At what scale does serverless stop being the right answer?
I keep telling teams that serverless is good for bursty, unpredictable workloads. But where is the real inflection point? At 10,000 RPS? At sustained constant load? I have heard very different answers from very credible people and I still do not have mine.
How do you actually measure ‘code quality’?
Code coverage? Cyclomatic complexity? Pull request review time? I have used all of these. None of them capture the thing I actually care about: can a new engineer understand and safely modify this code in six months? I am still looking for a metric that gets close.
Is event-driven architecture making distributed systems harder to reason about?
EventBridge gives you beautiful decoupling. But ‘who published this event and why did it trigger that Lambda?’ becomes genuinely hard to answer in a post-mortem. I wonder if we are solving coupling by creating complexity of a different kind.
What does ‘senior’ actually mean in engineering?
I have seen senior engineers who write brilliant code but cannot bring a junior along. And I have seen others who write mediocre code but ship teams that consistently deliver. Which one is more valuable? I think I know the answer, but it makes me uncomfortable.
Will AI write the boilerplate, or will it write the architecture?
Right now AI is great at plumbing — CRUD endpoints, test scaffolding, repetitive config. But the hard decisions — what to build, how services should talk, what to NOT build — still feel very human. I wonder how long that distinction holds.