diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 7da3597..38042a1 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -2,13 +2,13 @@
 # See https://pre-commit.com/hooks.html for more hooks
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v6.0.0
     hooks:
     -   id: trailing-whitespace
     -   id: end-of-file-fixer
     -   id: check-yaml
     -   id: check-added-large-files
 -   repo: https://github.com/igorshubovych/markdownlint-cli
-    rev: v0.45.0
+    rev: v0.49.0
     hooks:
     -   id: markdownlint-fix
diff --git a/examples/ml/fashion-mnist-robustness/spec.vcl b/examples/ml/fashion-mnist-robustness/spec.vcl
index 188dc42..7795691 100644
--- a/examples/ml/fashion-mnist-robustness/spec.vcl
+++ b/examples/ml/fashion-mnist-robustness/spec.vcl
@@ -4,7 +4,7 @@ p : Real
 
 @logic
 capucciAdditive : DifferentiableLogic
-capucciAdditive = 
+capucciAdditive =
   { trueElement                = -infinity
   , falseElement               = infinity
   , pointwiseNegation          = \x -> -x
@@ -19,4 +19,3 @@ capucciAdditive =
   , reduceConjunction          = \xs -> (1/p) * log(reduceAdd (exp (p * xs)))
   , reduceDisjunction          = \xs -> (1/p) * log(reduceAdd (exp (-p * xs)))
   }
-
diff --git a/examples/ml/fashion-mnist-robustness/train.py b/examples/ml/fashion-mnist-robustness/train.py
index 4f34b0f..39ddd47 100644
--- a/examples/ml/fashion-mnist-robustness/train.py
+++ b/examples/ml/fashion-mnist-robustness/train.py
@@ -26,7 +26,7 @@ def network(x: torch.Tensor) -> torch.Tensor:
 
 declarations = loss_pt.load_specification(
     "spec.vcl",
-    logic=vcl.DifferentiableLogic.Vehicle, 
+    logic=vcl.DifferentiableLogic.Vehicle,
     #logic=vcl.DifferentiableLogic.Custom(cappuci)
 )
 
@@ -49,4 +49,4 @@ def network(x: torch.Tensor) -> torch.Tensor:
         total_loss = alpha * task_loss + (1.0 - alpha) * constraint_loss
 
         total_loss.backward()
-        optimizer.step()
\ No newline at end of file
+        optimizer.step()