warm up

The most common screen opener, and interviewers use it to check whether hashmap thinking is reflexive. Here's the one-pass answer, the narration that earns points, and the duplicate-handling edge case most candidates fumble.

A 5-minute warm-up that quietly tests the most useful idea in practical coding: choosing a canonical key. The sorted-string vs character-count tradeoff is exactly what interviewers want to hear you reason about.

The interval pattern shows up everywhere in FDE loops: calendars, flight segments, log windows. Master the sort-then-sweep idiom here and three other interview questions fall out for free.

The follow-up interviewers reach for when merge-intervals goes too smoothly. The three-phase scan is elegant, but only if you've internalized the overlap condition most candidates have to re-derive under pressure.

Top-K is the most reused primitive in FDE interviews: it reappears inside log parsers, analytics questions, and retrieval ranking. Three solutions exist; knowing which one to lead with is the real test.

Looks like a sliding-window problem; isn't one. The prefix-sum + hashmap trick that solves it is the same idea behind sessionization and cumulative-metrics questions later in the loop: learn it once, reuse it three times.

The gentlest 'design a class' question in FDE screens, and the warm-up interviewers use before rate limiters and sessionization. The deque trick is easy; the API and time-window follow-ups are where the signal is.

Anthropic-style screens open with deceptively simple builds like this, then extend them three times. The grade isn't the counting; it's whether your first version survives the extensions without a rewrite.

Everyone quotes p95 in latency SLOs, but few can compute it without a library and fewer can explain why the index is ceil(p*n)-1, not p*n. The warm-up that screens whether you actually understand the metric you live by.

The arithmetic is three sums; the signal is what you do when a vector is all zeros and the denominator vanishes. The warm-up that gates every RAG and retrieval coding round.

Real LLM logs are JSONL, not JSON, and real log files have a malformed line halfway through. The warm-up that screens whether you can stream a file and aggregate without one bad record nuking the whole run.

Batching inputs is how you keep a GPU fed, but the source is an iterator you cannot index or len(). The warm-up that screens whether you can write a lazy generator that handles the ragged final batch.

A dashboard needs the average tokens/sec over the last N seconds, updated on every sample, forever. The warm-up that screens whether you evict by time and keep a running sum instead of re-summing the window each call.