diff --git a/04_modelling.md b/04_modelling.md
index f9d8c95..dbae64b 100644
--- a/04_modelling.md
+++ b/04_modelling.md
@@ -447,9 +447,9 @@ consider, although such scenarios are rare in practical applications.
 
 > [!TIP]
 >
-> If you have long sums of variables and coefficients, it can be more efficient to
-> use the sum-methods of LinearExpr than to use Python's sum-function. Note that
-> this function does currently not support generators.
+> If you have long sums of variables and coefficients, it can be more efficient
+> to use the sum-methods of LinearExpr than to use Python's sum-function. Note
+> that this function does currently not support generators.
 >
 > ```python
 > xs = [model.NewIntVar(0, 10, f"x{i}") for i in range(5)]
diff --git a/README.md b/README.md
index d431d92..dcc0625 100644
--- a/README.md
+++ b/README.md
@@ -885,6 +885,19 @@ multiplying all terms by 3. However, this requires manual intervention, which
 undermines the idea of using a solver. These limitations are important to
 consider, although such scenarios are rare in practical applications.
 
+> [!TIP]
+>
+> If you have long sums of variables and coefficients, it can be more efficient
+> to use the sum-methods of LinearExpr than to use Python's sum-function. Note
+> that this function does currently not support generators.
+>
+> ```python
+> xs = [model.NewIntVar(0, 10, f"x{i}") for i in range(5)]
+> weights = [i for i in range(5)]
+> model.add(cp_model.LinearExpr.sum(xs) >= 1)
+> model.minimize(cp_model.LinearExpr.weighted_sum(xs, weights))
+> ```
+
 If you have a lower and an upper bound for a linear expression, you can also use
 the `add_linear_constraint`-method, which allows you to specify both bounds in
 one go.
diff --git a/tests/test_lin_constraints.py b/tests/test_lin_constraints.py
index 3a504df..e7a16db 100644
--- a/tests/test_lin_constraints.py
+++ b/tests/test_lin_constraints.py
@@ -20,14 +20,13 @@ def test_lin_constraints():
     model.add(x < y + z)
     model.add(y > 300 - 4 * z)
 
+
 def test_lin_constraint_sum():
     model = cp_model.CpModel()
     vars = [model.NewIntVar(0, 10, f"x{i}") for i in range(5)]
     weights = [i for i in range(5)]
     model.add(cp_model.LinearExpr.sum(vars) >= 1)
     model.minimize(cp_model.LinearExpr.weighted_sum(vars, weights))
-    
-
 
 
 def test_infeasible_intersection():