https://speakerdeck.com/raphaeldelio 2024-02-22 04:39:17 -0500 Wed, 01 Apr 2026 00:00:00 -0400 https://speakerdeck.com/raphaeldelio/introducing-redis-agent-memory-server https://speakerdeck.com/raphaeldelio/introducing-redis-agent-memory-server Presented at AI Lowlands 2025 & Devnexus 2026 Presented at AI Lowlands 2025 & Devnexus 2026 Tue, 02 Dec 2025 00:00:00 -0500 https://speakerdeck.com/raphaeldelio/evolution-of-memory-in-humans-and-ai-agents https://speakerdeck.com/raphaeldelio/evolution-of-memory-in-humans-and-ai-agents What happens when you combine the Apollo program’s historical data with modern AI tools? You get a way to interact with one of humanity’s greatest adventures like never before! In this session, I’ll show you how I used Redis OM Spring and Spring AI to explore Apollo mission data—aligning transcripts, telemetry, and images to uncover hidden connections and insights. We’ll dive into how Semantic Search powered by vector embeddings makes sense of unstructured text, how Redis as a vector database enables lightning-fast retrieval, and why these tools unlock new ways to explore complex datasets. Don’t know what embeddings or vector databases are? No worries—I’ll break it all down and show you how it works. Come for the Moon missions, stay for the AI magic, and leave ready to build your own intelligent search experiences! What happens when you combine the Apollo program’s historical data with modern AI tools? You get a way to interact with one of humanity’s greatest adventures like never before! In this session, I’ll show you how I used Redis OM Spring and Spring AI to explore Apollo mission data—aligning transcripts, telemetry, and images to uncover hidden connections and insights. We’ll dive into how Semantic Search powered by vector embeddings makes sense of unstructured text, how Redis as a vector database enables lightning-fast retrieval, and why these tools unlock new ways to explore complex datasets. Don’t know what embeddings or vector databases are? No worries—I’ll break it all down and show you how it works. Come for the Moon missions, stay for the AI magic, and leave ready to build your own intelligent search experiences! Wed, 26 Nov 2025 00:00:00 -0500 https://speakerdeck.com/raphaeldelio/rediscovering-apollo-11-using-kotlin-spring-ai-plus-redis-om-spring-to-explore-the-mission-to-the-moon https://speakerdeck.com/raphaeldelio/rediscovering-apollo-11-using-kotlin-spring-ai-plus-redis-om-spring-to-explore-the-mission-to-the-moon AI projects often stall when models can’t deliver results at the speed and scale users expect. In this webinar, discover how Red Hat OpenShift AI and Redis combine to supercharge performance and unlock new possibilities for real-time AI. Our experts will show how the two platforms work together to enable retrieval-augmented generation (RAG), semantic caching, and LLM context management—helping reduce latency, cut costs, and deliver production-ready AI applications. You’ll also learn how to deploy Redis on OpenShift and integrate leading AI frameworks to accelerate your path from experimentation to enterprise-grade solutions. Join us to see how you can take your AI from promising prototypes to fast, scalable, production-ready systems. AI projects often stall when models can’t deliver results at the speed and scale users expect. In this webinar, discover how Red Hat OpenShift AI and Redis combine to supercharge performance and unlock new possibilities for real-time AI. Our experts will show how the two platforms work together to enable retrieval-augmented generation (RAG), semantic caching, and LLM context management—helping reduce latency, cut costs, and deliver production-ready AI applications. You’ll also learn how to deploy Redis on OpenShift and integrate leading AI frameworks to accelerate your path from experimentation to enterprise-grade solutions. Join us to see how you can take your AI from promising prototypes to fast, scalable, production-ready systems. Tue, 18 Nov 2025 00:00:00 -0500 https://speakerdeck.com/raphaeldelio/supercharge-your-agentic-application-with-openshift-plus-redis https://speakerdeck.com/raphaeldelio/supercharge-your-agentic-application-with-openshift-plus-redis LLMs are powerful, but calling them for everything gets expensive, slow, and energy-hungry fast. What if you could handle common tasks like classification, routing, and caching without reaching for a massive model every time? In this session, I’ll show you how to use vector search and semantic patterns to build smarter systems that skip unnecessary LLM calls and still deliver. We’ll cover: • How semantic classification can match intent without tokens or prompts • How to route requests based on meaning, not brittle rules • How semantic caching helps you reuse answers and cut costs You’ll see how to replace brute-force prompting with clean, efficient logic using embeddings, similarity, and lightweight decision-making. No complex ML pipelines, no GPU bills, just smart patterns that save time, money, and energy. This session will help you do it better with fewer calls, less waste, and a lot more control. LLMs are powerful, but calling them for everything gets expensive, slow, and energy-hungry fast. What if you could handle common tasks like classification, routing, and caching without reaching for a massive model every time? In this session, I’ll show you how to use vector search and semantic patterns to build smarter systems that skip unnecessary LLM calls and still deliver. We’ll cover: • How semantic classification can match intent without tokens or prompts • How to route requests based on meaning, not brittle rules • How semantic caching helps you reuse answers and cut costs You’ll see how to replace brute-force prompting with clean, efficient logic using embeddings, similarity, and lightweight decision-making. No complex ML pipelines, no GPU bills, just smart patterns that save time, money, and energy. This session will help you do it better with fewer calls, less waste, and a lot more control. Tue, 30 Sep 2025 00:00:00 -0400 https://speakerdeck.com/raphaeldelio/reduce-llm-calls-with-vector-search https://speakerdeck.com/raphaeldelio/reduce-llm-calls-with-vector-search 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. 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. Mon, 26 May 2025 00:00:00 -0400 https://speakerdeck.com/raphaeldelio/from-data-to-insights-building-a-bluesky-bot-powered-by-ai https://speakerdeck.com/raphaeldelio/from-data-to-insights-building-a-bluesky-bot-powered-by-ai A Count-Min Sketch, a Bloom Filter, and a TopK might sound fancy, but they’re just smart ways to work with huge amounts of data using very little memory. In this talk, we’ll explore three powerful probabilistic data structures that trade a bit of accuracy for a lot of speed and scalability. You’ll learn: What Count-Min Sketch, Bloom Filter, and TopK actually are How each of them works under the hood How I used them together to build an efficient version of Trending Topics for Bluesky By the end, you’ll see how these tools help you process large data streams without blowing up your memory, and how to apply them in real-world systems where being fast matters more than being perfect. A Count-Min Sketch, a Bloom Filter, and a TopK might sound fancy, but they’re just smart ways to work with huge amounts of data using very little memory. In this talk, we’ll explore three powerful probabilistic data structures that trade a bit of accuracy for a lot of speed and scalability. You’ll learn: What Count-Min Sketch, Bloom Filter, and TopK actually are How each of them works under the hood How I used them together to build an efficient version of Trending Topics for Bluesky By the end, you’ll see how these tools help you process large data streams without blowing up your memory, and how to apply them in real-world systems where being fast matters more than being perfect. Thu, 17 Apr 2025 00:00:00 -0400 https://speakerdeck.com/raphaeldelio/count-min-sketch-bloom-filter-topk-efficient-probabilistic-data-structures https://speakerdeck.com/raphaeldelio/count-min-sketch-bloom-filter-topk-efficient-probabilistic-data-structures Redis 8 is here. In this talk I go through a brief history of Redis. Then what's new in Redis 8. And wrap up with three short demos: full text search, vector similarity search, and probabilistic data structures. Redis 8 is here. In this talk I go through a brief history of Redis. Then what's new in Redis 8. And wrap up with three short demos: full text search, vector similarity search, and probabilistic data structures. Fri, 04 Apr 2025 00:00:00 -0400 https://speakerdeck.com/raphaeldelio/introducing-redis-8-15-years-of-redis https://speakerdeck.com/raphaeldelio/introducing-redis-8-15-years-of-redis What happens when you combine the Apollo program’s historical data with modern AI tools? You get a way to interact with one of humanity’s greatest adventures like never before! In this session, I’ll show you how I used Redis OM Spring and Spring AI to explore Apollo mission data—aligning transcripts, telemetry, and images to uncover hidden connections and insights. We’ll dive into how Semantic Search powered by vector embeddings makes sense of unstructured text, how Redis as a vector database enables lightning-fast retrieval, and why these tools unlock new ways to explore complex datasets. Don’t know what embeddings or vector databases are? No worries—I’ll break it all down and show you how it works. Come for the Moon missions, stay for the AI magic, and leave ready to build your own intelligent search experiences! What happens when you combine the Apollo program’s historical data with modern AI tools? You get a way to interact with one of humanity’s greatest adventures like never before! In this session, I’ll show you how I used Redis OM Spring and Spring AI to explore Apollo mission data—aligning transcripts, telemetry, and images to uncover hidden connections and insights. We’ll dive into how Semantic Search powered by vector embeddings makes sense of unstructured text, how Redis as a vector database enables lightning-fast retrieval, and why these tools unlock new ways to explore complex datasets. Don’t know what embeddings or vector databases are? No worries—I’ll break it all down and show you how it works. Come for the Moon missions, stay for the AI magic, and leave ready to build your own intelligent search experiences! Thu, 06 Feb 2025 00:00:00 -0500 https://speakerdeck.com/raphaeldelio/rediscovering-apollo-11-using-spring-ai-plus-redis-om-spring-to-explore-the-mission-to-the-moon https://speakerdeck.com/raphaeldelio/rediscovering-apollo-11-using-spring-ai-plus-redis-om-spring-to-explore-the-mission-to-the-moon A Count-Min Sketch is a data structure that estimates how often something appears in a large dataset while using very little memory. It relies on a table and hash functions to map items to specific spots in the table. Adding an item increases the values in those spots, and checking an item’s count returns the smallest value from them. While not exact due to possible collisions, it’s efficient and great for approximate counts when precision isn’t critical. In this talk, we’ll explore: • What this data structure is • How it works internally • How I used it to build an efficient version of Trending Topics for Bluesky By the end of this session, you’ll have a clear understanding of Count-Min Sketches, why they’re valuable for handling large-scale data efficiently, and how you can apply them to solve real-world problems. A Count-Min Sketch is a data structure that estimates how often something appears in a large dataset while using very little memory. It relies on a table and hash functions to map items to specific spots in the table. Adding an item increases the values in those spots, and checking an item’s count returns the smallest value from them. While not exact due to possible collisions, it’s efficient and great for approximate counts when precision isn’t critical. In this talk, we’ll explore: • What this data structure is • How it works internally • How I used it to build an efficient version of Trending Topics for Bluesky By the end of this session, you’ll have a clear understanding of Count-Min Sketches, why they’re valuable for handling large-scale data efficiently, and how you can apply them to solve real-world problems. Fri, 27 Dec 2024 00:00:00 -0500 https://speakerdeck.com/raphaeldelio/count-min-sketch-an-efficient-probabilistic-data-structure https://speakerdeck.com/raphaeldelio/count-min-sketch-an-efficient-probabilistic-data-structure AsyncAPI is an open-source initiative that provides a specification to standardize the documentation of event-driven APIs, much like the OpenAPI specification does for REST APIs. Springwolf, on the other hand, is a practical implementation of the AsyncAPI specification, specifically tailored for Spring Boot applications. It automates the process of documenting event-driven systems, ensuring that the documentation is always synchronized with the actual state of the application. In this talk, we will focus on learning how Springwolf can help us design and maintain accurate, protocol-agnostic API documentation, automate the generation of AsyncAPI specifications, and provide clear insights into the interactions and functionalities of our event-driven systems, enhancing collaboration and understanding among developers. Presented @ Devoxx Belgium 2024 AsyncAPI is an open-source initiative that provides a specification to standardize the documentation of event-driven APIs, much like the OpenAPI specification does for REST APIs. Springwolf, on the other hand, is a practical implementation of the AsyncAPI specification, specifically tailored for Spring Boot applications. It automates the process of documenting event-driven systems, ensuring that the documentation is always synchronized with the actual state of the application. In this talk, we will focus on learning how Springwolf can help us design and maintain accurate, protocol-agnostic API documentation, automate the generation of AsyncAPI specifications, and provide clear insights into the interactions and functionalities of our event-driven systems, enhancing collaboration and understanding among developers. Presented @ Devoxx Belgium 2024 Wed, 18 Dec 2024 00:00:00 -0500 https://speakerdeck.com/raphaeldelio/asyncapi-and-springwolf-3fc27e64-37b8-4edc-a716-dcf646f39c9e https://speakerdeck.com/raphaeldelio/asyncapi-and-springwolf-3fc27e64-37b8-4edc-a716-dcf646f39c9e AsyncAPI is an open-source initiative that provides a specification to standardize the documentation of event-driven APIs, much like the OpenAPI specification does for REST APIs. Springwolf, on the other hand, is a practical implementation of the AsyncAPI specification, specifically tailored for Spring Boot applications. It automates the process of documenting event-driven systems, ensuring that the documentation is always synchronized with the actual state of the application. In this talk, we will focus on learning how Springwolf can help us design and maintain accurate, protocol-agnostic API documentation, automate the generation of AsyncAPI specifications, and provide clear insights into the interactions and functionalities of our event-driven systems, enhancing collaboration and understanding among developers. Presented @ Spring IO 2024 AsyncAPI is an open-source initiative that provides a specification to standardize the documentation of event-driven APIs, much like the OpenAPI specification does for REST APIs. Springwolf, on the other hand, is a practical implementation of the AsyncAPI specification, specifically tailored for Spring Boot applications. It automates the process of documenting event-driven systems, ensuring that the documentation is always synchronized with the actual state of the application. In this talk, we will focus on learning how Springwolf can help us design and maintain accurate, protocol-agnostic API documentation, automate the generation of AsyncAPI specifications, and provide clear insights into the interactions and functionalities of our event-driven systems, enhancing collaboration and understanding among developers. Presented @ Spring IO 2024 Wed, 26 Jun 2024 00:00:00 -0400 https://speakerdeck.com/raphaeldelio/asyncapi-and-srpingwolf https://speakerdeck.com/raphaeldelio/asyncapi-and-srpingwolf In this lightning talk I will talk about RevenueCat, a platform to power in-app purchases, manage customer data, and grow revenue on iOS, Android, and the web and show how I was able to easily set it up on my own World Map Translator application. In this lightning talk I will talk about RevenueCat, a platform to power in-app purchases, manage customer data, and grow revenue on iOS, Android, and the web and show how I was able to easily set it up on my own World Map Translator application. Thu, 22 Feb 2024 00:00:00 -0500 https://speakerdeck.com/raphaeldelio/revenue-cat-in-app-purchases-made-easy-with-flutter https://speakerdeck.com/raphaeldelio/revenue-cat-in-app-purchases-made-easy-with-flutter