diff --git a/2018/day23-input b/2018/day23-input
new file mode 100644
index 0000000..a62d523
--- /dev/null
+++ b/2018/day23-input
@@ -0,0 +1,1000 @@
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+pos=<30909212,-6788078,64806357>, r=72854974
+pos=<55103952,23718679,78056077>, r=79792693
+pos=<81558894,16473383,56890444>, r=92327281
+pos=<-35677121,21362681,45633755>, r=60800540
+pos=<47781727,78466337,51608305>, r=96459543
+pos=<67173768,22063676,61734189>, r=77196289
+pos=<28600570,62264860,52363826>, r=61832422
+pos=<-16873581,-4279973,46227194>, r=68233097
+pos=<48128855,12001409,69590877>, r=76069932
+pos=<3970778,16725836,103187042>, r=83342764
+pos=<-3902795,-16226599,73717169>, r=94698904
+pos=<69143773,21219270,49278858>, r=67554968
+pos=<25455135,-25465662,42822008>, r=64094214
+pos=<88817272,24843336,55383440>, r=89708720
diff --git a/2018/day23.py b/2018/day23.py
new file mode 100644
index 0000000..e866ca4
--- /dev/null
+++ b/2018/day23.py
@@ -0,0 +1,45 @@
+with open('day23-input', 'r') as file:
+  data = [l.strip('\n') for l in file]
+import numpy as np
+import re
+
+numbers = [[int(s) for s in re.findall(r'-?\d+', d)] for d in data]
+arr = np.array(numbers, dtype=np.int64)
+
+def nanobots_in_range_of(index):
+  delta_positions = arr[:,:3] - arr[index,:3]
+  distances = np.abs(delta_positions).sum(axis=1)
+  return (distances <= arr[index, 3]).sum()
+
+largest_rad_bot = arr[:,3].argmax()
+print(nanobots_in_range_of(largest_rad_bot))  # Part 1
+
+
+min_coords = arr[:,:3].min(axis=0)
+max_coords = arr[:,:3].max(axis=0)
+coords_range = max_coords - min_coords
+
+# This is an astronomical number of potential coordinates. We can evaluate them at a resolution of 200,000 with int32s for 11GiB
+def nanobots_ranging(x, y, z, downsample=1):
+  delta_positions = (arr[:,:3]//downsample) - np.array((x,y,z))
+  distances = np.abs(delta_positions).sum(axis=1)
+  return (distances <= (arr[:,3]//downsample)).sum()
+
+
+def downsample_survey(start_coords, end_coords, ds_factor):
+  points_ds = np.zeros(tuple(end_coords-start_coords), dtype=np.int32)
+  for x in range(start_coords[0], end_coords[0]):
+    for y in range(start_coords[1], end_coords[1]):
+      for z in range(start_coords[2], end_coords[2]):
+        points_ds[x-start_coords[0],y-start_coords[1],z-start_coords[2]] = nanobots_ranging(x, y, z, ds_factor)
+  return points_ds
+
+
+ds_factor = 10000000
+mins_ds = min_coords//ds_factor
+maxs_ds = max_coords//ds_factor
+points_ds = downsample_survey(mins_ds, maxs_ds, ds_factor)
+
+hotspots = (np.argwhere(points_ds > points_ds.max()*0.9)+mins_ds)*ds_factor
+
+