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Tuesday, Mar 3, 2020
Recently I had to build an application that would store and order players in a Redis Sorted Set, but using multiple columns as criteria for the score, instead of just one. After trying to find a proper solution on the web and failing to do so; I decided to put my head to work and figure out an way. This post will show how did I accomplished that.
It turns out that my application has players with the following fields:
Saturday, Feb 29, 2020
If you are reading this blog post then there is a high chance that you’ve been looking for ways to make Confluent’s Go Client for Apache Kafka work with your Docker images but you’re struggling to. Am I right?
Don’t worry, you’ve came to the right place. After struggling with this myself I decided to share the solution that I’ve found with everybody else so we all can spend more time writing code than just wasting time with dependency plumbing.
Thursday, Oct 11, 2018
As mentioned in this post; I have acquired a new laptop from Lenovo, the ThinkPad X1 Extreme. Since I will be working from home sometimes, I thought it would be a good idea to buy an dock station for it.
So I decided to buy the following dock station:
It is a nice dock station solution, especially because it connects everything to the laptop via Thunderbolt 3. However; since I am running Fedora in my laptop, things couldn’t be that easy right?
Wednesday, Aug 29, 2018
August 2018 was a very productive month to me, where after some intense dog hours studying like crazy and practicing a lot - I was able to get certified in two of the most interesting AWS exams: Solutions Architect and Developer Associate.
That was quite of a challenge to me, because my Cloud background had been up to that point mainly focused on Oracle Cloud. It was interesting to see the differences between the two Cloud vendors, as well as seeing how AWS structures its services. Which BTW is amazing.
I contribute to LangChain4j, an idiomatic open source Java library for building LLM-powered applications on the JVM. Recent work includes adding native vector search embedding stores so developers can build RAG, recommendation engines, and AI memory systems.
I also ported RedisVL to Go, an open source, AI-native client that brings vector search, semantic caching, LLM memory, semantic routing, rerankers, and an MCP server to the Redis ecosystem for Golang developers.
I’ve been speaking at conferences since 2008 and doing it full time as part of my work with DevRel since 2018. Some of the events I’ve spoken at include AWS re:Invent, Microsoft Ignite, Google Cloud Next, KubeCon, Oracle OpenWorld, QCon, Strange Loop, Kafka Summit, Pulsar Summit, JavaOne, DevNexus, JFokus, JNation, and All Things Open.
You can find recordings of my talks live on my YouTube channel, and the code I write for talks, demos, and tutorials is on my GitHub.
I work at the intersection of distributed systems, AI, and data infrastructure, turning complex technology into things developers can understand, adopt, and build with.
Lately, that means hands-on AI engineering: building vector search, semantic caching, agent memory, and RAG into the data layer, and figuring out how to make AI agents secure enough to ship. I also contribute to open-source projects like LangChain4j and RedisVL for Golang.
The AI-native work isn’t a pivot. It draws on the same systems-design foundation I’ve built for 20+ years: designing data systems for scale, moving data fast, watching where systems break; now applied to vectors and agents. I have worked on RDBMS and Big Data at Oracle; event streaming with Apache Kafka and Flink at Confluent; observability at Elastic; AI and developer tooling at AWS; and NoSQL and vector stores at Redis. That foundation is exactly what separates AI demos that work on stage from AI systems that survive production.
