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)