A common challenge developers face when working with data streams is collecting and analyzing this data as fast as possible to uncover meaningful insights. It’s a complex problem that requires the right combination of real-time data technologies and AI for instant, intelligent decision-making.

In this talk, I’ll show you how I tackled this by building a Bluesky bot that turns raw data into actionable insights using GenAI. We’ll dive into the process of collecting data, transforming it into streams, and using Redis 8 to power real-time analysis. Along the way, I’ll explore how probabilistic data structures, like Count-Min Sketch and Bloom Filters, help optimize performance and enable scalable analytics without compromising accuracy.

I’ll also demonstrate how Redis 8 supports vector similarity search, making it possible to compare and classify data efficiently—an essential step for enhancing AI-driven insights. You’ll see how this can be applied to find patterns, group similar content, and make smarter recommendations.

Finally, I’ll bring it all together by showing how Redis and GenAI work hand in hand to extract patterns and generate insights, with practical examples implemented in Java.

Whether you’re curious about GenAI, interested in data-driven analytics, or simply love experimenting with creative tech solutions, this session will inspire you with practical techniques and real-world applications.

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Beyond Coding Podcast: https://youtu.be/aF7Pcvn0CdI?si=SwomwFaEWrPFVwGP

TedXKE: https://youtu.be/gJKuqonxRJQ?si=91zKMrZ1lqq4ePgf