I’m Matt Rogish.
I’m a technologist, a builder, a multiple-time founder w/exits, and a veteran CTO. Having navigated building & selling tech businesses, I now partner with companies as a fractional CTO/Advisor/Problem-Solver/Jack-of-all-trades to thoughtfully and successfully integrate AI, Agents, and smart automation into their operations.
If that’s what you need, let’s chat!
“Hey Siri, what’s the weather tomorrow?”
Looks like thunderstorms tomorrow. The high temperature will be 66 degrees, and low will be 33.
So you go outside with an umbrella and a t-shirt only to realize that it’s nearly snowing when you’re out. Why? Because of a chart that looks like this:
“Hey Siri, play Toxic”. And then “Toxic” by Britney Spears plays instead of the one by BoyWithUke that I intended.
It’s no secret that AI-produced slop is filling up social networks, websites, blogs, job search sites, homework. B2B marketers have completely clogged the internets here and - perhaps because they lack the confidence, or just value the convenience - I see clearly AI-produced “personal” content in Slack groups, newsletters, mailing lists and Discords of all shapes and sizes.
And I get it. The pull is undeniable. The magical autocomplete super tempting. I keep trying to resist; when I’m typing out a post I will have Sonnet critique my work and I have to fight taking the suggestions verbatim. To quote the famous Jim Gaffigan: “Hey, that’s something I’d say!”
It seems somewhat fitting that now, March 11, 2026, almost two years ago to the day that ChatGPT 4 was released (I distinctly remember asking GPT3.5 to create a song about something in the style of Nine Inch Nails, but my ChatGPT.com history only goes back to March 27, 2023.
Compare March 2023 with March 2026. (I have no idea why I was interested in this.)
), I’m writing my first thoughts on it.
There’s a lot of churn in the AI/LLM/Agent space, but I’ve landed on an approach that reliably produces production-quality code for non-trivial work. The core insight is simple: use multiple LLMs to develop a solid architecture, then implement it through a phased crawl/walk/run approach.
Getting the plan right before writing code is where most of the value is. Here’s how that plays out in practice.
The agent needs to run your tests, catch its own errors, and iterate without you babysitting it. Without that feedback loop, you’re stuck manually pasting error messages back in or clicking “yes, run bin/rails test” after every step.