multi part

A verbatim-reported multi-part at OpenAI and Anthropic. Each level breaks the previous design on purpose: the burst-at-the-boundary flaw is planted, and naming it before the interviewer does is how you win the round.

The question where production scars are the rubric. Anyone can write a retry loop; the grade lives in which errors you DON'T retry, why jitter exists, and what an idempotency key actually protects you from.

A three-level build that quietly tests whether your L1 design survives L3. Regex substitution wins level one and loses the round; here's the token-based structure that carries through conditionals and loops.

Exact-match pub/sub is a dict of lists. The round is decided at level two (wildcard matching on hierarchical topics) and at level three, when the interviewer asks what happens when a subscriber's callback throws.

Kahn's algorithm gets you level one. The round is won at level three, where 'run up to K tasks at once' breaks naive topo sort, and the indegree bookkeeping you chose at L1 either saves you or sinks you.

The practical build that's secretly a graph problem: formulas are a dependency DAG, evaluation is DFS with memoization, and the three-color cycle trick decides whether A1=B1, B1=A1 crashes you or earns the offer.

The OpenAI FDE signature question, reported near-verbatim for two years: four levels in ~60 minutes, where L4 silently breaks every naive L3 implementation. The lazy-expiry design that survives all four levels, with the pacing plan.

The other OpenAI signature build, reported in four parts for two years. Part C silently punishes anyone who hardcoded single-condition filtering in part B; here's the predicate-compiler design that absorbs all four parts.

An OpenAI bank regular: next() over a list, then save/restore position, then resume across multiple JSON files where some are empty. The whole question is one decision (what counts as state) and most candidates get it wrong on part one.