Clickmoat offers cutting-edge solutions for identifying and preventing click fraud in digital advertising. Utilizing advanced algorithms and machine learning, Clickmoat helps ensure that advertising budgets are spent on genuine interactions rather than fraudulent clicks, thereby enhancing the effectiveness and ROI of digital campaigns. The platform provides real-time monitoring, detailed reporting, and actionable insights to help advertisers make informed decisions and reduce ad spend wastage.

GoExchange is a unique browser extension designed for currency forecasting. By utilizing advanced machine learning algorithms alongside real-time data from the European Central Bank, it predicts exchange rate movements. Users can benefit from informed insights into currency trends, significantly enhancing trading strategies and financial planning. The extension is user-friendly, offering intuitive navigation and clear visualizations of currency trends that are vital for anyone involved in foreign exchange transactions.

Layly AI is a robust platform utilizing advanced machine learning and image analysis algorithms to identify and analyze deepfake content and other digital media anomalies. With its precise detection capability, Layly AI stands as a pivotal tool in preserving the authenticity of digital content, making it indispensable for users who require reliable verification of multimedia files against tampering and misinformation.

Point Is is an AI-based tool designed to help users quickly summarize and search various types of content, including web articles, emails, and documents. By leveraging powerful machine learning algorithms, Point Is extracts the most relevant information, enabling users to grasp key concepts without diving deep into lengthy texts. Its user-friendly interface makes it accessible for everyone, from students to professionals, looking to optimize their reading and research time. With Point Is, you can spend less time reading and more time understanding.

Mean-Field MARL provides a robust Python framework for implementing and evaluating mean-field multi-agent reinforcement learning algorithms. It approximates large-scale agent interactions by modeling the average effect of neighboring agents via mean-field Q-learning. The library includes environment wrappers, agent policy modules, training loops, and evaluation metrics, enabling scalable training across hundreds of agents. Built on PyTorch for GPU acceleration, it supports customizable environments like Particle World and Gridworld. Modular design allows easy extension with new algorithms, while built-in logging and Matplotlib-based visualization tools track rewards, loss curves, and mean-field distributions. Example scripts and documentation guide users through setup, experiment configuration, and result analysis, making it ideal for both research and prototyping of large-scale multi-agent systems.