By the end of last semester, I was preparing for a deep dive into deployments, monitoring, maintenance, and the operational crossroads where Backend meets DevOps. My goal was to bridge my previous cloud experience—especially with GCP—with the modern stack we rely on at JetRockets, such as Kamal, AppSignal, DigitalOcean, and Cloudflare. When the syllabus dropped for my second semester in the Artificial Intelligence program at UFRN, I was thrilled to spot MLOps (Machine Learning Operations) on the list. While the name might sound niche, MLOps is essentially the application of strict software engineering and DevOps principles to machine learning, ensuring models aren’t just local experiments, but are effectively deployed, monitored, and maintained in production.
Migrating Rails views to Jet UI: a guide with ViewComponent and Tailwind v4 06-05-2026Last week, I wrote an article about migrating Calcpace views to use jet_ui to JetRockets blog. Here is the full article:
Calcpace: open beta, maps, bot protection and a growing gem 06-04-2026Calcpace.app is now open to anyone — no invite code needed. This post covers what changed since the closed beta: new gem modules, GPS tracking with maps, bot protection, the infrastructure decisions behind each feature, and a few lessons learned the hard way.
Calcpace Web: the calculator now in the browser 26-03-2026The calcpace gem just hit 7,000 downloads, so I celebrated the best way I know how: I ran a 5k and finally built calcpace.app to bring those calculations to everyone, no Ruby console required.