diff --git a/tinygrad/schedule/rangeify.py b/tinygrad/schedule/rangeify.py
index 9811450ea7..314ff2eefa 100644
--- a/tinygrad/schedule/rangeify.py
+++ b/tinygrad/schedule/rangeify.py
@@ -530,7 +530,6 @@ add_tags = PatternMatcher([
 ])
 
 # support for using a contiguous permuted view instead of the parent view if one exists
-# modified from kernelize.py to not use ShapeTracker
 
 def found_contiguous(ctx:dict[UOp, UOp], contig:UOp, src:UOp):
   x = src
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index ad26051b8e..0f52ac2d7c 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -162,10 +162,10 @@ class Tensor(OpMixin):
 
     # data might be on a different device
     if isinstance(_device, str): self.uop:UOp = data if data.device == _device else data.copy_to_device(_device)
-    # if device is a tuple, we should have/construct a MultiLazyBuffer
+    # if device is a tuple, we should have/construct a multi-device UOp
     elif isinstance(data.device, str): self.uop = Tensor(data).shard(_device).uop
     else:
-      assert data.device == _device, f"MultiLazyBuffer device mismatch, {data.device} != {_device}"
+      assert data.device == _device, f"multi-device UOp device mismatch, {data.device} != {_device}"
       self.uop = data
 
     # add to all_tensors after construction succeeds
@@ -397,7 +397,7 @@ class Tensor(OpMixin):
     print(t.shard((t.device, t.device), axis=1).uop)
     ```
     """
-    if not isinstance(self.device, str): raise RuntimeError("can't shard a MultiLazyBuffer")
+    if not isinstance(self.device, str): raise RuntimeError("can't shard a multi-device tensor")
     if len(devices) == 1: return self.to(devices[0])
     devices = tuple(canonicalize_device(x) for x in devices)
     mlb = self.uop.shard(devices, self._resolve_dim(axis)) if axis is not None else self.uop.copy_to_device(devices)
diff --git a/tinygrad/uop/divandmod.py b/tinygrad/uop/divandmod.py
index 5779a1f0d1..ce917e33d6 100644
--- a/tinygrad/uop/divandmod.py
+++ b/tinygrad/uop/divandmod.py
@@ -3,7 +3,7 @@ from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp
 from tinygrad.dtype import dtypes
 from tinygrad.helpers import cdiv, cmod, CORRECT_DIVMOD_FOLDING, unwrap
 
-# NOTE: this cache is only on index UOps and matches the cache in the old ShapeTracker in spirit
+# NOTE: this cache is only on index UOps
 @functools.cache
 def fold_divmod_general(d: UOp, correct_divmod_folding: bool) -> UOp|None:
   x, y = d.src
diff --git a/tinygrad/uop/ops.py b/tinygrad/uop/ops.py
index e8cd8bb054..443dd5276d 100644
--- a/tinygrad/uop/ops.py
+++ b/tinygrad/uop/ops.py
@@ -236,7 +236,7 @@ class UOp(OpMixin, metaclass=UOpMetaClass):
       case Ops.RESHAPE:
         if self.src[0]._shape is None: return self.marg
 
-    # movement ops change the shape. this is the logic from the old ShapeTracker
+    # movement ops change the shape
     # NOTE: ssimplify is required because the shape needs to be canonical for broadcasting and same shape checking
     if self.op in GroupOp.Movement.union({Ops.MULTI, Ops.REDUCE_AXIS, Ops.WMMA}):
       ps = self.src[0]._shape
@@ -465,14 +465,12 @@ class UOp(OpMixin, metaclass=UOpMetaClass):
     return UOp(Ops.ALLREDUCE, self.dtype, (self, UOp(Ops.DEVICE, arg=device) if not isinstance(device, UOp) else device), op)
   def overflows(self, dtype:DType) -> bool: return self.vmin < dtype.min or dtype.max < self.vmax
 
-  # *** ShapeTracker helpers ***
-
   def split_uop(self:UOp, sep:Ops):
     if self.op is sep:
       for s in self.src: yield from s.split_uop(sep)
     else: yield self
 
-  # *** from MultiLazyBuffer ***
+  # *** multi-device helpers ***
 
   def multi(self, axis:int|None):
     assert isinstance(self.device, tuple), f"multi device must be tuple, {self.device} isn't"
@@ -514,8 +512,6 @@ class UOp(OpMixin, metaclass=UOpMetaClass):
     return self.shrink(tuple((0,s) if i != axis else (dnum*sz,dnum*sz+sz) for i,s in enumerate(self.shape)))
   def shard(self, devices:tuple[str, ...], axis:int) -> UOp: return self.copy_to_device(devices)._shard(axis).multi(axis)
 
-  # *** from LazyBuffer ***
-
   def copy_to_device(self, device:str|tuple[str, ...]|UOp, arg=None):
     assert arg is None or isinstance(self.device, tuple)
     inp = self if arg is None else UOp(Ops.MSELECT, self.dtype, src=(self,), arg=arg)