Podcasts about Dynatrace

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 AILinks 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=3012 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 discussedBook on Amazon: https://www.amazon.com/Observability-AI-Native-Era-Artificial-Intelligence-ebook/dp/B0GHZH1YFLHilliary 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/

S&P futures are down (0.2%) as of now, pointing to a slightly lower open. Asian equities traded mostly lower on Tuesday, with Japan's Nikkei closed (1%) lower following the BoJ rate decision. Greater China markets were also lower, while South Korea and Taiwan benchmarks extended record gains, supported by semiconductors and AI demand. European markets are firmer in early trading.Companies Mentioned: Meta Platforms, Dynatrace

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 discussedLinkedIn 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/

Dynatrace's Chief Technology Strategist Alois Reitbauer explains how AI-powered observability is moving beyond monitoring to autonomously fixing software issues — and why the best AI doesn't replace human judgment, it sharpens it.Topics Include:Dynatrace helps global enterprise companies observe, optimize, and protect their software.The platform goes beyond monitoring — it takes automated action to fix issues.Business observability connects technical data to real-world operational decisions.Dynatrace has been investing in AI for 14 years, starting with root cause analysis.AI eliminates human confirmation bias when diagnosing critical system failures.Generative AI now enables Dynatrace to propose and implement code-level fixes.AI works best augmenting humans — like a GPS, not an autopilot.The Dynatrace-AWS partnership began with aligning on a shared long-term vision.Joint engineering calls and shared roadmaps made the two teams feel like one.Dynatrace experienced Amazon's famous silent document-reading meeting culture firsthand.Good partnerships require honesty, investment, and knowing when to say no.AI is maturing from an efficiency play toward genuine human augmentation.Participants:Alois Reitbauer – Chief Technology Strategist, DynatraceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Send us Fan MailWe're celebrating our 100th episode of Get Amplified by bringing in Troy Stoll from Dynatrace (APAC), an energising leader who's lived what it really takes to make alignment stick. If you've seen strong teams slowed by silos, mixed metrics, or underlying friction, this is a clearer way forward. By aligning on a shared North Star and working back to remove friction between marketing, sales, services, and customer success, the APAC team is now executing with real pace and consistency. We get into why over-communication is a leadership responsibility, especially as teams evolve, new leaders join, and priorities shift. It's less about telling people what to do, and more about helping them understand why it matters and where they fit into the bigger picture.A big part of the story is the Switch change framework and why shared language matters more than most leaders realise. We break down its three core ideas: finding the bright spots (what's already working), shaping the path (making the right behaviours easier to follow), and motivating the elephant (tapping into what actually drives people to act). The shift here is simple but powerful: stop trying to fix everything, and start amplifying what already works. That's what shrinks change and helps it land faster across teams, cultures, and geographies.Troy also connects this to the Team Speed Check as a practical way to surface purpose, trust, clarity, and simplicity, so leaders aren't guessing what's going on, they're responding to reality.If you're looking for actionable ideas on cross-functional leadership, communication, and building trust that shows up in pipeline, renewals, and better customer outcomes, this one's worth your time.Subscribe to Get Amplified, share this milestone episode with a colleague.We would love you to follow us on LinkedIn! https://www.linkedin.com/company/amplified-group/

In this episode of the PurePerformance Podcast, Andi and Brian sit down with Chris LaBrado—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 discussedChris 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=20CEO of NVIDIA had an interesting excerpt from interview: https://x.com/MinusWells/status/2031974516155695414?s=20Elon Musk on speed of AI: https://x.com/r0ck3t23/status/2031639621465931903?s=20AI brain emulation of a fly (e.g. "a sign of the times"): https://x.com/alexwg/status/2030217301929132323?s=20Elon on fiat currency transforming based on AI manufacturing loop: https://x.com/elonmusk/status/2020202496547844312?s=20Fiat currency moves to model based on thermodynamics: https://x.com/r0ck3t23/status/2033371028202602547?s=20

In this episode of Mainframe Coven, Jessielaine Punongbayan (Product Manager, Dynatrace) and Richelle Anne Craw (Software Engineer, Beta Systems Software) talked about the importance of conjuring a safe space for women in tech.Full Transcript here: https://openmainframeproject.org/mainframe-coven/mainframe-coven-conjuring-a-safe-space/

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.Linkshttps://codescene.com/blog/agentic-ai-coding-best-practice-patterns-for-speed-with-qualityhttps://codescene.com/blog/strengthening-the-inner-developer-loop-turn-ai-into-a-reliable-engineering-partner

In dieser Folge spricht Markus mit Katrin Freihofner, Co-Founder von Straion, einer Plattform für AI Code Governance.Straion adressiert ein Problem, das viele Unternehmen gerade erst erkennen: AI kann zwar Code generieren, kennt aber keine firmenspezifischen Regeln, Sicherheitsanforderungen oder Architektur-Standards. Straion sorgt dafür, dass AI-generierter Code automatisch mit den internen Richtlinien eines Unternehmens abgeglichen wird und wirklich production-ready ist.Katrin erzählt von ihrem Weg vom Product Design bei Dynatrace und Elastic zur Startup-Gründerin – und wie aus einem Side-Project ein VC-finanziertes AI-Startup wurde.Wir sprechen darüber, wie Straion Enterprise-Kunden gewinnen will, warum Sicherheit und Zertifizierungen für junge Startups eine große Herausforderung sein können und wie AI den Arbeitsalltag von Gründer:innen verändert.Außerdem geht es um:wie Straion AI-generierten Code mit firmenspezifischen Regeln und Guidelines verbindetwarum große Softwareteams hunderte Coding-Standards verwalten müssenwarum Enterprise-Kunden für Startups besonders schwer zu gewinnen sindKatrins wichtigste Founder-Learnings: Fokus behalten und Prioritäten setzenwarum AI zwar produktiver macht, aber Arbeit gleichzeitig intensiver werden kannProduction: Hanna Moser Musik (Intro/Outro): www.sebastianegger.com

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 discussedAdrians LinkedIn: https://www.linkedin.com/in/adhorn/Resilium Labs: https://www.resiliumlabs.com/Upcoming Book: https://leanpub.com/whywestillsuckatresilience

What does autonomous IT really look like when you move beyond the slideware and start wiring systems together in the real world? At Dynatrace Perform in Las Vegas, I sat down with Pablo Stern, EVP and GM of Technology Workflow Products at ServiceNow, to unpack exactly that. Pablo leads the teams focused on CIOs and CISOs, building the workflows and security products that sit at the heart of modern IT organizations. From service desks and command centers to risk and asset management, his remit is clear: enable AI to work for people, not the other way around. We began with ServiceNow's deepening multi-year partnership with Dynatrace. While the announcement made headlines, Pablo was quick to point out that the real story starts with customers. This collaboration is rooted in a shared goal of helping joint customers reduce outages, improve SLA adherence, and shrink mean time to resolution. The vision of autonomous IT operations is not about hype. It is about connecting observability data with deterministic workflows so that insight can evolve into coordinated, system-level action. Pablo walked me through the maturity curve he sees emerging. First came AI-powered insight, summarizing data and surfacing signals from noise. Then came task automation, drafting knowledge articles, paging teams, triggering predefined playbooks. The next step, and the one that excites him most, is orchestrated autonomy. That means stitching together skills, agents, and workflows into systems that can drive end-to-end outcomes. It is a journey measured in years, not months, and it depends as much on digitizing process and building trust as it does on technology. We also explored root cause analysis, still one of the biggest time drains in IT. By combining Dynatrace's AI-driven observability with ServiceNow's workflow engine, enterprises can automate forensic steps, correlate events faster, and shorten the time spent on major incident bridges where teams debate ownership. Even incremental improvements in accuracy can save hours when incidents strike. Trust, of course, remains central. Pablo was candid that full self-healing systems are still some distance away. What we will see first is relief automation, controlled failovers, scripted actions suggested by machines but approved by humans. Over time, as confidence grows and processes become fully digitized, the balance will shift. Beyond the technology, a consistent theme ran through our conversation. Outcomes have not changed. Enterprises still want higher availability, faster resolution, better employee experiences. What is changing is the how. ServiceNow is reimagining its platform to deliver those outcomes at a much higher standard, not through incremental tweaks, but through rethinking workflows for an AI-first world. From design partnerships with banks building pre-flight change checks, to internal teams acting as the toughest customers, this was a grounded, practical conversation about where autonomous operations are headed and what it will take to get there. If you are a CIO, CISO, or IT leader wondering how to move from theory to execution, this episode offers a clear-eyed look behind the curtain.      

How do you protect millions in revenue during your busiest hour of the year when your entire business depends on digital performance? At Perform 2026, I caught up with Alex Hibbitt, Engineering Director responsible for the customer platform at Storio Group, to unpack what happens when observability moves from an engineering afterthought to a board-level priority. Storio Group was formed from the merger of Photobox and Albelli, bringing together multiple brands and five separate e-commerce platforms into one unified customer journey. That consolidation created opportunity, but it also exposed risk, especially during peak trading from Black Friday through Black Sunday and into the Christmas rush. Alex shared what it really looks like when downtime is non-negotiable. At peak, Storio's platform can generate up to 1.5 million euros per hour. A single poorly timed incident is not simply a technical problem, it is a direct threat to revenue and customer trust. Before partnering with Dynatrace, the team was relying heavily on centralized logging, processing over a billion log lines a day and depending on engineers to manually interpret signals. It was reactive, labor intensive, and left too much to chance. What stood out for me was how cultural change led the transformation. Rather than imposing a new tool from the top down, Alex and his team built a maturity model engineers could relate to, created internal champions, and framed observability as risk management and business protection. The result was a reported 65 to 70 percent reduction in log costs, a 50 percent drop in mean time to detect overall, and up to 90 percent improvement for the most severe incidents. We also explored how unifying logs, metrics, and traces into a single AI-driven platform helped Storio move from reactive firefighting to proactive detection. During one Black Sunday alone, three major issues were identified early enough to avoid an estimated 4.5 million euros in potential impact. This conversation goes beyond tooling. It is about protecting customer experience, safeguarding revenue during peak demand, and building an engineering culture that embraces change. If your organization is wrestling with cloud costs, fragmented monitoring, or the pressure to deliver flawless digital performance under load, there are some powerful lessons here.

A few weeks after Dynatrace acquired DevCycle, Michael Beemer and Andrew Norris discussed on The New Stack Makers podcast how feature flagging is becoming a critical safeguard in the AI era. By integrating DevCycle's feature flagging into the Dynatrace observability platform, the combined solution delivers a “360-degree view” of software performance at the feature level. This closes a key visibility gap, enabling teams to see exactly how individual features affect systems in production. As “agentic development” accelerates—where AI agents rapidly generate code—feature flags act as a safety net. They allow teams to test, control, and roll back AI-generated changes in live environments, keeping a human in the loop before full releases. This reduces risk while speeding enterprise adoption of AI tools. The discussion also highlighted support for the Cloud Native Computing Foundation's OpenFeature standard to avoid vendor lock-in. Ultimately, developers are evolving into “conductors,” orchestrating AI agents with feature flags as their baton.   Learn more from The New Stack about the latest around AI enterprise development:  Why You Can't Build AI Without Progressive Delivery  Beyond automation: Dynatrace unveils agentic AI that fixes problems on its own  Join our community of newsletter subscribers to stay on top of the news and at the top of your game.   

Perform 2026 felt like a turning point for Dynatrace, and when Steve Tack joined me for his fourth appearance on the show, it was clear this was not business as usual.  We began with a little Perform nostalgia, from Dave Anderson's unforgettable "Full Stack Baby" moment to the debut of AI Rick on the keynote stage. But the humor quickly gave way to substance. Because beneath the spectacle, Dynatrace introduced something that signals a broader shift in observability: Dynatrace Intelligence. Steve was candid about the problem they set out to solve. Too much focus on ingesting data. Too much time spent stitching tools together. Too many dashboards. Too many alerts. The real opportunity, he argued, is turning telemetry into trusted, automated action. And that means blending deterministic AI with agentic systems in a way enterprises can actually trust. We unpacked what that looks like in practice. From United Airlines using a digital cockpit to improve operational performance, to TELUS and Vodafone demonstrating measurable ROI on stage, the emphasis at Perform was firmly on production outcomes rather than pilot projects. As Steve put it, the industry has spent long enough in "pilot purgatory." The next phase demands real-world deployment and real return. A big part of that confidence comes from the foundations Dynatrace has laid with Grail and Smartscape. By combining unified telemetry in its data lakehouse with real-time topology mapping and causal AI, Dynatrace is positioning itself as the engine behind explainable, trustworthy automation. When hyperscaler agents from AWS, Azure, or Google Cloud call Dynatrace Intelligence, they are expected to receive answers grounded in causal context rather than probabilistic guesswork. We also explored what this means for developers, who often carry the burden of alert fatigue and fragmented tooling. New integrations into VS Code, Slack, Atlassian, and ServiceNow aim to bring observability directly into the developer workflow. The goal is simple in theory and complex in execution: keep engineers in their flow, reduce toil, and amplify human decision-making rather than replace it. Of course, autonomy raises questions about risk. Steve acknowledged that for now, humans remain firmly in the loop, with most agentic interactions still requiring checkpoints. But as trust grows, so will the willingness to let systems self-optimize, self-heal, and remediate issues automatically. We closed by zooming out. In a market saturated with AI claims, Steve encouraged listeners to bet on change rather than cling to the status quo. There will be hype. There will be agent washing. But there is also real value emerging for those prepared to experiment, learn, and scale responsibly. If you want to understand where AI observability is heading, and how deterministic and agentic intelligence can coexist inside enterprise operations, this episode offers a grounded, practical perspective straight from the Perform show floor.

Dynatrace is at a pivotal point, expanding beyond traditional observability into a platform designed for autonomous operations and security powered by agentic AI. In an interview on *The New Stack Makers*, recorded at the Dynatrace Perform conference, Chief Technology Strategist Alois Reitbauer discussed his vision for AI-managed production environments. The conversation followed Dynatrace's acquisition of DevCycle, a feature-management platform. Reitbauer highlighted feature flags—long used in software development—as a critical safety mechanism in the age of agentic AI. Rather than allowing AI agents to rewrite and deploy code, Dynatrace envisions them operating within guardrails by adjusting configuration settings through feature flags. This approach limits risk while enabling faster, automated decision-making. Customers, Reitbauer noted, are increasingly comfortable with AI handling defined tasks under constraints, but not with agents making sweeping, unsupervised changes. By combining AI with controlled configuration tools, Dynatrace aims to create a safer path toward truly autonomous operations. Learn more from The New Stack about the latest in progressive delivery: Why You Can't Build AI Without Progressive Delivery Continuous Delivery: Gold Standard for Software Development Join our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

What really happens to a business when payments stop working, even for a few minutes? I recorded this episode live at Dynatrace Perform in Las Vegas, inside the Venetian, surrounded by engineers, operators, and business leaders all wrestling with the same uncomfortable reality. Payment outages are no longer rare edge cases. They are becoming a routine operational risk, and the cost is far higher than many organizations realize. To unpack that shift, I sat down with Victoria Ruffo, Software Engineering Team Lead at FreedomPay, for a grounded and practical conversation about resilience, observability, and what failure actually looks like in modern commerce. Victoria explains how FreedomPay supports merchants by orchestrating every part of the payment journey through a single platform, from terminal management to remote updates and even on-device advertising. If you have checked into a hotel and noticed a payment terminal quietly branded "Secured By FreedomPay," there is a good chance you have already interacted with her team's work. That real-world exposure gives her a clear view of what happens when systems fail and why customers are far less patient than businesses often assume. We talk about new research from FreedomPay, Dynatrace, and Retail Economics that puts a stark number on the issue. $44.4 billion in U.S. retail and hospitality revenue is at risk every year due to payment disruptions. But as Victoria points out, the most alarming insight is not the headline figure. It is the gap between how long customers are willing to wait and how long outages actually last. Most consumers abandon a purchase after seven minutes, while many disruptions stretch on for hours. In those early minutes alone, the majority of revenue is already gone. The conversation moves beyond statistics into lived experience. From lunch breaks cut short by declined payments to stadiums losing an entire event's worth of revenue in a single outage, Victoria shares why these failures are not abstract technical issues. They directly affect staff wages, customer loyalty, and long-term brand trust. We also explore why cash-only backups and outdated terminals no longer reflect how people actually pay, and why uneven investment in resilience leaves many merchants dangerously exposed. AI plays a central role in the discussion, but not in the way hype cycles often suggest. Victoria is clear that FreedomPay is not using AI to touch cardholder data or write payment code. Instead, tools like Dynatrace Intelligence help teams detect issues faster, identify patterns humans might miss, and move from reaction to anticipation. That shift, she argues, is where real value shows up, especially when seconds and minutes matter. If you care about payments, customer experience, or the hidden connection between technical failure and business impact, this episode offers a timely reminder that outages do not have to be catastrophic if organizations plan for them properly. As consumers grow less patient and systems grow more complex, are your payment platforms designed to absorb disruption, or are they quietly waiting to fail at the worst possible moment? Useful Links Connect With Victoria Ruffo Learn More About Freedom Pay Whitepaper Payment Resilience in an Uncertain World UK Learn More About Dynatrace Perform Thanks to our sponsors, Alcor, for supporting the show.

Sean O'Dell of Dynatrace argues that enterprises are unprepared for a major shift brought on by AI: the rise of the developer. Speaking at Dynatrace Perform in Las Vegas, O'Dell explains that AI-assisted and “vibe” coding are collapsing traditional boundaries in software development. Developers, once insulated from production by layers of operations and governance, are now regaining end-to-end ownership of the entire software lifecycle — from development and testing to deployment and security. This shift challenges long-standing enterprise structures built around separation of duties and risk mitigation. At the same time, the definition of “developer” is expanding. With AI lowering technical barriers, software creation is becoming more about creative intent than mastery of specialized tools, opening the door to nontraditional developers. Experimentation is also moving into production environments, a change that would have seemed reckless just 18 months ago. According to O'Dell, enterprises now understand AI well enough to experiment confidently, but many are not ready for the cultural, operational, and security implications of developers — broadly defined — taking full control again.Learn more from The New Stack about the latest around enterprise developers and AI: Retool's New AI-Powered App Builder Lets Non-Developers Build Enterprise AppsSolving 3 Enterprise AI Problems Developers FaceEnterprise Platform Teams Are Stuck in Day 2 HellJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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/

#335: Observability tools have exploded in recent years, but most come with a familiar tradeoff: either pay steep cloud vendor markups or spend weeks building custom dashboards from scratch. Coroot takes a different path as a self-hosted, open source observability platform that prioritizes simplicity over flexibility. Using eBPF technology, Coroot automatically instruments applications without requiring code changes or complex configuration, delivering what co-founder Peter Zaitsev calls opinionated observability—a philosophy of less is more that aims to reduce cognitive overload rather than drowning users in endless metrics and dashboards. The conversation explores how Coroot differentiates itself in a crowded market with over a hundred observability vendors. Rather than competing head-to-head with cloud giants like Datadog and Dynatrace, Coroot focuses on developers who need answers fast without building elaborate monitoring systems. The platform combines systematic root cause analysis with AI-powered recommendations, using deterministic methods to trace how errors propagate through microservices before handing off to LLMs for actionable fix suggestions. Darin and Viktor dig into Coroot's business model with Peter, examining why the company chose Apache 2.0 licensing instead of more restrictive options, and how staying bootstrapped with minimal angel funding allows them to play the long game without pressure to chase every hype cycle.   Peter's contact information: X: https://x.com/PeterZaitsev Bluesky: https://bsky.app/profile/peterzaitsev.bsky.social LinkedIn: https://www.linkedin.com/in/peterzaitsev/   YouTube channel: https://youtube.com/devopsparadox   Review the podcast on Apple Podcasts: https://www.devopsparadox.com/review-podcast/   Slack: https://www.devopsparadox.com/slack/   Connect with us at: https://www.devopsparadox.com/contact/

Reality TV isn't just weekend entertainment. It's a blueprint for brand building.That's the lesson of Summer House, Bravo's long-running hit that turns everyday interactions into year-round engagement. In this episode, we break down its marketing lessons with the help of our special guest Kelly Cheng, Chief Marketing Officer at Goldcast.Together, we explore what B2B marketers can learn from playing the long game with their audience, making marketing more human by building in public, and creating a steady stream of content that keeps you top of mind long after the season ends.About our guest, Kelly ChengKelly Cheng is a seasoned marketing executive with over a decade of experience driving growth and leading successful marketing strategies for high-performing technology companies. As the Chief Marketing Officer at Goldcast, she is responsible for spearheading the company's global marketing initiatives, including brand development, demand generation, and digital marketing.Prior to her current role, Kelly served as the VP of Marketing at Goldcast, where she played a pivotal role in the company's successful rebrand and the implementation of a data-driven marketing approach. Before joining Goldcast, she held marketing leadership positions at Wistia and Dynatrace, where she demonstrated her expertise in growth marketing, media optimization, and digital acquisition strategies. Kelly's diverse background also includes experience in media planning and digital marketing at PagerDuty and Havas Media Group.Kelly holds a Bachelor of Arts degree in Communications from Boston University, where she graduated cum laude and was recognized for her academic excellence.What B2B Companies Can Learn From Summer House:Build long-term relationships with your audience. Reality TV wins through continuity. Keeping familiar faces and building trust season after season. Kelly explains, “The continuity piece is really important. Throughout the nine seasons, there's a lot of OGs that have been around since season one, and you really, really build that rapport with the audience, and people are super invested in what you do next.” In B2B, the same applies. Consistency and ongoing storytelling help audiences feel emotionally connected, not just informed. Your series or campaign shouldn't end when engagement dips. It should evolve, deepen, and reward loyalty.Build in public. Kelly draws a parallel between following a cast across nine seasons and showing your brand's journey transparently. “You're following on for nine years, learning about their development over time... It's kind of like building in public…I could just put up a show and say watch me learn about AI in marketing and watch me win and watch me fail.” B2B marketers can use this approach to humanize their brand: sharing learnings, experiments, and even missteps. The more your audience sees your process, the more invested they become in your success.Capture year-round mindshare through consistent content. Bravo doesn't just rely on one show. They have built an ecosystem that keeps fans engaged across formats and seasons. Kelly notes, “They're just really, really good at turning out content that people want to consume to keep them top of mind… There's an extra 10 months that you have to make sure that you have got air cover so people don't forget about you.” The lesson: don't go dark between campaigns. Extend your reach with follow-up content, micro-clips, events, and spin-offs. Sustained storytelling turns fleeting interest into durable brand awareness.Quote“I think there's a lot of learning in making B2B marketing a bit more human and drawing those learnings from reality TV about building in public. Because at the end of the day, you're selling software to help an individual that will ultimately help an organization.”Time Stamps[00:55] Meet Kelly Cheng, Chief Marketing Officer at Goldcast[01:08] Why Summer House?[07:13] What is Summer House?[17:37] B2B Marketing Takeaways from Summer House[36:43] Goldcast's Approach to Marketing[42:28] Goldcasts' Upcoming Agent Launches[43:29] Advice for CMOs[44:25] Final Thoughts and TakeawaysLinksConnect with Kelly on LinkedInLearn more about GoldcastAbout Remarkable!Remarkable! is created by the team at Caspian Studios, the premier B2B Podcast-as-a-Service company. Caspian creates both nonfiction and fiction series for B2B companies. If you want a fiction series check out our new offering - The Business Thriller - Hollywood style storytelling for B2B. Learn more at CaspianStudios.com. In today's episode, you heard from Ian Faison (CEO of Caspian Studios) and Meredith Gooderham (Head of Production). Remarkable was produced this week by Jess Avellino, mixed by Scott Goodrich, and our theme song is “Solomon” by FALAK. Create something remarkable. Rise above the noise. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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 discussedLinkedIn 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/

Tertulia previa al 33 Congreso Aslan en la que han intervenido: Isidre Royo, Senior Product Manager de Business Observability de Dynatrace; Andrés Miramontes, Regional Sales Manager Iberia de Quantum; Andrés Marín, Country Manager de MongoDB y Luis Miguel Domínguez, VP de Área Iberia de ServiceNow.

Jennifer Tejada is the Chairperson and CEO of PagerDuty, the global leader in digital operations management (NYSE: PD).  Jennifer brings to the role 25 years of diverse experience spanning mass consumer products to disruptive cloud and software solutions. She has a successful track record in product innovation, optimizing operations, and scaling public and private enterprise technology companies. She is also an active tech investor as an LP in multiple firms, including Operator Collective, Harlem Capital, and Penny Jar. Prior to her role at PagerDuty, Jennifer was CEO of Keynote Systems, where she led the company to strong, profitable growth before its acquisition by Dynatrace. Before Keynote, Jennifer was Executive Vice President and Chief Strategy Officer at Mincom, leading its global strategy up to its acquisition by ABB. She has also held senior positions at Procter & Gamble and i2 Technologies. Jennifer currently serves as a board member of The Estée Lauder Companies Inc. (NYSE: EL). Jennifer holds a bachelor's degree in organizational behavior and business management from the University of Michigan, where she was a proud member of the Michigan's Women's Golf Team. Outside of her professional roles, Jennifer enjoys spending time with her family and is an avid skier.

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 discussedWilliams LinkedIn: https://www.linkedin.com/in/william-david-louth/Humainary Research: https://humainary.io/research/Humainary GitHub: https://github.com/humainary-ioServentis 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 discussedAbhi's LinkedIn: https://www.linkedin.com/in/abhimanyuselvan/Cloud Native Austria Talk: https://www.youtube.com/watch?v=VjMPHWjawxM&list=PLtLBTEzR4SqU9GwgWiaDt10-yOVIN0nzM&index=9Cursor 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 discussedBartek'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=10Talk at Porto Tech Hub: https://www.youtube.com/watch?v=-ZuEaA2PoToKraken: https://github.com/krkn-chaos/krknChasoEater: https://github.com/ntt-dkiku/chaos-eater

In this episode of Mainframe Coven, Jessielaine Punongbayan (Product Manager, Dynatrace) and Richelle Anne Craw (Software Engineer, Beta Systems Software) chat with Lezlie Browder, A Technologist Devoted to Lifelong Learning. She shares her decades-long career as a mainframe mentor, instructor, and technologist. She reflects on inclusive tech culture, teaching across generations, volunteering, and offering grounded advice for women balancing caregiving, career growth, and lifelong learning.Links and Resources Mentioned in the Episode:- Open Mainframe Project Mentorship Program: https://openmainframeproject.org/projects/mentorship/- IBM TechXchange Community: https://community.ibm.com/community/user/my-community- Black Girls Code: https://www.wearebgc.org/- Blind Service Chicago: https://www.blindservicechicago.org/- Source: Isha Jain, “Care responsibilities impact career choices for 40% of women in tech,” Data Centre & Network News, Tech Talent Charter survey, retrieved from dcnnmagazine.com: https://dcnnmagazine.com/news/care-responsibilities-impact-career-choices-for-40-of-women-in-tech/

At KubeCon + CloudNativeCon 2025 in Atlanta, the panel of experts - Kate Goldenring of Fermyon Technologies, Idit Levine of Solo.io, Shaun O'Meara of Mirantis, Sean O'Dell of Dynatrace and James Harmison of Red Hat - explored whether the cloud native era has evolved into an AI native era — and what that shift means for infrastructure, security and development practices. Jonathan Bryce of the CNCF argued that true AI-native systems depend on robust inference layers, which have been overshadowed by the hype around chatbots and agents. As organizations push AI to the edge and demand faster, more personalized experiences, Fermyon's Kate Goldenring highlighted WebAssembly as a way to bundle and securely deploy models directly to GPU-equipped hardware, reducing latency while adding sandboxed security.Dynatrace's Sean O'Dell noted that AI dramatically increases observability needs: integrating LLM-based intelligence adds value but also expands the challenge of filtering massive data streams to understand user behavior. Meanwhile, Mirantis CTO Shaun O'Meara emphasized a return to deeper infrastructure awareness. Unlike abstracted cloud native workloads, AI workloads running on GPUs require careful attention to hardware performance, orchestration, and energy constraints. Managing power-hungry data centers efficiently, he argued, will be a defining challenge of the AI native era.Learn more from The New Stack about evolving cloud native ecosystem to an AI native eraCloud Native and AI: Why Open Source Needs Standards Like MCPA Decade of Cloud Native: From CNCF, to the Pandemic, to AICrossing the AI Chasm: Lessons From the Early Days of CloudJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Is it time to look past the AI bubble and focus on the infrastructure actually securing it? Today, we're pivoting to a top secular growth trend: Cybersecurity.With the industry projected to grow 12% annually and hit $215 billion in spending by 2025, Palo Alto Networks (PANW) is making aggressive moves to dominate the landscape. We discuss their M&A strategy—including the purchase of Chronosphere and the pending CyberArk deal—and what this means for their entry into the cloud observability market against competitors like Datadog and Dynatrace.In this video, we cover:-- AI-Native Security: Why AI agents and cloud workloads are driving the next wave of IT spending.--The Financials: a breakdown of PANW's cash pile, revenue acceleration, and rising stock-based compensation.-- Valuation Check: With the stock trading around 30-33x Free Cash Flow, is Palo Alto Networks a buy, a hold, or just fair value?.We analyze whether this cybersecurity giant can execute on its "platformization" strategy and if the recent sell-off offers a prime entry point for investors.Tickers mentioned: PANW,CYBR,DT,DDOG#PaloAltoNetworks #Cybersecurity #StockMarket #Investing #PANW #CloudSecurity #AIStocksJoin us on Discord with Semiconductor Insider, sign up on our website: www.chipstockinvestor.com/membershipCharts & Data provided by fiscal.ai. Get 25% off any paid plan (Nov 26 - Dec 1) using our link: https://fiscal.ai/csi/Sign Up For Our Newsletter: https://mailchi.mp/b1228c12f284/sign-up-landing-page-short-formIf you found this video useful, please make sure to like and subscribe!*********************************************************Affiliate links that are sprinkled in throughout this video. If something catches your eye and you decide to buy it, we might earn a little coffee money. Thanks for helping us (Kasey) fuel our caffeine addiction!Content in this video is for general information or entertainment only and is not specific or individual investment advice. Forecasts and information presented may not develop as predicted and there is no guarantee any strategies presented will be successful. All investing involves risk, and you could lose some or all of your principal.Nick and Kasey own shares of Palo Alto Networks

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

Jyoti Bansal is the co-founder and CEO of Harness, the software delivery platform used by thousands of engineering teams, and previously founded AppDynamics, which he led from inception to a multibillion-dollar acquisition by Cisco. In this episode, Jyoti unpacks what it really takes to move from mid-market to enterprise, why he thinks in terms of “product-market-sales fit,” and how he structures Harness as a collection of “startups within a startup” to launch multiple “best-of-breed” products. In today's episode, we discuss: Why companies get stuck in the mid-market and struggle to move up into enterprise Why Jyoti deliberately lost Netflix as their customer The difference between product-market-sales fit, and product-market-fit How to build a scalable, capacity-driven go-to-market machine (instead of chasing deals) Diagnosing whether you have a product problem or a distribution problem How to hire and evaluate your first head of sales and top sales leaders Why Jyoti sold AppDynamics three days before IPO The “binary differentiator” rule for launching new products into crowded markets Why Harness runs 16 product lines under one roof Where to find Jyoti: LinkedIn: https://www.linkedin.com/in/jyotibansal/ Twitter/X: https://x.com/jyotibansalsf Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast References: Amazon: https://www.amazon.com/ AppDynamics: https://www.appdynamics.com/ Barclays: https://home.barclays/ BIG Labs: https://www.biglabs.com/ Carlos Delatorre: https://www.linkedin.com/in/cadelatorre/ Charles Schwab: https://www.schwab.com/ Cisco: https://www.cisco.com/ Citi: https://www.citi.com/ Cloudability: https://www.apptio.com/products/cloudability/ Datadog: https://www.datadoghq.com/ Dynatrace: https://www.dynatrace.com/ Harness: https://www.harness.io/ Jeff Bezos: https://x.com/JeffBezos Microsoft: https://www.microsoft.com/ Nasdaq: https://www.nasdaq.com/ Netflix: https://www.netflix.com/ New Relic: https://newrelic.com/ Salesforce: https://www.salesforce.com/ Splunk: https://www.splunk.com/ Traceable: https://www.traceable.ai/ Unusual Ventures: https://www.unusual.vc/ VMware: https://www.vmware.com/ Timestamps: (01:48) Why do companies get stuck in the mid-market? (05:09) Designing a product for enterprise and mid-market (07:19) Why Jyoti lost Netflix as a customer - on purpose (10:18) Becoming a scalable GTM organization (12:32) The real signs of product-market fit (14:04) Have you delivered the value? (15:46) How to hire your first sales team (19:59) The four signs of excellent sales leaders (23:16) How to interview a sales leader (27:51) Where Jyoti developed his commercial taste (29:37) Why early founders need to learn sales (32:02) How AppDynamics began (36:36) Why Jyoti sold three days pre-IPO (41:55) What does a healthy board look like? (44:23) How Jyoti perceives competition (46:18) Why you need a binary differentiator (49:53) How to launch multiple products (52:00) “We need to be best of breed” (57:38) Why PMs are like mini-entrepreneurs (1:00:20) The startup within a startup (1:02:45) A culture of continuous improvement

Adriana is a CNCF Ambassador, blogger, host of the Geeking Out Podcast, and a maintainer of the OpenTelemetry End User SIG. By day, she's a Principal Developer Advocate at Dynatrace, focusing on Observability and OpenTelemetry. By night, she climbs walls. She also loves capybaras, because they make her happy.You can find Adriana on the following sites:BlueskyBlogLinkedInGitHubMastodonYouTubePLEASE SUBSCRIBE TO THE PODCASTSpotifyApple PodcastsYouTube MusicAmazon MusicRSS FeedYou can check out more episodes of Coffee and Open Source on https://www.coffeeandopensource.comCoffee and Open Source is hosted by Isaac Levin

In this episode of Mainframe Coven, Jessielaine Punongbayan (Product Manager, Dynatrace) and Richelle Anne Craw (Software Engineer, Beta Systems Software) chat with Sudharsana Srinivasan, a champion for mentorship and mainframe advocacy. She opens up about her journey from studying computer science in India to leading firmware development at IBM, sharing the lessons she's learned along the way. Sudharsana talks about her passion for helping the next generation, the value of connecting what you learn to real-world opportunities, and how mentorship can create a ripple effect of growth and empowerment.Links and Resources Mentioned in the Episode:Open Mainframe Project Mentorship Program: https://openmainframeproject.org/projects/mentorship/Open Mainframe Project COBOL Programming Course: https://openmainframeproject.org/projects/cobol-programming-course/ibm.biz/mentor-today: http://ibm.biz/mentor-todayibm.biz/newtoibmz: http://ibm.biz/newtoibmz

Sales performance is evolving fast - and in the age of AI, elite teams need more than just strategy. They need culture, coaching, and customer readiness. In this episode of B2B Sales Trends, Harry Kendlbacher sits down with Justin Geib, Regional VP NEMA at Dynatrace (formerly at Dell Technologies), to explore how top leaders are building high-performing sales cultures that thrive in a world where AI reshapes every customer interaction.

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 discussedLinkedIn 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_K8din5tsQlMCpXRNzlKUVoxxed Conference Talk: https://amsterdam.voxxeddays.com/talk/?id=39801Attention 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/OBobCtl GitHub Repo: https://github.com/k8sstormcenter/bobctlCloud Native Summit Munich Talk: https://www.youtube.com/watch?v=XETuwndd_mw&index=11&pp=iAQBnpm supply chain attack: https://www.infosecurity-magazine.com/news/npm-supply-chain-attack-averted/

In this episode of Mainframe Coven, Jessielaine Punongbayan (Product Manager, Dynatrace) and Richelle Anne Craw (Software Engineer, Beta Systems Software) chat with Meredith Stowell, Vice President of IBM Z & LinuxONE Ecosystem, a visionary leader helping more people find their way into tech. Meredith shares how her career shaped her leadership philosophy, her advocacy for non-traditional pathways that create a sustainable workforce, and her vision for building diverse, inclusive global ecosystems.For a transcript of this episode, visit https://openmainframeproject.org/mainframe-coven/mainframe-coven-meredith-stowell/Links and Resources Mentioned in the Episode:- ibm.biz/ztalent

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

In this episode of Mainframe Coven, Jessielaine Punongbayan (Product Manager, Dynatrace) and Richelle Anne Craw (Software Engineer, Beta Systems Software) chat with Former SHARE President Pam Taylor, a historian turned mainframer who ended up making history herself. Pam shares her journey into enterprise tech, her advocacy for standards and user-focused solutions, and how she blends technical expertise with creative storytelling.Mainframe Coven is a 10-part mini-series honoring the past, present, and future women of IT. It's about real stories from the essential yet unseen minds behind the machines.The podcast is sponsored by the Open Mainframe Project, a Linux Foundation project that aims to build community and adoption of Open Source on the mainframe by eliminating barriers to Open Source adoption on the mainframe, demonstrating the value of the mainframe.For a transcript of this episode, visit https://openmainframeproject.org/mainframe-coven/mainframe-coven-pam-taylor/Links and Resources Mentioned in the Episode:- SHARE's Women of Influence in Mainframe: https://blog.share.org/Article/shares-women-of-influence-in-mainframe- Pam Taylor's Website: https://pamela-taylor.com/

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

Dynatrace (DT) posted an earnings beat and raised guidance, and Steven Dickens points to its diversification in A.I. as the reason for growth. As he explains, it also serves as an indicator that automation is here to stay as companies search for ways to improve their bottom line. Steven later notes how we're starting to enter the "platform A.I. era."======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

In this episode of Mainframe Coven, Jessielaine Punongbayan (Product Manager, Dynatrace) and Richelle Anne Craw (Software Engineer, Beta Systems Software) look back at a time when women were central to computing and examine how and why that changed, even though the work didn't. Together they reflect on software engineering, cultural bias, institutional gatekeeping, and the motivation to rewrite the narrative.Mainframe Coven is a 10-part mini-series honoring the past, present, and future women of IT. It's about real stories from the essential yet unseen minds behind the machines.The podcast is sponsored by the Open Mainframe Project, a Linux Foundation project that aims to build community and adoption of Open Source on the mainframe by eliminating barriers to Open Source adoption on the mainframe, demonstrating the value of the mainframe.For a transcript of this episode, visit https://openmainframeproject.org/mainframe-coven/mainframe-coven-when-computers-wore-skirtsLinks and Resources Mentioned in the Episode:- She Was a Computer When Computers Wore Skirts: https://www.nasa.gov/centers-and-facilities/langley/she-was-a-computer-when-computers-wore-skirts/- Zeros and Ones: Digital Women and the New Technoculture by Sadie Plant: https://www.4thestate.co.uk/products/zeros-and-ones-digital-women-and-the-new-technoculture-sadie-plant-9781857026986/- Lovelace & Babbage and the creation of the 1843 'notes' by J. Fuegi and J. Francis, in IEEE Annals of the History of Computing, vol. 25, no. 4, pp. 16-26, Oct.-Dec. 2003: https://doi.org/10.1109/MAHC.2003.1253887- Broad Band: The Untold Story of the Women Who Made the Internet by Claire Evans: https://www.penguinrandomhouse.com/books/545427/broad-band-by-claire-l-evans/- Pioneer Programmer: Jean Jennings Bartik and the Computer That Changed the World by Jean Jennings Bartik: https://www.amazon.com/Pioneer-Programmer-Jennings-Computer-Changed/dp/1612480861/- The women of ENIAC by W. B. Fritz, in IEEE Annals of the History of Computing, vol. 18, no. 3, pp. 13-28, Fall 1996: https://doi.org/10.1109/85.511940- Jean J. Bartik and Frances E. “Betty” Snyder Holberton, interview by Henry Tropp, April 1973, Computer Oral History Collection, Archives Center, National Museum of American History, Smithsonian Institution: https://mads.si.edu/mads/id/NMAH-AC0196_bart730427/- When Computers Were Women by Jennifer S. Light, Technology and Culture, vol. 40, no. 3, 1999: https://www.jstor.org/stable/25147356- ENIAC Programmers Project: https://eniacprogrammers.org/- Great Unsung Women of Computing: The Computers, The Coders and The Future Makers: https://www.wmm.com/catalog/film/great-unsung-women-of-computing-the-computers-the-coders-and-the-future-makers/- The Untold History of Women in Science and Technology (White House Archives): https://obamawhitehouse.archives.gov/women-in-stem/- The Queen of Code, directed by Gillian Jacobs. FiveThirtyEight, 2015: https://vimeo.com/118556349/- “Making Programming Masculine” In Gender Codes: Why Women Are Leaving Computing by Nathan Ensmenger: https://homes.luddy.indiana.edu/nensmeng/posts/2010/09/09/misa2010/- The Computer Boys Take Over: Computers, Programmers, and the Politics of Technical Expertise by Nathan Ensmenger: https://thecomputerboys.com/

In this episode of Mainframe Coven, Jessielaine Punongbayan (Product Manager, Dynatrace) and Richelle Anne Craw (Software Engineer, Beta Systems Software) dive into the origins of the term "Kilogirl", explore its historical context, and discuss the importance of women's visibility in tech.Together, they reflect on the legacy of women in computing, share personal insights, and answer the powerful question:"Why is women visibility important?"Mainframe Coven is a 10-part mini-series honoring the past, present, and future women of IT. It's about real stories from the essential yet unseen minds behind the machines.The podcast is sponsored by the Open Mainframe Project, a Linux Foundation project that aims to build community and adoption of Open Source on the mainframe by eliminating barriers to Open Source adoption on the mainframe, demonstrating the value of the mainframe.For a transcript of this episode, visit https://openmainframeproject.org/main...Links Mentioned in the Episode:- Radical Software: Women, Art & Computing 1960–1991: https://kunsthallewien.at/ausstellung...Computing Power Used to Be Measured in 'Kilo-Girls': https://www.theatlantic.com/technolog...The Gendered History of Human Computers: https://www.smithsonianmag.com/scienc...The Glass Universe: The Hidden History of the Women Who took the Measure of the Stars by Dava Sobel: https://www.4thestate.co.uk/products/... as mentioned in An astronomical feat: https://www.spectator.co.uk/article/a...Broad Band: The Untold Story of the Women Who Made the Internet by Claire Evans: https://www.penguinrandomhouse.com/bo...Anita B.org: https://anitab.org/The Bletchley Circle (2012–2014, ITV): https://www.world-productions.com/pro...Hidden Figures (2016, directed by Theodore Melfi) Based on the book by Margot Lee Shetterly: https://family.20thcenturystudios.com...Re-writing the code: https://rewritingthecode.org/

"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 

What happens when you try to monitor something fundamentally unpredictable? In this featured guest episode, Wayne Segar from Dynatrace joins Corey Quinn to tackle the messy reality of observing AI workloads in enterprise environments. They explore why traditional monitoring breaks down with non-deterministic AI systems, how AI Centers of Excellence are helping overcome compliance roadblocks, and why “human in the loop” beats full automation in most real-world scenarios.From Cursor's AI-driven customer service fail to why enterprises are consolidating from 15+ observability vendors, this conversation dives into the gap between AI hype and operational reality, and why the companies not shouting the loudest about AI might be the ones actually using it best.Show Highlights(00:00) - Cold Open(00:48) – Introductions and what Dynatrace actually does(03:28) – Who Dynatrace serves(04:55) – Why AI isn't prominently featured on Dynatrace's homepage(05:41) – How Dynatrace built AI into its platform 10 years ago(07:32) – Observability for GenAI workloads and their complexity(08:00) – Why AI workloads are "non-deterministic" and what that means for monitoring(12:00) – When AI goes wrong(13:35) – “Human in the loop”: Why the smartest companies keep people in control(16:00) – How AI Centers of Excellence are solving the compliance bottleneck(18:00) – Are enterprises too paranoid about their data?(21:00) – Why startups can innovate faster than enterprises(26:00) – The "multi-function printer problem" plaguing observability platforms(29:00) – Why you rarely hear customers complain about Dynatrace(31:28) – Free trials and playground environmentsAbout Wayne SegarWayne Segar is Director of Global Field CTOs at Dynatrace and part of the Global Center of Excellence where he focuses on cutting-edge cloud technologies and enabling the adoption of Dynatrace at large enterprise customers. Prior to joining Dynatrace, Wayne was a Dynatrace customer where he was responsible for performance and customer experience at a large financial institution. LinksDynatrace website: https://dynatrace.comDynatrace free trial: https://dynatrace.com/trialDynatrace AI observability: https://dynatrace.com/platform/artificial-intelligence/Wayne Segar on LinkedIn: https://www.linkedin.com/in/wayne-segar/SponsorDynatrace: http://www.dynatrace.com 

"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 todayDusan'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 todayMarian'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