Meta interview LeetCode prep

You have probably been told to finish a large tagged list before the phone screen, and you want to know whether that number means anything. This section separates what LeetCode genuinely trains from what a Meta pad still tests. The short version: it builds the recall you need and skips the performance you are scored on.

What it trains well is real. Recognising which structure a prompt wants, writing correct code without a compiler, and knowing the complexity of what you just wrote all come from repetition. Meta's pad has no run button and no autocomplete, so that recall is necessary.

What it leaves out is everything social. Nobody on LeetCode interrupts you to ask why you chose a heap. Nobody adds a constraint and watches you rewrite the approach. You never have to say a wrong idea out loud and correct it while a stranger listens. That is the object Meta is grading.

Coverage matters more than volume. The families that recur are a short list:

• Arrays and strings. Two pointers, sliding windows, prefix sums, in-place rewrites.
• Hash maps. The hard part is what to key on, not the lookup.
• Trees. Depth-first and breadth-first, path sums, lowest common ancestor.
• Graphs. Grids, connected components, shortest path on unweighted graphs.
• Heaps. K-th elements, merge K streams, top-K in a window.
• Approachable dynamic programming. One-dimensional state, not contest-hard tableaux.

A useful stopping test: pick a solved problem at random and explain, out loud, why that pattern applies and what would break the alternative. If you can do that across the list, more volume adds little. Other companies' loops are broken down on the company interviews topic hub.

Four differences should change how you practise. You talk before you type, because interviewers expect an approach and a complexity estimate before code appears. A second problem arrives while you are still warm from the first, often with about 20 minutes on the clock. There is no submit button, so you are the interpreter, stepping through indices and failing cases by hand. And the whole thing happens in a shared pad while somebody watches, cameras on.

A candidate preparing for an E4 role had cleared a long Meta-tagged list and still stalled in the phone screen. A recording of a practice session showed the cause: nine minutes of silence before the first line. The fix was not more problems. It was re-solving three familiar ones while narrating every decision.

Running that kind of timed spoken rehearsal against an AI interviewer works on the mock interview page.

Turn the last stretch into performance practice. Re-solve known problems out loud on a 45-minute, two-problem timer. State the approach before typing. Give the complexity unprompted. Hand-trace one failing case even when you are sure the code is right.

SubcueAI is built for the spoken rounds of a loop rather than for the coding pad. The native macOS and Windows app captures system audio plus your microphone and shows suggestions in a floating local overlay. The browser extension's Side Panel does live assist for a meeting tab on Chromium browsers, capturing that tab's audio only. No meeting bot joins the call, and nothing is injected into the meeting page.

The limit is plain. A Meta coding round is a shared pad, and a screen-shared IDE puts any overlay inside the share. Proctored or company-managed devices are also out of scope. What the assistant captures and stores in spoken rounds is on the security page.