We are very excited to announce that Zaggy AI has been accepted to the Microsoft for Startups Founders Hub!

Nothing too crazy to report in this patch-level release: Latest package has been published on PyPi and latest docs are up. Let us know if you have any issues or feature requests on our GitHub project page!

Zaggy AI is proud to announce our first FOSS contribution, PashehNet. PashehNet is a tool for quickly and reproducibly creating simulated sensor networks (SSN) that can publish to a target system.

Leveraging AI at the edge is challenging at best. We see many IoT solutions trying to push AI all the way to the actual sensors, or all the way into the cloud. We think there is a better solution.

Hybrid AI aims to combine the best of both worlds to create more robust, reliable, and effective systems by leveraging a blend of both rule-based and learning-based approaches.

Edge computing is transforming the way data is being handled, processed, and delivered from millions of devices around the world. As the next wave in the evolution of internet architecture, edge computing is poised to redefine connectivity and provide new opportunities for businesses and consumers alike. Here’s an in-depth look at what edge computing is and why it is crucial.

In the rapidly advancing field of artificial intelligence (AI), unsupervised and self-supervised learning is emerging as a transformative paradigm that promises to reshape the landscape.

I’m sure by now you’ve noticed the oddly abstract post headers we’ve been using in lieu of clip art, stock art, or custom artwork. In the spirit of being an AI-focused venture ourselves, we decided it would be interesting to dabble in generative AI.

Deep learning, a subset of machine learning, has taken the technological world by storm, underpinning the advancements in various applications from autonomous vehicles to drug discovery. Three dominant paradigms within deep learning are supervised, unsupervised, and self-supervised learning. In this article, we will elucidate these methods, noting their similarities and distinctions.

Sensor fusion, the process of integrating data from multiple sensors to provide a more comprehensive and accurate view of the environment, has become increasingly significant in various fields. Deep learning, a subset of machine learning characterized by deep neural networks, has shown outstanding capabilities in extracting patterns and information from large datasets. Its integration with sensor fusion can bring transformative benefits.