PurePerformance

Hosted by PurePerformance

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346

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Sep 2026

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About the show

The brutal truth about digital performance engineering and operations. Andreas (aka Andi) Grabner and Brian Wilson are veterans of the digital performance world. Combined they have seen too many applications not scaling and performing up to expectations. With more rapid deployment models made possible through continuous delivery and a mentality shift sparked by DevOps they feel it’s time to share their stories. In each episode, they and their guests discuss different topics concerning performance, ranging from common performance problems for specific technology platforms to best practices in development, testing, deploying and monitoring software performance and user experience. Be prepared to learn a lot about metrics. Andi & Brian both work at Dynatrace, where they get to witness more real world customer performance issues than they can TPS report at.

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October 26, 2026Episode 27146 min

Observability for AI: Instrumentation, Patterns, Trends with Giovanni Liva

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

October 12, 2026Episode 27027 min

Privacy, Security and Performance: The Hard Lessons of Feature Flagging with Todd Baert

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

September 28, 2026Episode 26948 min

How AI is shaping Flag-Driven Development with Todd Baert

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

September 14, 2026Episode 26846 min

10x Engineers, 0.1x Platform: Scaling AI the Right Way with Michael Binz

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

August 31, 2026Episode 2671 hr 44 min

You Are Not Broken: A Candid Talk on Burnout & Mental Health with Roman Ferstl

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.

August 17, 2026Episode 26648 min

Can AI Save the Planet — Or Is It the Problem? Building Green Software With Anne Currie

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

August 3, 2026Episode 26552 min

80% AI-Written Code: How 1KOMMA5 Measured the Real Impact of AI on Engineering

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

July 20, 2026Episode 24652 min

Blueprints for OTel Success: Standardizing Observability at Scale with Dan Gomez Blanco

"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

July 6, 2026Episode 26351 min

OpenTelemetry and the Reality of Vendor Choice with Adriana Villela and Josh Lee

I still hear people say, “OpenTelemetry is vendor-neutral, so you can switch any time!” In this episode, Adriana Villela and Josh Lee (both active OpenTelemetry contributors) help bust that myth. While OTel standardizes instrumentation and signal transport—and unlocks a rich ecosystem of tools—switching vendors isn’t as simple as it sounds. There’s real cost in retraining engineers, migrating dashboards, SLOs, and alerts, and reworking deep integrations across your delivery pipeline. We also dive into a key challenge the community is tackling: helping engineers instrument by value, not by default—making it easier to capture the right signals with high quality instead of just collecting everything. Here the links we discussed: Adriana's LinkedIn: https://www.linkedin.com/in/adrianavillela/ Josh's LinkedIn: https://www.linkedin.com/in/joshuamlee/ The blog article: https://thenewstack.io/opentelemetry-vendor-neutrality-guide/ CND Austria Talk: https://www.youtube.com/watch?v=1gxLseuaTdM KCD Prague Talk: https://www.youtube.com/watch?v=pPXG20CXKxQ OpenTelemetry Project Website: https://opentelemetry.io/

June 22, 2026Episode 26256 min

AI Is a Gift: Rethinking Software Engineering Education and Hiring

In this episode, we explore how AI is transforming education, from classrooms to corporate training. What changes are needed in schools and universities? How does AI affect both students and educators? And how should companies rethink internal training and hiring to stay competitive? To answer these questions, we’re joined by Rainer Stropek, CEO of Software Architects and Chairman of Coding Club Linz. With decades of experience teaching at high schools and universities—and helping organizations upskill their engineers—Rainer brings a unique perspective on how software engineering education is evolving. While many view AI as a threat, Rainer sees it as a “Christmas gift”—opening up endless opportunities to learn, adapt, and innovate. Tune in to hear why curiosity is more important than ever, how educational institutions can prepare future engineers, and why organizations must step up to ensure everyone has a fair chance to succeed in the age of AI. Links we discussed Rainer's LinkedIn Profile: https://www.linkedin.com/in/rainerstropek/ Rainer's Website: https://rainerstropek.me/ CodeClub: https://codeclub.org/en/ Coder DoJo Linz: https://linz.coderdojo.net/