DevOps Paradox

Darin Pope & Viktor Farcic

What is DevOps? We will attempt to answer this and many more questions.

  1. The Assembly Line Problem: Why Adding AI to One Step Breaks Everything

    PRED 12 H

    The Assembly Line Problem: Why Adding AI to One Step Breaks Everything

    #338: Every company adding AI coding tools runs into the same wall. Developers produce more code, but features don't ship any faster. The bottleneck just slides downstream -- to QA, to security, to legal, to whoever comes next in the pipeline. And the team that got faster? They don't even realize the people upstream could be feeding them more work. Viktor's take: the fastest possible setup is one person carrying a feature from idea to production. Not one person doing everything alone -- a system designed so nobody waits. Tests run in CI. Deployments happen through Argo CD. Security scanning is automated. There's a real difference between wiring up a light switch and hiring a butler to flip it for you. None of this is new. The same thing happened with punch cards, client-server, cloud, Kubernetes. One group adopts the new thing, everyone else says it doesn't apply to them, and the market eventually forces their hand. Meanwhile, every team in every company says they'd love to change if only the rest of the organization would get on board. Every team says this. So who's actually blocked?   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/

    42 min
  2. Nanoseconds Matter - InfluxDB and the Future of Real-Time Data

    11. FEB.

    Nanoseconds Matter - InfluxDB and the Future of Real-Time Data

    #337: Time series databases have become essential infrastructure for the physical AI revolution. As automation extends into manufacturing, autonomous vehicles, and robotics, the demand for high-resolution, low-latency data has shifted from milliseconds to nanoseconds. The difference between a general-purpose database and a specialized time series solution is the difference between a minivan and an F1 car - both will get around the track, but only one is built for the demands of real-time operational workloads. The open source business model continues to evolve in unexpected ways. While companies like Elastic and Redis have seen hyperscalers fork their projects, a new partnership paradigm is emerging. Amazon Web Services now pays to license InfluxDB and offers it as a managed service, signaling a shift toward collaboration rather than competition. This approach benefits everyone: vendors maintain development velocity, cloud providers get workloads on their platforms, and customers receive better-supported products. Evan Kaplan, CEO of InfluxData, joins Darin and Viktor to discuss the trajectory from observability metrics to physical world instrumentation, why deterministic models matter more than probabilistic ones when your robot might run over your cat, and what it takes to build a sustainable open source company over a decade-plus journey.   Evan's contact information: X: https://x.com/evankaplan LinkedIn: https://www.linkedin.com/in/kaplanevan/   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/

    43 min
  3. Why Top Talent Won't Work for You Anymore

    4. FEB.

    Why Top Talent Won't Work for You Anymore

    #336: The workplace is on the verge of a transformation as significant as the Industrial Revolution. Just as Bring Your Own Device policies emerged after the iPhone disrupted corporate mobile standards, we are now entering an era where employees may arrive with their own AI teams in tow. The question is no longer whether AI will change hiring and employment - it is how quickly companies will adapt before being left behind by competitors who embrace this shift. Current AI productivity gains remain largely individual rather than organizational. Writing code twice as fast means nothing if the deployment pipeline stays the same speed. But within five to ten years, entire industries face disruption - from primary care physicians to transportation to knowledge work. Companies clinging to restrictive AI policies today risk driving away top talent who have already integrated these tools into their workflows. The intellectual property implications alone - who owns an AI stack trained on company processes when an employee leaves - will require entirely new frameworks for employment law. Darin and Viktor explore these scenarios through the lens of a hypothetical job interview where a candidate brings their own team of AI agents. The conversation surfaces uncomfortable questions about compensation models, corporate governance, and whether we are witnessing the emergence of a new kind of talent that blends human expertise with digital capabilities.   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/

    54 min

  4. 28. JAN.

    Stop Building Dashboards and Start Getting Answers With Coroot

    #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/

    51 min
  5. If Code Is the Easy Part, What Should Developers Actually Be Doing?

    21. JAN.

    If Code Is the Easy Part, What Should Developers Actually Be Doing?

    #334: The debate over whether AI saves developers time misses a fundamental truth: coding was never the hardest part of software development. Writing code is mechanical work - the real challenges have always been understanding problems, designing solutions, communicating with stakeholders, and navigating organizational complexity. AI is now forcing a reckoning with this reality, pushing developers at every level to reconsider what skills actually matter. The traditional separation between architects who design and developers who implement is breaking down. AI enables a return to something like pair programming, where the person thinking through problems can now work alongside a fast executor without the old bottleneck of slow human typing. This shift means developers need stronger communication skills - the ability to explain technical decisions to non-technical stakeholders and translate business requirements into technical direction. For juniors, the opportunity is unprecedented: you can upskill faster than ever in the history of software, but only if you balance building things with actually understanding how they work. Darin and Viktor explore what this means for developers at every career stage, from juniors who should focus on fundamentals and end-to-end understanding, to seniors who are becoming more like editors and supervisors of AI-generated work. The developers who will thrive are those who combine real experience with a willingness to embrace change - and that combination has always been the winning formula.   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/

    40 min

  6. 14. JAN.

    The Hidden Problems Behind Every Data Pipeline

    #333: Pete Hunt, CEO of Dagster and early React team member, explores the evolution from Facebook's early React development through trust and safety infrastructure at Twitter, to building modern data orchestration tools. The conversation reveals how similar infrastructure problems plague every industry - whether you're launching rockets or managing porta-potties, the core challenges remain consistent: late data, quality issues, and mysterious errors that require both automated solutions and human oversight. The discussion dives into the technical realities of scaling systems, from the microservices complexity trap to the current AI adoption wave. Hunt shares candid insights about leadership challenges, including how well-intentioned technology recommendations can backfire, and why most data projects fail despite sophisticated multi-agent orchestration. The conversation touches on career advancement pressures that drive unnecessary complexity and the importance of focusing on actual user adoption rather than technical sophistication. This episode features Pete Hunt in conversation with hosts Darin and Viktor, covering everything from regular expression nightmares to the future of data infrastructure and the lessons learned from building products that people actually use.   Pete's contact information: X: https://x.com/floydophone LinkedIn: https://www.linkedin.com/in/pwhunt/   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/

    51 min

  7. 7. JAN.

    2026 - The Year of Discovery

    #332: AI adoption in enterprise software development is accelerating, but operations teams are lagging behind. While application developers embrace AI tools at a rapid pace, those on the ops side remain skeptical—citing concerns about determinism, control, and a general resistance to change. This mirrors previous technology waves like containers, cloud, and Kubernetes, where certain groups initially pushed back before eventually adapting. The prediction for 2026: AI will not see widespread adoption in operations despite its growing presence elsewhere in the software lifecycle. The bigger challenge facing organizations is not just adopting AI but transforming entire processes to take advantage of it. Improving just one piece of the software delivery pipeline—like development speed—only creates bottlenecks elsewhere. Companies cannot hand developers AI tools while keeping everything else the same and expect transformational results. The future points toward a world where experts bring their own AI agents to companies: personal toolsets trained on their experience and best practices that integrate with organizational systems. Perhaps the most provocative insight centers on the value of writing code itself. The argument: writing code is the easiest and least valuable part of software development. The real cognitive load comes from thinking through requirements, architecture, and design. Developers who simply translate instructions to code without deeper engagement may find themselves in real danger as AI continues to advance. Darin and Viktor explore these predictions and more as they look ahead to what 2026 might bring for DevOps, platform engineering, and the evolving role of developers.   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/

    49 min

  8. 31. 12. 2025

    Looking Back on Our 2025 Predictions

    #331: At the end of 2024, predictions were made about what 2025 would bring to the tech industry. A year later, on New Year's Eve, it's time to look back and see what actually happened. The prediction episode from January 1st covered four major topics: rug pulls from companies switching to business source licenses, the rise of WebAssembly adoption, a wave of company acquisitions, and AI becoming embedded in existing tools. Some predictions hit the mark while others missed entirely, but what emerged was something nobody fully anticipated.   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/

    22 min
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Napovednik

Več informacij

What is DevOps? We will attempt to answer this and many more questions.

Informacije

  • Ustvarjalec
    Darin Pope & Viktor Farcic
  • Leta delovanja
    2019 - 2026
  • Epizode
    342
  • Ocena
    Primerno za vse
  • Avtorske pravice
    © PlanetPope, Inc. 2019-2026
  • Spetna stran oddaje
    DevOps Paradox
  • Ponudnik

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