diff --git a/_website/tutorials/introduction/upgrade_guide_020.ipynb b/_website/tutorials/introduction/upgrade_guide_020.ipynb
index 9961b29..5a1b103 100644
--- a/_website/tutorials/introduction/upgrade_guide_020.ipynb
+++ b/_website/tutorials/introduction/upgrade_guide_020.ipynb
@@ -39,6 +39,7 @@
     "def maximum(x, y):\n",
     "    return znp.maximum(x, y)\n",
     "\n",
+    "\n",
     "print(f\"sqrt with np:{np.sqrt(maximum(1., 2.))}, with z:{znp.sqrt(maximum(1., 2.))}\")\n",
     "print(f\"vectorized: {np.sqrt(maximum(znp.array([1., 2.]), znp.array([3., 4.])))}\")"
    ]
@@ -306,9 +307,46 @@
    "id": "24",
    "metadata": {},
    "source": [
-    "## Result and serialization\n",
+    "## Result\n",
+    "\n",
+    "The result has more usage for setting parameters, the `update_params` method is now available and can be used to set the parameters to the minimum as seen above.\n",
     "\n",
-    "The result stays similar but can now be pickled like any object in zfit!\n",
+    "It can also be used in a context manager to temporarily set the parameters to the minimum and restore them afterwards.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "param1again.set_value(1.5)\n",
+    "with result:\n",
+    "    print(f\"param1 set temporarily to {param1again}\")\n",
+    "print(f\"param1 is now {param1again} again\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# or to set the parameters to the minimum\n",
+    "zfit.param.set_values(result)  # supports also a dict of {param: value}!\n",
+    "print(param1again)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27",
+   "metadata": {},
+   "source": [
+    "## Serialization\n",
+    "\n",
+    "The result can now be pickled like any object in zfit!\n",
     "(this was not possible before, only after calling `freeze` on the result)\n",
     " \n",
     "This works directly using `dill` (a library that extends `pickle`), but can fail if the garbage collector is not run. Therefore, zfit provides a slightly modified `dill` that can work as a drop-in replacement.\n",
@@ -319,18 +357,30 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "25",
+   "id": "28",
    "metadata": {},
    "outputs": [],
    "source": [
     "result_serialized = zfit.dill.dumps(result)\n",
-    "result_deserialized = zfit.dill.loads(result_serialized)\n",
-    "result_deserialized.errors()"
+    "result_deserialized = zfit.dill.loads(result_serialized)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the result can be used as before\n",
+    "result_deserialized.hesse()  # the default name is now \"hesse\" and not \"minuit_hesse\"\n",
+    "result_deserialized.errors()  # the default name is now \"errors\" and not \"minuit_minos\"\n",
+    "print(result_deserialized)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "26",
+   "id": "30",
    "metadata": {},
    "source": [
     "## Parameters as arguments\n",
@@ -343,7 +393,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "27",
+   "id": "31",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -358,7 +408,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "28",
+   "id": "32",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -381,13 +431,24 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "29",
+   "id": "33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a result can also be used as argument for PDFs or, here, for losses\n",
+    "loss_before = loss.value()\n",
+    "loss_min = loss.value(params=result_deserialized)  # evaluate at minimum\n",
+    "print(f\"loss before (random from before): {loss_before:.7} minimum value: {loss_min:.7} vs {result_deserialized.fmin:.7}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34",
    "metadata": {},
    "outputs": [],
    "source": [
     "# creating a PDF looks also different, but here we use the name of the parametrization and the axis (integers)\n",
-    "\n",
-    "\n",
     "class MyGauss2D(zfit.pdf.ZPDF):\n",
     "    _PARAMS = (\"mu\", \"sigma\")\n",
     "    _N_OBS = 2\n",
@@ -404,7 +465,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "30",
+   "id": "35",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -415,7 +476,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "31",
+   "id": "36",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/introduction/upgrade_guide_020.ipynb b/introduction/upgrade_guide_020.ipynb
index 9961b29..5a1b103 100644
--- a/introduction/upgrade_guide_020.ipynb
+++ b/introduction/upgrade_guide_020.ipynb
@@ -39,6 +39,7 @@
     "def maximum(x, y):\n",
     "    return znp.maximum(x, y)\n",
     "\n",
+    "\n",
     "print(f\"sqrt with np:{np.sqrt(maximum(1., 2.))}, with z:{znp.sqrt(maximum(1., 2.))}\")\n",
     "print(f\"vectorized: {np.sqrt(maximum(znp.array([1., 2.]), znp.array([3., 4.])))}\")"
    ]
@@ -306,9 +307,46 @@
    "id": "24",
    "metadata": {},
    "source": [
-    "## Result and serialization\n",
+    "## Result\n",
+    "\n",
+    "The result has more usage for setting parameters, the `update_params` method is now available and can be used to set the parameters to the minimum as seen above.\n",
     "\n",
-    "The result stays similar but can now be pickled like any object in zfit!\n",
+    "It can also be used in a context manager to temporarily set the parameters to the minimum and restore them afterwards.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "param1again.set_value(1.5)\n",
+    "with result:\n",
+    "    print(f\"param1 set temporarily to {param1again}\")\n",
+    "print(f\"param1 is now {param1again} again\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# or to set the parameters to the minimum\n",
+    "zfit.param.set_values(result)  # supports also a dict of {param: value}!\n",
+    "print(param1again)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27",
+   "metadata": {},
+   "source": [
+    "## Serialization\n",
+    "\n",
+    "The result can now be pickled like any object in zfit!\n",
     "(this was not possible before, only after calling `freeze` on the result)\n",
     " \n",
     "This works directly using `dill` (a library that extends `pickle`), but can fail if the garbage collector is not run. Therefore, zfit provides a slightly modified `dill` that can work as a drop-in replacement.\n",
@@ -319,18 +357,30 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "25",
+   "id": "28",
    "metadata": {},
    "outputs": [],
    "source": [
     "result_serialized = zfit.dill.dumps(result)\n",
-    "result_deserialized = zfit.dill.loads(result_serialized)\n",
-    "result_deserialized.errors()"
+    "result_deserialized = zfit.dill.loads(result_serialized)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the result can be used as before\n",
+    "result_deserialized.hesse()  # the default name is now \"hesse\" and not \"minuit_hesse\"\n",
+    "result_deserialized.errors()  # the default name is now \"errors\" and not \"minuit_minos\"\n",
+    "print(result_deserialized)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "26",
+   "id": "30",
    "metadata": {},
    "source": [
     "## Parameters as arguments\n",
@@ -343,7 +393,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "27",
+   "id": "31",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -358,7 +408,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "28",
+   "id": "32",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -381,13 +431,24 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "29",
+   "id": "33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a result can also be used as argument for PDFs or, here, for losses\n",
+    "loss_before = loss.value()\n",
+    "loss_min = loss.value(params=result_deserialized)  # evaluate at minimum\n",
+    "print(f\"loss before (random from before): {loss_before:.7} minimum value: {loss_min:.7} vs {result_deserialized.fmin:.7}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34",
    "metadata": {},
    "outputs": [],
    "source": [
     "# creating a PDF looks also different, but here we use the name of the parametrization and the axis (integers)\n",
-    "\n",
-    "\n",
     "class MyGauss2D(zfit.pdf.ZPDF):\n",
     "    _PARAMS = (\"mu\", \"sigma\")\n",
     "    _N_OBS = 2\n",
@@ -404,7 +465,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "30",
+   "id": "35",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -415,7 +476,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "31",
+   "id": "36",
    "metadata": {},
    "outputs": [],
    "source": []