New Lenovo ThinkPad X1 Extreme

Tuesday, Oct 9, 2018 | 2 minute read

Ricardo Ferreira

“It’s not a laptop. It’s a ThinkPad.” - I really like this slogan because it helps to explain why I took the decision to buy the new ThinkPad X1 Extreme from Lenovo, while there are some good options out there - presumably better ones. But let’s start from the beginning.

I have been working with IT for quite some time now, and for some reason I have never had a laptop of my own. Instead, I always used the laptops given by the companies I worked for. This is not a bad decision per-se, but in the long run you may find yourself disappointed with the whole keep-moving-things-around every time you change jobs.

Yes… I know that there are some neat storage solutions out there that I could use to minimize this pain. And yes, I know what Cloud is. However, I am not talking strictly about files. I am talking about the whole time you devote in setting up an laptop for your own needs, such as fine tuning, hardware upgrades, home configuration, travel configuration, etc.

That is why I decided to treat myself with a new laptop. And I call this treat because I decided not to save money on this new laptop, and wanted the best out there. And after some serious digging, I have decided to go for the ThinkPad X1 Extreme. Isn’t a cheap laptop by far, but it delivers what you have paid for. In my case, I build a model with 64GB of RAM, 1 TB of Solid-State-Disk, Core i7 vPro Processor 8th Generation with 12 cores, NVIDIA GeForce GTX 1050 Ti 4 GB and of course: lots of Thunderbolts 3 ports.

This laptop is not only the fastest I have ever seen, but it is also very thin and quiet. And though a few people might argue that the MacBook Pro is nicer - I can confidently say that it doesn’t deliver the same performance of this laptop, nor the same price/value/configuration ratio.

It came with Windows 10 pre-installed, which I rapidly got rid of it and installed Fedora 28 instead. It was not easy, but after some troubleshooting regarding the NVIDIA GeForce drivers, I got this beast up-and-running. It is simply beautiful to see =)

I am really excited to start running some heavy workloads on this laptop, perhaps even using it to demonstrate some of the cool features of Apache Kafka.

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© 2018 - 2026 Ricardo Ferreira

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Open Source

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.

Speaking

I’ve been speaking at conferences since 2008 and doing it full time as part of my work 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.

Recordings of my talks live on my YouTube channel, and the code I write for talks, demos, and tutorials is on my GitHub.

Who am I?

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; contributing to LangChain4j and RedisVL for Golang; and figuring out how to make AI agents secure enough to ship.

The AI-native work isn’t a pivot. It draws on the same systems-design foundation I’ve built for 20+ years, moving data fast, at scale, close to compute, watching where systems break; now applied to vectors and agents. I have worked on event streaming with Apache Kafka and Flink at AWS and Confluent; observability at Elastic; as well as RDBMS, NoSQL, and Big Data at Oracle. That foundation is exactly what separates AI demos that work on stage from AI systems that survive production.

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