This is an excerpt from our Tanzu Catsup last week. In that episode we talked all about how this AI stuff is changing - for the better - how you can handle security problems at the app layer.
It’s Monday morning. Your boss walks up, says “scrap the backlog, we’ve got a list of CVEs longer than that curved screen we bought you last year, the CISO is coming, fix them,” and goes to brunch.
Posts in "videos"
Treat AI as a stoner
The right mental model for working with an AI, according to my co-host David.
If you’ve spent any real time with an AI, you know exactly what he means. The model can do impressive work in a tight scope. Step out of that scope, or feed it more than fits, and you’re suddenly explaining the same constraint for the third time that happened just a few minutes ago.
In the most recent Tanzu Catsup episode, we also talk about copy.
What cf push actually does
When I see a platform engineering conference talk about building an internal developer platform on Kubernetes, I think about cf push. Cloud Foundry has been doing this - the actual thing, the single command that takes you from source code to running app - for more than a decade. People keep rebuilding it on top of Kubernetes with Backstage plus a pile of CRDs and a bespoke yaml, and that’s.
Don't forget what I told you yesterday - AI memory and the mind palace - Tanzu Catsup
If you’ve spent any real time with Claude Code or Cursor, you know the feeling. The thing you told the agent five minutes ago is now optional as far as it’s concerned. The fix isn’t a smarter model. It’s architecture.
This week David Zendzian and I dig into memory for AI agents - what it actually means, why one giant context window isn’t it, and what a real structure for long-running agent work looks like.
Art Degrees, Sun Microsystems, and How Kubernetes Scales Contributions, with Josh Berkus - Software Defined Interviews #121
Our interview for this week is up, it’s with Josh Berkus:
Whitney and Coté discuss with Josh Berkus (Red Hat, Kubernetes contributor) how liberal and fine arts degrees (philosophy, photography, sculpture, pottery) apply to tech careers. Berkus details how early hardware experience influenced his database performance work, noting hardware’s renewed relevance with AI and multi-arch computing. The conversation covers Sun Microsystems’ 1990s internet role, internal politics, and its MySQL/Postgres strategy.
Does Platform Product Management & Design Really Happen? Or is it all just platform engineering? - Tanzu Catsup
Most organizations treat infrastructure as a series of projects to be “completed,” but successful platform engineering requires a permanent product mindset. In this episode, we explore why platform teams need dedicated product management to balance competing priorities—like security, cost, and developer experience—and why the “why” scales much better than the “what” in large enterprises. We also dive into the often-overlooked role of designers in creating platform tools that developers actually want to use.
Developers crave AI tools for various tasks beyond coding, but that’s only about 20% of their work. But, ops people freak out about security and control challenges, like cost, regulatory compliance, and usage tracking.
Bad advice from Wall Street on enterprise AI.
Why it's great to be a Spring developer now, and how to make it even better - State of Spring, 2026
This is a talk I give at the start of Spring workshops we do. Here is the recording. The point is to show people that being a Java and Spring developers is fantastic right now. Here’s the description:
Spring developers are in a strange position in 2026: everything is changing: AI, platform engineering, enterprise architecture. And yet Spring keeps getting stronger.
In this talk, Coté walks through why this is actually a great moment to be a Spring developer, especially in large organizations.
Your Boss Doesn’t Know What to Do With AI
Enterprise AI Has a Product-Market Fit Problem. Enterprise AI isn’t stalled because the models are weak. It’s stalled because we haven’t discovered product-market fit inside the enterprise yet.
You don’t find real AI value by theorizing in workshops. You find it by running experiments for months inside your actual systems - against real data - in a governed environment.
That requires a platform.
Without one, AI pilots turn into disconnected experiments, shadow infrastructure, and compliance risk. With one, experimentation compounds into institutional learning.
In this video, I break down:
- Why enterprise AI is still in discovery mode
- Why experimentation must be long-running, not one-off
- How governance enables innovation instead of blocking it
- Why a secure platform foundation is the baseline for AI ROI
If you’re thinking about AI strategy, platform engineering, or how to make AI experimentation safe and scalable, this is where to start.
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