Episode 39: Combining Humility & Accuracy with Danielle and Matt Noyes の全文書き起こし

the Weather Channel? 00:02:08.969 --> 00:02:11.189 And you were you weren't scared at all? You were just fascinated. 00:02:11.189 --> 00:02:12.780 No, always fascinated by it. 00:02:12.840 --> 00:02:15.389 And we had a similar kind of start with our love for weather. 00:02:15.569 --> 00:02:17.250 Were you on the beach too, in a different car 00:02:17.000 --> 00:02:17.430 No, I was actually growing up in Haverhill, which that day? 00:02:19.520 --> 00:02:22.099 is very much not on the beach, but near the Merrimack River. 00:02:22.669 --> 00:02:40.639 And so for me, it was a hurricane too. But being five years older than Danielle, it was Hurricane Gloria, which came exactly about what, six years sooner. I guess 1985 is when we got hurricane Gloria. I just thought it was the most amazing, awesome, powerful thing to watch my dad's apple tree split in half. Our driveway washed out. 00:02:37.520 --> 00:02:52.129 We had no power for like, 11 days out of that, it was incredible stuff. But if you had asked me, then, was I gonna have our own weather company sometime in the future, I would have told you absolutely not. Weather's just something cool to look at. 00:02:50.150 --> 00:02:52.129 You know, at the time. 00:02:52.729 --> 00:03:03.259 Now, did you have, like, role models or idols on TV or something that you sort of followed as you were developing this passion for weather? I know you said you said you didn't have the Weather Channel, but maybe later on? 00:03:03.530 --> 00:03:22.400 So later on. So I feel like Mish Michaels was one of the meteorologists that I always looked up to, because she was one of the only female meteorologists at the time that was on TV. That being said, anybody that was on The Weather Channel when I went to my friends houses, I thought that's really cool, because they were out in these storms in different parts of the country. Mish Michaels, though, was definitely one of my role models growing up. 00:03:22.789 --> 00:03:26.960 And you know, our role models came within the same weather center, if you talk about professional role models. 00:03:26.960 --> 00:03:29.689 So when I was growing up, I didn't want to be meteorologist. 00:03:29.689 --> 00:03:34.909 If you had asked Middle School, Matt, are you going to be a meteorologist someday, I would have said, No, I like the weather. If you'd asked High School, Matt, 00:03:34.939 --> 00:03:35.689 What'd you want to be? 00:03:35.960 --> 00:04:37.699 I wanted to be a State Trooper like my dad, or I wanted to be a doctor. When I went off to high school, I had decided I had settled okay, I know I want to be a doctor. That was that was it right? In fact, there's even a journal entry from my sophomore year where you had to write about what you're going to be. And I said, I love the weather. It's a great hobby, but it'll never be my career. I will never make that my job. And it was probably sometime around junior to senior year of high school that I started thinking, Wait a minute, I could

actually make my hobby my job. I could have fun every day and get paid to have fun every day. It had never occurred to me. I had just grown up thinking that your job is your job, and what you enjoy in life is something totally separate. And it was in that moment toward the end of high school that I said, this could be incredible if I could make my hobby my job. And that's where I decided, no, I'm gonna make weather my living my life, you know, yeah, but Harvey Leonard was my role model. He when I sent a letter out to all the stations at the time, and there were fewer than than there are now in Boston, I heard back from all three of the major stations. 00:04:33.980 --> 00:04:40.250 Two of them said, Sorry, we don't take high school kids come back when you're in college. 00:04:40.430 --> 00:04:47.810 Harvey said, Sorry, we don't take high school kids. Come back when you're college, but wait. I want you to come into the station. Come in with your parents. I'll show you around. 00:04:47.810 --> 00:04:51.230 We can spend the day together. 00:04:47.810 --> 00:05:03.350 And so then he when I went off to college, followed me there in terms of sending me an email, I see that you're studying meteorology. I'm so proud of you. Do you still want that internship? Justice? Super, super nice. That's always been my role model since then. 00:05:04.500 --> 00:05:13.379 Now, Danielle, You briefly mentioned there they're going out and standing in the storm and doing the reporting and everything. Have either of you ever been assigned to that where you go out and you stand in the storm? 00:05:13.000 --> 00:05:31.329 A few times, I was on the South Shore. One in particular I remember was in Scituate when we had a nor'easter coming in. I think it was back in 2015 because I had just gone to Channel Four, and there was like a moderate coastal flooding event, and we had to sleep over at one of the like little houses that was literally right on the coast. 00:05:31.329 --> 00:05:33.490 Not like in a concrete hotel three floors up. 00:05:33.939 --> 00:05:35.860 Down, right on the coast, in a house that's gonna wash away. 00:05:35.860 --> 00:05:51.730 No. Like a little bed and breakfast. And I was in this room, and I remember the whole house was like shaking through the overnight as the waves were crashing in. We actually ended up having to move our vehicles like the next day back because when the high tide was coming in. But that night, I didn't sleep too much because the wind was blowing the whole building. 00:05:51.790 --> 00:05:57.970 Then you went on to find out from her and from other folks that that's like, that's everyday life for anyone that lives right there at the beach. 00:05:58.089 --> 00:06:00.639 100% anytime they have that that's just regular. 00:06:00.639 --> 00:06:01.449 It's just how they live it, 00:06:01.449 --> 00:06:02.290 Yeah, exactly. 00:06:02.290 --> 00:06:03.819 The price you pay for living in that location. 00:06:03.000 --> 00:06:38.519 That's exactly it's beautiful, though. Yeah, right, it's beautiful. Yeah, for me, my days of standing out in the storms ended really early in my career here in Boston. I came, and it wasn't long before I was in front of the weather. The weather maps all the time, but the Merrimack River floods of 2005 I actually went out down to the river and actually went out with some of the crews that were making these rescues, house to house, and was on board some of the big flatbed trucks. They went and pulled people out of the the apartments and and really, that's because I'm a Merrimack Valley guy. So it was, it was folks that I knew that were out there doing the work and making the rescues. So I went down there and I gave him some briefings on the river. Was out there with him for that. 00:06:38.579 --> 00:06:44.160 That might have been the last time I think that I was actually out on location, so to speak, during a storm. The rest of it, I was always in the weather center. 00:06:44.730 --> 00:06:51.839 That's kind of exciting, and sounds terrifying, but also really exciting and a great way to contribute to the community, right? If you know it, you know how to give them advice. 00:06:52.379 --> 00:06:56.459 That's it. That's been kind of a big thing for both of us lifelong from around here, you know. 00:06:56.670 --> 00:07:00.870 So now, back in the studio for a little bit, as you're building your careers. 00:06:58.529 --> 00:07:04.410 Now, I've heard, and I can only imagine that broadcast meteorology, and I'm going to mess that word up 100 times. 00:07:04.709 --> 00:07:05.610 It's all right. 00:07:04.709 --> 00:07:05.610 Everybody does. 00:07:05.939 --> 00:07:09.180 Can really be a very high pressure environment. 00:07:09.449 --> 00:07:13.050 In other words, I'm sure something goes wrong sometimes. 00:07:14.189 --> 00:07:26.279 So I'm wondering, kind of, as you're building your careers, and you're new to this, and you're kind of making your name here in the Boston scene, do you have any memorable moments or funny stories you can share where you know, yeah, it just did not work the way that it was supposed to. 00:07:26.790 --> 00:07:37.199 I can think of many turning a microphone off just for a second when you're either going to the bathroom or doing something, and then go back in the studio. Your microphones off, dropping the clicker, 00:07:37.980 --> 00:07:39.600 Oh, sure. Doing that live on air. 00:07:40.230 --> 00:07:41.459 So you control your own stuff? 00:07:41.459 --> 00:07:44.879 Yes, so you drop the clicker. Oh, hold on. A second. Gonna pick that up. 00:07:45.689 --> 00:07:49.589 Didn't you have one where you had a fly that was flying around that go kind of almost viral? 00:07:49.000 --> 00:07:50.529 That was really bad. 00:07:50.529 --> 00:07:51.370 Yeah, sounds like a fun one. 00:07:51.370 --> 00:08:00.879 It was, I don't like bugs, but there was a fly while I was doing a live weather cast. It came right in my forehead, and I actually went'aaah' and

kind moved out of the way and it ended up being on YouTube, 00:08:02.740 --> 00:08:04.149 You've had a lot of fun moments like that. 00:08:04.149 --> 00:08:09.129 You have too, remember that, you have to remember that time you danced on, you did like, 00:08:09.689 --> 00:08:50.009 We used to have some fun stuff. When I was on the morning show, we would have a lot of fun with, you know, we did dance off on Fridays. That was a good time years ago. I'm thinking of the time that I was, I was new in the business, all right? I was, I was only probably 21, 22 and I was talking about how the jet stream was going to shift south. The problem was, when I set it on air, the F in shift didn't come out. And so then I mortified, and I'm like, oh no, oh, folks, folks, I am so sorry. I did not mean to say that, what I'm what I and now I'm so nervous, and I go, what I meant to say, was the jet streams gonna and I did it again. No, no, wait, no, no, it's gonna move south. It's gonna move south. So that was my first and only swear, yeah. 00:08:50.039 --> 00:09:01.950 Move is a good word. Which was kind of gonna be the follow up to where I was going with that question. How did that help you overcome sort of obstacles and roadblocks. 00:09:01.950 --> 00:09:10.679 And, I mean, looking at you guys now, you're so comfortable and so casual, like, I feel like, if you did that now, you would just roll right on. Is that just a matter of time? 00:09:11.169 --> 00:09:48.940 No, you know, you bring up a really great point. And this, and this ties into our business One Degree Outside too, right? Because you have to make this this acceptance. I was always a perfectionist. I mean, I grew up the son of a state trooper and a school teacher, right? And so they were very you know, there was right and it was wrong, and I was a perfectionist. I wanted to do everything right. Going into meteorology, being a perfectionist is a really bad choice, but it can also be a really liberating choice if you let it so in those moments of the jet stream, doing whatever it was South right at the same time, you realize that you're not going to be perfect. You're not going to speak perfectly every time, you're not going to forecast perfectly every time. 00:09:49.240 --> 00:09:51.549 And that's life, and that's life. No matter what we do, 00:09:51.580 --> 00:09:52.299 It doesn't matter. 00:09:52.330 --> 00:09:55.750 That's it. That's it. 00:09:52.330 --> 00:10:04.539 You move past it. So for me, those type of experiences were really liberating in that I discovered early on, which would go to be things like, you forecast an inch and a half of snow and you get 13 inches. 00:10:04.720 --> 00:10:11.950 Happened to me in upstate New York one time. But you realize you have to let yourself off the hook every now and again, or you got to drive yourself crazy. 00:10:11.980 --> 00:10:17.799 And we'll get into some of the more details. I'm assuming if the model tells you an inch and a half, what else are you supposed to do? 00:10:17.799 --> 00:10:19.899 Well, we can talk about that. We can talk about that. 00:10:19.899 --> 00:10:22.929 You can't just imagine it's going to be 13 inches randomly. 00:10:22.990 --> 00:10:23.590 Pretty true. 00:10:23.000 --> 00:10:27.440 Yeah, definitely. I would say it helped me develop thick skin too, when I first started. 00:10:27.559 --> 00:10:33.049 Similarly, when I first, you know, the first couple years, I was on air, I had clammy hands. 00:10:30.110 --> 00:10:47.419 And you remember this, every time I was nervous, I was fumbling words as well. And people write in, you know, and say stuff about that, but, and at first was like, oh, no, they really, you know, they care. I'm getting all these emails. I need to change this, this, and it just makes you think like you're human. You make mistakes too. 00:10:47.419 --> 00:10:56.899 It's a learning process, and maybe don't care as much about what others think. And helps you in that respect, in terms of developing thick skin and just moving forward. 00:10:57.500 --> 00:11:02.299 How are your hands now? They claim me? No, you're doing great, awesome. 00:11:05.029 --> 00:11:15.620 So let's, let's come to One Degree Outside, sure. So I got to start with the name. Is there a specific backstory to this, or is it just that, conveniently, 75 Degrees Outside and Sunny was already taken. 00:11:17.179 --> 00:11:37.460 No, you are. You're right on the money with thinking there's something behind it, because there absolutely is right. So really, the thought process behind this is that we are going to tell you that we are going to be as accurate as anyone's going to be, and we mean that, right? And we have the numbers to back it up, but we also are really honest about the fact that you're never going to be perfect all the time in weather, sometimes you're going to be one degree outside, right? 00:11:37.460 --> 00:11:38.539 So that was a part. 00:11:38.539 --> 00:11:42.080 That's what it is that was not outside, it's outside. 00:11:42.830 --> 00:11:57.259 It's combining humility with accuracy, saying, Yeah, we're going to be accurate, but we're humble enough to say we're not going to be dead on every single time, right? Because nobody is the outside part does play into it, though, because a lot of people, what are we appealing to right? 00:11:57.289 --> 00:12:11.000 People who are outside all the time? Right here in New England, I feel like a lot of people you know, are obsessed and really love the weather, and it revolves around our daily lives, no matter what season we're outside skiing or we're planting, or we're going to the beach or, you know, it really, 00:12:11.120 --> 00:12:15.049 Weather here doesn't seem to stop people from doing what they want to do. They just, they're just part of it. 00:12:15.080 --> 00:12:16.490 So that's the outside part of that. 00:12:16.519 --> 00:12:19.940 That's right. And you know, I'd love to say that we thought of it all on our own. 00:12:17.750

--> 00:12:39.200 We have three great business advisors. One of them is Larry Galco, has a company brand new day. And so his whole thing is about branding. And so he was great at guiding and leading our discussion about, okay, so where do you want to be? What do you want to convey? We would answer those questions. And then we kind of all together, landed on, well, this kind of conveys all those things, and it seems to be taking off. People seem to really love the name. 00:12:39.000 --> 00:12:41.309 Yeah, I love it. 00:12:39.000 --> 00:12:56.039 I'm glad, and I'm glad I asked, because I didn't, I mean, I thought it was just a reference to temperature. I did not realize that there was also this humility data aspect. Now, what made you want to go off and do this on your own in the first place? Or can you say? 00:12:56.000 --> 00:12:58.669 Oh, no, we certainly, I know. Look, it's, I 00:12:56.000 --> 00:13:30.889 Oh everyone's on their apps now, or whatever only look at Danielle and smile because she's, she's been, been wonderful about kind of 100% coming on board and being a part of this, you know, and at the same time, though, in the beginning, it was kind of, you know, I was at the height of my career at NBC, and NECN, I'd been there for 22 years. 25 in broadcast, things were going great. But the interesting thing was that Greg back in 2023 there was a YouGov poll that came out that showed that for the first time, only 47% were getting their weather from local news. 00:13:28.220 --> 00:13:34.610 And the reason that's important is because the first time ever in history, else. Okay. 00:13:34.340 --> 00:13:36.758 Never before had less than half turned to local 00:13:35.679 --> 00:13:46.899 Let's do it. 00:13:35.679 --> 00:13:46.899 YOLO. Now's the time. 00:13:36.809 --> 00:13:49.418 television news. So I said, Look, we're at this interesting crossroads, right? We can, I could ride this into the sunset and have a nice, comfortable end of the career, and that'd be great. That's just not what I always love to do. I always love 00:13:46.929 --> 00:14:20.299 That's right. That's right. You kept freelancing for to be on the cutting edge. And so, you know, while I cherish a while longer on television and radio. You're still on WBUR and all of my years in television, it was absolutely beautiful, and they're still serving a tremendously important need. At the same time, I said, if folks are going digital, and these are the people who helped me build my career and my life, it seemed like the right thing to do, to try something, to be on the cutting edge. And so I think this is the time to do it. And Danielle is tremendously supportive in almost anything and everything, but particularly when it comes to the crazy, imaginative ideas. And so you said, Yeah, let's do it. 00:14:28.000 --> 00:14:34.870 all that, right, but, but really, within the last few months, you've gotten 100% on board with this too and we're charging forward. 00:14:35.259 --> 00:14:38.049 Awesome. Now, what have you learned as entrepreneurs, kind of, along the way? 00:14:42.970 --> 00:15:08.740 I was gonna say it's, it's a balancing act, right? So we wear many hats as a bootstrapped startup, you know, there are unique challenges. But I would just say, for me personally, it's balancing it all. We have three kids. So on top of that, trying to figure out, you know, who's getting the kids today, who's taking them to. You know, we pack in the lunch box. Oh, someone's sick this week. Balancing the family and the business has been probably one of the most challenging parts for me. 00:15:08.860 --> 00:15:13.000 You're not, you're not just the CEO and the data guy and the janitor. You're also the parents. 00:15:13.210 --> 00:15:13.870 Exactly. 00:15:13.870 --> 00:15:16.090 That's right. You know that you put it perfectly. 00:15:16.090 --> 00:15:37.029 I mean, that, that I think, is the biggest challenge for us is that when you know when, when a kid, when a kid is sick, you just lost 50% of your workforce, right, right? So it's like it's so the the nice thing is, neither one of us ever had a problem with work ethic. Neither one of us had a problem complaining about the fact that we had too much workload. We just take it on and do it. So for us to carry load for each other is not really that big of a of a change. 00:15:37.000 --> 00:15:39.039 Well it's easy when you have a teammate like him. 00:15:39.039 --> 00:15:42.610 Do you have a little bit more flexibility and that you're not tied to a studio schedule? 00:15:43.389 --> 00:15:43.419 100% 00:15:43.450 --> 00:15:48.909 Even though, I mean, the weather still has to be predicted when it's happening you can't just take a whole day off. 00:15:48.929 --> 00:16:13.769 That's right. No, exactly, right. And so you know, for example, coming here to see you today, we made sure that we got our videos out before we came in, right? But one of the things that, for me has been really liberating is when you have a day that's quiet with sunshine, right? We can focus on business building after we take care of the weather. So we're not still doing 30 or 40 weather casts in a row all morning into the afternoon on a quiet day, it's all right, here's your weather. You've got it. We'll move on. You know. 00:16:13.769 --> 00:16:16.980 So you always have something you can use to build forward. 00:16:17.879 --> 00:16:27.570 That's right, exactly. So again, to being on the cutting edge, to moving forward. It's nice to have that ability to do that, instead of just be caught in the loop again and again, same thing over and over. 00:16:29.039 --> 00:16:34.620 After a bit of a rough start today, it's turning out to be a sunny day. So what's next for One Degree Outside? 00:16:34.620 --> 00:16:35.820 What are you gonna be working on this afternoon? 00:16:35.850 --> 00:16:48.179 Oh, that's great question. So look, you know, we've got basically two prongs in our business. One is the part that you see, the public facing

part, that is where we have our app and our website and all of our videos that go out on the YouTube channel and all that, 00:16:48.179 --> 00:16:49.320 And anybody can access those? 00:16:49.000 --> 00:16:57.049 That's it, yeah, and with this being, you know, there's a lot of, I know folks that really follow you guys who are looking at kind of the business angle of things and 00:16:54.610 --> 00:17:12.180 100%. We've been saving most of our clients, 41% growth of business, etc. So it's important to say that one of the first questions we always get is, so what's your business model with this? Because the app is totally free, right? So, so how do you make money and how do you survive? So the answer through that is for the app side of it, the public facing side. 00:17:12.229 --> 00:17:27.068 It's about partnerships and sponsorships. So you will see, for example, various great partners that show up through the app. Crack X, which is New England, waterproofing, actually has a great advertisement on our radar page, and we're really happy to be partnered with them. 00:17:23.964 --> 00:17:53.159 We are the official weather app of Boston Duck Tours for this year, so you should, before long, see the One Degree Outside duck boat rolling around town, believe it or not. And so we're happy to provide them with operational forecasts each day, and you'll see their their logo when you launch the app at the beginning of the day. But the other prong of our business is private forecasts for clients, right? So these are folks and companies and businesses and college campuses and organizations who are losing money on bad weather forecasts. 00:17:50.250 --> 00:17:57.670 And we look at this and say, there's an opportunity here to make a big difference, and we've been doing that. Haven't we? 00:18:01.539 --> 00:18:02.049 on average. 00:18:02.049 --> 00:18:04.720 That's the average savings over their previous weather providers. 00:18:04.720 --> 00:18:13.809 Compared to not, not to trash the mainstream media, but watching the Morning forecast on TV, you're providing something that's more what is it? Granular? 00:18:13.839 --> 00:18:19.059 Specific, you know, for whatever client we have, it's very pinpoint, right? 00:18:16.779 --> 00:18:26.799 So it's like a little microclimate, so it's not a more general, you know, all of the region forecast we are pinpointing to the exact spot and the exact needs of our client. 00:18:26.950 --> 00:18:31.150 So if they're in Acton versus in Auburn, they're getting totally different points from you. 00:18:30.920 --> 00:18:33.301 Totally different. 00:18:30.920 --> 00:18:52.736 You say, Well, can't you do that with an app? Yes, our app does that as a matter of fact. In fact, we've met with folks who are thinking about, Oh, would I like to hire you? We show them what we have in the app, and they go, Well, I can just get that for free. And we say you can, but what we do to take it to the next level is we have our we call it the One Degree Outside forecast engine, but basically we have our own proprietary data that we've 00:18:50.529 --> 00:19:26.289 Drivers and equipment and salt, who knows built on top of that. We're doing post processing, and we do use AI in that process, but the way that we use it is to fine tune and tailor exactly to the location. So let's say you're on the east side of a hill and your weather is always different than the west side of the hill. We've got you covered. You get that kind of improvement. So when we say that 41% savings, that's that's not over not having weather, that's over their previous weather provider, which is usually one of these major national companies, we're able to save them 40% over that, which when you talk about municipalities with $1.2 $1.4 what else. 00:19:21.231 --> 00:19:21.980 million budgets. 00:19:26.319 --> 00:19:34.269 So that's actually when folks will ask, What's your business model? That's the secret sauce. Just so, you know that's happening in the whole other problem. You don't see that in the app. 00:19:34.630 --> 00:19:38.710 So something you just said leads me perfectly into the next question. 00:19:36.940 --> 00:19:42.369 Actually, this question is from my daughter. Oh, she likes to listen to the podcast here and there. 00:19:42.369 --> 00:19:42.670 Oh that's great! 00:19:42.910 --> 00:19:43.569 And I mentioned her, 00:19:43.569 --> 00:19:43.930 What's her name? 00:19:43.930 --> 00:19:44.049 Avery, 00:19:44.049 --> 00:19:44.619 Hi, Avery, 00:19:44.650 --> 00:19:46.869 Hi Avery! That's awesome. 00:19:46.000 --> 00:20:05.890 And I mentioned to her that you guys were coming in today. And so she really wanted to know how long it actually takes to create a forecast. And then sort of the follow up to that is, is there a fundamental difference in the underlying data that you use now versus what you had on TV before, and you already kind of answered that you have a repertory model. 00:20:05.890 --> 00:20:07.269 This is great! How old's Avery? 00:20:07.269 --> 00:20:07.569 She's gonna be 11. 00:20:07.569 --> 00:20:08.170 She's hired. 00:20:08.170 --> 00:20:10.630 This is an amazing question from an almost 11 year old. 00:20:10.660 --> 00:20:11.950 Do you intern middle schoolers? 00:20:13.750 --> 00:20:21.220 Hold on, let me give the answer. We don't do internships, but you can come on in and take a tour, and we'll be having to show you all around the studio. That's right? No, I'll let you take that first. 00:20:21.220 --> 00:20:22.900 But yeah, how long it takes to create a forecast. 00:20:23.109 --> 00:20:25.089 If you have to have one out every single day, 00:20:25.299 --> 00:20:38.829 I would say it depends right on what we're forecasting. So there is a difference between if we're making a forecast specifically for a client or if we're doing something like a monthly forecast. The length of time varies depending on what you're doing. 00:20:38.859 --> 00:20:41.200 I wasn't even thinking of monthly forecasting. 00:20:41.269

--> 00:21:07.880 Oh yeah, so one of the things you have to keep in mind too is that we're already making a forecast for all of New England in the Northeast for the videos, right? So that's going to take couple of hours right out of the gate to answer the question directly for Avery and then when you're going to fine tune that for private partners, that's going to be more of a kind of a fine tune process that we bring in the AI for. So that's going to be honestly shorter, because we've already entered the human made forecast, that took a few hours to build. 00:21:07.910 --> 00:21:10.250 And then you're tweaking that exact fine tuning. 00:21:10.000 --> 00:21:45.400 That's right. The question that Avery asked about our data is a really great one, too. So yes, when we were working at TV stations, you have the general forecast data that either comes from the American government and the supercomputers there, the European government, the Canadian government, right? What we're doing is we're actually taking in over 120 different pieces of data from across the world, and then we're figuring out which ones are performing best and worst. And we take the worst performing out, we put the best performing in, so we can get the best solution. That's what we call our One Degree Outside prediction system. And then from there, we can make our forecast, put it into the forecast engine, and really get down, kind of drill down from there. 00:21:45.400 --> 00:21:46.000 It's fascinating. 00:21:46.150 --> 00:21:52.180 To Avery's point there is so much data, so this is the best way to be able to pick and choose what's best. 00:21:52.210 --> 00:22:22.869 That's right. This is a continuation of it always drove me crazy, and you'll still see it when you'll see, you know, on TV, or you'll see on the internet. Well, the American model says this, oh, it made my head want to explode, and I never did it, because there are hundreds of different varieties and versions of computer forecast models that are out there. It isn't just one for American right for Europe. So why make it seem that simple? If it's not right? So we try to take it, bring everything together, and then boil it down to something that's most specific. 00:22:23.799 --> 00:22:27.819 So I sort of threatened before we got started that I would math nerd out for a moment. 00:22:27.819 --> 00:22:28.269 I love this. 00:22:28.569 --> 00:22:29.259 Please do. 00:22:29.289 --> 00:22:55.839 So, so I studied, actually, a bit of chaos theory, which, for the listeners, is a branch of math that sort of explores this idea of sensitive dependence on initial conditions, in other words, that in a chaotic system, even one small change could greatly impact something else. And I think an example of that that might ring true for a lot of people is you're trying to drive to work, or even you're just trying to leave your house. 00:22:53.740 --> 00:23:08.440 Maybe there was an accident on the interstate. Now everybody's gotten off the interstate, and now they've all found your little sides. There's a traffic jam in front of your house, and you have no idea why, because this one incident over here caused this traffic jam on your street. 00:23:08.440 --> 00:23:11.440 That's a great analogy. I might have to steal it down the line. I'll give you credit when I do. 00:23:11.000 --> 00:23:32.059 Take it! So I guess the question for me is with weather, which is literally a global, chaotic, dynamic system, how do you even approach forecasting where you know that even the slightest shift in one of these variables in one of the many models that you're looking at could have such drastic differences, like you mentioned, the one and a half to the 13 inches of snow. 00:23:32.089 --> 00:23:34.190 Love this question. 00:23:32.089 --> 00:23:34.940 So do you want to take you want me to start out with it? 00:23:34.970 --> 00:23:46.460 Well, all I will say is that when I go to visit schools, I say to the kids, imagine you have like this little bundle of air, and you just go and you blow it, and it's already across the room. 00:23:43.250 --> 00:23:49.819 Put that on a global scale, and imagine how it impacts weather. 00:23:46.460 --> 00:23:51.559 It's pretty fascinating. How far technology has come. That's all I'll say on that. 00:23:50.980 --> 00:23:59.817 Yeah, I think that's a great way to look at it. I mean, you know, I look at the fact that we are in this, this spot in society right now, where it's amazing. You see it on 00:23:59.220 --> 00:24:41.910 Which gives you less day to day intel? 00:23:59.868 --> 00:24:24.675 social media all the time, that we question science, right? And we question science and say, well, but is it really because of this? Right? So here's the amazing thing about whether that a lot of people don't realize the laws of physics are the laws of physics. So when, when I talk about how we pull in 120 different pieces of data, it's important to note, that doesn't mean that those 120 plus different solutions are coming from different physics equations. Physics is physics. 00:24:24.728 --> 00:24:30.930 There are laws that govern science that doesn't change. So you say, Well, then why is the forecast wrong? So much right? 00:24:30.981 --> 00:25:02.094 Because of exactly what you're talking about, those 120 different solutions come because, if you tweak the initial condition just a little bit, that can change the outcome dramatically. When you get five or 10 days down the line where we're really seeing this and you're going to see worse forecasts in the short term. And the reason I say that is because without in any way, shape or form, getting political with the fact that funding and staffing has gone down now, with regard to some of the National Oceanic and Atmospheric Administration, there are fewer weather balloons that are getting launched 00:25:01.710 --> 00:25:03.180 Absolutely. In New England no longer. First, we had significantly fewer. 00:25:08.880 --> 00:25:26.309

Chatham, the weather balloon site out of Chatham got eroded due to beach erosion, never got replaced. Then we had Gray Maine, just had to discontinue their weather balloons. So what this means is to cover all of New England. There's a weather balloon out of Albany and a weather balloon out of Caribou Maine, up in the northern tip of Maine. That's it. That's your New England. 00:25:26.309 --> 00:25:27.509 And that's supposed to cover Boston? 00:25:27.539 --> 00:25:33.809 That's everything in between is not sampled in the atmosphere. So the reason that becomes important is because of your chaos theory. 00:25:33.809 --> 00:25:36.869 Hold on a second, sampled in the atmosphere? What is being sampled up there? 00:25:36.869 --> 00:25:38.460 Great. So you have a weather balloon, 00:25:38.490 --> 00:25:40.470 I hope you don't have anywhere to go, because this is fascinating. 00:25:39.980 --> 00:25:42.651 No, I'm glad you like it haha. You have a weather 00:25:40.000 --> 00:25:46.193 So when you guys make your first billion to reach you launch it up as it goes up. 00:25:42.704 --> 00:25:54.970 There's an electronic package on there that's recording temperature, humidity, barometric pressure, everything you need to know about the a critical mass. How many of these balloons would you atmosphere, wind speed, direction, as it as it goes up. 00:25:55.023 --> 00:26:04.891 And it goes up until that balloon pops, which is basically personally want to have in launch over New England to up into the stratosphere, all right, and then it comes back down. And by the way, the whole way down. It's also recording. 00:26:04.089 --> 00:26:10.900 actually, we will know more accurate. 00:26:04.944 --> 00:26:14.538 That's right, the entire way down. So what we do is we limit the effect of chaos theory by putting more of these up, because now you have a greater sampling, so you know better 00:26:11.829 --> 00:26:39.559 Yeah, we were nowhere close. It would be what's going on. Now if you take a bunch of those out, because you just cut staffing so substantially, you suddenly don't have those balloons going up anymore, and now the door is open to a tremendous increase in your Chaos Theory and your differences in initial condition. You're not measuring them. Think about you could have a whole thunderstorm complex between Albany and Caribou, Maine doesn't get measured by any balloon or sounding that goes up there. 00:26:51.490 --> 00:26:59.559 great. And there's been other things have been tried. For example, satellite soundings, where satellites can kind of interpret what's happening through the atmosphere, but you have issues with cloud cover and all that. 00:27:00.369 --> 00:27:03.549 Like do you need, like, 10 of these in New England, do you need 50 of them? 00:27:04.210 --> 00:27:04.869 As many as you can get. 00:27:04.900 --> 00:27:06.130 I was gonna say there's no limit. 00:27:06.000 --> 00:27:19.380 Seriously, because anything that happens between sites. So maybe another example would be surface observing. So weather stations at, let's say airports, for example, there's still, what would you say, 30 to 50 miles between each one, depending on the sight. 00:27:19.890 --> 00:27:30.779 Depending on the site, depending on the state, in between each observing, you know, we have one at Logan Airport, and then we have one in Worcester. We have one in Hanscom. Think about the amount, you know, right of space in between Boston and 128 and 495. 00:27:30.809 --> 00:27:33.240 Hyper local weather that's in that space in between. 00:27:32.720 --> 00:27:35.372 That's right. So a lot of you know, what we've seen 00:27:33.000 --> 00:27:39.750 And it becomes another subscription model. 00:27:35.423 --> 00:27:41.144 already is there's a lot of companies that are trying to fill those gaps with various types of technology. The 00:27:40.799 --> 00:27:47.640 That's correct, and that impacts research at interesting thing with that is that that may be, is that the universities and so many other things. 00:27:44.368 --> 00:27:47.541 direction that we end up going as a nation, because if we end 00:27:47.000 --> 00:27:59.920 This is why you need to use your own first up short staffed on the government side of things, private sector will have to come in and fill those voids. The interesting part of that becomes, is the data then for sale, rather than freely available, which has always been a hallmark. 00:28:07.309 --> 00:28:09.829 billion to do it? Because then you're gonna make the subscription model. 00:28:09.829 --> 00:28:11.869 Correct. You got it. 00:28:09.829 --> 00:28:11.869 You hit the nail on the head. 00:28:12.200 --> 00:28:16.609 So then you mentioned that weather forecasting might be a little less accurate in the short term. 00:28:17.599 --> 00:28:29.299 Is there kind of a certainty metric or percentage that you can apply to weather forecasts today, where, you know, maybe 10 days out it's 10% accurate, but one day out it's 90% accurate. 00:28:29.299 --> 00:28:31.099 Is there anything like that, or is it completely fluid? 00:28:31.000 --> 00:28:45.640 Nope, no. There are answers to that. Absolutely. So basically, you know, we look at something like this winter, for example, and we did forecast regularly for partners, both in Boston and also in Worcester. 00:28:41.470 --> 00:29:01.150 And so what we did is that's great, because there are climate sites in both of those places, meaning we can verify and see how we did exactly right. Okay, so three days out, we were an inch or less error on snow. So you could ask us how much it was gonna snow in three days, would tell you within an inch, which you're not really finding anywhere else. 00:29:01.150 --> 00:29:03.339 That's very reasonable for shoveling or anything else. 00:29:03.339 --> 00:29:31.359 Yes, wind gusts, all the wind that we had over this winter and spring, average wind gust error for us was four miles an hour, 4.4 miles an hour. So you can get very specific, the 10 day

forecast during stable seasons like summer or winter is about six and a half degree error at 10 days out during the fall and the spring, where we get the big cold fronts and all that, that's going to be more like eight degree error. But even that, you look and say that's actually really helpful if you're trying to plan energy load or anything along those lines. 00:29:31.960 --> 00:29:38.140 And has improved a lot compared to even this. You know, 510, obviously, 20 years ago. 00:29:38.289 --> 00:29:49.630 Totally, yeah. We hear from a lot of folks who've watched weather for decades and say it was better back in the 1970s than it is now. The numbers show that that's that's absolutely not true. 00:29:49.690 --> 00:30:17.799 Well the only place I ever was that the weather forecasts were always accurate was I lived in Arizona for a number of years, and it was just sunny in 100 for the next eight days. Never wrong, haha. So let's, let's move a little bit to the community. And I understand that you guys have a program where you go out to schools and you've interacted with hundreds of thousands of students through this. Tell us a little bit about this program, what that is it, and why it's important. 00:30:17.839 --> 00:30:43.339 So we bring enrichment programs to school, and this is so fun, you know, it's about inspiring the next generation of scientists. I remember being a kid and loving math and science and loving, you know, STEM programs. And I feel like, particularly where we are right now as a country, it's really important to, you know, get these young kids excited about math and science and weather. So we go, we do a whole program, we bring our lightning ball and our tornadoes in a bottle 00:30:43.339 --> 00:30:44.119 Wait you have a lightning ball? 00:30:44.150 --> 00:30:44.990 Yes, we do. 00:30:45.019 --> 00:30:47.299 I want a lightning ball? Where do I get a lightning ball? 00:30:47.750 --> 00:30:49.759 You can come to our come to our office. 00:30:49.849 --> 00:30:51.019 That's right. 00:30:49.849 --> 00:30:51.019 Avery's gonna get a tour. 00:30:51.410 --> 00:30:53.299 Exactly. She's gotta get to have the lightning ball too. 00:30:53.299 --> 00:31:05.180 So that's really something special that we get to do is go visit these schools, you know, anywhere from kindergarteners up to college age students. We've done adult group visits to to bring, you know, kind of math and science to life. 00:31:05.210 --> 00:31:20.480 Yeah. And Danielle has really led that, led the charge on that since, since we've launched One Degree Outside, I'm usually in the office and working on different things, and Danielle is always out in the community. How often would you at least once a week, on average. It's amazing. You do such a great job. 00:31:20.480 --> 00:31:22.579 At least once a week. 00:31:20.480 --> 00:31:43.430 It makes me so happy to see all these kids, you know, it could be a small group of just one classroom, or it's an entire, you know, school of several 100 kids that are sitting there, and I just look at their faces and their eyes are popping open when we start to talk about, you know, the science and meteorology and everything behind it. So it brings me a lot of joy to be able to do that and hopefully spark some interest for our future scientists. 00:31:43.450 --> 00:32:22.809 You know, it's, it's funny, because I always, always defer to Danielle on it, because it's, she's done such a great job. It's become her, her babies and and does a wonderful job with it. I can tell you that for me, I look at this and I say, America used to be number one in science, technology, engineering and math when you go back to the 1960s. We fell to 24th and so, you know, when you look at if you're like, we are parents of kids, right? We have to accept the fact that we are among the dumbest generation of Americans relative to the rest of the world, not relative to history, but relative to the rest of the world when it comes to STEM education. And so the issue with that, and again, you see this in so many different arenas, right? 00:32:22.960 --> 00:32:34.119 Is that we have this this certainty, and we feel like we we know better, but we actually, relative to a lot of other developed nations, don't when it comes to science. 00:32:34.599 --> 00:32:41.049 I would challenge one thing that you said, I don't necessarily think we're the dumbest. We might just be the least educated. And that goes back to what you were saying. 00:32:41.200 --> 00:32:56.769 These kids are not dumb, they're incredibly smart. And this is that that outreach that says, hey, instead of sort of choosing the dumber path, you can choose the smarter path, yes. And I think that that that wealth of smartness is still there, yes, right? 00:32:56.769 --> 00:32:57.460 And I apologize, 00:32:57.759 --> 00:33:06.549 No, I don't mean it that way, but I think it's a great segue into this idea of lifting them up rather than letting it continue down this path that sort of dumbing down. 00:33:06.000 --> 00:33:18.750 Yes, that is exactly what we're talking about. So for example, we will hear from folks oftentimes who will see, you know, plane exhaust in the sky, which is called a contrail, a condensation trail, and are convinced that 00:33:18.000 --> 00:33:19.859 That's what that stands for? 00:33:19.000 --> 00:33:22.359 Condensation trail. 00:33:19.000 --> 00:33:44.529 Yes, convinced this is mind altering chemicals that are being released by the government, right? We are under the control of folks because of these, these chemical sprays and and look, the science behind that is very determined. It's very it's very clear what it is, as do with relative humidity, vapor pressure, dust particles that are up there called condensation nuclei. But we don't as a society, we haven't been educated on these scientific principles. 00:33:44.559 --> 00:33:49.390 So we think of it as the same thing as the dark soot that comes out of a truck even though it's really not.

00:33:49.000 --> 00:33:56.559 Well it's funny because our daughter did a 00:33:49.359 --> 00:34:13.360 That's correct. And so it's this, it's this, this danger zone that we're in. And so you're right. The word choice that I had may not have been great, but it's this danger zone little science program at her school over the winter, and she that we are in currently where we either are going to accept and understand that we need to raise our level of STEM made a tornado in the in a bottle. So we bring that to the education awareness, or we're not going to remain competitive on a national level. And so I love that you're doing this school visit too. And I tell this story, and I say, she ran because you are out there inspiring. 00:34:18.039 --> 00:34:28.239 out and she said, Mom, look what I made. And I always ask the kids, I said, Do you think she know what it? Knew what it was. 00:34:28.239 --> 00:34:33.789 And they say, No. I said, Of course she did. She's got a parents who are meteorologists. 00:34:30.039 --> 00:35:09.670 But the somewhat disturbing part was there was a little boy behind her, and he said, Dad, look what I made. And he goes, Wow, look at that hurricane. It was a tornado in a bottle, but it was just slightly eye opening. It's like, okay, there's still some confusion about many things, about contrails, about the difference between a hurricane and a tornado. We don't watch much TV, but we watch some sometimes just relax at night. And there was a show the other day, and someone was talking about how, you know, auras and being. And how that, you know, she heard that maybe someone could, you can control the weather yourselves, remember? So, so back to your point. It's just, it's education is key. 00:35:09.699 --> 00:35:27.130 Yeah, right, yep, yeah. We're in a bit of a danger zone right now. So that's a way to put it danger zone. So I mean, our hope is that we can at least do our small part, whether it's through our podcast that we do, whether it's through our videos that we do to try and kind of bring that science in weather, education, awareness to all ages. 00:35:27.659 --> 00:35:49.530 Awesome. Yeah, all right, we're going to jump to a segment we call, what would you choose? And so this is a little rapid fire thing. And the interesting thing I think now is going to be, are you going to answer these as your like, parent people selves? Are you going to answer them as your professional weather selves? 00:35:46.469 --> 00:35:49.530 Right? But no wrong answers. 00:35:49.530 --> 00:35:54.059 It's just kind of a quick what would you prefer? A nor'easter or a heat wave? 00:35:56.099 --> 00:35:56.760 Heat wave. 00:35:56.940 --> 00:35:57.480 Nor'easter. 00:35:57.869 --> 00:36:08.699 See, I was going, I was thinking nor'easter because I was thinking you were going to go professional like this is more interesting and challenging. Interesting and challenging. You talking about being on the beach and watching it happen. But you want the heat wave.

You want the nice sunny days. 00:36:08.760 --> 00:36:13.199 Yeah I lived in Florida for a couple years that was nice having the heat and humidity. I like it. 00:36:13.440 --> 00:36:14.789 I wonder if you'd answer different August. 00:36:14.820 --> 00:36:16.320 I know I might, it's true. 00:36:16.349 --> 00:36:25.469 That's what I always tell my wife when she complains. She's like, I promise I won't complain in the summer, when it's she loves it. Are you more early birds or night owls? 00:36:26.219 --> 00:36:26.760 Neither. 00:36:27.239 --> 00:36:28.590 Neither. You're just in the middle? 00:36:28.579 --> 00:36:30.920 Just up all the time. 00:36:30.730 --> 00:36:33.010 I guess night owl. I don't like getting up early. 00:36:33.039 --> 00:36:36.550 Okay, and I'm an early bird. I love getting up early, seeing the sunrise, crisp air in the morning. 00:36:36.550 --> 00:36:39.429 See I was thinking early bird, because you have to get up and get your your stuff out. 00:36:39.920 --> 00:36:40.400 That's right. 00:36:40.000 --> 00:36:41.829 I said I don't like getting up early, but I do. 00:36:41.829 --> 00:36:43.210 Ah, you don't like it, but you have to. 00:36:43.300 --> 00:36:45.070 Just more painful for one of us than the other. 00:36:45.070 --> 00:36:45.969 Yeah, that's very true. 00:36:47.590 --> 00:36:53.590 Now, outside of the spaghetti models we talked about, are you more data driven or you more gut instinct? 00:36:53.619 --> 00:36:54.039 Oh, 00:36:55.300 --> 00:36:56.050 Data driven. 00:36:56.650 --> 00:37:03.909 Data driven when it comes to professional, yeah, gut when it comes to personal. 00:36:59.349 --> 00:37:04.150 Well, it's and I think you'd agree. 00:37:04.150 --> 00:37:05.559 Yeah, 100%. 00:37:05.599 --> 00:37:09.920 But in there sometimes you have to make that divide, right? You have to know when to be data driven and when to be done. 00:37:10.789 --> 00:37:16.579 That's a good point. Is your favorite part of your day, when you're behind the camera or when you're behind the scenes? 00:37:17.000 --> 00:37:17.869 Behind the scenes. 00:37:17.000 --> 00:37:19.340 Yeah, same for me. 00:37:17.000 --> 00:37:19.340 Yeah. 00:37:19.000 --> 00:37:26.170 Yeah. So all that time in the camera, and really what you prefer doing is the cool behind the scenes stuff. 00:37:26.170 --> 00:37:28.630 Love it. Love it. 00:37:26.170 --> 00:37:30.579 And that's not say we don't enjoy the camera part. 00:37:28.630 --> 00:37:44.469 Obviously, we both made a career out of that. But I think we both, you know, you've kind of heard us talk about today. We both really love kind of thinking about new ways to construct things, and new ways to be able to posit things for folks and and so doing that happens, most of the good work for that happens behind the scenes. 00:37:45.969 --> 00:37:59.619 And you both answer that very quickly, yeah, this is my answer. All right. Last one, this is, this is a bit to the dynamic of husband and wife duo working together, divide and conquer or tackle as a team? 00:37:59.949 --> 00:38:00.909 Tackle as a team. 00:38:00.940 --> 00:38:08.860 Yeah, and I would say both. Tackle as a team is what we do for whenever there's a big task that needs to be done, it's always tackled as a team. 00:38:08.860 --> 00:38:09.610 Although you're right. 00:38:09.639 --> 00:38:17.050 When there's smaller stuff, we divide and conquer. We may go, there be certain days, we may go the whole day without barely talking to one another, because you're either in your office or in the studio. 00:38:17.079 --> 00:38:24.610 Even just yesterday, when I was in, we were both at the office. Yeah, I was in the studio part. He was doing business stuff, and I was like, All right, I'm leaving to get the kids see you later. 00:38:24.559 --> 00:38:33.697 Yep, but when it comes to any type of major steering focus for our company, that's always tackled as a team, and that's going to be that way, right? We just recently had a 00:38:30.809 --> 00:38:58.920 That collaborative approach. 00:38:33.751 --> 00:38:57.639 new member of the team come on board, Vice President of Brand Integration, Annie Peters. She was for 30 years at NECN and NBC Boston, doing VP of sales there, and now has come on board with us is brand integration, and we make sure we're all in a text group together. All the ideas we have, we're bouncing off her, vice versa. So I think that for as long as we've got our company going, which we hope is a long time, that'll always be part of it. 00:38:59.460 --> 00:39:00.809 That's it. It's always better that way. 00:39:01.139 --> 00:39:12.869 I love it. You guys did great. You were like, really quick on the answer. Sometimes we get, ooh, I'm not sure, yeah, I like both. But you guys were like, This is my answer. You guys understand what rapid fire means. 00:39:13.050 --> 00:39:15.210 Oh, absolutely, we're ready to go. We're ready to go. 00:39:15.389 --> 00:39:28.469 All right, one more segment that we do, and this is the one where we give you a bit of homework and we ask you to bring or share with us something could be a memory. It could be something material. Could be whatever that brings you inspiration. 00:39:34.440 --> 00:39:41.280 Oh, you put it in your bag. So I haven't even seen this, by the way, she did not tell me what it was that Danielle was bringing. 00:39:48.300 --> 00:39:51.030 So the first is a little heart compass. Do you remember this? 00:39:51.030 --> 00:39:52.530 I've seen that above our kitchen sink. 00:39:52.800 --> 00:39:56.099 Follow your heart. I actually gave this to Matt. Oh, gosh, many, many years ago. 00:39:56.000 --> 00:39:58.639 You said it's a compass, aahh. 00:39:58.000 --> 00:40:27.039 Yup, it has a little compass on the back. It says, Follow your heart. I just feel like it's a reminder that I see every day that's on our windowsill, and it speaks to everything in life, right from from relationships to our business. Just gotta follow your heart. The other thing is a little metal

piece that says joy on it. And this reminds me of my friend Christine, who passed away from cancer last year, and my counsin's son who passed away from pediatric brain cancer. 00:40:29.139 --> 00:40:36.039 Terrible stuff, right? But they were both such about joy, and I just feel like it's an important reminder for perspective. 00:40:37.230 --> 00:40:39.360 Right, just to carry that with you. 00:40:38.840 --> 00:40:41.608 For everything. 00:40:38.840 --> 00:40:45.400 So I keep both of these on the window. So yeah, those are the two little things I brought in. 00:40:45.329 --> 00:40:53.190 I love that. Do you want to change your answer from data driven and gut instinct. 00:40:47.429 --> 00:40:54.420 That is not at all data driven, that is beautiful. 00:40:54.510 --> 00:41:17.670 Well, that's, that's the interesting part about us, right? Is it, remember I said you have to have that divider between when to be data driven one to be guts. So do for me, it's funny, because I do have a physical thing that's kind of a motivator that I keep but I can tell you that my two biggest motivators, one is right beside me, which is Danielle, because she is tremendously motivating to become a better person and to continue to be more compassionate and thoughtful. 00:41:18.030 --> 00:41:38.699 The other one I wear around my neck, which is my cross. So again, not data driven, right, right at the same time, you know, really, for me throughout life, you know God, and belief in God has been a really important motivating factor. And then the final thing, and this one you might enjoy, let's see if I can pull it out of my bag, because I did bring it, because you asked us to bring for homework, my can of Moxie. 00:41:39.000 --> 00:41:44.099 So have you ever had Moxie? I have not. Have you ever 00:41:39.119 --> 00:41:39.630 Oh, my. 00:41:44.099 --> 00:41:50.130 had Moxie? Anybody behind the scenes? Yes, okay, Sydney, you have no Eddie hasn't, okay, so you're gonna have to try this. 00:41:50.250 --> 00:42:02.190 In fact, I dedicate to, you know, I dedicate my can of Moxie. So Moxie is a quintessential, a quintessential New England. No, you have to fake like you like quintessential New England soda. 00:41:59.550 --> 00:42:14.429 So the word, do you know what the word Moxie means? Like to have Moxie, right? He's got, he's got gusto. So the word Moxie came from the soda. It didn't go the other way around. 00:42:11.280 --> 00:42:17.250 This is a Maine creation here in New England. In fact, there you go. Here we go. Let's see your reaction. 00:42:17.519 --> 00:42:18.059 That's good. 00:42:18.000 --> 00:42:21.960 You like it? Great. 00:42:18.000 --> 00:42:38.789 You know, my thing for me, when I first came to Boston in my 20s, I was scared to death. I was this young guy coming into a big, major TV market. So for me, I found a lot of comfort in Moxie, because it was a reminder that in order to survive in this industry, you got to have Moxie. 00:42:38.820 --> 00:42:42.869 You got to have spunk. So I keep these in my office at all times. 00:42:42.869 --> 00:42:48.300 I never knew that that was the reason. And now, looking at the can, it says, distinctively different. 00:42:48.300 --> 00:43:05.250 That's right. So here we are launching a brand new company that's digital first. That's the first of its kind in New England to do this, and we have to have Moxie in order to make it. So when things get stressful, or I'm like, are we gonna do this? Are we gonna crack open that Moxie? I take a sip and I go, we're good. 00:43:05.309 --> 00:43:09.900 It's got, like, a little like, orange soda with root beer, kind of flavor to it. 00:43:13.139 --> 00:43:25.800 Well, you should look it up honestly, the Moxie day in Maine, because there's a whole Moxie Day Parade. There's a celebration. It's fantastic. 00:43:19.710 --> 00:43:25.800 If you look it up, you will see. 00:43:25.949 --> 00:43:27.329 And this is just available anywhere? I can just 00:43:27.000 --> 00:43:36.960 Absolutely, go to your local, you know, go, supermarket. You'll get it. I've got a Ted Williams, an old tin sign in my office, Ted Williams. 00:43:33.659 --> 00:43:43.139 It says Ted Williams, says you should drink Moxie. And it's got him right on there. So it's become a big part of my career, and kind of professional side of 00:43:42.280 --> 00:43:43.090 I love that, because I never knew. 00:43:43.139 --> 00:43:43.199 me. 00:43:44.000 --> 00:43:45.739 What do you think? 00:43:44.000 --> 00:43:46.130 What do you think of the hair Danielle? 00:43:47.179 --> 00:43:49.550 it looks good. 00:43:47.179 --> 00:43:49.550 It's very similar. You know, 00:43:50.119 --> 00:43:51.590 Definitely you've been inspired. 00:43:51.619 --> 00:43:55.039 That's right, that's right. So none of our motivation is data driven. So there you go. 00:43:55.909 --> 00:44:12.590 There you go. Well, this has been really fun. This has been an awesome time. I learned a lot. I think it's absolutely fascinating. I've always enjoyed like this concept of chaos and weather and everything, and you guys do a really nice job of breaking it down and making it tangible and understandable. 00:44:14.210 --> 00:44:22.639 We hope, we hope that we'll be able to come back in another year or two and be able to show you the big weather Empire that's been built and have served the community. 00:44:22.670 --> 00:44:24.440 New levels of certainty and everything. 00:44:24.469 --> 00:44:39.679 That's right, that's right. You know, our big goal, folks say, what is your what's your goal in terms of revenue, in terms of growth? Look, our goal is just to continue to serve the community in a way that will make the community smarter, stronger, better and more ready for weather. So if we can do that in the next several years, we're gonna be thrilled. 00:44:39.769 --> 00:44:58.909 And for what I hear. I mean, you know, we see this at the Bank, as well as this adaptability, this changing environment, like there's, there's a different method of people interacting with companies now, whether it's banking or whether it's getting your weather forecast, and to have something new and unique for folks, I think is awesome, and I'm inspired by it. 00:44:59.239 --> 00:45:01.639 Thank you very much we appreciate you having us on. 00:45:01.690 --> 00:45:02.980 Thank you so much. Thank you. 00:45:02.000 --> 00:45:37.789 Don't forget to subscribe and rate our show. The Building Interest podcast is live on all podcast platforms and YouTube. We want to hear what you think of each episode, and encourage you to submit your questions that you want us to cover. So please find us on YouTube or wherever you prefer to listen to podcasts and comment your thoughts, all opinions expressed by Matt and Danielle Noyes are their own and not the opinions of Leader Bank NA. Leader Bank is not affiliated with One Degree Outside. For more information on today's subject, visit leaderbank.com in addition to past episodes, you can find our Learning Center blog for more insights. This podcast is a production of Leader Bank, an equal housing lender, member FDIC, NMLS number 449250.