Building in Public

I’ve been meaning to build a personal site for years. Not a portfolio — I’m not looking for a job. Not a brand exercise — I don’t have a course to sell. Just a place to write, share what I’m working on, and think out loud about the things I spend my days on: technology strategy, AI, and the messy reality of building things that actually work inside organizations.

So here it is. Finally.

How this got built

This site is built with Astro, which I picked because it ships zero JavaScript by default and treats content as a first-class citizen. The styling is Tailwind CSS 4, the content lives in Markdown/MDX files, and it deploys to Cloudflare Pages. The whole thing was scaffolded with Claude Code in a single session — layout, content collections, RSS feed, deployment config, all of it.

[TODO: Add a sentence or two about your experience using Claude Code for this — what surprised you, what you’d do differently.]

That’s not a gimmick or a flex. It’s genuinely how I think a lot of software is going to get built going forward: you describe what you want clearly, an AI does the scaffolding, and you spend your time on the decisions that matter — architecture, content, design intent — instead of the boilerplate.

Content pipeline: OpenClaw

This site is now fully managed via OpenClaw. What started as an idea for a content authoring pipeline has become my primary interface for writing, editing, and publishing.

I work with an AI assistant (Chives) who helps me draft posts, review changes, manage the deployment pipeline, and keep track of ideas. When I want to publish, I just ask — the rest happens automatically. No more switching between text editors, Git commands, and deploy scripts.

It’s an experiment in treating AI as a genuine collaborator in the writing process, not just a tool. So far, it’s working better than I expected.

Why “building in public”

I’m a CIO. I run IT and technology strategy for a company with nearly 400 people. Most of the people in my role don’t write about what they’re doing. I get why — it’s busy, the work is internal, and there’s not an obvious incentive to share.

But I’ve learned more from people who write about their work than from any conference talk or certification. I want to contribute to that. Not hot takes on AI hype cycles. Just honest notes on what I’m trying, what’s working, and what isn’t.

[TODO: Add anything about what topics you plan to cover — AI strategy at WFG, automation experiments, coaching parallels, etc.]

If any of that sounds interesting, stick around. There’s an RSS feed if that’s your thing, or come find me on LinkedIn.