diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index dca3c646..fe681410 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,12 +1,12 @@
 exclude: ^python/tests/__snapshots__/
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.14.3
+    rev: v0.14.10
     hooks:
       - id: ruff-check
         args: [--fix]
       - id: ruff-format
   - repo: https://github.com/astral-sh/uv-pre-commit
-    rev: 0.9.7
+    rev: 0.9.18
     hooks:
       - id: uv-lock
diff --git a/python/egglog/__init__.py b/python/egglog/__init__.py
index 7d20dfdb..66e7402b 100644
--- a/python/egglog/__init__.py
+++ b/python/egglog/__init__.py
@@ -4,7 +4,7 @@
 
 from . import config, ipython_magic  # noqa: F401
 from .bindings import EggSmolError, StageInfo, TimeOnly, WithPlan  # noqa: F401
-from .builtins import *  # noqa: UP029
+from .builtins import *
 from .conversion import *
 from .deconstruct import *
 from .egraph import *
diff --git a/python/tests/test_array_api.py b/python/tests/test_array_api.py
index 34564ddc..f3c50a7f 100644
--- a/python/tests/test_array_api.py
+++ b/python/tests/test_array_api.py
@@ -210,11 +210,14 @@ def linalg_norm(X: NDArray, axis: TupleIntLike) -> NDArray:
     return NDArray(
         outshape,
         X.dtype,
-        lambda k: LoopNestAPI.from_tuple(reduce_axis)
-        .unwrap()
-        .indices()
-        .foldl_value(lambda carry, i: carry + ((x := X.index(i + k)).conj() * x).real(), init=0.0)
-        .sqrt(),
+        lambda k: (
+            LoopNestAPI
+            .from_tuple(reduce_axis)
+            .unwrap()
+            .indices()
+            .foldl_value(lambda carry, i: carry + ((x := X.index(i + k)).conj() * x).real(), init=0.0)
+            .sqrt()
+        ),
     )
 
 
@@ -224,9 +227,11 @@ def linalg_norm_v2(X: NDArrayLike, axis: TupleIntLike) -> NDArray:
     return NDArray(
         X.shape.deselect(axis),
         X.dtype,
-        lambda k: ndindex(X.shape.select(axis))
-        .foldl_value(lambda carry, i: carry + ((x := X.index(i + k)).conj() * x).real(), init=0.0)
-        .sqrt(),
+        lambda k: (
+            ndindex(X.shape.select(axis))
+            .foldl_value(lambda carry, i: carry + ((x := X.index(i + k)).conj() * x).real(), init=0.0)
+            .sqrt()
+        ),
     )