diff --git a/docs/abstractions2.py b/docs/abstractions2.py
index 2775fe57f1..07e2670b85 100644
--- a/docs/abstractions2.py
+++ b/docs/abstractions2.py
@@ -84,8 +84,8 @@ a = UOp.metaop(Ops.EMPTY, (1,), dtypes.int32, DEVICE)
 b = UOp.metaop(Ops.EMPTY, (1,), dtypes.int32, DEVICE)
 a.buffer.allocate().copyin(memoryview(bytearray(struct.pack("I", 2))))
 b.buffer.allocate().copyin(memoryview(bytearray(struct.pack("I", 3))))
-del a.srcs
-del b.srcs
+a = a.buf_uop_view()
+b = b.buf_uop_view()
 
 # describe the computation
 out = a.alu(Ops.ADD, b)
diff --git a/test/test_tensor.py b/test/test_tensor.py
index e3a9ceea2a..7ab6c6354a 100644
--- a/test/test_tensor.py
+++ b/test/test_tensor.py
@@ -742,8 +742,8 @@ class TestTensorMetadata(unittest.TestCase):
     y = Tensor.rand(3, requires_grad=True)
     out = x.relu() * y.sigmoid()
     self.assertEqual(out.lazydata.metadata.name, "__mul__")
-    self.assertEqual(out.lazydata.srcs[0].metadata.name, "relu")
-    self.assertEqual(out.lazydata.srcs[1].metadata.name, "sigmoid")
+    self.assertEqual(out.lazydata.src[0].metadata.name, "relu")
+    self.assertEqual(out.lazydata.src[1].metadata.name, "sigmoid")
     si = create_schedule([out.lazydata])[-1]
     self.assertEqual(len(si.metadata), 3)
     self.assertEqual(set(m.name for m in si.metadata), {"relu", "sigmoid", "__mul__"})
diff --git a/tinygrad/engine/schedule.py b/tinygrad/engine/schedule.py
index 28ca361f51..f071a418c8 100644
--- a/tinygrad/engine/schedule.py
+++ b/tinygrad/engine/schedule.py
@@ -69,7 +69,7 @@ def to_uop(buf:UOp, ctx:ScheduleContext, cache:Dict[UOp, UOp]) -> UOp:
   # ASSIGN uses the target buffer, otherwise we create a new buffer
   else:
     buf_uop = buf.buf_uop if buf.op is Ops.ASSIGN else UOp.new_buffer(buf.device, buf.size, dtype)
-    op = buf.replace(dtype=dtype.base, src=tuple(to_uop(x, ctx, cache) for x in buf.srcs))
+    op = buf.replace(dtype=dtype.base, src=tuple(to_uop(x, ctx, cache) for x in buf.src))
   # track the underlying tensor uop for this op
   ctx.tensor_uops[buf_uop] = [buf]
   # (early) bufferize
diff --git a/tinygrad/ops.py b/tinygrad/ops.py
index 73c980a1e1..ffe9eef0ba 100644
--- a/tinygrad/ops.py
+++ b/tinygrad/ops.py
@@ -453,10 +453,6 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     return (self.st is not None and self._device is not None and self.st.consecutive and not self.is_unrealized_const() and
             not isinstance(self.dtype, ImageDType) and self.device.split(":")[0] in view_supported_devices)
   @property
-  def srcs(self): return self.src
-  @srcs.deleter
-  def srcs(self): self.become(self.buf_uop.view(unwrap(self.st)))
-  @property
   def lbs(self): return [self]
   @property
   def metadata(self): return all_metadata.get(self, None)
@@ -511,6 +507,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     if self.op is Ops.BUFFER: return self
     assert self.base.op in {*GroupOp.Buffer, Ops.ASSIGN, Ops.VIEW}, f"buf_uop called on {self.op}"
     return self.src[0].buf_uop
+  def buf_uop_view(self) -> UOp: return self.buf_uop.view(unwrap(self.st))
   @property
   def buffer(self) -> Buffer:
     if self.base.realized is not None: return self.base.realized
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 88feee46ce..2ff65d3874 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -52,8 +52,7 @@ def _fromnp(x: 'np.ndarray') -> UOp:  # type: ignore [name-defined] # noqa: F821
   ret = UOp.metaop(Ops.EMPTY, x.shape, _from_np_dtype(x.dtype), "NPY")
   # fake realize
   ret.buffer.allocate(x)
-  del ret.srcs
-  return ret
+  return ret.buf_uop_view()
 
 def get_shape(x) -> Tuple[int, ...]:
   # NOTE: str is special because __getitem__ on a str is still a str
@@ -70,8 +69,7 @@ def _frompy(x:Union[List, Tuple, bytes], dtype:DType) -> UOp:
     data = struct.pack(f"@{ret.size}{dtype.fmt}", *[truncate_function(xi) for xi in fully_flatten(x)])
   # fake realize
   ret.buffer.allocate(memoryview(data if Device.DEFAULT != "PYTHON" else bytearray(data)))
-  del ret.srcs
-  return ret
+  return ret.buf_uop_view()
 
 def _get_winograd_matcols(mat, dims:int, shp:Tuple[sint, ...], device:Union[str, Tuple[str, ...]], dtype:DType) -> List[List[Tensor]]:
   return [[Tensor.cat(*[Tensor.full(shp[:dim] + (1,) + shp[dim+1:], float(m[k]), device=device, dtype=dtype) for m in mat], dim=dim)
@@ -430,7 +428,7 @@ class Tensor(SimpleMathTrait):
 
     r = Tensor._metaop(Ops.EMPTY, shape, **kwargs)
     r.lazydata.buffer.allocate(external_ptr=ptr)
-    del r.lazydata.srcs # fake realize
+    r.lazydata.buf_uop_view()
     return r
 
   @staticmethod