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

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

"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

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/

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/

Its rare - but it happens: A guest-free episode of PurePerformance, allowing Andi Grabner and Brian Wilson reconnect to share real-world insights from recent months in the cloud-native and observability space. From KubeCon Amsterdam experiences and the strength of open-source collaboration to emerging challenges like AI-generated contributions, they explore how the industry is evolving beyond the hype. Your co-hosts of PurePerformance discuss the changing role of observability in the AI-native era—both as a foundation for understanding complex systems and as a tool to monitor AI itself. Brian shares his personal shift from AI skepticism to practical adoption, highlighting how AI can significantly improve productivity when used thoughtfully. Hope you all enjoy this episode!

In 2011 Heroku defined the 12 factor app to remove emerging bottlenecks as developers tried to scale their output when they moved from building monoliths to microservices. In Platform Engineer we see a repeating pattern called the "8 Factor Platform Producers".  AI allows engineering teams to speed up but they face bottlenecks as platform capabilities are not scaling with that demand as they are often depending on a central platform engineering team to be built and maintained. To learn more about 8 Factor Platform Producers we invited Abby Bangser,  Founding Principal Engineer at Syntasso and CNCF Ambassador. She gave an amazing talk at KubeCon in Amsterdam and today walks us through the need of defining both consumers and producers for platforms to eliminate any emerging bottlenecks in Platform Engineering and allow an organization to reap the benefit of speeding up with AI Links we discussed: Abby's LinkedIn: https://www.linkedin.com/in/abbybangser/ Abby's Kubecon Keynote: https://www.youtube.com/watch?v=8t0-5cvvMGM&list=PLj6h78yzYM2MXCOWSN9CqqID6OOvF7wxL&index=30 12 Factor Apps: https://12factor.net/ CNCF Whitepaper: https://cloudnativeplatforms.com/whitepapers/platforms/

As the software world is transforming from cloud native to AI-native, observability must transform with it. But how exactly? How do we apply this in an existing enterprise with established processes and practices? In this PurePerformance episode, Andi Grabner hosts Hilliary Lipsig and Rob Rati to discuss their new book, Observability in the AI‑Native Era. The conversation explores how AIOps, automation, and modern observability must evolve as systems become cloud‑native, data‑heavy, and AI‑driven. We talk about why old alerting and SLO models no longer scale, how to balance AI with automation and human judgment, and why trust, security, and compliance matter more than ever when machines start making operational decisions. A must‑listen for SREs, platform engineers, and engineering leaders navigating the AI‑native future. Links we discussed Book on Amazon: https://www.amazon.com/Observability-AI-Native-Era-Artificial-Intelligence-ebook/dp/B0GHZH1YFL Hilliary LinkedIn: https://www.linkedin.com/in/hilliary-lipsig-a5935245/ Rob LinkedIn: https://www.linkedin.com/in/roberthrati/ Andi LinkedIn: https://www.linkedin.com/in/grabnerandi/

AI coding agents are fast—but speed alone doesn’t guarantee quality. In this episode, Andi Grabner talks with Lukas Holzer (Straion) about why large context files and “almost right” AI code create new risks for engineering teams. You will learn about the "Lost in the Middle Syndrom" and why many organizations are not getting the promised 10x engineering boost right now! Andi and Lukas also explore rule adherence, dynamic context generation, enterprise readiness for AI-first development, and how software engineering roles are evolving in the age of AI. Tune in to learn more ... Links we discussed LinkedIn Profile: https://www.linkedin.com/in/lukas-holzer/ Straion Website: https://straion.com/ 90 Percent Rule Blog: https://straion.com/blog/90-percent-rule-adherence-straion-coding-agents/ 1million tokens Blog: https://straion.com/blog/1m-tokens-wont-save-your-engineering-standards/

In this episode of the PurePerformance Podcast, Andi and Brian sit down with https://www.linkedin.com/in/chrislabrado/—Solutions Architect for AI Enablement, FSO, SRE, and ITSM at HSN/QVC, where he has spent an incredible 27 years shaping operational excellence. Their conversation dives deep into how AI is transforming software creation, enterprise workflows, and even the very role of developers. Chris shares how the barrier to entry for building tools and automation has dropped overnight thanks to natural‑language-based development: “Everyone can now create automation or tools without having to worry about the syntax.” He explains why AI is rapidly becoming the primary interface into the enterprise—capable of navigating presentations, emails, and complex back‑office systems—and why the future of engineering may shift from human‑oriented coding to AI-driven development models such as MDCD (MarkDown Continuous Development). The discussion also takes unexpected but fascinating detours into Chris’s background as a former bowling‑industry podcaster, his recent work with generative agents like DynaClaude, his Vibe Coded Root Cause Agent, and a philosophical exploration of AI, creativity, and the concept of singularity. Amidst all the change, Chris remains optimistic: “AI opens up a lot of new opportunity for everyone willing to adapt. It will result in us creating more things that ultimately help us as humans.” This episode is a thoughtful, energizing look at where software engineering is headed—and why the future might be brighter than we think. Links we discussed Chris LaBrado on LinkedIn: https://www.linkedin.com/in/chrislabrado/ Mo Gawdat, former Google Executive on the Singularity "moment of truth": https://x.com/vitrupo/status/2008824930646057380?s=20 CEO of NVIDIA had an interesting excerpt from interview: https://x.com/MinusWells/status/2031974516155695414?s=20 Elon Musk on speed of AI: https://x.com/r0ck3t23/status/2031639621465931903?s=20 AI brain emulation of a fly (e.g. "a sign of the times"): https://x.com/alexwg/status/2030217301929132323?s=20 Elon on fiat currency transforming based on AI manufacturing loop: https://x.com/elonmusk/status/2020202496547844312?s=20 Fiat currency moves to model based on thermodynamics: https://x.com/r0ck3t23/status/2033371028202602547?s=20

In this episode, Andi and Brian welcome back Adam Tornhill—founder of CodeScene and author of Your Code as a Crime Scene—to explore how agentic AI is reshaping software engineering. Adam shares his personal journey from 40 years of hands-on coding to orchestrating AI-generated code, and what this shift really means for development teams. Together, they dive into new research on the hidden risks of AI-assisted coding, why low-quality or legacy code slows AI down, and how to measure the “AI-readiness” of a codebase. Adam breaks down practical strategies from his latest work on Agentic AI Coding, including guardrails, refactoring patterns, enforced processes, and why test coverage has become a surprising cornerstone for safe, fast AI iteration. Whether you're experimenting with AI coding tools or planning enterprise-scale adoption, this episode delivers actionable guidance rooted in data, engineering discipline, and real-world experience. Links https://codescene.com/blog/agentic-ai-coding-best-practice-patterns-for-speed-with-quality https://codescene.com/blog/strengthening-the-inner-developer-loop-turn-ai-into-a-reliable-engineering-partner

AI is transforming software engineering—faster than many teams can adapt. In this episode, Andi talks with Wolfgang Heider and Benedict Evert about what it really means to build “AI‑native” software, where prototypes turn into production apps in minutes. We explore why good engineering fundamentals still matter, how multi‑agent workflows mirror traditional roles, and why testing, governance, and clarity of intent become more important—not less. We also discuss the future of junior engineers, the risk of everyone reinventing the same solution, and why value—not code generation—is becoming the real differentiator. Links we discussed https://www.linkedin.com/posts/wolfgangheider_productmanagement-softwareengineering-ai-activity-7425746505883607042-D1OZ https://www.linkedin.com/pulse/machines-making-wolfgang-heider-5mvsf https://www.linkedin.com/pulse/i-built-app-between-final-stranger-things-episodes-wolfgang-heider-5penf/ https://futurelab.studio/ora/  https://futurelab.studio/htmlctl/

Why do we still struggle with resilience in 2026? Is it the growing complexity of systems, the pressure to ship fast, or a lack of education around resilient design? In this episode we welcome Adrian Hornsby from Resilium Labs to explore these questions and learn about chaos, complexity, and the importance of continuous learning! Adrian has learned his chaos engineering skills while working at AWS for many years. He shares insights from his upcoming book and his experience helping organizations embrace resilience as a continuous learning practice. We discuss: - Why traditional chaos engineering assumptions break down when AI starts writing your code. - The rise of AI-powered SRE agents—are they a blessing or a missed learning opportunity? - Organizational challenges and the importance of tracking near misses. Links we discussed Adrians LinkedIn: https://www.linkedin.com/in/adhorn/ Resilium Labs: https://www.resiliumlabs.com/ Upcoming Book: https://leanpub.com/whywestillsuckatresilience

Contributing to Open Source is easier than ever - especially because contributions are needed for documentation, demos, tutorials and code. But how to get started? Where to look for "first good issues"? Is everyone welcome? What are the prerequisites? Tune in and hear from Diana Todea, Developer Experience Engineer at Victoria Metrics, on how within a year she made it from Zero to Developer and receiving the Contributor Award for OpenTelemetry 2025 at KubeCon Atlanta. Diana shares her journey, how she started, how she found the right topic and how she keeps herself motivated. Diana is also the Co-lead of the Neurodiversity CNCF Working Group and gives us insights into the Merge Forward community.  And don't forget: Call for Papers for Cloud Native Days Romania and Austria are open and both Diana and Andi would be glad to see your proposals! So - what are you waiting for? Links we discussed: Diana's LinkedIn: https://www.linkedin.com/in/diana-todea-b2a79968/  From Zero to Developer Talk: https://www.youtube.com/watch?v=nPrxpEE5GpY  Contributor Award: https://siliconangle.com/2025/11/13/accessibility-meets-open-source-collaboration-kubeconna/  Her latest CNCF Blog Post: https://www.cncf.io/blog/2025/12/04/my-first-kubecon-cloudnativecon-a-journey-through-community-inclusivity-and-neurodiversity/ Start contributing to Open Source: https://contribute.cncf.io/contributors/getting-started/  Diana's Conference Talks: https://github.com/didiViking/Conferences_Talks  Diana on Medium: https://medium.com/@dianatodea/  Articles on OpenTelemetry for beginners:  https://medium.com/@dianatodea/the-unofficial-guide-to-contributing-to-opentelemetry-where-to-look-and-who-to-talk-to-9de04ae75fe0  CNCF Merge-Forward: https://community.cncf.io/merge-forward CNCF Neurodiversity initiative: https://community.cncf.io/neurodiversity  Cloud Native Days Romania: https://cloudnativedays.ro/ Cloud Native Days Austria: https://cloudnativedays.at/ 

From Systems Engineer in Aeronautics via many clouds to becoming an SRE in Observability! That's the path from our guest, Alexandra Franz who is a Lead Product Engineer in SRE at Dynatrace. Tune in and learn how their team plans ahead for expected high traffic around Black Friday, Cyber Monday or the Super Bowl. We discuss how regional traffic patterns and differences in available hardware get factored in for capacity management and cost control. We also learn why global cloud outages are stressful - but - how those incidents can also be the reward for a good SRE. Make sure to connect with Alexandra on LinkedIn: https://www.linkedin.com/in/alexandrafranz/

If you are still treating your AI Coding Agent like a chat bot and not like a development team then this is one more reason to tune into this episode. In his blog post series 31 Days of Vibe Coding, Jeff Blankenburg walks us through all the lessons learned when bringing an idea to life just with vibe coding. His idea was building a website for collectors of baseball cards. With now more than 950k cards from almost 10k players, he has proven that vibe coding, when done right, can truly boost the output of software engineers.  Tune in and learn about how to effectively use Git Issues as the backlog for your AI, the importance of going through different phases in your conversation with the AI and why it is important to ask the AI the question: "Do you have any questions for me?" Links we discussed LinkedIn Profile: https://www.linkedin.com/in/jeffblankenburg/ 31 Days of Vibe Coding: https://31daysofvibecoding.com/ Collect Your Cards: https://collectyourcards.com/ Claude: https://claude.ai/

How many people have you met that implemented distributed tracing in the early 2000s? Make it one more after you have tuned into our latest podcast with William Louth.  William, who can't seem to escape the observability space even though he keeps trying, has a track record in the space. He is an innovator and tool builder and is currently reimagining intelligent systems by shifting the focus from data collection to meaning-making. In our conversation we learn about situational awareness and how systems should use symbols to show their current state by also taking into account everything they are aware of happening in their ecosystem. This podcast episode has been long overdue and opens a fascinating new world beyond metrics, logs and traces! Links discussed Williams LinkedIn: https://www.linkedin.com/in/william-david-louth/ Humainary Research: https://humainary.io/research/ Humainary GitHub: https://github.com/humainary-io Serventis Signs: https://raw.githubusercontent.com/humainary-io/substrates-api-java/refs/heads/main/ext/serventis/SIGNS.md

It started with the prompt: "Create an Uber Clone"! Several iterations and some months later Abhi presents his lessons learned when vibing a Ride Share Platform for RoboTaxis at Cloud Native Days Austria! "Commit to one tool and go deep. Don't get distracted by all the options you have. Treat your agent like a human! Get better in expressing what you really want!", those are the many lessons learned in Abhi's journey applying the potential of the latest AI agents that are available for software engineers. Tune into our latest episode and understand what Abhi means when he says: Context is important! Give it Macro Context and do Micro Incremental Improvements! Links we discussed Abhi's LinkedIn: https://www.linkedin.com/in/abhimanyuselvan/ Cloud Native Austria Talk: https://www.youtube.com/watch?v=VjMPHWjawxM&list=PLtLBTEzR4SqU9GwgWiaDt10-yOVIN0nzM&index=9 Cursor AI: https://cursor.com/ OpenSpec: https://openspec.dev/

Chaos Engineering is the practice to introduced controlled failures into a system with the goal to improve the overall resiliency! What started with "lets see what happens when we unplug that server" to "lets simulate network latency issues" or "lets kill critical pods and see if the system recovers gracefully" is now seeing new experiments being conducted that are identified by a new companion: AI  In this episode we have invited Bartek Pisulak, Dir of Cloud Quality Engineering at Pegasystems, who has been educating quality engineers on AI-Augmented Chaos Testing in Practice. Tune in and learn about the how AI can improve efficiency in the 5 critical phases of a chaos experiment: Steady State, Hypothesis, Run Experiment, Verify, Improve! To learn more about the foundational principles make sure to watch some of the conference talks from Bartek listed below: Links discussed Bartek's LinkedIn: https://www.linkedin.com/in/bart%C5%82omiej-pisulak-82b94036/ Talk at Cloud Native Days Austria: https://www.youtube.com/watch?v=xUVCKNpMEz8&list=PLtLBTEzR4SqU9GwgWiaDt10-yOVIN0nzM&index=10 Talk at Porto Tech Hub: https://www.youtube.com/watch?v=-ZuEaA2PoTo Kraken: https://github.com/krkn-chaos/krkn ChasoEater: https://github.com/ntt-dkiku/chaos-eater

There is only one successful way to adopt new technology, and that is transformational! Sounds like a high-level consulting pitch but our industry has a track record to validate this statement. Just look at the recent web or cloud-native transformations! Pini Reznik has been helping organizations along the current AI-Native transformational journey. And what a timing: He just published his book on From Cloud Native to AI-Native where he provides a pragmatic approach to leveraging AI from Pioneering to Gradually Scaling! Tune in and hear from Pini why he thinks that AI projects are not failing because of bad AI, but because they approaching the problem the old and wrong way! And, stay until the end to hear how it was to write a book about AI using AI! Links we discussed Pini's LinkedIn: https://www.linkedin.com/in/pinireznik/ Link to Book: https://re-cinq.com/book Our previous episode: https://www.spreaker.com/episode/ai-native-the-next-revolution-after-cloud-native-with-pini-reznik--67692567 Prompt Engineering Conference Talk: https://www.youtube.com/watch?v=W7z5XMnvYt8

Don't get stuck using AI to build faster horses. Instead, find the opportunities and rethink your software delivery processes! That, and only that, will help you increase Developer Experience and Efficiency! This episode is all about how to measure and improve DevEx in the age of Artificial Intelligence. And with Laura Tacho, CTO at DX, we think we found a perfect guest! Laura has been working in the dev tooling space for the past 15 years. In her current role at DX she is working on the evolution of DORA and SPACE into DX Core 4 and the DXI Measurement Framework. In our episode we learn about those frameworks but also how tech leaders need to rethink where and how to apply AI to improve overall efficiency, quality and effectiveness!  The key takeaways from this conversation are - DevEx is all about the identifying and reducing friction in the end-2-end development process - Tech Leaders need to become better in articulating technical change requirements to business - As of today only 22% of code in git is really AI generated. Don't get fooled into believing AI is already better - Back to Basics makes companies successful with AI. That is: proper CI/CD, testing, documentation, observability! Here the links we discussed Laura's LinkedIn: https://www.linkedin.com/in/lauratacho/ DX: https://getdx.com/ Cloud Native Days Austria Talk: https://www.youtube.com/watch?v=kZ1F0-XS1l4 Engineering Leadership Community: https://www.engineeringleaders.io/

The AWS US-East problems on Oct 27th was a good reminder how depending we are on globally shared services. Built-in Resiliency is not guaranteed if systems have a hard dependency on a single region of a single vendor. Many of us have experienced systems being impacted that we use on a daily basis - some critical - some not so critical as Andi will tell you when he found out that is beloved Leberkas Pepi App didnt work! Besides this outage we discuss lessons learned from Cloud Native Days Austria, Observability and Platform Engineering Meetups in Gdansk and Tallinn as well as giving an outline to the upcoming Cloud and AI-Native US Tour from Henrik Rexed and Andi GrabnerAll the links we discussed are here - Leberkas Pepi: https://www.leberkaspepi.at/ - Cloud Native Austria: https://www.linkedin.com/company/cndaustria/ - Observability Meetup: https://www.meetup.com/observability-tech-community-meetup-group/ - US Tour from Henrik and Andi: https://events.dynatrace.com/noram-all-de-engineering-efficiency-tour-2025-28225/

While Artificial Intelligence seems to have just popped up when OpenAI brought ChatGPT to the consumer market it has its roots in the mids of the 20th century. But what is it that all of a sudden made it into every conversation we seem to have? Thomas Natschlaeger, Principal Data Scientist at Dynatrace, who has been working in the AI and Machine Learning space for the past 30 years gives us a brief historical overview and describes the critical evolutionary steps and compelling events in that technology that made it to what it is today.  Tune in and hear about how AIs are trained, how they are optimized and most importantly: how their outputs can be tested and validated! In our conversation we discuss current trends towards small language models that will help model digital twins of our existing roles and how AIs are used to Validate other AIs like we humans do when a senior engineer does pair programming with a junior and with that provides essential feedback on current accuracy and input to improve the outcome of future tasks. Links we discussed LinkedIn Profile from Thomas: https://www.linkedin.com/in/thomas-natschlaeger/ Ask Me Anything Session on Davis CoPilot: https://www.linkedin.com/posts/grabnerandi_llm-copilot-activity-7373837743971393536-QgxV?utm_source=share&utm_medium=member_desktop&rcm=ACoAAABLhVQBbh8Jkn_K8din5tsQlMCpXRNzlKU Voxxed Conference Talk: https://amsterdam.voxxeddays.com/talk/?id=39801 Attention is all you need paper: https://en.wikipedia.org/wiki/Attention_Is_All_You_Need

On September 8 the world saw the npm supply chain attack. Fortunately the community reacted in record time to avert a disaster.  In todays episode we have Constanze Roedig, Key Researcher at SBA Research, who introduces us to the new buddy of SBoM (Software Bill of Materials): SBoB (Software Bill of Behaviors) and her thoughts on how that new approach to fingerprinting software can help cyber security teams.  What's a BoB? It's a detailed runtime behavior profile of software. It expands on the static validation option through SBOMs as it allows security teams to validate the correct execution behavior of deployed software at deploy time or continuously in production. Thanks to eBPF, a malicious behavior such as opening non expected ports or accessing non expected files can therefore be detected. Listen to Constanze who shares the work she and Vadim Bauer, Owner of 8gear, have done on this topic. You will learn about how software vendors can create their own SBOBs, ship them with their container images and how security teams can get alerted or enforce any detected malicious behavior. Make sure to check out their GitHub repo, star it if you like it and try their hands-on tutorial! Links: Constanze LinkedIn: https://www.linkedin.com/in/croedig/ Vadim LinkedIn: https://www.linkedin.com/in/vadim-bauer/O BobCtl GitHub Repo: https://github.com/k8sstormcenter/bobctl Cloud Native Summit Munich Talk: https://www.youtube.com/watch?v=XETuwndd_mw&index=11&pp=iAQB npm supply chain attack: https://www.infosecurity-magazine.com/news/npm-supply-chain-attack-averted/

Defining AI-Native in 2025 is like trying to define Cloud Native back in 2014! We are in the early stages of understanding what AI really means to us. The ecosystem is just evolving, and many organizations are still struggling with re-architecting their digital systems to cloud native patterns! To learn more about the current transformational wave—the AI-Native Wave—we have invited Pini Reznik, CEO and Co-Founder of re:cinq. We will discuss what we can learn from previous "waves of innovation," why the business must care, and why the primary AI use case should not be just cost-cutting!  Make sure to get a copy of his book or catch his talk from Cloud Native Munich. All links we discussed here: Pini's LinkedIn: https://www.linkedin.com/in/pinireznik/ The Next Transformation Mini Book: https://re-cinq.com/mini-book Cloud Native Munich Talk: https://www.youtube.com/watch?v=CHb3TLEV8ZU

Most AI projects still fail, are too costly, or don't provide the value they hoped to gain. The root cause is nothing new: it's non-optimized models or code that runs the logic behind your AI Apps. The solution is also not new: tuning the system based on insights from Observability! To learn more about the state of AI Observability, we invited back Nir Gazit, CEO and Co-Founder of traceloop, the company behind OpenLLMetry, the open source observability standard that is seeing exponential adoption growth! Tune in and learn how OpenLLMetry became such a successful open source project, which problems it solves, and what we can learn from other AI project implementations that successfully launched their AI Apps and Agents Links we discussed Nir's LinkedIn: https://www.linkedin.com/in/nirga/ OpenLLMetry: https://github.com/traceloop/openllmetry Traceloop Hub LLM Gateway: https://www.traceloop.com/docs/hub

Did you know that the average salary for a Platform Engineer is 42.5% more than a DevOps engineer? But why is that? We sat down with Artem Lajko, CNCF Kubestronaut and Ambassador as well as Author of the book Implementing GitOps with Kubernetes. We dive into the role of a platform engineer, the common pitfalls in implementing IDPs and why Backstage and AI won't solve all your problems. And we touch upon a topic hot off the press around Terraform: Its not dead! Links we discussed Artem's LinkedIn: https://www.linkedin.com/in/lajko/ Talk slides from Cloud Land: https://lajko10-my.sharepoint.com/personal/artem_lajko_dev/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fartem%5Flajko%5Fdev%2FDocuments%2FAttachments%2Fcloud%20land%2D2025%5F%2Epdf&parent=%2Fpersonal%2Fartem%5Flajko%5Fdev%2FDocuments%2FAttachments&ga=1 State of Platform Engineering Report: https://platformengineering.org/reports/state-of-platform-engineering-vol-3 Upjet GitHub Project: https://github.com/crossplane/upjet

"Privacy engineering is the art of translating privacy laws and policies into code, figuring out how to make legal requirements such as ‘an individual must be able to request deletion of all their personal data’ a technical reality.", was the elegant explanation from Cat Easdon when asked about what she is doing in her day job. If you want to learn more then tune in to this episode. Cat, Privacy Engineer at Dynatrace, shares her learnings about things such as: When the right time is to form your own privacy engineering team, why privacy means different things for different people and regulators and what privacy considerations we specifically have in the observability industry so that our users trust our services! Links: Cat's LinkedIn Profile: https://www.linkedin.com/in/easdon/ Publications from Cat: https://www.dynatrace.com/engineering/persons/catherine-easdon/ Blog on Managing Sensitive Data at Scale: https://www.dynatrace.com/news/blog/manage-sensitive-data-and-privacy-requirements-at-scale/ Semgrep for lightweight code scanning: https://github.com/semgrep/semgrep The IAPP: https://iapp.org/ 'Meeting your users' expectations' is formally described by the theory of contextual integrity: https://www.open.edu/openlearncreate/mod/page/view.php?id=214540 Facebook's $5 billion fine from the FTC: http://ftc.gov/news-events/news/press-releases/2019/07/ftc-imposes-5-billion-penalty-sweeping-new-privacy-restrictions-facebook Fact-check: "The $5 billion penalty against Facebook is the largest ever imposed on any company for violating consumers’ privacy and almost 20 times greater than the largest privacy or data security penalty ever imposed worldwide. It is one of the largest penalties ever assessed by the U.S. government for any violation." I think that's still true; the largest fine under the GDPR was €1.2 billion (again for Facebook/Meta)

More than 50% of platform engineering leads don't know how to measure the impact of their platform! Many platform projects fall into common anti-pattern traps that make the platform look great on Day 1 but fail to scale and excite on Day 2! Daniel Bryant - who's profile tagline is "Helping you build better platforms" - is sharing his thoughts on how to measure the value of your platform, how to avoid common anti-patterns and why he believes that the future of platform engineering is in Platform Democracy! And of course, we wrap everything up with a discussion around the impact of Agentic AI towards platform engineering. So - tune in!   Here the links we discussed Daniel's LinkedIn Profile: https://www.linkedin.com/in/danielbryantuk/ Platform Engineering Book for Technical Product Leaders: https://www.amazon.de/Platform-Engineering-Technical-Product-Leaders/dp/1098153642/ref=asc_df_1098153642 Platform Engineering Day Talk: https://www.syntasso.io/post/syntasso-at-platengday-london-presentation-recap Kratix Website: https://www.kratix.io/ Ai-Driven Platform Engineering Blog: https://www.syntasso.io/post/what-we-learned-building-a-prototype-ai-driven-dev-interface-for-kratix Platform Democracy: https://www.syntasso.io/post/platform-democracy-rethinking-who-builds-and-consumes-your-internal-platform Platform Anti Patterns: https://www.syntasso.io/post/platform-building-antipatterns-slow-low-and-just-for-show Slide Deck on Platform Engineering for Devs and Architects: https://speakerdeck.com/danielbryantuk/platform-engineering-for-software-developers-and-architects-redux  

"How do you measure the impact you have with your platform engineering initiative?" is a question you should be able to answer. To show improvement you must first need to know what the status quo is. And this is where frameworks such as DX Core 4 come in. Never heard about it? Then tune into this episode where we have Dušan Katona, Sr Director of Platform Engineering at Ataccama, who is a big fan of the DX Core Four Metrics and who has just applied it in his current role to optimize developer experience. Dušan explains the details behind those 4 Core metrics: Speed, Effectiveness, Quality and Impact. He also shares how improving those metrics by a single point results in the equivalent of 10 hours saved per developer per year. And here the relevant links we discussed today Dusan's LinkedIn Profile: https://www.linkedin.com/in/dusankatona/ DX Core 4 Blog: https://getdx.com/research/measuring-developer-productivity-with-the-dx-core-4/ Marian's JIRA Analytics Open Source Project: https://github.com/marian-kamenistak/jira-lead-cycle-time-duration-extractor

"15 years ago it was enough to be smart - going forward its not a differentiator - being smart will just make you average!". But what is it? What makes great leaders worth following and how do they achieve tripling their value while others keep waiting for their 5% raise? 4 years ago Marian Kamenistak launched the Engineering Leadership Community out of Prague, Czech Republic. Feeding from his experience in the Silicon Valley this community has grown to 1500 members with the mission to create "Leaders worth following". Tune in and hear from Marian on how to think and talk about value impact vs being held up with trying to achieve technical perfection. Why its important to build a network around you, the difference between mentorship and management as well as how to proof the value to your leadership that you bring to the organization! Links we discussed today Marian's LinkedIn: https://www.linkedin.com/in/mariankamenistak/ Engineering Leadership Conference: https://www.elc-conference.io/ Engineering Leadership Community: https://www.engineeringleaders.io/ The Leadership Pipeline Book: https://www.amazon.com/Leadership-Pipeline-Build-Powered-Company/dp/0470894563

To predict the future, it's important to know the past. And that is true for Bernd Greifeneder, Founder and CTO of Dynatrace, who has been driving innovation in