Ship Happens

Docker

This is where serious devs level up. Each week, top engineers break down how they’re pushing productivity, locking down security, and building AI-first workflows—all in the cloud. It’s the real-world insight you won’t get from a blog post—straight from the people shipping at scale. We’re bringing you sharp minds, smart code, and battle-tested tactics. Hit play. Then out-build everyone else.

Episodes

  1. 10 Jun

    AI Coding vs. Enterprise Reality Standards, Reliability, and the Future of DevOps

    While AI-powered development has made it easier than ever to build prototypes and accelerate coding tasks, guests throughout this compilation caution that enterprise software demands far more than speed. Security, compliance, scalability, maintainability, governance, and reliability remain critical concerns that AI alone cannot solve. The discussion explores the rise of "vibe coding," the growing importance of developer experience in AI-assisted workflows, and the challenges organizations face when introducing AI into production environments. Guests explain why governance, standards, golden paths, and clear exit criteria are essential for preventing the rapid automation of bad processes. The episode also examines how AI is helping teams navigate legacy codebases, automate upgrades, improve pull request reviews, strengthen security practices, and reduce cognitive load for developers. At the same time, speakers warn that increased AI adoption can create operational complexity, reliability risks, and new management challenges if organizations lack proper controls and testing strategies. From platform engineering and DevOps automation to running open-source LLMs in Kubernetes environments, this compilation highlights the opportunities, tradeoffs, and realities of building software in the age of AI. Topics Covered AI coding assistants and coding agents Vibe coding versus enterprise software development Developer experience (DevEx) DevOps automation and AI adoption Governance, standards, and golden paths Reliability and software delivery stability DORA research and AI productivity tradeoffs Pull request reviews and engineering bottlenecks Legacy code modernization Platform engineering best practices Running LLMs in production Kubernetes and GPU infrastructure Engineering leadership in the AI era Timestamps (00:00) Welcome to Ship Happens (01:05) Vibe Coding vs Enterprise (03:01) Developer Experience Still Matters (05:13) AI in DevOps Today (07:32) Governance and Golden Paths (09:15) Speed vs Stability Tradeoffs (13:19) Standards Bots and Reviews (16:11) AI Reshaping Management (17:54) Agents, Trust, and Responsibility (18:58) Running LLMs in Production (20:03) Costs, Testing and Wrap Up Key Takeaways AI coding tools are powerful for prototyping and MVP development, but enterprise software requires stronger controls and governance. Developer experience becomes increasingly important as AI-assisted workflows become more common. Reliability is emerging as a critical success metric in organizations adopting AI at scale. Without standards and governance, AI can accelerate poor processes just as quickly as good ones. Golden paths and platform engineering practices help teams balance speed, consistency, and security. AI can reduce cognitive load, modernize legacy systems, and improve operational efficiency when implemented thoughtfully. Running LLMs in production introduces infrastructure, operational, and cost considerations that organizations must carefully manage. Engineering leadership is evolving as AI changes how teams build, review, and maintain software. Organizations that combine AI adoption with strong testing, security, and reliability practices will be best positioned for long-term success. Key Takeaways AI coding tools are powerful for prototyping and MVP development, but enterprise software requires stronger controls and governance. Developer experience becomes increasingly important as AI-assisted workflows become more common. Reliability is emerging as a critical success metric in organizations adopting AI at scale. Without standards and governance, AI can accelerate poor processes just as quickly as good ones. Golden paths and platform engineering practices help teams balance speed, consistency, and security. AI can reduce cognitive load, modernize legacy systems, and improve operational efficiency when implemented thoughtfully. Running LLMs in production introduces infrastructure, operational, and cost considerations that organizations must carefully manage. Engineering leadership is evolving as AI changes how teams build, review, and maintain software. Organizations that combine AI adoption with strong testing, security, and reliability practices will be best positioned for long-term success. Resources & Links 🎧 Subscribe to Ship Happens: Apple Podcasts Spotify YouTube 🌐 Learn more about Docker: https://www.docker.com 📚 Referenced Topics & Technologies: DORA (DevOps Research and Assessment) Kubernetes Large Language Models (LLMs) Platform Engineering Developer Experience (DevEx) AI Coding Assistants & Agents Open Source AI Models Enterprise DevOps & Reliability Engineering   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

    21 min

  2. 26 May

    Rethinking AI and Data: Incentives, Trust, and the Future of the Web with Ruben Verborgh

    In this Ship Happens episode, host Per Krogslund interviews Ruben Verborgh, a computer science professor at Ghent University and consultant about building a more responsible digital ecosystem where incentives align with ethics rather than exploitation. Ruben argues that today’s AI ecosystem is fundamentally unstable: companies often lack control over the models they deploy, sustainable business models remain unclear, and the industry continues relying on advertising-driven incentives that reward data extraction over trust. Instead of focusing purely on “ethical AI” messaging, Ruben says the real challenge is designing better economic systems and incentives. The conversation explores an alternative future for AI and personal data—one where raw user data stays under individual control and algorithms move to the data instead of endlessly collecting and centralizing it. Ruben explains how decentralized web standards like Solid aim to separate identity and storage from applications, giving users more portability, ownership, and freedom online. Per and Ruben also discuss mistrust in AI agents, why smaller task-specific models may ultimately be safer and more controllable than giant general-purpose systems, and why society must work alongside younger generations to shape technology rather than attempting to ban it outright. Chapters (00:00) Welcome and Guest Intro (00:32) Mission for Better Data (01:24) Incentives and AI Ethics (02:54) Packaging AI Like Utilities (05:14) Economic Model Will Implode (07:10) Beyond Ads and Big Data (08:36) Bringing the Algorithm to the Data (10:36) Consent Beats Tracking (11:45) The Myth of Retargeting Value (15:10) Trust and AI Agents (16:55) Guarantees and Smaller AI Models (18:03) Solid and the Decentralized Web (20:51) Why Identity Came Later (23:04) The Web as an Amplifier (26:22) Fix Incentives, Not Algorithms (27:53) There Is No “Responsible Tech,” Only Systems (29:06) Policy Shifts Beyond Finger Wagging (32:04) Believing in the Next Generation (33:11) Wrap Up and Outro   About the Guest Ruben Verborgh is a computer science professor at Ghent University and a researcher focused on decentralized web technologies, linked data, and data ownership. He works with Tim Berners-Lee’s Inrupt on the Solid project, which aims to separate applications from personal data storage to give users greater control, privacy, and interoperability across the web. Resources & Links Inrupt Official Website Solid Project Ghent University Ruben Verborgh Website Per Krogslund LinkedIn Docker Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

    36 min

  3. 12 May

    Shipping Safely in the Age of AI: Feature Ops with Ivar Conradi Østhus

    Software delivery is getting faster—but also more unpredictable. In this episode of Ship Happens, we sit down with Ivar Conradi Østhus, CTO of Unleash, to unpack how AI is reshaping the way teams build, ship, and operate software. At the center of the discussion is “Feature Ops,” a philosophy that combines DevOps practices, feature flags, and real-world feedback loops to enable safer, faster delivery cycles. Ivar explains why production is not the end of the pipeline—but the beginning of learning. We explore how teams use DORA metrics to measure delivery performance, and why AI adoption—while increasing developer velocity—can introduce new risks around deployment stability and system reliability. To counterbalance this, Ivar breaks down the importance of runtime control, observability, and automated feature management as essential safeguards in modern engineering systems. The conversation also dives into how AI is already being used to automate operational tasks like feature flag cleanup across distributed systems. Finally, we discuss how enterprise development tools are evolving—moving closer to developer workflows like pull requests, CI/CD, and runtime decision-making—and why the future is likely a best-of-breed ecosystem rather than a single unified platform.  We discuss: What “Feature Ops” actually means in practice, How DevOps and DORA metrics measure real delivery performance, Why AI increases both velocity and risk in software delivery, The role of feature flags in safe production releases, Runtime control, observability, and production learning loops, AI-driven automation in DevOps workflows, Enterprise tooling integration into CI/CD pipelines, Platform engineering vs best-of-breed ecosystems, The future of AI in developer productivity and delivery safety. Whether you're building engineering systems, leading platform teams, or navigating AI adoption, this episode explores what it really takes to ship safely in the age of AI. Key Topics Discussed Feature Ops as a modern delivery philosophy Shipping safely with frequent production releases DevOps and DORA metrics in practice AI’s impact on software delivery speed and stability Feature flags and runtime control systems Observability and production feedback loops Automated cleanup and management of feature flags AI-assisted DevOps workflows and tooling CI/CD integration for enterprise engineering teams Platform engineering vs best-of-breed architecture The future of developer productivity in the AI era Episode Timestamps (00:00) AI Threat Framing (00:52) Welcome and Guest Intro (02:00) What Is FeatureOps (04:03) Experiments and Fast Feedback (06:06) DevOps Metrics and DORA (09:55) Funding and AI Risk (14:04) Runtime Control and Rollouts (17:14) Testing Renaissance and Trust (20:46) Accountability and AI Ethics (24:35) AI Automation and Flag Cleanup (28:41) Agents and Smarter Software (29:36) Natural Language Integrations (31:07) MCP Versus CLI Debate (31:56) Enterprise Security Concerns (33:51) Standards and AI Translators (37:16) SOAP Makes a Comeback (38:23) SaaS Valuations and AI Fear (41:28) What Great Dev Tools Do (44:54) Platforms Versus Best of Breed (47:48) Platform Teams and Enterprise Data (50:18) Measuring AI Productivity ROI (54:29) Pricing Reality and Model Choice (56:44) Wrap Up and Sponsor Thanks Guest Bio Ivar Conradi Østhus is the CTO and creator of Unleash, one of the leading open-source feature management platforms used by engineering teams worldwide. He has spent his career building developer tooling and scaling software delivery systems, with a focus on helping teams ship safely through feature flags, controlled rollouts, and runtime decision-making. Ivar is a leading voice behind “Feature Ops,” a modern approach to DevOps that emphasizes production-first learning, fast iteration, and strong operational guardrails in complex engineering environments. Links & Resources Per Krogslund’s LinkedIn Ivar Conradi Østhus’s LinkedIn Learn more about Unleash Learn more about Docker Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

    57 min
  4. Why CI/CD Is Broken (And How to Fix It) | Jignesh Patel, Harness

    28 Apr

    Why CI/CD Is Broken (And How to Fix It) | Jignesh Patel, Harness

    In this episode of Ship Happens, host Per Krogslund sits down with Jignesh Patel, Field CTO at Harness, to unpack why CI/CD pipelines break down at scale—and what it takes to fix them. Drawing from his experience leading DevOps and cloud teams at major enterprises, Jignesh explains how fragmented tools, siloed teams, and heavy change management processes create what he calls “spaghetti pipelines”—complex, brittle paths to production that rely too heavily on individual expertise. They explore how DevOps should be viewed as a continuous journey focused on faster feedback loops, automation, and shifting security left. Jignesh breaks down how modern platforms like Harness aim to unify build, test, deploy, and security workflows—while introducing AI-assisted pipeline generation with guardrails to improve both speed and reliability. The conversation also dives into incident response, automated rollbacks, continuous verification, and why governance and human oversight are still critical—even as AI accelerates development workflows. For teams struggling with delivery bottlenecks, reliability issues, or scaling DevOps practices, this episode offers a practical look at what’s actually working in the enterprise today. In This Episode You’ll Hear: Why CI/CD pipelines break down in large organizations What “spaghetti pipelines” look like—and how to fix them How platform engineering simplifies DevOps at scale The role of AI in generating and optimizing pipelines Why DevSecOps requires shifting security earlier in development How automated rollback and verification improve reliability Why governance and human oversight still matter in AI workflows What metrics actually matter for developer experience and delivery Episode Timestamps: (00:00) Enterprise “Spaghetti Maps” in CI/CD (01:36) Podcast Intro & Guest Overview (02:32) Jignesh Patel’s Career Path to Field CTO (05:23) Proving DevOps ROI & Budget Value (06:05) Product Team Expectations vs Reality (08:48) Why CI/CD Pipelines Break Down (11:08) DevSecOps & Shifting Security Left (13:02) Fixing Release Chaos in Enterprises (15:08) What Harness Actually Does (19:17) Unified DevOps Platform vs Tool Sprawl (20:48) Reliability Engineering in the AI Era (21:50) Faster Rollbacks & Incident Recovery (22:34) Continuous Verification Explained (23:24) How AI Can Increase Chaos (24:40) Practical AI Use Cases for Developers (26:28) Governance, Guardrails & AI Safety (29:04) Reverse Demos & Hidden Workflow Gaps (31:43) Measuring Platform Adoption Success (33:24) Cloud Repatriation Trends Explained (34:55) AI Spend & ROI Reality Check (36:34) Risk, Accountability & AI Ownership (39:10) DevOps & AI: Next 5 Years Predictions (41:27) DevOps Strategy Advice for Teams (42:53) Closing Thoughts & Takeaways About the Guest: Jignesh Patel is the Field CTO at Harness, where he helps enterprises modernize software delivery through platform engineering, DevOps transformation, and AI-driven automation. With leadership experience at companies like United Airlines and Morningstar, Jignesh brings deep expertise in cloud infrastructure, CI/CD systems, and enterprise-scale DevSecOps. He is a strong advocate for simplifying delivery pipelines, improving developer experience, and building reliable, secure systems through better platform design. Links & Resources: Per Krogslund on LinkedIn Jignesh Patel on LinkedIn Learn more about Harness Learn more at Docker.com Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

    43 min
  5. The Modern Web: Why Open Source Still Wins — with Brian Alvey, WordPress VIP

    14 Apr

    The Modern Web: Why Open Source Still Wins — with Brian Alvey, WordPress VIP

    WordPress VIP, to trace the modern web’s evolution—from the first blogging platforms to today’s AI‑driven, platform‑dominated internet. Brian shares lessons from co‑founding Weblogs Inc.—the company behind Engadget and Autoblog—and how publishing challenges have both changed and stayed the same. He explains how AI is rewriting the economics of software development, making design and testing resources nearly limitless while undermining traditional publishing models. When bots and AI agents extract content without attention or attribution, the long‑standing audience‑to‑site contract breaks. Per and Brian explore how WordPress VIP supports enterprise publishers, regulated organizations, and compliant networks, including FedRAMP‑level customers. They discuss the constant tension between flexibility and control, why enterprise buyers often don’t use the products they approve, and how founders benefit more from domain and sales expertise than from technical pedigrees. Brian describes open source as the first of five “equalizing waves” in technology. In an AI era where large‑language models thrive on open data, open source has never mattered more. WordPress’s decades‑long “give it away” philosophy continues to prove that transparency and community outlast short‑term advantage. In this episode, you’ll learn: • How the early web became today’s platform economy • Why AI changes software’s cost and incentive model • What threats AI browsers pose to publishers • The challenges of scaling WordPress VIP for enterprise security & compliance • How open source fuels LLMs and long‑term innovation • Why openness remains the foundation of a durable internet Episode Timestamps: (00:00) AI Is Changing Everything About the Web (00:36) Introducing Brian Alvey from WordPress VIP (01:54) How the Early Web and CMS Shaped Modern Development (03:26) Why Brian Went Back to Coding (04:46) The Problem with Infinite Design and QA Cycles (07:00) From the Blogosphere to Web Enshittification (10:43) How Bots Broke the Open Web (13:44) Will AI Browsers Replace Websites? (15:43) WordPress VIP, Web Standards, and the Future (18:29) What Enterprise Really Needs from Software (21:03) Why Enterprise Software Has a Trust Problem (23:18) Why You Should Hire a Sales Cofounder First (25:15) The Reality of Enterprise Workflows (26:47) How Open Source Is Leveling Up (30:56) The Business of Monetizing Open Source (35:19) What Happens When You Give It All Away (39:59) Is WordPress Becoming Legacy Technology? (43:32) Final Thoughts with Brian Alvey About the Guest: Brian Alvey is the Chief Technology Officer at WordPress VIP, the enterprise platform from Automatic powering major publishers, brands, and government organizations worldwide. A veteran of early web publishing, Brian co‑founded Weblogs Inc.—creator of Engadget and Autoblog—later acquired by AOL. Over nearly three decades, he’s led teams at the intersection of media, software, and open source innovation. At WordPress VIP, he focuses on balancing enterprise‑grade security and compliance with the openness that drives web progress. Links & Resources: Brian Alvey — LinkedIn Per Krogslund - LinkedIn WordPress VIP Learn more at Docker.com Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

    45 min

  6. 31 Mar

    Infrastructure Engineering: Trust, Efficiency, and Change with Kristjan Elias

    In this episode of Ship Happens, host Per Krogslund sits down with Kristjan Elias, Director of Engineering Infrastructure at Pipedrive, for a conversation about what modern infrastructure teams actually own — and why that ownership matters. Kristjan reflects on nearly a decade at Pipedrive, spanning the company’s growth from roughly 100 employees through multiple stages of scale. He explains how infrastructure engineering differs from application development, with a longer-term focus on migrations, lifecycle planning, secure defaults, and building trust through stable, repeatable platforms. At Pipedrive, the infrastructure team owns cloud assets end-to-end, communicates upgrade timelines clearly, and balances a “golden path” with flexibility for team needs. Kristjan shares how north-star metrics like four-nines uptime, latency, cost per seat, and security shape the team’s decisions — and how platform quality directly impacts customer retention. Per and Kristjan also go deep on the technical stack: a long-lived PHP monolith, microservices fully on Kubernetes, a large MySQL footprint, Kafka, Elasticsearch, and infrastructure as code through Terraform and Ansible. They discuss lessons learned from AWS migration, including the realities of elasticity, cloud cost management, and cultural change after large infrastructure shifts. The conversation then turns to AI in production. Kristjan shares how 85% of engineers are already using AI coding tools, what it looks like to run open-source LLMs on GPU-backed Kubernetes with sglang, and why teams still rely heavily on external model providers. They also explore the uncertainty of AI unit economics, build-vs-buy decisions, supply-chain hardening, insider risk, and why autonomous agents need strong governance before they can be trusted in production. This episode is a practical look at infrastructure as a long-term systems discipline — one grounded in responsibility, customer value, and engineering judgment over hype. Episode Timestamps: (00:00) Why AI Models Keep Changing (00:43) Welcome and guest intro (01:46) Kristjan’s origin story (02:47) Why he stayed at Pipedrive for 10 years (05:25) What infrastructure engineers really do (07:14) How to run migrations without disruption (08:04) Ownership and accountability in platform teams (10:22) The infrastructure team’s north star (12:56) Uptime, latency, cost, and security metrics (14:28) Culture shifts after AWS migration (17:46) Pipedrive’s stack: Kubernetes, MySQL, and more (21:28) AI transformation and coding agents (23:59) What AI agents mean for SaaS (25:27) Agents as microservices (25:59) Trust, risk, and responsibility (26:34) Running LLMs on Kubernetes (28:01) Model sizing and infrastructure costs (28:53) Build vs. buy for LLMs (31:25) AI unit economics and pricing questions (35:38) Supply-chain security in the real world (40:04) Insider threats and access control (41:26) Governing autonomous agents (43:37) Infrastructure lessons and wrap-up (45:01) Closing and sponsor About the Guest: Kristjan Elias is the Director of Engineering Infrastructure at Pipedrive, where he leads platform and cloud strategy across a fast-growing SaaS environment. Over nearly a decade at Pipedrive, he has helped guide the company through major phases of scale, infrastructure modernization, cloud migration, and platform evolution. Kristjan’s work sits at the intersection of reliability, cost efficiency, security, and developer enablement, with a growing focus on how AI tools and autonomous systems can be introduced responsibly into production environments. Links & Resources: • Kristjan Elias — LinkedIn • Per Krogslund — LinkedIn • Learn more about Pipedrive • Learn more about Docker Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

    46 min

  7. 17 Mar

    Systems Thinking for Modern Engineering with Sergey Katsev

    In this episode of Ship Happens, host Per Krogslund sits down with Sergey Katsev, VP of Engineering at Catchpoint, for a deep dive into internet performance, distributed systems, and the realities of modern engineering management. Sergey explains why today’s websites rely on a sprawling network of dependencies — DNS providers, CDNs, cloud infrastructure, APIs, analytics, and third‑party scripts — and why traditional monitoring fails to capture real user experience. At Catchpoint, thousands of global “vantage points” act like automated secret shoppers, mapping outages, latency, and hidden bottlenecks before customers feel them. Per and Sergey explore why DNS remains one of the most fragile, overlooked layers on the internet, with recent large‑scale disruptions proving how easily the web breaks. From dependency chaos to observability as a shared language, Sergey reveals why systems thinking is the most important skill for today’s engineers and managers. They also unpack engineering leadership essentials: • Trust, communication, and psychological safety • Blameless postmortems and accountability • Hiring for curiosity and systems thinking over tool expertise • DevSecOps as a cultural connector • How AI will reshape management work and reduce “busywork” • The risks of unsafe data use and ungoverned “vibe coding” This episode blends internet architecture, leadership philosophy, and the future of engineering work — offering a grounded look at how modern teams can navigate complexity and build more resilient systems. Episode Timestamps  (00:00) Systems Thinking Wins (01:20) Show Introduction & Guest Overview (02:26) Sergey’s Origin Story (04:22) What Catchpoint Actually Measures (06:49) Why Networking Still Matters (08:58) Global “Secret Shopper” Vantage Points (10:12) DNS: The Internet’s Hidden Bottleneck (11:56) What Great Engineering Managers Do (15:07) Hiring for Systems Thinking (18:39) Blameless Culture & Continuous Learning (20:12) How AI Is Reshaping Management Work (21:57) Engineer Track vs Manager Track (24:07) Systems Thinking as a Leadership Skill (26:16) Should You Become a Manager? (28:08) Career Ladders, Coaching & Growth (29:57) Internet Dependencies & Modern Outages (31:55) DevSecOps as a Cultural Shift (35:52) Security Tools, Incentives & Reality (38:33) AI Data Leaks & Safe Controls (40:10) “Vibe Coding” and Safe Sandboxes (42:47) Final Systems Thinking Takeaways About the Guest: Sergey Katsev is the VP of Engineering at Catchpoint, a leader in internet performance monitoring and observability. With deep expertise in distributed systems, DNS performance, network dependencies, and engineering leadership, Sergey helps organizations understand how real users experience the modern web. He is a champion of systems thinking, blameless postmortems, DevSecOps culture, and hiring engineers who value curiosity and accountability over tool‑specific experience. Links & Resources: Sergey Katsev — LinkedIn Per Krogslund — LinkedInCatchpoint Learn more about Docker Learn more about Ship Happens Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

    44 min

  8. 3 Mar

    AI Coding vs Enterprise Reality — with Arun Gupta

    AI coding tools are shifting how developers work — but enterprise engineering still demands rigor. In this episode of Ship Happens, Arun Gupta joins us to talk about the true state of open source sustainability, the evolving role of OSPOs, and why AI‑generated code still requires human accountability. Arun brings 25 years of experience across Sun Microsystems, Amazon, Apple, Intel, the Linux Foundation, and CNCF, offering a uniquely balanced and grounded perspective on where engineering culture is headed. Arun explains why open source maintainers are burning out, how corporate motivations have changed, and why he believes structured investment in open source program offices is more important than ever. He also shares JetBrains' approach to integrating AI coding agents with IDE intelligence — and why guardrails matter in an era when software is written faster than it can be reviewed. In a candid and deeply human moment, Arun opens up about being laid off from Intel, navigating the grief, and rebuilding through daily routines, aggressive networking, and applying to 60+ companies. His reset led him to JetBrains, where he now leads DevEx initiatives in the age of AI. Whether you’re building developer tools, sustaining open source, or navigating your own career inflection point, this episode offers clarity, honesty, and actionable insight. Guest Bio Arun Gupta is the VP of Developer Experience at JetBrains and a globally recognized expert in open source sustainability, developer productivity, and platform strategy. With leadership roles at Sun Microsystems, Amazon, Apple, and Intel — as well as governance positions at the Linux Foundation and CNCF — Arun has shaped some of the industry’s most influential developer ecosystems. An early Docker Captain and longtime open source advocate, Arun is now focused on integrating AI coding agents into JetBrains IDEs while championing responsible AI, maintainers’ well‑being, and enterprise‑grade software delivery. Arun Gupta has two children, even though it can sometime feel like five.  Key Topics Discussed The gap between AI “vibe coding” and enterprise software The real state of open source sustainability Why OSPOs remain critical in large organizations How corporate incentives around open source have shifted AI coding agents: where they shine and where they break Developer experience in the AI era Accountability and guardrails for AI‑generated code Arun’s story of being laid off from Intel Rebuilding through routine, networking, and experimentation The path to JetBrains and the future of AI‑assisted IDEs Episode Timestamps (Optimized for Players) (00:00) Open Source Reality Check (00:30) Meet Arun Gupta (01:47) Life Outside Engineering (03:27) From Java to Big Tech (08:27) Why Open Source Wins (10:22) How Company Motives Shift (13:36) Do We Still Need OSPOs? (16:00) AI Coding vs Enterprise Needs (19:46) Intel Layoffs & Reset (21:21) Acceptance & Self‑Compassion (24:29) The Two‑Month Reset Routine (25:16) Building With AI Coding Tools (25:57) “Action Absorbs Anxiety” (27:07) The Modern Hiring Reality (28:09) Networking vs ATS (28:52) Applying to 60 Companies (30:35) Knowing Your Value (31:40) Interviewing: Rust to Rhythm (33:10) Automating the Job Hunt (34:30) Hungry, Humble, Learning (36:56) DevEx in the AI Era (38:12) Responsibility for AI Code (39:28) Diamond‑Shaped Careers (42:00) Guardrails for Coding Agents (43:01) Inside JetBrains DevEx (47:23) AI Guardrails in Practice (49:04) Wrap‑Up & Future Repo Links & Resources • Arun Gupta — LinkedIn • Docker Captain Program • Learn more about Docker • Learn more about JetBrains Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

    50 min

  9. 17 Feb

    Squads, Standards, and Scale: Pia Nilsson on Engineering at Spotify

    The Spotify Model has become one of the most referenced—and misunderstood—frameworks in modern software development. In this episode of Ship Happens, we sit down with Pia Nilsson of Spotify to explore what the model really means in practice. Beyond the buzzwords, Pia explains how Spotify balances creative autonomy with clear standards, cross-team synchronization, and operational accountability. The conversation dives into Spotify’s platform engineering evolution, including how internal tooling like Backstage helps reduce toil and improve developer effectiveness. Pia also shares how standardization, context-sharing, and intentional time for innovation—like hack weeks—have shaped engineering productivity across the organization. We discuss: How the Spotify Model has matured over timeWhy removing friction is central to developer experienceThe role of standards in enabling—not restricting—autonomyOpen-sourcing Backstage and what it means for the broader ecosystemHow AI is influencing engineering workflows today—and what’s nextWhether you're leading engineering teams, building internal platforms, or scaling organizational structures, this episode offers a grounded look at how squads, standards, and scale coexist in a real-world, high-performing tech organization. Hit play to go inside Spotify’s engineering culture. Guest BioPia Nilsson is a leader at Spotify focused on engineering culture, platform evolution, and developer effectiveness. She has played a key role in shaping how Spotify balances autonomous squads with shared standards, enabling teams to innovate quickly while maintaining alignment across a global organization. Key Topics DiscussedThe philosophy and evolution of the Spotify ModelBalancing autonomy with accountability at scaleSynchronization challenges across distributed engineering teamsPlatform engineering and the role of internal toolingHow Backstage improves developer productivityThe importance of reducing toil and increasing developer focusHack weeks and fostering structured innovationThe journey of open-sourcing BackstageAI adoption within engineering workflowsThe future of developer experience at SpotifyEpisode Timestamps(00:00) The hidden cost of “special stack” productivity — and why agents need order (00:39) Welcome and meet Pia Nilsson (Spotify Platform DevX & Backstage) (01:30) The real Spotify Model: autonomy for innovation (03:48) Where the model breaks at scale (04:53) Alignment at scale: OKRs, company bets, and synchronization (08:12) Translating DevX into leadership KPIs (10:20) The 2017 wake-up call: 60+ day onboarding (11:19) Backstage and golden paths: reducing fragmentation (13:48) Removing toil: fleet management and automation (15:36) Creating space for innovation and hack weeks (18:51) AI at Spotify: trials, metrics, and real productivity gains (20:52) Can you copy Spotify’s culture? Core principles explained (24:20) Backstage deep dive: onboarding and portal UX (25:37) Backstage as a single pane of glass (26:46) Beyond the web UI: IDE, CLI, Slack, and shared context (29:00) Will AI replace rich UIs? Trust and visibility challenges (32:26) Why Spotify open-sourced Backstage (34:51) From open source to commercial product (36:01) Product prioritization: internal vs. external customers (39:29) AI-ready engineering: standards, skills, and orchestration (43:28) New bottlenecks: PR review, security, and testing (47:53) What’s next: AI-powered fleet management and wrap-up Links & Resources:Per Krogslund’s LinkedInPia Nilsson’s LinkedInLearn more about SpotifyLearn more about Docker Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

    53 min

  10. 4 Feb

    Playing in the AI Sandbox: How E2B Is Powering the Future of AI Agents

    Ever wondered how developers safely build and scale AI agents?  From early coding experiments in Prague to launching a secure, scalable environment for AI code execution, Vasek Mlejnsky, founder of E2B shares the story behind one of the most exciting tools in AI infrastructure today. Discover why building in public, open-source models, and a focus on developer experience are transforming the way AI applications are built. He also talks about fundraising, navigating the AI boom, and the strategic move to San Francisco that helped E2B grow. Whether you’re a developer, founder, or just curious about the future of AI, this episode is packed with insights, practical advice, and a peek into the sandbox where the future of AI agents is being built. Hit play and join us as we explore the tools, strategies, and vision shaping the next generation of AI! Key Topics DiscussedHow E2B’s AI sandbox works and why developers need itThe origin story of building sandbox infrastructure for AI agentsEarly technical challenges and the importance of safe execution environmentsThe role of open source in scaling developer trust and adoptionBuilding in public as a strategic advantageFundraising lessons from early-stage AI startupsMoving from Europe to the U.S. to tap into networks and opportunitiesNavigating the AI boom and staying ahead of rapid changesWhy developer experience matters more than everVasek’s long-term vision for E2B as the foundational layer for agent-based appsEpisode Timestamps00:00 Introduction to Ship Happens Podcast 00:27 Meet Baek Linsky: Founder of E2B 01:09 The Journey to Founding E2B 02:17 Early Projects and Developer Tools 03:22 The Birth of E2B 04:17 Building a Community and Open Source 08:28 E2B's Technical Details and Vision 17:56 Fundraising and the Path to Success 22:15 Inspiration from Silicon Valley 23:34 The Decision to Move to San Francisco 24:44 Openness and Transparency in Business 26:14 Navigating the AI Boom 29:35 The Importance of Startups in Innovation 32:30 Future of Software Development with AI 38:30 The Role of Open Source Models 41:53 Conclusion and Future Plans   Guest BioVasek Mlejnsky is the founder of E2B, a platform offering secure, scalable sandboxes designed for AI code execution and agent development. With a background in developer tooling and open-source contributions, Vasek is shaping how developers safely build and deploy next-generation AI applications. Links & Resources:Per Krogslund’s LinkedInVasek Mlejnsky’s LinkedInLearn more about E2BLearn more about Docker Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

    44 min
  11. Introducing Ship Happens

    20 Jan

    Introducing Ship Happens

    I think it's a very interesting time to be in tech. Every day there's something new, a new revolution. As developers, we all of a sudden have advanced AI tooling in our hands. It's just a docker pull commander way. It's incredible. But how do we as developers, keep shipping great software when every day there's something new that apparently changes everything. You ask people who have already done it, and that's what this podcast is all about. I'll sit down with startup founders, technologists. It's open source maintainers, and so many more to talk about how they learn, make decisions, and how they essentially keep shipping great software. It's about tech, it's about ai, and of course, containers, but also about people. The podcast is called Ship Happens. It's hosted by me, Per Krogslund and powered by Docker. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

    1 min

Trailer

About

This is where serious devs level up. Each week, top engineers break down how they’re pushing productivity, locking down security, and building AI-first workflows—all in the cloud. It’s the real-world insight you won’t get from a blog post—straight from the people shipping at scale. We’re bringing you sharp minds, smart code, and battle-tested tactics. Hit play. Then out-build everyone else.

Information

  • Creator
    Docker
  • Years Active
    2k
  • Episodes
    11
  • Rating
    Clean
  • Copyright
    © 2026 Docker
  • Show Website
    Ship Happens