1 00:00:01,280 --> 00:00:04,609 S1: Welcome to Unsupervised Learning, a security, AI and meaning focused 2 00:00:04,610 --> 00:00:07,460 S1: podcast that looks at how best to thrive as humans 3 00:00:07,460 --> 00:00:11,690 S1: in a post AI world. It combines original ideas, analysis, 4 00:00:11,720 --> 00:00:14,930 S1: and mental models to bring not just the news, but 5 00:00:14,930 --> 00:00:18,080 S1: why it matters and how to respond. All right, welcome 6 00:00:18,079 --> 00:00:21,710 S1: to unsupervised Learning. This is Daniel Miessler, and I'm building 7 00:00:21,739 --> 00:00:25,280 S1: AI to upgrade humans. This is episode 458 of the 8 00:00:25,280 --> 00:00:30,200 S1: podcast Unsupervised Learning. And let's jump into it. So just 9 00:00:30,200 --> 00:00:34,309 S1: created my first fabric stitch, which is a combination of 10 00:00:34,310 --> 00:00:37,790 S1: fabric things all piped into each other to produce a result. 11 00:00:37,790 --> 00:00:40,040 S1: In this case, the name of the stitch is called 12 00:00:40,040 --> 00:00:44,210 S1: rate AI result, and it's actually running a pattern called 13 00:00:44,210 --> 00:00:49,310 S1: rate AI result as well. But what it is doing, 14 00:00:49,340 --> 00:00:52,760 S1: I will just click over there real quick. What it 15 00:00:52,760 --> 00:00:57,980 S1: is doing is actually see here it is actually using 16 00:00:57,980 --> 00:01:02,220 S1: a smart AI to rate a less smart I. So 17 00:01:02,220 --> 00:01:04,380 S1: this is the infrastructure that it's using. I'm actually going 18 00:01:04,410 --> 00:01:07,470 S1: to do a full write up on this. Well this 19 00:01:07,470 --> 00:01:08,730 S1: is the full write up. But I'm going to do 20 00:01:08,730 --> 00:01:12,030 S1: a standalone video on this. So you don't have to 21 00:01:12,060 --> 00:01:16,230 S1: necessarily worry about not getting everything here. So basically you 22 00:01:16,230 --> 00:01:19,740 S1: have your smartest AI on top. It's judging the judged 23 00:01:19,740 --> 00:01:24,210 S1: AI and the input and the prompt that it's given 24 00:01:24,209 --> 00:01:27,630 S1: and it's producing a result. Then you take the input, 25 00:01:27,630 --> 00:01:30,660 S1: the prompt and the results, and you send all of 26 00:01:30,690 --> 00:01:34,620 S1: that to the smarter AI, which is the judge. And 27 00:01:34,620 --> 00:01:36,420 S1: then what it does is it gives you an output 28 00:01:36,420 --> 00:01:39,750 S1: like this. It tells you is this at the level 29 00:01:39,750 --> 00:01:46,950 S1: of an uneducated human, a secondary, educated human, high school level, bachelor's, master's, PhD, 30 00:01:46,980 --> 00:01:51,300 S1: world class, human or superhuman. And it's giving you that 31 00:01:51,300 --> 00:01:54,779 S1: rating for whatever that task is. What's cool is that 32 00:01:54,780 --> 00:01:58,230 S1: this is universal. So it's not a just a summarization 33 00:01:58,230 --> 00:02:01,190 S1: one or just a threat modeling one. It looks at 34 00:02:01,190 --> 00:02:04,100 S1: the task that you've given it, and it looks at 35 00:02:04,100 --> 00:02:06,650 S1: the input, and it looks at the output, and it 36 00:02:06,650 --> 00:02:09,830 S1: judges universally for any task that you've given it for 37 00:02:09,830 --> 00:02:14,000 S1: any particular AI. It's basically saying that somebody at this 38 00:02:14,000 --> 00:02:17,179 S1: level would have been able to do it about that. Good. 39 00:02:17,180 --> 00:02:20,720 S1: So that was pretty exciting. I'm happy with the results 40 00:02:20,720 --> 00:02:24,709 S1: so far. Look forward to anyone being able to improve it. Okay. Um, 41 00:02:25,220 --> 00:02:28,370 S1: let's see here. Going back to the podcast. Yeah, I'm 42 00:02:28,370 --> 00:02:30,980 S1: going to be sending out the newsletter from newsletter at 43 00:02:30,980 --> 00:02:35,149 S1: Unsupervised learning.com instead of Daniel at Daniel. Com so just 44 00:02:35,150 --> 00:02:38,420 S1: go ahead and add newsletter at Unsupervised learning.com to your 45 00:02:38,419 --> 00:02:41,990 S1: contact list just so it won't get forwarded to a 46 00:02:41,990 --> 00:02:44,750 S1: bad folder. And I'm entering the fiber world. This is 47 00:02:44,750 --> 00:02:49,190 S1: pretty exciting. Upgraded to five gigabit fiber to the house. 48 00:02:49,190 --> 00:02:51,919 S1: And so I'm doing a whole bunch of stuff with, uh, 49 00:02:51,950 --> 00:02:54,950 S1: ten Gigabit Ethernet. So you got to do a bunch 50 00:02:54,950 --> 00:02:57,800 S1: of stuff on your switching, right? Because if you just 51 00:02:57,800 --> 00:02:59,580 S1: have it coming into the house, it doesn't mean it's 52 00:02:59,580 --> 00:03:02,220 S1: going to make it to your computer. So you have 53 00:03:02,250 --> 00:03:07,079 S1: your switches and your firewalls and your wall wiring, and 54 00:03:07,080 --> 00:03:08,670 S1: all the way to your device has to be able 55 00:03:08,669 --> 00:03:11,940 S1: to support that faster speed. So ten gigabit is pretty 56 00:03:11,940 --> 00:03:15,000 S1: much what most consumer hardware can go up to now. 57 00:03:15,030 --> 00:03:18,780 S1: The Mac studio that I use actually already has ten 58 00:03:18,780 --> 00:03:21,510 S1: gigabit in it. And for something else you can get, 59 00:03:21,540 --> 00:03:25,200 S1: like if it has USB and it's fairly modern, you 60 00:03:25,200 --> 00:03:29,910 S1: can get like a USB connector, like USB-C connector that 61 00:03:29,910 --> 00:03:35,730 S1: is actually a ten gigabit, uh, translator. So to be 62 00:03:35,730 --> 00:03:38,460 S1: able to handle that, ten gigabit, even if your Nic 63 00:03:38,460 --> 00:03:42,990 S1: card natively built into the system cannot. But anyway, here's 64 00:03:42,990 --> 00:03:47,910 S1: what I'm getting down and up now, basically 4.6 gigabit. 65 00:03:47,910 --> 00:03:50,040 S1: So it's supposed to be five. I'm going to try 66 00:03:50,040 --> 00:03:53,550 S1: to get it up to five. But uh, 4600 is 67 00:03:53,550 --> 00:03:56,340 S1: not bad at all. And I'm going to keep upgrading 68 00:03:56,340 --> 00:03:59,720 S1: stuff like in the walls and different switches and stuff 69 00:03:59,720 --> 00:04:03,170 S1: like that and see how fast we can go. And 70 00:04:03,170 --> 00:04:06,830 S1: had a great sponsor conversation with my best bud, Jason 71 00:04:06,830 --> 00:04:09,620 S1: Haddox with flare. We talked about a whole bunch of stuff, 72 00:04:09,620 --> 00:04:12,230 S1: but especially why he likes flare and why he thinks 73 00:04:12,230 --> 00:04:16,400 S1: it's the best out there for threat intelligence. So definitely 74 00:04:16,400 --> 00:04:19,190 S1: go check that video out and let's jump into security. 75 00:04:19,190 --> 00:04:23,240 S1: So six critical flaws have been found in Olama AI framework, 76 00:04:23,330 --> 00:04:27,560 S1: potentially allowing denial of service or model theft or poisoning attacks. 77 00:04:27,560 --> 00:04:31,220 S1: And remember, don't put your olama online. A lot of 78 00:04:31,220 --> 00:04:36,740 S1: people know that you can olama natively. It publishes APIs 79 00:04:36,740 --> 00:04:38,960 S1: so you can call the different models that you create. 80 00:04:38,960 --> 00:04:42,170 S1: So if you go get a model from hugging face 81 00:04:42,170 --> 00:04:43,790 S1: and you put it in a llama and you spin 82 00:04:43,790 --> 00:04:47,150 S1: it up, well, now there's an API that can listen 83 00:04:47,150 --> 00:04:49,850 S1: and answer requests, and you could do some cool stuff there, 84 00:04:49,850 --> 00:04:53,450 S1: but do not make that public unless you really, really 85 00:04:53,450 --> 00:04:55,250 S1: know what you're doing and you have auth and all 86 00:04:55,250 --> 00:04:58,010 S1: that kind of stuff because it could be bad as 87 00:04:58,010 --> 00:05:00,930 S1: we see here, we actually have vulnerabilities here. FBI is 88 00:05:00,930 --> 00:05:03,390 S1: warning about a rise in hacked police emails being used 89 00:05:03,390 --> 00:05:07,739 S1: to send fake subpoenas and emergency data requests. That's the 90 00:05:07,740 --> 00:05:11,489 S1: trick one, right? Emergency data requests. So it looks like 91 00:05:11,490 --> 00:05:15,030 S1: it's coming from a reputable place that has authority, like 92 00:05:15,029 --> 00:05:17,010 S1: a police department. So of course you're going to turn 93 00:05:17,010 --> 00:05:19,410 S1: over the data, right? I think this is a pretty 94 00:05:19,440 --> 00:05:22,770 S1: nasty general attack here, which is you find a low 95 00:05:22,770 --> 00:05:27,450 S1: security organization that's like understaffed in cyber, like a police department. 96 00:05:27,450 --> 00:05:30,719 S1: Then you send emails from the police department to a 97 00:05:30,720 --> 00:05:33,120 S1: place that you want to get data from, and then 98 00:05:33,120 --> 00:05:35,880 S1: they turn it over thinking that they have to. So 99 00:05:35,880 --> 00:05:38,010 S1: I think this is a general thing, and I think 100 00:05:38,040 --> 00:05:42,930 S1: police departments, also law firms, might be likely to respond 101 00:05:42,930 --> 00:05:45,719 S1: to this, although maybe they'll be smarter about asking more 102 00:05:45,720 --> 00:05:50,460 S1: questions anyway. I think it's a general interesting type of attack. 103 00:05:50,490 --> 00:05:55,020 S1: Google's AI security assessment tool, Bigsleep, found a zero day 104 00:05:55,020 --> 00:05:59,140 S1: vulnerability in SQLite, and it's the first time we've really seen. 105 00:05:59,170 --> 00:06:04,539 S1: I find something that in production software that hasn't been 106 00:06:04,540 --> 00:06:07,750 S1: found via another method. Right. Because they've done fuzzing on 107 00:06:07,750 --> 00:06:10,180 S1: this thing or fuzzing has been done on this thing, 108 00:06:10,180 --> 00:06:14,140 S1: I'm sure, many times. And this is a good example 109 00:06:14,140 --> 00:06:18,400 S1: of a AI actually finding something. So I mean, I 110 00:06:18,400 --> 00:06:22,060 S1: always thought that this was inevitable. I still think it's 111 00:06:22,060 --> 00:06:24,849 S1: inevitable that this is going to become like the main 112 00:06:24,850 --> 00:06:28,630 S1: way of finding things. But yeah, Google found it in 113 00:06:28,630 --> 00:06:31,299 S1: the real world. FBI is asking for public help in 114 00:06:31,300 --> 00:06:36,760 S1: identifying Chinese hacker, uh, hackers in groups like apt 31 115 00:06:36,760 --> 00:06:40,570 S1: and APT. 41. CrowdStrike has launched a new AI Red 116 00:06:40,600 --> 00:06:44,890 S1: team service to identify vulnerabilities in AI systems. So they've 117 00:06:44,890 --> 00:06:49,990 S1: spun up an AI security consultancy. Kind of strange inside 118 00:06:49,990 --> 00:06:54,669 S1: of CrowdStrike itself, which does endpoint protection, Synology is telling 119 00:06:54,670 --> 00:06:58,920 S1: its users to patch a critical zero click RC bug 120 00:06:59,279 --> 00:07:04,859 S1: affecting millions of disk, station and Photos NAS devices. Why 121 00:07:04,860 --> 00:07:09,210 S1: are the NAS devices online? Friends don't friend. Friends don't 122 00:07:09,210 --> 00:07:14,670 S1: let friends put NAS devices online. It is really nasty. 123 00:07:14,700 --> 00:07:16,920 S1: It's like the worst thing you could put online. Not 124 00:07:16,920 --> 00:07:19,860 S1: because they're the most vulnerable, which they are quite vulnerable 125 00:07:19,860 --> 00:07:23,369 S1: as we've seen. But that's not the real reason. The 126 00:07:23,370 --> 00:07:26,100 S1: real reason is it's a combination of how vulnerable is 127 00:07:26,100 --> 00:07:29,850 S1: it versus how sensitive is it a NAS network attached, 128 00:07:30,030 --> 00:07:33,300 S1: attached storage? It tends to have lots of really sensitive 129 00:07:33,300 --> 00:07:36,090 S1: stuff in it. So you need to be really careful 130 00:07:36,090 --> 00:07:38,400 S1: with what you do with that thing. And putting it 131 00:07:38,400 --> 00:07:42,210 S1: on online is not really careful. All right. Nokia is 132 00:07:42,210 --> 00:07:46,110 S1: investigating potential breach after a hacker Intel broker claimed to 133 00:07:46,140 --> 00:07:48,840 S1: have stolen their source code from a third party vendor. 134 00:07:48,870 --> 00:07:53,280 S1: Canada has ordered TikTok Technology Canada to shut down for 135 00:07:53,280 --> 00:07:58,690 S1: national security reasons, but doesn't actually block Canadians from using TikTok, 136 00:07:58,690 --> 00:08:01,240 S1: which is what I think most people think when they 137 00:08:01,240 --> 00:08:05,170 S1: see this story. It's actually just shutting down the Canadian 138 00:08:05,170 --> 00:08:11,230 S1: business operations. Researchers from GMU, George Mason University, have introduced 139 00:08:11,230 --> 00:08:14,650 S1: Mantis framework that uses prompt injections to hack back against 140 00:08:14,650 --> 00:08:18,130 S1: prompt injection. I'm not usually a fan of hack back stuff, 141 00:08:18,130 --> 00:08:22,960 S1: but I do like the exploration here. US is tightening 142 00:08:22,960 --> 00:08:27,850 S1: rules on foreign real estate deals near military bases, adding 143 00:08:27,850 --> 00:08:31,870 S1: 60 more installations to the list. And this is after 144 00:08:32,080 --> 00:08:35,560 S1: basically the military found China buying a whole bunch of 145 00:08:35,559 --> 00:08:40,930 S1: land around military bases to do crypto mining. And they're like, uh, 146 00:08:40,960 --> 00:08:44,350 S1: I don't think we like this. So there's now laws 147 00:08:44,380 --> 00:08:47,679 S1: in place to stop them from doing that. In some cases, 148 00:08:47,710 --> 00:08:51,070 S1: AI and tech robotic dogs are now patrolling Mar-A-Lago to 149 00:08:51,100 --> 00:08:55,030 S1: help protect president elect Trump. These high tech hounds are 150 00:08:55,030 --> 00:08:59,670 S1: part of the Astro program equipped with surveillance tech and sensors. 151 00:08:59,670 --> 00:09:01,589 S1: So if we open that one up. Yeah. Look at 152 00:09:01,590 --> 00:09:06,840 S1: these things. This is just like the Black Mirror episode. 153 00:09:06,870 --> 00:09:10,079 S1: Look at that. Scary little things. Now, if it's just 154 00:09:10,080 --> 00:09:13,500 S1: one and you're like, oh, there's probably dumb. It's only 2024. 155 00:09:13,530 --> 00:09:16,710 S1: What happens when it's 2026 and it's got guns on it? 156 00:09:16,710 --> 00:09:19,260 S1: And what happens when it's not one, but it's like 157 00:09:19,350 --> 00:09:23,280 S1: 700 and you could just like point a laser pointer 158 00:09:23,280 --> 00:09:26,160 S1: on something and be like, get em. And they just 159 00:09:26,160 --> 00:09:29,640 S1: swarm and start firing. Sounds like sci fi. It's not 160 00:09:29,640 --> 00:09:33,570 S1: really going to be sci fi. And China is crushing 161 00:09:33,570 --> 00:09:37,380 S1: in the drone market. They're building these things so fast. 162 00:09:37,410 --> 00:09:39,870 S1: They're really good at them. Look at DJI. Its number 163 00:09:39,870 --> 00:09:42,180 S1: one drone company in the world, at least on the 164 00:09:42,179 --> 00:09:46,199 S1: consumer side. So this is something we have to watch. Yeah. 165 00:09:46,230 --> 00:09:49,470 S1: What I said here is 2025 and 2026 are going 166 00:09:49,500 --> 00:09:53,760 S1: to become some serious utopia slash dystopia years. Lots of 167 00:09:53,760 --> 00:09:57,850 S1: sci fi happening except for in actual reality, Nvidia surpassed 168 00:09:57,880 --> 00:10:00,730 S1: Apple to become the world's largest company by market cap 169 00:10:00,730 --> 00:10:04,810 S1: 3.43 trillion. And that might have gone up or down. 170 00:10:04,809 --> 00:10:08,260 S1: Since that was written, OpenAI has introduced new feature called 171 00:10:08,260 --> 00:10:11,500 S1: predicted outputs lets you send expected content to speed up 172 00:10:11,500 --> 00:10:15,610 S1: API responses. I like this stuff similar to the caching stuff. 173 00:10:15,700 --> 00:10:18,040 S1: I like the little tweaks that are helping you day 174 00:10:18,040 --> 00:10:21,309 S1: to day, especially save some money. Waymo has launched its 175 00:10:21,309 --> 00:10:25,240 S1: robotaxi service across 80 square mile area in and around 176 00:10:25,240 --> 00:10:28,540 S1: Los Angeles. What is going on? Why can't we have 177 00:10:28,540 --> 00:10:32,079 S1: this for the whole Bay area? They did San Francisco 178 00:10:32,080 --> 00:10:34,209 S1: and they just left. Come on, we need it for 179 00:10:34,210 --> 00:10:36,280 S1: the whole Bay area. Apple's adding a new feature to 180 00:10:36,309 --> 00:10:40,480 S1: find my in iOS 8.2. So basically if you lose 181 00:10:40,480 --> 00:10:43,870 S1: your bag or your bag, your luggage doesn't get to 182 00:10:43,870 --> 00:10:47,620 S1: your destination in some foreign country, you can actually send 183 00:10:47,620 --> 00:10:52,630 S1: an AirTag for your bag to the airline so the 184 00:10:52,630 --> 00:10:55,540 S1: airline can actually find it inside the airport. I think 185 00:10:55,550 --> 00:11:01,490 S1: that's super cool. Apple Vision Pro. Vision Pro's Vision OS 2.2. Yeah. 186 00:11:01,520 --> 00:11:02,360 S1: Look at this thing. 187 00:11:02,420 --> 00:11:04,550 S2: The full size scopes with everything else. It's like having 188 00:11:04,550 --> 00:11:07,819 S2: a two monitor set up. Look at this. Better create 189 00:11:07,820 --> 00:11:10,370 S2: a lot more space for those in my I've used 190 00:11:10,370 --> 00:11:14,240 S2: this here. Almost have a full size video and a 191 00:11:14,240 --> 00:11:16,340 S2: full size scopes with everything else. It's like having a 192 00:11:16,340 --> 00:11:18,980 S2: two monitor set up, but better. So let's take this 193 00:11:18,980 --> 00:11:22,100 S2: to the logical extreme and go ultra wide. Look at that. 194 00:11:22,130 --> 00:11:24,380 S1: Wow, it's bent around you. 195 00:11:24,950 --> 00:11:27,590 S2: This is kind of amazing. Actually. I don't know that 196 00:11:27,620 --> 00:11:29,990 S2: using completely crazy. 197 00:11:30,020 --> 00:11:34,130 S1: Completely crazy. And actually the ultra one is too large. 198 00:11:34,160 --> 00:11:36,800 S1: Excuse me? You can't actually, you can't actually look at 199 00:11:36,800 --> 00:11:38,420 S1: this thing. You have to turn your head all the 200 00:11:38,420 --> 00:11:42,950 S1: way around it. So just the the bigger one, like 201 00:11:42,950 --> 00:11:45,470 S1: the middle one is actually the one that I use. 202 00:11:45,470 --> 00:11:49,790 S1: And they've made it so that it's not always in 203 00:11:49,790 --> 00:11:53,270 S1: focus wherever you, wherever for the whole screen, because that 204 00:11:53,270 --> 00:11:56,199 S1: would be too difficult. Right. So what happens is when 205 00:11:56,320 --> 00:12:00,340 S1: wherever you move your eyes, it comes into crystal clear focus. 206 00:12:00,340 --> 00:12:03,250 S1: And I'm looking at a really high quality monitor right now. 207 00:12:03,280 --> 00:12:06,130 S1: Actually it's the Apple monitor. It looks just as good 208 00:12:06,130 --> 00:12:10,089 S1: as that. It really does. It looks just as good 209 00:12:10,090 --> 00:12:13,420 S1: as like the six K Apple monitor once you focus 210 00:12:13,420 --> 00:12:18,520 S1: on it. Because I mean it's it's sending light right 211 00:12:18,520 --> 00:12:20,830 S1: into your eye. Right. So it's pretty good at it. 212 00:12:20,830 --> 00:12:24,310 S1: And it's crazy how fast it is too. Because when 213 00:12:24,309 --> 00:12:27,429 S1: you're moving your eyes around very quickly, first of all, 214 00:12:27,429 --> 00:12:29,410 S1: it knows exactly where you're looking. And it just takes 215 00:12:29,410 --> 00:12:34,000 S1: that spot and just crystallizes it. Really, really cool. It's 216 00:12:34,000 --> 00:12:36,820 S1: like it's like the most practical thing that I've done 217 00:12:36,850 --> 00:12:41,230 S1: actually with the Vision Pro, because mostly it's entertainment and 218 00:12:41,260 --> 00:12:45,219 S1: like playing with different apps. But this is the killer 219 00:12:45,220 --> 00:12:48,760 S1: app I've been waiting for. Is productivity an actual super 220 00:12:48,790 --> 00:12:53,170 S1: wide screen? TSMC is opening up its fab in Arizona 221 00:12:53,170 --> 00:12:56,300 S1: in December, So that's going to be cool for Onshoring 222 00:12:56,300 --> 00:12:59,990 S1: in the US. TSMC is halting the supply of advanced 223 00:13:00,020 --> 00:13:06,080 S1: AI processors to Chinese clients starting in November. Well, already 224 00:13:06,080 --> 00:13:09,290 S1: happened a couple of days ago following an investigation showing 225 00:13:09,320 --> 00:13:13,459 S1: chips were ending up in Huawei devices. Humans dollars at 226 00:13:13,460 --> 00:13:18,230 S1: its highest point in two years. Stock market going crazy. Uh, yeah. 227 00:13:18,260 --> 00:13:22,610 S1: Bitcoin over 90,000. Yeah, I did predict that Trump was 228 00:13:22,610 --> 00:13:24,920 S1: going to win and that investors were going to go crazy, 229 00:13:24,920 --> 00:13:26,150 S1: but I didn't think it was going to be this 230 00:13:26,150 --> 00:13:30,710 S1: crazy and this soon. Andreessen Horowitz is backing AI powered 231 00:13:30,710 --> 00:13:37,040 S1: parenting tools, with Justine Moore highlighting she's a partner on 232 00:13:37,040 --> 00:13:41,599 S1: this thing. New wave of parenting co-pilots I like this, 233 00:13:41,600 --> 00:13:44,449 S1: I like this, so my kid is doing this. What 234 00:13:44,450 --> 00:13:47,059 S1: should I do? And what I like about that is like, okay, 235 00:13:47,090 --> 00:13:50,090 S1: what would a helicopter parent do? What is like an 236 00:13:50,090 --> 00:13:53,300 S1: eastern way of doing this? What's a European way of 237 00:13:53,300 --> 00:13:56,810 S1: doing this. What is a modern US West Coast way 238 00:13:56,809 --> 00:14:00,110 S1: of doing this? So it's like you're hearing a bunch 239 00:14:00,110 --> 00:14:03,320 S1: of experts because nobody really knows how to raise kids, right? 240 00:14:03,320 --> 00:14:06,650 S1: It's very, very customized. Very there's not a perfect one 241 00:14:06,650 --> 00:14:09,199 S1: way to do it. Right. So I don't have kids, 242 00:14:09,200 --> 00:14:12,170 S1: but if I did, I would want to know, what 243 00:14:12,170 --> 00:14:15,410 S1: do the people who tend to raise the best kids, 244 00:14:15,500 --> 00:14:19,280 S1: what is their long term cultural way of handling this 245 00:14:19,280 --> 00:14:22,670 S1: particular problem? And AI is amazing for that. So that 246 00:14:22,670 --> 00:14:26,660 S1: seems pretty, uh, pretty cool. My buddies participating in a 247 00:14:26,660 --> 00:14:29,720 S1: real life bug bounty. Actually, it's actually a treasure hunt, 248 00:14:29,720 --> 00:14:32,660 S1: which is very similar to a bug bounty. So my buddy, 249 00:14:32,750 --> 00:14:35,900 S1: he's been going out to an island, and he's meeting 250 00:14:35,900 --> 00:14:39,830 S1: his other top bug bounty friend out there, and they're 251 00:14:39,830 --> 00:14:43,910 S1: actually looking for treasure. The treasure is worth half $1 million. 252 00:14:43,910 --> 00:14:49,040 S1: And this book that's coming out, I think it's already out, actually, um, 253 00:14:49,070 --> 00:14:51,200 S1: I'm going to click on this book. Yeah. It's called 254 00:14:51,200 --> 00:14:54,979 S1: There's Treasure Inside. Okay. So it's all about this person 255 00:14:54,980 --> 00:14:59,450 S1: who's launching these treasure hunts with tons, like millions of 256 00:14:59,450 --> 00:15:02,180 S1: dollars of prizes. And they're really hard to find. And 257 00:15:02,180 --> 00:15:04,370 S1: you have to travel there and you have to follow clues. 258 00:15:04,370 --> 00:15:07,820 S1: It's like puzzles. And I was talking to my buddy 259 00:15:07,820 --> 00:15:10,010 S1: about this, and it's like it's very similar to Bug Bounty, 260 00:15:10,010 --> 00:15:12,590 S1: where it's like, there's this joy of, you're the first 261 00:15:12,590 --> 00:15:15,410 S1: one to find this thing, and you get paid money 262 00:15:15,410 --> 00:15:19,489 S1: as a result of finding it. It's like very it like, 263 00:15:19,490 --> 00:15:22,730 S1: revives your childhood to be chasing something in this way. 264 00:15:22,730 --> 00:15:25,370 S1: So I think it's really cool. And he agreed with 265 00:15:25,370 --> 00:15:28,760 S1: that assessment. Genetic discrimination becoming a real thing as we 266 00:15:28,790 --> 00:15:32,960 S1: knew it would. Insurers use DNA data to deny coverage 267 00:15:32,960 --> 00:15:36,530 S1: or to raise prices. So this guy, Bill, who was 268 00:15:36,560 --> 00:15:40,670 S1: a healthy 60 year old, was denied long term care 269 00:15:40,670 --> 00:15:45,020 S1: insurance after he submitted his genetic stuff and it found 270 00:15:45,020 --> 00:15:49,250 S1: a genetic mutation linked to ALS. So he didn't have 271 00:15:49,250 --> 00:15:52,100 S1: the disease, but they were like, yeah, you can't get 272 00:15:52,100 --> 00:15:55,229 S1: this coverage because your genetics say you might get it. 273 00:15:55,230 --> 00:15:57,930 S1: Companies are already moving production out of China as Trump 274 00:15:57,930 --> 00:16:02,880 S1: plans massive tariffs. So Steve Madden cutting China made products 275 00:16:02,880 --> 00:16:07,980 S1: by 40 to 45%, shifting to Vietnam and Cambodia. Stanley 276 00:16:07,980 --> 00:16:13,590 S1: Black and Decker reworking its supply chain and H&M manufacturing. 277 00:16:13,590 --> 00:16:17,400 S1: And crews are eyeing increased U.S. production. Seems like the 278 00:16:17,400 --> 00:16:22,980 S1: tariffs could work in multiple ways. It definitely has a deterrent. 279 00:16:22,980 --> 00:16:25,620 S1: The way I'm excited about it is people wanted to 280 00:16:25,650 --> 00:16:28,560 S1: move to us onshoring, or they wanted to move out 281 00:16:28,560 --> 00:16:31,290 S1: of China or whatever, which everyone in the West kind 282 00:16:31,320 --> 00:16:34,950 S1: of wants to, but they're like, it's just too we 283 00:16:34,950 --> 00:16:37,260 S1: can't do it yet. We can't do it yet. I 284 00:16:37,260 --> 00:16:39,030 S1: want to do it, but I can't do it yet. 285 00:16:39,030 --> 00:16:43,800 S1: And this just pushes people. It pushes people to be like, okay, well, 286 00:16:44,130 --> 00:16:46,530 S1: now I might as well because I'm going to I 287 00:16:46,530 --> 00:16:49,230 S1: might get charged all this money to, to have to 288 00:16:49,260 --> 00:16:52,260 S1: use China. So now it might actually be worth the 289 00:16:52,260 --> 00:16:57,080 S1: money to switch. So hopefully the new administration doesn't go 290 00:16:57,080 --> 00:17:00,710 S1: crazy with this because it actually will cause inflation because 291 00:17:00,710 --> 00:17:03,800 S1: it'll just be more expensive for everyone to buy things. 292 00:17:03,830 --> 00:17:07,160 S1: And Elon talked about this recently and he's like, you 293 00:17:07,160 --> 00:17:09,170 S1: can't do too many of these and you can't do 294 00:17:09,170 --> 00:17:12,470 S1: them too quickly or you will break things. So you 295 00:17:12,470 --> 00:17:15,260 S1: have to be careful with them. But just the idea 296 00:17:15,260 --> 00:17:17,810 S1: of being out there, I think is going to have 297 00:17:17,810 --> 00:17:21,320 S1: the effect that they actually want. NASA's Juno spacecraft just 298 00:17:21,320 --> 00:17:26,510 S1: completed 66th flyby of Jupiter, sending back stunning images. Look 299 00:17:26,540 --> 00:17:30,560 S1: at this stuff. This is crazy. Look at these. That's ridiculous. 300 00:17:30,590 --> 00:17:32,990 S1: Isn't that gorgeous? Is that the spot or is that 301 00:17:32,990 --> 00:17:36,139 S1: just a different spot? I thought the spot was red. Well, 302 00:17:36,140 --> 00:17:38,660 S1: these are different colors anyway. But look at that. Isn't 303 00:17:38,660 --> 00:17:41,660 S1: that amazing? Look at this. These are all storms. Yeah. 304 00:17:41,690 --> 00:17:45,379 S1: Just gorgeous. Man, I love astronomy. Yeah, that's. These are 305 00:17:45,380 --> 00:17:47,960 S1: all spots. But I don't think. I don't think maybe 306 00:17:47,960 --> 00:17:50,990 S1: these are the big red spot. In this case, blue spot. 307 00:17:50,990 --> 00:17:53,730 S1: But that's definitely not it. That's a different one. Yeah, 308 00:17:53,820 --> 00:17:58,170 S1: really cool DNA Dikmen's Leaving and Waving is a brilliant 309 00:17:58,170 --> 00:18:01,470 S1: and touching photo series capturing her parents waving goodbye over 310 00:18:01,470 --> 00:18:03,990 S1: the years. Look at this thing. Look at this. Isn't 311 00:18:03,990 --> 00:18:07,440 S1: this cool? All these captures of them waving goodbye. And 312 00:18:07,440 --> 00:18:11,070 S1: presumably this is like, while she's growing up. Oh, look 313 00:18:11,100 --> 00:18:14,580 S1: at that. Switches from black and white to color. Yeah, really, 314 00:18:14,580 --> 00:18:17,790 S1: really cool. I love this kind of stuff. New study 315 00:18:17,790 --> 00:18:21,840 S1: from Ben-Gurion University shows that controlling blood sugar can slow 316 00:18:21,840 --> 00:18:27,359 S1: brain aging. Yeah, sugar, inflammation, brain aging. These are getting 317 00:18:27,359 --> 00:18:30,450 S1: more and more linked. And yeah, the science here is 318 00:18:30,480 --> 00:18:37,050 S1: is getting pretty solid. Astrobiologist Sara Imari Walker explores the 319 00:18:37,050 --> 00:18:40,110 S1: complex questions of what life truly is in her book, 320 00:18:40,109 --> 00:18:43,770 S1: Life as No One Knows It physics of Life's emergence. 321 00:18:43,770 --> 00:18:46,470 S1: This is on my list might be a candidate for 322 00:18:46,470 --> 00:18:50,100 S1: the UL Book Club as well. She says that modern 323 00:18:50,100 --> 00:18:52,510 S1: science is yet to develop a theory that fully integrates 324 00:18:52,510 --> 00:18:56,770 S1: life into the universe's description. Yeah, I love this life 325 00:18:56,770 --> 00:18:59,710 S1: emergent stuff. A mom in Georgia was jailed after her 326 00:18:59,710 --> 00:19:03,040 S1: 11 year old son walked alone to town, despite the 327 00:19:03,040 --> 00:19:05,110 S1: fact that she was doing this on purpose because she 328 00:19:05,109 --> 00:19:09,310 S1: believes in free range upbringing. I'd love for this libertarian 329 00:19:09,310 --> 00:19:12,669 S1: mindset of like, yeah, just leave me alone to manage 330 00:19:12,670 --> 00:19:16,840 S1: whatever to actually come to these places, right? Because here 331 00:19:16,840 --> 00:19:21,700 S1: they are in Georgia, which should be libertarian, right? And no, 332 00:19:21,700 --> 00:19:25,390 S1: she went to jail. The mom went to jail. I mean, 333 00:19:25,390 --> 00:19:27,280 S1: I grew up in the 80s. I used to walk 334 00:19:27,280 --> 00:19:29,980 S1: around all over the place. I could ride my bike anywhere. 335 00:19:29,980 --> 00:19:32,620 S1: I just had to be back before the streetlights came on. 336 00:19:32,619 --> 00:19:35,380 S1: Or you get your ass whipped. I mean, that's that's 337 00:19:35,380 --> 00:19:37,510 S1: that's the way it's supposed to work. Not really. You 338 00:19:37,510 --> 00:19:39,850 S1: would get in trouble. Our average age of US home 339 00:19:39,880 --> 00:19:44,290 S1: buyers jumped to 56. 56 years old for an average 340 00:19:44,290 --> 00:19:49,270 S1: home buyer. It was 49. This needs to be, like 29. 341 00:19:49,300 --> 00:19:52,740 S1: What is going on? 56 that is way too old. 342 00:19:52,740 --> 00:19:55,199 S1: Oliver Sacks explores the meaning of life through love and 343 00:19:55,200 --> 00:19:59,010 S1: despair in his letters, emphasizing that meaning is something we create, 344 00:19:59,040 --> 00:20:05,550 S1: not find. Marginalien. This is Maria Popova project. Really, really 345 00:20:05,550 --> 00:20:08,610 S1: cool and this is a great write up here. Excuse me. Ideas. 346 00:20:08,609 --> 00:20:12,720 S1: Crypto is back, but it's mostly as gambling and money stores, 347 00:20:12,750 --> 00:20:14,760 S1: and not so much on what it was promising with 348 00:20:14,790 --> 00:20:21,659 S1: like Ethereum. Although maybe Solana might be still like an 349 00:20:21,660 --> 00:20:25,680 S1: Ethereum type vibe. It's basically like fast Ethereum. And I 350 00:20:25,680 --> 00:20:28,800 S1: got a prediction here about this whole election thing, which 351 00:20:28,890 --> 00:20:32,250 S1: I'm purposely not talking too much about right now. I 352 00:20:32,250 --> 00:20:35,310 S1: actually think a lot of good can come from the 353 00:20:35,310 --> 00:20:43,740 S1: whole Elon Andreessen, Thiel, Trump, uh, Vivek Ramaswamy, like all 354 00:20:43,740 --> 00:20:47,790 S1: this stuff. I think all this stuff could go horribly bad. 355 00:20:47,790 --> 00:20:51,230 S1: I voted for Harris, by the way, because of election 356 00:20:51,230 --> 00:20:55,550 S1: denial essentially is the main reason um, wasn't happy with 357 00:20:55,580 --> 00:20:59,810 S1: Harris or the campaign or anything like that, but I yeah, anyway, 358 00:20:59,810 --> 00:21:01,280 S1: I'm not going to go into that too much. The 359 00:21:01,280 --> 00:21:04,609 S1: point is, now that this has happened, I'm trying to 360 00:21:04,609 --> 00:21:08,899 S1: be optimistic. I see no alternative other than being optimistic. 361 00:21:08,930 --> 00:21:13,160 S1: And I believe that a lot of these people Thiel, 362 00:21:13,190 --> 00:21:18,139 S1: Elon Andreessen, um, Rogan these are Bernie loving people. These 363 00:21:18,140 --> 00:21:23,540 S1: are populists. These are long time liberals. Trump is a 364 00:21:23,570 --> 00:21:27,679 S1: long time liberal. Trump is a previous Democrat, been a 365 00:21:27,680 --> 00:21:31,520 S1: Democrat his whole life. And he switched over. In my opinion, 366 00:21:31,520 --> 00:21:34,189 S1: a lot of these people switched over and kind of 367 00:21:34,220 --> 00:21:38,240 S1: like got triggered and went crazy because of the crazy left. 368 00:21:38,240 --> 00:21:41,750 S1: So my hope is and this is wishful thinking, I 369 00:21:41,750 --> 00:21:44,930 S1: understand that. My hope is we're going to see a 370 00:21:44,930 --> 00:21:49,390 S1: lot of good come out of these folks. Hopefully. Okay. 371 00:21:49,420 --> 00:21:53,500 S1: So watch this reduction of government, this whole dodge thing. 372 00:21:53,500 --> 00:21:55,390 S1: It's kind of a gimmick, but I think they might 373 00:21:55,420 --> 00:21:58,659 S1: actually do some cool stuff with it. Like, what if 374 00:21:58,690 --> 00:22:00,820 S1: a whole lot more of our money that we were 375 00:22:00,820 --> 00:22:05,290 S1: spending in taxes? What if that became 50% more effective 376 00:22:05,290 --> 00:22:09,609 S1: or 25% more effective, or 75% more effective? I think 377 00:22:09,609 --> 00:22:15,129 S1: we could have maybe clean streets, maybe nice roads, fewer potholes, 378 00:22:15,160 --> 00:22:20,199 S1: actual infrastructure get built. My hope and my belief, optimistic 379 00:22:20,200 --> 00:22:26,290 S1: belief is that these people, including possibly even Trump, are 380 00:22:26,290 --> 00:22:30,220 S1: actually trying to raise the bottom. They are actually actually 381 00:22:30,220 --> 00:22:34,510 S1: trying to raise everyone, not just their friends. There's no 382 00:22:34,510 --> 00:22:38,679 S1: question that a lot of their decisions are very selfish 383 00:22:38,680 --> 00:22:43,000 S1: and very focused on their business and very opportunistic. No question. 384 00:22:43,000 --> 00:22:46,330 S1: Everyone knows that. I mean, that's most people do that. 385 00:22:46,359 --> 00:22:49,960 S1: The question is the balance between these things. So my 386 00:22:49,990 --> 00:22:52,720 S1: thing is, if they're sitting on a razor's edge somewhere 387 00:22:52,720 --> 00:22:55,510 S1: in the middle or the middle, right? And they could 388 00:22:55,540 --> 00:23:00,639 S1: become crazy, right? People, or they could fall back towards 389 00:23:00,640 --> 00:23:04,840 S1: the center and actually be positive and try to lift everyone. 390 00:23:04,869 --> 00:23:07,180 S1: And a lot of these AI people, they're trying to 391 00:23:07,210 --> 00:23:13,750 S1: create free abundance. Okay. Free shelter, free food, not free, 392 00:23:13,750 --> 00:23:17,830 S1: but very, very cheap. Okay. Easy to make very, very 393 00:23:17,830 --> 00:23:22,389 S1: cheap things. Why? Because of AGI? Because of ASI. Because 394 00:23:22,390 --> 00:23:25,210 S1: a lot of these things have the ability to remove 395 00:23:25,210 --> 00:23:29,109 S1: how expensive it is for the whole world to have 396 00:23:29,109 --> 00:23:33,760 S1: what the best people in the world have. Right? Um, so, 397 00:23:33,760 --> 00:23:37,389 S1: so basically you have like the best health care, right? 398 00:23:37,390 --> 00:23:42,580 S1: The best, um, the best education. So you have Harvard education, 399 00:23:42,580 --> 00:23:46,960 S1: you have the best health care policy you could possibly have, 400 00:23:46,990 --> 00:23:51,830 S1: like Kaiser or whatever you have, um, PPO or something 401 00:23:51,830 --> 00:23:56,180 S1: like that. The best of everything. Why can't everyone have that? 402 00:23:56,210 --> 00:23:58,880 S1: Why is only why do only a few people go 403 00:23:58,880 --> 00:24:03,200 S1: to Stanford or Harvard? Right? So the promise of this 404 00:24:03,200 --> 00:24:06,830 S1: AI stuff is to be able to give that education 405 00:24:06,830 --> 00:24:10,250 S1: to everyone via like an AI agent, something that's always 406 00:24:10,250 --> 00:24:14,119 S1: with you. Abundant energy. Right. Not having these wars over 407 00:24:14,150 --> 00:24:17,180 S1: oil because they're solar farms everywhere. And we already have 408 00:24:17,180 --> 00:24:21,770 S1: enough energy. It's done. Solved problem. I helped us with that. 409 00:24:21,800 --> 00:24:25,550 S1: AGI helped us. Maybe ASI helped us. Doesn't matter. The 410 00:24:25,550 --> 00:24:29,389 S1: point is, you never have to need something. Okay, now, 411 00:24:29,390 --> 00:24:31,370 S1: now we have a meaning crisis. We're going to have 412 00:24:31,369 --> 00:24:32,930 S1: to figure out what we're actually going to do with 413 00:24:32,930 --> 00:24:36,859 S1: ourselves after that. But the point is, I think a 414 00:24:36,890 --> 00:24:39,469 S1: lot of these people are actually going for that better future. 415 00:24:39,500 --> 00:24:41,419 S1: A lot of these people, I think, are actually going 416 00:24:41,420 --> 00:24:44,929 S1: for the Star Trek The Next Generation model, which is 417 00:24:44,930 --> 00:24:47,600 S1: what I'm going after. And again, maybe it's about to 418 00:24:47,630 --> 00:24:51,190 S1: get really bad because it's going to go full authoritarian. 419 00:24:51,190 --> 00:24:54,700 S1: It's going to go full fascist. Maybe it will, and 420 00:24:54,700 --> 00:24:58,300 S1: I'm very worried that it will. Okay. It's very possible 421 00:24:58,300 --> 00:25:01,720 S1: that they got triggered and they're never coming back. Okay. 422 00:25:01,750 --> 00:25:05,440 S1: I actually talked to Sam Harris about this. Um, and 423 00:25:05,440 --> 00:25:07,540 S1: I'm not going to say anything that he said that 424 00:25:07,540 --> 00:25:10,690 S1: he didn't say in his podcast, but I responded to 425 00:25:10,720 --> 00:25:15,010 S1: him after he did that reckoning podcast. And, um, he 426 00:25:15,010 --> 00:25:17,800 S1: basically said, I think they're gone in the podcast. I 427 00:25:17,800 --> 00:25:20,139 S1: think they're gone and it's going to get really bad. 428 00:25:20,140 --> 00:25:23,080 S1: And so I challenged him on that with basically this argument, 429 00:25:23,080 --> 00:25:26,410 S1: and he thinks I'm wrong. He is my favorite thinker. 430 00:25:26,410 --> 00:25:29,920 S1: He is the smartest person I believe out there right 431 00:25:29,920 --> 00:25:33,490 S1: now in terms of like his ability to see things 432 00:25:33,490 --> 00:25:38,199 S1: and articulate them. He's just like God tier to me, um, 433 00:25:38,260 --> 00:25:41,020 S1: always has been. I feel like I've caught up in 434 00:25:41,020 --> 00:25:44,470 S1: a lot of ways, but he still sees things extremely clearly, 435 00:25:44,470 --> 00:25:47,350 S1: and the fact that he doesn't believe me is troubling 436 00:25:47,380 --> 00:25:49,700 S1: to me. I actually can't find anyone who believes me 437 00:25:49,700 --> 00:25:52,550 S1: about this particular thing. They're like, oh, so are you 438 00:25:52,550 --> 00:25:54,920 S1: going right with them? I'm like, no, I'm staying where 439 00:25:54,920 --> 00:25:58,340 S1: I'm staying. I am a Star Trek Next Generation liberal. 440 00:25:58,340 --> 00:26:01,430 S1: This is where I'm going to be. But I do 441 00:26:01,430 --> 00:26:06,770 S1: believe in conservative methods of achieving liberal goals. Okay. So 442 00:26:06,770 --> 00:26:10,520 S1: I'm trying to find optimism here. And here's what I'm 443 00:26:10,520 --> 00:26:13,250 S1: offering to you. Here's what I'm asking of you. If 444 00:26:13,250 --> 00:26:17,000 S1: you are like me and you were in this liberal center, 445 00:26:17,000 --> 00:26:20,120 S1: so you've moved away from the left because they're crazy, 446 00:26:20,150 --> 00:26:23,000 S1: you're not going to the right because they're crazy. You're 447 00:26:23,000 --> 00:26:27,859 S1: here in this, this first principles. Think through the problem 448 00:26:27,859 --> 00:26:31,910 S1: situation in the center somewhere. I'm asking you to consider 449 00:26:31,910 --> 00:26:35,240 S1: that these people might actually be trying to be doing 450 00:26:35,240 --> 00:26:38,210 S1: good things. And I think this comes down to what 451 00:26:38,210 --> 00:26:40,610 S1: I think a big problem in our politics is right 452 00:26:40,609 --> 00:26:45,650 S1: now is assuming the absolute worst, assuming that they are going, 453 00:26:45,680 --> 00:26:50,710 S1: they're just sitting somewhere right now doing Mr. Burns like, conniving, like, 454 00:26:50,710 --> 00:26:54,100 S1: how can we ruin the entire bottom 90%? We're just 455 00:26:54,100 --> 00:26:56,619 S1: going to make this amazing world for the top 10% 456 00:26:56,619 --> 00:26:59,200 S1: of all our friends. And it's just going to be rich. 457 00:26:59,200 --> 00:27:03,280 S1: People rule everything, and we're going to do, you know, eugenics. 458 00:27:03,280 --> 00:27:05,830 S1: And it's just I don't think they're trying to do that, 459 00:27:05,830 --> 00:27:08,740 S1: I really don't. I think they're capable of doing that. 460 00:27:08,740 --> 00:27:12,490 S1: Especially Trump is capable of doing that if pushed in 461 00:27:12,490 --> 00:27:15,340 S1: the wrong way. If ego gets involved, like we've already 462 00:27:15,340 --> 00:27:19,180 S1: seen him do some horrific stuff and very stupid stuff. 463 00:27:19,180 --> 00:27:22,300 S1: Bottom line, I'm trying to be optimistic, and I'm asking 464 00:27:22,300 --> 00:27:25,690 S1: you to consider trying to be so as well. It's 465 00:27:25,690 --> 00:27:30,010 S1: it discovery. Security is a useless controls problem. This is 466 00:27:30,010 --> 00:27:32,050 S1: a must read. You got to read this thing even 467 00:27:32,050 --> 00:27:35,590 S1: if you don't. You don't agree. Chain forge, open source 468 00:27:35,590 --> 00:27:39,550 S1: visual programming tool. This is like Yahoo pipes for running 469 00:27:39,550 --> 00:27:43,750 S1: evaluations against prompts. Have it messed with this fully. In fact, 470 00:27:43,750 --> 00:27:46,540 S1: I'm going to open another tab. But from what I 471 00:27:46,570 --> 00:27:50,929 S1: looked at before, uh, looks pretty cool. And I love 472 00:27:50,930 --> 00:27:53,750 S1: the Yahoo pipes like design mechanism. How do you run 473 00:27:53,750 --> 00:27:57,350 S1: away from an army of these? Yeah. You don't watch 474 00:27:57,350 --> 00:28:00,200 S1: this thing. Look at this thing. Dir. Dir. Dir. This 475 00:28:00,200 --> 00:28:03,410 S1: is exactly like the Black Mirror episode. Except for this 476 00:28:03,410 --> 00:28:06,740 S1: thing is faster and scarier. Okay, it could do backflips. 477 00:28:06,740 --> 00:28:11,390 S1: Not scary at all. Yep. So just imagine you're a soldier. 478 00:28:11,390 --> 00:28:14,510 S1: You went through basic training. Some eight. You did some. 479 00:28:14,510 --> 00:28:17,600 S1: What's it called? You go out and you see, like 480 00:28:17,600 --> 00:28:20,480 S1: over the mountain. You just see thousands of these things 481 00:28:20,510 --> 00:28:24,980 S1: pour over, except for they're bigger with bigger muscles, bigger 482 00:28:24,980 --> 00:28:28,790 S1: battery packs. Oh, and they got guns. Cool, cool. It's 483 00:28:28,790 --> 00:28:31,220 S1: gonna be a problem, I'm telling you. I just put 484 00:28:31,220 --> 00:28:34,190 S1: it on. Uh, x the other day. We really need 485 00:28:34,190 --> 00:28:36,859 S1: to be thinking about Anti-drone stuff. And there's a company 486 00:28:36,859 --> 00:28:41,360 S1: called Anduril. Andrill not sure how to pronounce it, but 487 00:28:41,360 --> 00:28:44,450 S1: they are definitely thinking a lot of drone stuff. Um, 488 00:28:44,450 --> 00:28:47,610 S1: some people sent me some links saying the Navy's thinking 489 00:28:47,610 --> 00:28:53,040 S1: about this, because here's the problem. F-35s, carrier fleets, aircraft 490 00:28:53,040 --> 00:28:58,860 S1: carriers themselves, they're very vulnerable to being swarmed by small drones, 491 00:28:58,860 --> 00:29:03,840 S1: especially autonomous drones. That can't be, like, jammed. A very 492 00:29:03,840 --> 00:29:06,210 S1: serious problem to worry about. And by the way, I 493 00:29:06,210 --> 00:29:10,050 S1: keep recommending this book. It's called Kill Decision by Daniel Suarez. 494 00:29:10,080 --> 00:29:14,070 S1: Definitely want to check that out. Um, he basically talks 495 00:29:14,070 --> 00:29:15,690 S1: about a lot of the stuff in sort of a 496 00:29:15,690 --> 00:29:18,780 S1: fiction sense, but it gives you a vibe for why 497 00:29:18,780 --> 00:29:23,190 S1: it's pretty scary. Toolkit smart name set of scripts that 498 00:29:23,190 --> 00:29:26,460 S1: allow new sub commands to get. I cluster using Mac 499 00:29:26,460 --> 00:29:28,830 S1: Minis and XO labs. This is probably what I'm going 500 00:29:28,860 --> 00:29:32,520 S1: to build my next AI rig on. Maybe. Maybe it 501 00:29:32,520 --> 00:29:35,340 S1: depends how the whole game is. But uh, yeah, you 502 00:29:35,340 --> 00:29:38,310 S1: could do like Thunderbolt five and connect these things together. 503 00:29:38,310 --> 00:29:41,370 S1: Then you use this project called XO labs and it 504 00:29:41,370 --> 00:29:44,160 S1: actually links them all together. How I ship products at 505 00:29:44,160 --> 00:29:48,440 S1: Big Tech companies. Diagrams tool for creating diagrams as code. 506 00:29:48,470 --> 00:29:52,459 S1: Everything I've learned so far about running local LMS, Patrick 507 00:29:52,490 --> 00:29:55,790 S1: or no, I keep wanting to say the author, but 508 00:29:55,790 --> 00:30:00,440 S1: Patrick McCormick encourages readers to spend less time doomscrolling and 509 00:30:00,440 --> 00:30:05,390 S1: more time reading books, drawing audio. New musical sketchpad using 510 00:30:05,390 --> 00:30:10,250 S1: web audio API. Create music directly in your browser. Recommendation 511 00:30:10,280 --> 00:30:13,820 S1: of the week. The CEO of anthropic thinks AGI is 512 00:30:13,820 --> 00:30:16,640 S1: coming within a couple of years. Sam Altman thinks it's 513 00:30:16,640 --> 00:30:20,930 S1: 25 or 26. I've already said it was 25 to 26. 514 00:30:20,930 --> 00:30:25,670 S1: I said that in 2003. So these numbers are probably 515 00:30:25,670 --> 00:30:30,380 S1: getting real. Of course, it does depend on your definition, 516 00:30:30,380 --> 00:30:34,370 S1: but my recommendation to you, given that AGI is coming, 517 00:30:34,370 --> 00:30:38,390 S1: start getting ready. Know your life mission. Know your goals. 518 00:30:38,390 --> 00:30:42,980 S1: Fill in in practice your most important sentence. Which is 519 00:30:42,980 --> 00:30:45,350 S1: this right here. I believe one of the biggest problems 520 00:30:45,350 --> 00:30:48,570 S1: in the world is X, which is why I'm building, 521 00:30:48,570 --> 00:30:54,630 S1: creating or doing Y. So fill in that sentence for yourself. 522 00:30:54,630 --> 00:30:57,420 S1: Start building your telos file. Get really good with your 523 00:30:57,420 --> 00:31:02,010 S1: AI tools like fabric ChatGPT whatever you're using. Get your 524 00:31:02,010 --> 00:31:07,500 S1: website up. Commit to reading 50 books in 2025 and 525 00:31:07,500 --> 00:31:11,850 S1: hit me up if you need book recommendations. And most importantly, 526 00:31:11,850 --> 00:31:14,670 S1: start writing. Even if you think you don't have anything 527 00:31:14,700 --> 00:31:17,550 S1: to say. Once you start reading this many books, you're 528 00:31:17,550 --> 00:31:19,800 S1: going to have something to say, especially if you have 529 00:31:19,800 --> 00:31:22,320 S1: your Telos file up. You know your goals. You know 530 00:31:22,320 --> 00:31:25,320 S1: your life mission. It's going to be like a particle accelerator. 531 00:31:25,350 --> 00:31:27,810 S1: Things are going to smash into your brain. Ideas are 532 00:31:27,810 --> 00:31:30,060 S1: just going to start flying off, and then you just 533 00:31:30,060 --> 00:31:32,550 S1: start writing online and you're like, hey, wouldn't it be 534 00:31:32,550 --> 00:31:35,130 S1: cool if this hey, what about this? Someone's like, hey, 535 00:31:35,130 --> 00:31:37,380 S1: that's stupid. This is why it's stupid. You're like, oh, 536 00:31:37,410 --> 00:31:41,850 S1: that's true. Yeah. Good point. Pretty soon you're learning publicly, 537 00:31:41,850 --> 00:31:46,250 S1: live in real time and you can just build a 538 00:31:46,250 --> 00:31:49,340 S1: lot of new intelligence based on that. And the aphorism 539 00:31:49,370 --> 00:31:51,980 S1: of the week, if you were offered $1 million not 540 00:31:51,980 --> 00:31:54,890 S1: to wake up tomorrow, you would not take it. Which 541 00:31:54,890 --> 00:31:59,420 S1: means waking up tomorrow is worth more than $1 million. 542 00:31:59,540 --> 00:32:02,030 S1: So treat it that way. If you were offered $1 543 00:32:02,030 --> 00:32:04,850 S1: million not to wake up tomorrow, you would not take it. 544 00:32:04,850 --> 00:32:09,200 S1: Which means waking up tomorrow is worth more than $1 million. 545 00:32:09,200 --> 00:32:12,920 S1: So treat it that way. See you next time. Unsupervised 546 00:32:12,920 --> 00:32:15,440 S1: learning is produced and edited by Daniel Miessler on a 547 00:32:15,440 --> 00:32:20,300 S1: Neumann U87 AI microphone using Hindenburg. Intro and outro music 548 00:32:20,300 --> 00:32:23,390 S1: is by zombie with a Y. And to get the 549 00:32:23,390 --> 00:32:25,459 S1: text and links from this episode, sign up for the 550 00:32:25,460 --> 00:32:31,100 S1: newsletter version of the show at Daniel miessler.com/newsletter. We'll see 551 00:32:31,100 --> 00:32:31,880 S1: you next time.