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

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In today's episode, I'm sharing the core insights from my new book, Love Quotient: Stop Dying of Thirst in an Ocean of Love — and my goal is to give you everything you need to transform your most important relationships.

Welcome to Now I Get It with Dr. Andy. I'm talking about one of the most painful and preventable dynamics in relationships: the slow drift that happens when two people are pouring love into a connection, but neither one can feel it. The culprit isn't a lack of caring — it's a mismatch in cognitive functions. I walk through the four core ways we process the world — sensing, thinking, feeling, and intuition — and why we each only mature some of these functions while others stay dormant. That gap is precisely where love gets lost.

Tune in as I explore how to identify your loved one's dominant cognitive type through something as simple as their gestures or walk, and how to bridge the gap between the love you're giving and the love they actually feel. Whether you're navigating a marriage, a friendship, or a professional dynamic, this episode gives you a practical framework for turning a drifting relationship into one that deepens every day.

In this episode, you will learn:

(00:27) We only fully mature half of our cognitive functions in a lifetime

(05:09) Most relationships break down because love is sent in a form the other can't perceive

(07:45) Each cognitive function has its own unique language of love

(08:30) Four hand gestures reveal a person's dominant cognitive type

(13:00) The way someone walks maps to their relationship dynamic

(19:54) Combining gesture and walk pinpoints exactly what your loved one needs

(22:10) Attuning to your loved one triggers them to give you the love you need in return

Let’s connect!

linktr.ee/drprandy

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

What if artificial intelligence doesn't replace human intelligence — it amplifies it? And what if the quality of what you bring to AI is exactly what determines what you get back?

Welcome to Now I Get It with Dr. Andy. I'm Andrew Winkler, and in this episode I'm taking a deep dive into one of the most consequential technologies of our time: large language models. I break down how these systems are built on surprisingly elegant mathematics, why language itself has a hidden statistical structure that makes AI possible, and what it really means for how we interact with these powerful tools.

Tune in as I explore the neural network foundations that underpin modern AI, unpack the "garbage in, garbage out" principle in its most precise form, and reveal why the most important thing you can bring to an AI conversation is your own intelligence and curiosity.

In this episode, you will learn:

(00:27) Neural networks are built on elegant mathematics

(01:15) One nonlinearity unlocks AI's power to model anything

(02:47) Models extract signal, not just memorize data

(04:30) Language has a hidden statistical structure AI can learn

(08:30) AI defaults to average intelligence without strong context

(09:03) Smarter input produces smarter AI output

(09:45) AI amplifies human intelligence — it doesn't replace it

Let’s connect!

linktr.ee/drprandy

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