PurePerformance

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

Three years ago, Roman Ferstl — entrepreneur, founder, and returning PurePerformance guest — found himself experiencing something he struggled to explain to anyone who hadn't lived it. In this episode, he joins Andi Grabner and Brian Wilson to talk openly about burnout, anxiety, and depression: how they overlap, how they build on each other, and what the science of sleep, the nervous system, and neuroplasticity reveals about why recovery is genuinely possible. Honest about therapy, medication, and mindfulness — what worked and what didn't — Roman's goal is simple: if even one person listening takes one sentence away that changes something for them, the conversation was worth having. You are not broken. And it can get better.

When AI can generate and optimize code at scale, does that make software greener — or just faster to get wrong - or does it cost more to train and run those AI models then the savings we have optimizing our own software? Anne Currie, co-author of O'Reilly's Building Green Software, joins us to untangle the paradox. US data centers are on track to consume over 10% of the national grid by 2030 with a growing part of that energy going to AI workloads, yet it may also be our best tool for writing more efficient software — if the training data is any good (spoiler: for C, it often isn't; for Rust, it's a different story).

In our episode Brian, Andi and Anne get into the fundamentals that most teams are skipping: operational efficiency. Turning off systems you don't need, rightsizing what you do — these unglamorous moves can slash your hosting bill in half, and they're the prerequisite for any code-level green gains to actually matter. Plus: graceful shutdowns, grayouts, and why we'll probably need AI to eventually rewrite itself.

Links we discussed
Anne's LinkedIn: https://www.linkedin.com/in/annecurrie/
Her podcast: https://www.asynchronousunreliable.com/
Chapter 3 of her O'Reilly book: https://www.strategically.green/chapter-3-code-efficiency
Full book on Amazon: https://www.amazon.com/dp/1098150627
Brian's story on Myst: https://www.youtube.com/watch?v=EWX5B6cD4_4

AI coding tools are everywhere, but how do you prove they're actually making engineers more productive?
In this episode of PurePerformance, hosts Brian Wilson and Andi Grabner welcome Michael Reichenbach, Platform Engineer at 1KOMMA5°, to discuss the company's journey toward more than 80% AI-written code. Rather than relying on anecdotes or hype, Michael shares how his team designed a real experiment to measure the impact of AI-assisted development.
We explore the metrics they chose, why traditional DORA metrics such as deployment frequency and change failure rate were not the right indicators, and how they instead focused on "Time to Code" from ticket creation and first commit to merged pull request. Michael also explains how AI lowered the barrier for contribution across the organization, enabling even non-engineering teams to prototype and build solutions faster.
The conversation also dives into the operational side of AI adoption, including AI observability dashboards, budget controls, Slack alerts, usage monitoring, and the surprising decision to intentionally limit AI spending during their proof of concept.
Whether you're evaluating Cursor, GitHub Copilot, or other AI coding tools, this episode offers practical lessons on measuring value, maintaining quality, and scaling AI adoption responsibly.

Links we discussed
Michael's LinkedIn: https://www.linkedin.com/in/michael-reichenbach/
Klaus's LinkedIn: https://www.linkedin.com/in/langenheldt/
Talk at Cloud Native Munich: https://www.youtube.com/watch?v=Ntf0h0vFuMQ
1Komm5 Website: https://1komma5.com/
Kenote from KubeCon: https://youtu.be/P1phxZHJGrA?t=570&is=9DYnbK8VGorMmaXo
Michael's YouTube Playlist: https://youtube.com/playlist?list=PLn-u2xOcMlXlVweZ0aB4pu6VM6Xv6895i&si=MZ6jzF-VwIAY3HFF

"There is no single way to deploy OpenTelemetry at scale—and that’s exactly the challenge."
As organizations adopt OTel across teams and environments, they face tough questions around standardization, configuration, and operating resilient observability pipelines.
To address these challenges, the OpenTelemetry community has introduced Blueprints and Reference Implementations—practical guidance on topics like data standards, consistent agent and collector configuration, pipeline resilience, and intelligent sampling.
In this episode, we’re joined by Dan Gomez Blanco, maintainer of the OpenTelemetry End-User SIG, to explore real-world reference architectures from organizations like Skyscanner, Adobe, and Mastodon.
Tune in to learn how the community is turning OTel complexity into shared best practices—and how you can contribute your own blueprint