AI Coding — Shared Substring in String List

AI-Enabled Coding variant outside the Maze / Card-Game canon: given a list of strings, find a string that contains another from the list as a substring (or any pair sharing a substring relationship), then improve beyond brute force across 2-3 successive approaches. Requirements Input: a list of strings (e.g. ["programming", "am", "pro"]). Output: a string from the list that contains at least one other list member as a substring (in the example, "programming" contains both "am" and "pro"). Test cases are pre-written in the prompt; the interviewer expects the brute-force solution first, then 2-3 progressively faster approaches along with their time and space complexity. Notes The interviewer in the reported round required the idea to come from the candidate; implementation could be delegated to the AI assistant. Time/space complexity still had to be stated explicitly — the assistant readily prints them, so practice arriving at the same numbers yourself before peeking. Standard progression: Brute force: for each string, check every other string with in — O(n² · L) worst-case. Sort by length ascending, then for each longer string only check shorter ones it could contain — prunes a constant factor and lets you exit early once the candidates are exhausted. Trie of all strings; for each starting position in each string, walk the trie and report matches — O(total_chars · L) and naturally handles the prefix family. Sort + trie — feed strings into the trie shortest-first so that once a longer string traverses the trie it can detect any contained shorter string in O(L). Watch for the interpretation ambiguity: "shared substring" can mean (a) one whole string appears inside another (this round's intent based on the example) or (b) any common substring across two strings. Confirm before coding. A common framing ("ParanoidEcho" / "Echo") hands you a working brute-force solution plus a provided benchmark harness; you brainstorm optimizations (the assistant typically surfaces both a Trie and a hash-set approach), implement each, and plug them into the benchmark. The two often land at similar runtime with the Trie using more memory — be ready to read that result and argue the trade-off. Preparation Drill the trie-of-strings + scan pattern; this same skeleton powers LC 720 (Longest Word in Dictionary) and Aho-Corasick warm-ups. Rehearse a 30-second monologue per approach covering correctness, time, and space — this round grades the explanation as much as the AI-produced code.