Requirements, architecture, and code evolve, but their historical rationale is easily lost. This article proposes keeping a historical change ledger alongside living engineering specifications.

Most of the good ideas for automotive climate controls already exist. The challenge is combining them: proven rotary knobs, direct MAX A/C shortcuts, adjustable AUTO intensity, and a spring-return airflow selector.

Not every feature that can use AI should keep AI in the runtime path. A useful distinction is whether the task and input space can be specified at design time. If they can, deterministic software is usually the better runtime architecture; if either remains open-ended, runtime AI starts to earn its place.

AI does not make every part of software delivery equally faster. As implementation accelerates, the bottleneck moves upstream to product definition and downstream to business validation — and that will push technical roles closer to the business domain.

A football argument led me to a broader question: why can we often recognize a good or bad result even when we could not produce the same result ourselves? The same generation–evaluation asymmetry appears in cognition, creativity, computer science, and now AI-assisted software development.

History and experience have already shown us which control models work. Yet, across industries, we keep ignoring proven solutions and rediscovering the same mistakes. This article applies that frustration to one very visible product-design problem: car steering wheel ergonomics.

Before adopting micro-frontends, check whether you truly need independent ownership and deployment—or whether you are just importing distributed-systems complexity into the browser.