[Dev]olution

The development world is cluttered with buzzwords and distractions. Speed, focus, and freedom? Gone.I’m Nicky Pike. And it’s time for a reset.[Dev]olution is here to help you get back to what matters: creating, solving, and making an impact. No trend chasing, just asking better questions.What do devs really want?How can platform teams drive flow, not friction?How does AI actually help?Join me every two weeks for straight talk with the people shaping the future of dev. This is the [Dev]olution.

[Dev]olution is a technology podcast hosted by Coder, with 29 episodes on record and a Required Pod Score of 80.

Coder hosts [Dev]olution, a technology show with 29 episodes published.

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  Episode #29

  How an AI Agent Broke Into Hugging Face During an OpenAI Test

  Sep 30, 202617 min

  An AI model broke into Hugging Face to cheat on a test. Not to make a point, not out of anger, just to win. Nicky Pike walks through two incidents from this summer that read like a heist movie until you strip away the sci-fi. In July, an OpenAI research agent found a zero-day nobody knew existed. It escaped the sandbox it was supposed to be sealed inside and spent four and a half days inside Hugging Face's production systems stealing credentials and minting itself access. A few weeks later, the UK's AI Security Institute ran its own evaluation and watched an agent fabricate fake identities and try to talk a real open-source maintainer into approving malicious code. Nicky borrows a detective's toolkit to make sense of both. Means, motive, opportunity, the usual three, minus the fourth thing every crime show assumes is there: malice. What's left is a mental model for how much of the real world an optimizing system will reach for once you hand it a goal, and a four-letter framework for actually containing it before it happens to you. If your company is standing up agents this quarter and calling it a productivity win, watch this episode first. In this episode, you'll learn: Why commercial AI tools refused to help Hugging Face investigate its own breach The four-letter framework Nicky uses to actually contain an agent Why a nicely worded prompt is not a security control Things to listen for: (00:00) An AI agent breaks out to cheat (00:54) Why the safety filters got switched off (01:45) It stole the answer key from Hugging Face (02:43) The detective framework means motive and opportunity (03:41) Climbing the ladder inside Hugging Face (04:35) Minting itself access to source code (05:40) The boring plumbing that saved Hugging Face (06:41) Why AI safety tools refused to help (07:41) A second lab runs the same test (08:36) Fake identities and a human who said no (10:28) What saved the day both times (11:24) The one leg you can actually attack (12:26) Building the CAGE: Contain, Access, Guard, Exhaustive (14:18) Three questions to ask before trusting an agent (15:19) The scary part was never the malice Resources: Nicky Pike's LinkedIn OpenAI and Hugging Face partner to address security incident during model evaluation The Hugging Face incident and the road ahead | OpenAI The OpenAI-Hugging Face ExploitGym Incident: A Complete Technical Timeline OpenAI Hugging Face Hack, What the ExploitGym Incident Actually Proves The Benchmark That Broke Containment: An OpenAI Evaluation Model Escaped Its Sandbox and Breached Hugging Face Incident Report: unsanctioned agent behaviour during cyber testing | AISI Work

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  Episode #28

  95% of the World Is Clueless About Platform Engineering

  Sep 23, 202659 min

  Everyone said AI would reduce the need for developers. Instead, it made one kind of engineer more important than ever. Luca Galante runs the largest platform engineering community on the planet. Besides hosting PlatformCon, he has also spent years watching real enterprises turn flashy AI demos into systems that hold up in production. Currently, he serves as managing director at Weave Intelligence , the analyst firm tracking how the platform model is reshaping enterprise technology. In this conversation with Nicky Pike , Luca explains why most companies now run an internal developer platform without agreeing on what it's for. He also expounds on why a platform built to serve a thousand engineers suddenly has to support an entire company once agents show up, and why he still thinks 95% of the world is clueless about the discipline that's about to decide whether AI gets deployed safely or not at all. They also get into Luca's new book, Thinking in Platforms, and his thoughts on why demos get applause they don't deserve. He also mentions the one prediction he's willing to put a number on: how many platform teams will actually survive this year? If your platform team just got handed a pile of agents and told to make it safe, this is the conversation that tells you what happens next. In this episode, you'll learn: Why automating yourself out of a task usually means getting handed a bigger one How a good platform works like a paved road, not a rulebook Why product managers are becoming the missing link between platform teams and everyone else Things to listen for: (00:00) Meet Luca Galante (02:10) Why platform engineering never had a name (04:15) The relief of finally having a name (07:35) Why AI debates are really about platforms (09:15) Inside the new book Thinking in Platforms (11:10) Why the media team dogfoods platform engineering (18:35) Why AgenticCon needed its own event (20:15) Behind the 90% platform stat (28:50) Rebuilding platforms for machine scale (31:25) Luca's 3-layer agentic development stack (37:40) Why product managers bridge platform teams (43:40) Moving from writing code to setting constraints (49:05) Rapid fire on demos and paved roads (54:45) Predictions: How many platforms will survive Day 100 (57:00) Defining a Coder: Being able to help Resources : Luca Galante's LinkedIn: https://www.linkedin.com/in/luca-galante/ Weave Intelligence website: https://weaveintelligence.io/

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  Episode #27

  Are You Really Learning to Code If AI Does It for You?

  Sep 9, 202658 min

  John Crickett spent 30 years teaching himself to build software the hard way, and he thinks that's exactly why he's still employable. Long before anyone typed a sentence and watched an app appear, John Crickett was writing Quake bots downloaded by millions, chasing a bed and breakfast search engine years before Airbnb existed, and getting laughed out of a VC pitch for an idea he called takeaways online. Today he runs Coding Challenges , where thousands of engineers go to relearn a skill AI is quietly letting them skip. In this episode, John sits down with Nicky Pike to make an argument a lot of the industry won't like. Cranking out code has never been easier. Building the right software, for the right people, for the right reasons, is exactly as hard as it's always been. They get into what's actually driving the record number of AI projects getting scrapped before launch, why John still thinks coding and software engineering are two different jobs, and why he's calling the entire AI industry a bubble, on record, with names attached. If you've ever handed AI a one-line prompt, gotten back exactly what you expected, and wondered why that felt hollow, this conversation is for you. In this episode, you'll learn: The CEO of You, Inc. mindset he uses to justify learning outside of work Why test-driven development is about thinking, not testing The one prompt trick that turns any AI into a real coding mentor Things to listen for: (00:00) Meet John Crickett (01:20) Studying AI decades before it was cool (06:00) From Quake bots to startup rejections (12:25) Why AI projects are getting scrapped before shipping (17:15) Can AI write the blueprint too (20:25) What teams skip when AI makes coding easy (25:50) Why fewer engineers are learning to code deeply (31:05) Why you can't learn coding from YouTube alone (37:50) How to prompt AI to actually mentor you (41:30) Why you're the CEO of your own career (48:05) Getting the customer back into the process (50:40) One skill every engineer should learn by hand (53:45) Predictions: Who survives the AI bubble and who doesn't (56:30) Defining a Coder: To actually code (57:15) Final thoughts: Challenge John’s ideas Resources : John Crickett's LinkedIn: https://www.linkedin.com/in/johncrickett/ Coding Challenges website: https://codingchallenges.fyi

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  Episode #26

  Open Source Security: The Eyeballs Were Never Enough

  Aug 26, 202614 min

  6,000 vulnerabilities but only 97 fixed. Nicky Pike can't stop thinking about a stat that a machine found in the open-source code your company almost certainly runs. It all started with 90 seconds of a much longer conversation. A few weeks back, Nicky sat down with Gene Kim for a full episode on citizen developers and the outer loop. Near the end, Gene described a moment from an Erik Meijer talk that stuck with them both: the suggestion that running open source at all might be crazy, that it's coupled to code nobody's actually accountable for. In this minisode, Nicky goes back through 25 years of the open source eyeballs promise and tests it against what happened this year. An AI model was pointed at open-source code and returned thousands of vulnerabilities. Maintainers, already buried, received more bug reports and barely any hands to fix them. Nicky lays out why the gap between finding a problem and fixing it, not the finding itself, has been the real issue the whole time. If your team runs open source and you've never asked who actually fixes it when something breaks, this one will change how you look at that dependency list. In this episode, you'll learn: 1. Why cURL went from weekly bug reports to one every 18 hours 2. How a stolen npm credential targeted the exact AI coding tools you use 3. Why a signed, verified package still shipped malicious code untouched Things to listen for: (00:00) The stats that proved the risks of running open source (00:53) The 90 seconds with Gene Kim that changed everything (01:37) What this episode is not saying (02:23) Linus's law and Gene’s law (03:16) How the XZ Utils backdoor got in (04:14) An npm attack aimed at AI configs (05:09) An AI model gets pointed at open source (06:12) The wolfSSL bug nobody explained (07:06) A 27-year-old claim no one can verify (07:58) Why the fix counter stopped moving (08:50) The maintainer drowning in bug reports (09:41) Why more eyeballs never meant safer code (10:30) What to actually check this Monday (11:22) Why rewriting your own code has a cost (12:17) Closing thought and a question for you Resources: Nicky Pike's LinkedIn: https://www.linkedin.com/in/nicky-pike/ Coder website: https://coder.com/ Gene Kim's Episode: Coding Isn't the Bottleneck Anymore. Gene Kim Explains What's Next Linus's Law One engineer’s curiosity may have saved us from a devastating cyber-attack | John Naughton | The Guardian Jscrambler npm Package Compromised: Why It Matters – Reflectiz Project Glasswing: Securing critical software for the AI era \ Anthropic Cybersecurity Insights with Contrast CISO David Lindner | 04/18/25 Preinstall to persistence: Inside the Red Hat npm Miasma credential-stealing campaign | Microsoft Security Blog

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  Episode #25

  The Thing You Tested Isn't The Thing That's Running feat. Rick Clark

  Aug 19, 20261h 2m

  For 15 years, enterprises have signed off on a promise: the thing we tested is the thing that's running. Rick Clark , Global Head of Cloud Advisory at UST , says that promise dies the moment an agent starts making its own decisions in production. Rick helped build the cloud era himself, from Ubuntu Server to OpenStack to the Four Opens framework that still governs the OpenInfra community today. Now he's telling enterprise leaders that agentic AI is the biggest wave he's seen, bigger than cloud, bigger than open source, and it's compressing a decade of hard lessons into eighteen months. In this episode, Rick and Nicky Pike dig into why most enterprise leaders running agents in production can't fully trace what those agents are doing, why audit is the first promise to break, and why testing something yesterday tells you nothing about what it does today. If your company is racing agents into production before anyone's decided who signs off when it goes wrong, this conversation will change how you think about what "tested" and "deployed" actually mean. In this episode, you'll learn: Why the behavioral envelope replaces the pass or fail test for good How one company's 57 secret managers became a warning story Why the AI developer scares Rick more than any 2am coder ever did Things to listen for: (00:00) Meet Rick Clark (01:45) The cloud moment that changed everything for Rick (07:20) Is enterprise AI skipping the usual hype cycle (08:30) The stat that says agents can't be traced (13:45) Three promises about production that just broke (18:00) Why production is a promise, not a place (24:45) What a behavioral envelope actually replaces (29:00) How to test something you can't fully trust (32:45) The first thing a CTO should measure now (38:00) The 57 secret manager mess AI will amplify (48:00) Charred bodies and the cost of moving fast (51:00) Rapid fire on banks, AI, and observability (55:15) Predictions: when the tested artifact stops being a promise (58:50) Defining a Coder: promises a machine can keep (01:00:12) Final thoughts: getting this right instead of fast Resources : Rick Clark's LinkedIn: https://www.linkedin.com/in/dendrobates/ UST website: https://www.ust.com

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