1 00:00:00,200 --> 00:00:04,000 Speaker 1: I think it is that the geeks shall inherit the earth, 2 00:00:04,360 --> 00:00:08,360 Speaker 1: not the meek. This is a glorious time, you know, 3 00:00:08,520 --> 00:00:12,360 Speaker 1: the old kind of jab learned to code that the 4 00:00:12,440 --> 00:00:14,760 Speaker 1: left would throw at people when they would lose their 5 00:00:14,840 --> 00:00:18,639 Speaker 1: jobs as a coal miner, Well, learn to code. My 6 00:00:18,760 --> 00:00:22,840 Speaker 1: late brother's son, Braden is a computer programmer, and I 7 00:00:22,840 --> 00:00:24,920 Speaker 1: don't know where that came from, because we are a 8 00:00:24,960 --> 00:00:28,680 Speaker 1: family full of cops and plant workers, with one talk 9 00:00:28,720 --> 00:00:32,400 Speaker 1: show host, nobody in our family. We can barely turn 10 00:00:32,440 --> 00:00:37,800 Speaker 1: a computer on, much less code and program. But he does, 11 00:00:37,840 --> 00:00:40,840 Speaker 1: and I love it. Well, this is such a glorious time. 12 00:00:41,560 --> 00:00:47,440 Speaker 1: The mindset of America is accountability and transparency. We know 13 00:00:47,600 --> 00:00:50,879 Speaker 1: that there is behind the curtain this wizard, and we 14 00:00:50,960 --> 00:00:53,400 Speaker 1: don't know what that wizard is, and we know we're 15 00:00:53,440 --> 00:00:56,560 Speaker 1: pouring all our money in and nuns coming back. But 16 00:00:56,640 --> 00:00:59,600 Speaker 1: how on earth do we solve this problem? It's intransigent, 17 00:01:00,080 --> 00:01:03,360 Speaker 1: it's too complex to ever solve. We've heard that, right, 18 00:01:04,280 --> 00:01:08,399 Speaker 1: And yet here comes along Elon, who's somewhere on the 19 00:01:08,440 --> 00:01:12,720 Speaker 1: autism scale, pure genius in a way that I can 20 00:01:12,800 --> 00:01:18,520 Speaker 1: hardly comprehend. His reasoning skills, his ability to process things 21 00:01:18,560 --> 00:01:24,480 Speaker 1: on a multiplanetary level, and he brings within these young, 22 00:01:25,560 --> 00:01:29,560 Speaker 1: really smart guys and they start tearing apart the details 23 00:01:29,600 --> 00:01:31,320 Speaker 1: and going, here's your waste, and here's your waste, and 24 00:01:31,360 --> 00:01:34,040 Speaker 1: here's your waste. Well, one of the names that has 25 00:01:34,040 --> 00:01:38,280 Speaker 1: come to my attention because of Elon is on Twitter 26 00:01:38,760 --> 00:01:42,759 Speaker 1: and the handle is data Republican and she says republican 27 00:01:42,800 --> 00:01:46,000 Speaker 1: with a small R, as in a person who believes 28 00:01:46,000 --> 00:01:49,160 Speaker 1: in the republic, the style of government, representative government. And 29 00:01:49,200 --> 00:01:51,320 Speaker 1: so I started reading everything that was said. I noticed 30 00:01:51,360 --> 00:01:55,600 Speaker 1: how many people were quoting this very influential woman, and 31 00:01:55,640 --> 00:01:58,920 Speaker 1: so I reached out by direct message to her and 32 00:01:59,000 --> 00:02:00,960 Speaker 1: she said, I'd be glad to do it, but I 33 00:02:01,000 --> 00:02:04,720 Speaker 1: need you to talk to my guy, and that's Sean Hendricks, 34 00:02:05,160 --> 00:02:08,000 Speaker 1: because I need a translator when we do the interview. 35 00:02:08,800 --> 00:02:11,400 Speaker 1: So Sean came on and I said, well, can we 36 00:02:11,440 --> 00:02:15,280 Speaker 1: talk to you first? And why do we need a translator? 37 00:02:15,320 --> 00:02:18,120 Speaker 1: Does she not speak? And he said, oh, she's deaf 38 00:02:18,320 --> 00:02:21,000 Speaker 1: and mute, And I thought, wow, it's you know, you 39 00:02:21,000 --> 00:02:23,680 Speaker 1: hear these stories aramon, people that don't have sight, so 40 00:02:23,720 --> 00:02:26,320 Speaker 1: they have extra you know, they can hear better. It's 41 00:02:26,360 --> 00:02:28,960 Speaker 1: like you have a heightened sense. But anyway, I think 42 00:02:28,960 --> 00:02:32,040 Speaker 1: it's an inspirational story in addition to everything else. Sean 43 00:02:32,160 --> 00:02:35,000 Speaker 1: Hendricks is our guest, and I appreciate you making yourself 44 00:02:35,040 --> 00:02:35,760 Speaker 1: available to us. 45 00:02:35,800 --> 00:02:39,600 Speaker 2: Good sir, No, absolutely, thanks for having us on. It's 46 00:02:39,600 --> 00:02:42,959 Speaker 2: always great to get outside the X space and talk 47 00:02:43,000 --> 00:02:45,359 Speaker 2: about this on a broader platform so people are aware. 48 00:02:45,600 --> 00:02:48,000 Speaker 1: Well, I told you before we started. I don't normally 49 00:02:48,000 --> 00:02:49,920 Speaker 1: prep guests, but I did Sean because I knew he 50 00:02:49,960 --> 00:02:52,399 Speaker 1: probably didn't know much about us. Is that a lot 51 00:02:52,440 --> 00:02:55,760 Speaker 1: of national talk shows do their show as if everybody 52 00:02:55,800 --> 00:02:57,640 Speaker 1: is on Twitter all day long and knows what the 53 00:02:57,639 --> 00:02:59,799 Speaker 1: top story is. I do our show so that the 54 00:02:59,840 --> 00:03:01,640 Speaker 1: guy that gets out of the plant on his drive 55 00:03:01,720 --> 00:03:04,000 Speaker 1: home can understand what we're talking about. So if there's 56 00:03:04,000 --> 00:03:06,680 Speaker 1: an acronym, I explain it. And I want to first 57 00:03:06,720 --> 00:03:09,600 Speaker 1: talk about who Data Republican is, and then we're going 58 00:03:09,680 --> 00:03:12,760 Speaker 1: to get to you and what Data Republican is doing. 59 00:03:12,800 --> 00:03:14,920 Speaker 1: That came to the attention of Elon that's been so 60 00:03:15,000 --> 00:03:18,640 Speaker 1: important to these numbers we're bandying about about government corruption. 61 00:03:21,240 --> 00:03:24,160 Speaker 2: Yeah, she's she was just well, she was one of 62 00:03:24,160 --> 00:03:26,680 Speaker 2: my early followers when I was doing Western North Carolina 63 00:03:26,720 --> 00:03:30,200 Speaker 2: disaster relief and that's how we connected. And she was basically, 64 00:03:30,240 --> 00:03:32,840 Speaker 2: you know, you said learn to code, it goes beyond that. 65 00:03:32,919 --> 00:03:35,160 Speaker 2: You have to learn to build. Like there's a you know, 66 00:03:35,200 --> 00:03:37,680 Speaker 2: AI can code, but it's like really knowing what you're 67 00:03:37,720 --> 00:03:39,640 Speaker 2: building is what matters. And that's what she did. She's 68 00:03:39,640 --> 00:03:43,440 Speaker 2: a tool builder, and so she's an old colonel database engineer, 69 00:03:43,440 --> 00:03:46,160 Speaker 2: but she's taken to building tools that parse all this 70 00:03:46,360 --> 00:03:49,360 Speaker 2: data and present it in a way that the regular 71 00:03:49,440 --> 00:03:52,560 Speaker 2: person can look and see how the money flows. And 72 00:03:52,920 --> 00:03:54,880 Speaker 2: I guess, you know, we just didn't realize how much 73 00:03:54,920 --> 00:03:57,440 Speaker 2: we needed and wanted that, And now that it's out there, 74 00:03:57,480 --> 00:04:00,400 Speaker 2: people are just blown away at how much money is 75 00:04:00,480 --> 00:04:04,800 Speaker 2: moving into these mysterious areas that no one has representation 76 00:04:04,920 --> 00:04:06,360 Speaker 2: of or transparency through. 77 00:04:07,880 --> 00:04:11,240 Speaker 1: So let's talk about those tools and explain those tools 78 00:04:11,240 --> 00:04:13,800 Speaker 1: to those of us who are not computer programmers and 79 00:04:13,880 --> 00:04:17,839 Speaker 1: not real tech savvy. Why are those tools important. 80 00:04:19,560 --> 00:04:23,560 Speaker 2: Well, she's taken like one point eight terabytes of publicly 81 00:04:23,560 --> 00:04:26,480 Speaker 2: available data and put it into the front end. It's 82 00:04:26,480 --> 00:04:29,680 Speaker 2: on a website called datarepublican dot com, so you can 83 00:04:29,720 --> 00:04:32,880 Speaker 2: go in and type in an officer search. You got 84 00:04:32,920 --> 00:04:37,160 Speaker 2: some guy who's ranting and raving about Trump shutting down USAID. Well, 85 00:04:37,200 --> 00:04:39,240 Speaker 2: funny enough, you type his name into the search and 86 00:04:39,320 --> 00:04:41,320 Speaker 2: you see the seven charities that are receiving money from 87 00:04:41,400 --> 00:04:44,120 Speaker 2: USAID that he's a part of. Well, now you can 88 00:04:44,120 --> 00:04:47,919 Speaker 2: filter his bias through where he's getting money from and 89 00:04:47,960 --> 00:04:50,560 Speaker 2: help understand why this guy's so upset. They used to 90 00:04:50,600 --> 00:04:52,920 Speaker 2: hide in the shadows of this stuff, you know, Now 91 00:04:52,920 --> 00:04:55,320 Speaker 2: we can go and look, go, well, he's actually tied 92 00:04:55,360 --> 00:04:59,160 Speaker 2: to these charities. Where are those charities getting their money from? 93 00:04:59,160 --> 00:05:01,160 Speaker 2: So you put their chair already number into the system 94 00:05:01,560 --> 00:05:03,839 Speaker 2: and it shows all the grants coming in and you 95 00:05:03,880 --> 00:05:07,479 Speaker 2: can basically do your research to find out these powerful 96 00:05:07,520 --> 00:05:09,960 Speaker 2: people where their money's coming from. And then you start 97 00:05:10,000 --> 00:05:13,560 Speaker 2: to understand their reasons they make the decisions they're making, 98 00:05:13,920 --> 00:05:15,960 Speaker 2: the reasons they're fighting so hard to keep this stuff 99 00:05:16,040 --> 00:05:19,320 Speaker 2: quiet because this has been a huge flush fund there. 100 00:05:19,360 --> 00:05:23,000 Speaker 2: And it's not Republican and Democrat, it's both right, it's 101 00:05:23,000 --> 00:05:25,240 Speaker 2: not one side of the other. Everyone's had their hand 102 00:05:25,240 --> 00:05:27,440 Speaker 2: in this cookie jar. And that's what we're looking to do, 103 00:05:27,520 --> 00:05:30,920 Speaker 2: is give people transparency through data, through facts, and that's 104 00:05:30,960 --> 00:05:33,159 Speaker 2: something that we've had pulled over our eyes for many, 105 00:05:33,160 --> 00:05:37,559 Speaker 2: many years, and with AI and you amazing toolbuilders like data. 106 00:05:38,640 --> 00:05:39,800 Speaker 2: We can see this stuff now. 107 00:05:40,279 --> 00:05:42,039 Speaker 1: I'm going to ask some dumb questions because I'm not 108 00:05:42,040 --> 00:05:44,200 Speaker 1: afraid to ask dumb questions. The only way I learned anything. 109 00:05:44,640 --> 00:05:47,840 Speaker 1: How do you get access to this data? Is this 110 00:05:47,960 --> 00:05:50,960 Speaker 1: data now being made available because somebody has to hand 111 00:05:50,960 --> 00:05:53,640 Speaker 1: the data set to you to make sense of it. 112 00:05:55,640 --> 00:05:58,159 Speaker 2: Well, right, So a lot of this because of how 113 00:05:58,720 --> 00:06:02,320 Speaker 2: charities work is transparent. Form nine nineties are kind of 114 00:06:02,320 --> 00:06:06,360 Speaker 2: like where you come out in detail, you know what 115 00:06:06,400 --> 00:06:08,640 Speaker 2: your charity's doing. I mean, you get a tax exemption, 116 00:06:08,880 --> 00:06:11,159 Speaker 2: so there is some transparency that comes along with that. 117 00:06:11,480 --> 00:06:13,840 Speaker 2: I can't just set up a nonprofit until the government like, hey, 118 00:06:13,839 --> 00:06:16,840 Speaker 2: you can't look inside, right, you know. So there's a 119 00:06:16,839 --> 00:06:19,200 Speaker 2: lot of uh, there's a lot more regulation over five 120 00:06:19,200 --> 00:06:21,599 Speaker 2: oh one C three, and then we have like there's 121 00:06:21,680 --> 00:06:24,080 Speaker 2: donor advised fun I mean, there's just so many layers 122 00:06:24,080 --> 00:06:27,360 Speaker 2: to this charity. It's funny. Even before this data republican 123 00:06:27,440 --> 00:06:30,040 Speaker 2: thing broke, when I was up in western North Carolina, 124 00:06:30,360 --> 00:06:32,679 Speaker 2: I saw so much fraud with the whole charity system. 125 00:06:32,680 --> 00:06:34,359 Speaker 2: I used to tell people the only thing I trust 126 00:06:34,400 --> 00:06:36,800 Speaker 2: less than the government is charities. And then all of 127 00:06:36,839 --> 00:06:39,200 Speaker 2: a sudden two three months later. That's why I think 128 00:06:39,200 --> 00:06:41,320 Speaker 2: I've connected so much with her. As she was showing 129 00:06:41,360 --> 00:06:43,680 Speaker 2: me why I felt that way. I could feel it. 130 00:06:43,839 --> 00:06:46,080 Speaker 2: I just didn't have the data to prove it. But yeah, 131 00:06:46,120 --> 00:06:48,560 Speaker 2: this data, these these the IRIS website has a lot 132 00:06:48,560 --> 00:06:51,560 Speaker 2: of these data sets available. But unless you can cross 133 00:06:51,680 --> 00:06:54,200 Speaker 2: connect all the data, it doesn't really mean anything. If 134 00:06:54,240 --> 00:06:57,120 Speaker 2: I look at the you know, Kaiser Foundation health Plan Incorporated, 135 00:06:57,400 --> 00:07:00,159 Speaker 2: and I see that it received eighty two million or 136 00:07:00,160 --> 00:07:06,599 Speaker 2: eighty two billion, sorry, eighty two billion dollars, right, what 137 00:07:06,760 --> 00:07:08,360 Speaker 2: does that mean? Then we go through and we parse 138 00:07:08,400 --> 00:07:10,360 Speaker 2: it out and see that actually only eleven million of 139 00:07:10,360 --> 00:07:14,280 Speaker 2: that was taxpayer funds, But it was eleven million of 140 00:07:14,360 --> 00:07:16,680 Speaker 2: taxpayer funds? Why did taxpayer money go there? So you 141 00:07:16,680 --> 00:07:19,520 Speaker 2: can start to dig in and and again, data does 142 00:07:19,560 --> 00:07:21,600 Speaker 2: not mean a conviction, right, doesn't mean to have eleven 143 00:07:21,600 --> 00:07:23,720 Speaker 2: millions are bad? Right? It could have been a very 144 00:07:23,720 --> 00:07:25,520 Speaker 2: good reason we sent that money there. And we've had 145 00:07:25,520 --> 00:07:27,520 Speaker 2: people use this and say, look at Kayser, they're stealing 146 00:07:27,520 --> 00:07:30,760 Speaker 2: money from the taxpayer. No, no, you've got to dig 147 00:07:30,800 --> 00:07:31,320 Speaker 2: deeper than that. 148 00:07:31,480 --> 00:07:34,400 Speaker 1: But Sean, what we were doing before you guys, is 149 00:07:34,440 --> 00:07:36,840 Speaker 1: we were sitting back and going, wait a second, how 150 00:07:36,880 --> 00:07:39,800 Speaker 1: do they drive a rolls Royce when they're a nonprofit 151 00:07:39,920 --> 00:07:41,880 Speaker 1: And he says he only makes any time. We had 152 00:07:41,920 --> 00:07:43,840 Speaker 1: no tools. You talk about a tool. We had not 153 00:07:44,080 --> 00:07:46,880 Speaker 1: tools to do these things with. Hold with me. His name, 154 00:07:46,960 --> 00:07:49,960 Speaker 1: if you're on Twitter, is sewn at Sean Hendricks and 155 00:07:50,000 --> 00:07:53,760 Speaker 1: that's the Rix Shaw Hendrick. She is Data Republican and 156 00:07:53,840 --> 00:07:57,160 Speaker 1: you can find them at datarepublican dot com. You can 157 00:07:57,240 --> 00:07:59,200 Speaker 1: sign up to subscribe for three bucks a month and 158 00:07:59,240 --> 00:08:02,280 Speaker 1: you get all their stuff. I did Data Republican dot com. 159 00:08:02,320 --> 00:08:04,760 Speaker 1: He's Sean Hendrix. More with him. 160 00:08:05,480 --> 00:08:08,080 Speaker 2: He just shows me what it's like to be, you know, 161 00:08:08,200 --> 00:08:11,320 Speaker 2: a real man. I have never met someone so wonderful. 162 00:08:11,360 --> 00:08:13,200 Speaker 2: I call him Michael Berry. 163 00:08:14,640 --> 00:08:17,760 Speaker 1: John Hendrich is our guests. He's with Data Republican dot 164 00:08:17,800 --> 00:08:22,880 Speaker 1: com and they are taking data of your money, your 165 00:08:22,920 --> 00:08:25,880 Speaker 1: taxpayer dollars, and where it's going. And they are contributing 166 00:08:25,920 --> 00:08:29,880 Speaker 1: to this whole overall transparency that Elon Musk has ushered 167 00:08:29,920 --> 00:08:33,800 Speaker 1: in through Doge and Elon and many others have been 168 00:08:33,960 --> 00:08:36,280 Speaker 1: very complementary of the work these folks are doing at 169 00:08:36,320 --> 00:08:40,560 Speaker 1: data Republican dot com. These are number cruncher computer genius 170 00:08:40,640 --> 00:08:43,520 Speaker 1: kind of people genius for me. They may not like 171 00:08:43,559 --> 00:08:46,120 Speaker 1: that title, but for me, it is sean when you 172 00:08:46,200 --> 00:08:49,760 Speaker 1: talk about this data from which you're pulling all the 173 00:08:50,280 --> 00:08:52,760 Speaker 1: you know, you're making sense of it and analyzing it 174 00:08:52,800 --> 00:08:57,040 Speaker 1: for us. Was that data available before or has something 175 00:08:57,120 --> 00:08:59,360 Speaker 1: happened with Trump that this was now the doors were 176 00:08:59,360 --> 00:09:01,319 Speaker 1: open and they go come in here and take a look. 177 00:09:02,880 --> 00:09:05,760 Speaker 2: You know, I'm not quite sure how long Form nine 178 00:09:05,840 --> 00:09:09,680 Speaker 2: nineties have been public. You know, I just know that 179 00:09:09,840 --> 00:09:13,040 Speaker 2: as a taxes empt organization, your Form nine ninety is 180 00:09:13,120 --> 00:09:17,480 Speaker 2: public disclosure and it's available. I mean, as far as 181 00:09:17,520 --> 00:09:21,280 Speaker 2: I know, it's always been that way. And so yeah, 182 00:09:21,320 --> 00:09:24,640 Speaker 2: the data is there, just there was no tool, right, 183 00:09:24,720 --> 00:09:27,360 Speaker 2: No one had sat down and built this tool. And 184 00:09:27,760 --> 00:09:30,560 Speaker 2: you know, with I think what happens is like, until 185 00:09:30,559 --> 00:09:32,000 Speaker 2: there's a problem, you don't need you don't know, you 186 00:09:32,000 --> 00:09:34,560 Speaker 2: don't know that you need a tool, right, And so 187 00:09:34,960 --> 00:09:37,080 Speaker 2: we see there's a problem, we see that there's mass 188 00:09:37,880 --> 00:09:42,280 Speaker 2: spending that with no direction and no transparency, and so 189 00:09:42,679 --> 00:09:44,880 Speaker 2: she's like, well, we need a tool for this. And 190 00:09:44,920 --> 00:09:48,000 Speaker 2: when she started building it, it just you know, when 191 00:09:48,040 --> 00:09:50,559 Speaker 2: you invent something, you know there's there's two ways. It 192 00:09:50,600 --> 00:09:52,719 Speaker 2: can go. Nobody wants that invention or everybody wants it, it 193 00:09:52,760 --> 00:09:57,080 Speaker 2: seems like right and just people wanted to know where 194 00:09:57,120 --> 00:09:59,320 Speaker 2: their money's going. And I think we're all so tired 195 00:09:59,400 --> 00:10:02,680 Speaker 2: of paying a premium on groceries, a premium on fuel, 196 00:10:03,040 --> 00:10:06,840 Speaker 2: property taxes, income taxes, you know, death tap. I mean, 197 00:10:06,840 --> 00:10:08,679 Speaker 2: it just goes on and on and on. All our 198 00:10:08,679 --> 00:10:10,439 Speaker 2: money goes out and we don't get much for it, 199 00:10:10,480 --> 00:10:13,320 Speaker 2: feels like. And so now there's a tool to go 200 00:10:13,400 --> 00:10:16,400 Speaker 2: see where is our money going, especially in this nine 201 00:10:16,400 --> 00:10:18,680 Speaker 2: to ninety world. And I think the biggest thing was 202 00:10:18,679 --> 00:10:21,400 Speaker 2: that if the charity system hadn't been used to move 203 00:10:21,520 --> 00:10:23,360 Speaker 2: so much cash around, there had been no need for 204 00:10:23,400 --> 00:10:26,439 Speaker 2: a tool. But that's the fact that it hadn't been 205 00:10:26,720 --> 00:10:31,360 Speaker 2: us ad. We're talking just an unimaginable amount of cash 206 00:10:31,880 --> 00:10:34,280 Speaker 2: that's being moved around and there's no way to sort 207 00:10:34,320 --> 00:10:37,440 Speaker 2: it out with some kind of very very elegant tool. 208 00:10:37,440 --> 00:10:39,560 Speaker 2: And that's what she but what she created, that's why 209 00:10:39,960 --> 00:10:41,520 Speaker 2: she's become so popular. 210 00:10:42,520 --> 00:10:46,360 Speaker 1: Well, because you know, if you can't if you've got 211 00:10:46,360 --> 00:10:49,160 Speaker 1: this amazing uranium but you can't refine it, or you've 212 00:10:49,160 --> 00:10:51,079 Speaker 1: got oil reserves but you can't pull it out of 213 00:10:51,120 --> 00:10:54,000 Speaker 1: the earth. It's worth nothing to anyone, but now it's 214 00:10:54,040 --> 00:10:57,080 Speaker 1: worth things because we can we can write stories about it. 215 00:10:57,120 --> 00:10:59,000 Speaker 1: I can do shows about it. But I could have 216 00:10:59,040 --> 00:11:00,760 Speaker 1: never We couldn't make sense of any of this. We 217 00:11:00,760 --> 00:11:04,280 Speaker 1: wouldn't have even really known it that it existed. Sean 218 00:11:04,320 --> 00:11:06,880 Speaker 1: Hendricks is our guest data Republican dot com. Sewan, you 219 00:11:07,000 --> 00:11:11,520 Speaker 1: use the term that these supposed nonprofits, these NGOs are 220 00:11:11,600 --> 00:11:14,920 Speaker 1: moving cash around so much that makes it harder to understand. 221 00:11:15,600 --> 00:11:18,880 Speaker 1: Do you get the sense that the moving of cash 222 00:11:19,080 --> 00:11:22,280 Speaker 1: in this way is an attempt to launder or obscure 223 00:11:23,000 --> 00:11:24,960 Speaker 1: or what Do you think the reasoning for that is? 224 00:11:25,080 --> 00:11:26,479 Speaker 1: To the extent you can tell. 225 00:11:26,360 --> 00:11:29,880 Speaker 2: I want to be I want to be careful to allege. Right, 226 00:11:30,600 --> 00:11:34,040 Speaker 2: What I can say is how they do it gives 227 00:11:34,120 --> 00:11:38,040 Speaker 2: the outcome of obscuring and confusing the data. Right. This 228 00:11:38,120 --> 00:11:40,320 Speaker 2: stuff goes overseas and we lose track of it. It 229 00:11:40,360 --> 00:11:42,839 Speaker 2: goes from company to company. It's harder to track. Now. 230 00:11:42,840 --> 00:11:45,120 Speaker 2: I can't speak to intention because I don't have you know, 231 00:11:45,520 --> 00:11:47,640 Speaker 2: I can't prove somebody's intention. What I can say is, 232 00:11:47,679 --> 00:11:50,719 Speaker 2: are actions one to one hundred percent make it very 233 00:11:50,720 --> 00:11:54,400 Speaker 2: hard to track where the money goes. Now, whether that's 234 00:11:54,400 --> 00:11:56,080 Speaker 2: intentional or not, I can't speak to that, and that 235 00:11:56,120 --> 00:11:58,120 Speaker 2: will come in time. We will start to find out 236 00:11:58,400 --> 00:12:01,600 Speaker 2: the intentionality behind it as we start finding people, and 237 00:12:01,640 --> 00:12:03,839 Speaker 2: it's real easy for us. They make it very easy. 238 00:12:03,840 --> 00:12:06,480 Speaker 2: Those who are screaming the loudest, we just go look 239 00:12:06,520 --> 00:12:09,360 Speaker 2: into them and it's like almost every time, you know, 240 00:12:09,600 --> 00:12:11,880 Speaker 2: you know, the senators are screaming the loudest. I went 241 00:12:11,920 --> 00:12:14,040 Speaker 2: and looked. Every one of them had voted from the 242 00:12:14,040 --> 00:12:17,400 Speaker 2: OMNIUMUS Spending Bill of twenty twenty four that funded this 243 00:12:17,600 --> 00:12:20,920 Speaker 2: US eight right, So when you find the person squeaking, 244 00:12:21,080 --> 00:12:24,240 Speaker 2: it's they had a piece of it going, you know. Now, again, it's. 245 00:12:24,160 --> 00:12:26,240 Speaker 1: Kind of funny because we always said in the country 246 00:12:26,280 --> 00:12:28,840 Speaker 1: the hit dogs squeels first. It's kind of funny because 247 00:12:28,840 --> 00:12:32,080 Speaker 1: they're drawing attention to themselves by the fact that they're 248 00:12:32,080 --> 00:12:33,000 Speaker 1: screaming about it. 249 00:12:34,400 --> 00:12:37,320 Speaker 2: Oh yeah, every time they get into a beef with 250 00:12:37,400 --> 00:12:40,160 Speaker 2: Elon or jd Vance. As soon as I see it, 251 00:12:40,440 --> 00:12:42,880 Speaker 2: I'm like straight into the engine. I start pumping names in. 252 00:12:43,200 --> 00:12:46,840 Speaker 2: We start doing research and lo and behold, you're tied 253 00:12:46,880 --> 00:12:49,880 Speaker 2: to six or seven different things. No wonder you're screaming 254 00:12:49,920 --> 00:12:52,800 Speaker 2: because you're you're gravy train's about to get cut off, 255 00:12:53,880 --> 00:12:58,520 Speaker 2: you know, And so yeah, I mean it's almost too 256 00:12:58,559 --> 00:13:00,679 Speaker 2: much to process and just give an idea. In the 257 00:13:00,760 --> 00:13:03,560 Speaker 2: charity funding tab, we have one hundred and fifty six 258 00:13:03,679 --> 00:13:10,320 Speaker 2: thousand different funds totaling seven hundred and twenty three billion dollars, 259 00:13:11,760 --> 00:13:16,559 Speaker 2: with three hundred and fourteen billion being government grants. Right, 260 00:13:16,640 --> 00:13:18,880 Speaker 2: so we're talking three hundred and twenty two billion dollars 261 00:13:18,880 --> 00:13:23,920 Speaker 2: of total taxpayer money that's being moved around. It's wild. 262 00:13:24,600 --> 00:13:27,200 Speaker 1: I mean, this is an amount of money that exceeds that. 263 00:13:27,280 --> 00:13:29,520 Speaker 1: This is more than double what we sent to Ukraine. 264 00:13:29,960 --> 00:13:34,040 Speaker 1: You could fight an entire world, an entire war with 265 00:13:34,200 --> 00:13:37,000 Speaker 1: a major military superpower for the kind of money we're 266 00:13:37,000 --> 00:13:37,720 Speaker 1: talking about here. 267 00:13:39,120 --> 00:13:42,960 Speaker 2: Oh yeah, I mean this is this is unimaginable amount 268 00:13:42,960 --> 00:13:47,120 Speaker 2: of dollars. And we wonder why everything's so expensive. I mean, 269 00:13:47,120 --> 00:13:49,480 Speaker 2: gold right now surging to three thousand dollars an ounce. 270 00:13:49,840 --> 00:13:53,520 Speaker 2: You're we're printing money and just pumping it into the globe. 271 00:13:53,720 --> 00:13:56,280 Speaker 2: Of course, everything's expensive. What do you expect when you 272 00:13:56,320 --> 00:13:59,560 Speaker 2: do that stuff? But this is just a small piece 273 00:13:59,600 --> 00:14:02,280 Speaker 2: of it. I think the next four years, I think 274 00:14:02,320 --> 00:14:05,120 Speaker 2: we're going to be awestruck at what we find what 275 00:14:05,200 --> 00:14:07,480 Speaker 2: our budget really looks like. And I hear people say 276 00:14:07,520 --> 00:14:09,480 Speaker 2: all the time, Oh well, I mean, even if you 277 00:14:09,480 --> 00:14:11,560 Speaker 2: took the stuff that we have to pay for, it 278 00:14:11,559 --> 00:14:14,240 Speaker 2: wouldn't put us in a deficit. And it's like somehow 279 00:14:14,280 --> 00:14:16,640 Speaker 2: that that's good enough reason just to stay in debt forever. 280 00:14:16,840 --> 00:14:19,480 Speaker 2: Imagine I told my wife, well, I can't make enough 281 00:14:19,480 --> 00:14:21,720 Speaker 2: money to payunt the credit cards, so please keep spending 282 00:14:21,760 --> 00:14:24,440 Speaker 2: as much as you want on the credit cards. They 283 00:14:24,480 --> 00:14:26,480 Speaker 2: don't live in the real world like we do. And 284 00:14:26,560 --> 00:14:29,200 Speaker 2: now we can use this data to hold our politicians accountable. 285 00:14:29,280 --> 00:14:34,160 Speaker 2: We should fire our politicians for not balancing our budget. 286 00:14:34,840 --> 00:14:42,120 Speaker 1: It's it's staggering. You know what's amazing is we're a 287 00:14:42,200 --> 00:14:47,600 Speaker 1: month into the Trump presidency and we've already discovered all 288 00:14:47,680 --> 00:14:51,000 Speaker 1: of these amazing things. And you guys are just getting 289 00:14:51,040 --> 00:14:54,640 Speaker 1: better by the day. You're just now getting your feed 290 00:14:54,680 --> 00:14:57,880 Speaker 1: wet to figure out how to dive into these data sets, 291 00:14:58,120 --> 00:15:00,600 Speaker 1: and you're going to notice the things at their coding. 292 00:15:00,680 --> 00:15:00,840 Speaker 2: You know. 293 00:15:01,200 --> 00:15:03,680 Speaker 1: I saw the other day that Elon was talking about 294 00:15:03,680 --> 00:15:08,760 Speaker 1: the fact that they had the Social Security Administration had 295 00:15:08,840 --> 00:15:12,520 Speaker 1: some some millions of people who had as their death 296 00:15:12,600 --> 00:15:15,440 Speaker 1: date false, so they can never die. So you've got 297 00:15:15,440 --> 00:15:18,720 Speaker 1: millions of people who could who are supposedly one hundred 298 00:15:18,720 --> 00:15:21,160 Speaker 1: and fifty years old, getting a Social Security check and 299 00:15:21,240 --> 00:15:23,800 Speaker 1: of course that doesn't exist. That means it's a check 300 00:15:23,880 --> 00:15:28,240 Speaker 1: going to some conspiracy ring, some fraud ring. Once these 301 00:15:28,280 --> 00:15:32,000 Speaker 1: things become clear, we're going to understand that we shouldn't 302 00:15:32,040 --> 00:15:34,640 Speaker 1: even be running a deficit as a country. It is 303 00:15:34,720 --> 00:15:38,120 Speaker 1: absurd that we would even be running a deficit as 304 00:15:38,120 --> 00:15:41,160 Speaker 1: a country when you consider the work you guys are 305 00:15:41,200 --> 00:15:44,760 Speaker 1: doing and all the dollars that are exposed. In just 306 00:15:44,800 --> 00:15:48,040 Speaker 1: a moment, we will continue with Sean Hendrix. The website 307 00:15:48,080 --> 00:15:51,720 Speaker 1: is data Republican dot com. It's three bucks a month 308 00:15:51,760 --> 00:15:54,320 Speaker 1: to subscribe. I encourage you to do it only because 309 00:15:54,360 --> 00:15:55,880 Speaker 1: I did, and I like to support the work of 310 00:15:55,920 --> 00:16:00,440 Speaker 1: folks like this. Sean Hendricks is not the Data Republican. 311 00:16:01,120 --> 00:16:03,000 Speaker 1: That is a woman with whom we will also be 312 00:16:03,040 --> 00:16:06,160 Speaker 1: speaking in the near future through a translator. Sean Hendrix 313 00:16:06,200 --> 00:16:09,040 Speaker 1: is part of her group and was willing to speak 314 00:16:09,040 --> 00:16:11,040 Speaker 1: to us today. And I am so excited about what 315 00:16:11,040 --> 00:16:13,440 Speaker 1: they're doing because I think this is how you bring 316 00:16:13,520 --> 00:16:17,360 Speaker 1: real change. I think this is how you make things 317 00:16:17,680 --> 00:16:21,080 Speaker 1: in a bite sized manner, in a way that it's 318 00:16:21,280 --> 00:16:23,960 Speaker 1: accessible to the general public, to people that don't turn 319 00:16:24,000 --> 00:16:27,120 Speaker 1: on computers. Oh that's where all my money's going. Okay, 320 00:16:27,520 --> 00:16:30,840 Speaker 1: now I can be enraged. This is how you message 321 00:16:30,880 --> 00:16:34,960 Speaker 1: a campaign strategically to win, and that I find to 322 00:16:35,000 --> 00:16:37,920 Speaker 1: be very excited. And when I don't mean elections, I 323 00:16:37,960 --> 00:16:41,080 Speaker 1: mean fixing our daddumb country. I have kids. John Hendrix 324 00:16:41,160 --> 00:16:43,240 Speaker 1: is our guest Data Republican dot com. More coming up, 325 00:16:44,560 --> 00:16:46,880 Speaker 1: we're going to be changing the name of the Gulf 326 00:16:46,960 --> 00:16:48,200 Speaker 1: of Mexico to. 327 00:16:48,600 --> 00:16:51,760 Speaker 2: The Gulf of Michael Berry, which as a beautiful way. 328 00:16:53,560 --> 00:16:57,640 Speaker 1: John Hendrix is our guest Data Republican dot com. This 329 00:16:57,640 --> 00:16:59,920 Speaker 1: this is a small group that Elon Musk has been 330 00:17:00,040 --> 00:17:02,760 Speaker 1: talking a lot about the great work that they're doing 331 00:17:02,800 --> 00:17:08,400 Speaker 1: and exposing the governmental fraud and waste. Sean, I don't 332 00:17:08,400 --> 00:17:09,720 Speaker 1: know if you have a list in front of you 333 00:17:09,800 --> 00:17:11,760 Speaker 1: or if there are a few that come to mind, 334 00:17:11,800 --> 00:17:14,840 Speaker 1: But when you look at the waste and fraud allegedly 335 00:17:15,280 --> 00:17:18,359 Speaker 1: that you guys have exposed, were there some things that 336 00:17:18,440 --> 00:17:21,639 Speaker 1: jumped out at you that really seemed more egregious than 337 00:17:21,640 --> 00:17:22,040 Speaker 1: the rest? 338 00:17:23,680 --> 00:17:25,560 Speaker 2: You know, I'm going to turn the social Security thing 339 00:17:25,600 --> 00:17:27,560 Speaker 2: on its head a little bit. And also real quick, 340 00:17:27,920 --> 00:17:31,680 Speaker 2: the data Republican site is free, you can. We're subscription 341 00:17:31,960 --> 00:17:34,480 Speaker 2: paid for, so if you donate, that's great. We don't 342 00:17:34,520 --> 00:17:36,600 Speaker 2: want to pay all this data. We want everyone to 343 00:17:36,600 --> 00:17:39,560 Speaker 2: have access to it, but we are donation driven, So 344 00:17:39,640 --> 00:17:42,000 Speaker 2: I do appreciate you putting that out there. The thing 345 00:17:42,040 --> 00:17:44,359 Speaker 2: on the social Security side, the thing that struck me 346 00:17:44,400 --> 00:17:47,760 Speaker 2: this morning when I was looking through it is, yeah, 347 00:17:47,760 --> 00:17:51,520 Speaker 2: we talk about the money, but social security numbers vote right? 348 00:17:51,920 --> 00:17:54,719 Speaker 2: What if? And this is I'm just theorizing here, but like, 349 00:17:54,800 --> 00:17:57,879 Speaker 2: that's a lot of people still on a voter record. 350 00:17:57,880 --> 00:18:00,000 Speaker 2: If they're alive in the social security system, are they 351 00:18:00,040 --> 00:18:02,280 Speaker 2: still alive in the voter roles? I mean that's managed 352 00:18:02,280 --> 00:18:05,479 Speaker 2: state by state, And so I want to dig deeper 353 00:18:05,560 --> 00:18:09,520 Speaker 2: and go buy the voter registration information from a state 354 00:18:09,800 --> 00:18:12,120 Speaker 2: and then go compare it to whatever we can find 355 00:18:12,160 --> 00:18:16,399 Speaker 2: on this this you know, death false flag on the 356 00:18:16,400 --> 00:18:19,280 Speaker 2: social Security numbers. Maybe it's bigger than just money. You know, 357 00:18:19,280 --> 00:18:22,680 Speaker 2: we always you know, the old joke is my dad, 358 00:18:22,760 --> 00:18:25,120 Speaker 2: my granddad voted Republican up until the day he died, 359 00:18:25,160 --> 00:18:27,800 Speaker 2: and he voted a Democratic over sense, you know, so 360 00:18:30,480 --> 00:18:32,119 Speaker 2: but I want to dig into that because like sometimes 361 00:18:32,119 --> 00:18:33,600 Speaker 2: we're focused on the money, and it's like, well, what 362 00:18:33,680 --> 00:18:36,760 Speaker 2: is the bigger what's the bigger purpose behind the money? 363 00:18:36,760 --> 00:18:38,840 Speaker 2: What are they doing with the money? And that's the 364 00:18:38,880 --> 00:18:40,479 Speaker 2: thing we really find out that it has a lot 365 00:18:40,480 --> 00:18:44,120 Speaker 2: to do with uniparty and this idea of democracy. Right, 366 00:18:44,280 --> 00:18:48,200 Speaker 2: these people on both sides, this uniparty believes without them 367 00:18:48,640 --> 00:18:51,280 Speaker 2: sitting in the ivory tower guiding our lives and making 368 00:18:51,280 --> 00:18:54,320 Speaker 2: our decisions, that the Western world would collapse without the 369 00:18:54,400 --> 00:18:57,680 Speaker 2: genius of these these government workers, right. I mean, they've 370 00:18:57,680 --> 00:19:00,760 Speaker 2: put themselves in a ruling class of truly a really elite, 371 00:19:01,160 --> 00:19:05,359 Speaker 2: and that money is to prop up that ideology. And 372 00:19:05,400 --> 00:19:07,199 Speaker 2: so it's yeah, sure it's a waste of money, but 373 00:19:07,280 --> 00:19:11,440 Speaker 2: even worse, it's paying for bad ideologies across the globe. 374 00:19:13,000 --> 00:19:16,640 Speaker 2: And you know, now we're seeing the damage of it, we. 375 00:19:16,680 --> 00:19:20,119 Speaker 1: Are, And and you know, I think part of why 376 00:19:20,160 --> 00:19:23,560 Speaker 1: these things are allowed to happen is because this information 377 00:19:23,720 --> 00:19:28,240 Speaker 1: is obfuscated, This information is withheld, It stays behind the curtain. 378 00:19:28,320 --> 00:19:32,280 Speaker 1: The public doesn't know, and so what we don't see 379 00:19:32,440 --> 00:19:34,800 Speaker 1: in front of us, we can't concentrate on. So we 380 00:19:34,920 --> 00:19:39,639 Speaker 1: tend to focus instead on congressional sex scandals on you 381 00:19:39,680 --> 00:19:41,600 Speaker 1: know who said a nasty thing to the other, and 382 00:19:41,640 --> 00:19:44,359 Speaker 1: it keeps us spatting with each other when what really 383 00:19:44,400 --> 00:19:46,600 Speaker 1: matters is the dollars and cents that we're wasting and 384 00:19:46,640 --> 00:19:49,760 Speaker 1: the problems that aren't being solved. Sean, when you look 385 00:19:49,800 --> 00:19:52,199 Speaker 1: at where Data Republican is going and this in this 386 00:19:52,359 --> 00:19:56,879 Speaker 1: very brief period of time, is this burst of popularity 387 00:19:56,920 --> 00:20:00,320 Speaker 1: and celebrity to some extent on Twitter? For sure? What 388 00:20:00,400 --> 00:20:03,120 Speaker 1: do you see as being the future for her and 389 00:20:03,160 --> 00:20:06,359 Speaker 1: you and all of this because there is a lot 390 00:20:06,400 --> 00:20:08,240 Speaker 1: to a lot of work to be done here. Do 391 00:20:08,320 --> 00:20:12,440 Speaker 1: you see yourself joining DOSEE, do you see yourself becoming 392 00:20:12,600 --> 00:20:15,080 Speaker 1: an official organization or have you even thought about that? 393 00:20:16,760 --> 00:20:18,720 Speaker 2: Well, we did talk about that, and I think staying 394 00:20:18,760 --> 00:20:21,520 Speaker 2: independent makes the most sense. There's a lot of politics 395 00:20:21,520 --> 00:20:26,320 Speaker 2: with dose and you know it's ran by people who 396 00:20:26,400 --> 00:20:29,360 Speaker 2: are in the Trump administration, and you know, for us, 397 00:20:29,359 --> 00:20:31,560 Speaker 2: we want to look at this holistically. I want to 398 00:20:31,560 --> 00:20:33,560 Speaker 2: be able to look and not have to worry about 399 00:20:33,560 --> 00:20:35,240 Speaker 2: if there's a D and R next to the person 400 00:20:35,400 --> 00:20:38,199 Speaker 2: that we're finding data on. And so we definitely, we 401 00:20:38,240 --> 00:20:40,639 Speaker 2: definitely had that conversation about staying independent. Now are we 402 00:20:40,760 --> 00:20:44,560 Speaker 2: here to help, absolutely, But the independent part I think 403 00:20:44,600 --> 00:20:47,280 Speaker 2: gives us more flexibility to focus. I mean I literally 404 00:20:47,280 --> 00:20:49,480 Speaker 2: messaged her two days ago, was like, hey, look, I 405 00:20:49,520 --> 00:20:52,119 Speaker 2: need a tool where I can do a bulk INNGO 406 00:20:52,119 --> 00:20:55,520 Speaker 2: officer search. And like the next morning she's like, it's done. 407 00:20:55,960 --> 00:20:59,040 Speaker 2: You know, there's no approvals, there's no you know, board 408 00:20:59,040 --> 00:21:02,560 Speaker 2: of directors. It's it's like, we find it valuable, we 409 00:21:02,640 --> 00:21:05,560 Speaker 2: jump on it, and we get it done amazingly, just 410 00:21:05,640 --> 00:21:09,080 Speaker 2: amazing speed. And so I love that flexibility and I 411 00:21:09,080 --> 00:21:12,439 Speaker 2: love not being tied to a certain political party or another. 412 00:21:12,480 --> 00:21:15,879 Speaker 2: The goal is for all Americans to have access to 413 00:21:15,960 --> 00:21:18,400 Speaker 2: where their money is going, and we'll just keep building 414 00:21:18,440 --> 00:21:22,720 Speaker 2: tools as problems arise. That's the future and that's the goal, 415 00:21:22,840 --> 00:21:24,960 Speaker 2: is just to keep serving the American people by giving 416 00:21:25,000 --> 00:21:26,240 Speaker 2: them transparency through data. 417 00:21:27,640 --> 00:21:30,520 Speaker 1: What kind of feedback have you received? I mean, obviously 418 00:21:31,160 --> 00:21:33,760 Speaker 1: I sent a nice message to Data Republican herself that 419 00:21:33,840 --> 00:21:35,960 Speaker 1: this is fantastic, and Elon has said you're great, and 420 00:21:36,040 --> 00:21:37,800 Speaker 1: Charlie Kirkis said you're great, and a lot of a 421 00:21:37,840 --> 00:21:40,960 Speaker 1: lot of Twitter fhows. What kind of feedback are you 422 00:21:41,000 --> 00:21:44,359 Speaker 1: getting in terms of hate or pushback or criticism and 423 00:21:44,400 --> 00:21:45,600 Speaker 1: what is that criticism. 424 00:21:47,359 --> 00:21:51,119 Speaker 2: Well, the biggest criticism is that we're causing people to 425 00:21:51,200 --> 00:21:54,159 Speaker 2: lose jobs. Right. You know a lot of these a 426 00:21:54,160 --> 00:21:56,360 Speaker 2: lot of this money was propping up, you know, these 427 00:21:56,359 --> 00:21:59,560 Speaker 2: businesses that you know, even like popping up media companies, 428 00:22:00,080 --> 00:22:02,440 Speaker 2: you know, political was taking. That's going to get blows 429 00:22:02,440 --> 00:22:05,160 Speaker 2: my mind the most. How much money political not political, 430 00:22:05,200 --> 00:22:08,000 Speaker 2: but media companies were getting like political like why are 431 00:22:08,040 --> 00:22:11,800 Speaker 2: they getting these huge subscription numbers? And so one of 432 00:22:11,840 --> 00:22:14,560 Speaker 2: the one of the criticisms, oh, you're costing jobs. Well 433 00:22:14,960 --> 00:22:17,639 Speaker 2: I'm sorry, but when the government decided to shut the 434 00:22:17,640 --> 00:22:20,000 Speaker 2: country down for COVID, I lost my job, you know 435 00:22:20,040 --> 00:22:22,080 Speaker 2: what I mean, Like nobody cried about it. Then it's 436 00:22:22,160 --> 00:22:24,320 Speaker 2: just part of how it works. If it's if it's 437 00:22:24,320 --> 00:22:26,480 Speaker 2: not an effective part of government, it's got to go. 438 00:22:26,880 --> 00:22:28,639 Speaker 2: And I know that sucks for the family, and I 439 00:22:28,680 --> 00:22:30,919 Speaker 2: know that sucks with the person that's that's there and working. 440 00:22:31,320 --> 00:22:34,280 Speaker 2: But this isn't a charity. Our government is not a charity, 441 00:22:34,320 --> 00:22:35,919 Speaker 2: and we got to get away from that mindset. It 442 00:22:35,920 --> 00:22:38,440 Speaker 2: needs to be efficient, and it needs to servetive people, 443 00:22:38,480 --> 00:22:42,000 Speaker 2: and it needs to steward our money well or will 444 00:22:42,000 --> 00:22:45,240 Speaker 2: stop trusting him with it. You know that the government 445 00:22:45,240 --> 00:22:48,080 Speaker 2: needs to fear the people again, and they don't. They 446 00:22:48,160 --> 00:22:52,119 Speaker 2: just they just pillage and pillage and pillage with no 447 00:22:52,359 --> 00:22:54,840 Speaker 2: end in sight. And you know, I hope we can 448 00:22:55,600 --> 00:22:57,720 Speaker 2: keep moving that ball forward. 449 00:22:58,400 --> 00:23:01,720 Speaker 1: Sean, I am. I'm so impressed with you, guys. There's 450 00:23:01,720 --> 00:23:05,320 Speaker 1: so much talent in this country. There's so much incredible 451 00:23:05,359 --> 00:23:08,879 Speaker 1: talent on the sidelines in this country that if we 452 00:23:09,680 --> 00:23:12,000 Speaker 1: call it to bear. You know, you see these nations 453 00:23:12,040 --> 00:23:15,800 Speaker 1: that rise up and fight off an imperialist country or 454 00:23:15,800 --> 00:23:18,639 Speaker 1: fight off their enemy, and everyone pitches in, you know, 455 00:23:18,720 --> 00:23:21,840 Speaker 1: the World War two, Rosie the Riveter. I see this 456 00:23:22,000 --> 00:23:24,920 Speaker 1: mindset of people like you. You know, you got your background, 457 00:23:25,000 --> 00:23:27,000 Speaker 1: mister beast. Who would have guessed? I mean, and I 458 00:23:27,000 --> 00:23:30,080 Speaker 1: don't know data Republicans background, but obviously she's brilliant. I 459 00:23:30,119 --> 00:23:32,919 Speaker 1: see these people, everybody kind of pitching in in a 460 00:23:33,040 --> 00:23:36,800 Speaker 1: sort of altruistic way that says, hey, let's fix our country, guys. 461 00:23:37,119 --> 00:23:39,399 Speaker 1: And I got it. I mean, it's a little corny 462 00:23:39,440 --> 00:23:41,680 Speaker 1: and a little hokey maybe that I feel this way, 463 00:23:41,920 --> 00:23:45,320 Speaker 1: but it really inspires me. It does. And by that 464 00:23:45,560 --> 00:23:47,160 Speaker 1: I mean to say thank you for the great work 465 00:23:47,200 --> 00:23:47,560 Speaker 1: you're doing. 466 00:23:49,720 --> 00:23:53,159 Speaker 2: Absolutely. I think, like a lot of people, I just 467 00:23:53,240 --> 00:23:57,080 Speaker 2: wanted to be left alone. And we realized after the 468 00:23:57,200 --> 00:23:59,639 Speaker 2: Hurricane Helene storm that we had to step back in 469 00:23:59,680 --> 00:24:01,040 Speaker 2: the people who want to be left low and have 470 00:24:01,119 --> 00:24:03,800 Speaker 2: to enter the chat. And you know, even I knew 471 00:24:03,800 --> 00:24:07,120 Speaker 2: that being political is a risk, and I even told 472 00:24:07,119 --> 00:24:09,120 Speaker 2: my family, Look, I'd rather lose my job than lose 473 00:24:09,119 --> 00:24:11,760 Speaker 2: my country. And so you know, about four months ago 474 00:24:11,920 --> 00:24:14,960 Speaker 2: I became political in the sense of getting active and 475 00:24:15,000 --> 00:24:17,320 Speaker 2: making sure that our government's doing what it's supposed to 476 00:24:17,359 --> 00:24:20,800 Speaker 2: be doing. And that shouldn't be that shouldn't be controversial. 477 00:24:20,840 --> 00:24:22,440 Speaker 2: We should all be active. We should be going under 478 00:24:22,440 --> 00:24:25,480 Speaker 2: our local city council meetings, see what these people are doing. 479 00:24:25,560 --> 00:24:27,360 Speaker 2: Hold them accountable. And that's the big thing with Data 480 00:24:27,400 --> 00:24:29,199 Speaker 2: Republican is we're giving people a tool to see that. 481 00:24:29,760 --> 00:24:32,240 Speaker 2: But you need to go on your local level. Hold 482 00:24:32,359 --> 00:24:36,080 Speaker 2: your local politicians accountable, hold your state accountable. They're all 483 00:24:36,200 --> 00:24:39,040 Speaker 2: wasting your money without a doubt, you know. Go look 484 00:24:39,040 --> 00:24:40,920 Speaker 2: at the things or spending. Go to the budget meetings, 485 00:24:41,040 --> 00:24:42,560 Speaker 2: ask for a copy of the budget and throw it 486 00:24:42,600 --> 00:24:45,840 Speaker 2: into AI, take it, cut, paste it into gross and 487 00:24:45,960 --> 00:24:49,240 Speaker 2: have it break down what they're spending your city budget 488 00:24:49,280 --> 00:24:51,600 Speaker 2: on and make sure it makes sense. And if it doesn't, 489 00:24:51,920 --> 00:24:55,840 Speaker 2: make it known it It takes everyone being involved. The 490 00:24:55,840 --> 00:24:57,159 Speaker 2: reason we got to where we're at is because we 491 00:24:57,280 --> 00:25:00,280 Speaker 2: all turned our head and just focused on us and said, 492 00:25:00,320 --> 00:25:02,520 Speaker 2: I'm sure they've got it. And we turned around decades 493 00:25:02,600 --> 00:25:04,639 Speaker 2: later and went, oh, my god, they don't got it. 494 00:25:04,680 --> 00:25:07,880 Speaker 1: They've been self dealing. John Hendricks, you are awesome. Will 495 00:25:07,920 --> 00:25:09,760 Speaker 1: be in touch. Keep up the great work, my man. 496 00:25:11,320 --> 00:25:12,080 Speaker 2: Thank you so much. 497 00:25:12,400 --> 00:25:14,720 Speaker 1: We's been little man Els nice. 498 00:25:14,520 --> 00:25:15,440 Speaker 2: Left for billing me. 499 00:25:15,920 --> 00:25:17,560 Speaker 1: Thank you and good night