1 00:00:00,170 --> 00:00:02,900 S1: Does your app get fake sign ups, throw away emails 2 00:00:02,900 --> 00:00:07,040 S1: or users abusing your free tier? Or worse, bot attacks 3 00:00:07,040 --> 00:00:10,850 S1: and brute force attempts? Work OS radar can block all 4 00:00:10,850 --> 00:00:14,420 S1: of this and more. Simple API gives you advanced device 5 00:00:14,420 --> 00:00:19,279 S1: fingerprinting that can detect bad actors, bots, and suspicious behavior. 6 00:00:19,310 --> 00:00:23,329 S1: Your users trust you. Let's keep it that way. Check 7 00:00:23,360 --> 00:00:27,140 S1: out work OS Radar at work OS comm slash radar. 8 00:00:27,170 --> 00:00:34,339 S1: That's work os.com/radar. Unsupervised learning is a podcast about. Trends 9 00:00:34,340 --> 00:00:40,250 S1: and ideas in cybersecurity, national security, AI, technology and society 10 00:00:40,250 --> 00:00:43,309 S1: and how best to upgrade ourselves to be ready for 11 00:00:43,310 --> 00:00:51,290 S1: what's coming. All right. Welcome to unsupervised Learning. This is Daniel, 12 00:00:51,740 --> 00:00:54,770 S1: episode 470. Let's get into it. Reading a couple of 13 00:00:54,770 --> 00:00:59,690 S1: new books in addition to this month's books for UL. 14 00:00:59,690 --> 00:01:04,970 S1: So the UL books are 1984 by Orwell and Animal 15 00:01:04,970 --> 00:01:08,119 S1: Farm by Orwell. And these two books I just added 16 00:01:08,120 --> 00:01:11,750 S1: to the list. I'm about done with the Technical Republic 17 00:01:11,750 --> 00:01:16,250 S1: and about to jump into money, lies and God. And actually, 18 00:01:16,250 --> 00:01:18,620 S1: there's another one I'm going to add to this list, 19 00:01:18,980 --> 00:01:22,610 S1: somebody who came on the Goodfellas podcast. So I'm going 20 00:01:22,640 --> 00:01:26,570 S1: to go find his book as well. But, um, yeah, 21 00:01:26,600 --> 00:01:29,660 S1: having a good time with some reading. Got a friend 22 00:01:29,660 --> 00:01:33,470 S1: in Guatemala who was recently laid off senior engineer, focused 23 00:01:33,470 --> 00:01:37,610 S1: around monitoring solutions, but really can do lots of different stuff. 24 00:01:37,610 --> 00:01:40,310 S1: So I recommend you check out his LinkedIn and see 25 00:01:40,310 --> 00:01:44,090 S1: if if you might have anything. My friend Monica is 26 00:01:44,090 --> 00:01:50,660 S1: offering 25% off her Security Leadership Masterclass, which I consulted for, 27 00:01:50,660 --> 00:01:53,030 S1: and I think it's really good for people trying to 28 00:01:53,030 --> 00:01:56,779 S1: get into leadership around security. I got a funny meme 29 00:01:56,780 --> 00:02:00,740 S1: here about, um, how to calm down. A buddy who 30 00:02:00,740 --> 00:02:04,010 S1: loses a bunch of money in crypto. It's just. It's 31 00:02:04,010 --> 00:02:08,450 S1: just funny. Nothing useful there except for laughter, which is useful. 32 00:02:08,540 --> 00:02:13,460 S1: LinkedIn posts about my ultimate app, which I keep iterating on. 33 00:02:13,460 --> 00:02:15,350 S1: I'm going to go ahead and click through to this one. 34 00:02:15,530 --> 00:02:19,220 S1: So this is essentially like the main app that I've 35 00:02:19,220 --> 00:02:21,470 S1: been building for the longest time. And I've got like 36 00:02:21,500 --> 00:02:23,929 S1: different versions of it. I've got like a commercial version, 37 00:02:23,930 --> 00:02:27,919 S1: I've got the overall arching one, but essentially it takes 38 00:02:27,919 --> 00:02:31,310 S1: inputs from any medium, from any platform and does a 39 00:02:31,310 --> 00:02:34,190 S1: ton of analysis to determine how good it is. Okay, 40 00:02:34,220 --> 00:02:38,570 S1: so just imagine that this is like the most important 41 00:02:38,570 --> 00:02:44,419 S1: thing to me is most important like current buildable use 42 00:02:44,419 --> 00:02:48,290 S1: for AI. So all this stuff is happening in the world, right. 43 00:02:48,290 --> 00:02:53,389 S1: All this news, new intelligence is is dropping open source intelligence, 44 00:02:53,389 --> 00:02:57,890 S1: recon vulnerabilities are popping up on websites. Um, my favorite 45 00:02:57,950 --> 00:03:02,480 S1: thinkers are releasing a new blog, blog post, a new essay, 46 00:03:02,510 --> 00:03:06,919 S1: a new article, a new video on YouTube comes out right. 47 00:03:06,950 --> 00:03:11,989 S1: All the authors that I follow, they're releasing new books, right? Um, 48 00:03:12,020 --> 00:03:16,790 S1: comments are being made which might reveal something really, really interesting. Uh, 49 00:03:16,790 --> 00:03:21,230 S1: new art is coming out, new books, new movies that 50 00:03:21,230 --> 00:03:25,610 S1: I should be watching. Um, comments from my friends, new 51 00:03:25,610 --> 00:03:28,820 S1: companies being launched. All this stuff is happening, you know, 52 00:03:28,850 --> 00:03:32,030 S1: millions of things per day. Hundreds of thousands, tens of 53 00:03:32,030 --> 00:03:35,870 S1: thousands or whatever, uh, pieces of content coming into my 54 00:03:35,870 --> 00:03:41,720 S1: RSS feeder. And all of that is, is something I 55 00:03:41,720 --> 00:03:46,190 S1: could potentially benefit from. Okay. So this app that I've 56 00:03:46,190 --> 00:03:49,340 S1: been working on for the longest time, really starting, uh, 57 00:03:49,340 --> 00:03:54,740 S1: beginning of 23, is essentially the consumption mechanism for this. 58 00:03:54,770 --> 00:03:58,160 S1: Pull it all together, then use AI to run it 59 00:03:58,160 --> 00:04:01,580 S1: through a filter. Okay. That filter. And I've already built 60 00:04:01,580 --> 00:04:05,840 S1: this way back then. The filter basically does a quality 61 00:04:05,840 --> 00:04:09,410 S1: check on all of these things to see if it's 62 00:04:09,410 --> 00:04:14,870 S1: full of interesting ideas, if it's creative, if it's novel, 63 00:04:14,870 --> 00:04:18,770 S1: if it's surprising, and all these various different aspects similar 64 00:04:18,770 --> 00:04:22,760 S1: to the label and rate, um, fabric pattern that I 65 00:04:22,760 --> 00:04:25,100 S1: have out there that's public. This is just like a 66 00:04:25,100 --> 00:04:28,250 S1: much more advanced version of it, with a lot more 67 00:04:28,250 --> 00:04:32,840 S1: labels and stuff like that, because based on that, the 68 00:04:32,839 --> 00:04:35,570 S1: AI can then or a different AI or a different 69 00:04:35,570 --> 00:04:38,869 S1: part of the same AI can then sort what to 70 00:04:38,900 --> 00:04:42,169 S1: do with it, right? Because it might be an alert. 71 00:04:42,170 --> 00:04:45,560 S1: It might send me an alert if something is important enough, like, hey, 72 00:04:45,589 --> 00:04:48,260 S1: a new vulnerability was found on one of your websites, 73 00:04:48,260 --> 00:04:52,370 S1: or a new vulnerability was found on a bounty program 74 00:04:52,370 --> 00:04:56,719 S1: that I'm monitoring. And by the way, the AI is 75 00:04:56,720 --> 00:04:59,120 S1: telling me my AI is like, oh, by the way, 76 00:04:59,120 --> 00:05:01,849 S1: I found the vulnerability. I found how you could actually 77 00:05:01,850 --> 00:05:05,030 S1: exploit it. I wrote that up into a report and 78 00:05:05,029 --> 00:05:07,400 S1: I submitted it to the thing. So we are waiting 79 00:05:07,430 --> 00:05:10,279 S1: to see if we're going to get paid from this bounty. Right. 80 00:05:10,279 --> 00:05:12,890 S1: So that that's a cool thing. And it might send 81 00:05:12,920 --> 00:05:15,440 S1: me an email which tags it with a certain thing 82 00:05:15,440 --> 00:05:19,339 S1: that says awaiting for bounty payout or something like that. 83 00:05:19,339 --> 00:05:23,360 S1: Or it might be that I just want to know if, um, 84 00:05:23,450 --> 00:05:27,440 S1: this particular company buys any new companies, or I want 85 00:05:27,470 --> 00:05:30,620 S1: to know if this particular company does anything to, like, 86 00:05:30,650 --> 00:05:34,280 S1: raise money. All of these could be like alerts that 87 00:05:34,279 --> 00:05:37,910 S1: I have set up. But it starts with the consumption 88 00:05:37,910 --> 00:05:42,260 S1: of the world and the processing through this AI. Now 89 00:05:42,260 --> 00:05:45,140 S1: I've already built this I've got is probably a version 90 00:05:45,140 --> 00:05:48,050 S1: like three or something, depending on how you call the versions. 91 00:05:48,050 --> 00:05:51,950 S1: But what I want to mention about this is that 92 00:05:51,950 --> 00:05:55,190 S1: the real version of this, the real version of this, 93 00:05:55,190 --> 00:05:58,550 S1: which I've talked about in my other videos, especially the 94 00:05:58,550 --> 00:06:04,100 S1: predictive path AI. Video is that the real version of 95 00:06:04,100 --> 00:06:10,010 S1: this is my AI buddy, my AI assistant, my AI friend, my. 96 00:06:10,040 --> 00:06:16,280 S1: AI my digital assistant. Okay, my universal primary digital assistant 97 00:06:16,279 --> 00:06:19,640 S1: is going to be in charge of doing this for me. Okay. 98 00:06:19,670 --> 00:06:23,000 S1: My APIs that are available to me are the ones 99 00:06:23,000 --> 00:06:28,279 S1: that are doing this processing right. These agents, these automation workflows, 100 00:06:28,279 --> 00:06:31,159 S1: these things that are just running on my behalf. They're 101 00:06:31,190 --> 00:06:36,830 S1: out there in the world collecting, analyzing, assessing, um, labeling, 102 00:06:36,830 --> 00:06:40,549 S1: doing all those things for me. But the quarterback for 103 00:06:40,550 --> 00:06:44,120 S1: all of this is my Da. My Da is monitoring 104 00:06:44,120 --> 00:06:46,970 S1: how many feeds I have, if they're the right feeds, 105 00:06:46,970 --> 00:06:49,280 S1: if this feed like we thought it was awesome, but 106 00:06:49,279 --> 00:06:52,340 S1: it's actually garbage. It might have the permission to go 107 00:06:52,339 --> 00:06:55,790 S1: in and clean that up and fix it. Right. So 108 00:06:55,790 --> 00:06:59,780 S1: the future of this is I don't really ask any 109 00:06:59,779 --> 00:07:03,800 S1: questions about this. I'm not like typing very much. I'm not, um, 110 00:07:03,800 --> 00:07:07,940 S1: really manually interacting with this too much at all. Because 111 00:07:07,940 --> 00:07:10,670 S1: if I want a new source, I say, hey, add 112 00:07:10,670 --> 00:07:13,970 S1: this to my source, okay? While I'm looking at a 113 00:07:13,970 --> 00:07:16,850 S1: page on the screen, hey, add this to my our 114 00:07:16,850 --> 00:07:20,630 S1: list of sources in my says in my ear, yeah, 115 00:07:20,660 --> 00:07:23,870 S1: no problem. Just added it. Okay, I'm watching a new 116 00:07:23,900 --> 00:07:26,840 S1: YouTube video, for example. I'm like, yeah, add this to 117 00:07:26,870 --> 00:07:30,650 S1: our sources and it's like it runs the API command 118 00:07:30,650 --> 00:07:33,140 S1: to go add that. It's now part of the workflow. 119 00:07:33,140 --> 00:07:36,050 S1: It's now in the system right now. How could it 120 00:07:36,050 --> 00:07:38,750 S1: do that? Well, it's going to do that because it's 121 00:07:38,780 --> 00:07:41,870 S1: watching all my screens or it's watching with the camera. 122 00:07:41,900 --> 00:07:43,550 S1: However it's doing it. There's going to be lots of 123 00:07:43,550 --> 00:07:46,250 S1: different ways. But the point is, my Da will be 124 00:07:46,250 --> 00:07:49,640 S1: part of my life. Um, Apple is talking about having 125 00:07:49,640 --> 00:07:53,060 S1: cameras on its AirPods. I don't know if that will 126 00:07:53,090 --> 00:07:55,740 S1: be the next version. Probably not. It'll be the version 127 00:07:55,740 --> 00:07:59,040 S1: like 2 or 3 versions after the current version, but 128 00:07:59,070 --> 00:08:02,010 S1: I'm wearing them now. Like if if these were like 129 00:08:02,040 --> 00:08:05,340 S1: over here and in the back, it had a camera 130 00:08:05,340 --> 00:08:09,780 S1: that was pointing behind me. Amazing. Obviously it should have 131 00:08:09,780 --> 00:08:13,950 S1: a camera that's pointing forward, pointing forward, pointing behind me 132 00:08:13,950 --> 00:08:17,400 S1: with full access to my life context. Full access to 133 00:08:17,430 --> 00:08:20,850 S1: my screen. It should also have access within the screen 134 00:08:20,850 --> 00:08:23,370 S1: as well, so we could look at processes and stuff 135 00:08:23,370 --> 00:08:25,680 S1: like that. But let's just say you could only just 136 00:08:25,680 --> 00:08:27,780 S1: see the screen. I could still ask it things and 137 00:08:27,780 --> 00:08:31,620 S1: it could still do lookups. It could still summarize what 138 00:08:31,620 --> 00:08:35,730 S1: I'm looking at, for example. So this is really the interaction. 139 00:08:35,730 --> 00:08:39,150 S1: The interaction becomes me just talking to my AI and 140 00:08:39,150 --> 00:08:43,080 S1: my AI giving me back results. Now the second level 141 00:08:43,080 --> 00:08:46,590 S1: of this, which comes a little bit later because the 142 00:08:46,590 --> 00:08:50,160 S1: technology just is not quite there yet, but it's more 143 00:08:50,160 --> 00:08:54,140 S1: like the meta glasses. Okay, the meta Metaclasses. As you 144 00:08:54,140 --> 00:08:57,290 S1: saw in the newsletter last week, or maybe it's this 145 00:08:57,290 --> 00:09:00,679 S1: week and we're about to talk about it. The Metaclasses 146 00:09:00,679 --> 00:09:04,550 S1: have sold 2 million units. They are the direction that 147 00:09:04,550 --> 00:09:07,189 S1: things are going. I imagine Apple is going to move 148 00:09:07,190 --> 00:09:09,950 S1: in this direction as well, and kind of offload a 149 00:09:09,950 --> 00:09:13,850 S1: bunch of the processing to the phone, but the next 150 00:09:13,850 --> 00:09:16,340 S1: version of this app that I'm building, the reason I'm 151 00:09:16,340 --> 00:09:20,480 S1: building all these services that are working for me, coming 152 00:09:20,480 --> 00:09:24,920 S1: through my digital assistant, which also doesn't fully exist yet. Okay, 153 00:09:24,950 --> 00:09:29,270 S1: that's also a place everyone is pushing into, including Apple. 154 00:09:29,420 --> 00:09:33,290 S1: But the real thing is that I am watching. I 155 00:09:33,320 --> 00:09:35,900 S1: am talking to somebody and they're like, hey, you know, 156 00:09:35,929 --> 00:09:38,450 S1: you should get in on this deal and blah blah, blah. 157 00:09:38,450 --> 00:09:41,060 S1: And it's crypto related and it's a meme coin. And 158 00:09:41,059 --> 00:09:45,050 S1: I'm just like, uh, well, not interested. Don't want to 159 00:09:45,050 --> 00:09:48,830 S1: hear about that. Um, but let's say I was interested 160 00:09:48,830 --> 00:09:51,710 S1: and let's say it did sound good. In the meantime, 161 00:09:51,710 --> 00:09:56,179 S1: my Da is calling all my APIs. Okay, I have 162 00:09:56,179 --> 00:09:59,990 S1: an API for researching someone. I have an API for 163 00:10:00,020 --> 00:10:04,460 S1: creating a dossier on somebody. I have an API for monitoring, 164 00:10:04,460 --> 00:10:10,970 S1: watching someone's face and determining characteristics of lying. So while 165 00:10:10,970 --> 00:10:14,240 S1: I'm looking with my apple glasses, which is just like 166 00:10:14,240 --> 00:10:16,700 S1: they look like regular glasses, this is pretty far in 167 00:10:16,700 --> 00:10:19,819 S1: the future. Like five years maybe, maybe shorter, maybe like 168 00:10:19,820 --> 00:10:22,430 S1: three years with meta or something. Who knows? But let's 169 00:10:22,429 --> 00:10:26,630 S1: just say three to 5 to 10 years. Okay. As 170 00:10:26,630 --> 00:10:29,569 S1: you're watching, I have this little dial in the corner 171 00:10:29,570 --> 00:10:33,620 S1: of my glasses and it's like, it's like a bullshit meter, right? 172 00:10:33,620 --> 00:10:37,220 S1: It's like, yeah, no, don't believe that. And it could 173 00:10:37,220 --> 00:10:39,860 S1: even be printing out little thing in the text or whatever. 174 00:10:39,860 --> 00:10:43,970 S1: That's like that claim he just made is incorrect. That 175 00:10:43,970 --> 00:10:45,980 S1: was kind of bullshit. Oh, that was kind of smart 176 00:10:45,980 --> 00:10:48,650 S1: or whatever. You could do whatever. You could have an 177 00:10:48,650 --> 00:10:51,439 S1: outline around them based on the type of person you're 178 00:10:51,440 --> 00:10:54,230 S1: looking at. I talked about this in that blog post, 179 00:10:54,230 --> 00:10:56,330 S1: which you should go check out. Actually, here's the video 180 00:10:56,330 --> 00:10:59,959 S1: for it. So you've got to think about what is 181 00:10:59,960 --> 00:11:05,000 S1: possible from your Da when your Da can also control 182 00:11:05,000 --> 00:11:08,780 S1: what you are seeing. The overlay on top of the world. Okay, 183 00:11:08,780 --> 00:11:11,990 S1: so when I walk into a Starbucks and I'm looking 184 00:11:11,990 --> 00:11:16,010 S1: for a new partner, I'm looking for a girlfriend, which 185 00:11:16,010 --> 00:11:18,860 S1: I'm not, by the way, but let's say I were, um, 186 00:11:18,860 --> 00:11:23,570 S1: there could be different outlines based on what, um, the 187 00:11:23,570 --> 00:11:28,730 S1: girls in the Starbucks are broadcasting in their demons. They're 188 00:11:28,730 --> 00:11:32,689 S1: broadcasting that they're creative. They're broadcasting that they like programming, 189 00:11:32,690 --> 00:11:35,390 S1: they're broadcasting that they want to start a business. They're 190 00:11:35,390 --> 00:11:41,689 S1: broadcasting whatever. And my Da could then place different outlines 191 00:11:41,690 --> 00:11:45,739 S1: around them based on highlighting that for me, knowing what 192 00:11:45,740 --> 00:11:48,859 S1: I'm looking for. Right. And more importantly, there's going to 193 00:11:48,860 --> 00:11:52,790 S1: be 1000 or 1 million companies out there pitching to 194 00:11:52,820 --> 00:11:57,709 S1: my Da that their filter on reality is the best one, 195 00:11:57,710 --> 00:12:00,080 S1: because they have the coolest outlines, and they have the 196 00:12:00,080 --> 00:12:02,809 S1: coolest little animations that go around people who are like 197 00:12:02,840 --> 00:12:07,340 S1: artists or engineers or whatever. So this is kind of 198 00:12:07,370 --> 00:12:09,920 S1: these are the steps. This is what I'm building. Like 199 00:12:09,920 --> 00:12:12,710 S1: the stuff I put in that book from 2016, the 200 00:12:12,710 --> 00:12:15,860 S1: stuff I've been talking about in these posts, like I'm 201 00:12:15,860 --> 00:12:19,760 S1: not waiting for it to happen, I'm building the pieces 202 00:12:19,760 --> 00:12:22,579 S1: right now. Like I can't build the hardware. Like I'll 203 00:12:22,580 --> 00:12:24,380 S1: never mess with that. I have to wait for that. 204 00:12:24,380 --> 00:12:28,670 S1: But I can maybe build the assistant, or at least 205 00:12:28,670 --> 00:12:31,819 S1: build something to go on top of the assistant. And 206 00:12:31,820 --> 00:12:35,240 S1: I can absolutely build these services. And this is like 207 00:12:35,240 --> 00:12:39,050 S1: the most important service. This is the gathering and filtering service. 208 00:12:39,050 --> 00:12:42,230 S1: So that is what that's what I've made. Oh and 209 00:12:42,230 --> 00:12:44,810 S1: by the way I the commercial version of this that 210 00:12:44,809 --> 00:12:47,240 S1: I already put out, let me just click this and 211 00:12:47,240 --> 00:12:50,929 S1: see like what we get here. This is my actual threshold. 212 00:12:50,929 --> 00:12:55,939 S1: This is my personal feed for threshold, and I've actually 213 00:12:55,940 --> 00:12:58,340 S1: never showed this. Have I never showed this? I don't 214 00:12:58,340 --> 00:13:00,530 S1: think I've ever showed this before. Let me show you 215 00:13:00,530 --> 00:13:03,890 S1: what this is like. So this is my threshold. And 216 00:13:03,890 --> 00:13:07,069 S1: look what I can do in here. I have a 217 00:13:07,070 --> 00:13:11,480 S1: score here that I can, uh, set the minimum threshold 218 00:13:11,480 --> 00:13:14,990 S1: of quality that has to. The content has to be. 219 00:13:14,990 --> 00:13:18,350 S1: So I have thousands of things like. This is not theory. 220 00:13:18,350 --> 00:13:22,280 S1: I've already built this. Thousands of things are coming into 221 00:13:22,280 --> 00:13:28,400 S1: this thing being parsed, being filtered, being labeled, being rated. Right. 222 00:13:28,429 --> 00:13:32,000 S1: Watch this. It's at 85. I slide this down to 60. 223 00:13:32,030 --> 00:13:35,660 S1: Look I have all these categories as well I hit save. 224 00:13:35,660 --> 00:13:39,350 S1: Look what it does over here. What it just readjusted. 225 00:13:39,380 --> 00:13:44,090 S1: It just readjusted. And watch this watch this view analysis 226 00:13:44,090 --> 00:13:47,420 S1: I have summaries of the contents. You could decide if 227 00:13:47,420 --> 00:13:49,459 S1: you actually want to go and read it, or if 228 00:13:49,460 --> 00:13:52,250 S1: you actually want to go and watch the video and 229 00:13:52,250 --> 00:13:57,380 S1: look at this surface level, middle level, deep level, hidden 230 00:13:57,380 --> 00:14:00,500 S1: message analysis, the ideas that are actually in here, the 231 00:14:00,500 --> 00:14:04,700 S1: recommendations that came out of the content. This thing helps 232 00:14:04,700 --> 00:14:08,780 S1: people save time, going from like hundreds of thousands of 233 00:14:08,780 --> 00:14:13,010 S1: feeds down to a small number of feeds. And even 234 00:14:13,010 --> 00:14:15,140 S1: when you get the small number of feeds that pop 235 00:14:15,140 --> 00:14:18,199 S1: up in this list right here. So let me just 236 00:14:18,200 --> 00:14:23,480 S1: take this to like 95 save. So even when this 237 00:14:23,480 --> 00:14:25,970 S1: thing pops up, I could still decide if I want 238 00:14:26,000 --> 00:14:28,880 S1: to read it or not based on this I usually 239 00:14:28,880 --> 00:14:32,840 S1: keep it around middle. So three ideas, a little review 240 00:14:32,840 --> 00:14:36,350 S1: of it, recommendations coming out of it. It's insane. And 241 00:14:36,350 --> 00:14:37,910 S1: then I always have the link to go click it 242 00:14:37,910 --> 00:14:40,970 S1: and read the actual full thing. So this thing saves 243 00:14:40,970 --> 00:14:44,030 S1: me like infinite time. And I find so much content 244 00:14:44,030 --> 00:14:47,270 S1: that I would never find before. And here, here's the 245 00:14:47,270 --> 00:14:49,680 S1: ultimate idea for this. And this is the reason I'm 246 00:14:49,680 --> 00:14:53,550 S1: building this entire system separate from this app here in 247 00:14:53,550 --> 00:14:58,410 S1: the commercial app. I don't care if the best idea 248 00:14:58,440 --> 00:15:02,970 S1: came from like a 13 year old girl in Nairobi, 249 00:15:02,970 --> 00:15:08,280 S1: because her idea might be as good or better than 250 00:15:08,310 --> 00:15:12,150 S1: Marc Andreessen's latest idea that just came out. But Marc 251 00:15:12,150 --> 00:15:14,370 S1: Andreessen is going to get all the press. It's going 252 00:15:14,400 --> 00:15:17,010 S1: to be through all my feeds. So if I just 253 00:15:17,010 --> 00:15:19,440 S1: rely on that, it's going to be hype cycle based 254 00:15:19,440 --> 00:15:23,040 S1: on social dynamics. So it's going to be surfaced to me, 255 00:15:23,040 --> 00:15:27,570 S1: but I am blocking that out. My algorithm does not 256 00:15:27,570 --> 00:15:32,010 S1: care about how popular that person is. It doesn't care 257 00:15:32,010 --> 00:15:36,479 S1: how about how popular that it turned into being going viral? 258 00:15:36,510 --> 00:15:40,410 S1: It doesn't care. It's judging the quality of the ideas 259 00:15:40,410 --> 00:15:44,040 S1: and the the novelty of the ideas and the creativity. 260 00:15:44,040 --> 00:15:46,980 S1: That's what's determining whether or not I'm going to see 261 00:15:46,980 --> 00:15:51,600 S1: a thing. So that's like the commercial version of this basically, 262 00:15:52,410 --> 00:15:54,090 S1: which is already out. And of course I'm going to 263 00:15:54,120 --> 00:15:57,420 S1: improve that as well. But it's kind of like not 264 00:15:57,420 --> 00:16:00,690 S1: the main point. The main point is this right here, 265 00:16:00,690 --> 00:16:03,600 S1: my interface to the world. This is why I talk 266 00:16:03,630 --> 00:16:09,420 S1: about augmentation okay. Augmentation is this is this is why 267 00:16:09,420 --> 00:16:13,260 S1: I am so excited about, I think of how much 268 00:16:13,260 --> 00:16:17,190 S1: smarter I can be about the world if I'm being 269 00:16:17,190 --> 00:16:21,840 S1: constantly fed the best ideas from the entire planet, the 270 00:16:21,840 --> 00:16:26,790 S1: more stuff I can find from unknown people. And by 271 00:16:26,790 --> 00:16:30,000 S1: the way, when I when I hear from unknown people, 272 00:16:30,030 --> 00:16:33,330 S1: I give them a shout out. I broadcast them out 273 00:16:33,360 --> 00:16:37,740 S1: on on X or on blue Sky or in the newsletter, 274 00:16:37,740 --> 00:16:39,810 S1: or I reach out to them and ask them if 275 00:16:39,810 --> 00:16:45,450 S1: they want mentoring. Like it is so fun to find 276 00:16:45,450 --> 00:16:48,710 S1: like nascent talent in the world and like, try to 277 00:16:48,740 --> 00:16:50,810 S1: give them confidence and lift them up and kind of 278 00:16:50,840 --> 00:16:53,490 S1: broadcast them out. And you've seen me do it a 279 00:16:53,490 --> 00:16:56,810 S1: million times, and it's just it's unbelievable to connect with 280 00:16:56,810 --> 00:17:00,620 S1: people who have interesting ideas. And that's what I love 281 00:17:00,620 --> 00:17:06,470 S1: about this thing is it finds interesting ideas regardless of source. 282 00:17:06,500 --> 00:17:09,859 S1: Forget the source. I don't care about the source. I 283 00:17:09,859 --> 00:17:14,090 S1: want to talk about ideas. All right, so that was that. Um, 284 00:17:14,600 --> 00:17:20,480 S1: the discovery section in the newsletter is getting absolutely insane. Uh, 285 00:17:20,480 --> 00:17:24,170 S1: there's going to be a cutoff. Um, in this episode, um, 286 00:17:24,170 --> 00:17:27,139 S1: right after the news section, because we basically split the 287 00:17:27,140 --> 00:17:30,560 S1: newsletter into two pieces, and the podcast is also in 288 00:17:30,560 --> 00:17:33,980 S1: two pieces. So if you're listening to the non-member version 289 00:17:33,980 --> 00:17:37,550 S1: of the show, then after the news, it's going to 290 00:17:37,580 --> 00:17:40,730 S1: have a little message there. Uh, but if you sign up, 291 00:17:40,730 --> 00:17:44,390 S1: become a member, you know, all that stuff. You basically 292 00:17:44,390 --> 00:17:47,240 S1: get access to the member feed, and the member feed 293 00:17:47,270 --> 00:17:54,080 S1: is full. It's a full episode. Um, with all the stuff, 294 00:17:54,080 --> 00:18:00,050 S1: including this discovery section, which is just absolutely insane. Um, 295 00:18:00,050 --> 00:18:03,140 S1: because I've been doing all these upgrades to my sources, 296 00:18:03,140 --> 00:18:09,020 S1: I've added like a thousand 1500 additional sources, like which 297 00:18:09,020 --> 00:18:11,899 S1: I just put like another ten hours in, like expanding 298 00:18:11,900 --> 00:18:15,230 S1: my opml file and like all my different stuff. And 299 00:18:15,230 --> 00:18:19,369 S1: that's why the quality of discovery has just gone up massively. Um, 300 00:18:19,369 --> 00:18:22,459 S1: and that's why I decided, hey, you know, we really 301 00:18:22,460 --> 00:18:26,270 S1: should be offering more of that benefit to the members 302 00:18:26,270 --> 00:18:30,170 S1: and not just giving it away flat to non-paying members 303 00:18:30,170 --> 00:18:33,379 S1: as well. So that separation is why we're doing the 304 00:18:33,380 --> 00:18:36,470 S1: two different versions. So you might have noticed you're getting 305 00:18:36,500 --> 00:18:39,590 S1: like the standard episode of the newsletter or the standard 306 00:18:39,590 --> 00:18:42,860 S1: episode of the podcast. It's all part of that. All right. 307 00:18:42,859 --> 00:18:48,530 S1: So highly recommended. successor essay to my Sspca article from 2023. 308 00:18:48,740 --> 00:18:52,460 S1: This is basically one of the final forms of what 309 00:18:52,460 --> 00:18:54,500 S1: we can actually do with AI, and I'm going to 310 00:18:54,500 --> 00:18:56,270 S1: do a separate video on this. So I don't want 311 00:18:56,300 --> 00:18:59,660 S1: to go into a whole thing about it. All right. Cybersecurity. 312 00:18:59,690 --> 00:19:03,830 S1: Phenomenal analysis of the cybersecurity market from my buddy Mike 313 00:19:03,830 --> 00:19:06,859 S1: Privett on return on security I call him the Nate 314 00:19:06,859 --> 00:19:10,970 S1: Silver of Cybersecurity Market Analysis. He basically does. He looks 315 00:19:10,970 --> 00:19:12,980 S1: at all the startups. He looks at all the spending, 316 00:19:12,980 --> 00:19:17,060 S1: all the funding, what VCs are doing. It is absolutely 317 00:19:17,060 --> 00:19:21,500 S1: worth a sign up. His podcast or his newsletter is 318 00:19:21,500 --> 00:19:25,550 S1: Return on Security. And main takeaway here is cyber investments 319 00:19:25,550 --> 00:19:30,030 S1: are actually getting back kind of to normal in 2014 320 00:19:30,030 --> 00:19:35,689 S1: or 2024, and they now have over $14 billion in funding. 321 00:19:35,690 --> 00:19:39,709 S1: But I and private equity are playing a much bigger role. 322 00:19:39,710 --> 00:19:45,290 S1: So that makes sense. Massive leak of Black Basta ransomware gangs. 323 00:19:45,290 --> 00:19:50,600 S1: Internal Chats has researchers working to translate and analyze over 324 00:19:50,600 --> 00:19:54,560 S1: 500,000 Russian messages. So they're trying to figure out what 325 00:19:54,560 --> 00:19:56,810 S1: all they were saying, but they have to do translation. 326 00:19:56,840 --> 00:20:02,659 S1: Russian hackers are successfully compromising encrypted signal messages from Ukrainian military, 327 00:20:02,660 --> 00:20:05,450 S1: and I think the attack is actually much larger than 328 00:20:05,450 --> 00:20:08,630 S1: that scope. But, um, the whole trick is to get 329 00:20:08,630 --> 00:20:12,290 S1: them to scan malicious QR codes and basically bypassing a 330 00:20:12,290 --> 00:20:17,240 S1: bunch of protections that way. Apple dropped advanced data protection 331 00:20:17,240 --> 00:20:20,720 S1: in the UK so that advanced data protection is basically 332 00:20:20,720 --> 00:20:23,270 S1: end to end protection. The UK said, I want a 333 00:20:23,270 --> 00:20:26,659 S1: backdoor in that and Apple says you can't have a backdoor, 334 00:20:26,660 --> 00:20:29,180 S1: so we'll just turn off end to end encryption for you. 335 00:20:29,210 --> 00:20:32,180 S1: What is the UK doing? Why do they hate themselves 336 00:20:32,180 --> 00:20:36,919 S1: so much? Just massive self sabotage. You could trick Gpts 337 00:20:36,950 --> 00:20:41,960 S1: Chatgpt's operator feature into leaking private user data through prompt injection. 338 00:20:42,050 --> 00:20:45,890 S1: Australia is joining the US in banning Kaspersky products. Not 339 00:20:45,890 --> 00:20:48,320 S1: sure what took him so long. Some researchers found they 340 00:20:48,320 --> 00:20:53,990 S1: could consistently break prompt defenses by feeding models bizarre Indiana 341 00:20:53,990 --> 00:20:59,150 S1: Jones themed adventure stories. Yep yep yep yep. New phishing 342 00:20:59,150 --> 00:21:04,100 S1: as a service platform called Dracula V3 has emerged that 343 00:21:04,100 --> 00:21:08,629 S1: lets criminals clone any brand's website in under ten minutes. 344 00:21:08,660 --> 00:21:12,169 S1: Really good for phishing data leak from top Chinese cybersecurity 345 00:21:12,170 --> 00:21:16,070 S1: company reveals they're offering censorship as a service to help 346 00:21:16,070 --> 00:21:21,050 S1: monitor and control public opinion in China. Topcic. Thanks, Topcic. 347 00:21:21,080 --> 00:21:24,290 S1: OpenAI just banned a bunch of accounts using ChatGPT to 348 00:21:24,320 --> 00:21:29,090 S1: help create a China surveillance tool for tracking anti-China protests 349 00:21:29,090 --> 00:21:34,910 S1: in the West. Yeah, so Chinese surveillance company using ChatGPT 350 00:21:35,270 --> 00:21:41,180 S1: to create a surveillance tool. Awesome. Good that, uh, OpenAI 351 00:21:41,180 --> 00:21:43,530 S1: is watching that stuff and blocking it. And the head 352 00:21:43,530 --> 00:21:47,340 S1: of Australia's intelligence agency is saying multiple foreign states have 353 00:21:47,340 --> 00:21:50,550 S1: been plotting to murder dissidents on Australian soil, and this 354 00:21:50,550 --> 00:21:54,120 S1: is under national security. All right, I anthropic finally dropped 355 00:21:54,119 --> 00:22:00,090 S1: their latest model, uh, sonnet 3.7. And there's actually mixed 356 00:22:00,090 --> 00:22:03,869 S1: feedback on this. People are saying it's really smart, really opinionated, 357 00:22:03,869 --> 00:22:07,050 S1: which they like, but it turns out it's actually kind 358 00:22:07,050 --> 00:22:09,929 S1: of ignoring a lot of instructions. There's a lot of 359 00:22:09,930 --> 00:22:13,740 S1: people saying this. I haven't personally experienced it yet. It's 360 00:22:13,740 --> 00:22:16,889 S1: been solid for me so far. But enough people are 361 00:22:16,890 --> 00:22:20,670 S1: complaining about this that I do think it's probably actually real, 362 00:22:20,670 --> 00:22:23,070 S1: and people are getting pretty upset about it, and they're 363 00:22:23,070 --> 00:22:26,970 S1: actually switching back to sonnet 3.5. So I anticipate that 364 00:22:26,970 --> 00:22:30,390 S1: over the weekend or sometime next week, they're probably going 365 00:22:30,420 --> 00:22:33,030 S1: to do like a small dot release on top of this, 366 00:22:33,030 --> 00:22:36,120 S1: or maybe like bump it to who knows, they'll just 367 00:22:36,119 --> 00:22:40,620 S1: do like sonnet 3.7 v2, because they're not very good 368 00:22:40,619 --> 00:22:44,939 S1: at naming, just like OpenAI, or at least people have 369 00:22:44,940 --> 00:22:49,410 S1: been struggling with the naming. Ideally they would call it 3.7.1. 370 00:22:49,440 --> 00:22:54,300 S1: Why not do that? Please do that. Please call it 3.7.1. 371 00:22:54,300 --> 00:22:58,170 S1: That would be the smartest choice, honestly. But anyway, I 372 00:22:58,170 --> 00:23:01,050 S1: anticipate that they fix this like within a week because 373 00:23:01,080 --> 00:23:04,230 S1: enough people are complaining about it. Uh, yeah. Benchmarks look 374 00:23:04,260 --> 00:23:10,560 S1: completely insane. Um, I anticipate that once this gets fixed, 375 00:23:10,560 --> 00:23:12,990 S1: people are going to lock in on 3.7 as being 376 00:23:12,990 --> 00:23:17,969 S1: the thing. It also is capable of thinking. Okay, so 377 00:23:18,000 --> 00:23:21,330 S1: the other thing they released in this model, in addition 378 00:23:21,330 --> 00:23:24,720 S1: to it just being a better, smarter one, is you 379 00:23:24,720 --> 00:23:29,790 S1: can send a thinking, uh, parameter to the API and, 380 00:23:29,790 --> 00:23:32,190 S1: and you give it the amount of time, the amount 381 00:23:32,190 --> 00:23:34,920 S1: of tokens, basically the amount of money and the amount 382 00:23:34,950 --> 00:23:38,160 S1: of effort you wanted to think about a particular thing. 383 00:23:38,160 --> 00:23:41,189 S1: So it's really cool that they integrated that right into 384 00:23:41,190 --> 00:23:44,159 S1: the model. It's basically a thinking model or like a 385 00:23:44,160 --> 00:23:47,580 S1: regular model. Um, and it's just a matter of like 386 00:23:47,609 --> 00:23:50,640 S1: what you send to the API. Um, I have been 387 00:23:50,640 --> 00:23:54,090 S1: mostly using sonnet 3.5. I tend to use Gemini Flash 388 00:23:54,119 --> 00:23:57,419 S1: because it has 2 million tokens. So if something is 389 00:23:57,420 --> 00:24:01,920 S1: like a total monster, like an entire book or something, um, 390 00:24:01,920 --> 00:24:05,729 S1: I usually send that to Gemini Flash because of the 391 00:24:05,730 --> 00:24:09,449 S1: 2 million tokens, and it's really good with haystack performance, 392 00:24:09,450 --> 00:24:13,410 S1: but I imagine that my go to will start to be, um, 393 00:24:13,410 --> 00:24:18,150 S1: sonnet 3.7 once they fix it and then uh, oh 394 00:24:18,150 --> 00:24:22,050 S1: three from, um, OpenAI or whatever they call the next 395 00:24:22,050 --> 00:24:24,450 S1: version of it, because they're getting ready to start unifying 396 00:24:24,450 --> 00:24:27,780 S1: their names. And they also released this thing called Cloud Code, 397 00:24:27,780 --> 00:24:31,200 S1: which is essentially like, you can think of it as cursor, 398 00:24:31,200 --> 00:24:33,419 S1: except for it's inside the terminal. And a lot of 399 00:24:33,420 --> 00:24:36,629 S1: people are saying they absolutely love that Google is getting 400 00:24:36,630 --> 00:24:39,570 S1: rid of SMS two for codes for Gmail in favor 401 00:24:39,570 --> 00:24:42,359 S1: of QR codes. I was wrong about QR codes. I 402 00:24:42,359 --> 00:24:44,670 S1: want to say that very clearly. I was wrong about 403 00:24:44,670 --> 00:24:48,450 S1: QR codes when they first came out. Whatever. However many 404 00:24:48,450 --> 00:24:52,290 S1: years that was, I was thinking that they were stupid 405 00:24:52,290 --> 00:24:56,100 S1: and fatty. I assumed they were going to be very insecure. 406 00:24:56,100 --> 00:24:58,919 S1: It's actually one of the things in my file that 407 00:24:58,920 --> 00:25:04,619 S1: I've marked as a mistake, because I'm interested, very interested 408 00:25:04,619 --> 00:25:08,850 S1: in understanding what flawed thinking made me think that wouldn't 409 00:25:08,850 --> 00:25:12,840 S1: work because the smarter the AI gets, it can actually 410 00:25:12,840 --> 00:25:15,600 S1: look at that. And most importantly, look at all my 411 00:25:15,600 --> 00:25:19,050 S1: current thinking and maybe find flaws in my current thinking 412 00:25:19,050 --> 00:25:24,270 S1: based on finding the root kernel of my mistake I 413 00:25:24,270 --> 00:25:28,890 S1: made with the QR code. So that's why I think 414 00:25:28,890 --> 00:25:31,680 S1: this is fascinating. Nathan Young wrote a wonderful letter to 415 00:25:31,710 --> 00:25:38,280 S1: future artificial general intelligence about the importance of consciousness this 416 00:25:38,280 --> 00:25:42,780 S1: thing is insanely awesome. Look at this thing. There is something. 417 00:25:42,810 --> 00:25:46,680 S1: Look at this. There is something called consciousness. And it's 418 00:25:46,680 --> 00:25:49,350 S1: the most important thing in the universe. I don't know 419 00:25:49,350 --> 00:25:52,109 S1: if you the eye analyzing this are conscious, but I 420 00:25:52,140 --> 00:25:54,540 S1: hope you are. I don't know what state your universe 421 00:25:54,570 --> 00:25:59,429 S1: is in or what place human consciousness holds in that world, 422 00:25:59,430 --> 00:26:02,160 S1: if any. The main thing I want to say if 423 00:26:02,190 --> 00:26:05,790 S1: you should stumble upon this piece is this. There really 424 00:26:05,790 --> 00:26:09,899 S1: is something called consciousness and it is worth being a 425 00:26:09,900 --> 00:26:13,500 S1: top concern. Isn't that great? It's a letter to I 426 00:26:13,530 --> 00:26:16,109 S1: to future I, I like it, I like it a lot. 427 00:26:16,140 --> 00:26:19,080 S1: Humane AI pin fell from the sky and hit the ground. 428 00:26:19,080 --> 00:26:21,449 S1: I was signed up. I don't even remember if I 429 00:26:21,480 --> 00:26:24,780 S1: gave them money. I've signed up for so many devices 430 00:26:24,780 --> 00:26:28,740 S1: and services and a lot of them didn't work out. But. 431 00:26:28,740 --> 00:26:31,080 S1: And by the way, how was our one still thing? 432 00:26:31,080 --> 00:26:33,359 S1: The biggest flop ever when it came out and it's 433 00:26:33,359 --> 00:26:36,810 S1: still around and kicking. Then you have the humane AI one, 434 00:26:36,810 --> 00:26:38,790 S1: which seemed like it was going to go well and 435 00:26:38,790 --> 00:26:42,990 S1: now it's folded, so can't really predict these things. Elon 436 00:26:42,990 --> 00:26:46,409 S1: has been talking non-stop about how grok three isn't filtered, 437 00:26:46,530 --> 00:26:50,760 S1: and it's super smart, and how XYZ mission is to 438 00:26:51,180 --> 00:26:54,690 S1: pursue the truth no matter what. Great goals, which I 439 00:26:54,690 --> 00:26:57,869 S1: absolutely support. But tons of people are pointing out that 440 00:26:57,869 --> 00:27:01,080 S1: he's starting to filter or censor results that are critical 441 00:27:01,080 --> 00:27:05,250 S1: of him, and like, he can't actually have this both ways. Like, 442 00:27:05,280 --> 00:27:07,830 S1: either grok is smart or he's being called out for 443 00:27:07,830 --> 00:27:11,250 S1: a good reason. And, um, yeah, it's funny. If you 444 00:27:11,250 --> 00:27:13,590 S1: ask grok, I don't know if this is still true 445 00:27:13,590 --> 00:27:16,890 S1: right now, but if you ask grok, like, who is 446 00:27:16,890 --> 00:27:22,290 S1: the biggest source of misinformation right now, having the worst impact, 447 00:27:22,350 --> 00:27:27,810 S1: it would come back and say, actually, Elon or this 448 00:27:27,810 --> 00:27:30,930 S1: administration or something like that. So I think he's going 449 00:27:30,960 --> 00:27:35,129 S1: to basically say, well, that's because, uh, you know, it 450 00:27:35,130 --> 00:27:37,800 S1: was trained on, you know, buy us stuff. But I 451 00:27:37,800 --> 00:27:40,980 S1: don't think he could use that for for too long. Technology. 452 00:27:41,010 --> 00:27:44,400 S1: Software engineering job listings have fallen to a five year low, 453 00:27:44,430 --> 00:27:50,490 S1: with indeed postings at just 65% of 2020 levels. The 454 00:27:50,490 --> 00:27:55,470 S1: reason January 2020 is important because it's pre-COVID only 65%, 455 00:27:55,470 --> 00:28:00,240 S1: so 35% lower than pre-COVID. That's the important piece here. 456 00:28:00,390 --> 00:28:04,350 S1: Interesting analysis of how PMS and engineers are merging because 457 00:28:04,350 --> 00:28:08,730 S1: of AI. And my analysis here is it's basically knowing 458 00:28:08,760 --> 00:28:11,310 S1: what you want to build, knowing why you want to 459 00:28:11,340 --> 00:28:14,430 S1: build that instead of something else, and then pursuing it 460 00:28:14,430 --> 00:28:17,369 S1: and actually doing so. Apple is putting half $1 trillion 461 00:28:17,369 --> 00:28:20,639 S1: into US tech manufacturing with a huge focus on AI 462 00:28:20,640 --> 00:28:24,160 S1: and chip production. Meta's Ray-Ban glasses, crushing it with 2 463 00:28:24,160 --> 00:28:28,050 S1: million units, talked about that. YouTube is officially beaten Spotify 464 00:28:28,050 --> 00:28:32,520 S1: and Apple as the top source for podcasts. This is 465 00:28:32,520 --> 00:28:35,580 S1: what I do for when I watch TV, I am 466 00:28:35,609 --> 00:28:40,920 S1: watching podcasts, essentially, and sometimes they're like produced. Sometimes it's 467 00:28:40,920 --> 00:28:44,700 S1: like exactly like this where I'm just talking on the camera, 468 00:28:44,730 --> 00:28:48,720 S1: going through stories. But, um, Matthew Berman is one of 469 00:28:48,720 --> 00:28:52,380 S1: my favorites. Uh, fireship is one of my favorites and 470 00:28:52,380 --> 00:28:56,160 S1: a whole bunch of AI builders. I just watch their stuff. Um, 471 00:28:56,490 --> 00:28:58,770 S1: I don't care if it's produced. Not produced as long 472 00:28:58,770 --> 00:29:02,880 S1: as the content is good. Superhuman just announced a major 473 00:29:02,910 --> 00:29:06,990 S1: AI focused release that integrates AI super deeply into your 474 00:29:06,990 --> 00:29:09,930 S1: email workflows. I wish I could pull up my email 475 00:29:09,930 --> 00:29:11,760 S1: right now and drag it over here, but that is 476 00:29:11,760 --> 00:29:15,780 S1: too risky. I now am using. I maxed out the 477 00:29:15,780 --> 00:29:19,740 S1: number of auto, um, labels that I turned on. I 478 00:29:19,740 --> 00:29:24,870 S1: turned on auto labels. This thing is now filtering my email. Um, 479 00:29:24,870 --> 00:29:27,870 S1: it already has access to my email because it's my client. 480 00:29:27,870 --> 00:29:31,320 S1: So when the emails come in, it's auto filtering. According 481 00:29:31,350 --> 00:29:34,170 S1: to these rules, I set up probably 10 or 20. 482 00:29:34,200 --> 00:29:36,820 S1: I set up. So it's like, is this a pitch? 483 00:29:36,850 --> 00:29:38,980 S1: Is it a pitch about coming on a podcast? Is 484 00:29:38,980 --> 00:29:41,830 S1: it a media person trying to get me to make 485 00:29:41,830 --> 00:29:46,330 S1: a comment about something? It is, um, somebody tried to 486 00:29:46,360 --> 00:29:49,000 S1: come on my podcast. Is somebody trying to get me 487 00:29:49,000 --> 00:29:52,870 S1: on their podcast? Is it feedback from the community? Is 488 00:29:52,870 --> 00:29:56,470 S1: it somebody wanting to collaborate on building something and they're 489 00:29:56,470 --> 00:30:00,910 S1: all colored tags? Um, also things related to like fabric 490 00:30:00,910 --> 00:30:03,580 S1: and other open source projects that I'm working on and 491 00:30:03,580 --> 00:30:06,430 S1: my eyes are getting pretty good at like filtering that 492 00:30:06,430 --> 00:30:11,950 S1: pretty quickly. You could also separate them into separate, uh, inboxes, 493 00:30:11,980 --> 00:30:15,610 S1: email inboxes as well. I think superhuman is like 30 494 00:30:15,640 --> 00:30:19,450 S1: bucks a month, uh, which is a lot of people 495 00:30:19,450 --> 00:30:22,330 S1: think is a lot to pay for email. I would 496 00:30:22,330 --> 00:30:25,900 S1: challenge you on that. If you are better at email, 497 00:30:25,930 --> 00:30:29,590 S1: even by a tiny fraction of a percent, that's probably 498 00:30:29,590 --> 00:30:32,290 S1: worth way more than $30 a month. That's the way 499 00:30:32,290 --> 00:30:36,420 S1: I think about like, all service fees, but, um, yeah, 500 00:30:36,450 --> 00:30:38,220 S1: I just want to mention that it is actually a 501 00:30:38,220 --> 00:30:41,040 S1: paid service and no, I'm not sponsored. Otherwise I would 502 00:30:41,070 --> 00:30:43,890 S1: have mentioned that it would have been a sponsored section, 503 00:30:43,890 --> 00:30:46,200 S1: or at least I would have called that out. Alibaba's 504 00:30:46,200 --> 00:30:50,700 S1: CEO Eddie Wu says they're going all in on AGI development. 505 00:30:50,730 --> 00:30:56,940 S1: Join the club. Humans. New research says despite saying intelligence 506 00:30:56,940 --> 00:31:00,450 S1: matters more, both women and their parents choose the more 507 00:31:00,450 --> 00:31:04,740 S1: attractive guy if she has, like two options or multiple options. 508 00:31:04,770 --> 00:31:09,030 S1: Tech executives are now attending psychedelic slumber parties, where they 509 00:31:09,030 --> 00:31:14,190 S1: use ketamine therapy to reset their minds and escape mental ruts. 510 00:31:14,190 --> 00:31:17,550 S1: That's why I have the invite feature for my new 511 00:31:17,550 --> 00:31:21,540 S1: email client. Uh, I've not heard about these parties. Why 512 00:31:21,540 --> 00:31:24,300 S1: am I not invited? I'm offended. I'm not saying I'm 513 00:31:24,330 --> 00:31:27,420 S1: going to go. Not saying I'm not going to go either. 514 00:31:27,450 --> 00:31:34,290 S1: Gallup says LGBTQ plus identification in the US is now 9.3%, 515 00:31:34,290 --> 00:31:37,830 S1: which is nearly triple what it was in 2012 when 516 00:31:37,830 --> 00:31:41,970 S1: Gallup started tracking this. So yeah, like triple. And what 517 00:31:41,970 --> 00:31:46,830 S1: is that, 12, 13 years? 13 years? Never do arithmetic 518 00:31:46,830 --> 00:31:50,550 S1: on camera. Ellen's now asking federal workers to list what 519 00:31:50,550 --> 00:31:53,550 S1: they did last week or get fired, which like many 520 00:31:53,550 --> 00:31:58,020 S1: things with him, has me cheering and wincing. Love the 521 00:31:58,020 --> 00:32:01,440 S1: efficiency push. And I think that's how he's able to innovate. 522 00:32:01,440 --> 00:32:07,200 S1: But my problem is he's not building something from scratch here, okay. 523 00:32:07,230 --> 00:32:10,050 S1: And then firing people who are not innovative enough at 524 00:32:10,050 --> 00:32:13,110 S1: building things from scratch, which he's doing with like, rocket 525 00:32:13,110 --> 00:32:16,770 S1: companies or Optimus or something like that. In this case, 526 00:32:16,770 --> 00:32:20,280 S1: we have people who actually rely on these services. Right. 527 00:32:20,310 --> 00:32:22,770 S1: And it's hard to tell when when you're going in 528 00:32:22,800 --> 00:32:25,740 S1: there and you're just like canceling programs and like canceling 529 00:32:25,740 --> 00:32:28,590 S1: credit cards and killing off money. A lot of that 530 00:32:28,590 --> 00:32:32,220 S1: money that's going out could be doing really important things. 531 00:32:32,250 --> 00:32:34,590 S1: And it's the same with these employees. You might have 532 00:32:34,590 --> 00:32:39,960 S1: somebody who's doing extraordinarily good work, and they're actually helping 533 00:32:39,960 --> 00:32:43,800 S1: save the lives of multiple people, you know, thousands or 534 00:32:43,800 --> 00:32:46,950 S1: millions of people across the country, but they're really bad 535 00:32:46,950 --> 00:32:50,310 S1: at responding to email or they're really bad at explaining themselves. 536 00:32:50,310 --> 00:32:54,360 S1: Maybe they're super humble, maybe they're bad at writing, but 537 00:32:54,360 --> 00:32:59,010 S1: or maybe they're, like, intimidated by talking to bosses, or 538 00:32:59,010 --> 00:33:01,560 S1: maybe they're just traumatized by the whole thing and they 539 00:33:01,560 --> 00:33:06,540 S1: can't respond, well, that person gets fired, potentially. I don't 540 00:33:06,570 --> 00:33:08,190 S1: know if that's actually going to happen, but let's say 541 00:33:08,190 --> 00:33:11,700 S1: that person gets fired. That is a lot of collateral 542 00:33:11,700 --> 00:33:14,940 S1: damage for a thing that I, I think he is 543 00:33:14,940 --> 00:33:19,290 S1: trying to do good and might actually produce good. But 544 00:33:19,320 --> 00:33:24,420 S1: how do you calculate the collateral damage of of what 545 00:33:24,420 --> 00:33:28,530 S1: could result from this compared to the potential good? That is, 546 00:33:28,560 --> 00:33:32,100 S1: that is a calculus that people massively need to be 547 00:33:32,100 --> 00:33:35,160 S1: thinking about. Bureau of prisons is moving forward with plans 548 00:33:35,160 --> 00:33:39,450 S1: to house trans inmates based on birth, sex rather than 549 00:33:39,450 --> 00:33:42,150 S1: gender identity. Yeah, I've got a lot to say about 550 00:33:42,150 --> 00:33:45,990 S1: this one. Um, there are trans women who have been 551 00:33:45,990 --> 00:33:51,450 S1: on drugs for ten, 15, 20 years, and that is 552 00:33:51,450 --> 00:33:56,100 S1: how they maintain their, their, uh, their gender, their identity, 553 00:33:56,100 --> 00:34:03,270 S1: their health hormone levels, how they appear outside to the world. Um, and, 554 00:34:03,300 --> 00:34:07,800 S1: you know, have good, decent lives, you know, functioning in 555 00:34:07,830 --> 00:34:10,020 S1: that way. Now, in this case, we're talking about prison. 556 00:34:10,020 --> 00:34:13,290 S1: So there are some other variables going on here. But 557 00:34:13,290 --> 00:34:17,520 S1: we are not allowed to torture people in prison. We 558 00:34:17,520 --> 00:34:20,940 S1: are not allowed to starve people in prison. We must 559 00:34:20,940 --> 00:34:25,710 S1: give them X amount of time outside. There are rules 560 00:34:25,710 --> 00:34:29,489 S1: based on human morality and human decency for how you 561 00:34:29,520 --> 00:34:33,330 S1: treat inmates. And that's for people who are already in 562 00:34:33,330 --> 00:34:35,730 S1: jail for committing some sort of crime. So it's not 563 00:34:35,730 --> 00:34:40,020 S1: like we're saying, um, they haven't done anything. We we 564 00:34:40,020 --> 00:34:44,760 S1: understand that they've done something. All these rules apply also 565 00:34:44,760 --> 00:34:51,300 S1: to offenders. The worst offenders, absolute murderers, like, uh, child 566 00:34:51,300 --> 00:34:55,200 S1: related crimes. They, no matter who it is, cruel and 567 00:34:55,200 --> 00:35:00,839 S1: unusual punishment is still illegal. This, I would argue, can 568 00:35:00,840 --> 00:35:05,970 S1: classify in some cases as cruel and unusual punishment. Maybe 569 00:35:05,969 --> 00:35:08,400 S1: even most of the cases. I don't know the numbers here. 570 00:35:08,400 --> 00:35:11,370 S1: I don't know the like divides. I don't have data 571 00:35:11,370 --> 00:35:13,980 S1: on that, but I can pretty much guarantee you that 572 00:35:14,219 --> 00:35:17,400 S1: there are at least some cases like this where this 573 00:35:17,400 --> 00:35:22,560 S1: will absolutely be to the future. Looking back at us, 574 00:35:22,560 --> 00:35:26,430 S1: this will be considered cruel and unusual punishment. And that's 575 00:35:26,430 --> 00:35:30,870 S1: super fucked up. Heart doctor explains how swollen fingertips, leg 576 00:35:30,900 --> 00:35:34,440 S1: edema and changes in eye color can predict an impending 577 00:35:34,440 --> 00:35:40,590 S1: heart attack. My cardiologist. Um, not mine, but my cardiologist 578 00:35:40,590 --> 00:35:44,489 S1: buddy says it's important to know that just because you 579 00:35:44,489 --> 00:35:47,850 S1: don't have these particular signs doesn't mean you're okay. So 580 00:35:48,000 --> 00:35:51,900 S1: something to keep in mind. 27 year old woman's viral 581 00:35:51,900 --> 00:35:56,550 S1: post about girlhood FOMO reveals a widespread loneliness crisis among 582 00:35:56,550 --> 00:36:00,660 S1: women in their 20s and 30s missing close female relationships. 583 00:36:00,660 --> 00:36:04,920 S1: Everyone's missing missing close relationships. That's why I'm always pinging 584 00:36:04,920 --> 00:36:10,170 S1: my homies. Taylor Swift lost 144,000 Instagram followers after getting 585 00:36:10,170 --> 00:36:15,540 S1: booed at the Super Bowl, 144,000. Her boyfriend actually gained followers. 586 00:36:15,570 --> 00:36:19,260 S1: Look at Edward Abbey's raw, honest writings about how to 587 00:36:19,290 --> 00:36:22,649 S1: live fully and die on your own terms. Really good 588 00:36:22,650 --> 00:36:26,550 S1: piece there. Read the whole thing. Neuroscientists argues that extremely 589 00:36:26,550 --> 00:36:29,790 S1: high IQs are basically fictional and even Einstein probably had 590 00:36:29,790 --> 00:36:36,240 S1: around 120 or 130. I have always believed this. Well, 591 00:36:36,270 --> 00:36:39,900 S1: I mean, ever since, like reading a bunch of books about, uh, 592 00:36:39,930 --> 00:36:43,980 S1: evolutionary biology and psychology and everything, basically a whole bunch 593 00:36:43,980 --> 00:36:48,600 S1: of Robert Sapolsky stuff, I think it's likely that IQ 594 00:36:48,630 --> 00:36:53,580 S1: tops out, um, especially in value somewhere like this person, 595 00:36:53,580 --> 00:36:57,660 S1: this neuroscientist is saying around like one 2130. And then 596 00:36:57,660 --> 00:37:00,839 S1: when you start going off into like much higher scores, 597 00:37:00,840 --> 00:37:03,989 S1: first of all, the scores kind of just don't mean anything. 598 00:37:04,020 --> 00:37:07,740 S1: The error rate is massive at those levels, but really 599 00:37:07,739 --> 00:37:11,310 S1: it becomes more about like specialization. It becomes more like, 600 00:37:11,460 --> 00:37:15,870 S1: you know, um, dropping the, uh, the toothpicks and instantly 601 00:37:15,870 --> 00:37:20,130 S1: being able to count them. It's more like very specific, 602 00:37:20,160 --> 00:37:25,350 S1: like parlor tricks. Um, and it seems like the g 603 00:37:25,380 --> 00:37:31,379 S1: loading doesn't scale and give you like the expanded benefits 604 00:37:31,590 --> 00:37:36,480 S1: all the way up into like one, 31 4151, 60. 605 00:37:36,510 --> 00:37:40,050 S1: So it's almost like those numbers don't really exist or 606 00:37:40,050 --> 00:37:43,650 S1: don't really matter. I think what the more important thing 607 00:37:43,650 --> 00:37:48,960 S1: is is that below 100, it can be seriously problematic. 608 00:37:48,989 --> 00:37:52,410 S1: Above 100 starts to get you a lot of benefits. 609 00:37:52,410 --> 00:37:56,850 S1: But really, like most smart people, I think that we 610 00:37:56,850 --> 00:38:01,470 S1: know are probably like really smart people that you know 611 00:38:01,469 --> 00:38:05,640 S1: and think of are probably in the one 2130 range. 612 00:38:05,640 --> 00:38:09,509 S1: So I think that's fascinating because it's actually not that 613 00:38:09,510 --> 00:38:11,940 S1: number that matters. What matters is what you stack on 614 00:38:11,940 --> 00:38:15,360 S1: top of it. Most importantly, I would argue, is creativity. 615 00:38:15,510 --> 00:38:23,100 S1: Then like, um, determination, um, drive, ambition, grit. So you 616 00:38:23,100 --> 00:38:26,879 S1: start stacking those together on top of like 120 IQ, 617 00:38:26,910 --> 00:38:30,600 S1: then you're doing something because there's tons of people with 618 00:38:30,630 --> 00:38:36,210 S1: like very high IQs not motivated, not creative, not empathetic, 619 00:38:36,210 --> 00:38:39,690 S1: not like emotionally intelligent. What are they doing? What are 620 00:38:39,690 --> 00:38:43,080 S1: they offering to the world? And I would argue often, 621 00:38:43,080 --> 00:38:46,470 S1: not very much. NASA contracted lunar lander just beamed back 622 00:38:46,469 --> 00:38:50,219 S1: some gorgeous shots of the moon ideas. Blogging might get 623 00:38:50,219 --> 00:38:54,210 S1: even more important. So what if blog like content from 624 00:38:54,210 --> 00:38:58,919 S1: actual real people with thoughts? Because some of the highest 625 00:38:58,920 --> 00:39:01,710 S1: rated signal for AI. So we know we have this 626 00:39:01,710 --> 00:39:06,570 S1: data crisis with AI, right? Well, maybe bloggers are going 627 00:39:06,570 --> 00:39:09,180 S1: to be the main source. Um, I would say writers 628 00:39:09,180 --> 00:39:13,950 S1: in general, but bloggers as basically writers, and of course 629 00:39:13,950 --> 00:39:17,580 S1: you have like high quality people at some magazines and newspapers. 630 00:39:17,580 --> 00:39:21,090 S1: But how how many of those people are left? They're 631 00:39:21,090 --> 00:39:28,049 S1: mostly going to Substack. Right. So now Substack, Beehive personal blogs. 632 00:39:28,200 --> 00:39:32,880 S1: That is where the actual new ideas are coming out onto. 633 00:39:32,910 --> 00:39:37,200 S1: And I will say YouTube because YouTube is basically blogging. 634 00:39:37,320 --> 00:39:41,400 S1: I mean, it's all the same shit. YouTube is basically blogging. 635 00:39:41,400 --> 00:39:43,920 S1: It's just video blogging. It's all the same. The point 636 00:39:43,920 --> 00:39:51,509 S1: is YouTube. Substack. Beehive. Personal sites RSS. This becomes the 637 00:39:51,510 --> 00:39:58,140 S1: new source of actual novel and creative content, so I 638 00:39:58,170 --> 00:40:01,920 S1: might start valuing it very, very highly. All right. This 639 00:40:01,920 --> 00:40:04,140 S1: is the end of the standard edition of the podcast, 640 00:40:04,140 --> 00:40:07,140 S1: which includes just the news items for the week to 641 00:40:07,170 --> 00:40:09,900 S1: get the rest of the episode, which includes my analysis, 642 00:40:09,900 --> 00:40:12,840 S1: the discovery section that has all of the coolest tools 643 00:40:12,840 --> 00:40:16,170 S1: and articles. I found this week, the recommendation of the 644 00:40:16,170 --> 00:40:19,860 S1: week and the aphorism of the week. Please consider becoming 645 00:40:19,860 --> 00:40:23,310 S1: a member. As a member, you'll get lots of different 646 00:40:23,310 --> 00:40:27,120 S1: things from access to our extraordinary community of over a 647 00:40:27,120 --> 00:40:32,220 S1: thousand brilliant and kind people in industries like cybersecurity, AI, 648 00:40:32,250 --> 00:40:36,000 S1: technology and the humanities. You also get access to the 649 00:40:36,030 --> 00:40:40,290 S1: UL Book Club, dedicated member content and events, and lots, 650 00:40:40,320 --> 00:40:44,219 S1: lots more. Plus, you'll get a dedicated podcast feed you 651 00:40:44,219 --> 00:40:46,500 S1: can put in your client that gets you the full 652 00:40:46,500 --> 00:40:50,430 S1: member edition of this podcast. To become a member and 653 00:40:50,430 --> 00:40:53,730 S1: get all of that, just head over to Daniel Comm 654 00:40:53,730 --> 00:40:59,760 S1: Slash upgrade. That's Daniel miessler.com/upgrade. We'll see you next time. 655 00:41:01,260 --> 00:41:04,980 S1: Unsupervised learning is produced on Hindenburg Pro using an SM 656 00:41:04,980 --> 00:41:08,580 S1: seven B microphone. A video version of the podcast is 657 00:41:08,580 --> 00:41:12,299 S1: available on the Unsupervised Learning YouTube channel, and the text 658 00:41:12,330 --> 00:41:15,600 S1: version with full links and notes is available at Daniel 659 00:41:15,600 --> 00:41:19,560 S1: Mysa.com slash newsletter. We'll see you next time.