diff --git a/examples/ndarray/jit-expr.py b/examples/ndarray/jit-expr.py
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+++ b/examples/ndarray/jit-expr.py
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+#######################################################################
+# Copyright (c) 2019-present, Blosc Development Team <blosc@blosc.org>
+# All rights reserved.
+#
+# This source code is licensed under a BSD-style license (found in the
+# LICENSE file in the root directory of this source tree)
+#######################################################################
+
+# Examples of using the jit decorator with expressions
+# You can find benchmarks for this example in the bench/ndarray directory
+
+import blosc2
+import numpy as np
+
+# Example 1: Basic usage of the jit decorator
+@blosc2.jit
+def expr_jit(a, b, c):
+    # This function computes a boolean array where the condition is met
+    return ((a ** 3 + np.sin(a * 2)) < c) & (b > 0)
+
+# Create some sample data
+a = blosc2.linspace(0, 1, 10 * 100, dtype="float32", shape=(10, 100))
+b = blosc2.linspace(1, 2, 10 * 100, dtype="float32", shape=(10, 100))
+c = blosc2.linspace(-10, 10, 10, dtype="float32", shape=(100,))
+
+# Call the function with the jit decorator
+result = expr_jit(a, b, c)
+print(result[1,:10])
+
+# Example 2: Using the jit decorator with an out parameter
+out = blosc2.zeros((10, 100), dtype=np.bool_)
+
+@blosc2.jit(out=out)
+def expr_jit_out(a, b, c):
+    # This function computes a boolean array and stores the result in the 'out' array
+    return ((a ** 3 + np.sin(a * 2)) < c) & (b > 0)
+
+# Call the function with the jit decorator and out parameter
+result_out = expr_jit_out(a, b, c)
+print(result_out[1,:10])
+print(out[1,:10])  # The 'out' array should now contain the same result
+
+# Example 3: Using the jit decorator with additional keyword arguments
+cparams = blosc2.CParams(clevel=1, codec=blosc2.Codec.LZ4, filters=[blosc2.Filter.BITSHUFFLE])
+
+@blosc2.jit(cparams=cparams)
+def expr_jit_cparams(a, b, c):
+    # This function computes a boolean array with custom compression parameters
+    return ((a ** 3 + np.sin(a * 2)) < c) & (b > 0)
+
+# Call the function with the jit decorator and custom parameters
+result_cparams = expr_jit_cparams(a, b, c)
+print(result_cparams[1,:10])
diff --git a/examples/ndarray/jit-reduc.py b/examples/ndarray/jit-reduc.py
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+++ b/examples/ndarray/jit-reduc.py
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+#######################################################################
+# Copyright (c) 2019-present, Blosc Development Team <blosc@blosc.org>
+# All rights reserved.
+#
+# This source code is licensed under a BSD-style license (found in the
+# LICENSE file in the root directory of this source tree)
+#######################################################################
+
+# Examples of using the jit decorator with reductions
+# You can find benchmarks for this example in the bench/ndarray directory
+
+import blosc2
+import numpy as np
+
+# Example 1: Basic usage of the jit decorator with reduction
+@blosc2.jit
+def expr_jit(a, b, c):
+    # This function computes a sum reduction along axis 1
+    return np.sum(((a ** 3 + np.sin(a * 2)) < c) & (b > 0), axis=1)
+
+# Create some sample data
+a = blosc2.linspace(0, 1, 10 * 100, dtype="float32", shape=(10, 100))
+b = blosc2.linspace(1, 2, 10 * 100, dtype="float32", shape=(10, 100))
+c = blosc2.linspace(-10, 10, 10, dtype="float32", shape=(100,))
+
+# Call the function with the jit decorator
+result = expr_jit(a, b, c)
+print("Example 1 result:", result)
+
+# Example 2: Using the jit decorator with an out parameter for reduction
+out = np.zeros((10,), dtype=np.int64)
+
+@blosc2.jit
+def expr_jit_out(a, b, c):
+    # This function computes a sum reduction along axis 1 and stores the result in the 'out' array
+    return np.sum(((a ** 3 + np.sin(a * 2)) < c) & (b > 0), axis=1, out=out)
+
+# Call the function with the jit decorator and out parameter
+result_out = expr_jit_out(a, b, c)
+print("Example 2 result:", result_out)
+print("Example 2 out array:", out)  # The 'out' array should now contain the same result
+
+# Example 3: Using the jit decorator with additional keyword arguments for reduction
+cparams = blosc2.CParams(clevel=1, codec=blosc2.Codec.LZ4, filters=[blosc2.Filter.BITSHUFFLE])
+out_cparams = blosc2.zeros((10,), dtype=np.int64, cparams=cparams)
+
+@blosc2.jit
+def expr_jit_cparams(a, b, c):
+    # This function computes a sum reduction along axis 1 with custom compression parameters
+    return np.sum(((a ** 3 + np.sin(a * 2)) < c) & (b > 0), axis=1, out=out_cparams)
+
+# Call the function with the jit decorator and custom parameters
+result_cparams = expr_jit_cparams(a, b, c)
+print("Example 3 result:", result_cparams[...])
+print("Example 3 out array:", out_cparams[...])  # The 'out_cparams' array should now contain the same result