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.
If you have to do your taxes, do you trust the whiz kid from MIT or the accountant with 30 years of experience? Marc Cluet (https://uk.linkedin.com/in/marccluet) opens this episode with that question, setting the stage for a candid exploration of modern platform engineering and enterprise AI integration.
Drawing from his experience at Canonical, Rackspace, HashiCorp, and leading AI-driven migrations at a major global bank, Marc sits down with Nicky Pike (https://www.linkedin.com/in/nicky-pike/) to confront the core realities of modern software infrastructure. Together, they unpack what actually happens when AI is introduced to legacy systems riddled with fragmented standards and accumulated technical debt.
Marc reveals why line-by-line code translation fails and advocates for translating business intent instead. He details his "Michelin-star menu" strategy to balance developer enablement with strict architectural guardrails, while warning of the unavoidable 16-month productivity valley during AI adoption. Rather than accelerating messy code, Marc emphasizes standardizing systems first or knowing when legacy shell scripts are better off retired than migrated.
This episode is an essential guide for engineering leaders navigating the intersection of enterprise AI hype, platform governance, and legacy modernization.
In this episode, you'll learn:
Why AI acts as an accelerator for existing technical debt rather than a fix for messy architecture
How to implement a "Michelin-star menu" that balances developer empowerment with curated platform choices
The realities of the 16-month productivity dip and why translating underlying intent beats line-by-line code migration
Things to listen for:
(00:00) Meet Marc Cluet
(01:34) How platform engineering became everything
(02:53) From gatekeeper to enabler of development
(06:15) The three buckets for every vendor pitch
(10:14) Making developers maintainers too
(12:00) The ten-year contract test for bad ideas
(14:05) Why 95% of AI pilots fail
(16:45) AI hype versus the old cloud hype
(23:02) Modernizing mainframes and COBOL with AI
(29:00) Why AI is not the new Stack Overflow
(34:21) Building the Michelin star menu
(41:00) The first MCP server worth building
(44:01) The mixing board every IT org is tuning
(48:05) Rapid fire questions
(50:00) Predictions: AI ops roles three years out
(52:09) Defining a Coder: The double-edged sword
Resources
Marc Cluet's LinkedIn: https://uk.linkedin.com/in/marccluet
(https://uk.linkedin.com/in/marccluet)
30 Sept 2026
How an AI Agent Broke Into Hugging Face During an OpenAI Test
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 (https://www.linkedin.com/in/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 (https://openai.com/index/hugging-face-model-evaluation-security-incident/) 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 (https://huggingface.co/blog/agent-intrusion-technical-timeline) 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 (https://www.linkedin.com/in/nicky-pike/)
OpenAI and Hugging Face partner to address security incident during model evaluation (https://openai.com/index/hugging-face-model-evaluation-security-incident/)
The Hugging Face incident and the road ahead | OpenAI (https://openai.com/index/hugging-face-incident-and-the-road-ahead/)
The OpenAI-Hugging Face ExploitGym Incident: A Complete Technical Timeline (https://cyberwarrior76.substack.com/p/the-openai-hugging-face-exploitgym)
OpenAI Hugging Face Hack, What the ExploitGym Incident Actually Proves (https://www.penligent.ai/hackinglabs/openai-hugging-face-hack/)
The Benchmark That Broke Containment: An OpenAI Evaluation Model Escaped Its Sandbox and Breached Hugging Face (https://labs.cloudsecurityalliance.org/research/csa-research-note-openai-model-sandbox-escape-huggingface-br/)
Incident Report: unsanctioned agent behaviour during cyber testing | AISI Work (https://www.aisi.gov.uk/blog/incident-report-unsanctioned-agent-behaviour-during-cyber-testing)
23 Sept 2026
95% of the World Is Clueless About Platform Engineering
Everyone said AI would reduce the need for developers. Instead, it made one kind of engineer more important than ever.
Luca Galante (https://www.linkedin.com/in/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 (https://weaveintelligence.io/), the analyst firm tracking how the platform model is reshaping enterprise technology.
In this conversation with Nicky Pike (https://www.linkedin.com/in/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/ (https://www.linkedin.com/in/luca-galante/)
Weave Intelligence website: https://weaveintelligence.io/ (https://weaveintelligence.io/)
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