diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index dc815cf..9c29094 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -8,7 +8,7 @@ jobs:
     - name: Check out
       uses: actions/checkout@v2
     - name: Build and Deploy Nikola
-      working-directory: ./site
       uses: getnikola/nikola-action@v4
       with:
         dry_run: false
+      working-directory: ./site
diff --git a/notebooks/05_the_many_variables_and_the_spurious_waffles.ipynb b/notebooks/05_the_many_variables_and_the_spurious_waffles.ipynb
index b5bb538..efbcb50 100644
--- a/notebooks/05_the_many_variables_and_the_spurious_waffles.ipynb
+++ b/notebooks/05_the_many_variables_and_the_spurious_waffles.ipynb
@@ -192,6 +192,7 @@
     "# compute percentile interval of mean\n",
     "A_seq = jnp.linspace(start=-3, stop=3.2, num=30)\n",
     "post = m5_1.sample_posterior(random.PRNGKey(1), p5_1, sample_shape=(1000,))\n",
+    "post.pop(\"mu\")\n",
     "post_pred = Predictive(m5_1.model, post)(random.PRNGKey(2), A=A_seq)\n",
     "mu = post_pred[\"mu\"]\n",
     "mu_mean = jnp.mean(mu, 0)\n",
@@ -551,6 +552,7 @@
    "outputs": [],
    "source": [
     "post = m5_4.sample_posterior(random.PRNGKey(1), p5_4, sample_shape=(1000,))\n",
+    "post.pop(\"mu\")\n",
     "post_pred = Predictive(m5_4.model, post)(random.PRNGKey(2), A=d.A.values)\n",
     "mu = post_pred[\"mu\"]\n",
     "mu_mean = jnp.mean(mu, 0)\n",
@@ -573,6 +575,7 @@
     "# call predictive without specifying new data\n",
     "# so it uses original data\n",
     "post = m5_3.sample_posterior(random.PRNGKey(1), p5_3, (int(1e4),))\n",
+    "post.pop(\"mu\")\n",
     "post_pred = Predictive(m5_3.model, post)(random.PRNGKey(2), M=d.M.values, A=d.A.values)\n",
     "mu = post_pred[\"mu\"]\n",
     "\n",
@@ -1394,6 +1397,7 @@
    "source": [
     "xseq = jnp.linspace(start=dcc.N.min() - 0.15, stop=dcc.N.max() + 0.15, num=30)\n",
     "post = m5_5.sample_posterior(random.PRNGKey(1), p5_5, sample_shape=(1000,))\n",
+    "post.pop(\"mu\")\n",
     "post_pred = Predictive(m5_5.model, post)(random.PRNGKey(2), N=xseq)\n",
     "mu = post_pred[\"mu\"]\n",
     "mu_mean = jnp.mean(mu, 0)\n",
@@ -1610,6 +1614,7 @@
    "source": [
     "xseq = jnp.linspace(start=dcc.N.min() - 0.15, stop=dcc.N.max() + 0.15, num=30)\n",
     "post = m5_7.sample_posterior(random.PRNGKey(1), p5_7, sample_shape=(1000,))\n",
+    "post.pop(\"mu\")\n",
     "post_pred = Predictive(m5_7.model, post)(random.PRNGKey(2), M=0, N=xseq)\n",
     "mu = post_pred[\"mu\"]\n",
     "mu_mean = jnp.mean(mu, 0)\n",