← Anthropic Interview Insights
Start by clarifying the problem and edge cases, then propose a brute-force solution to establish a baseline. Introduce the sliding window technique with a hash map to optimize to O(n), and explain how the window expands and contracts while tracking the maximum length.
Pro tip: Emphasize that the key to O(n) is that each character is visited at most twice (once by the right pointer, once by the left), so the inner while loop does not make it O(n^2). Also, mention that using an array of size 128 (for ASCII) can be faster than a hash map in practice.
Ask clarifying questions: character set (ASCII/Unicode), empty string, case sensitivity. Define the problem: longest substring without repeating characters.
Describe a naive O(n^3) or O(n^2) approach: check all substrings and verify uniqueness. This shows you can start simple and sets the stage for optimization.
Introduce two pointers (left, right) and a hash map (or array) to track characters in the current window. Expand right, and when a duplicate is found, move left until the duplicate is removed.
Explain that each character is processed at most twice, so time is O(n). Space is O(min(n, m)) where m is the size of the character set.
Walk through examples: empty string, all unique, all same, and mixed. Discuss potential optimizations like using an array for ASCII or storing last seen index to skip ahead.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the definition of a 'character' by distinguishing between code units, code points, and grapheme clusters, then explain how the solution changes based on the chosen interpretation. Discuss the trade-offs of each approach and propose a pragmatic solution that handles Unicode correctly, especially for emoji with ZWJ sequences.
Pro tip: Mention that many languages' built-in string length functions return code units, not grapheme clusters, so relying on them can lead to subtle bugs; always ask whether the requirement is user-perceived characters or code points.
Ask the interviewer whether 'character' means a code unit, code point, or grapheme cluster. This determines the entire approach.
Describe how strings are stored (UTF-8, UTF-16, etc.) and how emoji with ZWJ are composed of multiple code points joined by zero-width joiners.
Analyze how the solution changes if counting code points vs. grapheme clusters, including performance and complexity considerations.
Recommend using a grapheme cluster library (e.g., ICU) for user-perceived characters, or code point iteration for simpler needs, and explain the trade-offs.
Reiterate the importance of clarifying requirements and choosing the right abstraction for the use case.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.