Agilish

I’ve had some time recently to re-tool and refocus, which led me down an AI journey that ultimately resulted in building a showcase web application called Tee It Up Chicago . Tee It Up Chicago is a golf course catalog and utility platform for golfers in the Chicago area, featuring course information, scorecards, handicap tracking, and other tools designed for local players. I am working on adding round and match scoring and other features to make outings and tournaments easier to score and track. The best way I can describe the experience working with agentic AI agents is this: it feels like having the smartest kid from my computer science class sitting next to me, helping me build software. This “AI teammate” never gets tired, never complains when I ask a basic question, and can jump in and write code when I need it to. The more I’ve worked with these AI coding tools, the more I’ve learned how to collaborate with them effectively — and the faster I’ve been able to build web appl...

Some organizations decide to maintain only two environments: Development and UAT. The reasoning usually sounds practical. One fewer environment to maintain. One fewer pipeline to manage. One less budget line to defend. In practice, it eliminates the one thing that makes a pre-production release testable. When QA and UAT are the same environment, in-progress work and release candidates sit side by side. Business stakeholders doing acceptance testing are looking at code that may not even be part of the upcoming release. External partners are testing against something that does not represent what production will look like. Most critically, there is no stable, controlled place to validate the actual release before it ships. The result is that the final validation happens in production. Nobody sets out to test in production — but many organizations end up there because they removed the environment that would have prevented it. UAT as Your Most Valuable Diagnostic Tool Con...