diff --git a/examples/beautiful_mnist.py b/examples/beautiful_mnist.py
index cc0cba2e25..45feaa9779 100644
--- a/examples/beautiful_mnist.py
+++ b/examples/beautiful_mnist.py
@@ -27,7 +27,7 @@ if __name__ == "__main__":
   def train_step() -> Tensor:
     with Tensor.train():
       opt.zero_grad()
-      samples = Tensor.randint(512, high=X_train.shape[0])
+      samples = Tensor.randint(getenv("BS", 512), high=X_train.shape[0])
       # TODO: this "gather" of samples is very slow. will be under 5s when this is fixed
       loss = model(X_train[samples]).sparse_categorical_crossentropy(Y_train[samples]).backward()
       opt.step()
diff --git a/tinygrad/codegen/lowerer.py b/tinygrad/codegen/lowerer.py
index e9a13f7fc9..944a1116aa 100644
--- a/tinygrad/codegen/lowerer.py
+++ b/tinygrad/codegen/lowerer.py
@@ -23,7 +23,7 @@ render_ops: Any = { NumNode: lambda self, ops, ctx: UOp.const(dtypes.int, self.b
   AndNode: lambda self,ops,ctx: functools.reduce(lambda a,b: a*b.render(ops, ctx), self.nodes[1:], self.nodes[0].render(ops,ctx)) }
 
 if getenv("UOP_IS_SYMBOLIC"):
-  # TODO: change this once UOps is ready to replace symbolic. note: this doesn't work for variable shapetrackers now
+  # TODO: change this once UOps is ready to replace symbolic
   def _uop_view(view:View, idxs:List[UOp], vexpr:UOp) -> Tuple[UOp, UOp]:
     # TODO: dtypes.realint
     iexpr = variable_to_uop(view.offset)