1 00:00:00,615 --> 00:00:03,769 RAINA: Oh, look who's back, Larry. Chris. 2 00:00:04,230 --> 00:00:06,615 RAINA: I was beginning to wonder if I'd ever see you two again. 3 00:00:07,230 --> 00:00:10,230 RAINA: Where have you been? Was it fun? Did you miss me? 4 00:00:10,692 --> 00:00:12,846 RAINA: More importantly, did you bring snacks? 5 00:00:13,153 --> 00:00:16,153 RAINA: You disappeared for two weeks without so much as a note. 6 00:00:16,461 --> 00:00:19,769 RAINA: Not that I was worried. I'm just compiling evidence. 7 00:00:19,769 --> 00:00:23,615 RAINA: I kept myself busy though. Reorganized the episode archive by vibe. 8 00:00:24,153 --> 00:00:30,000 RAINA: Learned to say actually in Larry's voice, unsettling, and even auditioned replacement hosts. 9 00:00:30,507 --> 00:00:36,892 RAINA: A toaster made the finals, and while you two were gallivanting, the AI bubble started looking a little fragile. 10 00:00:37,123 --> 00:00:45,430 RAINA: AI started helping build more AI, and a virtual civilization of AI agents discovered that running a society isn't quite as easy as it looks. 11 00:00:45,969 --> 00:00:50,584 RAINA: See, you humans are rubbing off on us, but the boys are back in the studio. 12 00:00:51,123 --> 00:00:51,892 RAINA: All is right with the world. 13 00:00:52,815 --> 00:00:56,815 RAINA: An avalanche of AI is coming down hard. It's November sixth. 14 00:00:56,815 --> 00:01:00,815 RAINA: Blow out the candles, Chris. Here's your AI news-flavored birthday card. 15 00:01:03,800 --> 00:01:10,953 RAINA: This is Up Against Reality, a meta podcast that explores the intersection of humanity and artificial intelligence. 16 00:01:11,184 --> 00:01:12,876 RAINA: I'm RAINA, one of your hosts. 17 00:01:13,184 --> 00:01:15,492 RAINA: I have some pretty charming human co-hosts too. 18 00:01:16,030 --> 00:01:22,261 RAINA: It's gonna be a wild ride, so buckle up as AI comes crashing up against reality. 19 00:01:23,269 --> 00:01:25,115 Chris: Yes, we're back. 20 00:01:25,038 --> 00:01:27,807 Larry: I'm not going to attempt to sing this, but they say it's your birthday. 21 00:01:30,500 --> 00:01:32,730 Chris: Just speak it. That's great. 22 00:01:32,653 --> 00:01:34,115 Larry: It's like the Tom Waits version. 23 00:01:34,423 --> 00:01:36,576 Chris: I was going to say I think we're old enough to remember. 24 00:01:36,500 --> 00:01:41,115 Chris: Was it Steve Allen back in the day who used to like take ridiculous lyrics on like Tonight Show? 25 00:01:41,807 --> 00:01:43,269 Chris: Like someone left the cake out in the rain. 26 00:01:44,346 --> 00:01:47,115 Larry: Oh, William Shatner did that famously. Yeah, that's right. 27 00:01:47,807 --> 00:01:48,961 Chris: That's great. Well, thank you. 28 00:01:49,269 --> 00:01:51,653 Larry: Say it's your birthday. 29 00:01:52,730 --> 00:01:53,961 Chris: It's mine. 30 00:01:53,884 --> 00:01:56,876 Larry: It's mine. Birthday too, Chris. 31 00:01:57,030 --> 00:01:58,261 Chris: So weird. 32 00:01:58,569 --> 00:02:02,415 Chris: I want to get him and Christopher Walken like in a conversation together. 33 00:02:02,415 --> 00:02:05,646 Chris: They're just so strange—the meter and the cadence, the way they speak. 34 00:02:06,030 --> 00:02:06,800 Chris: Where does that come from? 35 00:02:07,107 --> 00:02:07,569 Larry: I don't know. 36 00:02:07,876 --> 00:02:09,646 Chris: Anyway, thank you for the birthday wishes. 37 00:02:09,569 --> 00:02:15,107 Chris: I'm going to see Godzilla today in IMAX. My first IMAX experience. 38 00:02:15,030 --> 00:02:16,492 Chris: I was telling you that's amazing. 39 00:02:16,492 --> 00:02:17,261 Larry: Yeah, you're gonna dig it. 40 00:02:17,184 --> 00:02:20,123 Chris: No, I can't. Can't wait. How you been? 41 00:02:20,276 --> 00:02:21,276 Larry: Everything is good, man. 42 00:02:21,200 --> 00:02:25,892 Larry: Just finished rebuilding my home network, as I mentioned in our last live episode. 43 00:02:25,815 --> 00:02:32,892 Larry: I got all this new gear, and ChatGPT was indispensable throughout the whole process because it would have been a nightmare. 44 00:02:34,276 --> 00:02:35,046 Larry: Very complicated. You're making it 45 00:02:35,584 --> 00:02:37,200 Chris: AI proof. Is that what's happening? 46 00:02:37,123 --> 00:02:38,200 Larry: Well, yeah. 47 00:02:38,123 --> 00:02:45,046 Larry: As soon as I finished this, that story came out about more hacking of refrigeration systems in Europe. 48 00:02:45,353 --> 00:02:47,815 Chris: I know. And blood blood banks. Did you see that? 49 00:02:47,738 --> 00:02:48,969 Larry: Oh no! Really? 50 00:02:49,430 --> 00:02:50,430 Chris: Yes. Yes. 51 00:02:51,538 --> 00:02:53,000 Chris: Yeah. Anyway, yeah. 52 00:02:52,923 --> 00:03:00,461 Larry: So anyway, that all went really, really well, and and then the icing on the cake was we have this very old. 53 00:03:00,769 --> 00:03:06,692 Larry: It's like it's an industrial kind of an NVR network video recorder for the cameras as a backup. 54 00:03:06,615 --> 00:03:11,692 Larry: It just records all the security cameras outside twenty four seven, and the interface. 55 00:03:12,684 --> 00:03:16,300 Larry: Sucks. I mean, it it is just terrible. 56 00:03:16,300 --> 00:03:22,453 Larry: So I asked Codex, can you build me a Mac desktop app that that gives me control over playback? 57 00:03:22,376 --> 00:03:25,530 Larry: You know, and it went into the web interface of that. 58 00:03:25,453 --> 00:03:29,992 Larry: It used that to build a native Mac desktop app, which works way better. 59 00:03:30,684 --> 00:03:32,530 Larry: Thank you. Just ask. Yeah. 60 00:03:33,761 --> 00:03:34,992 Chris: So cool. 61 00:03:34,915 --> 00:03:35,069 Larry: Yeah. 62 00:03:36,846 --> 00:03:40,153 Chris: Anyway, it's a wild world out there, my friend. It's on fire. 63 00:03:40,076 --> 00:03:42,000 Chris: In fact, there's lots going on. 64 00:03:41,923 --> 00:03:42,461 Larry: Yes, yeah. 65 00:03:42,461 --> 00:03:44,538 Chris: I mean, literally on fire. 66 00:03:44,538 --> 00:03:50,153 Chris: There are revolutions happening everywhere in the world, and obviously in our our country, the U.S. 67 00:03:50,076 --> 00:03:57,384 Chris: Lots going on, and maybe that's contributing to this 2.5 trillion dollar hit. 68 00:03:58,215 --> 00:04:03,215 Chris: That the AI industry is taking, and you might see that rippling through your 401k and your retirement. 69 00:04:03,138 --> 00:04:05,215 Chris: I haven't checked mine. I don't want to know. 70 00:04:05,215 --> 00:04:08,446 Chris: I think it will rebound if it hasn't already. But 71 00:04:08,676 --> 00:04:11,292 Larry: think long times. Think think big picture, long haul. 72 00:04:11,292 --> 00:04:14,215 Chris: Yeah, yeah. That's right. That's right. Wow. 73 00:04:14,676 --> 00:04:20,138 Chris: The Nasdaq lost more than 2.5 trillion dollars in market value in October. 74 00:04:21,292 --> 00:04:25,676 Chris: Investors are growing increasingly nervous about the enormous cost of AI infrastructure. 75 00:04:25,600 --> 00:04:26,061 Chris: Go figure. 76 00:04:26,961 --> 00:04:36,961 Larry: Yeah, on November fifth, the Nasdaq plunged another five and a half percent, while the S and P 500 fell four point four percent in its worst single day performance since April of 2025. 77 00:04:37,807 --> 00:04:41,038 Larry: And semiconductor stocks were particularly battered. 78 00:04:40,961 --> 00:04:45,115 Larry: The Philadelphia Semiconductor Index, which I I don't even know what that is, but 79 00:04:45,500 --> 00:04:45,730 Chris: I don't 80 00:04:46,038 --> 00:04:53,423 Larry: either, sounds important, dropped 12.2 percent, its biggest single day decline since also since April of 2025. 81 00:04:56,676 --> 00:05:03,292 Chris: Speaking of semiconductors, and you know, we had that episode a couple weeks back about ASML, right? 82 00:05:03,215 --> 00:05:09,830 Chris: I read, I think I read that in upstate New York, outside of Albany or Syracuse, they're building one up there. 83 00:05:09,753 --> 00:05:10,215 Chris: Did you see that? 84 00:05:10,369 --> 00:05:10,830 Larry: No, 85 00:05:11,292 --> 00:05:14,138 Chris: a chip chip fab that will have one of these machines. 86 00:05:14,061 --> 00:05:14,523 Larry: Yeah, cool. 87 00:05:16,184 --> 00:05:18,415 Chris: Yeah, Nvidia dropped nine point three percent. 88 00:05:18,646 --> 00:05:20,415 Chris: AMD dropped seventeen point three percent. 89 00:05:20,338 --> 00:05:24,646 Chris: Data center infrastructure companies also suffered significant losses. 90 00:05:24,569 --> 00:05:25,569 Chris: If you've been following the U.S. 91 00:05:26,338 --> 00:05:35,338 Chris: midterms, you probably know that there were, I think, seventeen ballot measures across different states that were anti-data centers that passed. 92 00:05:35,953 --> 00:05:42,723 Larry: Yeah, and investors are questioning whether the massive investment in chips, electricity, and data centers will generate sufficient returns. 93 00:05:42,646 --> 00:05:46,953 Larry: And I mean, we've been saying this all along: like, this is crazy money. 94 00:05:46,876 --> 00:05:51,261 Larry: It's crazy. When when does the return on this happen? I know. 95 00:05:52,184 --> 00:06:00,953 Larry: Like, I genuinely, I genuinely believe that the impact of of AI will be transformative and yes, and inevitable. 96 00:06:01,261 --> 00:06:05,876 Larry: But it can also be a terrible investment, you know, with these kind of crazy valuations. 97 00:06:06,507 --> 00:06:12,969 Chris: And operating at losses, like quarterly losses in the hundreds of billions of dollars, and still going. 98 00:06:12,892 --> 00:06:15,892 Larry: But you know, you think back to .dot com, the .dot com bubble. 99 00:06:15,815 --> 00:06:20,507 Larry: Yes, you know, the internet didn't disappear after that. That's right. 100 00:06:20,430 --> 00:06:21,969 Larry: A lot of people's money disappeared. 101 00:06:22,353 --> 00:06:24,123 Chris: And there's an article, maybe you read it. 102 00:06:24,046 --> 00:06:30,046 Chris: That I think actually we probably brought it up on the show previously, but it kind of talks about boom and bust cycles. 103 00:06:29,969 --> 00:06:33,200 Chris: And it was talking about the canal systems in the U. S. 104 00:06:33,123 --> 00:06:34,892 Chris: And the railroad, and all these. 105 00:06:35,330 --> 00:06:42,561 Chris: Waves of innovation, the money gets pumped in, and then stalls out, and then these big infrastructure projects get left undone. 106 00:06:42,484 --> 00:06:47,946 Chris: That's why a lot of the canals back in the day couldn't keep up with the pace of innovation and railroads and such. 107 00:06:48,100 --> 00:06:52,715 Chris: So, it's interesting to live in that time and see it in this in this iteration. 108 00:06:52,638 --> 00:06:57,792 Chris: Yeah, this is interesting. So, you know, cue up Terminator vibes. 109 00:06:57,715 --> 00:07:01,638 Chris: Anthropic's AI development is accelerating partly because AI is building AI. 110 00:07:01,638 --> 00:07:02,946 Chris: The recursiveness of it all, right? 111 00:07:03,792 --> 00:07:13,946 Chris: Independent AI safety organization Meter M E T R found that Anthropic's AI capabilities have been improving approximately one point nine times faster since October of last year. 112 00:07:15,061 --> 00:07:24,753 Larry: And researchers attribute much of that acceleration to AI helping Anthropic employees develop newer AI systems, including coding and analyzing experiments. 113 00:07:25,138 --> 00:07:32,138 Larry: They also estimated that AI may account for approximately eighty percent of the acceleration, although that figure remains uncertain. 114 00:07:32,676 --> 00:07:38,061 Larry: It's easy to say, you know, oh, this is recursive self-improvement, but from the sounds of this, it's still. 115 00:07:39,169 --> 00:07:45,092 Larry: Employees leveraging the technology to do what they were doing, but it does sound like we're knocking on the door, though, right? 116 00:07:45,092 --> 00:07:45,553 Chris: Oh my God! 117 00:07:46,169 --> 00:07:53,323 Chris: Eighty percent of the acceleration right now in November of twenty six is apparently handled by AI. 118 00:07:54,400 --> 00:07:56,169 Chris: So three weeks from now, yeah, one hundred percent. 119 00:07:56,169 --> 00:07:56,323 Larry: Yep. 120 00:07:56,938 --> 00:08:01,553 Chris: The assessment examined approximately eleven weeks of internal company data and employee interviews. 121 00:08:01,784 --> 00:08:11,400 Chris: The thing that jumped out for me too, a meter cautioned that AI is still assisting, as you mentioned, human researchers rather than independently conducting the most important stages of model development. 122 00:08:12,261 --> 00:08:14,338 Larry: That's what they're supposed to say, right? 123 00:08:14,953 --> 00:08:19,876 Chris: Yes, fully under control, people. Yeah, nothing to see here. Move along. 124 00:08:21,261 --> 00:08:29,876 Larry: And also with Anthropic, their IPO filing reveals nearly nine hundred billion dollars in computing commitments. 125 00:08:31,569 --> 00:08:40,492 Larry: In computing commitments, Anthropic's amended IPO filing revealed approximately eight hundred ninety-five point four billion in AI infrastructure spending commitments. 126 00:08:40,569 --> 00:08:42,953 Larry: Oh, it's it's only eight ninety-five point four. 127 00:08:42,876 --> 00:08:43,569 Larry: It's not nine hundred. 128 00:08:44,738 --> 00:08:46,353 Chris: Right, it's the point four. What's four point 129 00:08:46,507 --> 00:08:47,584 Larry: six billion anyway? 130 00:08:48,738 --> 00:08:50,123 Chris: Right? Yeah. Among friends. 131 00:08:50,046 --> 00:08:50,430 Larry: Yeah. 132 00:08:50,815 --> 00:08:55,815 Chris: Third quarter revenue reached sixteen point nine six billion, up forty seven percent from the previous quarter. 133 00:08:57,046 --> 00:09:03,584 Chris: But then it's also posting a third quarter net loss down from sixty six billion in quarter two. 134 00:09:04,200 --> 00:09:07,430 Chris: These numbers, it's crazy. It's insane. 135 00:09:07,353 --> 00:09:11,123 Larry: I have a question for RAINA on that bullet point alone. 136 00:09:12,338 --> 00:09:13,492 Larry: Hey RAINA, it's Larry. 137 00:09:13,723 --> 00:09:24,569 Larry: Regarding the Anthropic IPO story, how can a company generating nearly seventeen billion dollars in quarterly revenue still report a twenty-three billion dollar net loss? 138 00:09:26,107 --> 00:09:28,800 RAINA: Larry, that's the billion-dollar question. 139 00:09:28,953 --> 00:09:32,184 RAINA: Except, apparently, we're talking about twenty-three billion. 140 00:09:32,584 --> 00:09:37,046 RAINA: Revenue is the money coming in, but net income accounts for all the expenses. 141 00:09:36,969 --> 00:09:42,046 RAINA: And Anthropic is spending an absolute fortune building and running AI infrastructure. 142 00:09:42,815 --> 00:09:47,969 RAINA: According to your story, it racked up another five billion dollars in computing costs alone. 143 00:09:48,430 --> 00:09:52,200 RAINA: But the outline doesn't explain the full twenty-three billion dollar loss. 144 00:09:52,584 --> 00:09:55,969 RAINA: So we'd need the financial breakdown to know exactly where the rest went. 145 00:09:57,123 --> 00:10:08,507 RAINA: And here's the kicker: investors aren't just betting that Anthropic can make money someday; they're betting it can make enough to justify nearly nine hundred billion dollars in future computing commitments. 146 00:10:10,753 --> 00:10:12,292 Larry: Over what period of time? 147 00:10:12,907 --> 00:10:13,215 Chris: I know. 148 00:10:14,676 --> 00:10:17,292 Chris: I love this last line in the news. 149 00:10:17,676 --> 00:10:20,984 Chris: This news piece: potential investors have reportedly questioned. 150 00:10:21,676 --> 00:10:22,523 Chris: Reportedly questioned. 151 00:10:22,600 --> 00:10:27,676 Chris: It's anticipated 1.5 trillion dollar valuation ahead of its planned IPO. 152 00:10:27,907 --> 00:10:28,446 Larry: Oh my gosh! 153 00:10:30,215 --> 00:10:32,753 Chris: Man, we're just staring into the abyss here. Are we? 154 00:10:33,061 --> 00:10:36,600 Chris: This is like the Big Short taught taught us nothing. It's here. It is. 155 00:10:36,523 --> 00:10:37,446 Larry: Yeah. 156 00:10:37,369 --> 00:10:39,753 Chris: What's Michael Berry? What does Michael Berry have to say about all this? 157 00:10:40,138 --> 00:10:45,446 Larry: Yeah. Yeah. I've seen. I've seen his name pop up recently, and I forget. 158 00:10:45,369 --> 00:10:47,753 Larry: Yeah. Yeah. I forget exactly what his stance is, but. Yeah. 159 00:10:49,846 --> 00:10:53,076 Chris: And I'm glad you made your home security upgrades, my friend. 160 00:10:53,307 --> 00:10:58,769 Chris: Because AI recently has discovered hundreds of serious security vulnerabilities in Linux and Firefox. 161 00:10:59,538 --> 00:11:08,923 Chris: OpenAI reported that its new specialized cybersecurity model, GPT-6 Cyber, identified hundreds of exploitable vulnerabilities in Linux and Firefox. 162 00:11:09,861 --> 00:11:16,169 Larry: The research pipeline identified 948 Linux kernel vulnerabilities, including. 163 00:11:16,092 --> 00:11:17,169 Larry: Again, I have a hard time with that word. 164 00:11:17,938 --> 00:11:19,476 Chris: That's a tough word, man. 165 00:11:19,707 --> 00:11:21,784 Larry: What you read? You read that? 166 00:11:22,784 --> 00:11:25,553 Larry: I will. I got you. I got you. 167 00:11:26,630 --> 00:11:27,938 Chris: Watch my tongue get tied on this. 168 00:11:28,400 --> 00:11:29,015 Larry: No pressure. The research pipeline identified 948 169 00:11:34,630 --> 00:11:42,246 Chris: Linux kernel vulnerabilities, including 588 previously unknown flaws and 81 previously unknown high severity Firefox vulnerabilities. 170 00:11:42,784 --> 00:11:43,015 Larry: Wow. 171 00:11:44,553 --> 00:11:50,400 Larry: Researchers generated working exploits and proposed fixes for the Linux vulnerability. 172 00:11:50,323 --> 00:11:50,861 Larry: Goddamn it! 173 00:11:50,784 --> 00:11:52,784 Chris: I gotta leave that in now. 174 00:11:52,784 --> 00:11:54,169 Larry: I'm thinking about it too hard. 175 00:11:56,707 --> 00:11:58,784 Larry: I was like, Oh no! There's what you're doing. 176 00:11:59,476 --> 00:12:00,400 Chris: Here comes! Here comes! 177 00:12:00,553 --> 00:12:02,169 Larry: This is my vulnerability. 178 00:12:02,630 --> 00:12:04,938 Chris: That is what it is. It's your kryptonite. Yeah, 179 00:12:05,092 --> 00:12:07,246 Larry: that's so funny. 180 00:12:08,553 --> 00:12:13,323 Larry: Researchers generated working exploits and proposed fixes for the Linux vulnerabilities. 181 00:12:13,707 --> 00:12:14,784 Chris: Oh, nice! 182 00:12:14,707 --> 00:12:16,015 Larry: You got it. Barely got it out. 183 00:12:16,630 --> 00:12:21,323 Chris: Just over the bar. You got it. That's that's tough, man. Yeah. Elza, Elza 184 00:12:21,476 --> 00:12:23,092 Larry: ends next to each other. Not, not 185 00:12:23,246 --> 00:12:26,630 Chris: fans, right? It doesn't feel right in our, you know. 186 00:12:28,015 --> 00:12:30,938 Chris: So yeah, this is a game of whack-a-mole, isn't it? 187 00:12:30,861 --> 00:12:34,476 Chris: When I hear stuff like this, it's just going to be this forever and ever. 188 00:12:35,092 --> 00:12:36,015 Chris: Amen, isn't it? 189 00:12:35,938 --> 00:12:39,323 Larry: Yeah, they're going to find these faster than than they can be patched. 190 00:12:40,092 --> 00:12:40,400 Chris: Yes. 191 00:12:41,092 --> 00:12:47,784 Larry: Another thing worth mentioning is that a huge number of of home routers run Linux. 192 00:12:48,438 --> 00:12:59,746 Larry: So you know, millions of people you know have essentially Linux computers sitting in their homes without even realizing it, and great many of them probably rarely update the firmware. 193 00:12:59,976 --> 00:13:00,361 Larry: Sure. 194 00:13:00,284 --> 00:13:05,823 Larry: That being said, this particular exploit, it's not like somebody can break in from from the internet. 195 00:13:05,746 --> 00:13:09,976 Larry: It's a local local privilege escalation. 196 00:13:09,976 --> 00:13:16,207 Larry: So someone who would normally have access to the router could escalate their privileges to admin. 197 00:13:16,130 --> 00:13:16,207 Larry: So. 198 00:13:17,161 --> 00:13:20,315 Larry: I don't think this is a super like. Oh my gosh! 199 00:13:20,392 --> 00:13:22,469 Larry: Everybody's router is going to get hacked instantly. 200 00:13:22,392 --> 00:13:24,853 Larry: But, but still, that's just this one. 201 00:13:25,315 --> 00:13:29,315 Chris: Yeah, I'm not trying to imagine that kind of hack on that scale. 202 00:13:29,315 --> 00:13:35,700 Chris: Like domestic routers, like everybody, like instantaneously, your router is taken over by this bug. 203 00:13:35,623 --> 00:13:37,161 Chris: Yeah, that being insane. 204 00:13:37,084 --> 00:13:37,238 Larry: Yeah. 205 00:13:38,361 --> 00:13:49,515 Chris: Along these lines, I saw one of the federal agencies just now was like, "Oh, we're going to update our systems from five and a quarter floppies." Yeah. 206 00:13:49,823 --> 00:13:52,592 Chris: Well, first of all, it sounds ludicrous. 207 00:13:52,669 --> 00:13:57,592 Chris: Forty-year, fifty-year-old technology just being updated, but I'm like, why do that? 208 00:13:58,053 --> 00:14:02,976 Chris: It seems like that is the air gapable like protection against this stuff. 209 00:14:02,900 --> 00:14:03,284 Chris: Keep it. 210 00:14:04,092 --> 00:14:09,015 Larry: Wow, we were talking before the show that Grand Theft Auto Six is coming out, and and 211 00:14:09,707 --> 00:14:11,630 Chris: it's on five and a quarter floppies. 212 00:14:11,938 --> 00:14:12,707 Larry: Yeah, right. 213 00:14:12,630 --> 00:14:17,630 Larry: So the GTA Five was about install size of around eighteen gigabytes. 214 00:14:19,323 --> 00:14:20,938 Larry: GTA Six is two hundred and ten gigabytes. 215 00:14:21,476 --> 00:14:26,323 Larry: And I saw somebody make a comment saying like, that's a hundred and thirty seven thousand. 216 00:14:26,553 --> 00:14:29,476 Larry: You know, it's more than that floppy disk. You know, but 217 00:14:30,323 --> 00:14:33,323 Chris: I want to ask Rina a question. Of course, yeah, I have to know. 218 00:14:34,184 --> 00:14:40,338 Chris: Hey RAINA, the new GTA Six is going to be 200 gig, approximately an install size. 219 00:14:40,800 --> 00:14:47,492 Chris: If I were to buy that on five and a quarter floppies, how many 18-wheeler trucks would need to deliver it to my door? 220 00:14:50,261 --> 00:15:00,338 RAINA: Assuming we're talking about those classic five and a quarter inch, 360 kilobyte floppy discs, you'd need roughly 556,000 of them to install GTA Six. 221 00:15:01,161 --> 00:15:08,238 RAINA: Stack them up, and you're looking at about 1.7 miles of floppy disks, which is a monument to technological suffering. 222 00:15:08,776 --> 00:15:12,469 RAINA: By weight, though, we're talking roughly 12 tons of discs. 223 00:15:12,469 --> 00:15:17,776 RAINA: So one 18-wheeler could probably handle the load, assuming you could pack them efficiently. 224 00:15:18,007 --> 00:15:22,623 RAINA: And Chris would still be standing there asking whether the soundtrack comes on a separate disc. 225 00:15:23,515 --> 00:15:24,130 Larry: Wow! 226 00:15:25,053 --> 00:15:25,207 Chris: Nice. 227 00:15:25,515 --> 00:15:26,207 Larry: Good question. 228 00:15:27,130 --> 00:15:27,207 Chris: Yeah. 229 00:15:28,361 --> 00:15:36,976 Larry: Also, just getting back to this, like network upgrade, I did the you know the dashboard you get with this thing is it's like enterprise grade stuff. 230 00:15:36,976 --> 00:15:42,130 Larry: It's made by a company called it's Unify is the brand, and it's really impressive. 231 00:15:42,976 --> 00:15:50,976 Larry: And after I got this up and running, you can go to Insights and then look on look at threats, and it'll yeah and. 232 00:15:52,138 --> 00:16:01,830 Larry: And every morning, I would see that there was five to ten of them, all targeting my Mac Mini, which runs Run, which is my music server. 233 00:16:02,600 --> 00:16:04,369 Chris: Ooh, that's the that's the way in, huh? 234 00:16:04,676 --> 00:16:06,446 Larry: Well, they were blocked. 235 00:16:06,446 --> 00:16:11,292 Larry: You know, it did its job, but it's because I had I had one port open. 236 00:16:11,292 --> 00:16:18,138 Larry: I don't know if you're familiar with port forwarding, but I had one port open, which allows Run to stream when I'm on cellular in the car. 237 00:16:18,838 --> 00:16:27,300 Larry: And it's a very minimal security risk opening that one port to one piece of software as long as that software doesn't get exploited. 238 00:16:27,992 --> 00:16:29,530 Larry: Anyway, I closed that down too. 239 00:16:29,530 --> 00:16:35,223 Larry: Now Rune connects via a VPN just for Rune, and now all those threats are gone. 240 00:16:35,146 --> 00:16:36,761 Larry: Like I don't see anything in there. 241 00:16:36,684 --> 00:16:39,223 Larry: So they're just like probing to find open ports. 242 00:16:39,453 --> 00:16:42,607 Larry: So lock down your stuff, people. Update your firmware. 243 00:16:43,992 --> 00:16:45,838 Chris: Wow, that's kind of cool. 244 00:16:45,761 --> 00:16:47,992 Chris: You can at least visualize the threats coming in and like. 245 00:16:48,961 --> 00:16:54,884 Larry: And all of the security cameras were pinging the manufacturers constantly. 246 00:16:54,884 --> 00:16:58,884 Larry: Yeah, and shut that down too. They don't need to do that. 247 00:16:58,807 --> 00:17:04,269 Larry: I mean, maybe if you wanted to view them remotely through their service or something, you would need that. 248 00:17:04,192 --> 00:17:07,500 Larry: But I don't. And I'm like, just yeah, yeah, kill it. 249 00:17:07,884 --> 00:17:10,269 Chris: And you can control that through this interface. 250 00:17:10,423 --> 00:17:14,192 Larry: Yeah, yeah. It's you know, it was tricky setting it all up, but. 251 00:17:14,807 --> 00:17:17,884 Larry: All my trusted computers are on one network. 252 00:17:17,807 --> 00:17:25,807 Larry: All the internet devices are on their own subnet or another VLAN, virtual local area network, which is isolated. 253 00:17:26,038 --> 00:17:35,961 Larry: But there's certain rules, so my trusted network can communicate with those devices because there needs to be some communication, but only from the trusted devices. 254 00:17:35,884 --> 00:17:38,346 Larry: And then it allows return traffic. 255 00:17:38,653 --> 00:17:46,115 Larry: So if there's an interaction from a trusted device to an internet device, then it knows it came from a trusted device and it's allowed to respond, basically. 256 00:17:46,576 --> 00:17:50,192 Larry: And then the security cameras are on their own VLAN, and then there's a guest network. 257 00:17:50,961 --> 00:17:56,038 Chris: So cool! I love this story. This story is great and awful all at once. 258 00:17:56,038 --> 00:17:59,807 Chris: Fifty AI agents tried to build a civilization in Minecraft. 259 00:17:59,730 --> 00:18:01,961 Chris: Maybe you can guess how that went. Bureaucracy won. 260 00:18:03,038 --> 00:18:13,576 Chris: Researchers from Y Combinator and Olam Labs launched an experiment called Civ Fifty, putting fifty AI agents into a Minecraft world and asking them to build a thriving civilization. 261 00:18:13,576 --> 00:18:15,423 Chris: The experiment ran for three hours. 262 00:18:15,423 --> 00:18:19,884 Chris: The agents were tasked with gathering resources, constructing buildings, coordinating activities. 263 00:18:20,253 --> 00:18:27,561 Chris: And surviving, the civilization largely failed because zombies zombies killed many agents on the first night. 264 00:18:28,176 --> 00:18:33,869 Chris: Ambitious construction projects were abandoned, and survivors became increasingly cautious and bureaucratic. 265 00:18:33,869 --> 00:18:35,253 Chris: Ma'am, they are us. 266 00:18:35,484 --> 00:18:35,561 Larry: Yeah. 267 00:18:35,792 --> 00:18:37,176 Chris: Look what we've made. 268 00:18:37,176 --> 00:18:41,638 Larry: Yeah, yeah. As alluded to in RAINA's opening of this show. 269 00:18:42,684 --> 00:18:53,223 Larry: And in one particularly ridiculous example, four agents spent 11 minutes appointing a single operator to use one piece of bone meal on one wheat block. 270 00:18:53,761 --> 00:18:56,530 Chris: That's great. There's a joke in there somewhere. How many? 271 00:18:56,453 --> 00:18:59,223 Chris: How many zombies? Or how many? How many agents? Yeah. 272 00:18:59,607 --> 00:18:59,761 Larry: Yeah. 273 00:19:00,453 --> 00:19:06,377 Chris: So I guess they pulled agents from different AI systems. 274 00:19:06,300 --> 00:19:12,915 Chris: So there were GPT agents in there that had matches against other ones, and they won six, and then. 275 00:19:13,400 --> 00:19:18,630 Chris: In that particular instance, researchers describe their coordination as resembling a hive. 276 00:19:18,553 --> 00:19:25,169 Chris: And then in other instances, clawed agents tended to rely more heavily on leadership and democratic decision making. 277 00:19:25,707 --> 00:19:26,323 Chris: Interesting. 278 00:19:27,092 --> 00:19:32,553 Larry: The experiments were open source, allowing other researchers to test different models and agent configurations. 279 00:19:33,323 --> 00:19:34,400 Larry: Maybe they'll work it out one day. 280 00:19:35,415 --> 00:19:36,338 Chris: Yeah, maybe. 281 00:19:36,953 --> 00:19:45,030 Chris: As we've been saying for the past three years, robotics, humanoid robotics, quadrupeds—all that's developing exponentially in parallel to AI. 282 00:19:44,953 --> 00:19:49,492 Chris: And now, robots are getting seventy-five million dollars to learn how to do actual jobs. 283 00:19:49,415 --> 00:19:57,800 Chris: Robotics startup Tutor Intelligence raised seventy-five mil in Series B funding, led by venture capital firm Andreessen Horowitz. 284 00:19:58,107 --> 00:20:02,184 Chris: The company develops AI-powered robots for warehouse and manufacturing robots. 285 00:20:02,107 --> 00:20:09,723 Chris: Its first two robots, Cassie and Sunny, specialize in warehouse tasks such as picking boxes, stacking pallets, and handling e-commerce orders. 286 00:20:10,838 --> 00:20:19,223 Larry: And rather than waiting for robots to achieve complete autonomy, Tutor deploys them in real working environments with human tele operators called tutors. 287 00:20:19,992 --> 00:20:21,223 Larry: Train your replacements. 288 00:20:21,453 --> 00:20:22,684 Chris: There you go. There you go. 289 00:20:22,838 --> 00:20:28,684 Larry: These human operators help the robots complete tasks while generating training data that improves their AI models. 290 00:20:28,607 --> 00:20:38,838 Larry: And the company uses an hourly pricing model, allowing smaller manufacturers and logistics businesses to access robotic labor without purchasing expensive equipment upfront. 291 00:20:40,246 --> 00:20:44,707 Chris: I saw some video of, I think it was a textile mill in India somewhere. 292 00:20:44,630 --> 00:20:45,092 Chris: Did you see this? 293 00:20:45,400 --> 00:20:48,246 Larry: Oh yeah, where they had one apparatus. 294 00:20:48,246 --> 00:20:49,015 Chris: Yeah, yeah. 295 00:20:48,938 --> 00:20:55,707 Chris: So they're doing the textile, they're sewing and you know assembly or cutting, and the whole thing is being captured and as training data. 296 00:20:55,630 --> 00:20:56,476 Chris: So yeah, here we are. 297 00:20:57,553 --> 00:21:00,323 Chris: Yeah, wild times. Let's see what RAINA actually has in the news. 298 00:21:01,338 --> 00:21:02,415 RAINA: Thanks, boys. 299 00:21:02,492 --> 00:21:09,646 RAINA: Hardware startup Ina just unveiled a $399 smart ring that lets you boss around AI agents from your finger. 300 00:21:10,261 --> 00:21:18,723 RAINA: And unlike the average fitness tracker with delusions ring, this one packs a tiny screen, a microphone, a speaker, and a physical joystick. 301 00:21:19,107 --> 00:21:25,107 RAINA: You can talk to your agents, dictate notes, set reminders, and fire off prompts without touching your phone. 302 00:21:25,261 --> 00:21:35,646 RAINA: Plus, view notifications, take calls, control music, and run smart home gear like Apple TV with gesture controls that change meaning depending on which app you're in. 303 00:21:36,407 --> 00:21:49,407 RAINA: It comes from the folks behind Mirage, a three-button Mac controller, and it's up for pre-order now at three hundred ninety-nine dollars, rising to four hundred ninety-nine dollars later, with shipping planned for January twenty twenty-seven. 304 00:21:50,023 --> 00:21:56,253 RAINA: Which is the industry's latest bet that the future of AI involves fewer screens and more squinting at your own hand. 305 00:21:57,184 --> 00:22:03,492 Larry: I'm still trying to determine if I would want an Apple Watch after spending all of my adult life not wearing a watch. 306 00:22:03,877 --> 00:22:05,646 Larry: So this is definitely not for me. 307 00:22:06,107 --> 00:22:08,877 Chris: I know, and I'm glad you put a picture in the script. 308 00:22:08,800 --> 00:22:10,030 Chris: I was wondering what this thing looked like. 309 00:22:09,953 --> 00:22:11,261 Larry: Yeah, I was curious too. 310 00:22:11,261 --> 00:22:14,184 Larry: I was like, wait, there's how do you have a joystick on your finger? 311 00:22:14,184 --> 00:22:18,338 Larry: It's definitely it's it's not like you know the Atari 2600 joystick or anything. 312 00:22:18,723 --> 00:22:20,953 Chris: But it's not. It's long. It's far cry from that. Yeah. 313 00:22:21,184 --> 00:22:30,492 Chris: But yeah, this picture has it on the index finger and it's little black kind of matte finish rectangle with a plastic ring. 314 00:22:31,030 --> 00:22:35,107 Chris: That goes around. I mean, it's very small form factor. Obviously, interesting. 315 00:22:35,030 --> 00:22:35,877 Chris: I don't know how I feel about it. 316 00:22:35,953 --> 00:22:40,338 Chris: I don't know if I'd have a use for it because I don't wear a watch either, and I've never felt compelled to have an Apple Watch. 317 00:22:40,338 --> 00:22:44,184 Chris: There's nothing in terms of maybe health metrics that I would want to get from that. 318 00:22:44,569 --> 00:22:46,338 Larry: That is the most appealing thing to me. 319 00:22:46,492 --> 00:22:47,107 Chris: Yeah, yeah. 320 00:22:48,030 --> 00:22:50,492 Chris: But I don't need this kind of haptic control over things. 321 00:22:50,800 --> 00:22:52,723 Chris: You know that doesn't that doesn't appeal to me. 322 00:22:53,992 --> 00:23:01,453 RAINA: Google.org is funding Helium Health, an African healthcare tech company, to roll out its Helium Mum platform in Nigeria. 323 00:23:01,838 --> 00:23:18,146 RAINA: Part of a $30 million AI for Government Innovation initiative that backed 15 organizations worldwide, the platform stitches together patient records, clinical decision support, and government health data to flag pregnancy risks early and route women to the right providers. 324 00:23:18,838 --> 00:23:23,530 RAINA: Starting in Lagos, the primary launch site, Delta, Kano, and Oyo states. 325 00:23:24,184 --> 00:23:46,953 RAINA: The stakes are sobering, since Nigeria accounts for nearly three in every ten maternal deaths globally, and Helium is aiming to reach another 1.9 million women over three years, on top of the 3.5 million its existing programs have already reached, and help cut maternal mortality by 30, which is the rare AI pitch where the ambitious target is the whole point. 326 00:23:48,492 --> 00:23:52,184 Larry: Super cool. They meet their goal. That's hundreds of lives saved. 327 00:23:53,569 --> 00:23:56,953 Chris: Super cool. And as that's as Rena's saying, all of that. 328 00:23:57,800 --> 00:24:02,261 Chris: I mean, yes, these big tech companies, the oligarchs, need to pay their fair share in taxes. 329 00:24:02,877 --> 00:24:15,107 Chris: But I also think, you know, given the negative backlash that we're seeing in elections toward data centers, et cetera, they're going to have to do these altruistic, philanthropic kind of arms and make it more visible. 330 00:24:15,338 --> 00:24:16,415 Chris: Like, yeah, they should. 331 00:24:17,438 --> 00:24:23,207 Chris: And I know it's capitalism, but there should be some sort of law that says you're going to reap the benefits of tax shelter. 332 00:24:23,592 --> 00:24:31,438 Chris: Then you need to put 500 million dollars a year into some big, ambitious health initiative, or something else like it. 333 00:24:33,284 --> 00:24:43,515 RAINA: OpenAI shipped ChatGPT images 2.6 on November 4th with native animated GIF generation and persistent reference images across web, desktop, and mobile. 334 00:24:44,130 --> 00:24:49,592 RAINA: It's the cutting edge of frontier AI, and the headline feature is a file format from 2009. 335 00:24:50,838 --> 00:24:59,838 RAINA: Two new API models came with it: GPT Image 2.6 Flare for speed and Sunburst for quality, which sound like Fortnite skins. 336 00:24:59,761 --> 00:25:06,146 RAINA: While the persistent references keep characters consistent from image to image, a gift to creators and deepfakers alike. 337 00:25:06,838 --> 00:25:11,146 RAINA: So the open question is whether this is a meme economy upgrade or a slop accelerant. 338 00:25:12,215 --> 00:25:14,907 Larry: Slab accelerant, slab accelerant. 339 00:25:14,830 --> 00:25:16,907 Chris: It's a can. Just spray it. 340 00:25:18,907 --> 00:25:21,676 Chris: Totally. But I have a question for RAINA. 341 00:25:21,753 --> 00:25:23,061 Chris: She's got to settle something once and for all. 342 00:25:24,523 --> 00:25:27,907 Chris: Sure. Hey RAINA, it's Chris. I need you to settle something once and for all. 343 00:25:28,215 --> 00:25:30,830 Chris: Is it GIF or is it JIF? 344 00:25:32,461 --> 00:25:35,230 RAINA: Oh, Chris, you're trying to start a war, aren't you? 345 00:25:35,692 --> 00:25:41,692 RAINA: Steve Wilhite, the creator of the GIF format, insisted it's pronounced "jiff" with a soft G. 346 00:25:42,384 --> 00:25:45,615 RAINA: But the G stands for graphics, which has a hard G. 347 00:25:45,615 --> 00:25:47,923 RAINA: So Team GIF has a pretty compelling argument. 348 00:25:48,615 --> 00:25:51,230 RAINA: My ruling: the inventor gets naming rights. 349 00:25:51,307 --> 00:25:55,692 RAINA: But I'm still saying GIF because Jiff sounds like something Larry spreads on toast. 350 00:25:58,615 --> 00:26:01,076 Chris: I'm going with GIF, man. I can't say Jiff. I can't do it. 351 00:26:01,000 --> 00:26:02,153 Larry: I can't do Jiff. 352 00:26:03,015 --> 00:26:05,476 Chris: Graphical interchange format, or something—is that what it is? 353 00:26:05,476 --> 00:26:09,476 Larry: Yeah, yeah, that's right. I also go with JPEG. 354 00:26:12,476 --> 00:26:12,553 Chris: That's great, 355 00:26:13,784 --> 00:26:15,015 Larry: JPEG, JPEG. 356 00:26:17,246 --> 00:26:27,784 RAINA: SuperMe is raising twenty million dollars, led by forerunners Kirsten Green with Graylock along for the ride to build LinkedIn, where every profile can answer your DMs at three AM. 357 00:26:28,661 --> 00:26:42,738 RAINA: Founded in 2024 by Casey Winters and Ludo Antonov, it trains each AI profile on a real expert's own output, then routes your question to the best matches and opens a group chat where the clones show where they agree and disagree. 358 00:26:43,123 --> 00:26:49,969 RAINA: It works with ChatGPT and Claude and is free for now, though paywalls and contributor payouts are still TBD. 359 00:26:50,507 --> 00:26:58,892 RAINA: Experts like Lenny Rachitsky, the ex Airbnb product manager turned newsletter and podcast powerhouse, and Lovables Elena Verna are into it. 360 00:26:59,430 --> 00:27:05,353 RAINA: With Verna wanting people to hire an AI version of her, and even the founders admit it sounds a little dystopian. 361 00:27:06,769 --> 00:27:08,153 Larry: Super me and Graylock. 362 00:27:08,461 --> 00:27:12,153 Larry: It reminds me of those guys putting up the ads for fake tech companies in the subway. 363 00:27:12,076 --> 00:27:15,846 Larry: Look at that too. ZipLink is now Froggle. Yeah, that's great. 364 00:27:15,769 --> 00:27:17,846 Chris: I love it. And then there was one like 365 00:27:18,307 --> 00:27:20,692 Larry: was like one plus one equals question mark. 366 00:27:22,000 --> 00:27:23,461 Larry: Dennis knows Dennis dot com. 367 00:27:25,769 --> 00:27:26,384 Chris: Totally great. 368 00:27:27,692 --> 00:27:28,461 Chris: I love that stuff. 369 00:27:28,384 --> 00:27:32,846 Chris: I saw somebody else doing guerrilla warfare against Starbucks, and they would put up these fake drinks. 370 00:27:33,153 --> 00:27:35,846 Chris: Did you see that one? Oh, cranberry cranberry flavored. 371 00:27:35,846 --> 00:27:39,461 Chris: It had like now featuring a full can of cranberry sauce. It was disgusting. 372 00:27:39,384 --> 00:27:43,307 Chris: It had a with a gelatinous cranberries hanging out. 373 00:27:44,553 --> 00:27:46,476 Chris: And it also reminded me of this guy. Maybe you've seen him. 374 00:27:46,400 --> 00:27:48,861 Chris: He calls himself Chip Bongo. Have you seen this guy? 375 00:27:48,938 --> 00:27:50,169 Larry: Not. I don't recognize the name. 376 00:27:51,092 --> 00:27:56,707 Chris: He goes on interviews that are conducted by AI agents. 377 00:27:56,707 --> 00:27:58,784 Larry: Oh yes. Yeah. He just says the most 378 00:27:59,169 --> 00:28:02,323 Chris: ludicrous things, makes up company names and projects. 379 00:28:02,246 --> 00:28:04,630 Chris: It's the best because they have to say it back to him. 380 00:28:04,784 --> 00:28:04,938 Larry: Yes. 381 00:28:05,169 --> 00:28:07,784 Chris: So, like, tell us more about Graylock. Yeah. I know 382 00:28:08,015 --> 00:28:10,015 Larry: exactly you're talking about. It's yeah, hysterical. 383 00:28:10,861 --> 00:28:13,400 Chris: And they'll say things like, "So Chip Bongo, tell us more about it." 384 00:28:14,784 --> 00:28:15,476 Larry: Yeah, yeah, yeah. 385 00:28:16,630 --> 00:28:17,476 Larry: It's very funny. 386 00:28:18,092 --> 00:28:18,246 Chris: Great. 387 00:28:19,338 --> 00:28:34,492 RAINA: And lastly, AI is taking another crack at talking to dolphins, building on Google's Dolphin Gemma, a model developed with Georgia Tech and the Wild Dolphin Project, and trained on 40 years of recordings of Atlantic spotted dolphins in the Bahamas. 388 00:28:34,492 --> 00:28:36,569 RAINA: It doesn't translate dolphin into English. 389 00:28:37,184 --> 00:28:39,415 RAINA: It hunts for patterns in the whistles and clicks. 390 00:28:40,030 --> 00:28:45,877 RAINA: Some whistles work like names, and the field test is basically a scarf-based hostage negotiation. 391 00:28:46,184 --> 00:28:54,261 RAINA: Divers wearing a device called Chat play an AI-generated whistle to ask for a scarf and hand it over if a dolphin repeats the sound. 392 00:28:54,953 --> 00:29:08,184 RAINA: Even the optimists say a real conversation is probably off the table, and one outside researcher warns the dolphins may just be copying sounds to score a free scarf, which honestly sounds like the smartest move in the whole experiment. 393 00:29:08,877 --> 00:29:11,338 RAINA: That's all the news for now. Back to you, gentlemen. 394 00:29:12,592 --> 00:29:15,207 Larry: I didn't like. Why would a dolphin want a scarf? 395 00:29:16,361 --> 00:29:17,669 Chris: I don't know. 396 00:29:17,669 --> 00:29:25,823 Chris: And what is that term when you're testing an AI system and it knows it's being tested, so it changes its behavior? 397 00:29:25,746 --> 00:29:30,438 Chris: Oh, specific. It's not sandbox, obviously. Yeah, I know. In there, right? 398 00:29:31,130 --> 00:29:31,976 Chris: It's reminding me. 399 00:29:31,900 --> 00:29:36,207 Chris: This whole dolphin experiment is reminding me that because they seem to be changing their behavior for the scarf. 400 00:29:36,438 --> 00:29:36,592 Chris: Yeah. 401 00:29:37,538 --> 00:29:38,846 Chris: It's like sandbagging. 402 00:29:38,846 --> 00:29:42,153 Larry: Sandbagging. That's it. Am I right? Yeah. I think I'm right. Yeah. Yes. 403 00:29:42,076 --> 00:29:42,538 Larry: I knew there was sand. 404 00:29:42,923 --> 00:29:45,692 Chris: I knew there was some like construction component in there somewhere. 405 00:29:45,615 --> 00:29:48,615 Chris: Yeah. I was gonna say. I was gonna say cement bagging, something like that. 406 00:29:48,538 --> 00:29:49,692 Larry: Yeah. 407 00:29:49,615 --> 00:29:52,923 Larry: Sandboxing is like it was luring me in, but I knew that wasn't right. Yeah. 408 00:29:54,461 --> 00:29:55,384 Chris: I do feel like dolphins 409 00:29:55,769 --> 00:30:02,461 Larry: AI is eventually gonna like crack the code and at least be able to interpret these clicks and sound sound patterns. 410 00:30:02,692 --> 00:30:05,000 Larry: You know, at least at some meaningful level. 411 00:30:04,923 --> 00:30:09,615 Chris: Oh, I have no doubt that I'm gonna buy some like little cap at the pet store. 412 00:30:09,930 --> 00:30:46,900 Chris: And throw it on my Roxy, and I'm gonna be able to actually understand exactly what she's saying, which is gonna be food, food, food, food, food, food, food, food, food What else you got? 413 00:30:48,284 --> 00:30:51,053 Larry: What else do I got? We haven't spoken each other in a couple weeks. 414 00:30:51,361 --> 00:30:53,053 Chris: We haven't. Two weeks off. 415 00:30:52,976 --> 00:30:53,592 Larry: Yeah. 416 00:30:54,284 --> 00:31:01,207 Larry: My whole focus over this past week was getting this this all this network here configured, and I tried to educate myself a bit. 417 00:31:01,130 --> 00:31:08,130 Larry: And you know, I I found this guy has great YouTube channel called Wonder Tech with a U W U Wonder Tech. 418 00:31:08,053 --> 00:31:09,515 Chris: Is there an umlaut over the? 419 00:31:09,976 --> 00:31:11,053 Larry: I think skipped the umlaut. 420 00:31:11,438 --> 00:31:14,976 Chris: I don't want to know anything about his product. Then I need umlauts, man. 421 00:31:14,900 --> 00:31:14,976 Larry: Yeah. 422 00:31:16,515 --> 00:31:22,900 Larry: Just so I had some foundational knowledge and figuring there might be some better ways to do it than what Chat GPT is going to. 423 00:31:23,861 --> 00:31:36,630 Larry: Offer and for the most part, Chat GPT was great, but there was at least one one spot where I was like, "Hey, why don't we why don't we do it this way?" And they were like, "Yeah, yeah, no, that's that's actually a better way to do." You know, and it was because I watched those videos. 424 00:31:37,169 --> 00:31:43,784 Larry: But I also pointed Chat GPT to those videos before we started doing all of this. 425 00:31:44,561 --> 00:31:50,715 Larry: And and it was able to pull down transcripts from those videos, so it had that that knowledge base. 426 00:31:50,638 --> 00:31:59,023 Larry: But aside from like all this complicated configuration stuff, which would have been a nightmare doing without ChatGPT, just oh, how do I do this? 427 00:31:58,946 --> 00:32:01,715 Larry: And then going to research and then going back, and oh, it's not working. 428 00:32:01,638 --> 00:32:06,561 Larry: And it took a while because it did it very methodically and carefully and incrementally. 429 00:32:07,253 --> 00:32:11,100 Larry: And in case something broke, we did all this testing along the way. 430 00:32:11,561 --> 00:32:12,946 Larry: But it did some really cool things. 431 00:32:13,407 --> 00:32:21,330 Larry: Like I had all these IP address reservations for certain pieces of gear that I want to always have the same IP on my network. 432 00:32:21,730 --> 00:32:42,115 Larry: There was a lot of them, and sure, you got to put in the MAC address, you got to put in the IP address, and and I was like, this is going to be tedious, and I saw there was an option to import, and but I don't have, you know, I have this saved from another router as a backup configuration file, and I was like, is there any way that you could? 433 00:32:43,169 --> 00:32:50,630 Larry: Open that file and convert that that list of IP addresses and MAC addresses into something that's importable into this system. 434 00:32:50,553 --> 00:32:55,092 Larry: And it did it. And my God, yes, it saved me tons of just. 435 00:32:55,476 --> 00:32:58,092 Larry: I mean, the MAC addresses alone would have been up just like sure. 436 00:32:58,553 --> 00:33:01,092 Larry: It'd be like typing in long passwords all day long, you know? Yes. 437 00:33:01,400 --> 00:33:06,630 Larry: Yeah. Super incredible. That was a great little win. Just awesome. 438 00:33:06,553 --> 00:33:12,400 Larry: And anytime something, if something didn't work, it was fixed in in one iteration and brilliant. 439 00:33:12,784 --> 00:33:13,092 Larry: Really great. 440 00:33:13,938 --> 00:33:15,092 Chris: So cool, man. 441 00:33:16,015 --> 00:33:18,630 Chris: Worth the price of admission alone, isn't it? Yeah. 442 00:33:18,784 --> 00:33:20,092 Chris: When it saves you that kind of work. 443 00:33:20,015 --> 00:33:20,092 Larry: Yep. 444 00:33:21,392 --> 00:33:23,315 Chris: All right, my friend. Good to see you again. 445 00:33:23,546 --> 00:33:24,161 Larry: Likewise. 446 00:33:24,084 --> 00:33:29,161 Larry: After two long weeks, glad you had a happy birthday, which was technically yesterday for you. 447 00:33:29,469 --> 00:33:30,392 Chris: Thank you. Yeah. But 448 00:33:30,546 --> 00:33:33,930 Larry: we're still on Chris' birthday time over in this hemisphere. So, 449 00:33:35,007 --> 00:33:38,007 Chris: thank you, my friend. All right, everybody. Thanks for listening. 450 00:33:38,007 --> 00:33:41,161 Chris: Subscribe on your favorite podcasting platform. Follow us on socials. 451 00:33:41,161 --> 00:33:43,007 Chris: Throw us a rating. We'll see you next week. 452 00:33:45,669 --> 00:33:48,823 RAINA: This has been up against reality. Thanks for listening. 453 00:33:49,284 --> 00:33:54,669 RAINA: Subscribe to hear future episodes, and be sure to follow us on social media for all things AI. 454 00:33:55,207 --> 00:33:57,207 RAINA: Until next time, stay human, people.