{"action":"created","comment":{"url":"https://api.github.com/repos/rrrutledge/innersource-intro/pulls/comments/180220632","pull_request_review_id":110605413,"id":180220632,"diff_hunk":"@@ -1,77 +1,255 @@\n-<h1>A Simple Oriole</h1>\n-<p>A really simple example:</p>\n-<p>You can write paragraphs like this.</p>\n-<pre data-code-language=\"python\" data-executable=\"true\" data-type=\"programlisting\">hey = &quot;hello world&quot;, 38+4\n-print(hey hello. This is an executable code block )!\n-</pre>\n-<div id=\"the-id\">\n-<p>This is a block inside a div with an id the-id.\n-Very cool. Yes.</p>\n-</div>\n-<pre data-code-language=\"\" >hey = &quot;hello world&quot;, 38+4\n-print(hey hello. This is a non executable block )!\n-</pre>\n-<div id=\"another-id\">\n-<h2>More complicated example</h2>\n-</div>\n-<pre data-code-language=\"python\" data-executable=\"true\" data-type=\"programlisting\">x = [1,1]\n-for i in range(12):\n-    x.append(x[-1] + x[-2]) \n-print(', '.join(str(y) for y in x))\n-</pre>\n-<h2>A Chart</h2>\n-<div id=\"this-example\">\n-</div>\n-<pre data-code-language=\"python\" data-executable=\"true\" data-type=\"programlisting\">%matplotlib inline\n-import matplotlib.pyplot as plt\n-import numpy as np\n-x = np.linspace(0, 3*np.pi, 500)\n-plt.plot(x, np.sin(x**2))\n-plt.title('A simple chirp');\n-</pre>\n-<h2>Seaborn Example</h2>\n-<pre data-code-language=\"python\" data-executable=\"true\" data-type=\"programlisting\">from __future__ import print_function, division\n+<pre>Where will we stop for feedback and assessment questions?</pre>\n \n-%matplotlib inline\n-import matplotlib.pyplot as plt\n-import numpy as np\n-import pandas as pd\n+<h2>Introduction</h2>\n \n-x = np.linspace(0, 10, 1000)\n-plt.plot(x, np.sin(x), x, np.cos(x));\n+<p>\n+  InnerSource is the methodology of applying open source practices and principles to internal software development.\n+  This methodology provides a low-friction process for ensuring that the features and functionality of shared projects are responsive and nimble to the changing needs of their many internal consumers.\n+</p>\n \n-import seaborn as sns\n-sns.set()\n-plt.plot(x, np.sin(x), x, np.cos(x));\n-</pre>\n-<h2>Interactive widgets</h2>\n-<pre data-code-language=\"python\" data-executable=\"true\" data-type=\"programlisting\">%matplotlib inline\n-from IPython.display import Image\n-from IPython.html.widgets import interact\n-from numpy import pi, cos, sin\n-import numpy as np\n-import pylab as plt\n-import seaborn as sns\n-sns.set()\n-</pre>\n-<pre data-code-language=\"pyt\" data-executable=\"true\" data-type=\"programlisting\">def plot_fringe(bl_length, wavelength):\n-    &quot;&quot;&quot; Plot the fringe function for a baseline (see Fig 1)\n-\n-    bl_length:      distance between antennas, in m\n-    wavelength:     wavelength, in m\n-    &quot;&quot;&quot;\n-    theta = np.linspace(-np.pi, np.pi, 401)\n-    l = sin(theta)\n-    F = cos(2 * pi * bl_length * l / wavelength)\n-    F2 = cos(2 * pi * 2 * bl_length * l / wavelength)\n-\n-    plt.plot(l, F, c='#cc0000', label=&quot;Baseline 1-2&quot;)\n-    plt.plot(l, F2, c='#0000cc', label=&quot;Baseline 1-3&quot;)\n-    plt.xlabel(&quot;$sin(\\\\theta)$&quot;)\n-    plt.ylabel(&quot;Fringe amplitude&quot;)\n-    plt.ylim(-2, 2)\n-    plt.legend()\n-\n-f = interact(plot_fringe, bl_length=(1, 100), wavelength=(1, 100))\n-&lt;/pre&gt;\n+<p>\n+  In this Oriole, we'll define some key terms used when discussing InnerSource.\n+  We'll walk through the high-level mechanics of how InnerSourcing can work and enumerate the key principles upon-which these mechanics are based.\n+  Finally, we'll share other references that can help you deepen your understanding and involvement in the craft of InnerSource.\n+</p>\n+\n+<h2>Context</h2>\n+\n+<p>\n+  Imagine two teams delivering separate pieces of software with one team's software depending on that of the other.\n+  This situation is common in a large enterprise where a single team producing software may have dozens or hundreds of consumers.\n+  When consuming teams need many features, producing teams normally have some sort of requirements and prioritization process to decide which features they will work on.\n+  For feature requests that are not prioritized, the consuming team is faced with 3 choices, each of which come with their own drawbacks.\n+</p>\n+\n+<ol>\n+  <li>\n+    <p>\n+      <strong>Wait it out</strong>.\n+      In this option, the consuming team does nothing and limps along without the requested functionality.\n+      Depending on the benefit of the feature request, <em>Wait it out</em> might be just fine or it may come with real amounts of pain for the consuming team. \n+    </p>\n+  </li>\n+  <li>\n+    <p>\n+      <strong>Workaround</strong>.\n+      A consuming team that doesn't want to wait may do extra work somewhere else to compensate for the absence of their requested feature.\n+      This extra work may come as a modification (hack) in the consuming 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