If you’re going to DockerCon in San Francisco this week, drop by the Weaveworks booth (#B2)and say hello to Neil Gehani.
Neil is Weaveworks’ Director of Product Management, and is responsible for the overall direction as well as determining the features that get implemented in Weave Cloud. Neil’s also a former software engineer and besides helping teams build great products, he still dabbles in writing code and geeking out on new technologies like machine learning. When he’s out of the office, you can find Neil conquering the hills of the bay area on his bike.
Five Questions for Neil Gehani
What does a typical day look like for you and what are you currently working on?
Day to day my job is to help the Weaveworks team ship cool and useful features for developers building and deploying Kubernetes applications. Development teams are who we building Weave Cloud for. We want to take the Kubernetes ops pain away by building our expertise into our Weave Cloud platform.
Any words of advice for others trying to learn about containers and Kubernetes?
Keep experimenting and building Kubernetes apps. Learn to break things down into smaller deliverable chunks - microservices in containers.
What’s your top 3 Talks/Books?
I read about 2 hours a day and listen to podcasts like “The Hidden Brain” and “Intelligence Squared”. I read everything from politics to philosophy to psychology. Books I have read recently is Radical Candor, Checklist and Tribal Leadership. Mostly, I read The Atlantic and other long form stories that get surfaced to me via Pocket, Medium, or Flipboard. I like to know what and how people think.
What do you wish other people knew about Weaveworks?
That we are a fun and easy going bunch and we do our best to build things that Developers and Teams would actually use.
Any favourite cool new technologies you’ve been looking at lately?
I am into music so my HomePod is my favorite right now. My taste changes over time. I am also learning more about AI/AR which will have fascinating applications if used ethically and without data bias.