[laughs] Okay.

But that's the concept of impedance.

And so to kind of explain what I mean by that is I'm sure everyone's probably had the experience of being in, for instance, a well-lit home at night, and you're able to see your reflection in your windows.

Um, that's the light inside of the home bouncing off of that window and reflecting back to where you can see it.

But if you were to go outside, you'd also be able to see what's going on inside the house, the light's also traveling through that window.

And so you have this mixture both of reflection and transmission through that glass.

The reason for that is because the glass itself has a different impedance to the light passing through it than the air on either side of the glass.

So I'm sure that we- we're, we're not 100%, but I, I'm wondering if, uh, anybody has any ideas on that to kind of wrap us up here.

Without using techie words or math, what are you adjusting? What are you tuning? And why is the conventional load pull taking so long? Then we're going to get, after you explain, we're going to get the use of AI and actually mitigating this problem.

Yeah, so if it's all right, I may introduce one techie word, but I promise I'll explain what it means.

But that's the concept of impedance.

And so to kind of explain what I mean by that is I'm sure everyone's probably had the experience of being in, for instance, a well-lit home at night and you're able to see your reflection in your windows.

That's the light inside of the home bouncing off of that window and reflecting back to where you can see it.

But if you were to go outside, you'd also be able to see what's going on inside the house, the light's also traveling through that window.

And so you have this mixture both of reflection and transmission through that glass.

So I'm sure that we're not 100%, but I'm wondering if anybody has any ideas on that to kind of wrap us up here.