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!

linktr.ee/drprandy

Read, with computer assistance, the best literature the world has ever produced

andrew-winkler-reading-room.overskill.app

Love Quotient: Stop Dying of Thirst in an Ocean of Love Kickstarter

https://www.kickstarter.com/projects/amwphd/love-quotient

Hosted on Acast. See acast.com/privacy for more information.

Today I'm talking about something that's been on my mind as I've been writing Love Quotient: the two feedback loops that quietly corrode almost every relationship we have. I explain how our dominant cognitive function becomes our personal "channel" for expressing and receiving love, and why a mismatch with the people we care about can spiral into the exact same withdrawal on both sides, until the relationship falls apart.

From there I dive into Pride and Prejudice, using what I know about personality and temperament to offer a fresh, more sympathetic read on Mr. Bennett and the entailment that hangs over the Bennet family. I also share a personal story about my border collies that mirrors his situation surprisingly well. Then I take a hard turn into artificial intelligence, walking through how I used "vibe coding" to build a free tool that helps you read the world's great literature, in the original Greek, Latin, Hebrew, and Aramaic, with computer-assisted definitions built right in.

In this episode you will learn:

(00:00) Why our relationships are being steadily corroded by two feedback loops we rarely notice

(02:27) How our dominant cognitive function becomes our personal channel for expressing and receiving love

(05:15) What entailment meant for the Bennet family and why it set the entire plot of Pride and Prejudice in motion

(08:22) Why Mrs. Bennett was so openly focused on marrying off her daughters to rich men

(11:57) How "goodness of fit" and temperament theory give us a more sympathetic read on Mr. Bennett's parenting

(15:32) What my border collies taught me about Mr. Bennett's distance from his younger daughters

(18:45) Why artificial intelligence is simultaneously overhyped and underhyped right now

(18:45) How human and machine intelligence complement each other when it comes to parsing language

(22:01) How I used "vibe coding" to build a reading tool from scratch in a single day

(24:45) What's inside the Andrew Winkler Reading Room, from the Septuagint to Shakespeare to Ulysses

Let’s connect!

linktr.ee/drprandy

Read, with computer assistance, the best literature the world has ever produced

andrew-winkler-reading-room.overskill.app

Love Quotient: Stop Dying of Thirst in an Ocean of Love Kickstarter

https://www.kickstarter.com/projects/amwphd/love-quotient

Hosted on Acast. See acast.com/privacy for more information.

Today I'm tackling something a little different: taxes. After spending years writing my book Love Quotient and other projects in physics and math, I funded that work through earnings in the tech sector, specifically long-term capital gains. That led me into a maze of federal versus California tax treatment, amended returns, and a frustrating "screw you letter" from the IRS demanding paperwork they could have processed themselves if they'd just shared their own software with taxpayers. I talk about why that doesn't happen, who benefits from keeping it that way, and the surprising silver lining: California's tax agency was actually reasonable to deal with, which genuinely surprised me.

Then I shift gears completely into something I've been working on for the past year: a new way of understanding scalar and vector quantities in physics. I unpack why "scalars" like mass, temperature, and length aren't really scalars at all, but one-dimensional vectors tied to units, and how units themselves function as bases for these vector spaces. From there, I show how ordinary vector quantities like force and velocity are tensor products of geometric vectors with these one-dimensional magnitude spaces, and how this framework recovers the classic magnitude-and-direction picture of physics in a cleaner, more unified way.

In this episode you will learn:

(00:00) Why I ended up filing amended tax returns after years of writing my book and research projects

(03:43) Why government tax agencies don't give taxpayers access to their own processing software

(04:18) How political incentives keep tax prep companies like H&R Block and Liberty Tax in business

(06:00) Why "scalar" quantities like mass and temperature aren't actually scalars

(09:27) How units function as bases for one-dimensional vector spaces

(10:00) Why changing units works exactly like a change of basis for vectors

(10:47) How to recover both magnitude and direction from this unified framework

Let’s connect!

linktr.ee/drprandy

Hosted on Acast. See acast.com/privacy for more information.