PurePerformance

Around 90% of commercial software is built on open source, yet only a small fraction of the companies using it give anything back. Meanwhile, the maintainers who keep it running are burning out, and a 2025 survey found 58% of them have considered quitting.

In this episode, Brian Wilson and Andi Grabner talk with Sebastian Kister, Chief AI Architect and Lead of OSPO Contribution & Maintainership at Volkswagen Group (assigned by Audi AG). Andi met Sebastian at Cloud Native Days Austria, where his talk on enterprise AI foundations made one point very clear: passive consumption of open source is a long-term risk.

Sebastian explains why upstream contribution is a business case and not charity. It shapes the standards your products depend on, extends your product lifecycle, and prevents short-sighted migrations. It also gets you ready for regulation like the EU AI Act and SBOM mandates, and it’s one of the strongest recruiting signals for top engineering talent. He also shows how it can be organized in practice: a group-level OSPO that frees employees to contribute to projects like OpenTelemetry and OpenSearch during work time, and connects the inside world with the outside one.

We also dig into:
Sovereignty: why it has less to do with geography than with control, business continuity, and being able to leave the room on your own terms.

Make or buy: Andi plays devil’s advocate on whether buying enterprise software is a safe alternative to contributing.

Evaluating vendors: why contribution should become a criterion when enterprises choose vendors.

Writing for AI: whether to write blogs for humans or for AI agents.

And yes, the world’s best limoncello of 2025 doesn’t come from Italy. It comes from Sebastian’s distillery in Bavaria.

Links we discussed
Sebastians LinkedIn: https://www.linkedin.com/in/sebastiankister/
Cloud Native Austria Talk: https://www.youtube.com/watch?v=zue4cg8lcR0
Sebastians new blog: https://sebastiankister.de/keynote/

What does it take to truly observe an AI application? In this episode, we sit down with Giovanni Lima — Open Source Contributor to OpenTelemetry, OpenFeature, and Keptn, and Principal Software Engineer at Dynatrace working on AI Observability.

We explore how to instrument AI apps with frameworks like OpenInference, what distributed traces reveal about agent topology, model cost, performance, and accuracy, and why prompt engineering and observability go hand in hand.

We also look at emerging trends: internal agentic platforms for back-office automation, and how organizations self-hosting models use observability to optimize hardware, inference, and caching.

Links
Giovanni's LinkedIn: https://www.linkedin.com/in/giovanni-liva-101a1532/
OpenTelemetry: https://opentelemetry.io/
OTel AI Semantic Conventions: https://github.com/open-telemetry/semantic-conventions-genai
OpenInference: https://github.com/Arize-ai/openinference

In the second part of our conversation with Todd, Maintainer of OpenFeature, we learn about why implementing feature flagging is not as easy as at looks at first glance. As a framework developer you need to think about where feature flag evaluations are done. Ideally as close as possible to where your confidential data is you use for making a decision. But that comes with other challenges such as propagating your rule engine potentially to millions of mobile devices.
If you think you know all the edge cases then think again and tune in to this episode. Learn about how to not leak any confidential data, how to avoid propagating rules with PII data to millions of devices, and how to build a framework that can evaluate within flags within milliseconds regardless of where your end users execute your code to avoid any impact on end user experience.
Links
Todd’s LinkedIn: https://www.linkedin.com/in/toddbaert/
OpenFeature: https://openfeature.dev/
Introducing OpenFeature Podcast: https://www.spreaker.com/episode/introducing-openfeature-stepping-into-the-footsteps-of-opentelemetry-with-mike-beemer-and-todd-baert--50043067

Feature flagging is almost as old as software engineering – but only since the increased adoption of AIDLC (AI- Delivery Life Cycle) it has really taking off like AI has taken off as the default way to generate code!
In this episode we welcome back Todd Baert, Maintainer of OpenFeature, and learn about the many different use cases of Feature Flagging. Todd explains how feature flagging evolved from simple variables that pivot code execution towards dynamic evaluations that are supported by modern frameworks like OpenFeature and feature flag management systems that are based on that cloud native standard.
Tune in and learn what the biggest competitors of OpenFeature are, why feature flagging requires less thinking during development than you think and how it mitigates the risk of AI generated code having negative impact on your systems resiliency!
Links
Todd’s LinkedIn: https://www.linkedin.com/in/toddbaert/
OpenFeature: https://openfeature.dev/
Introducing OpenFeature Podcast: https://www.spreaker.com/episode/introducing-openfeature-stepping-into-the-footsteps-of-opentelemetry-with-mike-beemer-and-todd-baert--50043067

What happens when your AI coding tools outpace your delivery platform? Andi Grabner and Brian Wilson find out with Michael Binz, DevRel for Juno - the Internal Developer Platform (IDP) at Dynatrace. Together they walk through the full journey of building an IDP that keeps up with AI-speed engineering. Juno serves as a knowledge base, automation hub, and organizational memory across the entire software delivery lifecycle. If your platform can't scale with your AI ambitions, your 10x engineers are working on a 0.1x foundation. This episode shows you what to do about it.

Links we discussed:
Michaels LinkedIn: https://www.linkedin.com/in/michael-binz-a1307a111/
YouTube Video of their talk: https://www.youtube.com/watch?v=3Ow9rIdv-x8
Bluebox: https://www.bluebox.ai 
Max Headroom: https://www.youtube.com/watch?v=egCOO7Zzq0U