Demos: ensure 720px wide, reduce indentation

orx-kdtree/src/jvmDemo/kotlin/DemoNearestNeighbour01.kt

@@ -3,8 +3,8 @@ import org.openrndr.extra.kdtree.kdTree
 import org.openrndr.math.Vector2
 
 /**
- * Initializes an interactive graphical application that displays 1000 randomly distributed 2D points
- * on a canvas of dimensions 1280x720. The points are organized into a KD-tree for efficient spatial querying.
+ * Initializes an interactive graphical application that displays 1000 randomly distributed 2D points.
+ * The points are organized into a KD-tree for efficient spatial querying.
  *
  * Key functionality:
  * - Displays the points as small circles on the canvas.
@@ -14,24 +14,22 @@ import org.openrndr.math.Vector2
  * - KD-tree structure enables efficient nearest-neighbor searches.
  * - The nearest point to the cursor is determined and visually emphasized in real-time as the cursor moves.
  */
-fun main() {
-    application {
-        configure {
-            width = 1280
-            height = 720
+fun main() = application {
+    configure {
+        width = 720
+        height = 720
+    }
+    program {
+        val points = MutableList(1000) {
+            Vector2(Math.random() * width, Math.random() * height)
         }
-        program {
-            val points = MutableList(1000) {
-                Vector2(Math.random() * width, Math.random() * height)
-            }
-            val tree = points.kdTree()
-            extend {
-                drawer.circles(points, 5.0)
-                val nearest = tree.findNearest(mouse.position)
-                nearest?.let {
-                    drawer.circle(it.x, it.y, 20.0)
-                }
+        val tree = points.kdTree()
+        extend {
+            drawer.circles(points, 5.0)
+            val nearest = tree.findNearest(mouse.position)
+            nearest?.let {
+                drawer.circle(it.x, it.y, 20.0)
             }
         }
     }
-}
+}