Best AI tools for< Research User Behavior >

Synthetic Users is an AI-powered platform that enables user research without the need for recruitment. It utilizes advanced AI architecture to create accurate synthetic interviews and surveys, mimicking human-like interactions. The platform allows users to run quantitative research at scale, conduct in-depth interviews, and enrich their synthetic users with proprietary data. Synthetic Users offers a multi-agent framework for dynamic interactions, continuous learning, and adaptation to provide valuable insights for various applications.

Looppanel is a user research analysis and repository tool that uses AI to help researchers save time and improve the quality of their work. It offers a range of features, including automated transcription, AI note-taking, video snipping, and advanced search capabilities. Looppanel is designed to make it easy for researchers to capture, organize, and analyze their research data, so they can focus on what matters most: uncovering insights and making better decisions.

VisualEyes is a user experience (UX) optimization tool that uses attention heatmaps and clarity scores to help businesses improve the effectiveness of their digital products. It provides insights into how users interact with websites and applications, allowing businesses to identify areas for improvement and make data-driven decisions about their designs. VisualEyes is part of Neurons, a leading neuroscience company that specializes in providing AI-powered solutions for businesses.

Entropik is an AI-powered integrated market research company that offers consumer and user research platforms with Insights AI capabilities. Their platforms, Decode and Qatalyst, provide a range of research solutions such as Quantitative, Qualitative, In-Context, Media Insights, Diary Studies, Shopper Insights, and Co-Pilot. Entropik specializes in understanding human insights, consumer preferences, and user feedback at scale through innovative AI technologies.

FB Group Extractor is an AI-powered tool designed to scrape Facebook group members' data with one click. It allows users to easily extract, analyze, and utilize valuable information from Facebook groups using artificial intelligence technology. The tool provides features such as data extraction, behavioral analytics for personalized ads, content enhancement, user research, and more. With over 10k satisfied users, FB Group Extractor offers a seamless experience for businesses to enhance their marketing strategies and customer insights.

UXSquid is a comprehensive UX research software platform and tool that guides users through the user research process with interview question examples, plans, templates, and a cheat sheet. It offers a free trial, requires no credit card, and allows users to cancel anytime. UXSquid's platform makes it easy to conduct user interviews and gather feedback. Users can use its automation tools to set up interviews with their target audience and gather valuable information. UXSquid analyzes user experiences and interactions with a company using cutting-edge artificial intelligence. It then makes important suggestions and enhancements to improve a product for its users.

User Persona is a free AI-powered tool that enables users to create detailed user personas for their products or services in seconds. It helps businesses in designing and marketing by providing comprehensive profiles based on demographic details, behavior patterns, motivations, and goals. By leveraging research and data from real users, User Persona guides decisions throughout the development process, leading to tailored offerings for specific target audiences and improved user experiences.

RealEye is an online research platform that uses webcam eye-tracking to collect data on user behavior. It allows researchers to conduct studies on attention, emotions, and mouse/key tracking. RealEye is easy to use and does not require any special equipment or software. It is a valuable tool for researchers who want to gain insights into how users interact with websites and other online content.

Metaforms is an AI-powered form builder that helps businesses create personalized and engaging forms. It uses AI to automatically generate conditions, logics, and branching, which can save businesses up to 85% in setup time. Metaforms also uses AI to provide real-time question framing and acknowledgements, which can increase completion rates by up to 30%. Additionally, Metaforms' AI can ask follow-up questions based on other user responses in a cohort, which can provide businesses with more insights about user behavior, intent, and preferences.

MapsScraperAI is an AI-powered tool designed to extract leads and data from Maps. It offers businesses the ability to generate local B2B leads, conduct research, monitor competition, and obtain business contact details. With features like batch lookup, lightning-fast results, and the unique ability to extract email addresses, MapsScraperAI streamlines the process of data extraction without the need for coding. The tool mimics real user behavior to reduce the risk of being blocked by Maps and ensures timely updates to accommodate any changes on the Maps website.

Parroview is a revolutionary AI-powered user research platform that automates the process of conducting user interviews. It uses natural language processing (NLP) to engage with users in real-time conversations, asking follow-up questions and uncovering insights that would be difficult to obtain through traditional methods. Parroview is designed to be fully autonomous, allowing researchers to set up interviews and gather insights without the need for manual intervention. It supports multiple languages, making it accessible to a global audience. Parroview offers a range of features, including the ability to conduct interviews via text or voice, analyze insights in real-time, and generate detailed transcripts. It is suitable for a wide range of research needs, including product validation, consumer behavior analysis, post-purchase evaluations, brand perception studies, and customer persona development.

Probz is an AI-powered end-to-end Insights Platform that helps brands gather detailed insights on their ideas through Quant, Qual, and Video Research. With a panel of over 10 million users worldwide, Probz enables brands to supercharge their decision-making process, understand customer behavior, collect feedback for product development, optimize marketing strategies, explore new initiatives, and obtain feedback on technology preferences and designs. The platform offers AI-generated hypotheses, customizable surveys, and real-user tests to enhance sales, customer satisfaction, and brand loyalty.

Research Studio is a next-level UX research tool that helps you streamline your user research with AI-enhanced analysis. Whether you're a freelance UX designer, user researcher, or agency, Research Studio can help you get the insights you need to make better decisions about your products and services.

Ivie is an AI-powered user research tool that automates the collection and analysis of qualitative user insights to help product teams build better products. It offers features such as AI-powered insights, processed user insights, in-depth analysis, automated follow-up questions, multilingual support, and more. Ivie provides advantages like human-like conversations, scalable surveys, customizable AI researchers, quick research setup, and multiple question types. However, it has disadvantages such as limited customization options, potential language barriers, and the need for user training. The frequently asked questions cover topics like supported research types, data security, multilingual research, and research findings presentation. Ivie is suitable for jobs related to user research, product development, customer satisfaction analysis, market research, and concept testing. The application can be used for tasks like conducting customer interviews, analyzing user feedback, creating surveys, synthesizing research findings, and building user personas.

Maze is a continuous product discovery platform that enables users to enrich product decisions with intuitive user research. It offers a wide range of features such as prototype testing, website testing, surveys, interview studies, and more. With AI-powered tools and integrations with popular design tools, Maze helps users scale user insights and speed up product launches. The platform provides Enterprise-level protection, encrypted transmission, access control, data center security, GDPR compliance, SSO, and private workspaces to ensure data security and compliance. Trusted by companies of all industries and sizes, Maze empowers teams to make user-informed decisions and drive faster product iteration for a better user experience.

User Evaluation is an AI-powered insights and analysis tool that offers a comprehensive platform for customer understanding and data analysis. It provides advanced features such as AI-generated reports and presentations, sentiment analysis, transcription services, and chat capabilities. The tool helps businesses extract valuable insights from various data sources, including audio, video, text, and CSV files, to enhance decision-making processes and improve customer experiences. User Evaluation prioritizes data security and confidentiality, ensuring that user information is protected by encryption protocols and not used for training AI models.

EchoQuery is an AI-powered user research tool that helps businesses gain valuable insights into their customers' needs. It offers a comprehensive analysis tool that guides users through four simple steps: creating analysis, sharing surveys, analyzing results using AI, and digging deeper for more insights. With features like unlimited analysis, AI-powered interviews, AI findings and themes, and an AI querying tool, EchoQuery empowers businesses to make data-driven decisions. The tool also provides email and phone support to assist users along their research journey.

Hubble is an all-in-one user research software that provides tools for continuous discovery. It offers a variety of features such as in-product research, contextual surveys, user targeting, prototype tests, usability tests, and more. Hubble empowers product teams to collect feedback from users, make informed decisions, and enhance product development cycles.

Marvin is an AI research software that serves as the perfect AI research assistant for qualitative research. It automates tedious parts of qualitative research, allowing users to analyze hours of research in minutes. Marvin helps in centralizing, searching, and sharing user research data, making it easily accessible for the whole team. With AI-powered enhancements, Marvin assists in consolidating user insights, finding patterns, and backing decisions with evidence. The tool streamlines the research journey by managing user interview panels, recruiting participants effectively, and providing features like automatic transcripts, video clips, and privacy filters for compliance. Marvin offers different pricing plans suitable for small teams, startups, companies with multiple teams, and large organizations.

InsightQ.ai is a pioneering force in the world of Artificial Intelligence for Product Management. Its groundbreaking AI product manager, CoPilot, is an indispensable companion on the path to product success, offering a seamless and transformative experience for product managers. InsightQ.ai CoPilot can effortlessly create a variety of essential documents that are invaluable to product managers, including product roadmaps, user personas, competitive analysis, and go-to-market strategies. It seamlessly integrates with key platforms like Atlassian and Slack, and connects with over 5,000 other services through Zapier, ensuring a cohesive and efficient workflow across all project management tools.

This repository is a collection of papers and resources related to recommendation systems, focusing on foundation models, transferable recommender systems, large language models, and multimodal recommender systems. It explores questions such as the necessity of ID embeddings, the shift from matching to generating paradigms, and the future of multimodal recommender systems. The papers cover various aspects of recommendation systems, including pretraining, user representation, dataset benchmarks, and evaluation methods. The repository aims to provide insights and advancements in the field of recommendation systems through literature reviews, surveys, and empirical studies.

Awesome-LLM-in-Social-Science is a repository that compiles papers evaluating Large Language Models (LLMs) from a social science perspective. It includes papers on evaluating, aligning, and simulating LLMs, as well as enhancing tools in social science research. The repository categorizes papers based on their focus on attitudes, opinions, values, personality, morality, and more. It aims to contribute to discussions on the potential and challenges of using LLMs in social science research.

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.

A repository that lists papers related to Large Language Model (LLM) based agents. The repository covers various topics including survey, planning, feedback & reflection, memory mechanism, role playing, game playing, tool usage & human-agent interaction, benchmark & evaluation, environment & platform, agent framework, multi-agent system, and agent fine-tuning. It provides a comprehensive collection of research papers on LLM-based agents, exploring different aspects of AI agent architectures and applications.

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

This repository compiles a list of academic papers that evaluate, align, simulate, and provide surveys or perspectives on the use of Large Language Models (LLMs) in the field of Social Science. The papers cover various aspects of LLM research, including assessing their alignment with human values, evaluating their capabilities in tasks such as opinion formation and moral reasoning, and exploring their potential for simulating social interactions and addressing issues in diverse fields of Social Science. The repository aims to provide a comprehensive resource for researchers and practitioners interested in the intersection of LLMs and Social Science.

This paper list is about Large Language Model-enhanced Recommender System. It also contains some related works. Keywords: recommendation system, large language models

**Promptulate** is an AI Agent application development framework crafted by **Cogit Lab** , which offers developers an extremely concise and efficient way to build Agent applications through a Pythonic development paradigm. The core philosophy of Promptulate is to borrow and integrate the wisdom of the open-source community, incorporating the highlights of various development frameworks to lower the barrier to entry and unify the consensus among developers. With Promptulate, you can manipulate components like LLM, Agent, Tool, RAG, etc., with the most succinct code, as most tasks can be easily completed with just a few lines of code. 🚀

CAMEL is an open-source library designed for the study of autonomous and communicative agents. We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks. To facilitate research in this field, we implement and support various types of agents, tasks, prompts, models, and simulated environments.

OpenRL is an open-source general reinforcement learning research framework that supports training for various tasks such as single-agent, multi-agent, offline RL, self-play, and natural language. Developed based on PyTorch, the goal of OpenRL is to provide a simple-to-use, flexible, efficient and sustainable platform for the reinforcement learning research community. It supports a universal interface for all tasks/environments, single-agent and multi-agent tasks, offline RL training with expert dataset, self-play training, reinforcement learning training for natural language tasks, DeepSpeed, Arena for evaluation, importing models and datasets from Hugging Face, user-defined environments, models, and datasets, gymnasium environments, callbacks, visualization tools, unit testing, and code coverage testing. It also supports various algorithms like PPO, DQN, SAC, and environments like Gymnasium, MuJoCo, Atari, and more.

Awesome Agents is a curated list of open source AI agents designed for various tasks such as private interactions with documents, chat implementations, autonomous research, human-behavior simulation, code generation, HR queries, domain-specific research, and more. The agents leverage Large Language Models (LLMs) and other generative AI technologies to provide solutions for complex tasks and projects. The repository includes a diverse range of agents for different use cases, from conversational chatbots to AI coding engines, and from autonomous HR assistants to vision task solvers.

AirGuard is an anti-tracking protection app designed to protect Android users from being tracked by AirTags and other Find My devices. The app periodically scans the surroundings for potential tracking devices and notifies the user if being followed. Users can play a sound on AirTags, view tracked locations, and participate in a research study on privacy protection. AirGuard does not monetize through ads or in-app purchases and ensures all tracking detection and notifications happen locally on the user's device.

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.

Sunnypilot is a fork of comma.ai's openpilot, offering a unique driving experience for over 250+ supported car makes and models with modified behaviors of driving assist engagements. It complies with comma.ai's safety rules and provides features like Modified Assistive Driving Safety, Dynamic Lane Profile, Enhanced Speed Control, Gap Adjust Cruise, and more. Users can install it on supported devices and cars following detailed instructions, ensuring a safe and enhanced driving experience.

ControlFlow is a Python framework designed for building agentic AI workflows. It provides a structured approach for defining tasks, assigning specialized AI agents, and orchestrating complex behaviors. By balancing AI autonomy with precise oversight, users can create sophisticated AI-powered applications with confidence. ControlFlow offers a task-centric architecture, structured results with type-safe outputs, specialized agents for efficient problem-solving, ecosystem integration with LangChain models, flexible control over workflows, multi-agent orchestration, and native observability and debugging capabilities.

OpenAdapt is an open-source software adapter between Large Multimodal Models (LMMs) and traditional desktop and web Graphical User Interfaces (GUIs). It aims to automate repetitive GUI workflows by leveraging the power of LMMs. OpenAdapt records user input and screenshots, converts them into tokenized format, and generates synthetic input via transformer model completions. It also analyzes recordings to generate task trees and replay synthetic input to complete tasks. OpenAdapt is model agnostic and generates prompts automatically by learning from human demonstration, ensuring that agents are grounded in existing processes and mitigating hallucinations. It works with all types of desktop GUIs, including virtualized and web, and is open source under the MIT license.

AI-Driven Research Assistant is an advanced AI-powered system utilizing specialized agents for data analysis, visualization, and report generation. It integrates LangChain, OpenAI's GPT models, and LangGraph for complex research processes. Key features include hypothesis generation, data processing, web search, code generation, and report writing. The system's unique Note Taker agent maintains project state, reducing overhead and improving context retention. System requirements include Python 3.10+ and Jupyter Notebook environment. Installation involves cloning the repository, setting up a Conda virtual environment, installing dependencies, and configuring environment variables. Usage instructions include setting data, running Jupyter Notebook, customizing research tasks, and viewing results. Main components include agents for hypothesis generation, process supervision, visualization, code writing, search, report writing, quality review, and note-taking. Workflow involves hypothesis generation, processing, quality review, and revision. Customization is possible by modifying agent creation and workflow definition. Current issues include OpenAI errors, NoteTaker efficiency, runtime optimization, and refiner improvement. Contributions via pull requests are welcome under the MIT License.

MaxText is a high-performance, highly scalable, open-source LLM written in pure Python/Jax and targeting Google Cloud TPUs and GPUs for training and inference. MaxText achieves high MFUs and scales from single host to very large clusters while staying simple and "optimization-free" thanks to the power of Jax and the XLA compiler. MaxText aims to be a launching off point for ambitious LLM projects both in research and production. We encourage users to start by experimenting with MaxText out of the box and then fork and modify MaxText to meet their needs.

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