docs: rename category folders to remove parentheses and update heap meta title

apps/docs/content/trie/208-implement-trie-prefix-tree.mdx

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+---
+title: '208. Implement Trie (Prefix Tree)'
+description: A trie (pronounced as "try") or prefix tree is a tree data structure used to efficiently store and retrieve keys in a dataset of strings. There are various applications of this data structure, such as autocomplete and spellchecker
+sidebar:
+  label: 'Implement Trie (Prefix Tree)'
+  badge: 'Medium'
+---
+
+<Badge variant="accent">Hash Table</Badge>
+
+### Example 1:
+- Input: ``
+- Output: ``
+- Explanation: Input ["Trie", "insert", "search", "search", "startsWith", "insert", "search"] `[[]`, `["apple"]`, `["apple"]`, `["app"]`, `["app"]`, `["app"]`, ["app"]] Output [null, null, true, false, true, null, true] Explanation Trie trie = new Trie(); trie.insert("apple"); trie.search("apple");   // return True trie.search("app");     // return False trie.startsWith("app"); // return True trie.insert("app"); trie.search("app");     // return True
+
+### Constraints:
+
+- `1 <= word.length, prefix.length <= 2000`
+- `word` and `prefix` consist only of lowercase English letters.
+- At most 3 * 10^4 calls in total will be made to insert, search, and startsWith.
+
+## Approach
+
+```mermaid
+flowchart TD
+  subgraph state["state — root TrieNode; every node holds a children dict and an endOfWord flag"]
+    R["root"] --- C["... children[c] ..."] --- L["endOfWord = True — a word ends here"]
+  end
+  subgraph insert["insert(word)"]
+    I1["cur = root"] --> I2{"more c in word?"}
+    I2 -- yes --> I3{"c not in cur.children?"}
+    I3 -- yes --> I4["cur.children[c] = TrieNode()"]
+    I4 --> I5["cur = cur.children[c]"]
+    I3 -- no --> I5
+    I5 --> I2
+    I2 -- no --> I6["cur.endOfWord = True"]
+    I6 --> I7(["done"])
+  end
+  subgraph search["search(word)"]
+    S1["cur = root"] --> S2{"more c in word?"}
+    S2 -- yes --> S3{"c not in cur.children?"}
+    S3 -- yes --> S4(["return False"])
+    S3 -- no --> S5["cur = cur.children[c]"]
+    S5 --> S2
+    S2 -- no --> S6(["return cur.endOfWord — the path exists, but is it a whole word?"])
+  end
+  subgraph startsWith["startsWith(prefix)"]
+    P1["cur = root"] --> P2{"more c in prefix?"}
+    P2 -- yes --> P3{"c not in cur.children?"}
+    P3 -- yes --> P4(["return False"])
+    P3 -- no --> P5["cur = cur.children[c]"]
+    P5 --> P2
+    P2 -- no --> P6(["return True — every prefix char had a path"])
+  end
+  state --> insert
+  state --> search
+  state --> startsWith
+```
+
+## Solution
+
+```py
+class TrieNode:
+    def __init__(self):
+        self.children = {}
+        self.endOfWord = False
+
+
+class Trie:
+    def __init__(self):
+        self.root = TrieNode()
+
+    def insert(self, word: str) -> None:
+        cur = self.root
+
+        for c in word:
+            if c not in cur.children:
+                cur.children[c] = TrieNode()
+            cur = cur.children[c]
+        cur.endOfWord = True
+
+    def search(self, word: str) -> bool:
+        cur = self.root
+
+        for c in word:
+            if c not in cur.children:
+                return False
+            cur = cur.children[c]
+
+        return cur.endOfWord
+
+    def startsWith(self, prefix: str) -> bool:
+        cur = self.root
+
+        for c in prefix:
+            if c not in cur.children:
+                return False
+            cur = cur.children[c]
+
+        return True
+
+
+# Your Trie object will be instantiated and called as such:
+# obj = Trie()
+# obj.insert(word)
+# param_2 = obj.search(word)
+# param_3 = obj.startsWith(prefix)
+```
+
+## Explanation
+
+<YouTube id="oobqoCJlHA0" title="Implement Trie (Prefix Tree) - Leetcode 208" />

apps/docs/content/trie/211-design-add-and-search-words-data-structure.mdx

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+---
+title: '211. Design Add and Search Words Data Structure'
+description: Design a data structure that supports adding new words and finding if a string matches any previously added string
+sidebar:
+  label: 'Design Add and Search Words Data Structure'
+  badge: 'Medium'
+---
+
+<Badge variant="accent">String</Badge>
+
+### Constraints:
+
+- `1 <= word.length <= 25`
+- `word` in addWord consists of lowercase English letters.
+- `word` in search consist of '.' or lowercase English letters.
+- There will be at most 2 dots in `word` for search queries.
+- At most 10^4 calls will be made to addWord and search.
+
+## Approach
+
+```mermaid
+flowchart TD
+  subgraph state["state — root TrieNode; every node holds a children dict and an endOfWord flag"]
+    R["root"] --- C["... children[c] ..."] --- L["endOfWord = True — a word ends here"]
+  end
+  subgraph addWord["addWord(word)"]
+    A1["cur = root"] --> A2{"more c in word?"}
+    A2 -- yes --> A3{"c not in cur.children?"}
+    A3 -- yes --> A4["cur.children[c] = TrieNode()"]
+    A4 --> A5["cur = cur.children[c]"]
+    A3 -- no --> A5
+    A5 --> A2
+    A2 -- no --> A6["cur.endOfWord = True"]
+    A6 --> A7(["done"])
+  end
+  subgraph search["search(word)"]
+    S1["dfs(0, root) — match from index 0 at the root"] --> S2["cur = root"]
+    S2 --> S3{"more i in range(j, len(word))?"}
+    S3 -- no --> S4(["return cur.endOfWord"])
+    S3 -- yes --> S5["c = word[i]"]
+    S5 --> S6{"c == '.'?"}
+    S6 -- yes --> S7{"more child in cur.children.values()?"}
+    S7 -- yes --> S8{"dfs(i + 1, child)? — try the rest of the word from this child"}
+    S8 -- yes --> S9(["return True"])
+    S8 -- no --> S7
+    S7 -- no --> S10(["return False — no child matched the wildcard"])
+    S6 -- no --> S11{"c not in cur.children?"}
+    S11 -- yes --> S12(["return False"])
+    S11 -- no --> S13["cur = cur.children[c]"]
+    S13 --> S3
+  end
+  state --> addWord
+  state --> search
+```
+
+## Solution
+
+```py
+class TrieNode:
+    def __init__(self):
+        self.children = {}
+        self.endOfWord = False
+
+
+class WordDictionary:
+    def __init__(self):
+        self.root = TrieNode()
+
+    def addWord(self, word: str) -> None:
+        cur = self.root
+        for c in word:
+            if c not in cur.children:
+                cur.children[c] = TrieNode()
+            cur = cur.children[c]
+        cur.endOfWord = True
+
+    # Leverage Recursion
+    def search(self, word: str) -> bool:
+        def dfs(j, root):
+            cur = root
+            for i in range(j, len(word)):
+                c = word[i]
+
+                if c == ".":
+                    for child in cur.children.values():
+                        if dfs(i + 1, child):
+                            return True
+                    return False
+                else:
+                    if c not in cur.children:
+                        return False
+                    cur = cur.children[c]
+            return cur.endOfWord
+
+        return dfs(0, self.root)
+
+
+# Your WordDictionary object will be instantiated and called as such:
+# obj = WordDictionary()
+# obj.addWord(word)
+# param_3 = obj.search(word)
+```
+
+## Explanation
+
+<YouTube id="BTf05gs_8iU" title="Design Add and Search Words Data Structure - Leetcode 211 - Python" />

apps/docs/content/trie/meta.ts

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+import { defineMeta } from "blume";
+
+export default defineMeta({ title: "Trie", order: 15 });