diff --git a/content/posts/2024-08-17-kd-tree-revisited/index.md b/content/posts/2024-08-17-kd-tree-revisited/index.md
new file mode 100644
index 0000000..be15f48
--- /dev/null
+++ b/content/posts/2024-08-17-kd-tree-revisited/index.md
@@ -0,0 +1,112 @@
+---
+title: "Kd Tree Revisited"
+date: 2024-08-17T14:20:22+01:00
+draft: false # I don't care for draft mode, git has branches for that
+description: "Simplifying the nearest neighbour search"
+tags:
+  - algorithms
+  - data structures
+  - python
+categories:
+  - programming
+series:
+  - Cool algorithms
+favorite: false
+disable_feed: false
+---
+
+After giving it a bit of thought, I've found a way to simplify the nearest
+neighbour search (i.e: the `closest` method) for the `KdTree` I implemented in
+[my previous post]({{< relref "../2024-08-10-kd-tree/index.md" >}}).
+
+<!--more-->
+
+## The improvement
+
+That post implemented the nearest neighbour search by keeping track of the
+tree's boundaries (through `AABB`), and each of its sub-trees (through
+`AABB.split`), and testing for the early exit condition by computing the
+distance of the search's origin to each sub-tree's boundaries.
+
+Instead of _explicitly_ keeping track of each sub-tree's boundaries, we can
+implicitly compute it when recursing down the tree.
+
+To check for the distance between the queried point and the splitting plane of
+inner nodes: we simply need to project the origin onto that plane, thus giving
+us a minimal bound on the distance of the points stored on the other side.
+
+This can be easily computed from the `axis` and `mid` values which are stored in
+the inner nodes: to project the node on the plane we simply replace its
+coordinate for this axis by `mid`.
+
+## Simplified search
+
+With that out of the way, let's now see how `closest` can be implemented without
+needing to track the tree's `AABB` at the root:
+
+```python
+# Wrapper type for closest points, ordered by `distance`
+@dataclasses.dataclass(order=True)
+class ClosestPoint[T](NamedTuple):
+    point: Point = field(compare=False)
+    value: T = field(compare=False)
+    distance: float
+
+class KdTree[T]:
+    def closest(self, point: Point, n: int = 1) -> list[ClosestPoint[T]]:
+        assert n > 0
+        res = MaxHeap()
+        # Instead of passing an `AABB`, we give an initial projection point,
+        # the query origin itself (since we haven't visited any split node yet)
+        self._root.closest(point, res, n, point)
+        return sorted(res)
+
+class KdNode[T]:
+    def closest(
+        self,
+        point: Point,
+        out: MaxHeap[ClosestPoint[T]],
+        n: int,
+        projection: Point,
+    ) -> None:
+        # Same implementation
+        self.inner.closest(point, out, n, bounds)
+
+class KdLeafNode[T]:
+    def closest(
+        self,
+        point: Point,
+        out: MaxHeap[ClosestPoint[T]],
+        n: int,
+        projection: Point,
+    ) -> None:
+        # Same implementation
+        for p, val in self.points.items():
+            item = ClosestPoint(p, val, dist(p, point))
+            if len(out) < n:
+                out.push(item)
+            elif out.peek().distance > item.distance:
+                out.pushpop(item)
+
+class KdSplitNode[T]:
+    def closest(
+        self,
+        point: Point,
+        out: list[ClosestPoint[T]],
+        n: int,
+        projection: Point,
+    ) -> None:
+        index = self._index(point)
+        self.children[index].closest(point, out, n, projection)
+        # Project onto the splitting plane, for a minimum distance to its points
+        projection = projection.replace(self.axis, self.mid)
+        # If we're at capacity and can't possibly find any closer points, exit
+        if len(out) == n and dist(point, projection) > out.peek().distance:
+            return
+        # Otherwise recurse on the other side to check for nearer neighbours
+        self.children[1 - index].closest(point, out, n, projection)
+```
+
+As you can see, the main difference is in `KdSplitNode`'s implementation, where
+we can quickly compute the minimum distance between the search's origin and all
+potential points in that subspace.