diff --git a/tinygrad/codegen/opt/heuristic.py b/tinygrad/codegen/opt/heuristic.py
index b0d0b97d8c..1867b2eafc 100644
--- a/tinygrad/codegen/opt/heuristic.py
+++ b/tinygrad/codegen/opt/heuristic.py
@@ -27,15 +27,15 @@ def hand_coded_optimizations(k:Scheduler) -> Scheduler:
   # NOTE: unless TC_OPT is > 0, we only trigger tensor cores if there's only one reduce axis
   if USE_TC > 0 and (len(k.axes_of(AxisType.GROUP_REDUCE, AxisType.REDUCE)) == 1 or (TC_OPT.value >= 1)):
     good_tc_opt = False
+    tk = k.copy()
     try: # check TC first and apply hand-coded opts if successful
-      tk = k.copy()
       rngs = tk.apply_opt(Opt(OptOps.TC, 0, (TC_SELECT.value, TC_OPT.value, USE_TC.value)))
       good_tc_opt = True
     except KernelOptError:
       pass
-    if good_tc_opt:
-      # skip hand-coded TC opts if AMX, upcasting will make kernel slower
-      if rngs is not None and not AMX:
+    # skip hand-coded TC opts if AMX, upcasting will make kernel slower
+    if good_tc_opt and not AMX:
+      if rngs is not None:
         for tc_dim in [1,0]: # attempt to upcast M and N
           szs = [sz for sz in [5,4,3,2] if rngs[tc_dim].src[0].divides(sz) is not None]
           if szs:
@@ -149,7 +149,6 @@ def hand_coded_optimizations(k:Scheduler) -> Scheduler:
 
   # if nothing at all is upcasted and it's easy to, do an upcast
   for splits in [4]:
-    # TODO: somehow this never hits a reduce
     if not k.upcasted and k.upcastable_dims and k.full_shape[k.upcastable_dims[-1]] % splits == 0:
       k.apply_opt(Opt(OptOps.UPCAST, k.upcastable_dims[-1], splits))