diff --git a/hands_on/local_maxima/local_maxima.py b/hands_on/local_maxima/local_maxima.py
new file mode 100644
index 0000000..a6be11d
--- /dev/null
+++ b/hands_on/local_maxima/local_maxima.py
@@ -0,0 +1,16 @@
+import numpy as np
+
+def local_maxima(numlist):
+    #numlist = input('enter list')
+    loc = 0
+    curr = numlist[0]
+    for idx, elem in enumerate(numlist):
+        if elem>curr:
+            loc = idx
+            curr = elem
+    print(loc)
+    return loc
+
+#local_maxima([1, 3, -1, 2, 1])
+#local_maxima([1,4,-5,0,2,1])
+#local_maxima([-1,-1,0,1])
diff --git a/hands_on/numerical_fuzzing/test_var.py b/hands_on/numerical_fuzzing/test_var.py
new file mode 100644
index 0000000..d034321
--- /dev/null
+++ b/hands_on/numerical_fuzzing/test_var.py
@@ -0,0 +1,22 @@
+#Create a directory called numerical_fuzzing in hands_on.
+#In a module called test_var.py, write two tests for the variance function, numpy.var:
+#    First, a deterministic test
+#    Then, a numerical fuzzing test
+from math import isclose
+import numpy
+from numpy import testing
+
+def test_var_deterministic():
+    x = numpy.array([-2.0, 2.0, 6.0])
+    expected = 10.6
+    assert isclose(numpy.var(x), expected, abs_tol = 0.1)
+
+def test_var_fuzzing():
+    rand_state = numpy.random.RandomState(1333)
+    N, D = 100000, 5
+    #Goal var: [0.1 , 0.45, 0.8 , 1.15, 1.5]
+    expected = numpy.linspace(0.1, 1.5, D)
+    # Generate random, D-dimensional data with the desired mean
+    x = rand_state.randn(N, D) * expected
+    vars = numpy.var(x, axis=0)
+    numpy.testing.assert_allclose(vars, expected*expected, rtol=1e-2)
diff --git a/hands_on/pyanno_voting/test_something.py b/hands_on/pyanno_voting/test_something.py
new file mode 100644
index 0000000..38b3829
--- /dev/null
+++ b/hands_on/pyanno_voting/test_something.py
@@ -0,0 +1,34 @@
+from math import isclose
+import numpy
+#from numpy import assert_array_equal
+
+def test_floating():
+    assert isclose(1.1 + 2.2, 3.3)
+
+#def test_tol():
+#    assert isclose(1.1 + 2.2, abs_tol=0.1)
+
+def test_arraysum():
+    x = numpy.array([1,1])
+    y = numpy.array([2,2])
+    z = numpy.array([3,3])
+    assert all(x + y == z)
+    #assert_array_equal(x + y, z)
+
+
+def test_arithmetic():
+    assert 1 == 1
+    assert 2 * 3 == 6
+
+def test_len_list():
+    lst = ['a', 'b', 'c']
+    assert len(lst) == 3
+
+def test_equalthree():
+    assert 1 + 2 == 3
+
+def test_truthy():
+    assert 1
+
+#def test_floatsum():
+#    assert 1.1 + 2.2 == 3.3