Taylor Mullen

So we write something called a test.

And the test means verifies when I click the button, it sent the email.

We do this so that when we write the next feature, we don't accidentally break the ability to send tests.

Because if we accidentally break that, that's a problem, right?

Turns out AI is really, really good at, well, what if you were just to write the test to say, did it send the email?

And then write the feature.

Like what if you changed it?

And this is really impactful with AI.

Why?

Because it enables the system to look at this like this problem to say, hey, it can't send an email.

How do I make it send an email?

And it can iterate with that kind of in mind versus the backwards of like just trying to figure something out.

And then after the fact, trying to write something that validates the experience.

Each developer is going to respond a little bit differently because of the surfaces that they interact with.

But I can give you a little bit of insight so I can say, hey, how I actually interact with that.

And so one of my normal flows is very much, I will open Gemini CLI.

And we're actually open source on GitHub.

Actually, I'll show my screen again.

I'll show a little bit here.

GitHub.com, Gemini CLI.