Michael Stahnke is a seasoned engineering executive, having spent the last 15 years working in the development and operational tooling space where also did research and was an author on Puppet’s State of DevOps Reports.

Michael is VP of Engineering at Flox and is fascinated with the intersection of software engineering and business.  He was previously in senior engineering leadership at CircleCI and Puppet where he grew engineering teams by 5x or more. He has spent time building high performing teams, organizations and researching engineering effectiveness in addition to hacking on packaging and release systems. He’s been speaking at DevOps and Automation events since 2007. He founded the package repository Extra Packages for Enterprise Linux (EPEL) and wrote a book on OpenSSH in 2005.

Presentations

23x

When Everything Looks Like a Container: Rethinking 15 Years of Cloud-Native Defaults

We explore how containers have empowered teams and transformed collaboration while also becoming an unquestioned default in many workflows. This talk reflects on where containers have genuinely improved developer experience and reliability, where we adopted them out of convenience rather than design, and how newer tools and patterns can help simplify today’s increasingly complex stacks. It invites attendees to revisit long-held assumptions with empathy, not to replace containers but to use them more intentionally, and it offers a human-centered perspective on building systems and teams that thrive.

See Presentation
23x

Nix and AI, are we there yet?

AI stacks look like the perfect use-case for Nix: Massively multiplying dependency matrices, double-digit-GB OCI images, tedious, Sisyphean build → push → pull → test loops. In ML/AI dev, "It runs" literally means "It runs … on this machine." So … why aren't more ML teams using Nix? This talk is a field guide to the logistics and sociotechnics of what it takes to make Nix happen in AI. Its point of departure is the following question: "Why do we ship what we ship the way we ship it? Either Nix fits the conveyor belt people already ship on, or ML teams learn a new way to build → ship → deploy software. So what will it take to fit Nix to this conveyor belt?

See Presentation