Posted without comment so that you can see what an AI post-mortem looks like. For all those folks who let your coding agents run off and do things, this is an example of where they can get lost. Environment: Cursor, Ruby on Rails 8, Tailwind CSS, Hotwire/Stimulus/Turbo. Executive Summary A layout issue (sidebar appearing below content instead of … Read more
One of the challenges that’s come up repeatedly in the Rails app I’m building is managing access to features effectively. With multiple user roles and types and a growing set of capabilities (thanks in part to easier access to coding tools) I needed a way to control who sees what, without constant code deploys or … Read more
After getting back from KubeCon + CloudNativeCon North America 2025 in Atlanta (always a great event for catching up on what’s next in cloud native). I was lucky enough to spend time with the team at ControlTheory while I was there as part of our media coverage for the event. They were showing off their … Read more
Modern ML engineers and platform developers know that scaling machine learning workloads isn’t a matter of just adding GPUs. Optimizing ML system performance requires orchestrating compute, storage, and data pipelines to maximal efficiency under production constraints. As models grow more complex, what once was processed within a single rack now requires distributed GPU clusters, shared … Read more