Perplexity strikes multi-year licensing deal with Getty Images

The development of Dew, an AI character inspired by Duolingo, aims to bridge the gap between artificial intelligence and human-like interaction. Unlike traditional AI, which often lacks emotional expression, Dew is designed to communicate with users through facial expressions and reactions, making interactions feel more personal and engaging. This innovation is significant as it could enhance user experience and acceptance of AI technologies, making them more relatable and effective in everyday applications.

The rise of agentic browsers, which operate autonomously without user input, raises significant concerns about vulnerabilities that could lead to data and financial losses. As these AI systems become more prevalent, understanding their risks is crucial for users to protect their information and assets. This discussion highlights the importance of awareness and caution in the evolving landscape of AI technology.

The recent licensing deal between Perplexity and Getty Images marks a notable shift in how AI companies access visual content. Unlike previous agreements, Perplexity is allowed to use Getty's media but cannot train its AI model on this content. This distinction raises important questions about the future of AI training and content usage, highlighting the need for clearer guidelines in the evolving landscape of AI and media.

Getty Images has entered into a multi-year licensing deal with Perplexity, marking a significant partnership in the digital content space. This collaboration is expected to enhance the accessibility of high-quality images for users of Perplexity's platform, which is known for its innovative approach to information retrieval. Such agreements are crucial as they not only benefit the companies involved but also improve the overall user experience by providing better resources for content creators and consumers alike.

Perplexity has signed a significant deal to utilize Getty Images, enhancing its content offerings. This partnership is crucial as it allows Perplexity to access a vast library of high-quality images, which can improve user engagement and enrich the overall experience on their platform. Such collaborations are vital in the competitive landscape of digital content, showcasing Perplexity's commitment to providing valuable resources to its users.

Perplexity has launched a free AI-powered patent research tool in beta, allowing users to search for patents using natural language queries. This innovation is significant as it simplifies the often complex process of patent research, making it more accessible for inventors, entrepreneurs, and researchers. By leveraging AI, users can find relevant patents more efficiently, potentially accelerating innovation and development in various fields.

Getty Images has partnered with AI startup Perplexity in a multi-year licensing agreement, allowing the startup to incorporate Getty's extensive image library into its search and discovery tools. This collaboration is significant as it enhances the visual capabilities of AI-powered search, providing users with richer and more engaging content. By leveraging Getty's high-quality images, Perplexity aims to improve user experience and drive innovation in the AI space.

If you're looking to make some extra cash, Perplexity has an exciting referral program that rewards you with $15 for every friend you bring on board to Comet. The best part? There appears to be no cap on how much you can earn, making it a fantastic opportunity for those with a wide social circle. This initiative not only helps you boost your income but also introduces your friends to a valuable service.

This article discusses the modern need for efficient background processes in application development and introduces a simple solution using .NET's System.Threading.Channels. It highlights how this approach can streamline communication with APIs, making it easier for developers to implement background services without the complexity of traditional methods. This matters because it can significantly enhance application performance and developer productivity.

This article dives into the efficient processing of large datasets using Laravel by introducing a clean architecture for batch jobs. It emphasizes the importance of breaking down tasks into manageable chunks, which not only enhances performance but also ensures safety and extensibility in job handling. This approach is crucial for developers looking to optimize their applications and manage resources effectively.

MongoDB's latest enhancements to its aggregation framework, particularly with the $group and $sum operations, are making waves in the tech community. By leveraging indexes, users can now achieve significantly faster performance, especially with the DISTINCT_SCAN optimization. This is crucial for developers and businesses that rely on efficient data processing, as it not only speeds up queries but also improves overall application performance. As MongoDB continues to innovate, these advancements highlight its commitment to providing powerful tools for data management.

Tonight's Game 7 of the 2025 MLB World Series between the Dodgers and Blue Jays is set to be an exciting showdown, and fans can catch all the action without cable. This matchup is significant as it showcases two of the league's top teams battling for the championship title, making it a must-watch event for baseball enthusiasts.

The article introduces Monetzly, a new solution for monetizing AI applications through dual revenue streams. It highlights the potential for developers to earn money not only from subscriptions but also by integrating relevant ads into their apps. This innovative approach allows creators to focus on enhancing their applications while still benefiting financially, making it a significant development in the AI app market.

Researchers have found that large language models, often celebrated for their problem-solving abilities, tend to operate under the assumption that conditions remain constant while they process information. This discovery is significant because it highlights a limitation in AI's adaptability to real-world scenarios where interruptions or new data can occur unexpectedly. Understanding this behavior could lead to improvements in AI systems, making them more responsive and effective in dynamic environments.