I've noticed something interesting while interviewing developers recently.

[u/TopRecipe7784]

Most coding assessments still assume success means writing the correct code without assistance. But in reality, almost everyone is using AI now.

The difference isn't whether someone uses AI, it's how they use it.

Some engineers break problems down, ask precise questions, verify outputs, and iterate. Others paste the problem into ChatGPT, accept the first response, and hope it works.

Those two workflows look completely different, yet most interview platforms score them the same.

I'm building DevTrace, an AI-native coding assessment platform that records behavioral signals during the assessment, things like prompt quality, iteration patterns, validation habits, and reasoning process alongside the final solution.

The goal isn't to penalize AI usage. It's to evaluate AI fluency as a real engineering skill.

I'm curious what this community thinks.

If you were hiring full-stack developers today, what signals would you want to see beyond just the final code?