I've been experimenting with AI tools lately, and my newest project came out of that: CardSmart, a free app that solves a problem I kept running into myself. Credit cards pay you back differently depending on where you shop, and keeping track of which card wins where, especially with rotating categories and payout caps, is more than anyone should have to manage in their head. CardSmart uses your phone's location, which never leaves your device, to tell you which of your cards earns the most wherever you are, or which card you don't have yet would do better.

There's also a referral layer built in. Most credit card companies pay hundreds or even thousands of dollars when someone you refer gets approved, so CardSmart lets you enter your affiliate codes and share your card link automatically when it recommends that card to someone else. I don't make money on this myself; I use the app exactly like anyone else. I built it this way because I want people to have a real reason to share it, since it means their friends and family get the same savings too.

In this episode you will learn:

(00:00) Why I built CardSmart and the philosophy behind making apps I wish existed

(01:20) How CardSmart uses your phone's location to find the best card for wherever you're shopping, and why rotating categories and payout caps make this hard to track on your own

(03:00) How the referral bonus feature works: entering your affiliate ID lets the app share your card link and earn you money when others sign up

(04:44) My real motivation for the referral feature, and why I want you to spread the word

(05:13) Why I'd rather you complain loudly about bugs than stay quiet

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.

I've been thinking a lot lately about AI, a subject I explored back in grad school and postdoc. In this episode, I walk through a hackathon project to democratize AI by distributing neural network training across home computers. I explain why it failed: first the obvious reason, a bad actor can silently poison a distributed model, then a subtler one rooted in the physics of computation. I break down how neural networks work, why a single nonlinearity lets them approximate almost anything, and why these models need the low latency bandwidth only a data center can provide.

From there I connect to something bigger: why AI is destined to concentrate power into a few organizations, and why that oligopoly could tip into an outright monopoly. I use the mathematics of pandemic spread to show how a tiny edge in AI self improvement compounds into a runaway advantage, and I compare it to UK land ownership to explain what it means to be a rent seeker over an entire economy. I close with why every business is now a software business, why AI generated code brings new unintended behavior, and why we need to stay engaged rather than assume it's someone else's problem.

In this episode you will learn:

(00:00) The hackathon idea that started this, distributing AI training across home computers so everyone could have a stake, and why it failed, starting with undetectable poisoning by bad actors.

(03:44) How neural networks work, from linear functions in algebra to the single nonlinearity that lets them approximate virtually any function.

(06:36) The matrix computations and graphics chips these models need, and why low latency communication means the work must happen inside one data center, not distributed machines.

(10:00) Why AI is destined to be an oligopoly controlled by a handful of corporations and nation states, and how AI improving AI can turn a breakout model into a runaway one.

(12:57) The mathematics of pandemic spread, the replication number separating exponential growth from decline, and how a small edge in AI self improvement compounds into an overwhelming advantage.

(15:56) The idea of a rent seeker, illustrated through UK land ownership dating back nearly a thousand years, and how a runaway AI could end up in a similar position over the global economy.

(18:53) Dan Kennedy's insight that whoever can spend the most to acquire a customer wins, and why that means every business is now a software business built on AI.

(21:32) The unintended behaviors in AI generated software, and why engaging with these risks now matters more than treating them as a distant problem.

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.

I'm taking you through my own origin story this episode, from getting swept into a Johns Hopkins study on intellectual talent as a sixth grader to skipping straight into college at fifteen. I share how that unusual path, being identified as gifted, accelerated through school, and landing in a college honors program that clashed with my own academic ambitions, shaped the way I think about learning today. Along the way I talk about my late AP English teacher Adrian Morris, and how I completely missed the point of literature even while being taught by the best.

That early misstep, wanting to read everything in its original language rather than in translation, ended up defining my life's work. I explain why I built reader.winklers.us, my free computer assisted reading tool with over a hundred classic texts, and why I deliberately left out syntax support so readers still get the meditative wrestling that makes literature transformative. I also dig into the psychology behind why this matters: how literature builds theory of mind, how naming our emotions helps us process implicit memories, and why untranslatable words like Schadenfreude and saudade reveal blind spots in our own emotional vocabulary.

In this episode you will learn:

(00:00) How a Johns Hopkins study on intellectual talent led me from taking the SAT in sixth grade to skipping into college at fifteen

(02:43) Why my college honors program wanted humanities depth I didn't have, and how I vastly overestimated my ability to learn languages fast enough to read literature in the original

(04:42) What inspired me to build reader.winklers.us, a free tool that handles the tedious dictionary work while deliberately leaving syntax to the reader for a deeper, more meditative experience

(07:09) How literature builds theory of mind and why that matters for long term relationships, a concept I explore further in my book Love Quotient: Stop Dying of Thirst in an Ocean of Love

(08:00) What "name it to tame it" means, and how explicit versus implicit memory processing shapes our emotional coping

(09:35) Why words like Schadenfreude, Waldeinsamkeit, and saudade reveal the blind spots baked into any single language or culture

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.