I'm digging into something that's genuinely changed how I spend my free time: using AI to build the tool I've wished existed for decades, one that helps me read the world's great literature in its original language. I tell the story of a chance dinner with a Hertz Fellow who turned out to be a major figure at Anthropic, and how that connection eventually led me back to a project I'd tried and abandoned before, a computer-assisted reader for Greek, Latin, Hebrew, and Aramaic texts. Six months ago, the AI models I tried couldn't get past writing a single working line of code. This time was different, and I walk through exactly what changed, what these models are actually good at, and where they still fall apart.
I also get into the weeds a bit, sharing my process for getting better results out of these tools, why my public writing on Quora turns out to matter, and a wild multi-round debugging session with Gemini that kept insisting it had \"finally\" found all the bugs. I close with a personal aside into a physics insight I've been chasing since the 1970s, and how an AI helped me push it further, even if I lost most of the conversation to a very avoidable software glitch. Along the way I make my case for why AI right now is best thought of as a tireless, brilliant, but occasionally hallucinating army of interns, not a replacement for human judgment.
In this episode you will learn:
(00:00) How a chance dinner with a Hertz Fellow connected to Anthropic changed my skepticism about the current state of AI
(03:37) Why I've always wished a computer could do the tedious lexical lookup work of reading Greek, Latin, Hebrew, and Aramaic texts
(06:35) What large language models actually are under the hood, and why they can \"spout like an idiot\" and \"pontificate like a sage\" with equal ease
(08:57) The multi-round debugging saga where Gemini kept insisting it had found the last bug, and the last bug, and the last bug
(11:48) What gets lost in translation, using the hidden joke behind Don Quixote's horse Rocinante as an example
(14:43) Inside the Andrew Winkler Reading Room, from the Septuagint to Shakespeare, and why computers and humans make a great reading team
(17:10) My feature requests for Google and Apple after losing valuable AI conversations to a silent clipboard failure
(19:36) Why I think of AI right now as an army of knowledgeable but wisdom-less interns, and what that means for companies rushing to replace workers with it
(22:01) The physics insight I've been sitting on since the 1970s, and how AI helped me extend it into the standard model
(24:28) What was lost when my breakthrough AI conversation on unit-free physics vanished, and why I'm sharing the story anyway
Let’s connect!
Read, with computer assistance, the best literature the world has ever produced
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