diff --git a/test/test_schedule.py b/test/test_schedule.py
index 5037b47748..f333e89a7c 100644
--- a/test/test_schedule.py
+++ b/test/test_schedule.py
@@ -445,7 +445,7 @@ class TestSchedule(unittest.TestCase):
 
   def test_double_from(self):
     x = Tensor([1,2,3,4])
-    out = x.to('python')
+    out = x.to('npy')
     check_schedule(out, 0, filter_loadops=False)
 
   def test_pow_const_tensor_simplified(self):
diff --git a/test/test_tensor.py b/test/test_tensor.py
index df9c17b09d..c791aa2c79 100644
--- a/test/test_tensor.py
+++ b/test/test_tensor.py
@@ -320,6 +320,14 @@ class TestTinygrad(unittest.TestCase):
     # data = data + [-x for x in data]
     # np.testing.assert_allclose(Tensor(data, dtype=dtypes.int32).numpy(), np.array(data, dtype=np.int32))
 
+  def test_tensor_list_ndarray(self):
+    data = [np.array([1, 2, 3]), np.array([1, 2, 3]), np.array([1, 2, 3])]
+    np.testing.assert_equal(Tensor(data).numpy(), np.array(data))
+    data = [np.array([1.0, 2.0, 3.0]), np.array([1, 2, 3]), np.array([1, 2, 3])]
+    np.testing.assert_equal(Tensor(data).numpy(), np.array(data))
+    data = [np.array(1.0), np.array(2.0), np.array(3.0)]
+    np.testing.assert_equal(Tensor(data).numpy(), np.array(data))
+
   def test_tensor_bytes(self):
     data = b"abc123"
     t = Tensor(data)
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 7896e4c00f..8b6a2d4cb6 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -46,6 +46,13 @@ def _loadop(op, shape:Tuple[sint,...], dtype:DType, device:Union[str, Tuple[str,
 def _from_np_dtype(npdtype:type) -> DType: return dtypes.fields()[np.dtype(npdtype).name]
 def _to_np_dtype(dtype:DType) -> Optional[type]: return np.dtype(dtype.fmt).type if dtype.fmt is not None else None
 
+def _fromnp(x: np.ndarray) -> LazyBuffer:
+  ret = LazyBuffer.loadop(LoadOps.EMPTY, x.shape, _from_np_dtype(x.dtype), "NPY")
+  # fake realize
+  ret.buffer.allocate(x)
+  del ret.srcs
+  return ret
+
 def _frompy(x:Union[List, Tuple, bytes], dtype:DType) -> LazyBuffer:
   if isinstance(x, bytes): ret, data = LazyBuffer.loadop(LoadOps.EMPTY, (len(x),), dtype, "PYTHON"), x
   else:
@@ -117,20 +124,15 @@ class Tensor:
     elif isinstance(data, Variable): data = _loadop(LoadOps.CONST, tuple(), dtype or dtypes.from_py(data.unbind()[1]), device, data)
     elif isinstance(data, bytes): data = _frompy(data, dtypes.uint8)
     elif isinstance(data, (list, tuple)):
-      if dtype is None: dtype = dtypes.from_py(data)
-      if dtype == dtypes.bfloat16: data = Tensor(_frompy(data, dtypes.float32), device=device).cast(dtypes.bfloat16).lazydata
-      else: data = _frompy(data, dtype)
+      if dtype is None:
+        if (d := fully_flatten(data)) and all(isinstance(s, bool) for s in d): dtype = dtypes.bool
+        else: dtype = dtypes.default_int if d and all_int(d) else dtypes.default_float
+      if dtype == dtypes.bfloat16: data = Tensor(_fromnp(np.array(data, np.float32)), device=device).cast(dtypes.bfloat16).lazydata
+      else: data = _fromnp(np.array(data, _to_np_dtype(dtype)))
     elif data is None: data = _loadop(LoadOps.EMPTY, (0,), dtype or dtypes.default_float, device)
     elif isinstance(data, np.ndarray):
       if data.shape == (): data = _loadop(LoadOps.CONST, tuple(), dtype or _from_np_dtype(data.dtype), device, data.item())
-      else:
-        def _fromnp(x: np.ndarray) -> LazyBuffer:
-          ret = LazyBuffer.loadop(LoadOps.EMPTY, x.shape, _from_np_dtype(x.dtype), "NPY")
-          # fake realize
-          ret.buffer.allocate(x)
-          del ret.srcs
-          return ret
-        data = _fromnp(data.astype(npdtype) if dtype is not None and (npdtype:=_to_np_dtype(dtype)) is not None else data)
+      else: data = _fromnp(data.astype(npdtype) if dtype is not None and (npdtype:=_to_np_dtype(dtype)) is not None else data)
 
     # by this point, it has to be a LazyBuffer
     if not isinstance(data, (LazyBuffer, MultiLazyBuffer)):