PurePerformance

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 discussedPini's LinkedIn: https://www.linkedin.com/in/pinireznik/Link to Book: https://re-cinq.com/bookOur previous episode: https://www.spreaker.com/episode/ai-native-the-next-revolution-after-cloud-native-with-pini-reznik--67692567Prompt 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 areDevEx is all about the identifying and reducing friction in the end-2-end development processTech Leaders need to become better in articulating technical change requirements to businessAs of today only 22% of code in git is really AI generated. Don't get fooled into believing AI is already betterBack to Basics makes companies successful with AI. That is: proper CI/CD, testing, documentation, observability!Here the links we discussedLaura's LinkedIn: https://www.linkedin.com/in/lauratacho/DX: https://getdx.com/Cloud Native Days Austria Talk: https://www.youtube.com/watch?v=kZ1F0-XS1l4Engineering 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 hereLeberkas 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/

Thomas Natschlaeger, Principal Data Scientist at Dynatrace, brings nearly 30 years of AI experience to the table. He shares fascinating insights into the historical milestones that made AI mainstream, emphasizing the importance of hardware and recent breakthroughs. The discussion dives into how LLMs can validate outputs, reducing hallucinations while boosting accuracy. Thomas also explores the trend towards specialized agents and the rethinking of roles within AI-native systems, making for a thought-provoking conversation on the future of technology.

Constanze Roedig, a key researcher at SBA Research, dives into the innovative concept of the Software Bill of Behaviors (SBoB). This approach provides a dynamic runtime behavior profile of software, enhancing cybersecurity practices. She explains the critical differences between static SBOMs and dynamic SBoBs, emphasizing the importance of real-time monitoring for detecting malicious behavior. Constanze also discusses how vendors can create their own SBoBs for better trust and security, while advocating for community engagement to strengthen this emerging framework.

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-bookCloud 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 AgentsLinks we discussedNir's LinkedIn: https://www.linkedin.com/in/nirga/OpenLLMetry: https://github.com/traceloop/openllmetryTraceloop 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 discussedArtem'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=1State of Platform Engineering Report: https://platformengineering.org/reports/state-of-platform-engineering-vol-3Upjet 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/semgrepThe 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=214540Facebook'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-facebookFact-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 discussedDaniel'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_1098153642Platform Engineering Day Talk: https://www.syntasso.io/post/syntasso-at-platengday-london-presentation-recapKratix 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-kratixPlatform Democracy: https://www.syntasso.io/post/platform-democracy-rethinking-who-builds-and-consumes-your-internal-platformPlatform Anti Patterns: https://www.syntasso.io/post/platform-building-antipatterns-slow-low-and-just-for-showSlide Deck on Platform Engineering for Devs and Architects: https://speakerdeck.com/danielbryantuk/platform-engineering-for-software-developers-and-architects-redux