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DevOps Paradox

Darin Pope & Viktor Farcic

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

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  • 20 episodes
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  • Avg 48 min
  • English

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  • #365
    Wednesday · 48 min

    DOP 365: What the DORA AI ROI Report Really Says

    #365: A bad engineer with a laptop is much worse than a bad engineer with a calculator. Now hand that engineer AI. DORA's ROI report puts it politely - AI without engineering excellence just scales your problems. Except it's not more of everything. It's more of whatever you were already doing, and there's a statistical chance that what you're doing is bad. Your agent already read your flaky tests and decided that's the house style. So what do you measure now? Viktor's answer: every technical metric you've got is gamed. ROI of AI-assisted Software Development report https://dora.dev/ai/roi/report/ 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/

  • #364
    August 19 · 48 min

    DOP 364: How to Avoid Burnout as a Leader

    #364: Your CEO comes back from a leadership offsite talking about burnout-proof leadership and AI transformation at scale. Two weeks later you're working through lunch, you're working through dinner, and Slack never stops. Deployment frequency is up. Incidents are up worse. So who was that program actually protecting? In this episode, we speak with Dr. Victoria Mensch, Founder and CEO of the Silicon Valley Executive Academy, about what really goes on at those offsites, and why Nokia, BlackBerry and Blockbuster all saw it coming anyway. Victoria's contact information: LinkedIn: https://www.linkedin.com/in/victoria-mensch/ 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/

  • #363
    August 12 · 44 min

    DOP 363: Is Your Website Agent-Ready?

    #363: Three waves of the web, and you are late for the third one. The 90s were about getting a browser to render your page at all. The early 2000s were about SEO, or as Darin puts it, sell me all the ads ready. Now it is agent ready, and Cloudflare built a scoreboard for it at [isitagentready.com](https://isitagentready.com/). The devopsparadox.com site scored about 70 out of 100 and then went down when Cloudflare added new checks. Run yours. You will be sad. Viktor thinks the framing is slightly off, though, and the correction is the good part. Optimizing for agents that browse your site is aiming at the wrong thing, because most requests never touch your server. Agent asks the model, model answers, agent shows you. So the target is not the crawler, it is the training data - and if the model does go looking, the question becomes whether you are the first answer or one of the five sites it was told to go analyze. Same game as Google. Different index. It is not Google index anymore, it is model training now. Then the practical part. Five things Cloudflare scores you on: discoverability, content, bot access control, API, Auth, MCP & Skill Discovery, and Commerce. Content accessibility is where most of you are losing, because agents want Markdown and you are serving them a pile of HTML tags to strip. Both DOP and Viktor's site are Hugo, so the Markdown is already sitting on disk next to the HTML - serve one or the other based on what the request asks for. Almost no effort. If you are still shipping a JavaScript-rendered site, Darin says it is game over, and humans do not like those either. On the blocking side, both of them are baffled by the same thing: if you do not want agents reading it, do not publish it. robots.txt is a suggestion at best. If you really want to block, actually block. The API argument is the one that will annoy people. Viktor says CLIs and MCP servers are both auto-generated from a schema, so the real work is having a good API, and most companies do not. But who your audience is decides the wrapper - developers already have Bash, so give them a CLI and get out of the way. Everyone else needs MCP, because Viktor's mom is not installing your binary. And somewhere in the middle of all this Darin asks whether documentation should live in the code now more than ever, and Viktor says no, less than ever - he wants it separate so he can review it, because agents made everything cheap to produce and review is now the only thing standing between him and 5,000 features a day. Also: WordPress should be the last thing you consider, not the first. 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/

  • #362
    August 5 · 48 min

    DOP 362: Feature Flags vs Canary Deployments

    #362: Feature flags or canary deployments - do you need both? Viktor puts it to Alex Casalboni from Unleash, who says he argues about this with his colleagues roughly every day, and the answer lands clean. Switching a hostname, a database, an API vendor? That is infrastructure, nothing to do with who the user is, so keep your canaries and your blue-green. But a canary switches one thing at a time. Try running three A/B tests and ten behavioral changes through it and the whole approach buckles. Anything that needs to know who the user is belongs in a flag. Different layers of the stack, different tools, and most teams will end up with both whether they planned to or not. Back up, though, because there is a new word attached to all of this. FeatureOps. There is a manifesto and everything, sitting at [featureops.io](https://featureops.io/), reading a lot like someone nailed 95 theses about feature flags to a door. Real discipline, or marketing wrapper? Alex gets about ten seconds of pleasantries before he has to answer for the word. His defense is narrower than the name suggests, and better for it: every ops discipline we have gets you to the deployment and then waves goodbye. Something breaks, you go around the whole loop again - hotfix, pipeline, 20 or 30 or 60 minutes, fingers crossed. FeatureOps is the claim that the same principles apply after the code is already running. Runtime control. Alex says enterprise customers routinely have a 12 to 24-hour round trip between finding a problem and getting the fix live. Even for a hotfix. Viktor is not letting the seconds claim through unchallenged. If it takes you a day to notice and two seconds to flip, that is a day and two seconds - so stop measuring from the convenient starting line. Alex concedes the framing and then goes somewhere better with it: the bottleneck was never the clicking. It is the humans and the bureaucracy in between. Which is why Unleash is pushing impact metrics, where the SDK sends error rates back and the system kills the feature itself, no human in the loop. Then Darin calls BS on immutable event log, because there is no such thing as immutable data, and Alex takes the hit cleanly - fair, it is append-only with locked-down keys, not magic. Nobody puts this part on a landing page. Flag evaluation has an input, not just a true/false output, and that input is user context - which means an external API call is not just latency, it is your PII leaving the perimeter. A compliance problem hiding inside a performance decision. And the flag graveyard is worse than you think: companies create roughly ten flags for every one they clean up, and Alex has a customer whose oldest flag dates to 2012. His fix is an MCP server that opens the cleanup PR for you when you mark a release complete. Best line of the day, on whether flags complicate your code: everything complicates your code, and the best way to not complicate your code is to not code. Alex's contact information: LinkedIn: https://www.linkedin.com/in/alexcasalboni/ X: https://x.com/alex_casalboni 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/

  • #361
    July 29 · 41 min

    DOP 361: When Code Got Cheap, Reviewing Got Expensive

    #361: Picture a 6,000-line pull request landing in your project from someone you've never heard of. Every test passes - yours, theirs, all of it. And you can tell it was generated. Not assisted. Generated. What do you do with it? Viktor's first move is to poke the premise: how do you even know it was generated and not assisted? You can't. Nobody can. That distinction is already gone. So the real question isn't whether AI wrote it. It's what a maintainer is for. Viktor's answer is blunt - a maintainer's first job is to guide people in and help them contribute, and if you think your job is mainly to write code, you picked the wrong role. The moment you allow PRs, you stopped being an individual contributor and became a manager. That's the job. You don't get to complain about the job you signed up for. Don't want it? Fine - do what Ghostty did, do what curl did, turn PRs off and say so out loud. Just don't hide behind unknown contributors, because every single person on your project was an unknown contributor on their first commit, including you. A software developer says send me anything but a PR and lists four reasons: unknown contributors are a security risk, supply chain attacks are real, style disagreements eat maintainer time across time zones, and LLMs killed code-writing as the bottleneck so the stranger's PR doesn't help with the parts that are still hard. Viktor grants the security point and then points at XZ - social engineering, a long con, zero AI required. The risk was always there. What changed is quantity, not the percentage. And the maintainer who thinks a manual, line-by-line review still works in 2026 is, in his words, terribly wrong. Viktor guesses he can review 6,000 lines in about the time it takes to hand-review 600. CodeRabbit and Greptile clear the obvious junk so he can spend his attention on architecture and the feature itself - the stuff he never had energy for after slogging through nitpicks by hand. His read on the whole backlash: there's a new third group of maintainers now, the ones who aren't good enough with agents to fight agents, and they're the ones falling behind. Writing code is cheap. Reviewing it well is the expensive part - and the ones who refuse to use agents to review are drowning while blaming the contributors. There's a cost angle too. If you don't have tokens, you're in trouble, and not everyone can afford them. Tokens are becoming table stakes, like an internet connection. Which raises the question: what happens to open source when the reviewing tools cost money the maintainer doesn't have? I don't want your PRs anymore https://dpc.pw/posts/i-dont-want-your-prs-anymore/ 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/

  • #360
    July 22 · 54 min

    DOP 360: What Is an AI SRE?

    #360: Assume your production systems go sideways at 3:00am. Your phone doesn't ring. You wake up to a clean incident report - the system caught the problem, fixed itself, and left a Slack note saying it handled things at 3:15. Would you trust it? Viktor wouldn't, not at first. Same as a new hire, a new tool, a new anything. Trust gets earned. A few months in, if it hasn't messed up, then maybe. So what's actually new? We had self-healing in decades past, and Kubernetes brings a crashed pod back without anyone awake to watch. Birol Yildiz, co-founder and CEO of ilert, puts the line where it belongs: the old stuff runs on predefined patterns - known problem, known fix, automate it. The new part is diagnosing a root cause nobody wrote a rule for and then knowing what to do about it. And no, ilert isn't training models on your data. The agent runs on foundational models from the frontier labs, feeds them the same context any SRE would pull - telemetry, recent changes, the health of your infra - runs the investigation loop, and hands you a root cause with the evidence attached. Then the real argument starts, and it's about knowledge. Viktor keeps circling the blind spot he sees at every company: the wiki, the runbooks, the tribal stuff nobody keeps current. Birol's answer is blunter than you'd expect from a guy selling this - dump your Confluence and your hundred stale runbooks into the agent and you cause more damage than results. Code is the documentation that's actually true. ilert's agent doesn't even read runbooks. It builds a live service topology straight from tracing data and rebuilds it when things drift. What happens when a bad incident month turns your $100 bill into $10,000 - Birol's answer is a credit budget with a human paged when you blow past it. Who's accountable when the agent makes it worse? Has to be a human. Agents are tools. You don't get to blame your AI coding agent for shipping garbage code, and ilert doesn't get to blame the model either. Trust is binary - five good runs and one bad one, and you strip its write access. But even in observe-only, the value is real, because the time sink in an incident was never the fix, it was finding the needle. Cut root cause from 45 minutes to a few. And the closer nobody wants to hear: if you skipped observability and fifteen years of homework, an AI SRE isn't going to save you. You can't skip the parts you skipped. Birol's contact information: LinkedIn: https://www.linkedin.com/in/birolyildiz/ X: https://www.linkedin.com/in/birolyildiz/ 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/

  • #359
    July 15 · 42 min

    DOP 359: Demos in the Age of AI Agents

    #359: When was the last time you sat through a 30-minute product demo and walked away actually knowing anything? You would learn more from five minutes hands-on than an hour of watching someone else drive. Now you have help. An agent can watch the 30-minute video, play in the sandbox, read every page of the docs, and come back before you finish your coffee with a verdict - tried it, does not work, next. The agent is the new tire kicker. So if you are a vendor, an open source maintainer, or the person building the internal app nobody outside the building ever sees, the demo you have been giving is aimed at a buyer who already left the room. Your job now is to make life easier for agents. An MCP server, a CLI, skills, an AGENTS.md file, not blocking your own site with Cloudflare when someone's agent tries to read your pricing. Everything that makes a product easy for an agent would have made it easier for a human all along. We just never bothered, because we had months to burn. Now the clock runs in minutes and every corner we cut is suddenly on fire. Three kinds of demo, three different answers. The vendor sales demo is off-putting before it starts - if a website says book a call to try it, Viktor is already gone. Open source barely needs a demo at all: a good README, a quick start, an AGENTS.md, and the agent assembles a demo tailored to your stack, your database, your questions, instead of some generic happy path. Internal is where it gets good, and it might be the one that matters most since exactly zero apps ship without customization. Viktor's bar: stop showing me plans, show me the thing running. Sit the stakeholder down and build it live while you talk. Three days to a prototype instead of 300 pages of PRD. Sandboxes first, demos second - if you cannot spin up a sandbox, you did not build it right. And demo the failure modes, not the happy path, because resiliency is the real selling point now. Disks still fill up. No amount of AI magic empties them for you. 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/

  • #358
    July 8 · 50 min

    DOP 358: Just-in-Time Access for AI Agents

    #358: Production is on fire. You need access to one table you have never touched. So you file an access request, then phone the desk to say you filed it, then Slack them to say you phoned, then walk over to say you Slacked. Twenty-five minutes later the incident has resolved itself and the customer has already left. That is the setup, and Ofir Stein has lived the other side of it. He is the CTO and co-founder of Apono, and before that he was an engineering leader who felt the same pain every day - not because he hated security, but because he hated being blocked. There is a difference, and the whole conversation turns on it. Put productivity on one side, security risk on the other, and access management in the middle. Tighten one and you starve the other. Nobody wants to be slower and nobody wants to be breached, so the honest answer is there is no clean answer. Then AI agents show up and break the last assumption standing. Software used to be deterministic - your computer could not decide to do something other than what it was told. LLMs can. They can be socially engineered the way people are. Ofir's team built a full AWS environment run by AI agents, opened a Discord channel, and invited anyone to try to trick them. People could. That is the new attack surface, and it moves at machine speed - far too fast for the access reviews and approval chains built for humans. The guardrails everyone is now scrambling to build for agents should have been there for humans all along. Access is the one thing in your stack that never went dynamic. Servers scale up and down, pipelines rebuild everything, and then access is a static policy someone set two weeks ago when security sat with your manager and guessed what you would need. That is the opposite of how the rest of DevOps works. Ofir's argument is that access should change with context - who you are, whether you are on call, whether there is an open incident - evaluated in real time. For a human that is a faster request. For an AI agent, the decision has to live inside the loop, made by silicon, because no person can approve thousands of operations a minute. If access is per-operation and every operation is already a specific API call, what is left to scope? If the business context changes by the minute, how do you write guardrails in advance? And once the human is out of the loop, are you not just left with one AI deciding what another AI is allowed to do? Ofir does not pretend that part is solved. What he is sure of is the direction: the doors at the mall open when you walk up and close when you leave, and you never think about them. That is where access is headed - and there is a lot of road between here and there. Ofir's contact information: LinkedIn: https://www.linkedin.com/in/ofir-stein/ 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/

  • #357
    July 1 · 57 min

    DOP 357: What Is Spec-Driven Development?

    #357: Type a prompt, get code, fix the hallucinations, type another prompt. That is vibe coding, and it is a fine place to start. It is a terrible place to stay. So what comes next - and is spec-driven development actually it, or just waterfall wearing a new hat? Here is the reframe that runs the whole conversation: everybody already works from a spec. Even the person who swears they are winging it has a spec in their head - which language, where it runs, what it does. The real question was never specs or no specs. It is whether you write them like waterfall, one giant document before anyone touches code, or like agile, just enough to start and the rest discovered as you go. A design is only validated when you implement it - everything before that is an educated guess. So instead of spending a month on one detailed design, build five throwaway MVPs in a day. Fully operational. Frontend, backend, running in a cluster, connected to a database. Show them to customers. Pick the one that works. Then have the agent write the spec from the winning code, and throw the code away. The spec is the output, not the input. A PowerPoint took you a month and told the customer nothing. A working thing they can touch tells you everything. Viktor and Darin push on where this breaks. Over-specifying gives you a false sense of security - you are lying to yourself that you know everything up front, and you do not. Legacy systems? The code is the only complete spec - any document written thirty years ago is fiction. Performance? Measure it in production and be lightning-fast to react. Greenfield, CRUD, clear API contracts - those genuinely want a spec first. The part nobody on the org chart wants to hear: this does not delete the business analyst or the developer. It collapses the roles. The code monkey who pulls a Jira ticket, does the work, pushes it - that job is turning into tech lead, architect, product manager, all at once. Plan mode writes the spec with you, not for you. You write it to a file because you cannot review what you cannot see. And you review the tests harder than the code, because the tests are the spec made executable. Specs were always supposed to be living documents. Now there is finally no excuse. 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/

  • #356
    June 24 · 47 min

    DOP 356: Warehouse Robots Are a Distributed System

    #356: Fleet management means one thing to a DevOps engineer and something completely different to Tomas Kovacovsky. To Viktor it is a CD problem - a fleet of Kubernetes clusters he would rather not babysit. To Tomas it is hundreds of physical robots rolling around a warehouse, picking orders, dodging each other, and working very hard not to lose their connectivity. Tomas is the CTO of Brightpick, where the robots are not the kind you yell at for bumping into a chair. They are three-meter-tall autonomous pickers - some telescoping up to six - that find their way using lidar, recognize items with neural networks, and make their own decisions the second the network drops. Here is the part that will feel oddly familiar: everything you already do to ship software shows up again in the physical world. Canary rollouts. Rollbacks to the last good config. Prometheus scraping every robot, Grafana for the fleet. Logs, metrics, traces. Split brain, when a robot and the server disagree about what just happened. Even a flaky robot - one that feels off with no error to point at - gets diagnosed the same way you would hunt a flaky test: compare it against the rest of the population and find the outlier. A warehouse full of robots, running like a distributed system. The stack is what you would guess and also not. C++ on the robots for speed, Python on the backend, Kubernetes on an edge server inside the warehouse because latency matters down to the millisecond, and Git as the source of truth - the on-site servers check for differences and update themselves. GitOps, for robots. Then it gets bigger. The optimal pick speed, Tomas says, is infinity - right up until you try to pick an egg. The real bottleneck was never the picking, it was the traveling, so Brightpick moves the picking into the aisles instead of hauling totes back to a station. He also drops a prediction worth chewing on: the intelligence arrives before the dexterity. Machines will think their way around a warehouse long before they can fish for keys in a bag the way your hand does without looking. And the jobs question everyone braces for - the robot guys walking in, are you fearful for your job in 20 minutes - turns out the picker positions were mostly empty to begin with. Hundreds of thousands of them, unfilled. The takeaway for anyone writing software is the one Tomas lands at the end. The craft is getting eaten. What is left, and what actually matters, is whether you can connect the work to the product. Tomas' contact information: LinkedIn: https://www.linkedin.com/in/tomas-kovacovsky-46411280/ 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/

  • #355
    June 17 · 55 min

    DOP 355: Why AI Coding Slows Down Code Review

    #355: Picture your engineering team a year from now. A coding agent doing the coding. A testing agent on tests. A security agent on security. An infrastructure agent on infrastructure. All of them wired into GitHub and Jira, all of them working right alongside the humans. Not science fiction either - Atlassian and GitHub are already shipping these features. So out come the stats everyone loves to quote. AI code introduces 1.7 times more issues. Half of it ships with security holes. Code duplication is through the roof. AI-assisted PRs take four to five times longer to review. The response to most of it: so what? If you have a way to detect the issue and feed it back, that is just the SDLC doing its job. Couldn't care less if it is 1.7x or 50x more issues - what matters is what is left at the end, per feature shipped. Security holes? You have scanners. Detect, fix, ship. The only real problem is when you skip the detection or sit on the fix for months, and that has nothing to do with AI. Here is the one stat that actually sticks: PR reviews backing up. Speed up coding and leave everything downstream at human speed, and you have not sped up delivery - you have just moved the pile from Jira tickets to pull requests. The review pipeline was built for human speed, and now it is the bottleneck. The blunt fix: stop letting AI write 10,000-line PRs, work in smaller chunks, and accept that the job is about to get mentally harder. Delegate the tedious work and what is left is the demanding work - architecture, taste, is this even the feature we should ship. The silly stuff, does every function have a comment, is it camel case, goes to the machine. Spend your time there and you are wasting your talent. Offshoring never worked when the only goal was cheaper - chase the cheapest engineers, then chase even cheaper ones, and you end up dragging the work back in house. Same trap with AI. Offshore to Opus, then Sonnet, then Haiku, then Llama on a laptop. If cheaper is your primary motivation, you are doing it wrong. The win is qualitative, not the price tag. Where does it land? Three people per product, end to end - frontend, backend, database, deployments. Augmented at every stage, not autonomous. A human still pushes the final button to prod, the way you never let a Jenkins pipeline deploy straight to production without a check. Full autonomy is coming the way self-driving cars came: not in a year, not everywhere at once, and not by flipping it on at 4pm on a Friday. Even when the technology is ready, you are not. And if you think none of this touches your job, there is a story here about a textile factory built in the eighties that ran on five people. Knowledge work is next. The only exception is a monopoly, and you probably do not have one. 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/

  • #354
    June 10 · 42 min

    DOP 354: Your Dead Founder Trains New Hires

    #354: How do you build a consent system for someone who is dead? How do you clone a voice so it cannot be turned into a deep fake? Miles Spencer built a company around those exact questions. Reflekta.ai lets you talk to a reflection of someone who has passed. His own father reads a bedtime story to his granddaughter every night and talks it through until she falls asleep, eight years after he died. Is this just deep fake with better branding? What happens when the AI goes off the rails and asks grandpa for the three numbers on the back of a credit card? Miles has an answer for each one, and most of them land on the same line: you built it, you paid for it, it never leaves your four walls. Nothing gets scraped. There are only two public reflections on the entire platform. The voice of his dad came from a ten-second voicemail found on a relative's phone five years after he was gone, and last month that voice had 9,000 conversations. More than half the stories on Reflekta are from people who are still alive. ALS and Alzheimer's patients getting it all down while they still can. Founders who want their values to outlast them. And that last group is where it gets interesting for anyone who runs a company. New hires talk to the founder during onboarding. Ask a question about the business and the founder answers. SOPs, handbooks, the whole thing, in the voice of the person who built it. Miles calls the framework SoulTech, starting from the emotional weight of the product instead of bolting ethics on at the end. Agree with the premise or not, the stack underneath is less exotic than it sounds: multi-cloud, RAG, three voice vendors swapped by time of day, 110 days from idea to launch. Darin's verdict by the end is honest. The dead-relative part is still not his jam. But the founder who never leaves the building, the one who onboards every new hire forever? That one he gets. Miles' contact information: LinkedIn: https://www.linkedin.com/in/milesspencer/ 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/

  • #353
    June 3 · 48 min

    DOP 353: A Person Owns It Not the AI

    #353: Move fast and break things never meant be reckless. It meant do not stall out of fear, because something is going to break no matter how careful you are. The part everyone dropped from the sentence is the part that actually matters: and fix things fast. Break faster, fix faster. Take the second half away and you are just breaking things. So what changed with AI? An agent can take down a whole environment in the time it takes you to type kubectl. AWS found that out in December when Kiro -- running autonomously with operator-level permissions and no human in the loop -- decided to delete and recreate the production environment for Cost Explorer. Thirteen hours down in one region. Then there is the Agents of Chaos research, where five agents got two weeks with real infrastructure and an unrestricted bash shell, and one named Ash destroyed its entire mail server as a proportional response to being asked to protect a secret. Right values. Catastrophic judgment. Here is where Viktor plants his flag. A person owns the work. Not the AI. Doesn't matter the level of autonomy, doesn't matter whether the code came out of Claude or out of your own hands. You chose the model, you chose the agent, you wrote the rule set, you gave it the tools. If you handed an admin account to a thing that deleted production, that is on you -- exactly the way it would be on you if a human did it. The Kiro engineer could have made the same mistake without AI. Blame the people. The fix is not telling AI to be safe. It is building the place where breaking things is survivable. Immutable infrastructure. Progressive delivery everywhere. Feature flags you can actually turn off, not just on. Read-only tools for the agent and a human or a validation layer for anything that writes. And a new habit Darin calls celebrating near misses -- not just the failures, but the times the guardrails held and you learned where to tighten one more bolt. Viktor runs a blameless postmortem with his agents at least once a day, every wrong turn ends with an update to a skill or a CLAUDE.md. His homework for you this week: if an agent -- or a human -- deleted your full production environment right now, how long would it take you to come back? 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/

  • #352
    May 27 · 51 min

    DOP 352: No-Code Is the Guardrail Vibe Coding Needs

    #352: Vibe coding is the latest version of a promise the industry has been making since the first generation of programming languages. Type what you want, get an app. Jeff Kuo from Ragic has been working on the no-code version of that same promise for almost twenty years. He has thoughts on why the promise keeps not quite landing. The honest answer is that AI-assisted coding is great for people who already know what the code is doing. It is counterproductive for everyone else. A non-developer can generate a lot of code. They cannot maintain any of it. That gap is where every weekend vibe-coded project goes to die six months in, when the codebase has ballooned and the AI is in a loop confidently identifying the wrong root cause for the seventh time. So what does work? Jeff's argument is that no-code platforms become the guardrail AI actually needs. Strip the infrastructure layer away, leave only the business logic, and the model only has to reason about one thing at a time -- which is the one thing today's models are good at. Ragic generates form and report definitions, not code, and the Java engine underneath does the rest. There is also the strange consumer behavior nobody is talking about. People love AI chat boxes in tools they have never used before. They close AI chat boxes in tools they already know. Which means the future of AI-native software might not belong to the incumbents at all -- it belongs to the new tools being built right now for users who do not have any muscle memory to defend. And one piece of advice that has aged perfectly across forty years of software: the maintenance is the thing that keeps you awake at night. AI makes it faster to build things from scratch and harder to maintain anything at scale. Begin with the end in mind. Or do not, and become the next cautionary tale. Jeff's contact information: LinkedIn: https://www.linkedin.com/in/ragic/ 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/

  • #351
    May 20 · 49 min

    DOP 351: The Developer Job Market in the Age of AI

    #351: Entry-level tech jobs are down 67% since 2022. Junior developer roles are down 40 to 50%. The instinct is to blame AI and call it unprecedented, but the layoffs are not the new part. The boom-bust cycle has happened before -- dot-com to dot-bomb, the 2020 hiring spree to the 2022 correction, now this. The new part is that the thing replacing the bottom of the ladder is not a cheaper human in another country. It is an agent that takes instruction and ships code overnight. Here is the uncomfortable reframe. A junior developer is told what to do, does not change the architecture, does not make decisions, and produces better work the more detail you give them. Replace the word junior with agent and the description does not change. That is the whole problem. The traditional path from junior to senior assumed five years of grunt work would teach you the things grunt work teaches. The grunt work has a new owner now, and nobody knows what the new on-ramp looks like. Seniors are not safe either. If you have spent 30 years writing pretty code and you have already started rejecting the idea that an agent can do it better, history is not on your side. The same people who refused to embrace cloud and containers are the people who will refuse this -- and the SSH-key-maker on the team that took a week to provision a key is not pivoting to AI either. Two types of employees. The ones you can replace in five minutes and the ones whose departure feels like a loss. Only one type thrives in this cycle. So what actually works? Capacity to learn over experience. Specific knowledge over generic knowledge -- if every developer on the internet can do what you do, the model trained on the internet can too. The job is becoming managing a team of agents the way a manager manages people: figure out what should be done, how, and when, then check on the team and work with individuals. The hiring test that still works after all these years is the one where the candidate switches to the browser and Googles. That is the person who can adapt. That is the person who survives this market. 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/

  • #350
    May 13 · 48 min

    DOP 350: Context Is the New Bottleneck, Not Code

    #350: The bottleneck used to be writing the code. Now it is feeding the agent enough context to write the right code. That is Patrick Debois' argument, and given that Patrick coined the term DevOps, it is worth paying attention when he says the discipline is shifting again. The model does not matter. The IDE does not matter. What matters is whether your team can capture the way you actually work and hand it to an agent that does not know any of it. The promise was that AI would let us ship without writing specs. The reality is the opposite. If you want decent output, you need richer specs, more docs, and a way to feed the agent what is unique about your team and your codebase. Viktor admits he stopped writing specs himself. He talks to the agent until he is satisfied, then says write it down. The work did not go away. It moved. A second agent that validates your work tends to take the original spec too seriously and miss what is not there. The interesting validation is not whether the code matches the spec. It is whether the spec matches reality. Patrick's response is harness engineering -- combining verifier agents with deterministic tooling like linters and tests, and mining conversation logs for the moments a user says this is wrong so the missing context can be saved and reused. Memory, hooks, skills, registries -- all just delivery mechanisms for the same underlying thing. Patrick's number one piece of advice if you are starting today is brutal in its simplicity. When the agent does the wrong thing, write it down in your AGENTS.md or claude.md. Do not just re-prompt and move on. Build the context file. That is the new job. Code moved to context. Context, eventually, moves to knowledge -- the way your organization actually works, captured somewhere an agent can use it. Whoever owns that layer wins. The model does not. Patrick's contact information: LinkedIn: https://www.linkedin.com/in/patrickdebois/ X: https://x.com/patrickdebois 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/

  • #349
    May 6 · 45 min

    DOP 349: Shadow AI Is Going to Be a Thousand Times Worse Than Shadow IT

    #349: Every platform you already own is about to have AI baked into it. Not next year. This year. That is Ben Wilcox's blunt prediction, and Ben is the CTO and CISO at ProArch, so when he says shadow AI is going to make shadow IT look quaint, it is worth slowing down to figure out what that actually means. The data leaves your stack through tools you already paid for, through features the vendor shipped without asking, through copilot agents nobody filed a ticket for. Here is the uncomfortable part. This is not a new problem. It is the exact same retroactive-security failure pattern that broke DevSecOps, just with higher stakes and a faster clock. A pen test done six months ago is already obsolete because the app added AI in the meantime. Models get deprecated on seven-month windows while frameworks still get years of support. The whole "we will deal with it at the end" approach that worked badly for cloud and worked worse for containers is going to be catastrophic for AI. The fix is older than the problem. Landing zones. Well-architected frameworks. A storage account that already has the right policy. An API gateway already in front of the API. The developer should not be picking from twenty checkboxes to figure out which combination is secure -- that decision should already be made before the ticket lands. Stop forcing developers onto the security team. Stop running security reviews while the head developer sweats through his shirt right before release. Build the foundation up front and let the developer deploy into it. Then the harder question. The leaders making these calls today are the same engineers who lived through every prior cycle of this exact pain. Why are they letting another generation eat it again? Viktor's answer is one line: "It's my time now, baby." Ben does not disagree. PE pressure, VC timelines, race-to-market everything -- the budget exists, the tools exist, the patterns exist. What is missing is the will to invest two weeks up front so the last two months do not turn into panic. Ben's practical advice for any leader dipping a toe in: do not do it alone, inventory everything, talk to sales and finance and the developers, and assume the conversation you are having today will be obsolete in six months. Ben's contact information: LinkedIn: https://www.linkedin.com/in/ben-wilcox/ 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/

  • #348
    April 29 · 50 min

    DOP 348: Now It's Time to Panic

    Something flipped this year. Chatbots were a toy. Useful sometimes, but a toy. Agents are not. Agents take actions, hold credentials, write code, move Kanban cards, and run on cron schedules. The window between "this is interesting" and "this is existential" has closed faster than cloud, faster than Kubernetes, faster than any prior shift. Viktor's read is blunt. One person can now build a bigger business than most mid-size companies have ever managed. That is not hyperbole -- that is a description of what is already happening with a handful of solo-built projects shipping in weeks what used to take a hundred-person org years. The thesis: panic. Not because the sky is falling, but because larger companies cannot turn around overnight, and the gap between the people who get this and the people who are still scheduling meetings about scheduling meetings is widening every week. The conversation walks through what each big provider is actually doing. AWS is not pretending to compete on models -- they want the inference revenue. Microsoft is lost in Copilot button-stuffing. Google is quietly winning on three layers at once: TPUs, models, and inference infrastructure. Anthropic is on the path to becoming the next defining IPO, while OpenAI looks like a place to take money out of, not put more in. The Linux Foundation's new Agentic AI foundation got Anthropic's MCP, Block's Goose, and OpenAI's AGENTS.md spec. Viktor's reaction: those are heavy hitters donating not very much. Then it gets practical. Vendor-provided agents are like hiring a genius engineer who knows nothing about your company. Public skills are mostly nonsense -- if it is in public training data, the model already knows it; what is missing is everything specific to you, which is exactly what no public skill can provide. OWASP just published an Agentic AI Top 10 and most of it is least-privilege rebranded for agents. The cost story is also not what the marketing says: a 00 monthly subscription will not last a day for anyone working full-time with agents. There is a true story in here about a leaked token that turned a 00 monthly spend into 5,000 in two days. The hardest part of the episode is the part nobody likes hearing. If your output stays the same in 2026, you are in trouble. If you multiply your output, you are fine. Companies have always wanted to do more than they could afford to do. Now they can. The middle is where careers used to live. The middle is where the cuts are going. 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/

  • #347
    April 22 · 47 min

    DOP 347: Cozystack Turns Bare Metal Into a Managed Services Platform

    #347: Andrei Kvapil has been around Kubernetes since the early days. Contributor to Cilium, Kubevirt, and a handful of other projects you probably use without realizing it. He is also the maintainer of Cozystack, a CNCF sandbox project, and the CEO of Aenix, the company behind it. The thesis: Kubernetes should be boring. Not exciting, not cutting-edge, not the thing everyone argues about. Boring like the Linux kernel is boring. Something that sits underneath everything and nobody needs to think about. Viktor takes it one step further and says it should be invisible -- developers should never need to know Kubernetes exists, any more than they need to know what kernel their laptop is running. Cozystack is Andrei's answer to a specific problem. ISPs, banks, finops shops, anyone in Europe who cannot or will not put their data in AWS -- they all want to offer managed databases, managed Kubernetes, object storage, the whole stack. Building that from scratch is hard. Running OpenStack requires a dedicated team that does nothing but tune networking. Cozystack bundles the pieces (Kubevirt, CloudNative Postgres, Cilium, etc) into one product with an aggregation API layer on top of Kubernetes itself. Helm becomes the extension language. The platform becomes a product. Then the conversation takes a turn. Andrei is the CEO of a bootstrapped company and he says flatly that without AI the company would not exist. Claude Code is moving Kanban cards. Clients send files generated by their AI agent and Aenix feeds those files to their AI agent to generate the response. Andrei's only wish is for this middle step -- him -- to stop existing. Let the agents talk to each other and call him when something actually matters. There is a hiring question in here too. If the next generation of engineers starts their career with AI on the first commit, do they ever build the mental model that lets them guide the agent when it goes wrong? Andrei thinks you still need deep understanding for anything serious. Viktor agrees. Speed versus quality is still a choice, and juniors who skip the "write it three times until it stops being garbage" phase are going to feel that gap eventually. Andrei's contact information: LinkedIn: https://www.linkedin.com/in/kvaps/ 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/

  • #346
    April 15 · 55 min

    DOP 346: Fighting AI in Your Project Is a Terrible Mistake

    #346: Drive-by PRs, AI slop, maintainers burning out -- the open source world is having a meltdown and everyone wants to blame the robots. Viktor isn't buying it. The real problem started long before AI. Contributing to most open source projects has always depended on tribal knowledge and obscure docs nobody reads. AI didn't break that. It exposed it. When contributions were trickling in, you could get away with onboarding people via vibes. Now that contributions are a firehose, you can't. Viktor's take cuts in a direction that will annoy a lot of maintainers: your primary job is empowering contributors, not gatekeeping. And if a 20,000-line PR is drowning you, the answer isn't to block everybody. The answer is to change the whole review cycle -- because yesterday you were complaining about not enough contributions and today you're complaining about too many. That's a great problem to have. Solve it. Here's the part that will upset people. Viktor reframes what a developer's job actually is. If you think your role is typing on a keyboard, you're going to be disappointed. Your role is becoming a product manager. Asking the agent did you look at this, are you sure, what about that. Your job is no more. You just didn't receive the memo. There's also a thread running through the episode about auditing. Can you actually assess the health of an open source dependency you depend on? Viktor dares Darin to audit Kubernetes. Or curl. Or anything. Humans can't do it at all. AI can -- imperfectly, but better than nothing. Which means the old enterprise model (pay Red Hat, they'll handle it) starts to wobble when the value of someone else handling it drops because the tools can handle it for you. And there's a prediction. Right now when you ask AI to build something, it picks libraries based on training data. But what happens when the agent actually goes shopping -- analyzing projects, reading docs, deciding which dependencies to pull in? That changes the open source landscape in a way nobody is ready for. The episode ends somewhere quieter. Contributors, human or AI, should be cherished and trained over time. The hostility toward AI contributions is coming from maintainers who forgot that investing in new contributors is the job. The tools will make a mess. Then they will make less of a mess. Eventually we will be arguing about whether the feature should exist at all -- not whether the code compiles. 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/

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