Artificial intelligence
fromZDNET
3 days agoThe overselling of AI - and how to resist it
AI coding models succeed less than 23% of the time in real production code.
We build production platforms with AI every day, and we work with teams doing the same with their own stack -Cursor, Claude Code, Copilot. The difference shows up fast. By day two, some codebases are already harder to change than they were yesterday. Others keep getting easier. The difference is never the model. It's what the code lands in. The teams we work with that hit a wall? It's always the same story.
Every few years our team ends up reevaluating our test management setup and it always leads to the same debate. Most tools look similar on paper, but the real difference shows up six months later when the suite grows and the process gets messy. Some teams love the structure in tools like TestRail or Qase, others prefer something lighter that does not slow them down.
In any software development effort, there is always too much to do and not enough time or resources to do it all. The problem is that the number of things we could build is infinitely large, and our available time and resources are, by comparison, almost infinitely small. This applies especially to architecting. The art in software architecting is deciding what decisions need to be made now and which ones can wait.
Refactoring code by eliminating redundant attributes enhances clarity and encapsulation, reduces cognitive load, and ensures methods directly access the object's state for improved maintainability.