ThinkPad Thunderbolt 3 Dock | Suspend Problem

Thursday, Oct 11, 2018 | 2 minute read

Ricardo Ferreira

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:

ThinkPad Thunderbolt 3 Dock

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?

Firstly, by default any bolt-based device is not enabled in Fedora. So I have found this blog that helped on this matter. After that all ports were working as expected. FYI, I haven’t had to turn the SELinux off in order to work.

Secondly; I have found that any time I boot the laptop with the lid closed, the O.S would turn to a suspended state after the login. It was like if after the login - I would have closed the lid off. After some digging across many Fedora forums, I found a thread where people were discussing some-old-knobs-that-used-to-work-on-previous-versions.

Anyone that uses Linux might be used to these kind of thing, but I guess I haven’t used Linux as much as I should to get used to it. Anyway, in one of those threads I have found the proper solution. Here are the steps:

   1) sudo vi /etc/systemd/logind.conf
   2) Uncomment the property **HandleLidSwitch
**   3) Set the value of this property to "ignore"
   4) sudo systemctl restart systemd-logind

That is it. After that you should have everything working =)

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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.

Public Speaking

I’ve been speaking at conferences since 2008 and doing it full time as part of my work with DevRel since 2018. My talks go deep on the systems I build with: distributed systems and event streaming, AI engineering and vector search, and the data infrastructure that has to hold up when the demo ends and production begins. 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.

Ricardo Ferreira presenting on the main stage at AI DevWorld
On the main stage at AI DevWorld

You can find my upcoming and past talks on my speaking calendar. Recordings also live on my YouTube channel, and the code I write for talks, demos, and workshops is on my GitHub.

Consulting and Professional Services

If you’d like to hire me as a consultant for your projects, speak at your event, or lead a hands-on workshop for your team, contact me at riferrei@riferrei.com. I can understand the scope of your request and provide a free estimate.

Who am I?

I work at the intersection of AI, data infrastructure, and distributed systems, turning complex technology into things developers can understand and products users love.

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 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 Apache 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.

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