diff --git a/notebooks/CubevizExample.ipynb b/notebooks/CubevizExample.ipynb
index 73ef66238f..48e89d593c 100644
--- a/notebooks/CubevizExample.ipynb
+++ b/notebooks/CubevizExample.ipynb
@@ -29,14 +29,7 @@
     "from photutils.aperture import CircularAperture\n",
     "from regions import PixCoord, CirclePixelRegion\n",
     "\n",
-    "from jdaviz import Cubeviz"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We create a Cubeviz instance and show it."
+    "import jdaviz"
    ]
   },
   {
@@ -80,9 +73,26 @@
     "fn = \"jw02732-o004_t004_miri_ch1-shortmediumlong_s3d.fits\"\n",
     "result = Observations.download_file(f\"mast:JWST/product/{fn}\", local_path=f'{data_dir}/{fn}')\n",
     "\n",
+    "with warnings.catch_warnings():\n",
+    "    warnings.simplefilter('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can ask Jdaviz to automatically load the data. Jdaviz will automatically detect Cubeviz as the most appropriate configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "with warnings.catch_warnings():\n",
     "    warnings.simplefilter('ignore')\n",
-    "    cubeviz.load_data(f'{data_dir}/{fn}')"
+    "    cubeviz = jdaviz.open(f'{data_dir}/{fn}')"
    ]
   },
   {
@@ -225,7 +235,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Specviz2dExample.ipynb b/notebooks/Specviz2dExample.ipynb
index 32d7990d1a..69c56c9e60 100644
--- a/notebooks/Specviz2dExample.ipynb
+++ b/notebooks/Specviz2dExample.ipynb
@@ -28,27 +28,7 @@
     "\n",
     "from astroquery.mast import Observations\n",
     "\n",
-    "from jdaviz import Specviz2d\n",
-    "\n",
-    "specviz2d = Specviz2d()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ed07713",
-   "metadata": {},
-   "source": [
-    "## Display Specviz2d"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "45802673",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "specviz2d.show()"
+    "import jdaviz"
    ]
   },
   {
@@ -94,7 +74,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "specviz2d.load_data(f'{data_dir}/{fn}')"
+    "jdaviz.open(f'{data_dir}/{fn}')"
    ]
   }
  ],
@@ -114,7 +94,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/SpecvizExample.ipynb b/notebooks/SpecvizExample.ipynb
index 5f671b4864..c21c221acf 100644
--- a/notebooks/SpecvizExample.ipynb
+++ b/notebooks/SpecvizExample.ipynb
@@ -26,60 +26,16 @@
     "\n",
     "# Suppress warnings\n",
     "with warnings.catch_warnings():\n",
-    "    warnings.simplefilter(\"ignore\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Specviz via Helper"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from jdaviz import Specviz\n",
-    "specviz = Specviz()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Display Specviz\n",
+    "    warnings.simplefilter(\"ignore\")\n",
     "\n",
-    "Note: you probably want to pick one of the three options below depending on preference. Also not the latter two will only work in JupyterLab, not the \"classic\" notebook interface."
+    "import jdaviz"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This will show Specviz inline in the notebook"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "specviz.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load a file to Specviz\n",
+    "## Download Data\n",
     "\n",
     "Here we download a JWST spectrum from [MAST](https://masttest.stsci.edu/) using `astroquery`. \n",
     "By default the downloaded file goes to your temp directory, and thus may eventually be \n",
@@ -106,9 +62,24 @@
     "#data_dir = \"/User/username/Data/\"\n",
     "\n",
     "fn = \"jw02732-o004_t004_miri_ch1-shortmediumlong_x1d.fits\"\n",
-    "result = Observations.download_file(f\"mast:JWST/product/{fn}\", local_path=f'{data_dir}/{fn}')\n",
-    "\n",
-    "specviz.load_spectrum(f'{data_dir}/{fn}', \"myfile\")"
+    "result = Observations.download_file(f\"mast:JWST/product/{fn}\", local_path=f'{data_dir}/{fn}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Open in Jdaviz\n",
+    "Jdaviz can automatically detect the correct configuration and load the data by using `jdaviz.open`. By default, jdaviz will display the application automatically. To only return the helper, set `show=False`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "specviz = jdaviz.open(f'{data_dir}/{fn}', show=True, data_label=\"myfile\")"
    ]
   },
   {
@@ -293,7 +264,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,