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Welcome back to Data Driven. This is episode 400, and

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no matter what Andy's weather station tells you, it's always sunny in

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farmville. Today we're talking AI vibe,

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coding, building real systems with small teams, and how the last

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few years have completely changed how we ship software and podcasts.

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Let's do this with some different music this time.

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Hello, and welcome back to Data Driven, the podcast podcast where we explore the emerging

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field of data, AI, and, of course, data engineering.

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With me today on a special most auspicious day. I

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think I use that word auspicious, right? I'm not sure. Is my

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favorite data engineer in the world. How's it going, Eddie?

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It's going well, Frank. How are you doing? I'm doing

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fantastic. We're recording this on January

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7, 2026. Can you

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believe it? My goodness. 26. This

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is the future, man. I'm waiting for my flying car, but

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until then, any day. Any day now.

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There actually is something at CES that they were showing was, like a personal, like,

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drone that you can, like, stand in and, like, fly around.

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No way. Way. I saw it and I was like. But now we're at the

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point. Now I'm like, could that have been an AI video?

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Never really can't tell anymore. Yeah, that's true.

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They are getting better, aren't they? They really are.

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You've seen some of the experiments I've been doing on Frank's world

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on YouTube. Oh, yeah. See, like, you know,

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a lot of that. You know, I'm not going to tell you which ones, but

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you. You know, some of them are AI, so it's. It's.

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It's pretty amazing. Plus, I can generate

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videos longer than 8 seconds on my Spark.

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Nice. My DGX Spark. So. Which I don't think we've

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talked much about on the show is kind of like the. This 2025 was the

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year for me of the home lab. Right. Where. Yeah.

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And you, too, as well. Right? Like, it was an interesting. It was an interesting

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year. So I, in October, convinced my wife

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to let me get the DGX Spark. And if you're

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watching this on video, there's the box right there. I

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leave the box out because I know more than anything, the DTX

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Spark is as much of a status symbol as

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anything else. And

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seeing you cough is making me cough. I know I choked you up there.

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I know. Scrambling for the mute. I think the mouse was over

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on the third, like, three screens over. That's funny.

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Trying to get to the mute button. So,

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yeah, so I got DGX Spark. And it

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basically, it's. You know, Nvidia sells it as a personal

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AI supercomputer. It basically has

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shared. A shared memory model, so it has 128 gigs of

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RAM, which means that, you know, I have about.

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Depending on how much the system is using, when you're using it, I have about

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120 gigs of video RAM,

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which, if you were to replicate that with a traditional kind of desktop

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PC, would be very pricey. I mean, you'd have to get

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three or four of the 5070s, like, working in unison to get that much

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VRAM. Right. So that is. And it also

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has a Grace Blackwell chip in it, which is one of their, you know, one

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of their more professional ones. I know we're recording this while CES

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is going on, so there's. They've announced the new Vera Rubin stuff and

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all that. So there's definitely a lot more hardware innovation

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happening that I can't keep

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up with it, man. Like, it's just, you know, you can either pick

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the hardware side or the software side and, you know,

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but. And then also this year, I picked up

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a. I think I did talk about this on the show where I picked up

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the an i9 with a 4070 in it.

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That's a gaming PC, but it's a mini PC, so it's, you know,

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probably about the size of a cable. Cable box. Nice.

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Which. This will make listeners. I know it made me feel old, but

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I. I told my. My teenager, he's like, you know,

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what's it look like? And I'm like, well, it's about the size of a cable

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box. You know what he said? What's a

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cable box? So I'm not

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surprised. Yeah. So

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that. That is the world we live in today, I suppose.

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Frank, you mentioned we were the OGs. We really. We really are.

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So you were just on another podcast. So tell them. Tell. Tell our good

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listeners why this one's special. Although I think Bailey may have

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ruined it by now. Well, probably. But that's okay.

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Bailey's doing her job. And. And thank you, by the way,

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for helping Bailey do her job. You are the man, the

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creator of Bailey. This is our

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400th episode of the Data

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Driven Podcast. Can you believe that? 400?

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No, I can't, man. It's. It's wild.

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And it was about, what, nine years ago, we started,

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you know, figuring out the logistics of the recording and things like that

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and working through it, and it. It's been.

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It's been an interesting ride. That's for sure. Definitely.

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It definitely has. And yeah, show has evolved.

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You and I have grown and we've,

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we're 10 years older and. Yep. You know, and

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hopefully 10 years wiser. Hopefully, hopefully

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our families have grown. Yep, your family

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has grown. I. And, and you've added to your

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family in the last 10 years. I, I have not.

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But I did reach a milestone last August.

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My old, my youngest son turned,

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turned 18. And I realized

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in, in thinking about that, I realized that

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for 43 consecutive years

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I was the father of a minor, at least one

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minor child. Now in order to, to kind of

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span a gap there in the middle,

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I have to count time that Stephen was in the

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womb, but I knew he was there. When my younger daughter

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from my first marriage turned 18,

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we, we knew, we knew Christy was pregnant with Stephen. But

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43 consecutive years, Frank, that's a.

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Solid lifetime that is of being a dad.

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So it's different. It's, you know, it's, I don't like

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regret or, you know, have regrets or anything about. Well, I do

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have regrets, obviously, but I don't. It's not like a big loss.

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It's like I changed, I shifted gears. I'm now in this

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new phase of it. But, you know, it's not just me shifting

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gears, Frank. You've done a lot of gear shifting. The market has done

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a lot of. You mentioned keeping up. And I'll say this and then I'll shut

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up. The way that I've found to keep up with both,

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at least the kind of hitting the high spots, both software technology

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and even physics when it comes to quantum, is

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when GROK enabled the ability to create

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something called Grok tasks. And I want to say it was in

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2025, early in 2025, I set one up

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for technology news,

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really. And I haven't really played with Grok that much.

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So at 5:40am every every

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weekday, actually it's every day at 5:40

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an announcement shows up. That's when I scheduled it. It pops

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up and, you know, I get a little thing on my phone and I'll hit

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it. Or if I happen to be on Grock, and I am, sometimes

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I'll see the task gets populated there. Just click on it and

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it gives me six or eight, sometimes 12

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paragraphs on how things are going. I don't

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know if it's limited to Super Grock

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or not. I got in on Super Grok when it was

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basically an add on. If you went from

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paying x 8 bucks a month

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to 16 bucks a month, you could, you Got

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a subscription to Super Crock. So I think now if you buy it

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out of the box, it's like 30 bucks a month, but I'm still

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paying 16 and I get to use it. It is 30 bucks a month. Yeah.

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Yeah. Apparently I'm on the free plan. I didn't know that.

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I thought I was, I thought I was paying for this, but

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now I don't feel so bad about not using it as much.

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Will they let you do a task on a free plan? I don't

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know what's available. It says. It does. It says the task.

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Yeah. I have to take a look at that because that looks interesting.

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So to find it in the ui,

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but interesting. I've got the client on my phone. That's one of the few.

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I've got one of the few AI clients I've got on the phone. I know

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you use some too. Yeah, I have. So one of the things I did was

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I don't know if this deal is still available. If you sign up for

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perplexity using PayPal, you get a year of Perplexity for free.

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Oh, neat. Yeah, I don't know if that's still the deal, but that's. I

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have Perplexity, so I propel. I have Perplexity,

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I have Gemini And I have ChatGPT and,

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and the free version of Grok. I also have

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Claude because I love Claude code and I thank

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you for turning me on to that because it is, it has been

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transformative. Right. Like so, you know, you did mention I have a

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three year old and I have a teenager and I have a tween,

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so I don't get a lot of focus time.

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So I

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have to be very judicious with what I choose to focus on.

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And the big

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advancement is cloud code. Right.

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Because there's a lot of things I want to code up. I just don't have

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time for. Right. I don't have time to do that. So this one project I'm

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working on, I'll kind of give a preview of it. It's called podz

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and Podsy is Ponzi, is, is. Is meant

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to solve a problem that we're having with the creation of the podcast.

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Because we have this show and we have the Impact Quantum show

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which is doing gangbusters. If you're not already subscribed,

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check it out. Impact quantum.com. but you know, we

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won, it's won awards being the top quantum, you know, computing

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podcast by Feed Spot. It's, it's, it's on a

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really solid growth trajectory. Because again, we really,

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we've been podcasting now for nine years, right?

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And that makes us kind of OGs in this game, right? This

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is, you know, absolutely. And you know, I was talking to somebody and I was

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like, you know, like, I'm good at this and I don't say that at arrogance

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because I've done it about four or five hundred times, right?

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You know, if you something four or five hundred times, you're not, you don't get

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improved at it. You know, you really have to be trying not to improve.

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So, you know, and you know, we have enormous, we

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have fantastic numbers on this show. But like, you know, if you look at the

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growth trajectory, I mean, the potential is enormous. But one

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of the big challenges is, is that how do you track

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all of the assets that are related to a particular episode?

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And that's the problem that PODSI is meant to, to address, right? So

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every podcast is going to have a transcript, an audio

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file or video file, maybe both. And it's also going to have

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a, you know, a thumbnail image, right? So every show

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has at least four assets, right?

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How do you deal with that? Right? Because then at this point we're looking at,

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you know, 1600 assets for all our shows, right? That's

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not a trivial data problem to master or

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to manage. So that's what podzi is meant to do. So

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the reason why I say this the way, the reason why I think this is

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an interesting use case for Claude Code is because

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on December 5, I had a car accident and, you know, we're all okay.

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Car was totaled. The airbag did, though, leave

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a Honda shaped imprint in my hand

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because it was a Honda and the logo went smack into my hand.

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But aside from the hand injury, you know, we're all okay. And,

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but that night I just was like, I had

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this idea because I was getting frustrated because if I want to add a third

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show and that's, you know, we've talked about adding a third show, maybe, you

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know, more shows. Beyond that, the logistics of managing

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all of that, you know, content becomes

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an issue, right? And that's, you

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know, that's what POSI was and how that ties is the accident is

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that I, I was feeling frustrated with the lack of progress of

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Ponzi. I, I, I had thought I had this grand vision that we don't in

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a week, right. Even with,

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you know, vibe coding it or whatever,

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you know, when it got to like Christmas, I was like, all right, what the

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heck has taken so long here? Right? And yep,

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I basically looked at it,

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and it turns out that by that point I'd written about 30,000 lines

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of code, or it had written 30,000 lines of code.

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Right. And it does about 90% of what I wanted to

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do. Now think about that.

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Two of my boys have birthdays in December. I had a car accident.

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I had to close out a lot of things for my day job. And you

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know, you have the general, you know, run up to Christmas. There's

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no way in hell I would have been able to do that

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normally. 30,000 lines code, right? Yeah, yeah. Even if I

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was doing it full time, no job, no children, no responsibilities. That's

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a stretch, you know, in 20ish days to hit

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30,000 lines of code. Absolutely. That is,

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I'd have to, you know, I mean, that's a full time, maybe two full time

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coders. Right. Again, I'm not going to

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shell out the money to do that. Right. So in a sense,

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I'm not so much taking a job away.

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I'm adding to my productivity as an

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individual. Does that make sense? Yep. And

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that's exactly, you know, that's all of it. And I mentioned

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to you that I was listening, I am listening still to an audio book called

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Vibe Coding. And it occurred to me, and I, I think I posted this

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on social media yesterday or the day before. It occurs to me

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in this that there's more than one way

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to change the ratio for return on

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investment. And the role AI plays in that

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is exactly what you just described. And what it

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does is it takes one person, you or me,

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and it makes us ten times more productive.

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Right. And it turns out that, you know, we don't need to jack up

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the price, which would be one way to do it. Kind of a, you know,

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maybe not in a very effective way to do it. Increasing the value

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would certainly support jacking up the price. But to do,

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to increase value, you need to add features or functionality

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or some, some kind of way improve the code. Well, because,

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you know, ostensibly using an AI like Claude code

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to do that, you, you nailed it. You don't have the

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inclination to spend the money to hire a couple of Vibe coders, or, sorry, a

255
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couple of coders to, you know, knock out 30,000 lines of

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code. And it costs you the cost of a

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cloud subscription to do that in the

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middle of all the rest of your life. And what's happening is

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all across the board, all of these projects that have been on our

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list of when I get some time, I'll do that. Right. Or

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it's, you know, it's I joke about if it's not in the top

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three on my to do list, it doesn't get done. That's

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changed. It's now probably the top 20. Because

264
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a couple of things that were hanging out there that A couple of weeks ago

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I spent some. I spent a day and a half revamping

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all of my Andy weather stuff surfacing. The data that's

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collected from my weather station sitting out here on the deck and it took a

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day and a half. I had two applications that I rewrote.

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One of them was the one that posts on X. That's running again.

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It had run since 2018,

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rewriting the one that transfers the CSV blob

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from the old Emachine sitting over here up to

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Azure Blob storage container.

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Finally, the website, you know, it looked like an engineer built it because an

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engineer built it. And that's not a compliment

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and I'm the engineer that built it. But I had, I had

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Claude rewrite it. Now it's got a little bit of savvy to it and in

278
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addition to that, Frank, it's got two APIs running behind it,

279
00:17:43,590 --> 00:17:47,230
so. Interesting. Why do you need a weather station? Isn't it always sunny in

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Farmville? It is always sunny in Farm,

281
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except when it's not. You know, I. I noticed

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you're wearing a jacket, but it's January and I know you're in a basement and

283
00:17:58,470 --> 00:18:01,230
it's probably chilly down there. Yeah. It is actually

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74 degrees as we record

285
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on January 7th. It is 3:46pm

286
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in Farmville, Virginia. 74 degrees is what my instruments are showing

287
00:18:13,350 --> 00:18:16,040
me right now. Yeah, that's crazy hot.

288
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It went from being like really cold. I mean, I am in the

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basement and you can always tell if I'm running an AI workload or not based

290
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on, you know, and I'm not running a workload

291
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currently, but when I have the machines going, it does get toasty in here.

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So. Nice. We're in an

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abnormal heat wave for January in the mid Atlantic. And I know you

294
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guys are warm up there too. We're like at 50 today.

295
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Wow. And like a few days ago it was, you know,

296
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1720 degrees Fahrenheit. Right. So it's

297
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not. Yeah, it's kind of unusual, which is going to have. Yeah,

298
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amazing impact on my sinuses. So

299
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that'll be fun. That'll be fun to experience. So it is always funny in

300
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Farmville. You're right. I got these glasses. I love those

301
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glasses. These, these are blue blockers. And so if you're not

302
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watching. I kind of look. I don't know what I look like. He says I

303
00:19:11,330 --> 00:19:14,890
look cool. I look like a cool. So.

304
00:19:16,090 --> 00:19:19,610
No, like, one of the things that some people think could be

305
00:19:19,930 --> 00:19:23,770
triggering the migraines is. And headaches is kind of like too much blue

306
00:19:23,770 --> 00:19:27,570
light. So I figured, I'll give this a shot. I'll give it a shot. Plus,

307
00:19:27,570 --> 00:19:31,050
I. I feel like I look like AJ from the Y Files because. Like, you'll

308
00:19:31,050 --> 00:19:34,730
see, like, he wears glasses like that. He wears glass like that. And then

309
00:19:35,120 --> 00:19:37,840
I saw that and I. I looked up and I was like, what are those?

310
00:19:37,840 --> 00:19:41,520
And I find out. And I think the exact pair he wears

311
00:19:41,520 --> 00:19:45,360
is like $200. But we don't sell

312
00:19:45,360 --> 00:19:48,480
merch, like hacklefish and things like that, so.

313
00:19:49,920 --> 00:19:52,440
And if you don't know what I'm talking about. Well, the Y Files is an

314
00:19:52,440 --> 00:19:56,160
interesting YouTube channel. It's also a podcast, but it's very, very well

315
00:19:56,160 --> 00:19:59,080
done. I think when I first discovered it, I binge watched it. Then I shared

316
00:19:59,080 --> 00:20:02,670
it to you, and I think you binge watched it. I did, too, yeah. Lot

317
00:20:02,670 --> 00:20:05,950
of good stuff on that channel. Well, and he has a lot of

318
00:20:05,950 --> 00:20:09,790
throwbacks to some other stuff. And for listeners that, that are

319
00:20:09,790 --> 00:20:13,630
thinking to type the. And the letter Y and files. It's a

320
00:20:13,630 --> 00:20:17,269
W, H, Y. The Y Files. Yeah.

321
00:20:17,430 --> 00:20:21,070
But the throwbacks go back for me to

322
00:20:21,070 --> 00:20:24,510
start. So I used to work third shift as a plant

323
00:20:24,510 --> 00:20:27,830
electrician back in the old days and when the years began with A one.

324
00:20:28,640 --> 00:20:32,280
And it wasn't uncommon for, you know, me being on

325
00:20:32,280 --> 00:20:35,840
maintenance to have plenty of time to sit around after I'd done my,

326
00:20:36,000 --> 00:20:39,680
you know, my scheduled maintenance stuff and fix whatever may have broken.

327
00:20:39,840 --> 00:20:43,600
I was often idle. It was not always, but,

328
00:20:43,680 --> 00:20:47,440
you know, probably greater. Only so much happened. Almost only so

329
00:20:47,440 --> 00:20:51,240
Much happens at 3am. But I had the radio on and I would

330
00:20:51,240 --> 00:20:54,840
listen to. To WRVA 11:40am in

331
00:20:54,840 --> 00:20:58,330
Richmond, and the Art Bell show would play overnight

332
00:20:59,130 --> 00:21:02,890
on that. And he's a. AJ Gentile, the

333
00:21:02,970 --> 00:21:06,570
host of the show. Huge Art Bell fan.

334
00:21:06,650 --> 00:21:10,170
Oh, yeah. If you don't know who we're talking about, it's this whole. So

335
00:21:10,490 --> 00:21:14,010
welcome to conspiracy theory world here, right? Yeah. He was

336
00:21:14,010 --> 00:21:17,690
Alex Jones. He was Alex Jones before there was Alex Jones.

337
00:21:18,170 --> 00:21:21,970
Art. And he covered all of them. So Alex is, I think, a

338
00:21:21,970 --> 00:21:25,590
little more political. Yeah. You know, cultural art was

339
00:21:25,590 --> 00:21:29,350
more like sci fi, UFOs. Yeah. Really

340
00:21:29,670 --> 00:21:33,430
X Files type stuff, right? Exactly. Mel's Hole.

341
00:21:34,310 --> 00:21:37,950
And you'll see, you'll see stuff about Mel's

342
00:21:37,950 --> 00:21:41,630
Hole, even on Y Files merch. They've got some much formula sold out

343
00:21:41,630 --> 00:21:45,270
there so. But it's a fantastic podcast. Just

344
00:21:45,350 --> 00:21:47,990
the art of it. And AJ has

345
00:21:49,160 --> 00:21:52,920
is a career in. In entertainment and I didn't

346
00:21:52,920 --> 00:21:56,320
realize this particularly definitely radio. But he was also one of the

347
00:21:56,320 --> 00:21:59,840
producers or associate producers on Family Jewels. The

348
00:21:59,840 --> 00:22:03,680
Gene Simmons. Oh, I didn't know that. I

349
00:22:03,680 --> 00:22:07,360
didn't either. But there's somewhere I want to say in

350
00:22:07,360 --> 00:22:11,160
social media and stuff. He posted some pictures a few years ago about

351
00:22:11,160 --> 00:22:14,960
him and Gene, you know, collaborating on his head. Gene Simmons, if you don't know.

352
00:22:14,960 --> 00:22:18,720
Not everybody knows. Bass player and leader of the band

353
00:22:18,720 --> 00:22:22,320
Kiss, huge rock band in the

354
00:22:22,320 --> 00:22:25,600
70s and 80s and their.

355
00:22:25,920 --> 00:22:29,680
Their lead guitarist passed away a few weeks ago. Ace Fraley

356
00:22:30,160 --> 00:22:33,800
passed away and they actually did something for him at the White House.

357
00:22:33,800 --> 00:22:37,520
I believe there was a. I think so. Yeah. Some sort of ceremony there.

358
00:22:38,000 --> 00:22:41,720
So yeah. Yep. We're telling on ourselves

359
00:22:41,720 --> 00:22:45,560
Frank, but we're. Going, we're going on our off road thing which I think

360
00:22:45,560 --> 00:22:49,340
that's. And 400 shows we

361
00:22:49,340 --> 00:22:52,980
managed to. To miss this. 399 shows in a

362
00:22:52,980 --> 00:22:56,340
row. And here we are going off on a tangent. First time ever.

363
00:22:56,900 --> 00:23:00,580
I don't know about the first time. First time in the four hundreds. But

364
00:23:01,540 --> 00:23:05,260
the. No, I mean it was. It's. It's a cool. It's a. It's an interesting

365
00:23:05,260 --> 00:23:07,780
podcast and I think what, what I like about it is

366
00:23:10,100 --> 00:23:13,940
he's advancing the art of podcasting. Right. With Heckle Fish and like as a

367
00:23:13,940 --> 00:23:15,380
character and things like that.

368
00:23:17,680 --> 00:23:21,480
A lot of innovation. You know. I think one of the

369
00:23:21,480 --> 00:23:25,280
things that you know, also is, you know, we've had Bailey. Bailey is

370
00:23:25,280 --> 00:23:28,920
no longer in the Quantum show because some of our list. She

371
00:23:28,920 --> 00:23:32,720
doesn't really apply there. I think so. But we

372
00:23:32,720 --> 00:23:35,600
know we're doing. We're all going to do fun stuff like that.

373
00:23:37,280 --> 00:23:39,920
I'm toying with the idea of like a Professor Cubitt

374
00:23:41,520 --> 00:23:45,120
kind of like a thing that could work. Wasn't really ready in

375
00:23:45,120 --> 00:23:48,930
time but you know, just conceptually. But again like I

376
00:23:48,930 --> 00:23:52,610
think people focus on the jobs that AI will take away.

377
00:23:52,610 --> 00:23:56,250
But I also would, would, would pull from our own experience. When we hired the

378
00:23:56,250 --> 00:24:00,010
voice actor to do the first, you know,

379
00:24:00,010 --> 00:24:03,250
couple of seasons of, you know, the, the intro,

380
00:24:04,930 --> 00:24:08,770
we eventually replaced her with, you know, AI Once

381
00:24:08,850 --> 00:24:12,540
synthetic voices got good enough and somebody's like, well,

382
00:24:12,540 --> 00:24:16,300
you know, you really. You took away a job from a

383
00:24:16,300 --> 00:24:19,340
voice actor. I was like, well no, because getting a custom

384
00:24:19,660 --> 00:24:23,260
recording for every show would have cost. Would have been cost prohibitive would have been

385
00:24:23,660 --> 00:24:27,020
somewhere between 75 and $100. Right. Yeah. And

386
00:24:27,660 --> 00:24:30,300
you know, that's not really feasible

387
00:24:31,260 --> 00:24:34,780
and I didn't think it was, that. That was going away no matter what.

388
00:24:35,420 --> 00:24:38,500
But I wasn't going to do that anyway. Right. Like, so I think also with

389
00:24:38,500 --> 00:24:41,900
this, with this idea of, you know, most of us, you know,

390
00:24:43,420 --> 00:24:46,860
every developer I know, every data engineer I know always has like a back,

391
00:24:47,020 --> 00:24:50,060
like a side project in the back of their mind they want to build. But

392
00:24:50,220 --> 00:24:53,820
life, responsibility. Now that excuse is

393
00:24:53,820 --> 00:24:56,380
really a lot lower. Right. Like,

394
00:24:57,420 --> 00:25:00,380
I mean, now the question I have,

395
00:25:02,380 --> 00:25:05,700
now the stuff, now the question I have is, you know, how many machines can

396
00:25:05,700 --> 00:25:09,500
I have running code at the same time? Right. Because, like, I have

397
00:25:09,500 --> 00:25:13,190
all these other ideas. Like, you know, I have Dingo, which is, you know, right

398
00:25:13,190 --> 00:25:16,910
now is just a command line tool. I want to convert that

399
00:25:16,910 --> 00:25:20,630
to a web application. Sure. Like podzi.

400
00:25:20,630 --> 00:25:24,310
And you know, I don't know, like, podzi is amazing.

401
00:25:24,950 --> 00:25:27,670
Cool. Dingo is amazing. Thank you.

402
00:25:28,630 --> 00:25:32,430
It's good work, Frank. Frank does all the work. I've said this before, but

403
00:25:32,430 --> 00:25:35,350
I never say it enough. Frank does all the hard work here on the show.

404
00:25:35,830 --> 00:25:39,500
And it's as our schedules have just gotten crazy,

405
00:25:39,820 --> 00:25:43,660
you show up here less and less. Well, I show up

406
00:25:44,460 --> 00:25:48,100
and not even, I don't even show up that, that often anymore. That's so

407
00:25:48,100 --> 00:25:51,900
sad. But it's, you know, I, all I have is

408
00:25:51,900 --> 00:25:55,660
excuses, but it just, you know, it's a good

409
00:25:55,660 --> 00:25:58,860
thing that work is as busy as it is. Yeah, yeah.

410
00:25:59,260 --> 00:26:02,940
You know, the consulting and I, I, I'm in a,

411
00:26:02,940 --> 00:26:06,780
I've been in an interesting situation for about the past six

412
00:26:06,780 --> 00:26:10,440
months. I don't know if it's going to last. We, but there's been enough work

413
00:26:10,920 --> 00:26:14,120
out there that the teams have been doing for,

414
00:26:15,160 --> 00:26:18,800
for me to not, I can, I can pay the bills and

415
00:26:18,800 --> 00:26:22,360
I don't have to do consulting billing and

416
00:26:22,360 --> 00:26:25,920
that's unusual. It's the first time ever in my life

417
00:26:25,920 --> 00:26:28,280
that's been true. So I had time and money

418
00:26:29,640 --> 00:26:32,440
and I invested that money pretty heavily

419
00:26:33,560 --> 00:26:36,600
in. Oh, sorry. I invested the time pretty heavily

420
00:26:37,300 --> 00:26:40,900
and some stuff that's coming out of data integration, lifecycle management

421
00:26:40,900 --> 00:26:44,380
suite stuff. But the big boost was

422
00:26:44,380 --> 00:26:46,180
exactly what you were describing earlier.

423
00:26:48,020 --> 00:26:51,620
Applying Claude code specifically to these

424
00:26:51,620 --> 00:26:55,260
coding tasks has made it possible for me to

425
00:26:55,260 --> 00:26:57,460
finish up stuff that I've been working on for 10 years.

426
00:26:59,300 --> 00:27:02,380
Yeah, I know. You were showing me some stuff that you were building and some

427
00:27:02,380 --> 00:27:05,460
cool code names that you have as well as you have some acronyms.

428
00:27:06,700 --> 00:27:09,420
I Like to think that I inspired you to come up with the cooler code

429
00:27:09,420 --> 00:27:12,460
names, but we don't know. Absolutely. Well,

430
00:27:13,980 --> 00:27:17,420
I think the coolest code names actually Claude code

431
00:27:17,980 --> 00:27:21,739
suggested. Oh, really? It's been cool. Yeah.

432
00:27:21,739 --> 00:27:25,460
The one I think the one you reacted to most, the

433
00:27:25,460 --> 00:27:29,100
new big project I shared with you, that's the biggest thing I've ever even conceived.

434
00:27:30,700 --> 00:27:34,480
It picked the name and the name of the roles and so it's

435
00:27:34,480 --> 00:27:37,920
first, it's very applicable what it, what it shows the analogy holds

436
00:27:38,320 --> 00:27:41,600
and, and then the name names of the roles

437
00:27:42,000 --> 00:27:45,640
that are going to be parts, kind of like sub parts of that

438
00:27:45,640 --> 00:27:48,960
project. Those are also very apropos.

439
00:27:49,280 --> 00:27:52,640
Oh, very cool. A Ponzi I got name. I got. I asked

440
00:27:52,640 --> 00:27:56,320
ChatGPT to come up with a list of interesting names describing what

441
00:27:56,320 --> 00:27:59,920
it's going to do and Ponzi was one of them. Nice. So, yeah,

442
00:28:00,310 --> 00:28:03,990
great minds, Frank. Great minds. There you go. There you go. I'm actually, I'm

443
00:28:03,990 --> 00:28:07,830
actually talking about Podsy because I think I'm building it from the get go

444
00:28:08,390 --> 00:28:11,310
with the idea that it could be a SaaS for other, it could be a

445
00:28:11,310 --> 00:28:14,910
service for other podcasters. Absolutely. Yeah. We're having

446
00:28:14,910 --> 00:28:18,630
this problem. We can't be the only ones. Right.

447
00:28:18,630 --> 00:28:21,830
And if nobody buys it, at least I solve the problem for myself. Right.

448
00:28:23,430 --> 00:28:27,160
You know, and for you too. Like, I mean, one of the, you know, one

449
00:28:27,160 --> 00:28:30,800
of the things this does leans pretty heavily into the AI side of

450
00:28:31,040 --> 00:28:34,880
how do you generate content from a podcast that you already made?

451
00:28:35,120 --> 00:28:38,920
Right. How do you track, how do you track the content that you've, you've

452
00:28:38,920 --> 00:28:42,680
created as a result of, you know, other tools like Opus

453
00:28:42,680 --> 00:28:46,400
or. And if you've seen, if you've seen us on LinkedIn,

454
00:28:46,400 --> 00:28:50,160
like a lot of those short clips are generated by Opus where it'll, it'll

455
00:28:50,160 --> 00:28:53,770
show, you know, kind of us talking. I know you use it

456
00:28:55,290 --> 00:28:59,010
and, you know, it does the captioning, does the

457
00:28:59,010 --> 00:29:02,770
slick editing. You could have it at B roll. I mean, it's just amazing what's

458
00:29:02,770 --> 00:29:06,570
possible, right? Like. Yep. And I

459
00:29:06,570 --> 00:29:09,850
mean, people are amazed to find out how small of a team we are

460
00:29:11,210 --> 00:29:14,170
between this podcast and the other podcast. Right. Like, yeah,

461
00:29:14,810 --> 00:29:18,090
I mean, it's a lot of content if you think about it. If you're doing

462
00:29:18,090 --> 00:29:21,930
content per headcount, we're up there. Oh, yeah. And I

463
00:29:21,930 --> 00:29:25,730
say we. And it's mostly you. Again, I would say you're

464
00:29:25,730 --> 00:29:28,930
doing all of the, you're doing the, you're doing the lion's share of it. You

465
00:29:28,930 --> 00:29:32,450
know, north of 90%. Not so toot my own horn.

466
00:29:33,810 --> 00:29:37,610
But yeah, I mean, you started this with, with an eye

467
00:29:37,610 --> 00:29:41,130
towards automation and you kept looking for

468
00:29:41,130 --> 00:29:44,890
shortcuts and. And shortcuts for the shortcuts, and that's what's

469
00:29:44,890 --> 00:29:48,550
grown these tools. And I mean, it was. Frank, we had only

470
00:29:48,550 --> 00:29:52,190
been doing this a couple of years when you started coming up with, you know,

471
00:29:52,190 --> 00:29:55,950
scripts and stuff like that, that would. That would, you know,

472
00:29:55,950 --> 00:29:59,350
grab a transcript or, you know, parse

473
00:30:00,150 --> 00:30:03,830
the URLs and, you know, post things for

474
00:30:03,830 --> 00:30:07,270
us. It wasn't long at all, and it's just. It was what was available

475
00:30:07,430 --> 00:30:10,950
at the time. And I think you should do a demo of Potsy

476
00:30:11,750 --> 00:30:15,430
just to show people. How cool it is, like right

477
00:30:15,430 --> 00:30:19,110
now. Yeah, I'm gonna need a

478
00:30:19,110 --> 00:30:22,910
second to. And you got to keep in mind that Frank's been.

479
00:30:22,910 --> 00:30:26,590
You've only been working on us, what, a couple months? A month. Maybe

480
00:30:26,590 --> 00:30:28,870
a month today. Yeah, yeah.

481
00:30:30,070 --> 00:30:33,750
And you started, you know, you started with the idea.

482
00:30:34,470 --> 00:30:38,230
Yep. And. And, well, I was actually. I was

483
00:30:38,230 --> 00:30:41,420
thinking about this for a while, like, how to do it. Yeah.

484
00:30:41,820 --> 00:30:45,500
The real challenge was the real

485
00:30:45,500 --> 00:30:48,460
thing that broke the cat, the straw that broke the camel's back, so to speak,

486
00:30:49,420 --> 00:30:53,260
was the Advent calendar that we did for Impact

487
00:30:53,260 --> 00:30:56,940
Quantum. Okay. Which if you go. We'll make sure it's in the show

488
00:30:56,940 --> 00:31:00,380
notes. Right. But if you go to impactquantum.com advent calendar,

489
00:31:01,740 --> 00:31:04,540
filling in all of that content into one place.

490
00:31:06,390 --> 00:31:09,950
Was. That was vive

491
00:31:09,950 --> 00:31:13,750
coded, actually. Right. That whole HTML experience. But the issue

492
00:31:13,750 --> 00:31:17,350
I had was the effort to collect all the

493
00:31:17,350 --> 00:31:19,430
content. And the data that I had

494
00:31:22,630 --> 00:31:26,310
was. I'm typing, I'm telling Claude to start the dev server.

495
00:31:28,150 --> 00:31:30,710
I'm so lazy now. I don't have to type in.

496
00:31:33,350 --> 00:31:36,750
But that's efficient. It's efficient operational

497
00:31:36,750 --> 00:31:40,150
efficiency. It's not lazy. So

498
00:31:40,390 --> 00:31:44,110
ultimately I realized, like, you know,

499
00:31:44,110 --> 00:31:47,950
we have this, we have the transcripts, we have all this, but, like, collecting it

500
00:31:47,950 --> 00:31:51,590
in the one place is just way harder than it needed to

501
00:31:51,590 --> 00:31:55,430
be. And I kind of had this in

502
00:31:55,430 --> 00:31:58,070
the back of my mind, like, what is this going to look like? And things

503
00:31:58,070 --> 00:32:01,810
like that, and what am I going to do with it? And then really

504
00:32:01,810 --> 00:32:04,770
kind of the accident, actually, like, I had a lot of free time to kind

505
00:32:04,770 --> 00:32:07,450
of think, you know, later that day

506
00:32:08,650 --> 00:32:11,690
and, you know, nothing will jar you out of

507
00:32:12,410 --> 00:32:15,850
anything faster than like an accident,

508
00:32:16,410 --> 00:32:20,090
you know. And so this, ladies and gents,

509
00:32:20,090 --> 00:32:23,610
and AIs of varying levels of sentiency.

510
00:32:24,410 --> 00:32:28,210
This is the current homepage for Pozzi. I actually do have A domain name that's

511
00:32:28,210 --> 00:32:31,890
registered. Podzi Studio, I believe. Okay.

512
00:32:32,370 --> 00:32:35,930
And you know there's going to be more marketing material

513
00:32:35,930 --> 00:32:39,730
here. Right. But I'm going to sign in.

514
00:32:41,010 --> 00:32:44,770
These are all test accounts, so you're not gonna.

515
00:32:46,450 --> 00:32:49,410
I'll sign in. So right now we have two shows.

516
00:32:51,890 --> 00:32:55,690
There's Data Driven and Impact Quantum. And so I can

517
00:32:55,690 --> 00:32:56,930
see from my dashboard

518
00:32:59,490 --> 00:33:02,930
what I. What I have. Right? This tells me I have

519
00:33:02,930 --> 00:33:06,610
97 episodes in the system, which I think is about.

520
00:33:06,610 --> 00:33:10,290
Right. Based on what the feed would be pulling because we don't share every

521
00:33:10,290 --> 00:33:13,890
episode on one of the feeds because it would make the feed file super long.

522
00:33:14,530 --> 00:33:18,210
Right. But anyway, so you'll see my

523
00:33:18,210 --> 00:33:21,930
4 to 1 ratio is pretty spot on. Right. There's

524
00:33:21,930 --> 00:33:25,230
401 assets here and 97

525
00:33:25,230 --> 00:33:29,070
episodes, right. So it's about a 4 to 1 ratio. So let's

526
00:33:29,070 --> 00:33:32,470
just go to. From Molecules to

527
00:33:32,470 --> 00:33:35,630
Medicine. This was an episode

528
00:33:36,430 --> 00:33:38,270
36 of

529
00:33:40,110 --> 00:33:43,910
Impact Quantum. And you will see that

530
00:33:43,910 --> 00:33:47,630
I have these tracked assets now where it

531
00:33:47,630 --> 00:33:49,870
pulls. Most of these tracked assets are from.

532
00:33:52,120 --> 00:33:55,880
Are from the RSS feed, right? So every RSS feed. Yeah. So,

533
00:33:55,880 --> 00:33:58,680
like, and I think you and I were talking and was like, we use

534
00:33:59,000 --> 00:34:02,840
Captivate, so there's other fields we could also capture too. But I want to start

535
00:34:02,840 --> 00:34:06,280
with the vanilla. If you have a podcast, you have an RSS feed,

536
00:34:06,440 --> 00:34:09,800
full stop. Yeah, right. Good call. Because I think

537
00:34:09,800 --> 00:34:13,400
that's. I could always add on later.

538
00:34:15,720 --> 00:34:19,470
And so basically you have media, which is audio or

539
00:34:20,990 --> 00:34:24,590
this. You have video, you also have the audio and you have the

540
00:34:24,590 --> 00:34:28,190
transcript. So what I have here is I

541
00:34:28,190 --> 00:34:31,670
have the web page that's associated with it. So I can go here. I click

542
00:34:31,670 --> 00:34:34,990
on this. This takes me to the webpage for it,

543
00:34:36,270 --> 00:34:39,950
that episode. Right. Now, some of the stuff you do have to add manually,

544
00:34:40,110 --> 00:34:43,790
but that's fine. You can just add an asset and tag it to this. I'm

545
00:34:43,790 --> 00:34:47,479
also working on the ability to add a person,

546
00:34:47,479 --> 00:34:51,279
right? So we would add the name of the person, the guest, what organization they

547
00:34:51,279 --> 00:34:54,839
work for. My ultimate goal is to get a map of a graph

548
00:34:54,839 --> 00:34:57,999
of how many people work in this industry. How many people work in this industry,

549
00:34:58,239 --> 00:35:02,039
and kind of see that. And that could tell a story too,

550
00:35:02,039 --> 00:35:04,959
right? Because at my heart, I am a data visualization nerd.

551
00:35:05,759 --> 00:35:08,919
So that's kind of the thing. But as you can see, I have the thumbnail,

552
00:35:08,919 --> 00:35:12,289
I have the transcript. So if I click on this, this is the.

553
00:35:13,000 --> 00:35:16,640
The transcript from that episode. And then what I can do from

554
00:35:16,640 --> 00:35:20,080
there is I've using the power, the magic of

555
00:35:20,080 --> 00:35:23,480
AI Right. So if I want to create an

556
00:35:23,480 --> 00:35:27,240
infographic or pull quotes, right? Let's

557
00:35:27,240 --> 00:35:30,120
just say let's create a LinkedIn post. I know if you follow me on LinkedIn,

558
00:35:30,200 --> 00:35:33,920
this may be like telling you about

559
00:35:33,920 --> 00:35:36,600
the Easter Bunny, right? So I'm going to click on this

560
00:35:38,610 --> 00:35:42,250
and I'm going to click generate. So what this is doing

561
00:35:42,250 --> 00:35:45,410
here is this is saying, Please write a LinkedIn post about

562
00:35:45,810 --> 00:35:49,570
this episode, right? And I will open,

563
00:35:50,850 --> 00:35:54,009
I'll open a Grok because I just added this today based on a conversation you

564
00:35:54,009 --> 00:35:57,850
and I had. Cool. So I click

565
00:35:57,850 --> 00:36:01,650
on that. I want you to notice two things. One, that prompt

566
00:36:01,650 --> 00:36:03,970
was automatically put into that text box.

567
00:36:05,610 --> 00:36:09,450
Nice. And Grok is working on it right now.

568
00:36:10,890 --> 00:36:14,610
I could do the same thing in Claude, Gemini, et cetera,

569
00:36:14,610 --> 00:36:18,170
et cetera. But while that cooks, I will show you that prompts.

570
00:36:20,890 --> 00:36:24,330
I have a template, little template language you could do for a prompt.

571
00:36:24,810 --> 00:36:28,610
Right. So if I go here, I start

572
00:36:28,610 --> 00:36:32,180
off the prompt. This is actually based on the. The

573
00:36:32,180 --> 00:36:35,940
guest that we had on the

574
00:36:35,940 --> 00:36:39,300
first show of the season, Jeff woods, was it? Yes.

575
00:36:41,620 --> 00:36:44,740
Great show. That was a great show. And it was basically

576
00:36:47,460 --> 00:36:51,260
how his book, the AI Driven

577
00:36:51,260 --> 00:36:55,100
Leader and his prompt framework. This is really kind of based largely

578
00:36:55,100 --> 00:36:58,830
on that conversation, right? So I have a thing here. This

579
00:36:58,830 --> 00:37:02,270
is what I type. And I say based on the following data about this

580
00:37:02,270 --> 00:37:05,510
podcast episode, provide a list. Oh, that's a typo.

581
00:37:07,030 --> 00:37:10,630
Right? Can you give me a list of

582
00:37:11,910 --> 00:37:15,630
comma separated tags for WordPress? Right? So podcast

583
00:37:15,630 --> 00:37:19,430
name, I can just inject that as part of this script kind

584
00:37:19,430 --> 00:37:22,970
of code here, right? So podcast name.com,

585
00:37:23,680 --> 00:37:26,800
podcast name, episode title,

586
00:37:27,280 --> 00:37:31,040
episode description, here's the URL. So transcript, etc, etc,

587
00:37:31,040 --> 00:37:34,640
etc. I'll save changes. And so if I go back.

588
00:37:36,960 --> 00:37:40,080
Let's go back to here.

589
00:37:42,880 --> 00:37:43,520
Okay,

590
00:37:47,120 --> 00:37:50,920
so if I go here now, I will see, I

591
00:37:50,920 --> 00:37:54,200
will go tags,

592
00:37:54,920 --> 00:37:58,520
generate and we'll do this one in Claude just for grins

593
00:38:00,040 --> 00:38:01,480
and you'll see that it's there.

594
00:38:07,000 --> 00:38:10,800
I click the button now it's going to generate the comma

595
00:38:10,800 --> 00:38:14,480
separated tags. So this is useful for things like YouTube, where they want

596
00:38:14,480 --> 00:38:18,280
this type of metadata for, to help us with SEO and things like that. Sure.

597
00:38:19,240 --> 00:38:22,920
And then while that's cooking, we can see, look at, this is the finished product.

598
00:38:24,520 --> 00:38:28,360
Nice here. Now there's obviously things I need to do to

599
00:38:28,360 --> 00:38:31,400
clean it up, right? Because it says host Frank Lavinia. I wouldn't post this on

600
00:38:31,400 --> 00:38:34,600
my LinkedIn, like talk about myself in the third person, but

601
00:38:35,320 --> 00:38:39,120
just think about what's possible with this, right? And

602
00:38:39,120 --> 00:38:42,360
all you have to do is load your RSS feed into the system

603
00:38:42,990 --> 00:38:45,790
and all of this stuff becomes available. I can also add,

604
00:38:47,710 --> 00:38:51,310
I can add more metadata. What's really cool about this is

605
00:38:52,830 --> 00:38:56,470
that I picked this one because I'll put fun facts. So that way when we

606
00:38:56,470 --> 00:38:59,990
do like a retrospective show or like someone out wants an

607
00:38:59,990 --> 00:39:03,750
anecdote about a particular episode we can talk about,

608
00:39:03,750 --> 00:39:06,310
I can say like, well, I was actually in a lot of pain recording this

609
00:39:06,310 --> 00:39:09,720
and you can actually see it in my face. And I was attending

610
00:39:10,200 --> 00:39:13,320
ignite and you can see the hotel room in the background.

611
00:39:14,120 --> 00:39:17,240
And the reason why I was in pain was because we had a hot water

612
00:39:17,240 --> 00:39:21,040
leak like literally 36 hours before I had

613
00:39:21,040 --> 00:39:24,760
to fly cross country. So I had to clean up the basement,

614
00:39:24,760 --> 00:39:28,560
put everything in a dumpster, and then get on a cross

615
00:39:28,560 --> 00:39:32,120
country flight, which I don't recommend it at any age, but I can tell you

616
00:39:33,320 --> 00:39:36,930
anything that bent or moved hurt. Oh,

617
00:39:37,090 --> 00:39:40,690
Frank. So that's. And then the other thing I'm going to add is like, you

618
00:39:40,690 --> 00:39:44,290
know, add a sponsors, affiliates like you mentioned. Yeah.

619
00:39:44,450 --> 00:39:48,170
You mentioned an audiobook called Vibe Coding. Fun fact. Did

620
00:39:48,170 --> 00:39:51,450
you know that we have a sponsor? That sponsor is

621
00:39:51,450 --> 00:39:55,250
Audible and if you go to thedatadrivenbook.com

622
00:39:56,530 --> 00:40:00,250
you will be routed to Audible and you will get a free audiobook on

623
00:40:00,250 --> 00:40:02,850
us and you can have it be Andy's book

624
00:40:03,890 --> 00:40:07,490
or that he mentioned called Vibe Coding or Jeff

625
00:40:07,810 --> 00:40:10,370
woods book called the AI Driven Leader.

626
00:40:12,290 --> 00:40:15,810
So it's really kind of taking this to the next level,

627
00:40:15,810 --> 00:40:19,130
right? So the advantage of, the advantage of this is that

628
00:40:19,130 --> 00:40:22,850
realistically, if you're going to launch a podcast, and I say this to any

629
00:40:23,490 --> 00:40:27,330
podcaster or budding podcaster out there, right?

630
00:40:28,150 --> 00:40:31,510
Obviously the microphone's important, the camera's important, all that stuff.

631
00:40:32,150 --> 00:40:35,750
But there's a lot of other things to think about, right? Not just the mark,

632
00:40:36,230 --> 00:40:39,310
a lot of it is the marketing of it. Right? Who's your target audience? You

633
00:40:39,310 --> 00:40:43,070
know, what's your. The cool kids call that your avatar, right? Your typical

634
00:40:43,070 --> 00:40:46,470
thing. What's your audience? What are you trying to do? But the other thing,

635
00:40:46,470 --> 00:40:50,190
increasingly, in a noisier and noisier world, how are you going to

636
00:40:50,190 --> 00:40:53,780
market this show across various social media networks? Right?

637
00:40:53,780 --> 00:40:57,380
Right. And we've solved that problem

638
00:40:57,540 --> 00:41:00,900
with things like dingo and buying

639
00:41:01,300 --> 00:41:05,060
opus and things like that. But the

640
00:41:05,060 --> 00:41:07,140
next problem you have is

641
00:41:08,820 --> 00:41:12,420
creating content, right? Like, not just creating content, but

642
00:41:12,420 --> 00:41:16,180
managing it. Right? So generally speaking, opus is the

643
00:41:16,180 --> 00:41:19,930
clip that'll take a video and kind of cut it into like little

644
00:41:19,930 --> 00:41:23,370
short form videos, you get about 1 of those per minute,

645
00:41:23,370 --> 00:41:26,850
roughly, right? So let's just say, so a, a 60 minute

646
00:41:26,850 --> 00:41:30,090
podcast or. I already mentioned that you already have four bits of content,

647
00:41:30,490 --> 00:41:34,130
right? You have the actual audio of the show, the transcript, the

648
00:41:34,130 --> 00:41:37,850
thumbnail, possibly video as well, like the full on

649
00:41:37,930 --> 00:41:41,290
full length video. But then now you add,

650
00:41:41,690 --> 00:41:45,340
if it's 60 minutes, you're going to have roughly 60 short

651
00:41:45,340 --> 00:41:49,180
videos. So now you go from, you know, now

652
00:41:49,180 --> 00:41:52,980
you have 64, 65, 64 items

653
00:41:53,140 --> 00:41:56,860
now to track, right? And it doesn't sound like a big

654
00:41:56,860 --> 00:41:59,940
deal, but when you're trying to organize things, it becomes a very,

655
00:42:00,500 --> 00:42:04,340
very tedious work right away, right? And

656
00:42:04,660 --> 00:42:07,700
what if you make an. What if you write a blog post about, you know,

657
00:42:07,700 --> 00:42:11,540
the particular episode? Well, that's another asset. You have the track. Be nice to know

658
00:42:11,700 --> 00:42:15,380
you. Because up until then I was googling it

659
00:42:15,380 --> 00:42:18,340
basically against my own site. Like, where did I write that? Where did I put

660
00:42:18,340 --> 00:42:22,020
that? So now with this ability, you have the ability to track

661
00:42:22,020 --> 00:42:25,660
all that metadata in one place. And

662
00:42:26,300 --> 00:42:30,100
as it goes further along, I'm going to add the graph feature

663
00:42:30,100 --> 00:42:33,860
where I'll be able to upload metadata about each guest. Like

664
00:42:33,860 --> 00:42:37,700
this person works here, this is what they do. And then I want to be

665
00:42:37,700 --> 00:42:41,550
able to kind of track that. And I think that that will also, aside

666
00:42:41,550 --> 00:42:45,270
from satiating my inner data visualization nerd, I think it

667
00:42:45,270 --> 00:42:48,630
also could help people figure out where the next

668
00:42:49,110 --> 00:42:52,790
opportunity is, where the next opportunity to

669
00:42:54,150 --> 00:42:57,830
find guests. Maybe I'm over covering one type of industry,

670
00:42:57,990 --> 00:43:00,230
maybe I'm not covering this.

671
00:43:01,350 --> 00:43:03,830
Eventually I'd like to tie it into

672
00:43:04,890 --> 00:43:07,770
download statistics so I can say like, hey, every time I talk about

673
00:43:07,770 --> 00:43:10,490
bioinformatics, like this happens, right?

674
00:43:11,770 --> 00:43:15,530
That sort of thing. And also help manage kind of, you know, all these

675
00:43:15,530 --> 00:43:19,330
affiliate programs that we have, right? We have a program for Audible, we

676
00:43:19,330 --> 00:43:22,250
have a program for Opus. It'd be nice to kind of have that in one

677
00:43:22,250 --> 00:43:26,090
place as opposed to going around. What I do now

678
00:43:26,090 --> 00:43:29,810
is I go around different websites and find it. Candace, to her

679
00:43:29,810 --> 00:43:33,330
credit, shout out to you, Candace. She has them all in one spreadsheet. But that's

680
00:43:33,330 --> 00:43:37,160
still, I. It's still a cognitive kind of

681
00:43:37,160 --> 00:43:39,960
switch of, oh, I got to go to a spreadsheet. It'd be nice to have

682
00:43:39,960 --> 00:43:42,240
everything, one place to rule them all.

683
00:43:45,040 --> 00:43:48,760
So that, that's my stump speech. I know I got to work on

684
00:43:48,760 --> 00:43:52,400
shortening my elevator pitch for it, but you're

685
00:43:52,400 --> 00:43:56,200
muted, Andy, so we can't hear you. I'm sorry, I. I

686
00:43:56,200 --> 00:43:59,890
didn't Want to cough last time. It's a great stump speech, Frank. Thank

687
00:43:59,890 --> 00:44:03,650
you. So if folks are interested, let me know. Not that hard to

688
00:44:03,650 --> 00:44:07,130
find LinkedIn or whatever if you reach out to me.

689
00:44:07,450 --> 00:44:11,290
And we do go into wider beta testing unless you have it.

690
00:44:11,290 --> 00:44:14,650
And obviously if you're a longtime listener or even a short time listener,

691
00:44:15,449 --> 00:44:18,970
whatever. I tried to decide to charge, you'll get a solid discount from

692
00:44:19,770 --> 00:44:23,050
Gotcha but cool, man.

693
00:44:23,610 --> 00:44:27,290
Testing the data driven book.com

694
00:44:27,290 --> 00:44:29,930
and I believe the,

695
00:44:31,370 --> 00:44:35,210
that that was first off, I know the link has changed.

696
00:44:35,210 --> 00:44:38,570
It's been. I don't, I don't want to use the word hijacked.

697
00:44:39,290 --> 00:44:42,810
When did it work? Did it work the data

698
00:44:42,810 --> 00:44:46,010
driven dot com? It doesn't.

699
00:44:46,810 --> 00:44:49,770
No. That works for me.

700
00:44:50,730 --> 00:44:52,810
Does it? Yeah, it works on my machine.

701
00:44:54,650 --> 00:44:58,250
You know what, we'll include willing in the show notes. We'll include the actual

702
00:44:59,610 --> 00:45:00,410
affiliate link.

703
00:45:03,370 --> 00:45:06,490
I'm, I'm not sure what's going on.

704
00:45:07,610 --> 00:45:11,050
I've got us. I've got a sneaking suspicion I do know what's going on.

705
00:45:13,850 --> 00:45:17,530
All right. So in any case, we'll make sure we put in the proper link.

706
00:45:17,770 --> 00:45:21,610
But you know, there are other things too that like you said, like I've been

707
00:45:21,610 --> 00:45:24,570
working on optimizing things and things like that. It's more about,

708
00:45:26,350 --> 00:45:29,950
you know, it's a testament to 1%

709
00:45:29,950 --> 00:45:33,670
improvement every day will compound into a

710
00:45:33,670 --> 00:45:37,510
ridiculous amount of optimization. So, I mean, like I can turn one bit of

711
00:45:37,510 --> 00:45:41,190
content into, no exaggeration,

712
00:45:41,190 --> 00:45:44,710
like I said, like 60, 70 bits of content. Oh,

713
00:45:44,710 --> 00:45:48,510
goodness. Yeah, Easy. Opus is a big driver. Opus is going

714
00:45:48,510 --> 00:45:51,790
to be a big driver of that. But there's other, there's other secret sauce I

715
00:45:51,790 --> 00:45:55,370
haven't shared yet. Like, like shared with you, but I'm not shared publicly yet.

716
00:45:55,610 --> 00:45:58,650
Right. So there's definitely things you can do and if you use your imagination, it's

717
00:45:58,650 --> 00:46:01,930
a lot hard to figure out. Right. With all the other tool, generative AI tools

718
00:46:01,930 --> 00:46:05,170
that we have, you know, whether it's

719
00:46:05,170 --> 00:46:08,850
infographics, Notebook, LM like that sort of thing, you can

720
00:46:08,850 --> 00:46:12,610
get kind of, you know, one of the things I discovered is you can

721
00:46:12,610 --> 00:46:15,850
get kind of orthogonal views across different AI models

722
00:46:16,970 --> 00:46:20,020
off of one transcript and compare them

723
00:46:20,580 --> 00:46:24,180
and see what resonates in one engine and what

724
00:46:24,180 --> 00:46:27,940
resonates versus another. There's a lot of opportunity there. Right.

725
00:46:29,380 --> 00:46:33,140
And I certainly, I think if nothing else,

726
00:46:34,020 --> 00:46:37,540
transcripts are incredibly important, not just for

727
00:46:37,540 --> 00:46:40,980
accessibility but like just for the ability for

728
00:46:40,980 --> 00:46:44,460
AI to ingest the content and it

729
00:46:44,460 --> 00:46:48,130
becomes very malleable. And it's,

730
00:46:48,370 --> 00:46:52,130
you know, I think That I don't think people in Mass have realized

731
00:46:52,130 --> 00:46:55,010
that just yet. Hopefully. I totally agree.

732
00:46:55,730 --> 00:46:59,530
Hopefully Podzi will change that. Hopefully Podzi will be a big driver of that. But

733
00:46:59,530 --> 00:47:03,290
we shall see. And if you think about, you know, what LLMs

734
00:47:03,290 --> 00:47:06,730
actually do, it's right, they're. They're all about the words

735
00:47:06,730 --> 00:47:10,450
Brother. Right? 100%.

736
00:47:11,900 --> 00:47:15,500
And so transcripts filled with words.

737
00:47:16,620 --> 00:47:19,820
I like that. That should be the. That should be the tagline

738
00:47:20,940 --> 00:47:23,500
filled with words. You heard it here first.

739
00:47:24,700 --> 00:47:27,340
And I know you've been doing some exciting things with Claude.

740
00:47:29,340 --> 00:47:33,140
It's just. It's such a. It's very easy to

741
00:47:33,140 --> 00:47:36,980
get into the Debbie Downer mode of oh, my God, AI is

742
00:47:36,980 --> 00:47:40,730
going to take over. But I see opportunity here

743
00:47:40,970 --> 00:47:44,410
left and right. You have to.

744
00:47:45,130 --> 00:47:48,330
There's a lot of opportunity here when it comes to

745
00:47:48,810 --> 00:47:52,450
how you can use AI to be more efficient. Right. The

746
00:47:52,450 --> 00:47:56,290
idea of us having a podcast. Doing a podcast is one thing, but

747
00:47:56,290 --> 00:47:57,450
doing a podcast and

748
00:48:00,090 --> 00:48:03,890
having it appear everywhere. One of the best compliments I got was when I was

749
00:48:03,890 --> 00:48:07,710
in. When I was at Ignite, somebody knows me from LinkedIn,

750
00:48:07,710 --> 00:48:09,830
they're like, oh, my God, you. You're everywhere.

751
00:48:11,750 --> 00:48:15,510
You know, and it's funny. And, you know, we get emails

752
00:48:15,670 --> 00:48:18,470
and. And it's like, you know, this is a ghost for you and your team.

753
00:48:18,470 --> 00:48:22,070
And I'm like, I don't think they. The team's not as large as they think.

754
00:48:22,230 --> 00:48:25,590
I take it as a compliment. It is, you know. Yeah.

755
00:48:26,390 --> 00:48:30,150
So. And I'm excited because, you know, again,

756
00:48:30,150 --> 00:48:33,830
we're going to. Sometime in the spring, we'll will have season 10 of

757
00:48:33,830 --> 00:48:37,430
Data Driven, if you can believe that. Wow,

758
00:48:37,590 --> 00:48:39,750
is that crazy? I know.

759
00:48:42,310 --> 00:48:46,030
And that's like nine more seasons than I

760
00:48:46,030 --> 00:48:46,870
thought we'd have.

761
00:48:50,070 --> 00:48:53,390
And I didn't expect us to get nearly to this many episodes. I mean, we

762
00:48:53,390 --> 00:48:57,070
were excited, don't get me wrong. And we, you know, we got out there and

763
00:48:57,070 --> 00:49:00,310
did the best that we could at the time. And I thought we had a

764
00:49:00,310 --> 00:49:03,910
fair start. You know, we. We lined up some

765
00:49:04,150 --> 00:49:07,590
excellent guests. I think we nailed that to start with.

766
00:49:07,990 --> 00:49:11,750
And that may be the secret sauce that propelled the podcast

767
00:49:12,390 --> 00:49:15,750
through to episode 400 here and beyond.

768
00:49:16,630 --> 00:49:20,150
So, yeah, 101 of the best.

769
00:49:20,230 --> 00:49:23,750
One of the next logistic challenges, logistical challenges I gotta address

770
00:49:23,750 --> 00:49:26,170
is scheduling. Right.

771
00:49:27,050 --> 00:49:30,010
Calendaring. We. We did use Microsoft bookings.

772
00:49:32,170 --> 00:49:35,850
I want to keep our clean language rating, so I won't give people my opinion

773
00:49:35,850 --> 00:49:39,690
of there. There's two versions of bookings.

774
00:49:39,690 --> 00:49:42,570
There's the version that, the way you think it's going to work or the way

775
00:49:42,570 --> 00:49:46,170
the advertiser works and the way it actually works. Yeah.

776
00:49:46,170 --> 00:49:49,130
And so I actually switched back to calendarly,

777
00:49:51,340 --> 00:49:53,980
so as soon as I get that fixed up, that'll be on the site. I

778
00:49:53,980 --> 00:49:56,660
know we have a lot of folks reaching out to be in the show. Don't

779
00:49:56,660 --> 00:50:00,500
take it personally. It's just. It's been. I was talking

780
00:50:00,500 --> 00:50:04,260
to somebody this the other day, like, between, like mid November when

781
00:50:04,260 --> 00:50:07,740
the water heater broke till basically this week. I feel like I've been,

782
00:50:07,980 --> 00:50:11,820
like, running behind, you know, trying to catch up, but.

783
00:50:12,140 --> 00:50:15,780
But Again, thanks to AI, I was able to crank out over 30,000

784
00:50:15,780 --> 00:50:19,500
lines of code in the spite of this. Right. That,

785
00:50:19,500 --> 00:50:22,380
you know, and I. I think we all have had those projects that have been

786
00:50:22,380 --> 00:50:26,220
back burner ideas that you think I'll get to it someday. And

787
00:50:26,220 --> 00:50:29,300
someday never comes. Right, Right, right.

788
00:50:30,020 --> 00:50:32,580
You know, if you look at a calendar, there's seven days of the week. None

789
00:50:32,580 --> 00:50:36,260
of them are someday. I mean.

790
00:50:38,500 --> 00:50:42,260
Very true. So thank you.

791
00:50:42,260 --> 00:50:45,700
I want to say thank you to everyone who helped us get to 400 episodes.

792
00:50:45,700 --> 00:50:49,040
It's pretty wild. Like, I remember listening to

793
00:50:49,040 --> 00:50:52,520
podcasts just, you know, when I was just a young lad living in

794
00:50:52,520 --> 00:50:56,280
Richmond, I was listening to.net rocks and they

795
00:50:56,280 --> 00:51:00,000
were already at, like, episode. I think by then they were like, at episode

796
00:51:00,000 --> 00:51:03,720
three, 400 they were up there. And I was

797
00:51:03,720 --> 00:51:07,480
like, wow, man, that must be some achievement to get to that many episodes.

798
00:51:07,480 --> 00:51:11,160
Not just, you know, having a podcast and doing that, but getting to that

799
00:51:11,160 --> 00:51:14,730
point. And here we are. I

800
00:51:14,730 --> 00:51:18,330
know, it just seems surreal that we, you know, that we got

801
00:51:18,330 --> 00:51:22,090
here and we definitely

802
00:51:22,170 --> 00:51:25,610
could not have made it here this far without our

803
00:51:25,610 --> 00:51:29,370
audience. Y' all rock. And we do really

804
00:51:29,370 --> 00:51:33,090
appreciate y' all hanging with us through all this. We

805
00:51:33,090 --> 00:51:35,530
had, I'd say we had a couple of challenges with.

806
00:51:36,650 --> 00:51:40,450
With, like, scheduling. Certainly you mentioned that, the schedule with the

807
00:51:40,450 --> 00:51:44,240
calendaring part. But even there was.

808
00:51:44,240 --> 00:51:47,480
There were a couple of times where we had trouble getting guests lined up

809
00:51:48,040 --> 00:51:51,320
and the number of shows being put out lapsed.

810
00:51:51,720 --> 00:51:55,400
And part of that was just due to stuff

811
00:51:55,400 --> 00:51:59,080
going on in my friend's life that happens.

812
00:52:00,120 --> 00:52:03,080
It certainly wasn't intentional. And

813
00:52:05,000 --> 00:52:08,800
one thing I took away from it, even then, I was surprised by how

814
00:52:08,800 --> 00:52:12,320
many people would reach out to me and say, when's the next episode of Data

815
00:52:12,320 --> 00:52:15,970
Driven coming out? You know, it weren't mean or anything about it. It

816
00:52:15,970 --> 00:52:19,690
wasn't accusations. It was all right, right. Lines of gosh,

817
00:52:19,690 --> 00:52:23,490
you know, we missed the show. We miss hearing from you guys, and that's

818
00:52:23,490 --> 00:52:27,210
Good. That's nice. That's nice. It was a compliment. Yeah, yeah, yeah. We

819
00:52:27,210 --> 00:52:29,930
are going to work on that. Like even if it means that we have. We

820
00:52:29,930 --> 00:52:33,770
had Candace as sub in for Andy. I don't want

821
00:52:33,770 --> 00:52:37,610
people to think that, you know, if Andy doesn't appear for a while, there's nothing

822
00:52:37,610 --> 00:52:41,130
personal. It's just that he couldn't make it. No, right. Like I don't want to

823
00:52:41,130 --> 00:52:44,890
start that. I don't want. Candace is a lot more strict with

824
00:52:44,890 --> 00:52:48,370
the scheduling, so she's effectively

825
00:52:48,370 --> 00:52:51,610
joined our team. So she's a lot stricter with the stress. You know,

826
00:52:51,610 --> 00:52:55,250
she's awesome. She's

827
00:52:55,250 --> 00:52:58,770
joined our team and you know, so if Andy can't make it, she'll show up

828
00:52:58,770 --> 00:53:02,570
and maybe we'll have a few more surprise like stand in guests.

829
00:53:02,570 --> 00:53:06,050
Johnny Carson did it. Yeah. Work for him.

830
00:53:06,630 --> 00:53:10,270
Yeah, he did pretty well. Most kids today won't know who Johnny. Carson

831
00:53:10,270 --> 00:53:13,350
is, but he was the host

832
00:53:13,830 --> 00:53:17,670
before Jay Leonard. And you know what? Some kids won't know who Jay Le. I

833
00:53:17,670 --> 00:53:20,550
was gonna say. Was it Jimmy Kimmel?

834
00:53:21,270 --> 00:53:25,110
No, it was. Who. Who has guest hosted,

835
00:53:25,110 --> 00:53:28,870
I think on the Tonight show for a while. He did. He did.

836
00:53:28,870 --> 00:53:32,670
And then. Yeah. Who got the Tonight Show? Was it. It was David Letterman? Was

837
00:53:32,670 --> 00:53:36,500
it Conan? No, it was Conan and then Leno took it

838
00:53:36,500 --> 00:53:38,500
back and then now it's somebody else. I think it's.

839
00:53:40,260 --> 00:53:44,060
He used to be on Saturday Night Live. Yeah. I don't know who's doing

840
00:53:44,060 --> 00:53:47,260
it now. It's been so long since I. I don't stay awake that late at

841
00:53:47,260 --> 00:53:50,940
night anymore, Frank. And you. Only I know you don't either because

842
00:53:50,940 --> 00:53:54,580
there's been plenty of times when I've been here, been in, in the office

843
00:53:54,580 --> 00:53:58,260
here at like four in the morning. And

844
00:53:58,260 --> 00:54:01,900
I'm texting with you. I'm gonna check. Yeah. Get a chat, a

845
00:54:01,900 --> 00:54:05,200
text. This is Frank. Who

846
00:54:05,520 --> 00:54:08,240
has the Tonight Show. Now I gotta know now who runs.

847
00:54:09,360 --> 00:54:13,200
Yeah, it's. I can see the guy's face. I

848
00:54:13,200 --> 00:54:16,000
can't think of it. It's Jimmy Fallon.

849
00:54:16,720 --> 00:54:18,879
Jimmy Fallon, that's right.

850
00:54:21,440 --> 00:54:25,120
So apparently it used to be here. The. The former host was Jimmy

851
00:54:25,120 --> 00:54:28,960
Fallon. Johnny Carson, Jay Leno, Conan o', Brien, Steve Allen, Jack Par.

852
00:54:30,410 --> 00:54:34,010
Wow. Yeah, that goes. Jack Parr.

853
00:54:34,730 --> 00:54:38,490
Steve Allen. Jack Parr. Like our parents. Generation would

854
00:54:38,490 --> 00:54:42,250
know them. Yeah. Yeah. So

855
00:54:43,450 --> 00:54:47,290
that's wild. I don't know how we got on that tangent.

856
00:54:47,290 --> 00:54:50,770
It's kind of what we do. I really would like to start making money with

857
00:54:50,770 --> 00:54:54,250
it so we could have an off road race, rally, sponsorship Car.

858
00:54:54,820 --> 00:54:57,900
Imagine like this data driven on it. It's like, why do you do that? Because

859
00:54:57,900 --> 00:55:01,220
we're always going off track. But dumb, dumb.

860
00:55:02,020 --> 00:55:05,540
That was Steve Gainsworth's idea actually. I think he said that.

861
00:55:05,540 --> 00:55:09,340
Yes. Yeah, yeah. Stu and I are in a

862
00:55:09,340 --> 00:55:13,060
very similar place because he posted recently, recently like

863
00:55:13,140 --> 00:55:16,660
four to six weeks ago, about how he's largely

864
00:55:16,820 --> 00:55:19,700
doing work outside the Microsoft data ecosystem.

865
00:55:21,210 --> 00:55:24,650
I think you and I were talking about that and I'm largely, I'm

866
00:55:24,650 --> 00:55:28,290
largely outside the Microsoft ecosystem these days.

867
00:55:28,290 --> 00:55:31,930
I'm lagging behind you guys, but I'm moving that direction.

868
00:55:33,130 --> 00:55:35,850
I'm telling you, man, I know you finally installed Linux

869
00:55:37,210 --> 00:55:40,970
and you did that earlier on when you got your new laptop.

870
00:55:42,490 --> 00:55:46,130
True. I'm, you know, I'm

871
00:55:46,130 --> 00:55:49,450
not going to bet use this space to bash Windows 11. I already have done

872
00:55:49,450 --> 00:55:52,580
that on my LinkedIn newsletter multiple times.

873
00:55:53,540 --> 00:55:57,020
But our next episode, which is set to air, recorded with Andrew

874
00:55:57,020 --> 00:56:00,340
Brust, who is a rd, which is like a

875
00:56:00,340 --> 00:56:04,060
Microsoft mvp. And he, we, we

876
00:56:04,060 --> 00:56:07,740
have an interesting chat about fabric and kind of all of

877
00:56:07,740 --> 00:56:11,420
that. So. Okay. Yeah, I'm really sorry I

878
00:56:11,420 --> 00:56:15,140
missed that recording. Yeah. He says to say hello. He says that at the end

879
00:56:15,140 --> 00:56:18,780
of the episode. Tell Andy I said hello. But we definitely, we got to have

880
00:56:18,780 --> 00:56:21,540
him back on. Right. Because there's only so much you could talk about fabric. Right.

881
00:56:21,990 --> 00:56:25,470
There are definitely a lot of things I wanted to say about fabric that. But

882
00:56:25,470 --> 00:56:29,310
anyway. But I think fabric is

883
00:56:29,310 --> 00:56:33,150
in the right direction in terms of how

884
00:56:33,150 --> 00:56:35,910
you do that. But I, I think though that the world at large,

885
00:56:36,790 --> 00:56:40,470
and this could be my bias is

886
00:56:40,470 --> 00:56:43,270
it's becoming more and more about private AI, local AI,

887
00:56:45,190 --> 00:56:49,020
I think in a very real sense. Right. You know,

888
00:56:49,260 --> 00:56:52,180
because that, that was my number one goal was to be able to run an

889
00:56:52,180 --> 00:56:55,580
LLM locally. Right. And then after that

890
00:56:55,820 --> 00:56:58,940
I want to learn more about how I could train

891
00:56:59,900 --> 00:57:02,700
that LLM, you know, kind of shift its focus

892
00:57:03,580 --> 00:57:06,700
even. And you know, I totally get it.

893
00:57:07,580 --> 00:57:11,260
We, we have some clients at Enterprise Data and Analytics

894
00:57:11,340 --> 00:57:14,710
and some, I guess

895
00:57:16,310 --> 00:57:19,590
I'll use the word partners, businesses that we're

896
00:57:20,550 --> 00:57:24,150
engaged with or communicating about engaging with.

897
00:57:25,030 --> 00:57:28,870
And I learned from one group that is in Europe

898
00:57:30,230 --> 00:57:33,990
that, you know, there are a number of countries where part of the

899
00:57:33,990 --> 00:57:37,510
culture, the technology culture in that country is very

900
00:57:37,510 --> 00:57:41,230
much anti cloud. And it's, it's not paranoia,

901
00:57:41,230 --> 00:57:45,030
it's just a lack of trust. I don't think

902
00:57:45,030 --> 00:57:48,470
the tech industry, big tech industry has done a lot to

903
00:57:48,470 --> 00:57:52,150
engender trust. If you look at. Totally agree, you know, some of

904
00:57:52,150 --> 00:57:55,950
the, the privacy violations that have happened across different

905
00:57:55,950 --> 00:57:59,150
social networks and things like that. I can totally understand it.

906
00:58:00,350 --> 00:58:02,990
And you also remember, like, if you're in the United States,

907
00:58:04,840 --> 00:58:08,440
you have your choice of multiple cloud providers across multiple

908
00:58:08,520 --> 00:58:12,040
time zones. There are not

909
00:58:12,040 --> 00:58:15,800
Microsoft or AWS or Google data centers in every country in the world,

910
00:58:15,800 --> 00:58:19,600
which means your data has to leave your country. Which it turns

911
00:58:19,600 --> 00:58:23,080
out that reality strike. We should call this

912
00:58:23,400 --> 00:58:27,040
phase of the Internet reality strikes back. The

913
00:58:27,040 --> 00:58:30,280
whole idea of the cyberspace is its own

914
00:58:30,280 --> 00:58:34,090
independent, separate thing is not held true. Right. Because

915
00:58:34,170 --> 00:58:36,730
at the end of the day, everything virtual has to exist

916
00:58:37,770 --> 00:58:41,570
somewhere on planet Earth, right? Yeah. What will be

917
00:58:41,570 --> 00:58:45,370
interesting is that if they. If the talk of building,

918
00:58:48,170 --> 00:58:50,890
you know, data centers in orbit becomes true,

919
00:58:51,930 --> 00:58:55,130
that will be interesting. It'll be interesting, right? Will they.

920
00:58:55,850 --> 00:58:59,490
Will they flag them? Will it work like Merchant? I don't know anything about space

921
00:58:59,490 --> 00:59:02,560
law, but, you know, I would imagine

922
00:59:03,120 --> 00:59:06,840
it would probably be kind of like maritime law in a sense. Right? Like, would

923
00:59:06,840 --> 00:59:10,320
it. Would the data centers be. Would they be

924
00:59:10,320 --> 00:59:14,080
flagged? Would they be like, you know, this is operating under US law,

925
00:59:14,080 --> 00:59:17,680
this is operating under, you know, EU

926
00:59:17,680 --> 00:59:21,280
law. That'll be interesting to see how. Interesting

927
00:59:21,280 --> 00:59:24,720
point. Yeah. See how it all works out. It's almost like we should have a

928
00:59:24,720 --> 00:59:28,330
podcast about future facing tech and AI.

929
00:59:35,050 --> 00:59:37,610
Foreshadow much. Foreshadow much.

930
00:59:40,330 --> 00:59:43,970
But once I get Podzi finished and I have a

931
00:59:43,970 --> 00:59:46,490
nice. I have an even smoother workflow,

932
00:59:48,250 --> 00:59:51,690
the barriers that I currently have will no longer

933
00:59:52,160 --> 00:59:55,760
apply. So if you think we're doing a lot barriers,

934
00:59:56,560 --> 00:59:58,320
we don't need no stinking barriers.

935
01:00:00,400 --> 01:00:03,600
So with that in mind, I have multiple

936
01:00:04,000 --> 01:00:07,840
text windows, like people needing to reach me.

937
01:00:09,200 --> 01:00:12,960
Definitely. Stay tuned. We'll talk more about Podzi in the future episode.

938
01:00:12,960 --> 01:00:16,480
And Andy's. Andy's still with us. And

939
01:00:16,720 --> 01:00:20,480
we'll look forward to wrapping up season nine and then kicking

940
01:00:20,480 --> 01:00:24,200
off season 10. It's going to be

941
01:00:24,200 --> 01:00:28,040
awesome, Frank, 100%. And thank you, everyone. Once again,

942
01:00:30,440 --> 01:00:34,200
we'll let Bailey finish the show. That wraps up

943
01:00:34,200 --> 01:00:38,000
episode 400 of Data Driven. Thanks to everyone who's

944
01:00:38,000 --> 01:00:41,800
listened, shared, and supported the show over the years. It truly

945
01:00:41,800 --> 01:00:45,320
means a lot. We've got more coming as we close out Season

946
01:00:45,320 --> 01:00:49,100
9 and head into Season 10. Until then, thanks for

947
01:00:49,100 --> 01:00:51,420
listening and we'll catch you on the next one.