Why most interview practice fails before it starts
You walk into an onsite cold because the practice you've done doesn't match the loop you're walking into. ChatGPT will play interviewer if you ask, but it doesn't know that Google L4 weights system design scoping in the first 10 minutes, or that Amazon's Bar Raiser is a different kind of question than the team rounds. Friend mocks soften feedback because no one wants to tell you your STAR collapsed in round three. Generic interview-prep books cover everything and target nothing.
The result is a candidate who has practiced "interviewing" but hasn't practiced this interview. They've drilled algorithms but never been pushed on complexity analysis under time pressure. They've rehearsed STAR stories but never had a follow-up question break the structure. They've thought about system design but never been forced to scope before drawing.
A good mock interview reproduces the exact loop you're about to walk into — the question style, the signal weighting, the follow-up patterns, the time pressure — and tells you precisely where you would have failed.
Built on the rubrics, not around them
The AI was trained on the actual rubrics used at Google, Meta, Amazon, Apple, and Microsoft, plus a growing list of unicorns. Not "tips from people who interviewed there once" — the structured rubrics themselves: which signals are weighted at L3 versus L4 versus L5, which Leadership Principle is being tested in which question, what scoping behavior separates a passing system design from a failing one. The question bank is sourced from public reports across Glassdoor, Reddit, and 一亩三分地, mapped to the levels the questions came from.
This is the reason a Google L4 mock interview here looks different from a Meta E4 mock interview. Same role title, different rubrics, different question banks, different follow-up styles. The AI loads the right one before the loop starts.