docs(docs): add MDX page for “230. Kth Smallest Element in a BST”

apps/docs/content/(tree)/230-kth-smallest-element-in-a-bst.mdx

@@ -0,0 +1,54 @@
+---
+title: '230. Kth Smallest Element in a BST'
+description: Given the root of a binary search tree, and an integer k, return the k^th smallest value (1-indexed) of all the values of the nodes in the tree
+sidebar:
+  label: 'Kth Smallest Element in a BST'
+  badge: 'Medium'
+---
+
+<Badge variant="accent">Binary Search Tree</Badge>
+
+::::warning
+If the BST is modified often (i.e., we can do insert and delete operations) and you need to find the kth smallest frequently, how would you optimize?
+::::
+
+### Example 1:
+- Input: `root = [3,1,4,null,2], k = 1`
+- Output: `1`
+
+### Example 2:
+- Input: `root = [5,3,6,2,4,null,null,1], k = 3`
+- Output: `3`
+
+### Constraints:
+
+- The number of nodes in the tree is n.
+- `1 <= k <= n <= 10^4`
+- `0 <= Node.val <= 10^4`
+
+## Solution
+
+```py
+# Definition for a binary tree node.
+# class TreeNode:
+#     def __init__(self, val=0, left=None, right=None):
+#         self.val = val
+#         self.left = left
+#         self.right = right
+class Solution:
+    def kthSmallest(self, root: Optional[TreeNode], k: int) -> int:
+        n = 0
+        stack = []
+        cur = root
+
+        while cur or stack:
+            while cur:
+                stack.append(cur)
+                cur = cur.left
+
+            cur = stack.pop()
+            n += 1
+            if n == k:
+                return cur.val
+            cur = cur.right
+```