre-training, continual fine-tuning, model refinement, model alignment, multimodal LLMs, and miscellaneous aspects. The survey includes a collection of relevant papers, each focusing on different areas within the field of continual learning of large language models.

OpenAI-DotNet is a simple C# .NET client library for OpenAI to use through their RESTful API. It is independently developed and not an official library affiliated with OpenAI. Users need an OpenAI API account to utilize this library. The library targets .NET 6.0 and above, working across various platforms like console apps, winforms, wpf, asp.net, etc., and on Windows, Linux, and Mac. It provides functionalities for authentication, interacting with models, assistants, threads, chat, audio, images, files, fine-tuning, embeddings, and moderations.

Awesome-LLM-Quantization is a curated list of resources related to quantization techniques for Large Language Models (LLMs). Quantization is a crucial step in deploying LLMs on resource-constrained devices, such as mobile phones or edge devices, by reducing the model's size and computational requirements.

Langbase Examples is an open-source repository showcasing projects built using Langbase, a composable AI infrastructure for creating and deploying AI agents with hyper-personalized memory. Langbase offers AI Pipes for building custom AI agents as APIs and Memory (RAG) for managed search engine capabilities. The platform also includes AI Studio for collaboration and deployment of AI projects, providing a complete AI developer platform for teams to work together on building and deploying AI features.

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