The agency in agentic AI feels a lot more like giving the users - the humans - agency they didn’t have. That’s what’s making it useful for me, from sorting out dumb-shit home-networking incompatibilities, figuring out taxes, and otherwise sorting my shit out. When you unleash something like Claude code on all the messy and neglected rooms in your life, you start to clean-up and pay attention more.
There’s a very bottoms-up thing here.
Posts in "tech"
Using AI to help with SRE, ops, etc.:
The problem, he said, is that Claude “will get wrong correlation versus causation.” It’s like a new joiner on the team, they will think “oh, it’s a capacity problem, when actually you lost your cache.” “This is why we can’t trust LLMs for incident response,” said Palcuie. The problem is its inability to “step back and start discerning between causation and correlation… For us humans, it is hard as well.”
And:
The Jevons Paradox, said Palcuie, is “the favorite paradox in the AI industry. It’s when technological improvements increase the efficiency of our resources used, but the resulting lower cost causes consumption to rise rather than fall.”
In the case of software, “it’s easier to write software, so we write much more of it, so the complexity goes up and not down, which means things break in more interesting ways, which means more incidents, more on call… all the improvements in the tooling will be cancelled by this ever-growing complexity.”
From: Fixing Claude with Claude: Anthropic reports on AI site reliability engineering
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.
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.
You Can Feel It Coming - Software Defined Talk
This week, we discuss personal AI hype cycles, bottoms-up adoption, and “The Modern Stack” simplifying cloud. Plus, thoughts on new cars and the dogs that ride in them.
See the traditional podcast listing for links and more.
Progressive Delivery, with Heidi Waterhouse - Software Defined Interviews
See the traditional podcast version for more and Heidi links.
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.
Attention, Autonomy, and AI in the Critical Path - Related to your interests - February 17th, 2026
From “CEO said a thing” journalism and sovereign payments to product teams in code-gen land, Deming vs. OKRs, sex recessions, Neanderthals, fonts, and hype cycles - a tour through power, productivity, and what actually holds systems together.
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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