Question
Simulate a fixed-size slab allocator with `n_slots` slots indexed 0..n_slots-1, all initially free. Process a list of operations. ["alloc"] returns the lowest-indexed free slot, marks it used, and that index is the allocation's handle; if no slot is free the alloc fails (no slot consumed). ["free", idx] frees slot idx if it is currently used (no-op if already free or out of range). After processing all ops, return a list of length n_slots where element i is 1 if slot i is used and 0 if free. n_slots >= 1; <= 10^5 ops.
slab_allocator(n_slots: int, ops: list[list]) → list[int][3,[["alloc"],["alloc"],["free",0],["alloc"]]]out[1,1,0]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.