AI Agents: Agentic Design Patterns

Personalized recommendations in Zalando's newsletters

Ever imagined receiving emails that feel uniquely tailored just for you? 💌 Zalando Engineering team walks us through their journey of creating personalized newsletter emails using AWS. They provide insights how they harnessed AWS services—like Lambda, S3, and SES—to dynamically generate and distribute custom content at scale. It’s a fascinating look into building a system that adapts to individual user preferences, ensuring that each newsletter hits the mark. slys.dev is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Curious to see how cloud technology can transform email marketing? 🚀 Listen to discover the architecture, techniques, and best practices that made personalized newsletters a reality at Zalando! Get full access to slys.dev at iam.slys.dev/subscribe [https://iam.slys.dev/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

AI Agents: Agentic Design Patterns

Ever wish your chatbot could double-check its own answers or plan out a complicated task before diving in? That’s where Agentic Design Patterns come in! They’re a set of clever strategies that make AI more reliable and capable by adding features like: * Reflection – A built-in ‘review phase’ where AI spots mistakes and polishes its answers. * Tool Use – The power to call external APIs or databases, so it’s always up-to-date and accurate. * Planning – Step-by-step thinking, helping AI tackle multi-stage tasks without getting lost. * Multi-Agent Collaboration – Multiple AIs working together, each with its own specialty, to deliver better (and faster) results. slys.dev is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Whether you’re an AI newbie or just tired of chatbots spouting random trivia, agentic design patterns can transform simple software into a sharper, more helpful AI sidekick! Get full access to slys.dev at iam.slys.dev/subscribe [https://iam.slys.dev/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

LinkedIn’s journey of scaling to the exabyte

Ever wondered how a platform like LinkedIn handles the sheer scale of its data - enough to fill over a billion USB drives? Behind the scenes, they’ve been pushing Hadoop’s limits, managing an exabyte of data while tackling challenges like keeping things fast, secure, and endlessly scalable. From clever tweaks to Java heap management to building tools like Wormhole for lightning-fast data transfers, their journey offers a fascinating look into the art (and science) of mastering Big Data. Curious to dive deeper? Let’s unravel the story! slys.dev is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to slys.dev at iam.slys.dev/subscribe [https://iam.slys.dev/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

Moving Twitter's exabyte of data to Google Cloud

Ever wondered how Twitter manages the vast ocean of data generated daily by millions of users? What challenges arise when migrating over an exabyte of data to the cloud, and how can such a colossal task be executed seamlessly? We delve into Twitter's ambitious journey of transitioning its massive data infrastructure to Google Cloud. Discover the innovative strategies employed, the obstacles overcome, and the transformative impact on data accessibility and analytics. Whether you're a tech enthusiast or a data professional, this discussion offers invaluable insights into the future of large-scale data management. slys.dev is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to slys.dev at iam.slys.dev/subscribe [https://iam.slys.dev/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

How Uber manages a million writes per second

Curious about how Uber powers a global platform that processes millions of data points every second? We dive deep into the technology that makes it possible: from Apache Mesos’ dynamic resource management to Cassandra’s high-speed data storage and Kafka’s real-time data streaming. Discover how Uber’s architecture handles massive demand with low latency and high resilience, and what it takes to keep this system running smoothly across multiple data centers worldwide. Tune in to explore the fascinating, behind-the-scenes tech that drives one of the world’s largest and most innovative real-time platforms! slys.dev is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to slys.dev at iam.slys.dev/subscribe [https://iam.slys.dev/subscribe?utm_medium=podcast&utm_campaign=CTA_4]