diff --git a/test/test_tensor.py b/test/test_tensor.py
index 80ca087b0d..e8a0f16f91 100644
--- a/test/test_tensor.py
+++ b/test/test_tensor.py
@@ -303,6 +303,18 @@ class TestTinygrad(unittest.TestCase):
     # force device copy - to() is opt'd away - Tensor(dev)/1 is ignored
     np.testing.assert_allclose(ua_arr, (Tensor(ua_arr)/Tensor(1)).numpy())
 
+  def test_item_to_tensor_to_item(self):
+    for a in [0, 1, 2, 3, -1, -100, 100, -101.1, 2.345, 100.1, True, False]:
+      item = Tensor(a).item()
+      assert type(item) == type(a), a
+      np.testing.assert_allclose(item, a), a
+      buffered_item = Tensor([a]).item()
+      assert type(buffered_item) == type(a), a
+      np.testing.assert_allclose(buffered_item, a), a
+      reshaped_item = Tensor([a]).reshape((1, 1, 1, 1, 1)).item()
+      assert type(reshaped_item) == type(a), a
+      np.testing.assert_allclose(reshaped_item, a), a
+
 class TestZeroShapeTensor(unittest.TestCase):
   def test_shape_stride(self):
     t = Tensor.rand(3, 2, 0)
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index a91ecc0ad3..c47d5df8e2 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -118,9 +118,9 @@ class Tensor:
   def detach(self) -> Tensor: return Tensor(self.lazydata, device=self.device, requires_grad=False)
 
   # TODO: these are good places to start removing numpy
-  def item(self) -> Union[float, int]:
+  def item(self) -> Union[float, int, bool]:
     assert self.numel() == 1, "must have one element for item"
-    return cast(Buffer, self.contiguous().realize().lazydata.realized).toCPU().item()
+    return cast(Buffer, self.contiguous().realize().lazydata.base.realized).toCPU().item()
   def data(self) -> memoryview: return self.numpy().data
 
   # TODO: this should import numpy and use .data() to construct the array
@@ -128,7 +128,7 @@ class Tensor:
     assert all_int(self.shape), f"no numpy if shape is symbolic, {self.shape=}"
     assert self.dtype.np is not None, f"no numpy dtype for {self.dtype}"
     if 0 in self.shape: return np.zeros(self.shape, dtype=self.dtype.np)
-    return self.detach().cast(dtypes.from_np(self.dtype.np)).contiguous().to('CPU').realize().lazydata.base.realized.toCPU().astype(self.dtype.np, copy=True).reshape(self.shape)  # noqa: E501
+    return self.cast(self.dtype.scalar()).contiguous().realize().lazydata.base.realized.toCPU().astype(self.dtype.np, copy=True).reshape(self.shape)
 
   def to(self, device:Optional[str]) -> Tensor:
     if device is None or device == self.device: return self