diff --git a/test/test_renderer_failures.py b/test/test_renderer_failures.py
index 8092914be7..9e0559d44f 100644
--- a/test/test_renderer_failures.py
+++ b/test/test_renderer_failures.py
@@ -1,5 +1,4 @@
 import unittest
-from typing import List, cast
 import numpy as np
 from tinygrad.device import Buffer, Device, is_dtype_supported
 from tinygrad.dtype import dtypes, ConstType
@@ -15,15 +14,15 @@ from tinygrad.tensor import Tensor, _to_np_dtype
 from tinygrad.codegen import full_rewrite
 from tinygrad.engine.realize import lower_schedule_item
 
-def _test_uop_result(inputs:List[Tensor], stores:List[UOp], local_size=None):
+def _test_uop_result(inputs:list[Tensor], stores:list[UOp], local_size=None):
   for x in inputs: x.realize()
   # NOTE: we only toposort the stores
-  uops: List[UOp] = []
-  def _recursive_add(uop:UOp) -> List[UOp]: return flatten([_recursive_add(x) for x in uop.src])+[uop]
+  uops: list[UOp] = []
+  def _recursive_add(uop:UOp) -> list[UOp]: return flatten([_recursive_add(x) for x in uop.src])+[uop]
   uops = dedup(flatten(_recursive_add(st) for st in stores))
   outbufs = [Buffer(Device.DEFAULT, sz:=(1 if local_size is None else prod(local_size)), (dtype:=u.src[1].dtype), \
       initial_value=np.zeros(sz, dtype=_to_np_dtype(dtype)).data) for u in uops if u.op is Ops.STORE]
-  inbufs = [cast(UOp,x.uop).base.buffer for x in inputs]
+  inbufs = [x.uop.base.buffer for x in inputs]
   src = Device[Device.DEFAULT].renderer.render(uops)
   ei = CompiledRunner(ProgramSpec(uops[-1].arg.name if uops[-1].arg is not None else "test",
                                   src, Device.DEFAULT, uops[-1], uops=uops, local_size=local_size))
diff --git a/tinygrad/renderer/ptx.py b/tinygrad/renderer/ptx.py
index b94ff21d61..e61fc3eda1 100644
--- a/tinygrad/renderer/ptx.py
+++ b/tinygrad/renderer/ptx.py
@@ -6,7 +6,7 @@ from tinygrad.uop.ops import Ops, UOp, PatternMatcher, UPat, GroupOp
 from tinygrad.dtype import dtypes, DType, PtrDType, AddrSpace
 from tinygrad.renderer import Renderer
 from tinygrad.renderer.cstyle import CUDARenderer
-from tinygrad.helpers import flatten, get_single_element, prod
+from tinygrad.helpers import flatten, get_single_element, prod, unwrap
 
 def render_val(x, dtype):
   if dtypes.is_float(dtype):
@@ -181,7 +181,7 @@ class PTXRenderer(Renderer):
 
     def ssa(prefix:str, u:UOp|None=None, dtype:str|None=None) -> str:
       nonlocal c, r
-      prefix += f"_{dtype if dtype is not None else self.types[cast(UOp, u).dtype.base]}_"
+      prefix += f"_{dtype if dtype is not None else self.types[unwrap(u).dtype.base]}_"
       c[prefix] += 1
       return f"%{prefix}{c[prefix]-1}"
 
diff --git a/tinygrad/uop/symbolic.py b/tinygrad/uop/symbolic.py
index f22ac72c70..b22590e3a3 100644
--- a/tinygrad/uop/symbolic.py
+++ b/tinygrad/uop/symbolic.py
@@ -1,5 +1,4 @@
 # all of symbolic lives here now
-from typing import cast
 import math, operator, struct, functools
 from collections import defaultdict
 from tinygrad.uop.ops import Ops, PatternMatcher, UPat, UOp, GroupOp, exec_alu
@@ -131,7 +130,7 @@ symbolic_simple = propagate_invalid + PatternMatcher([
 def lt_folding(x:UOp, c:int) -> UOp|None:
   p, np = partition(x.split_uop(Ops.ADD), lambda u: u.const_factor() == 1)
   if np and (d:=math.gcd(*[u.const_factor() for u in np], c)) > 1 and 0 <= sum(u.vmin for u in p) and sum(u.vmax for u in p) < d:
-    return cast(UOp, UOp.sum(*np).divides(d))<(c//d)
+    return unwrap(UOp.sum(*np).divides(d))<(c//d)
   return None
 
 def canonicalize_simplex(X:UOp) -> UOp|None: