<story><title>Show HN: BBC “In Our Time”, categorised by Dewey Decimal, heavy lifting by GPT</title><url>https://genmon.github.io/braggoscope/</url><text>I&amp;#x27;m a big fan of the BBC podcast In Our Time -- and (like most people) I&amp;#x27;ve been playing with the OpenAI APIs.&lt;p&gt;In Our Time has almost 1,000 episodes on everything from Cleopatra to the evolution of teeth to plasma physics, all still available, so it&amp;#x27;s my starting point to learn about most topics. But it&amp;#x27;s not well organised.&lt;p&gt;So here are the episodes sorted by library code. It&amp;#x27;s fun to explore.&lt;p&gt;Web scraping is usually pretty tedious, but I found that I could send the minimised HTML to GPT-3 and get (almost) perfect JSON back: the prompt includes the Typescript definition.&lt;p&gt;At the same time I asked for a Dewey classification... and it worked. So I replaced a few days of fiddly work with 3 cents per inference and an overnight data run.&lt;p&gt;My takeaway is that I&amp;#x27;ll be using LLMs as function call way more in the future. This isn&amp;#x27;t &amp;quot;generative&amp;quot; AI, more &amp;quot;programmatic&amp;quot; AI perhaps?&lt;p&gt;So I&amp;#x27;m interested in what temperature=0 LLM usage looks like (you want it to be pretty deterministic), at scale, and what a language that treats that as a first-class concept might look like.</text></story><parent_chain><item><author>genmon</author><text>Some unlinked features...&lt;p&gt;If you put the Dewey division in the URL, the directory auto-opens. e.g. here are episodes about prehistoric life (my current jumping-off point)&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;genmon.github.io&amp;#x2F;braggoscope&amp;#x2F;directory#560&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;genmon.github.io&amp;#x2F;braggoscope&amp;#x2F;directory#560&lt;/a&gt;&lt;p&gt;There&amp;#x27;s a visual map of episodes. After principal component analysis of the episode embedding vectors, these are the most significant two components as the x,y&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;genmon.github.io&amp;#x2F;braggoscope&amp;#x2F;map.html&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;genmon.github.io&amp;#x2F;braggoscope&amp;#x2F;map.html&lt;/a&gt;&lt;p&gt;(it&amp;#x27;s not super useful tbh -- e.g. the Manhattan Project and the Cambrian Explosion have the same x,y... presumably because they are both about explosions?)&lt;p&gt;Many episodes have a reading list, and these are all linked to Google Books (so you can purchase&amp;#x2F;check out from a library), e.g. this episode page&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;genmon.github.io&amp;#x2F;braggoscope&amp;#x2F;2022&amp;#x2F;10&amp;#x2F;20&amp;#x2F;the-fishtetrapod-transition.html&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;genmon.github.io&amp;#x2F;braggoscope&amp;#x2F;2022&amp;#x2F;10&amp;#x2F;20&amp;#x2F;the-fishtetr...&lt;/a&gt;&lt;p&gt;There are ~4,600 books, and I have ~88% coverage on getting a Google Books page from the original data. Any ideas about what to do with this big list of academic-recommended books v welcome!</text></item></parent_chain><comment><author>flir</author><text>This is great. I&amp;#x27;ll certainly be thinking of &amp;quot;classification&amp;quot; uses for ChatGPT in the future.&lt;p&gt;Thinking out loud: Add the experts, not just the reading lists. They&amp;#x27;re a jumping-off point into academic-paper-space. What have they published? In what journals? Who have they collaborated with?</text></comment>
<story><title>Show HN: BBC “In Our Time”, categorised by Dewey Decimal, heavy lifting by GPT</title><url>https://genmon.github.io/braggoscope/</url><text>I&amp;#x27;m a big fan of the BBC podcast In Our Time -- and (like most people) I&amp;#x27;ve been playing with the OpenAI APIs.&lt;p&gt;In Our Time has almost 1,000 episodes on everything from Cleopatra to the evolution of teeth to plasma physics, all still available, so it&amp;#x27;s my starting point to learn about most topics. But it&amp;#x27;s not well organised.&lt;p&gt;So here are the episodes sorted by library code. It&amp;#x27;s fun to explore.&lt;p&gt;Web scraping is usually pretty tedious, but I found that I could send the minimised HTML to GPT-3 and get (almost) perfect JSON back: the prompt includes the Typescript definition.&lt;p&gt;At the same time I asked for a Dewey classification... and it worked. So I replaced a few days of fiddly work with 3 cents per inference and an overnight data run.&lt;p&gt;My takeaway is that I&amp;#x27;ll be using LLMs as function call way more in the future. This isn&amp;#x27;t &amp;quot;generative&amp;quot; AI, more &amp;quot;programmatic&amp;quot; AI perhaps?&lt;p&gt;So I&amp;#x27;m interested in what temperature=0 LLM usage looks like (you want it to be pretty deterministic), at scale, and what a language that treats that as a first-class concept might look like.</text></story><parent_chain><item><author>genmon</author><text>Some unlinked features...&lt;p&gt;If you put the Dewey division in the URL, the directory auto-opens. e.g. here are episodes about prehistoric life (my current jumping-off point)&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;genmon.github.io&amp;#x2F;braggoscope&amp;#x2F;directory#560&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;genmon.github.io&amp;#x2F;braggoscope&amp;#x2F;directory#560&lt;/a&gt;&lt;p&gt;There&amp;#x27;s a visual map of episodes. After principal component analysis of the episode embedding vectors, these are the most significant two components as the x,y&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;genmon.github.io&amp;#x2F;braggoscope&amp;#x2F;map.html&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;genmon.github.io&amp;#x2F;braggoscope&amp;#x2F;map.html&lt;/a&gt;&lt;p&gt;(it&amp;#x27;s not super useful tbh -- e.g. the Manhattan Project and the Cambrian Explosion have the same x,y... presumably because they are both about explosions?)&lt;p&gt;Many episodes have a reading list, and these are all linked to Google Books (so you can purchase&amp;#x2F;check out from a library), e.g. this episode page&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;genmon.github.io&amp;#x2F;braggoscope&amp;#x2F;2022&amp;#x2F;10&amp;#x2F;20&amp;#x2F;the-fishtetrapod-transition.html&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;genmon.github.io&amp;#x2F;braggoscope&amp;#x2F;2022&amp;#x2F;10&amp;#x2F;20&amp;#x2F;the-fishtetr...&lt;/a&gt;&lt;p&gt;There are ~4,600 books, and I have ~88% coverage on getting a Google Books page from the original data. Any ideas about what to do with this big list of academic-recommended books v welcome!</text></item></parent_chain><comment><author>CrypticShift</author><text>Excellent! I&amp;#x27;d love to see a script that sends a list of &amp;quot;descriptions&amp;quot; (1-100 words) to ChatGPT and directly gives you back a ready-made (embedding vectors closeness) map in a (textual) graph&amp;#x2F;chart format (like your above map or your plot &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;interconnected.org&amp;#x2F;more&amp;#x2F;2023&amp;#x2F;02&amp;#x2F;in_our_time-PCA-plot.html&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;interconnected.org&amp;#x2F;more&amp;#x2F;2023&amp;#x2F;02&amp;#x2F;in_our_time-PCA-plot...&lt;/a&gt;)</text></comment>