A design team can work nonstop, ship without pause, and still fail to move the business. The cause is almost never competence. It's the order of the conversation: it starts at the interface when it should start with the business outcome that justifies the work.
by Carlos Pinheiro
Can you really trust AI to write production-ready code? We tested Claude Code across 100 executions in five progressively complex scenarios to measure not just capability, but consistency. The results were clear: clean codebases and clear guidance dramatically improve reliability, while messy environments significantly reduce success rates. AI doesn’t eliminate the need for discipline—it amplifies whatever standards already exist. Clean patterns produce clean output. Chaos produces chaos.
by Gustavo Dutra
Deploying constantly doesn’t mean chaos. With small changes, tests, and a production mirror, releases stay safe and predictable.
by César Felipe
Many teams say “CI/CD” but only test inside each repo. In this article, we show how separating CI from CD and adding a dedicated, cross-repo E2E test suite as a release gate helps catch ecosystem issues before production. By centralizing Playwright tests in their own repository and running them on staging, engineers own availability end-to-end and ship with more confidence.
by César Felipe
Open-source models have evolved from an ideological statement into a strategic advantage. They offer control, transparency, and flexibility that closed APIs simply can’t match. In an era where AI powers the core of many businesses, the real question isn’t which model performs best today, but which approach lets you build on your own terms.
by César Felipe