Question
A value is being disseminated through a cluster via deterministic gossip. The cluster is described by an adjacency list `neighbors` (a dict from node id to the list of nodes it gossips to). In each round, EVERY node that already knows the value pushes it to ALL of its neighbors simultaneously; a neighbor that did not previously know the value learns it (becomes 'knowing') at the END of that round. Starting from the single seed node `seed` (which knows the value before round 1), return the sorted list of node ids that know the value after exactly `k` rounds.
gossip_known_after(neighbors: dict[str,list[str]], seed: str, k: int) → list[str][{"A":["B","C"],"B":["D"],"C":["D"],"D":[]},"A",1]out["A","B","C"]State your approach and its time/space complexity out loud before you optimize. Handle the edge cases (empty input, duplicates, overflow), and say why you chose this over the brute force. Green tests are the floor, not the grade.
Vibe coding: describe the solution in plain language (or narrate it) and the coach grades your approach. Generating runnable code from your description is coming next.