diff --git a/test/external/fuzz_schedule.py b/test/external/fuzz_schedule.py
index 78ec91418d..9a712e9e96 100644
--- a/test/external/fuzz_schedule.py
+++ b/test/external/fuzz_schedule.py
@@ -11,7 +11,7 @@ from tinygrad.tensor import Tensor
 
 ctx_vars = { MULTIOUTPUT: (0, 1) }
 
-def fuzz_schedule(outs: List[LazyBuffer]):
+def fuzz_schedule(outs:List[LazyBuffer]):
   # find toposorts across all tunable params
   unique_ts: Dict[Tuple[LazyBuffer, ...], Tuple[Dict, Dict[LazyBuffer, _LBScheduleItem]]] = {}
   for combination in itertools.product(*ctx_vars.values()):
@@ -66,7 +66,7 @@ def fuzz_schedule(outs: List[LazyBuffer]):
           print(f"FAILED FOR {out}")
           raise e
 
-def _exec_si(si: ScheduleItem, seed:int):
+def _exec_si(si:ScheduleItem, seed:int):
   ei = lower_schedule_item(si)
   if len(capturing): capturing[0].add(ei)
   if isinstance(ei.prg, CustomOp): Tensor._seed = seed
diff --git a/tinygrad/codegen/linearizer.py b/tinygrad/codegen/linearizer.py
index 5d37e68cec..a7203a9fc8 100644
--- a/tinygrad/codegen/linearizer.py
+++ b/tinygrad/codegen/linearizer.py
@@ -52,16 +52,13 @@ class Linearizer(Kernel):
   # NOTE: the consts have to be cached for deduping of downstream uops to work
   def const(self, b:ConstType, dtype:DType=dtypes.int32) -> UOp:
     if isinstance(b, Variable): return self.uops.add(UOps.DEFINE_VAR, dtype, tuple(), b.unbind()[0])
-    else: return self.uops.add(UOps.CONST, dtype, tuple(), dtypes.as_const(b, dtype))
-
-  def cast(self, val: UOp, dtype) -> UOp: return self.uops.add(UOps.CAST, dtype, (val,)) if val.dtype != dtype else val
+    return self.uops.add(UOps.CONST, dtype, tuple(), dtypes.as_const(b, dtype))
 
   def get_reduce_acc(self, reduceop:LazyOp):
-    dtype = reduceop.dtype
-    if reduceop.op is ReduceOps.SUM: return 0.0 if dtypes.is_float(dtype) else 0
+    if reduceop.op is ReduceOps.SUM: return 0.0 if dtypes.is_float(reduceop.dtype) else 0
     elif reduceop.op is ReduceOps.MAX:
-      if dtypes.is_int(dtype): return 0 if dtypes.is_unsigned(dtype) else -2**(dtype.itemsize*8-1)
-      return -math.inf if dtypes.is_float(dtype) else False
+      if dtypes.is_int(reduceop.dtype): return 0 if dtypes.is_unsigned(reduceop.dtype) else -2**(reduceop.dtype.itemsize*8-1)
+      return -math.inf if dtypes.is_float(reduceop.dtype) else False
 
   # NOTE: once images are loaded, we uop them as their base float
   def get_base_dtype(self, dt:DType): return dt.base if isinstance(dt, ImageDType) else dt
diff --git a/tinygrad/renderer/cstyle.py b/tinygrad/renderer/cstyle.py
index d74e9b5f6a..d9d7a5fb86 100644
--- a/tinygrad/renderer/cstyle.py
+++ b/tinygrad/renderer/cstyle.py
@@ -43,9 +43,9 @@ class CStyleLanguage(Renderer):
   def render_const(self, x:ConstType, dtype:DType) -> str:
     if math.isnan(x): val = "NAN"
     elif math.isinf(x): val = ("-" if x < 0 else "") + "INFINITY"
-    elif dtype == dtypes.float64: val = f"{x}"
-    elif dtype == dtypes.bool: val = "1" if x else "0"
-    else: val = f"{x}f" if dtypes.is_float(dtype) else f"{x}"
+    elif dtype is dtypes.bool: val = "1" if x else "0"
+    elif dtype is dtypes.float: val = f"{x}f"
+    else: val = str(x)
     return (self.render_cast([val] * dtype.count, dtype) if dtype.count > 1 or dtype not in [dtypes.float, dtypes.int, dtypes.bool] else val)
 
   # returns a str expression of the loaded value with the output type