 Hi there! Are you looking for a way to accelerate your STM32 MP1 development? At ST, we have been working really hard with a large number of partners, about 14 in fact, to create solutions that make your life easier. Here behind me, I have two of them, Timesys and Fondris, with whom we have been collaborating to create a solution based on the MP1 that will bring you the support you need to get to market faster. As ST, we propose a solution based on open source software. We collaborate with two partners on security aspects, giving us the capabilities to offer you an opportunity in deploying and maintaining securely your devices thanks to Fondris Factory Edge Platform and thanks to Timesys Vigilus to create a complete end-to-end security solution that will enable you to implement security early in the design of your embedded Linux-based product, all that around our MP1 family. Let's take a closer look, starting with Fondris, who have a demo showing an IoT dashboard deployed as a containerized microservice inside a Fondris Factory on a STM32 MP1. That demonstrates three containerized services working together in harmony, each one updateable, independent of the others. And now, let's check out Timesys, who have a demo showing the Vigilus solution to monitor your STM32 MP1-based product for vulnerabilities and available fixes. Integrated with open ST Linux. So now that we have seen our partners' demo, let's check out one that we have put together to showcase AI enablement on the MP1 using our open source offering. So here, we have a use case mixing computer vision, connectivity and privacy protection. In this demo, the STM32 MP1 is used as an headless camera device from Sienna Systems that will compute video frames and transfer non-sensitive data to another device via the cloud or local server, and meet maintaining people's privacy. In this demo, it's based on X-Linux AI Open ST Linux Expansion Package that can be downloaded from ST MicroXonics GitHub. The STM32 MP1 is managing camera streaming, neural network inference for people detection and counting is executed on the CPU. So here, we are talking about true edge AI. The data is then sent to the cloud via Wi-Fi. And here, once it gets interesting, you can see how we protect people's privacy and we save bandwidth. On the application side, we are retrieving people coordinates, data only, on the few bytes of data. It's transmitted on the cloud, so saving bandwidth globally. On this desktop UI, we display the information on the people's coordinates in a virtual environment. And the user can control the STM32 MP1 computing behavior based on user constraints. For more information on the STM32 MP1 and its partner, check out ST.com.