diff --git a/doc/releases/0.100.6.rst b/doc/releases/0.100.6.rst
index 99f34f534f..7f2bb5cd66 100644
--- a/doc/releases/0.100.6.rst
+++ b/doc/releases/0.100.6.rst
@@ -7,6 +7,7 @@ SpikeInterface 0.100.6 release notes
 
 Minor release with bug fixes
 
+* Avoid np.prod in make_shared_array (#2621)
 * Improve caching of MS5 sorter (#2690)
 * Allow for remove_excess_spikes to remove negative spike times (#2716)
 * Update ks4 wrapper for newer version>=4.0.3 (#2701, #2774)
diff --git a/doc/whatisnew.rst b/doc/whatisnew.rst
index 48f566087f..2ba199eb94 100644
--- a/doc/whatisnew.rst
+++ b/doc/whatisnew.rst
@@ -40,11 +40,18 @@ Release notes
   releases/0.9.1.rst
 
 
+Version 0.100.6
+===============
+
+* Minor release with bug fixes
+
+
 Version 0.100.5
 ===============
 
 * Minor release with bug fixes
 
+
 Version 0.100.4
 ===============
 
diff --git a/src/spikeinterface/core/core_tools.py b/src/spikeinterface/core/core_tools.py
index 3b82436d5c..3725fcfba8 100644
--- a/src/spikeinterface/core/core_tools.py
+++ b/src/spikeinterface/core/core_tools.py
@@ -7,6 +7,7 @@
 import json
 from copy import deepcopy
 
+from math import prod
 
 import numpy as np
 from tqdm import tqdm
@@ -163,7 +164,8 @@ def make_shared_array(shape, dtype):
     from multiprocessing.shared_memory import SharedMemory
 
     dtype = np.dtype(dtype)
-    nbytes = int(np.prod(shape) * dtype.itemsize)
+    shape = tuple(int(x) for x in shape)  # We need to be sure that shape comes in int instead of numpy scalars
+    nbytes = prod(shape) * dtype.itemsize
     shm = SharedMemory(name=None, create=True, size=nbytes)
     arr = np.ndarray(shape=shape, dtype=dtype, buffer=shm.buf)
     arr[:] = 0
diff --git a/src/spikeinterface/core/waveform_tools.py b/src/spikeinterface/core/waveform_tools.py
index f9e39382df..8864ae0d39 100644
--- a/src/spikeinterface/core/waveform_tools.py
+++ b/src/spikeinterface/core/waveform_tools.py
@@ -483,7 +483,7 @@ def extract_waveforms_to_single_buffer(
     if sparsity_mask is None:
         num_chans = recording.get_num_channels()
     else:
-        num_chans = max(np.sum(sparsity_mask, axis=1))
+        num_chans = int(max(np.sum(sparsity_mask, axis=1)))  # This is a numpy scalar, so we cast to int
     shape = (num_spikes, nsamples, num_chans)
 
     if mode == "memmap":