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  1. <?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.9.3">Jekyll</generator><link href="http://icepick.info//feed.xml" rel="self" type="application/atom+xml" /><link href="http://icepick.info//" rel="alternate" type="text/html" /><updated>2024-02-06T11:00:09-05:00</updated><id>http://icepick.info//feed.xml</id><title type="html">Shutup and Code</title><subtitle></subtitle><author><name>Myers Carpenter</name></author><entry><title type="html">Practical Deep Learning: Lesson 2: Is it a Hotdog? meets the Internet</title><link href="http://icepick.info//2023/04/30/practical-deep-learning-lesson-is-it-a-hotdog-meets-the-internet/" rel="alternate" type="text/html" title="Practical Deep Learning: Lesson 2: Is it a Hotdog? meets the Internet" /><published>2023-04-30T16:16:52-04:00</published><updated>2023-04-30T16:16:52-04:00</updated><id>http://icepick.info//2023/04/30/practical-deep-learning-lesson--is-it-a-hotdog-meets-the-internet</id><content type="html" xml:base="http://icepick.info//2023/04/30/practical-deep-learning-lesson-is-it-a-hotdog-meets-the-internet/">&lt;p&gt;&lt;a href=&quot;https://course.fast.ai/&quot;&gt;Practical Deep Learning for Coders&lt;/a&gt; &lt;a href=&quot;https://course.fast.ai/Lessons/lesson2.html&quot;&gt;Lesson 2&lt;/a&gt;: Let’s share this with the world.&lt;/p&gt;
  2.  
  3. &lt;p&gt;Take aways from video:&lt;/p&gt;
  4.  
  5. &lt;ul&gt;
  6.  &lt;li&gt;make a model, then clean the data.  The initial model you create will help you find the data that doesn’t seem to fit it’s generalization hypothesis.&lt;/li&gt;
  7. &lt;/ul&gt;
  8.  
  9. &lt;p&gt;I have shipped even more ML code: &lt;a href=&quot;https://huggingface.co/spaces/myers/hotdog-or-not&quot;&gt;Hotdog or Not?&lt;/a&gt;&lt;/p&gt;
  10.  
  11. &lt;figure&gt;
  12.  &lt;img src=&quot;/2023/04/30/hf-hotdog-or-not.jpg&quot; width=&quot;2568&quot; height=&quot;1122&quot; /&gt;
  13.  &lt;figcaption&gt;my first model deployed on Hugging Face 🤗&lt;/figcaption&gt;
  14. &lt;/figure&gt;
  15.  
  16. &lt;p&gt;Hugging Face 🤗 is darn slick.  Your project is built into a docker image and then launched as needed.  Github could learn a thing or two about showing status.  There was some errors due to the &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;fastai&lt;/code&gt; API changing to no longer needing you to wrap an image that you want to predict in a &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PILImage&lt;/code&gt; and another problem with adding example images, that I solved by just removing them.&lt;/p&gt;</content><author><name>Myers Carpenter</name></author><category term="machine-learning" /><summary type="html">Practical Deep Learning for Coders Lesson 2: Let’s share this with the world.</summary></entry><entry><title type="html">Practical Deep Learning: Lesson 1: Is it a Hotdog?</title><link href="http://icepick.info//2023/04/30/practical-deep-learning-lesson-is-it-a-hotdog/" rel="alternate" type="text/html" title="Practical Deep Learning: Lesson 1: Is it a Hotdog?" /><published>2023-04-30T15:51:34-04:00</published><updated>2023-04-30T15:51:34-04:00</updated><id>http://icepick.info//2023/04/30/practical-deep-learning-lesson--is-it-a-hotdog</id><content type="html" xml:base="http://icepick.info//2023/04/30/practical-deep-learning-lesson-is-it-a-hotdog/">&lt;p&gt;&lt;a href=&quot;https://course.fast.ai/&quot;&gt;Practical Deep Learning for Coders&lt;/a&gt; &lt;a href=&quot;https://course.fast.ai/Lessons/lesson1.html&quot;&gt;Lesson 1&lt;/a&gt;&lt;/p&gt;
  17.  
  18. &lt;p&gt;The biggest change since I last took a course on Machine Learning is one of the key points of this course:  the use of foundational models that you fine tune to get great results.  In this lesson’s video we fine tune an image classifier to see if a picture has a bird in it.&lt;/p&gt;
  19.  
  20. &lt;p&gt;While building my own model I attempted to get the classifier fine tuned to look at comic book covers and tell me what publisher it was from.  I thought with the publishers mark on 100 issues from Marvel, DC, Dark Horse, and Image the classifier would be able to tell.  The best I was able to do was about 30% error rate, a far cry from the 0% in the example models.  I tried a few different ideas of how to improve:&lt;/p&gt;
  21.  
  22. &lt;ul&gt;
  23.  &lt;li&gt;train with larger images.  The notebook used in the video makes the training go faster by reducing the size of the image.  As I write this I wonder if it is even possible to use larger images in a model that might have been trained on a fixed size.&lt;/li&gt;
  24.  &lt;li&gt;create a smaller image by getting the 4 corners of the cover into one image.&lt;/li&gt;
  25.  &lt;li&gt;clean the data so that all the covers in the dataset had a publisher mark on them.&lt;/li&gt;
  26. &lt;/ul&gt;
  27.  
  28. &lt;figure&gt;
  29.  &lt;img src=&quot;/2023/04/30/X-Men - Red (2022-) 001-000.jpg&quot; width=&quot;372&quot; height=&quot;573&quot; /&gt;
  30.  &lt;figcaption&gt;sample cover from &lt;a href=&quot;https://www.comics.org/issue/2403738/&quot;&gt;X-Men Red #2&lt;/a&gt; with only the corners&lt;/figcaption&gt;
  31. &lt;/figure&gt;
  32.  
  33. &lt;p&gt;Nothing moved the needle.  I’m hoping something I learn later in the course will give me the insight I need to do better.&lt;/p&gt;
  34.  
  35. &lt;p&gt;I took a second attempt with a simpler project.  Of course I remembered that Silicon Valley episode with the hot dog detector, and make a hot dog vs hamburger classifier.  It works great.  The next lesson covers getting a model like that into production, so hang tight.&lt;/p&gt;</content><author><name>Myers Carpenter</name></author><category term="machine-learning" /><summary type="html">Practical Deep Learning for Coders Lesson 1</summary></entry><entry><title type="html">Practical Deep Learning: Learning Game Plan</title><link href="http://icepick.info//2023/04/30/practical-deep-learning-learning-game-plan/" rel="alternate" type="text/html" title="Practical Deep Learning: Learning Game Plan" /><published>2023-04-30T15:31:21-04:00</published><updated>2023-04-30T15:31:21-04:00</updated><id>http://icepick.info//2023/04/30/practical-deep-learning-learning-game-plan</id><content type="html" xml:base="http://icepick.info//2023/04/30/practical-deep-learning-learning-game-plan/">&lt;p&gt;I dug this video up from the older version of &lt;a href=&quot;https://course.fast.ai/&quot;&gt;Practical Deep Learning for Coders&lt;/a&gt;.  It’s a “learn how to learn” type video.&lt;/p&gt;
  36.  
  37. &lt;iframe width=&quot;560&quot; height=&quot;315&quot; src=&quot;https://www.youtube.com/embed/gGxe2mN3kAg&quot; title=&quot;YouTube video player&quot; frameborder=&quot;0&quot; allow=&quot;accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share&quot; allowfullscreen=&quot;&quot;&gt;&lt;/iframe&gt;
  38.  
  39. &lt;p&gt;My take aways:&lt;/p&gt;
  40.  
  41. &lt;ul&gt;
  42.  &lt;li&gt;Practical work is the goal.  Don’t fool yourself into thinking you know what is taught by just watching the videos or reading the book.  Watch the video, watch it again but with the tools at hand, pausing to try stuff out, do a project using what you learned.  The title of this blog is me trying to remind myself of this concept.&lt;/li&gt;
  43.  &lt;li&gt;I have my own ML workstation setup with a GPU.  I used poetry to set it up.  This was a pain due to lack of attention to repeatability in Jupyter Notebooks.  It would be very useful if they had a “lock” feature that records all python packages in the environment they are run it and their exact version.  Below is what it looks like after finishing Lesson 1 (the next video).&lt;/li&gt;
  44.  &lt;li&gt;Be tenacious.  Finish a project.&lt;/li&gt;
  45.  &lt;li&gt;One message that’s very close to my heart is not keep on getting ready to do a project, like stopping to learn &lt;a href=&quot;https://www.khanacademy.org/math/linear-algebra&quot;&gt;linear algebra&lt;/a&gt; (and then remembering how I never learned all the math terms, and therefore want to go back even deeper) in order to do well on this course.  Try to do a complete project, then on the next project go deeper, dig in deeper when the code needs it.&lt;/li&gt;
  46.  &lt;li&gt;Show your work to the world.  Blog not to be a breaking news source, but blog for the audience of yourself 6 months ago.&lt;/li&gt;
  47. &lt;/ul&gt;
  48.  
  49. &lt;p&gt;&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;pyproject.toml&lt;/code&gt;&lt;/p&gt;
  50.  
  51. &lt;div class=&quot;language-toml highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;&lt;span class=&quot;nn&quot;&gt;[tool.poetry]&lt;/span&gt;
  52. &lt;span class=&quot;py&quot;&gt;name&lt;/span&gt; &lt;span class=&quot;p&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;dl&quot;&lt;/span&gt;
  53. &lt;span class=&quot;py&quot;&gt;version&lt;/span&gt; &lt;span class=&quot;p&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;0.1.0&quot;&lt;/span&gt;
  54. &lt;span class=&quot;py&quot;&gt;description&lt;/span&gt; &lt;span class=&quot;p&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;
  55. &lt;span class=&quot;py&quot;&gt;authors&lt;/span&gt; &lt;span class=&quot;p&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;p&quot;&gt;[]&lt;/span&gt;
  56. &lt;span class=&quot;py&quot;&gt;readme&lt;/span&gt; &lt;span class=&quot;p&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;README.md&quot;&lt;/span&gt;
  57.  
  58. &lt;span class=&quot;nn&quot;&gt;[tool.poetry.dependencies]&lt;/span&gt;
  59. &lt;span class=&quot;py&quot;&gt;python&lt;/span&gt; &lt;span class=&quot;p&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;^3.10&quot;&lt;/span&gt;
  60. &lt;span class=&quot;py&quot;&gt;fastai&lt;/span&gt; &lt;span class=&quot;p&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;2.7.11&quot;&lt;/span&gt;
  61. &lt;span class=&quot;py&quot;&gt;duckduckgo-search&lt;/span&gt; &lt;span class=&quot;p&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;^2.8.5&quot;&lt;/span&gt;
  62. &lt;span class=&quot;py&quot;&gt;ipykernel&lt;/span&gt; &lt;span class=&quot;p&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;^6.22.0&quot;&lt;/span&gt;
  63. &lt;span class=&quot;py&quot;&gt;ipywidgets&lt;/span&gt; &lt;span class=&quot;p&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;^8.0.5&quot;&lt;/span&gt;
  64. &lt;span class=&quot;py&quot;&gt;jupyterlab&lt;/span&gt; &lt;span class=&quot;p&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;^3.6.3&quot;&lt;/span&gt;
  65. &lt;span class=&quot;py&quot;&gt;jupyterlab-git&lt;/span&gt; &lt;span class=&quot;p&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;^0.41.0&quot;&lt;/span&gt;
  66.  
  67. &lt;span class=&quot;nn&quot;&gt;[build-system]&lt;/span&gt;
  68. &lt;span class=&quot;py&quot;&gt;requires&lt;/span&gt; &lt;span class=&quot;p&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;[&quot;poetry-core&quot;]&lt;/span&gt;
  69. &lt;span class=&quot;py&quot;&gt;build-backend&lt;/span&gt; &lt;span class=&quot;p&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;poetry.core.masonry.api&quot;&lt;/span&gt;
  70. &lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;/div&gt;</content><author><name>Myers Carpenter</name></author><category term="machine-learning" /><summary type="html">I dug this video up from the older version of Practical Deep Learning for Coders. It’s a “learn how to learn” type video.</summary></entry><entry><title type="html">Do Your Own Deep Neural Network in Rust idea</title><link href="http://icepick.info//2023/04/30/do-your-own-deep-neural-network-in-rust-idea/" rel="alternate" type="text/html" title="Do Your Own Deep Neural Network in Rust idea" /><published>2023-04-30T12:59:32-04:00</published><updated>2023-04-30T12:59:32-04:00</updated><id>http://icepick.info//2023/04/30/do-your-own-deep-neural-network-in-rust-idea</id><content type="html" xml:base="http://icepick.info//2023/04/30/do-your-own-deep-neural-network-in-rust-idea/">&lt;p&gt;Project idea:  Take &lt;a href=&quot;https://monadmonkey.com/dnns-from-scratch-in-zig&quot;&gt;DNNs from Scratch in Zig&lt;/a&gt; and build it in Rust.&lt;/p&gt;</content><author><name>Myers Carpenter</name></author><category term="machine-learning" /><category term="rust" /><category term="ideas" /><summary type="html">Project idea: Take DNNs from Scratch in Zig and build it in Rust.</summary></entry><entry><title type="html">LLMs and the Truth™</title><link href="http://icepick.info//2023/04/30/llms-and-the-truth/" rel="alternate" type="text/html" title="LLMs and the Truth™" /><published>2023-04-30T12:01:46-04:00</published><updated>2023-04-30T12:01:46-04:00</updated><id>http://icepick.info//2023/04/30/llms-and-the-truth</id><content type="html" xml:base="http://icepick.info//2023/04/30/llms-and-the-truth/">&lt;p&gt;The Economist covers &lt;a href=&quot;https://archive.is/5l1k3&quot;&gt;It doesn’t take much to make machine-learning algorithms go awry&lt;/a&gt;.  Will we see a core of knowledge built that considered “The Truth”, and then all other input data is evaluated on how likely that’s true based on the givens?  LLMs judging what is fed to their younger siblings?&lt;/p&gt;</content><author><name>Myers Carpenter</name></author><category term="machine-learning" /><category term="llm" /><summary type="html">The Economist covers It doesn’t take much to make machine-learning algorithms go awry. Will we see a core of knowledge built that considered “The Truth”, and then all other input data is evaluated on how likely that’s true based on the givens? LLMs judging what is fed to their younger siblings?</summary></entry><entry><title type="html">Back to Machine Learning</title><link href="http://icepick.info//2023/04/27/back-to-machine-learning/" rel="alternate" type="text/html" title="Back to Machine Learning" /><published>2023-04-27T18:38:13-04:00</published><updated>2023-04-27T18:38:13-04:00</updated><id>http://icepick.info//2023/04/27/back-to-machine-learning</id><content type="html" xml:base="http://icepick.info//2023/04/27/back-to-machine-learning/">&lt;p&gt;In 2012 I spent a lot of my rare free time working thru both &lt;a href=&quot;https://twitter.com/SebastianThrun&quot;&gt;Sebastian Thrun&lt;/a&gt; and &lt;a href=&quot;https://github.com/norvig&quot;&gt;Peter Norvig&lt;/a&gt;’s
  71. Intro to Artificial Intelligence (which I can’t find anymore) and &lt;a href=&quot;https://twitter.com/AndrewYNg&quot;&gt;Andrew Ng&lt;/a&gt;’s Machine Learning course.  Andrew’s was the better of the two.&lt;/p&gt;
  72.  
  73. &lt;p&gt;One thing that blew me away was how he used k-means clustering on our homework submissions to discover where there were a large number of students that had a common misconception, and make a clarification video.&lt;/p&gt;
  74.  
  75. &lt;p&gt;At the time I was working in R&amp;amp;D at Rosetta Stone, and we wanted to bring data science and machine learning to bear on how to improve our language learning offerings.  It was a linear course, and we dreamed of building a model of what our learners knew, and then challenging them on what they didn’t know.  &lt;a href=&quot;https://archive.is/8X5GR&quot;&gt;Duolingo&lt;/a&gt; had a much better vision and execution for this.&lt;/p&gt;
  76.  
  77. &lt;p&gt;With the breakthroughs in the last few years: &lt;a href=&quot;https://stablediffusionweb.com/&quot;&gt;Stable Diffusion&lt;/a&gt;, &lt;a href=&quot;https://ai.facebook.com/blog/large-language-model-llama-meta-ai/&quot;&gt;LLaMA&lt;/a&gt;, &lt;a href=&quot;https://github.com/haotian-liu/LLaVA&quot;&gt;LLaVA&lt;/a&gt; (yes, I’m ignoring things I can’t run on my own computer), I wanted to dive back in and learn more.  To that end I’m taking &lt;a href=&quot;https://twitter.com/jeremyphoward&quot;&gt;Jeremy Howard’s&lt;/a&gt; &lt;a href=&quot;https://course.fast.ai/&quot;&gt;Practical Deep Learning for Coders&lt;/a&gt;.&lt;/p&gt;
  78.  
  79. &lt;p&gt;I have a lot of data on comic books.  I’m hoping to build some practical applications for this.  One idea is to feed each panel into something like LLaVA and have it describe what’s going on in it, then have it summarize the story.&lt;/p&gt;</content><author><name>Myers Carpenter</name></author><category term="machine-learning" /><summary type="html">In 2012 I spent a lot of my rare free time working thru both Sebastian Thrun and Peter Norvig’s Intro to Artificial Intelligence (which I can’t find anymore) and Andrew Ng’s Machine Learning course. Andrew’s was the better of the two.</summary></entry><entry><title type="html">Hands On Rust</title><link href="http://icepick.info//2022/11/21/hands-on-rust/" rel="alternate" type="text/html" title="Hands On Rust" /><published>2022-11-21T22:36:57-05:00</published><updated>2022-11-21T22:36:57-05:00</updated><id>http://icepick.info//2022/11/21/hands-on-rust</id><content type="html" xml:base="http://icepick.info//2022/11/21/hands-on-rust/">&lt;p&gt;I’ve been working thru &lt;a href=&quot;https://pragprog.com/titles/hwrust/hands-on-rust/&quot;&gt;Hands On
  80. Rust&lt;/a&gt;.  You learn rust
  81. and make a game.  &lt;a href=&quot;https://icepick.info/myersrogue/&quot;&gt;Play my game&lt;/a&gt;.  &lt;a href=&quot;https://github.com/myers/myersrogue&quot;&gt;See it
  82. on github&lt;/a&gt;.&lt;/p&gt;</content><author><name>Myers Carpenter</name></author><category term="rust" /><category term="video-games" /><category term="wasm" /><category term="bevy" /><summary type="html">I’ve been working thru Hands On Rust. You learn rust and make a game. Play my game. See it on github.</summary></entry><entry><title type="html">HotWire’s Turbo with Django Bootstrap 5</title><link href="http://icepick.info//2022/08/10/hotwires-turbo-with-django-bootstrap/" rel="alternate" type="text/html" title="HotWire’s Turbo with Django Bootstrap 5" /><published>2022-08-10T20:59:42-04:00</published><updated>2022-08-10T20:59:42-04:00</updated><id>http://icepick.info//2022/08/10/hotwires-turbo-with-django-bootstrap-</id><content type="html" xml:base="http://icepick.info//2022/08/10/hotwires-turbo-with-django-bootstrap/">&lt;p&gt;If you use turbo and django-bootstrap5 drop downs will not work once you
  83. load another page.  You can fix this by adding the
  84. &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;data-turbolinks-eval=false&lt;/code&gt; attribute to bootstrap’s &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;&amp;lt;script&amp;gt;&lt;/code&gt;.&lt;/p&gt;
  85.  
  86. &lt;p&gt;&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;settings.py&lt;/code&gt;&lt;/p&gt;
  87.  
  88. &lt;div class=&quot;language-python highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;&lt;span class=&quot;kn&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;django_bootstrap5.core&lt;/span&gt; &lt;span class=&quot;kn&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;BOOTSTRAP5_DEFAULTS&lt;/span&gt;
  89.  
  90. &lt;span class=&quot;n&quot;&gt;BOOTSTRAP5&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;p&quot;&gt;{&lt;/span&gt;
  91.    &lt;span class=&quot;s&quot;&gt;&quot;javascript_in_head&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;:&lt;/span&gt; &lt;span class=&quot;bp&quot;&gt;False&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;
  92.    &lt;span class=&quot;s&quot;&gt;&quot;javascript_url&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;:&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;BOOTSTRAP5_DEFAULTS&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;javascript_url&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;].&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;copy&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;()&lt;/span&gt;
  93. &lt;span class=&quot;p&quot;&gt;}&lt;/span&gt;
  94. &lt;span class=&quot;n&quot;&gt;BOOTSTRAP5&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;javascript_url&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;][&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;data-turbolinks-eval&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;false&quot;&lt;/span&gt;
  95. &lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;/div&gt;</content><author><name>Myers Carpenter</name></author><category term="django" /><category term="hotwired" /><category term="turbo" /><summary type="html">If you use turbo and django-bootstrap5 drop downs will not work once you load another page. You can fix this by adding the data-turbolinks-eval=false attribute to bootstrap’s &amp;lt;script&amp;gt;.</summary></entry><entry><title type="html">Higher level protocols for websockets</title><link href="http://icepick.info//2013/04/19/higher-level-protocols-websockets/" rel="alternate" type="text/html" title="Higher level protocols for websockets" /><published>2013-04-19T06:07:46-04:00</published><updated>2013-04-19T06:07:46-04:00</updated><id>http://icepick.info//2013/04/19/higher-level-protocols-websockets</id><content type="html" xml:base="http://icepick.info//2013/04/19/higher-level-protocols-websockets/">&lt;p&gt;I ran across &lt;a href=&quot;http://wamp.ws/spec#welcome&quot;&gt;WAMP&lt;/a&gt; recently.  When I built my DC++ client who’s primary GUI is a jquery/jquery ui web app connected to the twisted python backend via websocket I had to create something just like this.  Later reading over the &lt;a href=&quot;http://www.jsonrpc.org/specification&quot;&gt;JSON RPC 2.0&lt;/a&gt; spec I realized you could just use their format and leverage json rpc libs already written.&lt;/p&gt;
  96.  
  97. &lt;p&gt;Small note: you might want to remove the “jsonrpc” key from the request and response to save some bytes.&lt;/p&gt;
  98.  
  99. &lt;p&gt;There is also this: &lt;a href=&quot;http://blog.gradientstudios.com/2012/08/15/cutting-down-bandwith-with-json-alternatives/&quot;&gt;Cutting Down Bandwidth with JSON Alternatives&lt;/a&gt;&lt;/p&gt;</content><author><name>Myers Carpenter</name></author><category term="ideas" /><summary type="html">I ran across WAMP recently. When I built my DC++ client who’s primary GUI is a jquery/jquery ui web app connected to the twisted python backend via websocket I had to create something just like this. Later reading over the JSON RPC 2.0 spec I realized you could just use their format and leverage json rpc libs already written.</summary></entry><entry><title type="html">Google Glass App Idea: Am I talking to my kids enough?</title><link href="http://icepick.info//2013/04/15/google-glass-app-idea-am-i-talking-my-kids-enough/" rel="alternate" type="text/html" title="Google Glass App Idea: Am I talking to my kids enough?" /><published>2013-04-15T07:00:36-04:00</published><updated>2013-04-15T07:00:36-04:00</updated><id>http://icepick.info//2013/04/15/google-glass-app-idea-am-i-talking-my-kids-enough</id><content type="html" xml:base="http://icepick.info//2013/04/15/google-glass-app-idea-am-i-talking-my-kids-enough/">&lt;p&gt;&lt;a href=&quot;http://opinionator.blogs.nytimes.com/2013/04/10/the-power-of-talking-to-your-baby/&quot;&gt;The Power of Talking to Your Baby&lt;/a&gt;&lt;/p&gt;
  100.  
  101. &lt;blockquote&gt;
  102.  &lt;p&gt;If you haven’t heard of Hart and Risley’s work, you are not alone — and you may be wondering why. These findings should have created a policy whirlwind: Here was a revolutionary way to reduce inequities in school achievement that seemed actually possible. How hard could it be to persuade poor parents to talk to their children more?&lt;/p&gt;
  103. &lt;/blockquote&gt;
  104.  
  105. &lt;blockquote&gt;
  106.  &lt;p&gt;Very hard, it turned out — because there was no practical way to measure how much parents talk. Each hour of recording took many hours to transcribe and classify: to count the words uttered near a child and attribute them to a parent, the main child, a sibling, someone else or a TV. The cost was prohibitive.&lt;/p&gt;
  107. &lt;/blockquote&gt;
  108.  
  109. &lt;p&gt;With &lt;a href=&quot;http://www.google.com/glass/start/&quot;&gt;Google Glass&lt;/a&gt; you could monitor everything you say, you could track if your kids are around you.&lt;/p&gt;</content><author><name>Myers Carpenter</name></author><category term="ideas" /><summary type="html">The Power of Talking to Your Baby</summary></entry></feed>

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