Galo Navarro

The pipeline had degraded to the point of collapse.

One of the hardest parts of integrating AI in our workflows is realizing that you can, and probably should, throw away assumptions and re-evaluate trade-offs from scratch.

I’m no longer convinced that the classic code review process is the right tool for the job.

AI workloads break the “cattle” 1 approach to infrastructure management that made Kubernetes an effective IaaS platform.

Was it ever possible to build a Proof of Concept that didn’t end up being rushed into production as a nest of bugs, instability, and technical debt, regardless of any advance warning from engineering that it will be “just a PoC”?

Everyone is focused on the impact of AI on the production of code.

The median product engineer should reason about applications as composites of high-level, functional Lego blocks where technical low-level details are invisible.

Once at Last.fm we had an integer overflow in an identify field.

When you are bringing a new system to production you know that you ought to define SLIs, set up instrumentation, alerting, etc.

I have found that the concept of test types (unit, integration, and so on) does more harm than good.