diff --git a/README.md b/README.md
index de9ec21db..5ee2c292b 100644
--- a/README.md
+++ b/README.md
@@ -47,40 +47,43 @@ Nodes can be used by themselves and -- other than being "delayed" in that their
 
 ```
 
-But the intent is to collect them together into a workflow and leverage existing nodes:
+But the intent is to collect them together into a workflow and leverage existing nodes. We can directly perform (many but not quite all) python actions natively on output channels, can build up data graph topology by simply assigning values (to attributes or at instantiation), and can package things together into reusable macros with customizable IO interfaces:
 
 ```python
 >>> from pyiron_workflow import Workflow
+>>> Workflow.register("plotting", "pyiron_workflow.node_library.plotting")
 >>>
 >>> @Workflow.wrap_as.single_value_node()
-... def add_one(x):
-...     return x + 1
+... def Arange(n: int):
+...     import numpy as np
+...     return np.arange(n)
 >>>
 >>> @Workflow.wrap_as.macro_node()
-... def add_three_macro(macro):
-...     macro.start = add_one()
-...     macro.middle = add_one(x=macro.start)
-...     macro.end = add_one(x=macro.middle)
-...     macro.inputs_map = {"start__x": "x"}
-...     macro.outputs_map = {"end__x + 1": "y"}
->>> 
->>> Workflow.register(
-...     "plotting", 
-...     "pyiron_workflow.node_library.plotting"
-... )
+... def PlotShiftedSquare(macro):
+...     macro.shift = macro.create.standard.UserInput(0)
+...     macro.arange = Arange()
+...     macro.plot = macro.create.plotting.Scatter(
+...         x=macro.arange + macro.shift,
+...         y=macro.arange**2
+...     )
+...     macro.inputs_map = {
+...         "shift__user_input": "shift",
+...         "arange__n": "n",
+...     }
+...     macro.outputs_map = {"plot__fig": "fig"}
 >>> 
->>> wf = Workflow("add_5_and_plot")
->>> wf.add_one = add_one()
->>> wf.add_three = add_three_macro(x=wf.add_one)
->>> wf.plot = wf.create.plotting.Scatter(
-...     x=wf.add_one,
-...     y=wf.add_three.outputs.y
-... )
+>>> wf = Workflow("plot_with_and_without_shift")
+>>> wf.n = wf.create.standard.UserInput()
+>>> wf.no_shift = PlotShiftedSquare(shift=0, n=10)
+>>> wf.shift = PlotShiftedSquare(shift=2, n=10)
+>>> wf.inputs_map = {
+...     "n__user_input": "n",
+...     "shift__shift": "shift"
+... }
 >>> 
 >>> diagram = wf.draw()
 >>> 
->>> import numpy as np
->>> fig = wf(add_one__x=np.arange(5)).plot__fig
+>>> out = wf(shift=3, n=10)
 
 ```
 
@@ -90,7 +93,7 @@ Which gives the workflow `diagram`
 
 And the resulting `fig`
 
-![](docs/_static/readme_shifted.png)
+![](docs/_static/readme_fig.png)
 
 ## Installation
 
diff --git a/docs/_static/readme_diagram.png b/docs/_static/readme_diagram.png
index b5e242731..45e495153 100644
Binary files a/docs/_static/readme_diagram.png and b/docs/_static/readme_diagram.png differ
diff --git a/docs/_static/readme_shifted.png b/docs/_static/readme_shifted.png
deleted file mode 100644
index 7daa81dac..000000000
Binary files a/docs/_static/readme_shifted.png and /dev/null differ
diff --git a/notebooks/atomistics_nodes.ipynb b/notebooks/atomistics_nodes.ipynb
index 1ecd1c5a1..cfd988d1d 100644
--- a/notebooks/atomistics_nodes.ipynb
+++ b/notebooks/atomistics_nodes.ipynb
@@ -29,7 +29,8 @@
    "source": [
     "Workflow.register(\"calculator\", \"pyiron_workflow.node_library.atomistics.calculator\")\n",
     "Workflow.register(\"macro\", \"pyiron_workflow.node_library.atomistics.macro\")\n",
-    "Workflow.register(\"task\", \"pyiron_workflow.node_library.atomistics.task\")"
+    "Workflow.register(\"task\", \"pyiron_workflow.node_library.atomistics.task\")\n",
+    "Workflow.register(\"plotting\", \"pyiron_workflow.node_library.plotting\")"
    ]
   },
   {
@@ -37,24 +38,49 @@
    "execution_count": 3,
    "id": "bc0e6187-236d-4cba-8674-de14a0520257",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "wf = Workflow(\"ev_curve\")\n",
     "\n",
     "wf.structure = wf.create.task.Bulk(\"Al\")\n",
     "wf.calculator = wf.create.calculator.Emt()\n",
+    "\n",
     "wf.ev = wf.create.macro.EnergyVolumeCurve(\n",
     "    structure=wf.structure, \n",
     "    calculator=wf.calculator,\n",
     ")\n",
+    "wf.ev_plot = wf.create.plotting.Scatter(\n",
+    "    wf.ev.outputs.result_dict['volume'],\n",
+    "    wf.ev.outputs.result_dict['energy']\n",
+    ")\n",
+    "\n",
     "wf.elastic = wf.create.macro.ElasticMatrix(\n",
     "    structure=wf.structure, \n",
     "    calculator=wf.calculator,\n",
     ")\n",
+    "wf.C = wf.elastic.outputs.result_dict[\"C\"]\n",
+    "\n",
     "wf.phonons = wf.create.macro.Phonons(\n",
     "    structure=wf.structure, \n",
     "    calculator=wf.calculator,\n",
-    ")"
+    ")\n",
+    "wf.dos_plot = wf.create.plotting.Scatter(\n",
+    "    wf.phonons.outputs.result_dict[1][\"frequency_points\"],\n",
+    "    wf.phonons.outputs.result_dict[1][\"total_dos\"],\n",
+    ")\n",
+    "\n",
+    "out = wf()"
    ]
   },
   {
@@ -62,36 +88,51 @@
    "execution_count": 4,
    "id": "585b69dc-7140-4891-be1d-9250827f96ac",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "out = wf()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "a523bcd8-c7f0-4b55-9592-5a3c37c6f34e",
-   "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel accumulate_and_run was not connected to ran, andthus could not disconnect from it.\n",
+      "  warn(\n",
+      "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel ran was not connected to accumulate_and_run, andthus could not disconnect from it.\n",
+      "  warn(\n",
+      "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel run was not connected to ran, andthus could not disconnect from it.\n",
+      "  warn(\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "39.544084907317895"
+       "<matplotlib.collections.PathCollection at 0x149cb2590>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "out.ev__result_dict[\"bulkmodul_eq\"]"
+    "wf.ev_plot()  \n",
+    "# We should be able to look at .value.figure\n",
+    "# but we need to get matplotlib to clear the previous double plot\n",
+    "# This is something to fix in the plotting package."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "65c4cd50-85dd-405f-9d2f-a0f89cb39c7f",
+   "execution_count": 5,
+   "id": "56ec091c-c05a-4d98-8ec3-a98099a26857",
    "metadata": {},
    "outputs": [
     {
@@ -111,74 +152,44 @@
        "        32.8950073 ]])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "out.elastic__result_dict[\"C\"]"
+    "wf.C"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "f36b7134-fdaf-439c-a8ed-8e0d12dea6ac",
+   "execution_count": 6,
+   "id": "b738be10-20f0-4be1-abf7-b2fe8053d170",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
-       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
-       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
-       "       0.        , 0.        , 0.00694078, 0.00988315, 0.01328576,\n",
-       "       0.01714862, 0.02147171, 0.02625505, 0.02985557, 0.037727  ,\n",
-       "       0.0434232 , 0.04946553, 0.05566541, 0.06215958, 0.07115592,\n",
-       "       0.08064991, 0.09079663, 0.09922499, 0.107314  , 0.11598167,\n",
-       "       0.12677452, 0.13892215, 0.15277843, 0.16509068, 0.17714585,\n",
-       "       0.18908449, 0.20207234, 0.21663715, 0.23193372, 0.24787345,\n",
-       "       0.26531108, 0.28151195, 0.29720939, 0.31555894, 0.33441182,\n",
-       "       0.35381457, 0.37348888, 0.39467179, 0.41600753, 0.43856869,\n",
-       "       0.46236269, 0.48653458, 0.51129518, 0.53659094, 0.56254718,\n",
-       "       0.59054813, 0.62101398, 0.65275413, 0.68514049, 0.71793811,\n",
-       "       0.75144708, 0.78583379, 0.82253802, 0.86542963, 0.90976761,\n",
-       "       0.95550071, 1.00263393, 1.05112517, 1.10148731, 1.33991857,\n",
-       "       1.36193113, 1.34430559, 1.32697925, 1.30233052, 1.2507499 ,\n",
-       "       1.49092299, 1.63962028, 3.26506443, 2.06872102, 2.14988263,\n",
-       "       2.25562045, 2.26389212, 2.24957095, 2.23068922, 2.19906969,\n",
-       "       2.0990768 , 1.92248659, 2.05381061, 2.22262812, 2.26534629,\n",
-       "       2.39290591, 2.44805769, 2.51126138, 2.56481713, 2.44897559,\n",
-       "       2.39584353, 2.4114828 , 2.49193391, 2.57081989, 2.57019593,\n",
-       "       2.41341987, 2.25573881, 2.29896005, 2.57661295, 2.6898915 ,\n",
-       "       2.52674647, 2.40732058, 2.39797664, 2.6860019 , 2.77461177,\n",
-       "       2.74267508, 2.68147413, 2.65588543, 2.70193114, 2.76928622,\n",
-       "       2.6439207 , 2.65435265, 2.90982222, 2.73490294, 1.98457979,\n",
-       "       1.72106217, 1.58627925, 1.56213379, 1.62154002, 1.73544337,\n",
-       "       1.70256902, 1.65407997, 1.66713389, 1.67643965, 1.63648852,\n",
-       "       1.60695654, 1.56655864, 1.61633361, 1.61150893, 1.55133986,\n",
-       "       1.53831926, 1.50303936, 1.46605859, 1.56752315, 1.43714305,\n",
-       "       1.24968842, 1.45944483, 1.66145587, 1.75923322, 1.73118812,\n",
-       "       1.5623509 , 1.49846885, 1.41120813, 1.37756017, 1.49546786,\n",
-       "       1.17761685, 1.07980165, 0.98605362, 1.08530243, 1.21699949,\n",
-       "       1.93599882, 2.10774636, 2.30948133, 2.46933119, 2.71827848,\n",
-       "       2.83882417, 2.97242386, 3.17702959, 3.24129876, 3.4949328 ,\n",
-       "       3.60987129, 3.83610362, 2.90427366, 2.52017744, 2.24833264,\n",
-       "       2.03412564, 1.82270575, 1.63369566, 1.45666495, 1.27615313,\n",
-       "       1.07133396, 0.8484739 , 0.60012783, 0.15450282, 0.        ,\n",
-       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
-       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
-       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
-       "       0.        ])"
+       "<matplotlib.collections.PathCollection at 0x149c72590>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "out.phonons__result_dict[1][\"total_dos\"]"
+    "wf.dos_plot()"
    ]
   }
  ],
diff --git a/notebooks/deepdive.ipynb b/notebooks/deepdive.ipynb
index 1c4d22eea..781dd5369 100644
--- a/notebooks/deepdive.ipynb
+++ b/notebooks/deepdive.ipynb
@@ -524,8 +524,6 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel ran was not connected to run, andthus could not disconnect from it.\n",
-      "  warn(\n",
       "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel run was not connected to ran, andthus could not disconnect from it.\n",
       "  warn(\n"
      ]
@@ -636,6 +634,210 @@
     "This let's us set up nodes which only start running after _all_ of their up-data-stream nodes have fired their `ran` signal. This is the default behaviour when `Composite` `Workflow` or `Macro` nodes automate their execution flow for DAG data graphs. We'll look at it again near the end of the notebook when we talk about remote execution."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6d464066-4271-41be-a34f-20c78d75867c",
+   "metadata": {},
+   "source": [
+    "# Output manipulation\n",
+    "\n",
+    "Most (but not all) python operations can be performed _directly on output channels_. This works by injecting new nodes after the output channels to perform the requested operation.\n",
+    "\n",
+    "Let's look at how we can repeat some of the above examples much more succinctly using this feature:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "98312fbb-0e87-417c-9780-d22903cdb3f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Subtract and square\n",
+    "\n",
+    "x = Linear(x=4)\n",
+    "y = Linear(x=1)\n",
+    "((x.outputs.x - y.outputs.x)**2).pull()  # It's just a node so we can pull it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b0e8fc87-fba1-4501-882b-f162c4eadf97",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Times two\n",
+    "l = Linear(x=10, run_after_init=True)\n",
+    "(2*l.outputs.x).value  # These nodes will try to run right away if everything upstream is ready"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3052c26c-3559-4d61-8c93-08708293b88c",
+   "metadata": {},
+   "source": [
+    "This also works with more sophisticated features like attribute and item access, including slicing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "8a195c41-233e-4076-ad77-008c93297f9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "foo = [1, 2, 3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "6805b0c3-9103-49f4-bc29-569b0b4d6ed0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function list.reverse()>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "foo.reverse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "7c4cbe66-9b0a-428b-835f-31959a7f75bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a_list = Linear(x=[1,2,3,4], run_after_init=True)\n",
+    "a_list.outputs.x[:2].value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "16c2d0de-de6f-4b33-84e4-aefbe5db4177",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a_dict = Linear(x={\"a\": 1, \"b\": 2}, run_after_init=True)\n",
+    "a_dict.outputs.x[\"a\"].value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "30b4ed75-bb73-44bb-b6d9-fe525b924652",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class Foo:\n",
+    "    bar = 42\n",
+    "    \n",
+    "an_object = Linear(x=Foo(), run_after_init=True)\n",
+    "an_object.outputs.x.bar.value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eab9c6b6-c954-471a-8613-792590e0464f",
+   "metadata": {},
+   "source": [
+    "Some features don't work this way, e.g. overriding the `==` operator has other deterious effects so we don't do that. Most of these operators are available as a method on output channels, based on their dunder name. E.g."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "786b1402-b595-4337-8872-fd58687c2725",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(True, False)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = Linear(x=42)\n",
+    "b = Linear(x=42)\n",
+    "c = Linear(x=0)\n",
+    "\n",
+    "a_eq_b = a.outputs.x.eq(b.outputs.x)\n",
+    "a_eq_c = a.outputs.x.eq(c.outputs.x)\n",
+    "\n",
+    "(a_eq_b | a_eq_c).pull(), (a_eq_b & a_eq_c).pull()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "e5c531a3-77e4-48ad-a189-fed619e79baa",
@@ -645,14 +847,14 @@
     "\n",
     "Many functions return just a single value. In this case, we can take advantage of the `SingleValue` node class which employs a bunch of syntactic tricks to make our lives easier.\n",
     "\n",
-    "The main difference between this and it's parent the `Function` class is that attribute and item access fall back to looking for attributes and items of this single output value.\n",
+    "The main difference between this and it's parent the `Function` class is that attribute and item access fall back to looking for attributes and items of this single output channel. I.e. you can use a single value node in many places you'd use an output channel, including in connection formation and output manipulation\n",
     "\n",
-    "Let's look at a use case:"
+    "Let's look at a use case for output manipulation:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 31,
    "id": "1a4e9693-0980-4435-aecc-3331d8b608dd",
    "metadata": {},
    "outputs": [],
@@ -664,7 +866,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 32,
    "id": "7c4d314b-33bb-4a67-bfb9-ed77fba3949c",
    "metadata": {},
    "outputs": [
@@ -689,8 +891,8 @@
     "lin()\n",
     "\n",
     "print(type(lin.outputs.linspace.value))  # Output is just what we expect\n",
-    "print(lin[1:4])  # Gets items from the output\n",
-    "print(lin.mean())  # Finds the method on the output -- a special feature of SingleValueNode"
+    "print(lin[1:4].value)  # Gets items from the output\n",
+    "print(lin.mean.value())  # Outputs the method on the output, which we can then call"
    ]
   },
   {
@@ -698,12 +900,12 @@
    "id": "eef23cb0-6192-4fe6-b9cc-007e261e347a",
    "metadata": {},
    "source": [
-    "The other advantage is that single value nodes can also be connected directly to input, since there is only one possible data connection. Of course it has a construction decorator just like `Function`, so let's replace `@function_node` with `@single_value_node` in one of our examples above to see how it tightens up the syntax a bit:"
+    "Our examples above also become more compact:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 33,
    "id": "61ae572f-197b-4a60-8d3e-e19c1b9cc6e2",
    "metadata": {},
    "outputs": [
@@ -713,7 +915,7 @@
        "4"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -734,6 +936,35 @@
     "t2.pull()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "01780601-f2fd-4730-acb8-95a7359d4b3c",
+   "metadata": {},
+   "source": [
+    "Or even just"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "5dd7ebc2-b45f-4759-bfc4-d4dd29afe216",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(2*l).value"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b2e56a64-d053-4127-bb8c-069777c1c6b5",
@@ -744,7 +975,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 35,
    "id": "6569014a-815b-46dd-8b47-4e1cd4584b3b",
    "metadata": {},
    "outputs": [
@@ -758,7 +989,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -813,7 +1044,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 36,
    "id": "1cd000bd-9b24-4c39-9cac-70a3291d0660",
    "metadata": {},
    "outputs": [],
@@ -840,7 +1071,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 37,
    "id": "7964df3c-55af-4c25-afc5-9e07accb606a",
    "metadata": {},
    "outputs": [
@@ -866,7 +1097,7 @@
     "for i, (label, node) in enumerate(wf.nodes.items()):\n",
     "    x = i / len(wf)\n",
     "    node(x=x)\n",
-    "    print(f\"{label} == {node.label}) {x} > 0.5 {node.single_value}\")"
+    "    print(f\"{label} == {node.label}) {x} > 0.5 {node.value}\")"
    ]
   },
   {
@@ -887,7 +1118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 38,
    "id": "809178a5-2e6b-471d-89ef-0797db47c5ad",
    "metadata": {},
    "outputs": [
@@ -941,7 +1172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 39,
    "id": "52c48d19-10a2-4c48-ae81-eceea4129a60",
    "metadata": {},
    "outputs": [
@@ -951,7 +1182,7 @@
        "{'ay': 3, 'a + b + 2': 7}"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -979,7 +1210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 40,
    "id": "bb35ba3e-602d-4c9c-b046-32da9401dd1c",
    "metadata": {},
    "outputs": [
@@ -989,7 +1220,7 @@
        "(7, 3)"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1008,7 +1239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 41,
    "id": "2b0d2c85-9049-417b-8739-8a8432a1efbe",
    "metadata": {},
    "outputs": [
@@ -1350,10 +1581,10 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x10c35b450>"
+       "<graphviz.graphs.Digraph at 0x142cbe050>"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1380,14 +1611,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 42,
    "id": "ae500d5e-e55b-432c-8b5f-d5892193cdf5",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a4dc82761024407e909840aab64b54de",
+       "model_id": "f77a418c673c4579b9a3a6cbd090ef8e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1406,10 +1637,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x14e7c5d50>"
+       "<matplotlib.collections.PathCollection at 0x1590fb090>"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1452,7 +1683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 43,
    "id": "2114d0c3-cdad-43c7-9ffa-50c36d56d18f",
    "metadata": {},
    "outputs": [
@@ -1666,10 +1897,10 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x14e8bfb90>"
+       "<graphviz.graphs.Digraph at 0x142519950>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1706,7 +1937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 44,
    "id": "c71a8308-f8a1-4041-bea0-1c841e072a6d",
    "metadata": {},
    "outputs": [],
@@ -1716,7 +1947,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 45,
    "id": "2b9bb21a-73cd-444e-84a9-100e202aa422",
    "metadata": {},
    "outputs": [
@@ -1734,7 +1965,7 @@
        "13"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1781,7 +2012,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 46,
    "id": "3668f9a9-adca-48a4-84ea-13add965897c",
    "metadata": {},
    "outputs": [
@@ -1791,7 +2022,7 @@
        "{'intermediate': 102, 'plus_three': 103}"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1829,7 +2060,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 47,
    "id": "9aaeeec0-5f88-4c94-a6cc-45b56d2f0111",
    "metadata": {},
    "outputs": [],
@@ -1848,18 +2079,12 @@
     "    macro.outputs_map = {\n",
     "        \"calc__energy_pot\": \"energy\",\n",
     "        \"structure__structure\": \"structure\",\n",
-    "    }\n",
-    "\n",
-    "@Workflow.wrap_as.single_value_node()\n",
-    "def PerAtomEnergyDifference(structure1, energy1, structure2, energy2):\n",
-    "    # The unrelaxed structure is fine, we're just using it to get n_atoms\n",
-    "    de = (energy2[-1]/len(structure2)) - (energy1[-1]/len(structure1))\n",
-    "    return de"
+    "    }"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 48,
    "id": "a832e552-b3cc-411a-a258-ef21574fc439",
    "metadata": {},
    "outputs": [],
@@ -1868,12 +2093,28 @@
     "wf.element = wf.create.standard.UserInput()\n",
     "wf.min_phase1 = LammpsMinimize(element=wf.element)\n",
     "wf.min_phase2 = LammpsMinimize(element=wf.element)\n",
-    "wf.compare = PerAtomEnergyDifference(\n",
-    "    wf.min_phase1.outputs.structure,\n",
-    "    wf.min_phase1.outputs.energy,\n",
-    "    wf.min_phase2.outputs.structure,\n",
-    "    wf.min_phase2.outputs.energy,\n",
-    ")\n",
+    "\n",
+    "wf.e1 = wf.min_phase1.outputs.energy[-1]\n",
+    "wf.n1 = wf.min_phase1.outputs.structure.len()\n",
+    "wf.e2 = wf.min_phase2.outputs.energy[-1]\n",
+    "wf.n2 = wf.min_phase2.outputs.structure.len()\n",
+    "wf.compare = (wf.e2 / wf.n2) - (wf.e1 / wf.n1)\n",
+    "\n",
+    "\n",
+    "# Or we could write a single node to do that:\n",
+    "\n",
+    "# @Workflow.wrap_as.single_value_node()\n",
+    "# def PerAtomEnergyDifference(structure1, energy1, structure2, energy2):\n",
+    "#     # The unrelaxed structure is fine, we're just using it to get n_atoms\n",
+    "#     sub = (energy2[-1]/len(structure2)) - (energy1[-1]/len(structure1))\n",
+    "#     return sub\n",
+    "\n",
+    "# wf.compare = PerAtomEnergyDifference(\n",
+    "#     wf.min_phase1.outputs.structure,\n",
+    "#     wf.min_phase1.outputs.energy,\n",
+    "#     wf.min_phase2.outputs.structure,\n",
+    "#     wf.min_phase2.outputs.energy,\n",
+    "# )\n",
     "\n",
     "wf.inputs_map = {\n",
     "    \"element__user_input\": \"element\",\n",
@@ -1886,7 +2127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 49,
    "id": "b764a447-236f-4cb7-952a-7cba4855087d",
    "metadata": {},
    "outputs": [
@@ -1899,1251 +2140,1715 @@
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+       "</g>\n",
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+       "<title>clusterphase_preferencee1Outputs</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1380.3\" y=\"-2198.95\" font-family=\"Times,serif\" font-size=\"14.00\">Outputs</text>\n",
+       "</g>\n",
+       "<g id=\"clust15\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencen1</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1246.97\" y=\"-2456.95\" font-family=\"Times,serif\" font-size=\"14.00\">n1: Length</text>\n",
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+       "<title>clusterphase_preferencen1Inputs</title>\n",
+       "<defs>\n",
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+       "</g>\n",
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+       "<title>clusterphase_preferencen1Outputs</title>\n",
+       "<defs>\n",
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+       "</defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1380.3\" y=\"-2399.95\" font-family=\"Times,serif\" font-size=\"14.00\">Outputs</text>\n",
+       "</g>\n",
+       "<g id=\"clust18\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencee2</title>\n",
+       "<defs>\n",
+       "<linearGradient id=\"clust18_l_17\" gradientUnits=\"userSpaceOnUse\" x1=\"1251.68\" y1=\"-30.25\" x2=\"1251.68\" y2=\"-304.25\" >\n",
+       "<stop offset=\"0\" style=\"stop-color:#17becf;stop-opacity:1.;\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"1251.68\" y=\"-286.95\" font-family=\"Times,serif\" font-size=\"14.00\">e2: GetItem</text>\n",
+       "</g>\n",
+       "<g id=\"clust19\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencee2Inputs</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1151.27\" y=\"-256.95\" font-family=\"Times,serif\" font-size=\"14.00\">Inputs</text>\n",
+       "</g>\n",
+       "<g id=\"clust20\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencee2Outputs</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1380.3\" y=\"-256.95\" font-family=\"Times,serif\" font-size=\"14.00\">Outputs</text>\n",
+       "</g>\n",
+       "<g id=\"clust21\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencen2</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1246.97\" y=\"-514.95\" font-family=\"Times,serif\" font-size=\"14.00\">n2: Length</text>\n",
+       "</g>\n",
+       "<g id=\"clust22\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencen2Inputs</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1151.27\" y=\"-484.95\" font-family=\"Times,serif\" font-size=\"14.00\">Inputs</text>\n",
+       "</g>\n",
+       "<g id=\"clust23\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencen2Outputs</title>\n",
+       "<defs>\n",
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+       "</linearGradient>\n",
+       "</defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1380.3\" y=\"-484.95\" font-family=\"Times,serif\" font-size=\"14.00\">Outputs</text>\n",
+       "</g>\n",
+       "<g id=\"clust24\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencee2__getitem_Divide_n2__len</title>\n",
+       "<defs>\n",
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+       "<stop offset=\"0\" style=\"stop-color:#17becf;stop-opacity:1.;\"/>\n",
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+       "</linearGradient>\n",
+       "</defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1573.85\" y=\"-832.95\" font-family=\"Times,serif\" font-size=\"14.00\">e2__getitem_Divide_n2__len: Divide</text>\n",
+       "</g>\n",
+       "<g id=\"clust25\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencee2__getitem_Divide_n2__lenInputs</title>\n",
+       "<defs>\n",
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+       "</g>\n",
+       "<g id=\"clust26\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencee2__getitem_Divide_n2__lenOutputs</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1656.47\" y=\"-802.95\" font-family=\"Times,serif\" font-size=\"14.00\">Outputs</text>\n",
+       "</g>\n",
+       "<g id=\"clust27\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencee1__getitem_Divide_n1__len</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1573.85\" y=\"-2336.95\" font-family=\"Times,serif\" font-size=\"14.00\">e1__getitem_Divide_n1__len: Divide</text>\n",
+       "</g>\n",
+       "<g id=\"clust28\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencee1__getitem_Divide_n1__lenInputs</title>\n",
+       "<defs>\n",
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+       "</g>\n",
+       "<g id=\"clust29\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencee1__getitem_Divide_n1__lenOutputs</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1656.47\" y=\"-2252.95\" font-family=\"Times,serif\" font-size=\"14.00\">Outputs</text>\n",
+       "</g>\n",
+       "<g id=\"clust30\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencecompare</title>\n",
+       "<defs>\n",
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+       "<text text-anchor=\"middle\" x=\"1840.6\" y=\"-940.95\" font-family=\"Times,serif\" font-size=\"14.00\">compare: Subtract</text>\n",
+       "</g>\n",
+       "<g id=\"clust31\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencecompareInputs</title>\n",
+       "<defs>\n",
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+       "</g>\n",
+       "<g id=\"clust32\" class=\"cluster\">\n",
+       "<title>clusterphase_preferencecompareOutputs</title>\n",
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        "<!-- clusterphase_preferencemin_phase1Inputsstructure__cubic -->\n",
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        "<title>clusterphase_preferencemin_phase1Inputsstructure__cubic</title>\n",
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        "<!-- clusterphase_preferenceInputsmin_phase1__structure__cubic&#45;&gt;clusterphase_preferencemin_phase1Inputsstructure__cubic -->\n",
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        "<title>clusterphase_preferenceInputsmin_phase1__structure__cubic&#45;&gt;clusterphase_preferencemin_phase1Inputsstructure__cubic</title>\n",
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        "<!-- clusterphase_preferencemin_phase1Inputscalc__n_ionic_steps -->\n",
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        "<title>clusterphase_preferencemin_phase1Inputscalc__n_ionic_steps</title>\n",
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        "<!-- clusterphase_preferencemin_phase1Outputscalc__energy_tot -->\n",
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        "<title>clusterphase_preferencemin_phase1Outputscalc__energy_tot</title>\n",
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        "<!-- clusterphase_preferencemin_phase1Outputscalc__forces -->\n",
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        "<!-- clusterphase_preferencemin_phase1Outputscalc__indices -->\n",
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        "<!-- clusterphase_preferencemin_phase1Outputscalc__pressures -->\n",
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        "<!-- clusterphase_preferencemin_phase2Outputscalc__pressures -->\n",
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+       "<!-- clusterphase_preferencee1__getitem_Divide_n1__lenOutputstruediv -->\n",
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+       "<title>clusterphase_preferencecompareInputsother</title>\n",
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+       "</g>\n",
+       "<!-- clusterphase_preferencee1__getitem_Divide_n1__lenOutputstruediv&#45;&gt;clusterphase_preferencecompareInputsother -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>clusterphase_preferencee1__getitem_Divide_n1__lenOutputstruediv&#45;&gt;clusterphase_preferencecompareInputsother</title>\n",
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        "<!-- clusterphase_preferencecompareInputsrun -->\n",
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        "</g>\n",
        "<!-- clusterphase_preferencecompareOutputsran -->\n",
-       "<g id=\"node117\" class=\"node\">\n",
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+       "<!-- clusterphase_preferencecompareOutputssub -->\n",
+       "<g id=\"node152\" class=\"node\">\n",
+       "<title>clusterphase_preferencecompareOutputssub</title>\n",
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        "</g>\n",
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x14e987250>"
+       "<graphviz.graphs.Digraph at 0x1591ab990>"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3154,7 +3859,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 50,
    "id": "b51bef25-86c5-4d57-80c1-ab733e703caf",
    "metadata": {},
    "outputs": [
@@ -3170,12 +3875,12 @@
    ],
    "source": [
     "out = wf(element=\"Al\", phase1=\"fcc\", phase2=\"hcp\", lattice_guess1=4, lattice_guess2=4)\n",
-    "print(f\"{wf.inputs.element.value}: E({wf.inputs.phase2.value}) - E({wf.inputs.phase1.value}) = {out.compare__de:.2f} eV/atom\")"
+    "print(f\"{wf.inputs.element.value}: E({wf.inputs.phase2.value}) - E({wf.inputs.phase1.value}) = {out.compare__sub:.2f} eV/atom\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 51,
    "id": "091e2386-0081-436c-a736-23d019bd9b91",
    "metadata": {},
    "outputs": [
@@ -3199,7 +3904,7 @@
    ],
    "source": [
     "out = wf(element=\"Mg\", phase1=\"fcc\", phase2=\"hcp\", lattice_guess1=3, lattice_guess2=3)\n",
-    "print(f\"{wf.inputs.element.value}: E({wf.inputs.phase2.value}) - E({wf.inputs.phase1.value}) = {out.compare__de:.2f} eV/atom\")"
+    "print(f\"{wf.inputs.element.value}: E({wf.inputs.phase2.value}) - E({wf.inputs.phase1.value}) = {out.compare__sub:.2f} eV/atom\")"
    ]
   },
   {
@@ -3216,7 +3921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 52,
    "id": "4cdffdca-48d3-4486-9045-48102c7e5f31",
    "metadata": {},
    "outputs": [
@@ -3230,9 +3935,9 @@
       "  warn(\n",
       "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel element was not connected to user_input, andthus could not disconnect from it.\n",
       "  warn(\n",
-      "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel structure was not connected to structure1, andthus could not disconnect from it.\n",
+      "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel structure was not connected to obj, andthus could not disconnect from it.\n",
       "  warn(\n",
-      "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel energy was not connected to energy1, andthus could not disconnect from it.\n",
+      "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel energy was not connected to obj, andthus could not disconnect from it.\n",
       "  warn(\n"
      ]
     }
@@ -3254,7 +3959,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 53,
    "id": "ed4a3a22-fc3a-44c9-9d4f-c65bc1288889",
    "metadata": {},
    "outputs": [
@@ -3279,12 +3984,12 @@
    "source": [
     "# Bad guess\n",
     "out = wf(element=\"Al\", phase1=\"fcc\", phase2=\"hcp\", lattice_guess1=3, lattice_guess2=3.1)\n",
-    "print(f\"{wf.inputs.element.value}: E({wf.inputs.phase2.value}) - E({wf.inputs.phase1.value}) = {out.compare__de:.2f} eV/atom\")"
+    "print(f\"{wf.inputs.element.value}: E({wf.inputs.phase2.value}) - E({wf.inputs.phase1.value}) = {out.compare__sub:.2f} eV/atom\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 54,
    "id": "5a985cbf-c308-4369-9223-b8a37edb8ab1",
    "metadata": {},
    "outputs": [
@@ -3309,7 +4014,7 @@
    "source": [
     "# Good guess\n",
     "out = wf(element=\"Al\", phase1=\"fcc\", phase2=\"hcp\", lattice_guess1=4.05, lattice_guess2=3.2)\n",
-    "print(f\"{wf.inputs.element.value}: E({wf.inputs.phase2.value}) - E({wf.inputs.phase1.value}) = {out.compare__de:.2f} eV/atom\")"
+    "print(f\"{wf.inputs.element.value}: E({wf.inputs.phase2.value}) - E({wf.inputs.phase1.value}) = {out.compare__sub:.2f} eV/atom\")"
    ]
   },
   {
@@ -3352,7 +4057,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 55,
    "id": "aa575249-b209-4e0c-9ea6-a82bc69dc833",
    "metadata": {},
    "outputs": [
@@ -3361,25 +4066,21 @@
      "output_type": "stream",
      "text": [
       "None 1\n",
-      "<Future at 0x1512f00d0 state=pending> <class 'pyiron_workflow.channels.NotData'>\n"
+      "<Future at 0x15872c9d0 state=pending> <class 'pyiron_workflow.channels.NotData'>\n"
      ]
     }
    ],
    "source": [
-    "@Workflow.wrap_as.single_value_node(\"sum\")\n",
-    "def Add(x, y):\n",
-    "    return x + y\n",
-    "\n",
     "wf = Workflow(\"with_executor\")\n",
-    "wf.a1 = Add(0, 1)\n",
-    "wf.a2 = Add(2, 3)\n",
-    "wf.b = Add(wf.a1, wf.a2)\n",
+    "wf.a1 = wf.create.standard.Add(0, 1)\n",
+    "wf.a2 = wf.create.standard.Add(2, 3)\n",
+    "wf.b = wf.a1 + wf.a2\n",
     "\n",
     "wf.a2.executor = wf.create.Executor()\n",
     "wf()\n",
     "\n",
-    "print(wf.a1.future, wf.a1.outputs.sum.value)\n",
-    "print(wf.a2.future, wf.a2.outputs.sum.value)"
+    "print(wf.a1.future, wf.a1.outputs.add.value)\n",
+    "print(wf.a2.future, wf.a2.outputs.add.value)"
    ]
   },
   {
@@ -3392,7 +4093,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 56,
    "id": "c1b7b4e9-1c76-470c-ba6e-a58ea3f611f6",
    "metadata": {},
    "outputs": [
@@ -3420,7 +4121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 57,
    "id": "7e98058b-a791-4cb1-ae2c-864ad7e56cee",
    "metadata": {},
    "outputs": [],
@@ -3438,7 +4139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 58,
    "id": "0d1b4005-488e-492f-adcb-8ad7235e4fe3",
    "metadata": {},
    "outputs": [
@@ -3447,7 +4148,7 @@
      "output_type": "stream",
      "text": [
       "None 1\n",
-      "<Future at 0x151389a90 state=pending> <class 'pyiron_workflow.channels.NotData'>\n",
+      "<Future at 0x15bb49bd0 state=pending> <class 'pyiron_workflow.channels.NotData'>\n",
       "Finally 5\n",
       "b (Add) output single-value: 6\n"
      ]
@@ -3456,15 +4157,15 @@
    "source": [
     "with Workflow.create.Executor() as executor:\n",
     "    wf = Workflow(\"with_executor\")\n",
-    "    wf.a1 = Add(0, 1)\n",
-    "    wf.a2 = Add(2, 3)\n",
-    "    wf.b = Add(wf.a1, wf.a2)\n",
+    "    wf.a1 = wf.create.standard.Add(0, 1)\n",
+    "    wf.a2 = wf.create.standard.Add(2, 3)\n",
+    "    wf.b = wf.a1 + wf.a2\n",
     "\n",
     "    wf.a2.executor = executor\n",
     "    wf()\n",
     "    \n",
-    "    print(wf.a1.future, wf.a1.outputs.sum.value)\n",
-    "    print(wf.a2.future, wf.a2.outputs.sum.value)\n",
+    "    print(wf.a1.future, wf.a1.outputs.add.value)\n",
+    "    print(wf.a2.future, wf.a2.outputs.add.value)\n",
     "    \n",
     "    print(\"Finally\", wf.a2.future.result())\n",
     "    print(wf.b)"
@@ -3482,7 +4183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 59,
    "id": "d03ca074-35a0-4e0d-9377-d4eaa5521f85",
    "metadata": {},
    "outputs": [],
@@ -3501,7 +4202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 60,
    "id": "a7c07aa0-84fc-4f43-aa4f-6498c0837d76",
    "metadata": {},
    "outputs": [
@@ -3509,7 +4210,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "6.029127317946404\n"
+      "6.018927805998828\n"
      ]
     }
    ],
@@ -3533,7 +4234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 61,
    "id": "b062ab5f-9b98-4843-8925-b93bf4c173f8",
    "metadata": {},
    "outputs": [
@@ -3541,7 +4242,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.678937633987516\n"
+      "3.0853979130042717\n"
      ]
     }
    ],
@@ -3615,7 +4316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 62,
    "id": "0b373764-b389-4c24-8086-f3d33a4f7fd7",
    "metadata": {},
    "outputs": [
@@ -3629,7 +4330,7 @@
        " 17.230249999999995]"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3666,7 +4367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 63,
    "id": "0dd04b4c-e3e7-4072-ad34-58f2c1e4f596",
    "metadata": {},
    "outputs": [
@@ -3725,7 +4426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 64,
    "id": "2dfb967b-41ac-4463-b606-3e315e617f2a",
    "metadata": {},
    "outputs": [
@@ -3749,7 +4450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 65,
    "id": "2e87f858-b327-4f6b-9237-c8a557f29aeb",
    "metadata": {},
    "outputs": [
@@ -3757,8 +4458,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.189 <= 0.2\n",
-      "Finally 0.189\n"
+      "0.676 > 0.2\n",
+      "0.208 > 0.2\n",
+      "0.919 > 0.2\n",
+      "0.834 > 0.2\n",
+      "0.952 > 0.2\n",
+      "0.031 <= 0.2\n",
+      "Finally 0.031\n"
      ]
     }
    ],
diff --git a/notebooks/quickstart.ipynb b/notebooks/quickstart.ipynb
index 38eda8dfb..344a454ee 100644
--- a/notebooks/quickstart.ipynb
+++ b/notebooks/quickstart.ipynb
@@ -189,7 +189,9 @@
     "\n",
     "We can work with nodes all by themselves, but since the whole point is to connect them together to make a computation graph, we can get extra tools by intentionally making these children of a `Workflow` node.\n",
     "\n",
-    "The `Workflow` class not only gives us access to the decorators for defining new nodes, but also lets us register modules of existing nodes and use them. Let's put together a workflow that uses both an existing node from a package, and a `Function` node that is more general than we used above in that it allows us to have multiple return values. This function node will also exploit our ability to name outputs and give type hints:"
+    "The `Workflow` class not only gives us access to the decorators for defining new nodes, but also lets us register modules of existing nodes and use them. Let's put together a workflow that uses both an existing node from a package, and a `Function` node that is more general than we used above in that it allows us to have multiple return values. This function node will also exploit our ability to name outputs and give type hints. \n",
+    "\n",
+    "We can also take output channels (or single value nodes) and perform many (but not all...) python operations on them to dynamically create new output nodes! Below see how we do math and indexing right on the output channels:"
    ]
   },
   {
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       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x1175b8490>"
+       "<graphviz.graphs.Digraph at 0x10f79ba50>"
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      },
      "execution_count": 7,
@@ -451,17 +826,19 @@
     "\n",
     "wf = Workflow(\"my_workflow\")\n",
     "\n",
-    "@Workflow.wrap_as.function_node(\"x\", \"x_sq\")\n",
-    "def SquareRange(x: int) -> tuple[np.ndarray, np.ndarray]:\n",
-    "    x = np.arange(x)\n",
-    "    return x, (x**2)\n",
+    "@Workflow.wrap_as.function_node(\"arange\", \"len\")\n",
+    "def Arange(n: int) -> tuple[np.ndarray, int]:\n",
+    "    \"\"\"\n",
+    "    Two outputs is silly overkill, but just to demonstrate how Function nodes work\n",
+    "    \"\"\"\n",
+    "    return np.arange(n), n\n",
     "\n",
     "wf.register(\"plotting\", \"pyiron_workflow.node_library.plotting\")\n",
     "\n",
-    "wf.arrays = SquareRange()\n",
+    "wf.arange = Arange(10)\n",
     "wf.plot = wf.create.plotting.Scatter(\n",
-    "    x=wf.arrays.outputs.x,\n",
-    "    y=wf.arrays.outputs.x_sq\n",
+    "    x=wf.arange.outputs.arange[:wf.arange.outputs.len -1],\n",
+    "    y=wf.arange.outputs.arange[:wf.arange.outputs.len -1]**2\n",
     ")\n",
     "\n",
     "wf.draw()"
@@ -472,7 +849,7 @@
    "id": "ffc897e4-0f12-4231-8ebe-82862c890de5",
    "metadata": {},
    "source": [
-    "We can see that the workflow automatically exposes unconnected IO of its children and gives them a name based on the child node's name and that node's IO name.\n",
+    "We can see that the workflow automatically exposes unconnected IO of its children and gives them a name based on the child node's name and that node's IO name. Further, the math and indexing we do automatically injects new nodes after the output. Note that the slicing nodes get re-used in both occurrences for computational efficiency.\n",
     "\n",
     "Let's run our workflow and look at the result:"
    ]
@@ -483,10 +860,18 @@
    "id": "c499c0ed-7af5-491a-b340-2d2f4f48529c",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/node.py:613: UserWarning: The keyword 'arrays__x' was not found among input labels. If you are trying to update a node keyword, please use attribute assignment directly instead of calling\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1179f9c10>"
+       "<matplotlib.collections.PathCollection at 0x10ff903d0>"
       ]
      },
      "execution_count": 8,
@@ -495,7 +880,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -527,13 +912,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The channel x cannot take the value `5.5` because it is not compliant with the type hint <class 'int'>\n"
+      "Can only set Channel object or connect to existing channels, but the attribute x got assigned 5.5 of type <class 'float'>\n"
      ]
     }
    ],
    "source": [
     "try:\n",
-    "    wf.arrays.inputs.x = 5.5\n",
+    "    wf.arange.inputs.x = 5.5\n",
     "except TypeError as e:\n",
     "    message = e.args[0]\n",
     "    print(message)"
@@ -560,15 +945,15 @@
    "source": [
     "@Workflow.wrap_as.macro_node()\n",
     "def MySquarePlot(macro):\n",
-    "    macro.arrays = SquareRange()\n",
+    "    macro.arange = Arange()\n",
     "    macro.plot = macro.create.plotting.Scatter(\n",
-    "        x=macro.arrays.outputs.x,\n",
-    "        y=macro.arrays.outputs.x_sq\n",
+    "        x=macro.arange.outputs.arange,\n",
+    "        y=macro.arange.outputs.arange**2\n",
     "    )\n",
-    "    macro.inputs_map = {\"arrays__x\": \"n\"}\n",
+    "    macro.inputs_map = {\"arange__n\": \"n\"}\n",
     "    macro.outputs_map = {\n",
-    "        \"arrays__x\": \"x\",\n",
-    "        \"arrays__x_sq\": \"y\",\n",
+    "        \"arange__arange\": \"x\",\n",
+    "        \"arange__len\": \"n\",\n",
     "        \"plot__fig\": \"fig\"\n",
     "    }\n",
     "    # Note that we also forced regularly hidden IO to be exposed!\n",
@@ -584,8 +969,9 @@
     {
      "data": {
       "text/plain": [
-       "{'square_plot__fig': <matplotlib.collections.PathCollection at 0x117de73d0>,\n",
-       " 'shifted_square_plot__fig': <matplotlib.collections.PathCollection at 0x117de1490>}"
+       "{'square_plot__n': 10,\n",
+       " 'square_plot__fig': <matplotlib.collections.PathCollection at 0x1100aab10>,\n",
+       " 'plus_one_square_plot__fig': <matplotlib.collections.PathCollection at 0x10fe77710>}"
       ]
      },
      "execution_count": 11,
@@ -594,7 +980,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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gkLwHlFAopGg0mrN222236dixY5KkYDAoSaN2SwYGBkbtqlzgdrvl8/lyHgAAYPrKe0C555579N577+Wsvf/++yotLZUkRSIRBYNBtbe3Z18/e/asOjo6VFlp9m21AQDA1Mj7vXiefvppVVZWqqGhQStXrlRXV5f27NmjPXv2SMpc2qmtrVVDQ4PKyspUVlamhoYGzZo1S6tWrcp3OwAAoADlPaDcddddev3117V161Y988wzikQi2r17tx555JFszaZNm3TmzBmtX79ep0+f1uLFi7V//355vd58twMAAApQ3uegTAXmoAAAUHhsnYMCAABwpQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAca6yuwEAwGVIp6SjndLQSWl2QCqtlJwuu7u6LKm0pa7eUxoYHFax16NFkTlyOR12twWbEVAAoNDEWqS2zVLyxMdrvrBUvUOKrrCvr8vQ1hNXfWtM8cRwdi3k96iuJqrq8pCNncFuXOIBgEISa5FeW5MbTiQpGc+sx1rs6esytPXEtW7voZxwIkn9iWGt23tIbT1xmzqDCQgoAFAo0qnMzomsS7x4fq1tS6bOcKm0pfrW2HjvRPWtMaXSl6rATEBAAYBCcbRz9M5JDktKHs/UGa6r99SonZNPsiTFE8Pq6j01dU3BKAQUACgUQyfzW2ejgcGxw8nl1GH6IaAAQKGYHchvnY2KvZ681mH6IaAAQKEorcyc1tFYR3Adku/GTJ3hFkXmKOT3jPdOFPJnjhxjZiKgAEChcLoyR4kljQ4p559XP1cQ81BcTofqaqKSxnwnqquJMg9lBiOgAEAhia6QVr4s+S6aEeILZ9YLaA5KdXlIzasrFPTnXsYJ+j1qXl3BHJQZzmFZVsGd4Uomk/L7/UokEvL5fHa3AwBTj0myKEAT+fxmkiwAFCKnS4ossbuLvHA5Hbr71rl2twHDcIkHAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxpn0gNLY2CiHw6Ha2trsmmVZ2r59u8LhsIqKirRs2TIdOXJkslsBAAAFYlIDSnd3t/bs2aPPfe5zOes7d+7Url271NTUpO7ubgWDQS1fvlyDg4OT2Q4AACgQkxZQhoaG9Mgjj+jFF1/U9ddfn123LEu7d+/Wtm3b9JWvfEXl5eV66aWX9OGHH2rfvn2T1Q4AACggkxZQNmzYoAceeED3339/znpvb6/6+/tVVVWVXXO73Vq6dKk6Ozsv+btGRkaUTCZzHgAAYPq6ajJ+6SuvvKJDhw6pu7t71Gv9/f2SpEAgkLMeCAR09OjRS/6+xsZG1dfX579RAABgpLzvoPT19empp57S3r175fF4xqxzOBw5zy3LGrV2wdatW5VIJLKPvr6+vPYMAADMkvcdlIMHD2pgYEALFizIrqVSKR04cEBNTU167733JGV2UkKhULZmYGBg1K7KBW63W263O9+tAgAAQ+V9B+W+++7TO++8o8OHD2cfCxcu1COPPKLDhw/rlltuUTAYVHt7e/Znzp49q46ODlVWVua7HQAAUIDyvoPi9XpVXl6es3bttddq7ty52fXa2lo1NDSorKxMZWVlamho0KxZs7Rq1ap8twMAAArQpHxJ9tNs2rRJZ86c0fr163X69GktXrxY+/fvl9frtaMdAABgGIdlWZbdTUxUMpmU3+9XIpGQz+ezux0AAPAZTOTzm3vxAAAA4xBQAACAcWz5DgoA2Cadko52SkMnpdkBqbRScrrs7mrCUmlLXb2nNDA4rGKvR4sic+RyXnqWFFCICCgAZo5Yi9S2WUqe+HjNF5aqd0jRFfb1NUFtPXHVt8YUTwxn10J+j+pqoqouD43zk0Dh4BIPgJkh1iK9tiY3nEhSMp5Zj7XY09cEtfXEtW7voZxwIkn9iWGt23tIbT1xmzoD8ouAAmD6S6cyOye61KHF82ttWzJ1BkulLdW3xsZ7F6pvjSmVLrjDmcAoBBQA09/RztE7JzksKXk8U2ewrt5To3ZOPsmSFE8Mq6v31NQ1BUwSAgqA6W/oZH7rbDIwOHY4uZw6wGQEFADT3+xL34j0sutsUuwd+w7xl1MHmIyAAmD6K63MnNbRWMdwHZLvxkydwRZF5ijk94z3LhTyZ44cA4WOgAJg+nO6MkeJJY0OKeefVz9n/DwUl9OhupqopDHfhepqosxDwbRAQAEwM0RXSCtflnwXzQnxhTPrBTIHpbo8pObVFQr6cy/jBP0eNa+uYA4Kpg1uFghgZmGSLGCbiXx+M0kWwMzidEmRJXZ3ccVcTofuvnWu3W0Ak4ZLPAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHGusrsBAAUinZKOdkpDJ6XZAam0UnK67O5qwlJpS129pzQwOKxir0eLInPkcjrsbgvARQgoAD5drEVq2ywlT3y85gtL1Tuk6Ar7+pqgtp646ltjiieGs2shv0d1NVFVl4ds7AzAxbjEA2B8sRbptTW54USSkvHMeqzFnr4mqK0nrnV7D+WEE0nqTwxr3d5DauuJ29QZgEshoAAYWzqV2TmRdYkXz6+1bcnUGSyVtlTfGhvvXai+NaZU+lIVAOxAQAEwtqOdo3dOclhS8nimzmBdvadG7Zx8kiUpnhhWV++pqWsKwLgIKADGNnQyv3U2GRgcO5xcTh2AyUdAATC22YH81tmk2OvJax2AyUdAATC20srMaR2NdQzXIfluzNQZbFFkjkJ+z3jvQiF/5sgxADMQUACMzenKHCWWNDqknH9e/Zzx81BcTofqaqKSxnwXqquJMg8FMAgBBcD4oiuklS9LvovmhPjCmfUCmYNSXR5S8+oKBf25l3GCfo+aV1cwBwUwjMOyrII7V5dMJuX3+5VIJOTz+exuB5gZmCQL4ApN5PObSbIAPhunS4ossbuLK+ZyOnT3rXPtbgPAp+ASDwAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcfIeUBobG3XXXXfJ6/WquLhYDz30kN57772cGsuytH37doXDYRUVFWnZsmU6cuRIvlsBAAAFKu8BpaOjQxs2bNBbb72l9vZ2nTt3TlVVVfrggw+yNTt37tSuXbvU1NSk7u5uBYNBLV++XIODg/luBwAAFKBJnyT7hz/8QcXFxero6NDnP/95WZalcDis2tpabd68WZI0MjKiQCCgHTt2aO3atZ/6O5kkCwBA4ZnI5/ekfwclkUhIkubMydwltLe3V/39/aqqqsrWuN1uLV26VJ2dnZf8HSMjI0omkzkPAAAwfU1qQLEsSxs3btS9996r8vJySVJ/f78kKRAI5NQGAoHsaxdrbGyU3+/PPkpKSiazbQAAYLNJDShPPPGEfve73+k//uM/Rr3mcOTenMuyrFFrF2zdulWJRCL76Ovrm5R+AQCAGSbtZoFPPvmkWlpadODAAd10003Z9WAwKCmzkxIKfXx784GBgVG7Khe43W653e7JahUAABgm7zsolmXpiSee0E9+8hP96le/UiQSyXk9EokoGAyqvb09u3b27Fl1dHSosrIy3+0AAIAClPcdlA0bNmjfvn366U9/Kq/Xm/1eid/vV1FRkRwOh2pra9XQ0KCysjKVlZWpoaFBs2bN0qpVq/LdDgAAKEB5DyjNzc2SpGXLluWs/+AHP9Bf/dVfSZI2bdqkM2fOaP369Tp9+rQWL16s/fv3y+v15rsdAABQgCZ9DspkYA4KAACFx6g5KAAAABNFQAEAAMaZtGPGAM5Lp6SjndLQSWl2QCqtlJwuu7uasFTaUlfvKQ0MDqvY69GiyBy5nJeeXQQAV4qAAkymWIvUtllKnvh4zReWqndI0RX29TVBbT1x1bfGFE8MZ9dCfo/qaqKqLg+N85MAcHm4xANMlliL9Nqa3HAiScl4Zj3WYk9fE9TWE9e6vYdywokk9SeGtW7vIbX1xG3qDMB0RkABJkM6ldk50aUOyZ1fa9uSqTNYKm2pvjU23rtQfWtMqXTBHQYEYDgCCjAZjnaO3jnJYUnJ45k6g3X1nhq1c/JJlqR4YlhdvaemrikAMwIBBZgMQyfzW2eTgcGxw8nl1AHAZ0VAASbD7Evf+PKy62xS7PXktQ4APisCCjAZSiszp3U01jFch+S7MVNnsEWROQr5PeO9C4X8mSPHAJBPBBRgMjhdmaPEkkaHlPPPq58zfh6Ky+lQXU1U0pjvQnU1UeahAMg7AgowWaIrpJUvS76L5oT4wpn1ApmDUl0eUvPqCgX9uZdxgn6PmldXMAcFwKTgZoHAZGOSLABImtjnN5NkgcnmdEmRJXZ3ccVcTofuvnWu3W0AmCG4xAMAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA43AvHpiLm+wBwIxFQIGZYi1S22YpeeLjNV9Yqt4hRVfY19cEtfXEVd8aUzwxnF0L+T2qq4mqujxkY2cAYDYu8cA8sRbptTW54USSkvHMeqzFnr4mqK0nrnV7D+WEE0nqTwxr3d5DauuJ29QZAJiPgAKzpFOZnRNZl3jx/FrblkydwVJpS/WtsfHehepbY0qlL1UBACCgwCxHO0fvnOSwpOTxTJ3BunpPjdo5+SRLUjwxrK7eU1PXFAAUEAIKzDJ0Mr91NhkYHDucXE4dAMw0BBSYZXYgv3U2KfZ68loHADMNAQVmKa3MnNbRWMdwHZLvxkydwRZF5ijk94z3LhTyZ44cAwBGI6DALE5X5iixpNEh5fzz6ueMn4ficjpUVxOVNOa7UF1NlHkoADAGAgrME10hrXxZ8l00J8QXzqwXyByU6vKQmldXKOjPvYwT9HvUvLqCOSgAMA6HZVkFd84xmUzK7/crkUjI5/PZ3Q4mC5NkAWBamcjnN5NkYS6nS4ossbuLK+ZyOnT3rXPtbgMACgqXeAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIdTPNMRx3MBAAWOgDLdxFqkts25dwT2hTPTWQtkwJkktfXEVd8ay7kjcMjvUV1NlAFnADADcIlnOom1SK+tyQ0nkpSMZ9ZjLfb0NUFtPXGt23soJ5xIUn9iWOv2HlJbT9ymzgAAU4WAMl2kU5mdE11qMPD5tbYtmTqDpdKW6ltj470L1bfGlEoX3ABkAMAEEFCmi6Odo3dOclhS8nimzmBdvadG7Zx8kiUpnhhWV++pqWsKADDlCCjTxdDJ/NbZZGBw7HByOXUAgMJEQJkuZgfyW2eTYq/n04smUAcAKEwElOmitDJzWkdjHcN1SL4bM3UGWxSZo5DfM967UMifOXIMAJi+CCjThdOVOUosaXRIOf+8+jnj56G4nA7V1UQljfkuVFcTZR4KAExzBJTpJLpCWvmy5LtoTogvnFkvkDko1eUhNa+uUNCfexkn6PeoeXUFc1AAYAZwWJZVcOc1k8mk/H6/EomEfD5f/n7xdJnAeu6c/u//+YXOnD6uoutv1LzFX5DrqsKbycckWQCYXiby+V14n1qTZdpNYJWkGyVJoQMdBTmB1eV06O5b59rdBgDABlzikZjACgCAYQgoTGAFAMA4tgaUF154QZFIRB6PRwsWLNAbb7wx9U0wgRUAAOPYFlBeffVV1dbWatu2bXr77be1ZMkSffGLX9SxY8emthEmsAIAYBzbAsquXbv02GOP6a//+q912223affu3SopKVFzc/OU9pG6tjivdXZhAisAYDqxJaCcPXtWBw8eVFVVVc56VVWVOjtHX0oZGRlRMpnMeeRLV2qeTlhzNNZXM9KWdMKaq67UvLz9m5OBCawAgOnEloDyxz/+UalUSoFA7n1hAoGA+vv7R9U3NjbK7/dnHyUlJXnrZeCDj1T/0RpJGhVSLjyv/+gbGvjgo7z9m5OBCawAgOnE1i/JOhy5H5aWZY1ak6StW7cqkUhkH319fXnrodjr0S/Si7Tuo1r1K3d3oV9zte6jWv0ivaggLo0wgRUAMF3YMqjthhtukMvlGrVbMjAwMGpXRZLcbrfcbvek9HLh0sj+xCK1jyzUIuf/VbH+nwZ0nbrS82TJWVCXRqrLQ1oeDTKBFQBQ0GzZQbnmmmu0YMECtbe356y3t7ersnJq77b7yUsjlpx6Kx1VS7pSb6Wjss7/5ym0SyMXJrB++c4bdfetcwuqdwAAJBsv8WzcuFH/+q//qn//93/Xu+++q6efflrHjh3T448/PuW9cGkEAACz2HYvnq997Wv63//9Xz3zzDOKx+MqLy/Xz372M5WWltrSD5dGAAAwB3czBgAAU2Iin9/ciwcAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGMe2UfdX4sLw22QyaXMnAADgs7rwuf1ZhtgXZEAZHByUJJWUlNjcCQAAmKjBwUH5/f5xawryXjzpdFonTpyQ1+uVw5Hfm/klk0mVlJSor6+P+/wYgL+HWfh7mIW/h3n4m4zPsiwNDg4qHA7L6Rz/WyYFuYPidDp10003Teq/4fP5+D+XQfh7mIW/h1n4e5iHv8nYPm3n5AK+JAsAAIxDQAEAAMYhoFzE7Xarrq5Obrfb7lYg/h6m4e9hFv4e5uFvkj8F+SVZAAAwvbGDAgAAjENAAQAAxiGgAAAA4xBQAACAcQgon/DCCy8oEonI4/FowYIFeuONN+xuacZqbGzUXXfdJa/Xq+LiYj300EN677337G4L5zU2NsrhcKi2ttbuVmas48ePa/Xq1Zo7d65mzZqlO++8UwcPHrS7rRnp3Llz+s53vqNIJKKioiLdcssteuaZZ5ROp+1uraARUM579dVXVVtbq23btuntt9/WkiVL9MUvflHHjh2zu7UZqaOjQxs2bNBbb72l9vZ2nTt3TlVVVfrggw/sbm3G6+7u1p49e/S5z33O7lZmrNOnT+uee+7R1VdfrZ///OeKxWL63ve+p+uuu87u1makHTt26Pvf/76ampr07rvvaufOnfrud7+r559/3u7WChrHjM9bvHixKioq1NzcnF277bbb9NBDD6mxsdHGziBJf/jDH1RcXKyOjg59/vOft7udGWtoaEgVFRV64YUX9E//9E+68847tXv3brvbmnG2bNmiX//61+zyGuLBBx9UIBDQv/3bv2XX/uIv/kKzZs3Sj370Ixs7K2zsoEg6e/asDh48qKqqqpz1qqoqdXZ22tQVPimRSEiS5syZY3MnM9uGDRv0wAMP6P7777e7lRmtpaVFCxcu1Fe/+lUVFxdr/vz5evHFF+1ua8a699579ctf/lLvv/++JOm3v/2t3nzzTX3pS1+yubPCVpA3C8y3P/7xj0qlUgoEAjnrgUBA/f39NnWFCyzL0saNG3XvvfeqvLzc7nZmrFdeeUWHDh1Sd3e33a3MeL///e/V3NysjRs36u///u/V1dWlb33rW3K73VqzZo3d7c04mzdvViKR0Lx58+RyuZRKpfTss8/q4Ycftru1gkZA+QSHw5Hz3LKsUWuYek888YR+97vf6c0337S7lRmrr69PTz31lPbv3y+Px2N3OzNeOp3WwoUL1dDQIEmaP3++jhw5oubmZgKKDV599VXt3btX+/bt0+23367Dhw+rtrZW4XBYjz76qN3tFSwCiqQbbrhBLpdr1G7JwMDAqF0VTK0nn3xSLS0tOnDggG666Sa725mxDh48qIGBAS1YsCC7lkqldODAATU1NWlkZEQul8vGDmeWUCikaDSas3bbbbfpxz/+sU0dzWzf/va3tWXLFn3961+XJN1xxx06evSoGhsbCShXgO+gSLrmmmu0YMECtbe356y3t7ersrLSpq5mNsuy9MQTT+gnP/mJfvWrXykSidjd0ox233336Z133tHhw4ezj4ULF+qRRx7R4cOHCSdT7J577hl17P79999XaWmpTR3NbB9++KGcztyPU5fLxTHjK8QOynkbN27UN77xDS1cuFB333239uzZo2PHjunxxx+3u7UZacOGDdq3b59++tOfyuv1Zne3/H6/ioqKbO5u5vF6vaO+/3Pttddq7ty5fC/IBk8//bQqKyvV0NCglStXqqurS3v27NGePXvsbm1Gqqmp0bPPPqubb75Zt99+u95++23t2rVL3/zmN+1urbBZyPqXf/kXq7S01LrmmmusiooKq6Ojw+6WZixJl3z84Ac/sLs1nLd06VLrqaeesruNGau1tdUqLy+33G63NW/ePGvPnj12tzRjJZNJ66mnnrJuvvlmy+PxWLfccou1bds2a2RkxO7WChpzUAAAgHH4DgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxvn/lASCsr1Dz/AAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -607,10 +993,10 @@
     "wf2 = Workflow(\"my_composed_workflow\")\n",
     "\n",
     "wf2.square_plot = MySquarePlot(n=10)\n",
-    "wf2.shift = AddOne(wf2.square_plot.outputs.x)\n",
-    "wf2.shifted_square_plot = wf2.create.plotting.Scatter(\n",
-    "    x=wf2.shift,\n",
-    "    y=wf2.square_plot.outputs.y,\n",
+    "wf2.plus_one = wf2.square_plot.outputs.x + 1\n",
+    "wf2.plus_one_square_plot = wf2.create.plotting.Scatter(\n",
+    "    x=wf2.square_plot.outputs.x,\n",
+    "    y=wf2.plus_one**2,\n",
     ")\n",
     "wf2()"
    ]
diff --git a/pyiron_workflow/channels.py b/pyiron_workflow/channels.py
index 63f4561c3..6065691db 100644
--- a/pyiron_workflow/channels.py
+++ b/pyiron_workflow/channels.py
@@ -183,9 +183,6 @@ def connected(self) -> bool:
     def __iter__(self):
         return self.connections.__iter__()
 
-    def __len__(self):
-        return len(self.connections)
-
     @property
     def channel(self) -> Channel:
         return self
@@ -264,6 +261,17 @@ class DataChannel(Channel, ABC):
     which is to say it is data (not `NotData`) and that it conforms to the type hint
     (if one is provided and checking is active).
 
+    Output data facilitates many (but not all) python operators by injecting a new
+    node to perform that operation. Where the operator is not supported, we try to
+    support using the operator's dunder name as a method, e.g. `==` gives us trouble
+    with hashing, but this exploits the dunder method `.__eq__(other)`, so you can call
+    `.eq(other)` on output data.
+    These new nodes are instructed to run at the end of instantiation, but this fails
+    cleanly in case they are not ready. This is intended to accommodate two likely
+    scenarios: if you're injecting a node on top of an existing result you probably
+    want the injection result to also be immediately available, but if you're injecting
+    it at the end of something that hasn't run yet you don't want to see an error.
+
     TODO:
         - Storage (including priority and history)
         - Ontological hinting
@@ -491,6 +499,221 @@ class OutputData(DataChannel):
     def connection_partner_type(self):
         return InputData
 
+    @staticmethod
+    def _other_label(other):
+        return (
+            other.channel.scoped_label if isinstance(other, HasChannel) else str(other)
+        )
+
+    def get_injected_label(self, injection_class, other=None):
+        suffix = f"_{self._other_label(other)}" if other is not None else ""
+        return f"{self.scoped_label}_{injection_class.__name__}{suffix}"
+
+    def _get_injection_label(self, injection_class, *args):
+        other_labels = "_".join(self._other_label(other) for other in args)
+        suffix = f"_{other_labels}" if len(args) > 0 else ""
+        return f"{self.scoped_label}_{injection_class.__name__}{suffix}"
+
+    def _node_injection(self, injection_class, *args, inject_self=True):
+        """
+        Create a new node with the same parent as this channel's node, and feed it
+        arguments, or load such a node if it already exists on the parent (based on a
+        name dynamically generated from the injected node class and arguments).
+
+        Args:
+            injection_class (type[Node]): The new node class to instantiate
+            *args: Any arguments for that function node
+            inject_self (bool): Whether to pre-pend the args with self. (Default is
+                True.)
+
+        Returns:
+            (Node): The instantiated or loaded node.
+        """
+        label = self._get_injection_label(injection_class, *args)
+        try:
+            # First check if the node already exists
+            return self.node.parent.nodes[label]
+        except (AttributeError, KeyError):
+            # Fall back on creating a new node in case parent is None or node nexists
+            node_args = (self, *args) if inject_self else args
+            return injection_class(
+                *node_args, parent=self.node.parent, label=label, run_after_init=True
+            )
+
+    # We don't wrap __all__ the operators, because you might really want the string or
+    # hash or whatever of the actual channel. But we do wrap all the dunder methods
+    # that should be unambiguously referring to an operation on values
+
+    def __getattr__(self, name):
+        from pyiron_workflow.node_library.standard import GetAttr
+
+        return self._node_injection(GetAttr, name)
+
+    def __getitem__(self, item):
+        # Break slices into deeper injections, if any slice arguments are channel-like
+        if isinstance(item, slice) and any(
+            isinstance(slice_input, HasChannel)
+            for slice_input in [item.start, item.stop, item.step]
+        ):
+            from pyiron_workflow.node_library.standard import Slice
+
+            item = self._node_injection(
+                Slice, item.start, item.stop, item.step, inject_self=False
+            )
+
+        from pyiron_workflow.node_library.standard import GetItem
+
+        return self._node_injection(GetItem, item)
+
+    def __lt__(self, other):
+        from pyiron_workflow.node_library.standard import LessThan
+
+        return self._node_injection(LessThan, other)
+
+    def __le__(self, other):
+        from pyiron_workflow.node_library.standard import LessThanEquals
+
+        return self._node_injection(LessThanEquals, other)
+
+    def eq(self, other):
+        from pyiron_workflow.node_library.standard import Equals
+
+        return self._node_injection(Equals, other)
+
+    def __ne__(self, other):
+        from pyiron_workflow.node_library.standard import NotEquals
+
+        return self._node_injection(NotEquals, other)
+
+    def __gt__(self, other):
+        from pyiron_workflow.node_library.standard import GreaterThan
+
+        return self._node_injection(GreaterThan, other)
+
+    def __ge__(self, other):
+        from pyiron_workflow.node_library.standard import GreaterThanEquals
+
+        return self._node_injection(GreaterThanEquals, other)
+
+    def bool(self):
+        from pyiron_workflow.node_library.standard import Bool
+
+        return self._node_injection(Bool)
+
+    def len(self):
+        from pyiron_workflow.node_library.standard import Length
+
+        return self._node_injection(Length)
+
+    def contains(self, other):
+        from pyiron_workflow.node_library.standard import Contains
+
+        return self._node_injection(Contains, other)
+
+    def __add__(self, other):
+        from pyiron_workflow.node_library.standard import Add
+
+        return self._node_injection(Add, other)
+
+    def __sub__(self, other):
+        from pyiron_workflow.node_library.standard import Subtract
+
+        return self._node_injection(Subtract, other)
+
+    def __mul__(self, other):
+        from pyiron_workflow.node_library.standard import Multiply
+
+        return self._node_injection(Multiply, other)
+
+    def __rmul__(self, other):
+        from pyiron_workflow.node_library.standard import RightMultiply
+
+        return self._node_injection(RightMultiply, other)
+
+    def __matmul__(self, other):
+        from pyiron_workflow.node_library.standard import MatrixMultiply
+
+        return self._node_injection(MatrixMultiply, other)
+
+    def __truediv__(self, other):
+        from pyiron_workflow.node_library.standard import Divide
+
+        return self._node_injection(Divide, other)
+
+    def __floordiv__(self, other):
+        from pyiron_workflow.node_library.standard import FloorDivide
+
+        return self._node_injection(FloorDivide, other)
+
+    def __mod__(self, other):
+        from pyiron_workflow.node_library.standard import Modulo
+
+        return self._node_injection(Modulo, other)
+
+    def __pow__(self, other):
+        from pyiron_workflow.node_library.standard import Power
+
+        return self._node_injection(Power, other)
+
+    def __and__(self, other):
+        from pyiron_workflow.node_library.standard import And
+
+        return self._node_injection(And, other)
+
+    def __xor__(self, other):
+        from pyiron_workflow.node_library.standard import XOr
+
+        return self._node_injection(XOr, other)
+
+    def __or__(self, other):
+        from pyiron_workflow.node_library.standard import Or
+
+        return self._node_injection(Or, other)
+
+    def __neg__(self):
+        from pyiron_workflow.node_library.standard import Negative
+
+        return self._node_injection(Negative)
+
+    def __pos__(self):
+        from pyiron_workflow.node_library.standard import Positive
+
+        return self._node_injection(Positive)
+
+    def __abs__(self):
+        from pyiron_workflow.node_library.standard import Absolute
+
+        return self._node_injection(Absolute)
+
+    def __invert__(self):
+        from pyiron_workflow.node_library.standard import Invert
+
+        return self._node_injection(Invert)
+
+    def int(self):
+        from pyiron_workflow.node_library.standard import Int
+
+        return self._node_injection(Int)
+
+    def float(self):
+        from pyiron_workflow.node_library.standard import Float
+
+        return self._node_injection(Float)
+
+    def __round__(self):
+        from pyiron_workflow.node_library.standard import Round
+
+        return self._node_injection(Round)
+
+    # Because we override __getattr__ we need to get and set state for serialization
+    def __getstate__(self):
+        return self.__dict__
+
+    def __setstate__(self, state):
+        # Update instead of overriding in case some other attributes were added on the
+        # main process while a remote process was working away
+        self.__dict__.update(**state)
+
 
 class SignalChannel(Channel, ABC):
     """
diff --git a/pyiron_workflow/function.py b/pyiron_workflow/function.py
index 42f8aafc6..82e6498c4 100644
--- a/pyiron_workflow/function.py
+++ b/pyiron_workflow/function.py
@@ -582,13 +582,15 @@ class SingleValue(Function, HasChannel):
     """
     A node that _must_ return only a single value.
 
-    Attribute and item access is modified to finally attempt access on the output value.
-    Note that this means any attributes/method available on the output value become
-    available directly at the node level (at least those which don't conflict with the
-    existing node namespace).
+    Attribute and item access is modified to finally attempt access on the output
+    channel, and other operations (those supported by the output channel) are also
+    passed there automatically.
+    This means that the node itself can be used in place of its output channel,
+    and that the `value` attribtue directly accesses the output value.
 
     Promises (in addition parent class promises):
-    - Attribute and item access will finally attempt to access the output value
+    - Attribute and item access will finally attempt to access the output
+    - Other operators supported by the output channel operate there immediately.
     - The entire node can be used in place of its output value for connections, e.g.
         `some_node.input.some_channel = my_svn_instance`.
     """
@@ -602,40 +604,114 @@ def _get_output_labels(self, output_labels: str | list[str] | tuple[str] | None)
             )
         return output_labels
 
-    @property
-    def single_value(self):
-        return self.outputs[self.outputs.labels[0]].value
-
     @property
     def channel(self) -> OutputData:
         """The channel for the single output"""
-        return list(self.outputs.channel_dict.values())[0]
+        return self.outputs[self.outputs.labels[0]]
 
     @property
     def color(self) -> str:
         """For drawing the graph"""
         return SeabornColors.cyan
 
-    def __getitem__(self, item):
-        return self.single_value.__getitem__(item)
-
-    def __getattr__(self, item):
-        try:
-            return getattr(self.single_value, item)
-        except Exception as e:
-            raise AttributeError(
-                f"Could not find {item} as an attribute of the single value "
-                f"{self.single_value}"
-            ) from e
-
     def __repr__(self):
-        return self.single_value.__repr__()
+        return self.channel.value.__repr__()
 
     def __str__(self):
         return f"{self.label} ({self.__class__.__name__}) output single-value: " + str(
-            self.single_value
+            self.channel.value
         )
 
+    def __getattr__(self, item):
+        return getattr(self.channel, item)
+
+    def __getitem__(self, item):
+        return self.channel.__getitem__(item)
+
+    def __lt__(self, other):
+        return self.channel.__lt__(other)
+
+    def __le__(self, other):
+        return self.channel.__le__(other)
+
+    def eq(self, other):
+        return self.channel.eq(other)
+
+    def __ne__(self, other):
+        return self.channel.__ne__(other)
+
+    def __gt__(self, other):
+        return self.channel.__gt__(other)
+
+    def __ge__(self, other):
+        return self.channel.__ge__(other)
+
+    def bool(self):
+        return self.channel.bool()
+
+    def len(self):
+        return self.channel.len()
+
+    def contains(self, other):
+        return self.channel.contains(other)
+
+    def __add__(self, other):
+        return self.channel.__add__(other)
+
+    def __sub__(self, other):
+        return self.channel.__sub__(other)
+
+    def __mul__(self, other):
+        return self.channel.__mul__(other)
+
+    def __rmul__(self, other):
+        return self.channel.__rmul__(other)
+
+    def __matmul__(self, other):
+        return self.channel.__matmul__(other)
+
+    def __truediv__(self, other):
+        return self.channel.__truediv__(other)
+
+    def __floordiv__(self, other):
+        return self.channel.__floordiv__(other)
+
+    def __mod__(self, other):
+        return self.channel.__mod__(other)
+
+    def __pow__(self, other):
+        return self.channel.__pow__(other)
+
+    def __and__(self, other):
+        return self.channel.__and__(other)
+
+    def __xor__(self, other):
+        return self.channel.__xor__(other)
+
+    def __or__(self, other):
+        return self.channel.__or__(other)
+
+    def __neg__(self):
+        return self.channel.__neg__()
+
+    def __pos__(self):
+        return self.channel.__pos__()
+
+    def __abs__(self):
+        return self.channel.__abs__()
+
+    def __invert__(self):
+        return self.channel.__invert__()
+
+    def int(self):
+        return self.channel.int()
+
+    def float(self):
+        return self.channel.float()
+
+    def __round__(self):
+        return self.channel.__round__()
+
 
 def _wrapper_factory(
     parent_class: type[Function], output_labels: Optional[list[str] | tuple[str]]
diff --git a/pyiron_workflow/meta.py b/pyiron_workflow/meta.py
index 570ac4dc8..1e221e7fd 100644
--- a/pyiron_workflow/meta.py
+++ b/pyiron_workflow/meta.py
@@ -216,17 +216,17 @@ def while_loop(
         >>>
         >>> AddWhile = Workflow.create.meta.while_loop(
         ...     loop_body_class=Add,
-        ...     condition_class=LessThanTen,
+        ...     condition_class=Workflow.create.standard.LessThan,
         ...     internal_connection_map=[
-        ...         ("Add", "a + b", "LessThanTen", "value"),
+        ...         ("Add", "a + b", "LessThan", "obj"),
         ...         ("Add", "a + b", "Add", "a")
         ...     ],
-        ...     inputs_map={"Add__a": "a", "Add__b": "b"},
+        ...     inputs_map={"Add__a": "a", "Add__b": "b", "LessThan__other": "cap"},
         ...     outputs_map={"Add__a + b": "total"}
         ... )
         >>>
         >>> wf = Workflow("do_while")
-        >>> wf.add_while = AddWhile()
+        >>> wf.add_while = AddWhile(cap=10)
         >>>
         >>> wf.inputs_map = {
         ...     "add_while__a": "a",
diff --git a/pyiron_workflow/node.py b/pyiron_workflow/node.py
index 2026c0afd..125c3717b 100644
--- a/pyiron_workflow/node.py
+++ b/pyiron_workflow/node.py
@@ -71,6 +71,10 @@ def wrapped_method(node: Node, *args, **kwargs):  # rather node:Node
     return wrapped_method
 
 
+class ReadinessError(ValueError):
+    pass
+
+
 class Node(HasToDict, ABC, metaclass=AbstractHasPost):
     """
     Nodes are elements of a computational graph.
@@ -120,6 +124,8 @@ class Node(HasToDict, ABC, metaclass=AbstractHasPost):
         held by the output channels
     - If an error is encountered _after_ reaching the state of actually computing the
         node's task, the status will get set to failure
+    - Nodes can be instructed to run at the end of their initialization, but will exit
+        cleanly if they get to checking their readiness and find they are not ready
     - Nodes have a label by which they are identified
     - Nodes may open a working directory related to their label, their parent(age) and
         the python process working directory
@@ -247,7 +253,10 @@ def __init__(
 
     def __post__(self, *args, run_after_init: bool = False, **kwargs):
         if run_after_init:
-            self.run()
+            try:
+                self.run()
+            except ReadinessError:
+                pass
 
     @property
     @abstractmethod
@@ -376,7 +385,7 @@ def run(
             self.inputs.fetch()
 
         if check_readiness and not self.ready:
-            raise ValueError(
+            raise ReadinessError(
                 f"{self.label} received a run command but is not ready. The node "
                 f"should be neither running nor failed, and all input values should"
                 f" conform to type hints.\n" + self.readiness_report
diff --git a/pyiron_workflow/node_library/standard.py b/pyiron_workflow/node_library/standard.py
index 2783aa08d..1e7820df3 100644
--- a/pyiron_workflow/node_library/standard.py
+++ b/pyiron_workflow/node_library/standard.py
@@ -17,7 +17,7 @@ def UserInput(user_input):
 
 class If(SingleValue):
     """
-    Has two extra signal channels: true and false. Evaluates the input as a boolean and
+    Has two extra signal channels: true and false. Evaluates the input as obj otheroolean and
     fires the corresponding output signal after running.
     """
 
@@ -29,7 +29,9 @@ def __init__(self, **kwargs):
     @staticmethod
     def if_(condition):
         if isclass(condition) and issubclass(condition, NotData):
-            raise TypeError(f"Logic 'If' node expected data but got NotData as input.")
+            raise TypeError(
+                f"Logic 'If' node expected data otherut got NotData as input."
+            )
         return bool(condition)
 
     def process_run_result(self, function_output):
@@ -44,7 +46,247 @@ def process_run_result(self, function_output):
             self.signals.output.false()
 
 
+@single_value_node("slice")
+def Slice(start=None, stop=NotData, step=None):
+    if start is None:
+        if stop is None:
+            raise ValueError(
+                "Slice must define at least start or stop, but both are None"
+            )
+        elif step is not None:
+            raise ValueError("If step is provided, start _must_ be provided")
+        else:
+            s = slice(stop)
+    elif stop is None:
+        raise ValueError("If start is provided, stop _must_ be provided")
+    else:
+        s = slice(start, stop, step)
+    return s
+
+
+# A bunch of (but not all) standard operators
+# Return values based on dunder methods, where available
+
+
+@single_value_node("str")
+def String(obj):
+    return str(obj)
+
+
+@single_value_node("bytes")
+def Bytes(obj):
+    return bytes(obj)
+
+
+@single_value_node("lt")
+def LessThan(obj, other):
+    return obj < other
+
+
+@single_value_node("le")
+def LessThanEquals(obj, other):
+    return obj <= other
+
+
+@single_value_node("eq")
+def Equals(obj, other):
+    return obj == other
+
+
+@single_value_node("neq")
+def NotEquals(obj, other):
+    return obj != other
+
+
+@single_value_node("gt")
+def GreaterThan(obj, other):
+    return obj > other
+
+
+@single_value_node("ge")
+def GreaterThanEquals(obj, other):
+    return obj >= other
+
+
+@single_value_node("hash")
+def Hash(obj):
+    return hash(obj)
+
+
+@single_value_node("bool")
+def Bool(obj):
+    return bool(obj)
+
+
+@single_value_node("getattr")
+def GetAttr(obj, name):
+    return getattr(obj, name)
+
+
+# These are not idempotent and thus not encouraged
+# @single_value_node("none")
+# def SetAttr(obj, name, value):
+#     setattr(obj, name, value)
+#     return None
+#
+#
+# @single_value_node("none")
+# def DelAttr(obj, name):
+#     delattr(obj, name)
+#     return None
+
+
+@single_value_node("getitem")
+def GetItem(obj, item):
+    return obj[item]
+
+
+@single_value_node("dir")
+def Dir(obj):
+    return dir(obj)
+
+
+@single_value_node("len")
+def Length(obj):
+    return len(obj)
+
+
+@single_value_node("in")
+def Contains(obj, other):
+    return other in obj
+
+
+@single_value_node("add")
+def Add(obj, other):
+    return obj + other
+
+
+@single_value_node("sub")
+def Subtract(obj, other):
+    return obj - other
+
+
+@single_value_node("mul")
+def Multiply(obj, other):
+    return obj * other
+
+
+@single_value_node("rmul")
+def RightMultiply(obj, other):
+    return other * obj
+
+
+@single_value_node("matmul")
+def MatrixMultiply(obj, other):
+    return obj @ other
+
+
+@single_value_node("truediv")
+def Divide(obj, other):
+    return obj / other
+
+
+@single_value_node("floordiv")
+def FloorDivide(obj, other):
+    return obj // other
+
+
+@single_value_node("mod")
+def Modulo(obj, other):
+    return obj % other
+
+
+@single_value_node("pow")
+def Power(obj, other):
+    return obj**other
+
+
+@single_value_node("and")
+def And(obj, other):
+    return obj & other
+
+
+@single_value_node("xor")
+def XOr(obj, other):
+    return obj ^ other
+
+
+@single_value_node("or")
+def Or(obj, other):
+    return obj ^ other
+
+
+@single_value_node("neg")
+def Negative(obj):
+    return -obj
+
+
+@single_value_node("pos")
+def Positive(obj):
+    return +obj
+
+
+@single_value_node("abs")
+def Absolute(obj):
+    return abs(obj)
+
+
+@single_value_node("invert")
+def Invert(obj):
+    return ~obj
+
+
+@single_value_node("int")
+def Int(obj):
+    return int(obj)
+
+
+@single_value_node("float")
+def Float(obj):
+    return float(obj)
+
+
+@single_value_node("round")
+def Round(obj):
+    return round(obj)
+
+
 nodes = [
-    UserInput,
+    Absolute,
+    Add,
+    And,
+    Bool,
+    Bytes,
+    Contains,
+    Dir,
+    Divide,
+    Equals,
+    Float,
+    FloorDivide,
+    GetAttr,
+    GetItem,
+    GreaterThan,
+    GreaterThanEquals,
+    Hash,
     If,
+    Int,
+    Invert,
+    Length,
+    LessThan,
+    LessThanEquals,
+    MatrixMultiply,
+    Modulo,
+    Multiply,
+    Negative,
+    NotEquals,
+    Or,
+    Positive,
+    Power,
+    RightMultiply,
+    Round,
+    Slice,
+    String,
+    Subtract,
+    UserInput,
+    XOr,
 ]
diff --git a/tests/integration/test_output_injection.py b/tests/integration/test_output_injection.py
new file mode 100644
index 000000000..3fc550434
--- /dev/null
+++ b/tests/integration/test_output_injection.py
@@ -0,0 +1,205 @@
+import unittest
+
+from pyiron_workflow import Workflow
+from pyiron_workflow.node import Node
+
+
+class TestOutputInjection(unittest.TestCase):
+    """
+    I.e. the process of inserting new nodes on-the-fly by modifying output channels"
+    """
+    def setUp(self) -> None:
+        self.wf = Workflow("injection")
+        self.int = Workflow.create.standard.UserInput(42, run_after_init=True)
+        self.list = Workflow.create.standard.UserInput(
+            list(range(10)), run_after_init=True
+        )
+
+    def test_equality(self):
+        with self.subTest("True expressions"):
+            for expression in [
+                self.int < 100,
+                0 < self.int,
+                self.int <= 100,
+                self.int <= 42,
+                0 <= self.int,
+                self.int.eq(42),
+                self.int != 43,
+                self.int > 0,
+                100 > self.int,
+                self.int >= 0,
+                100 >= self.int,
+                self.int >= 42,
+            ]:
+                with self.subTest(expression.label):
+                    self.assertTrue(expression.value)
+
+        with self.subTest("False expressions"):
+            for expression in [
+                self.int > 100,
+                0 > self.int,
+                self.int >= 100,
+                0 >= self.int,
+                self.int != 42,
+                self.int.eq(43),
+                self.int < 0,
+                100 < self.int,
+                self.int <= 0,
+                100 <= self.int,
+            ]:
+                with self.subTest(expression.label):
+                    self.assertFalse(expression.value)
+
+    def test_bool(self):
+        b = self.int.bool()
+        self.assertTrue(b.value)
+        self.int.inputs.user_input = False
+        self.assertFalse(b())
+
+    def test_len(self):
+        self.assertEqual(10, self.list.len().value)
+
+    def test_contains(self):
+        self.assertTrue(self.list.contains(1).value)
+        self.assertFalse(self.list.contains(-1).value)
+
+    def test_algebra(self):
+        x = self.int  # 42
+        for lhs, rhs in [
+            (x + x, 2 * x),
+            (2 * x, x * 2),
+            (x * x, x**2),
+            (x - x, 0 * x),
+            (x + x - x, x),
+            (x / 42, x / x),
+            (x // 2, x / 2),
+            (x // 43, 0 * x),
+            ((x + 1) % x, x + 1 - x),
+            (-x, -1 * x),
+            (+x, (-x)**2 / x),
+            (x, abs(-x)),
+        ]:
+            with self.subTest(f"{lhs.label} == {rhs.label}"):
+                self.assertEqual(lhs.value, rhs.value)
+
+    # This passes fine, but requires numpy so don't include it
+    # def test_matmul(self):
+    #     import numpy as np
+    #
+    #     a = np.random.rand(2, 2)
+    #     b = np.random.rand(2, 2)
+    #     self.wf.a = Workflow.create.standard.UserInput(a, run_after_init=True)
+    #     self.wf.b = Workflow.create.standard.UserInput(b, run_after_init=True)
+    #     self.assertListEqual(
+    #         (self.wf.a @ self.wf.b).value.tolist(),
+    #         (a @ b).tolist()
+    #     )
+
+    def test_logic(self):
+        # Note: We can't invert with not etc. because overloading __bool__ does not work
+        self.true = Workflow.create.standard.UserInput(True, run_after_init=True)
+        self.false = Workflow.create.standard.UserInput(False, run_after_init=True)
+
+        with self.subTest("True expressions"):
+            for expression in [
+                self.true & True,
+                # True & self.true,  # There's no __land__ etc.
+                self.true & self.true,
+                self.true ^ False,
+                # False ^ self.true,
+                self.true ^ self.false,
+                self.false ^ self.true,
+                self.true | False,
+                self.true | self.false,
+                self.false | self.true,
+                self.false | False | self.true,
+                # False | self.true,
+            ]:
+                with self.subTest(expression.label):
+                    self.assertTrue(expression.value)
+
+        with self.subTest("False expressions"):
+            for expression in [
+                self.true & False,
+                self.false & self.false,
+                self.false & self.true,
+                self.true & self.false,
+                self.true ^ self.true,
+                self.false ^ self.false,
+                self.false | self.false,
+                self.false | False,
+            ]:
+                with self.subTest(expression.label):
+                    self.assertFalse(expression.value)
+
+    def test_casts(self):
+        self.float = Workflow.create.standard.UserInput(42.2, run_after_init=True)
+
+        self.assertIsInstance(self.int.float().value, float)
+        self.assertIsInstance(self.float.int().value, int)
+        self.assertEqual(self.int.value, round(self.float).value)
+
+    def test_access(self):
+
+        self.dict = Workflow.create.standard.UserInput(
+            {"foo": 42}, run_after_init=True
+        )
+
+        class Something:
+            myattr = 1
+
+        self.obj = Workflow.create.standard.UserInput(
+            Something(), run_after_init=True
+        )
+
+        self.assertIsInstance(self.list[0].value, int)
+        self.assertEqual(5, self.list[:5].len().value)
+        self.assertEqual(4, self.list[1:5].len().value)
+        self.assertEqual(3, self.list[-3:].len().value)
+        self.assertEqual(2, self.list[1:5:2].len().value)
+        
+        self.assertEqual(42, self.dict["foo"].value)
+        self.assertEqual(1, self.obj.myattr.value)
+
+    def test_chaining(self):
+        self.assertFalse((self.list[:self.int//42][0] != 0).value)
+
+    def test_repeated_access_in_parent_scope(self):
+        wf = Workflow("output_manipulation")
+        wf.list = Workflow.create.standard.UserInput(list(range(10)))
+
+        a = wf.list[:4]
+        b = wf.list[:4]
+        c = wf.list[1:]
+
+        self.assertIs(
+            a,
+            b,
+            msg="The same operation should re-access an existing node in the parent"
+        )
+        self.assertIsNot(
+            a,
+            c,
+            msg="Unique operations should yield unique nodes"
+        )
+
+    def test_without_parent(self):
+        d1 = self.list[5]
+        d2 = self.list[5]
+
+        self.assertIsInstance(d1, Node)
+        self.assertIsNot(
+            d1,
+            d2,
+            msg="Outside the scope of a parent, we can't expect to re-access an "
+                "equivalent node"
+        )
+        self.assertEqual(
+            d1.label,
+            d2.label,
+            msg="Equivalent operations should nonetheless generate equal labels"
+        )
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/tests/integration/test_workflow.py b/tests/integration/test_workflow.py
index 464b0d560..128ca00d7 100644
--- a/tests/integration/test_workflow.py
+++ b/tests/integration/test_workflow.py
@@ -147,23 +147,19 @@ def GreaterThan(x: float, threshold: float):
 
         with self.subTest("Self-data-loop"):
 
-            @Workflow.wrap_as.single_value_node()
-            def Add(a, b):
-                return a + b
-
-            @Workflow.wrap_as.single_value_node()
-            def LessThanTen(value):
-                return value < 10
-
             AddWhile = Workflow.create.meta.while_loop(
-                loop_body_class=Add,
-                condition_class=LessThanTen,
+                loop_body_class=Workflow.create.standard.Add,
+                condition_class=Workflow.create.standard.LessThan,
                 internal_connection_map=[
-                    ("Add", "a + b", "LessThanTen", "value"),
-                    ("Add", "a + b", "Add", "a")
+                    ("Add", "add", "LessThan", "obj"),
+                    ("Add", "add", "Add", "obj")
                 ],
-                inputs_map={"Add__a": "a", "Add__b": "b"},
-                outputs_map={"Add__a + b": "total"}
+                inputs_map={
+                    "Add__obj": "a",
+                    "Add__other": "b",
+                    "LessThan__other": "cap",
+                },
+                outputs_map={"Add__add": "total"}
             )
 
             wf = Workflow("do_while")
@@ -171,11 +167,12 @@ def LessThanTen(value):
 
             wf.inputs_map = {
                 "add_while__a": "a",
-                "add_while__b": "b"
+                "add_while__b": "b",
+                "add_while__cap": "cap"
             }
             wf.outputs_map = {"add_while__total": "total"}
 
-            out = wf(a=1, b=2)
+            out = wf(a=1, b=2, cap=10)
             self.assertEqual(out.total, 11)
 
     def test_executor_and_creator_interaction(self):
diff --git a/tests/unit/test_channels.py b/tests/unit/test_channels.py
index 66ab99ec8..b501875b6 100644
--- a/tests/unit/test_channels.py
+++ b/tests/unit/test_channels.py
@@ -59,20 +59,6 @@ def test_connection_validity(self):
         self.inp.connect(self.out)
         # A conjugate pair should work fine
 
-    def test_length(self):
-        self.inp.connect(self.out)
-        self.out2.connect(self.inp)
-        self.assertEqual(
-            2,
-            len(self.inp),
-            msg="Promised that channel length was number of connections"
-        )
-        self.assertEqual(
-            1,
-            len(self.out),
-            msg="Promised that channel length was number of connections"
-        )
-
     def test_connection_reflexivity(self):
         self.inp.connect(self.out)
 
@@ -359,13 +345,13 @@ def test_connections(self):
 
         with self.subTest("Ignore repeated connection"):
             self.out.connect(self.inp)
-            self.assertEqual(len(self.inp), 1)
-            self.assertEqual(len(self.out), 1)
+            self.assertEqual(len(self.inp.connections), 1)
+            self.assertEqual(len(self.out.connections), 1)
 
         with self.subTest("Check disconnection"):
             self.out.disconnect_all()
-            self.assertEqual(len(self.inp), 0)
-            self.assertEqual(len(self.out), 0)
+            self.assertEqual(len(self.inp.connections), 0)
+            self.assertEqual(len(self.out.connections), 0)
 
         with self.subTest("No connections to non-SignalChannels"):
             bad = InputData(label="numeric", node=DummyNode(), default=1, type_hint=int)
diff --git a/tests/unit/test_function.py b/tests/unit/test_function.py
index 5a993c18d..285aa586c 100644
--- a/tests/unit/test_function.py
+++ b/tests/unit/test_function.py
@@ -480,31 +480,42 @@ def returns_foo() -> Foo:
             return Foo()
 
         svn = SingleValue(returns_foo, output_labels="foo")
+
+        self.assertEqual(
+            svn.connected,
+            False,
+            msg="Should return the _node_ attribute, not acting on the output channel"
+        )
+
+        injection = svn[0]  # Should pass cleanly, even though it tries to run
         svn.run()
 
         self.assertEqual(
-            svn.some_attribute,
+            svn.some_attribute.value,  # The call runs the dynamic node
             "exists",
-            msg="Should fall back to looking on the single value"
+            msg="Should fall back to acting on the output channel and creating a node"
         )
 
         self.assertEqual(
             svn.connected,
-            False,
-            msg="Should return the _node_ attribute, not the single value attribute"
+            True,
+            msg="Should now be connected to the dynamically created nodes"
         )
 
-        with self.assertRaises(AttributeError):
+        with self.assertRaises(
+            AttributeError,
+            msg="Aggressive running hits the problem that no such attribute exists"
+        ):
             svn.doesnt_exists_anywhere
 
         self.assertEqual(
-            svn[0],
+            injection(),
             True,
-            msg="Should fall back to looking on the single value"
+            msg="Should be able to query injection later"
         )
 
         self.assertEqual(
-            svn["some other key"],
+            svn["some other key"].value,
             False,
             msg="Should fall back to looking on the single value"
         )
@@ -531,7 +542,7 @@ def test_str(self):
         svn = SingleValue(plus_one)
         svn.run()
         self.assertTrue(
-            str(svn).endswith(str(svn.single_value)),
+            str(svn).endswith(str(svn.value)),
             msg="SingleValueNodes should have their output as a string in their string "
                 "representation (e.g., perhaps with a reminder note that this is "
                 "actually still a Function and not just the value you're seeing.)"