Title: I&#x27;m a big fan of the BBC podcast In Our Time -- and (like most people) I&#x27;ve been playing with the OpenAI APIs.<p>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&#x27;s my starting point to learn about most topics. But it&#x27;s not well organised.<p>So here are the episodes sorted by library code. It&#x27;s fun to explore.<p>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.<p>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.<p>My takeaway is that I&#x27;ll be using LLMs as function call way more in the future. This isn&#x27;t &quot;generative&quot; AI, more &quot;programmatic&quot; AI perhaps?<p>So I&#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.
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