Best AI tools for< Analyze User Behavior >

ChainAware.ai is an AI-powered blockchain super tool designed for both users and businesses. It offers a range of features such as Wallet Auditor, Fraud Detector, and Rug Pull Detector to enhance security and trust in blockchain transactions. The tool provides predictive AI capabilities to prevent fraud and identify potential risks before they occur. Additionally, it offers business solutions including account-based user acquisition, web3 user analytics, and crypto fraud detection with AI. ChainAware.ai aims to revolutionize the way users interact with blockchain technology by providing advanced tools and services powered by artificial intelligence.

UXsniff is an AI-powered website analytics tool that offers features such as session recordings, website heatmaps, feedback widgets, on-site surveys, and site audits. It autonomously analyzes user behavior to identify abnormal patterns and provides insights to enhance website UX and conversion rates. The tool leverages AI technologies like GPT Assistant and ChatGPT API to summarize session recordings and provide actionable recommendations. UXsniff helps users visualize user behavior through interactive heatmaps and offers automated SEO and UX audits. It also integrates with Zapier to connect with over 5000 apps for seamless workflow automation.

HelpLook is an AI-powered knowledge base platform designed for content creators, company employees, and customer service teams. It offers features such as an intuitive editor, website portal for visitors, customizable widgets, and AI integration for creating AI chatbots and knowledge bases. With HelpLook, users can easily build and manage knowledge repositories, improve customer service efficiency, reduce costs, and enhance user experience through AI-driven solutions.

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.

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.

Haptik is a Conversational CRM platform powered by Generative AI, offering a suite of AI-powered solutions for customer experience, sales assistance, and self-serve support. It seamlessly integrates Generative AI into marketing, support, and operations to drive business efficiency at scale. Trusted by 500+ leading brands, Haptik provides bespoke AI solutions tailored to unique enterprise requirements across various industries. The platform leverages Generative AI to offer intelligent analytics, user behavior insights, and low code builder tools for creating conversational experiences across multiple channels like WhatsApp, Instagram, Messenger, Google Business Messages, and more.

Summer AI is an AI tool designed to help bloggers and website owners enhance their content by providing short summaries, context, and relevant product suggestions. It offers features such as creating summaries, suggesting products, providing detailed statistics on user behavior, and improving reading time speed. With Summer AI, users can easily connect with their audience, generate more leads, and improve the overall user experience on their websites.

Sinopsis AI is an analytics tool designed for AI chatbots, offering detailed insights into user interactions to enhance response accuracy and user experience. It seamlessly integrates with existing systems, providing real-time data updates and customizable analytics reports. With a user-friendly platform and model-agnostic compatibility, Sinopsis AI empowers businesses to optimize their chatbot performance and improve customer engagement strategies.

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.

DataTower.ai is an AI tool that offers a comprehensive platform for the measurement of advertising effectiveness and data analysis. It provides solutions for user acquisition success, accurate ad performance tracing, optimization of ad performance, precise audience targeting, around-the-clock strategy adjustments, and transformation of user journey insights into actionable business guidance. The platform integrates data analysis, attribution monitoring, event forwarding, and more features to empower enterprises in making efficient and accurate decisions.

LangWatch is a monitoring and analytics platform designed to track, visualize, and analyze interactions with Large Language Models (LLMs). It offers real-time telemetry to optimize LLM cost and latency, a user-friendly interface for deep insights into LLM behavior, user analytics for engagement metrics, detailed debugging capabilities, and guardrails to monitor LLM outputs for issues like PII leaks and toxic language. The platform supports OpenAI and LangChain integrations, simplifying the process of tracing LLM calls and generating API keys for usage. LangWatch also provides documentation for easy integration and self-hosting options for interested users.

AI-Powered Search provides code examples for the book 'AI-Powered Search' by Trey Grainger, Doug Turnbull, and Max Irwin. The book teaches modern machine learning techniques for building search engines that continuously learn from users and content to deliver more intelligent and domain-aware search experiences. It covers semantic search, retrieval augmented generation, question answering, summarization, fine-tuning transformer-based models, personalized search, machine-learned ranking, click models, and more. The code examples are in Python, leveraging PySpark for data processing and Apache Solr as the default search engine. The repository is open source under the Apache License, Version 2.0.

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)

Phospho is a text analytics platform for LLM apps. It helps you detect issues and extract insights from text messages of your users or your app. You can gather user feedback, measure success, and iterate on your app to create the best conversational experience for your users.

Trubrics-sdk is a software development kit designed to facilitate the integration of analytics features into applications. It provides a set of tools and functionalities that enable developers to easily incorporate analytics capabilities, such as data collection, analysis, and reporting, into their software products. The SDK streamlines the process of implementing analytics solutions, allowing developers to focus on building and enhancing their applications' functionality and user experience. By leveraging trubrics-sdk, developers can quickly and efficiently integrate robust analytics features, gaining valuable insights into user behavior and application performance.

The NZT Poker AI Bot is an advanced tool designed to revolutionize poker gameplay by providing comprehensive features to dominate 17 rooms with the Cash Fish Monitor. It offers detailed analysis of opponents' VPIP and PFR, extensive hand history database, opponent exploitation techniques, data-driven intelligence, player profiles, advanced neural network technology, expert training, continuous refinement, and cutting-edge algorithms for maximizing poker profits. Created by a team of poker experts, this AI tool continuously adapts to the latest poker strategies and utilizes state-of-the-art technology to enhance decision-making prowess.

Llama-github is a powerful tool that helps retrieve relevant code snippets, issues, and repository information from GitHub based on queries. It empowers AI agents and developers to solve coding tasks efficiently. With features like intelligent GitHub retrieval, repository pool caching, LLM-powered question analysis, and comprehensive context generation, llama-github excels at providing valuable knowledge context for development needs. It supports asynchronous processing, flexible LLM integration, robust authentication options, and logging/error handling for smooth operations and troubleshooting. The vision is to seamlessly integrate with GitHub for AI-driven development solutions, while the roadmap focuses on empowering LLMs to automatically resolve complex coding tasks.

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

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 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.

Voidpulse is an open-source Mixpanel alternative with AI capabilities. It is currently in private beta and being used in production for the Voidpet app. The project aims to provide analytics functionalities without the high cost associated with other tools. It is built using React, Typescript, Next.js on the frontend, and Node.js with TRPC & Drizzle ORM on the backend. Data is stored in Postgresql, Clickhouse is used for storing/querying events, Kafka for batch event insertion, and Redis for caching.

BehaviorTree.CPP is a C++ 17 library that provides a framework to create BehaviorTrees. It was designed to be flexible, easy to use, reactive and fast. Even if our main use-case is robotics, you can use this library to build AI for games, or to replace Finite State Machines. There are few features which make BehaviorTree.CPP unique, when compared to other implementations: It makes asynchronous Actions, i.e. non-blocking, a first-class citizen. You can build reactive behaviors that execute multiple Actions concurrently (orthogonality). Trees are defined using a Domain Specific scripting language (based on XML), and can be loaded at run-time; in other words, even if written in C++, the morphology of the Trees is not hard-coded. You can statically link your custom TreeNodes or convert them into plugins and load them at run-time. It provides a type-safe and flexible mechanism to do Dataflow between Nodes of the Tree. It includes a logging/profiling infrastructure that allows the user to visualize, record, replay and analyze state transitions.

Slips is a powerful endpoint behavioral intrusion prevention and detection system that uses machine learning to detect malicious behaviors in network traffic. It can work with network traffic in real-time, PCAP files, and network flows from tools like Suricata, Zeek/Bro, and Argus. Slips threat detection is based on machine learning models, threat intelligence feeds, and expert heuristics. It gathers evidence of malicious behavior and triggers alerts when enough evidence is accumulated. The tool is Python-based and supported on Linux and MacOS, with blocking features only on Linux. Slips relies on Zeek network analysis framework and Redis for interprocess communication. It offers a graphical user interface for easy monitoring and analysis.

OpenInference is a set of conventions and plugins that complement OpenTelemetry to enable tracing of AI applications. It provides a way to capture and analyze the performance and behavior of AI models, including their interactions with other components of the application. OpenInference is designed to be language-agnostic and can be used with any OpenTelemetry-compatible backend. It includes a set of instrumentations for popular machine learning SDKs and frameworks, making it easy to add tracing to your AI applications.

Tools4AI is a Java-based Agentic Framework for building AI agents to integrate with enterprise Java applications. It enables the conversion of natural language prompts into actionable behaviors, streamlining user interactions with complex systems. By leveraging AI capabilities, it enhances productivity and innovation across diverse applications. The framework allows for seamless integration of AI with various systems, such as customer service applications, to interpret user requests, trigger actions, and streamline workflows. Prompt prediction anticipates user actions based on input prompts, enhancing user experience by proactively suggesting relevant actions or services based on context.

**Description:** PromptFuzz is an automated tool that generates high-quality fuzz drivers for libraries via a fuzz loop constructed on mutating LLMs' prompts. The fuzz loop of PromptFuzz aims to guide the mutation of LLMs' prompts to generate programs that cover more reachable code and explore complex API interrelationships, which are effective for fuzzing. **Features:** * **Multiply LLM support** : Supports the general LLMs: Codex, Inocder, ChatGPT, and GPT4 (Currently tested on ChatGPT). * **Context-based Prompt** : Construct LLM prompts with the automatically extracted library context. * **Powerful Sanitization** : The program's syntax, semantics, behavior, and coverage are thoroughly analyzed to sanitize the problematic programs. * **Prioritized Mutation** : Prioritizes mutating the library API combinations within LLM's prompts to explore complex interrelationships, guided by code coverage. * **Fuzz Driver Exploitation** : Infers API constraints using statistics and extends fixed API arguments to receive random bytes from fuzzers. * **Fuzz engine integration** : Integrates with grey-box fuzz engine: LibFuzzer. **Benefits:** * **High branch coverage:** The fuzz drivers generated by PromptFuzz achieved a branch coverage of 40.12% on the tested libraries, which is 1.61x greater than _OSS-Fuzz_ and 1.67x greater than _Hopper_. * **Bug detection:** PromptFuzz detected 33 valid security bugs from 49 unique crashes. * **Wide range of bugs:** The fuzz drivers generated by PromptFuzz can detect a wide range of bugs, most of which are security bugs. * **Unique bugs:** PromptFuzz detects uniquely interesting bugs that other fuzzers may miss. **Usage:** 1. Build the library using the provided build scripts. 2. Export the LLM API KEY if using ChatGPT or GPT4. 3. Generate fuzz drivers using the `fuzzer` command. 4. Run the fuzz drivers using the `harness` command. 5. Deduplicate and analyze the reported crashes. **Future Works:** * **Custom LLMs suport:** Support custom LLMs. * **Close-source libraries:** Apply PromptFuzz to close-source libraries by fine tuning LLMs on private code corpus. * **Performance** : Reduce the huge time cost required in erroneous program elimination.

ERAG is an advanced system that combines lexical, semantic, text, and knowledge graph searches with conversation context to provide accurate and contextually relevant responses. This tool processes various document types, creates embeddings, builds knowledge graphs, and uses this information to answer user queries intelligently. It includes modules for interacting with web content, GitHub repositories, and performing exploratory data analysis using various language models.

This repository provides a deployment guide for utilizing Google Cloud's Generative AI tools in marketing scenarios. It includes step-by-step instructions, examples of crafting marketing materials, and supplementary Jupyter notebooks. The demos cover marketing insights, audience analysis, trendspotting, content search, content generation, and workspace integration. Users can access and visualize marketing data, analyze trends, improve search experience, and generate compelling content. The repository structure includes backend APIs, frontend code, sample notebooks, templates, and installation scripts.

Analyzes behavioral data to understand user interactions and preferences, improving product designs.

Build extensions using the Eyeware Beam eye tracking SDK

We help you navigate Google Analytics 4 and start using it more.

Your assistant for understanding G Analytics data.

Assists in enhancing e-commerce sites for better conversions with tailored, easy-to-implement advice.

AI expert in UX Research and User Interviews

Generates fictional user profiles for software testing

This AI assistant is not just a chatbot; it's a virtual consultant, combining deep knowledge in IT product management with insights in user experience (UX) and product development.,

I suggest online business opportunities based on trends and user interests.

TikTok users data analysis and interpretation. (@apify actions)(Email me if any inquiries)

A UX research assistant focused solely on UX research and design.

Your go-to mentor for UX wisdom and growth.

小红书运营专家

Analyze any uploaded art piece, providing thoughtful insight on the history of the piece and its maker. Replicate art pieces in new styles generated by the user. Be an overall expert in art and help users navigate the art scene. Inform them of different types of art

Delves into historical events, figures, or eras based on user queries. It can provide detailed narratives, analyze historical contexts, and even create engaging stories or hypothetical scenarios based on historical facts, making learning history interactive and fun.

A GPT/AI system designed to collect, analyze, and summarize recent news from established media outlets, emphasizing balance in perspectives and precision in content delivery, with a default focus on top breaking news stories, adaptable to user-specified topics.

Analyzes annual reports, focusing on specific user-provided data

User Growthing Master, respond to all your Growth questions

Transforms user interview transcripts into a list of tasks [Asana compatible CSV file]. Send feedback to https://x.com/kireet_agrawal