// SOLVING THIS WITH AN AI ASSISTANT (2026)
If you are working through this problem with an AI coding assistant — Claude, ChatGPT, Cursor chat, Gemini, GitHub Copilot, Aider, or any agent — the goal isn’t to ask for the answer. It is to use the tool to understand the pattern. The prompt sequence I’d run:
The pattern matters more than the answer. If the agent just hands you optimized code, you’ve trained yourself to lose interviews.
Given an array of strings, group anagrams together.
Example:
Input: ["eat", "tea", "tan", "ate", "nat", "bat"],
Output:
[
["ate","eat","tea"],
["nat","tan"],
["bat"]
] Note:
Solution 1:
class Solution {
public List<List<String>> groupAnagrams(String[] strs) {
Map<String, List<String>> anMap = new HashMap<String, List<String>>();
List<List<String>> retList = new ArrayList<List<String>>();
for(int i = 0; i<strs.length;i++){
char[] charArray = strs[i].toCharArray();
Arrays.sort(charArray);
String str = new String(charArray);
if(anMap.containsKey(str)){
List<String> innerList = anMap.get(str);
innerList.add(strs[i]);
anMap.put(str, innerList);
}else{
List<String> innerList = new ArrayList<String>();
innerList.add(strs[i]);
anMap.put(str, innerList);
}
}
for(String key:anMap.keySet()){
retList.add(anMap.get(key));
}
return retList;
}
} For the AI-native engineering side of HackerHeap — building MCP servers, comparing agents (Claude Code, Cursor, Windsurf, Codex, Gemini, Copilot), and weekly working code — see the Friday Build newsletter and the MCP archive.
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