About Me

I'm currently an MBA student at Stanford Graduate School of Business. Previously, I was a software engineer working on the Kubernetes open source project at Google.

At a cloud computing conference called KubeCon in 2018, giving a talk about open source containerization tools I built at Google.


I’m Matt. I’m a programmer, writer, and entrepreneur. I’ve worked on open-source at Google, and as a developer at a private equity firm in New York.

I have a BA in Mathematics from Columbia University, and working towards an MBA at Stanford Graduate School of Business (2021).

You can find me on LinkedIn and @mattrickard on Twitter. You can find my open-source work on GitHub. All of my work at Google is public, and you can find interviews, talks, and podcasts below.

Interviews

Conference Talks

Kubernetes/Minikube

Website: https://minikube.sigs.k8s.io/docs/
GitHub: https://github.com/kubernetes/minikube

Minikube is the developer tool for Kubernetes. It runs an entire single-node distributed system on any laptop. I contributed a lot f major features to the project, building out the actual provisioning strategies and using native hypervisor frameworks to boot a custom, lightweight linux distribution that ran Kubernetes.

Skaffold

Website: https://skaffold.dev/
GitHub: https://github.com/GoogleContainerTools/skaffold

Skaffold was created from my frustration developing applications that run on Kubernetes. It’s a cloud-native application development tool focused on speeding up the inner developer loop. File syncing to Kubernetes deployments, automatic cached docker builds, and smart deployments make application development quick and repeatable on any machine. It supports local, cloud, and hybrid development.

Kubeflow

Website: https://www.kubeflow.org/
GitHub: https://github.com/kubeflow/kubeflow

Kubeflow is a machine learning toolkit for Kubernetes. It allows developers and operators to build out distributed training and inference machine learning jobs on Kubernetes infrastructure. I focused on the experience of going from a Jupyter notebook to a distributed training job automagically.

Mockerfile

GitHub: https://github.com/r2d4/mockerfile

An alternative to the Dockerfile that can be built with the existing Docker CLI. It uses a proof-of-concept YAML declaration for images and works through the native buildkit API found in every Docker distribution.

Distroless

GitHub: https://github.com/GoogleContainerTools/distroless

A set of rules and minimal docker images that can be used for language runtimes that don’t contain all of the extra things found in a Linux distribution. Instead of starting from Ubuntu, we build only the necessary files into a container image tarball. It also can be used to build docker images on unprivileged infrastructure, like building docker-in-docker without any extra permissions.