1 00:00:00,050 --> 00:00:03,440 S1: Whether you're starting or scaling your company's security program, demonstrating 2 00:00:03,470 --> 00:00:06,980 S1: top notch security practices and establishing trust is more important 3 00:00:06,980 --> 00:00:12,110 S1: than ever. Vanta automates compliance for Soc2, ISO 27,001 and more, 4 00:00:12,110 --> 00:00:16,070 S1: saving you time and money while helping you build customer trust. Plus, 5 00:00:16,070 --> 00:00:20,329 S1: you can streamline security reviews by automating questionnaires and demonstrating 6 00:00:20,329 --> 00:00:24,050 S1: your security posture with a customer facing trust center, all 7 00:00:24,050 --> 00:00:28,610 S1: powered by advanced AI. Over 7000 global companies like Atlassian, 8 00:00:28,610 --> 00:00:31,750 S1: Flow Health and Quora use Vanta to manage risk and 9 00:00:31,750 --> 00:00:35,710 S1: prove security in real time. Get $1,000 off Vanta when 10 00:00:35,710 --> 00:00:42,159 S1: you go to Vanta comm slash unsupervised. That's vanta.com/supervised for 11 00:00:42,159 --> 00:00:47,379 S1: $1,000 off. Welcome to Unsupervised Learning, a security, AI, and 12 00:00:47,380 --> 00:00:50,110 S1: meaning focused podcast that looks at how best to thrive 13 00:00:50,110 --> 00:00:55,050 S1: as humans in a post AI world. It combines original ideas, analysis, 14 00:00:55,050 --> 00:00:58,290 S1: and mental models to bring not just the news, but 15 00:00:58,290 --> 00:01:05,880 S1: why it matters and how to respond. All right, welcome 16 00:01:05,880 --> 00:01:08,819 S1: to unsupervised learning. This is Daniel Miessler. All right. So 17 00:01:08,819 --> 00:01:11,850 S1: here's what's up this week gearing up for Las Vegas. 18 00:01:11,880 --> 00:01:14,490 S1: Going to be there for 11 days. Assuming I don't 19 00:01:14,490 --> 00:01:16,650 S1: get sick after like four of those. And if you 20 00:01:16,650 --> 00:01:19,140 S1: see me, please, uh, please know that I'm a bit, uh, 21 00:01:19,140 --> 00:01:22,740 S1: shy and awkward. A bit, uh, like 20 to 60% 22 00:01:22,740 --> 00:01:24,900 S1: of the time. I mean, sometimes it's just not there 23 00:01:24,900 --> 00:01:27,570 S1: at all, and sometimes it's pretty bad. So just, uh, 24 00:01:27,569 --> 00:01:31,200 S1: say hi. Anyway, I love, uh, seeing people, and, uh, 25 00:01:31,200 --> 00:01:32,970 S1: it's a good training for me. Need to get better 26 00:01:32,970 --> 00:01:35,970 S1: at this. Anyway, watch a number of videos last night, 27 00:01:35,970 --> 00:01:39,600 S1: I believe, about people losing their jobs and starting, like, 28 00:01:39,600 --> 00:01:42,369 S1: a YouTube channel and, like, just trying to branch out 29 00:01:42,370 --> 00:01:46,240 S1: and maybe start a new career. And it was extremely sad. 30 00:01:46,240 --> 00:01:48,580 S1: And I watched like two of these. And of course 31 00:01:48,580 --> 00:01:51,130 S1: the feed showed me like ten more. And I ended 32 00:01:51,130 --> 00:01:53,410 S1: up just watching these videos for like an hour and 33 00:01:53,410 --> 00:01:55,960 S1: a half, two hours. And it was extremely sad. I 34 00:01:55,960 --> 00:01:59,770 S1: feel like people are seeing the ground shift underneath their 35 00:01:59,770 --> 00:02:03,670 S1: feet and they're like, why does nobody want me, right? 36 00:02:03,670 --> 00:02:06,650 S1: This one particular one was really sad because he was like, 37 00:02:06,650 --> 00:02:08,840 S1: I thought I was a good product manager. And then 38 00:02:08,840 --> 00:02:12,739 S1: after ten and a half months of interviewing and getting 39 00:02:12,740 --> 00:02:15,590 S1: all the way through interviews and getting to the very end, 40 00:02:15,590 --> 00:02:19,190 S1: and then not being given an offer or being given 41 00:02:19,190 --> 00:02:21,830 S1: an offer and then being ghosted after ten and a 42 00:02:21,830 --> 00:02:24,110 S1: half months, I no longer believe in myself. I no 43 00:02:24,110 --> 00:02:26,900 S1: longer believe I'm a good product manager, and it was 44 00:02:26,900 --> 00:02:30,369 S1: just devastating to hear this. And he was being so 45 00:02:30,370 --> 00:02:33,220 S1: like just open and honest and vulnerable. And it was 46 00:02:33,220 --> 00:02:36,339 S1: just it was hard to watch and just felt very human. 47 00:02:36,340 --> 00:02:39,070 S1: And this is essentially why I'm doing what I'm doing 48 00:02:39,070 --> 00:02:42,490 S1: now is to basically try to help people who are 49 00:02:42,490 --> 00:02:45,430 S1: in that situation and try to hopefully explain what I 50 00:02:45,430 --> 00:02:49,990 S1: see coming in terms of like, you shouldn't judge yourself 51 00:02:49,990 --> 00:02:54,690 S1: based on what these companies think about your skills and 52 00:02:54,690 --> 00:02:57,750 S1: what they think about your worth. Um, they do not 53 00:02:57,750 --> 00:03:00,480 S1: determine your worth. And this is why we need something 54 00:03:00,480 --> 00:03:06,180 S1: like human 3.0 is because human 3.0 is how to 55 00:03:06,180 --> 00:03:10,980 S1: measure yourself. Okay, you in your entirety. Full spectrum is 56 00:03:10,980 --> 00:03:13,890 S1: how you should be thinking about yourself, not in terms 57 00:03:13,889 --> 00:03:17,960 S1: of do you have these particular product management skills that 58 00:03:17,960 --> 00:03:21,170 S1: this particular type of startup needs or this particular type 59 00:03:21,169 --> 00:03:23,960 S1: of company needs? That is a very old world way 60 00:03:23,960 --> 00:03:26,930 S1: of thinking about things. And I don't fault anyone for 61 00:03:26,930 --> 00:03:29,120 S1: thinking that way because you're trying to get a job, 62 00:03:29,120 --> 00:03:31,310 S1: you're trying to pay bills, right? So I understand that. 63 00:03:31,310 --> 00:03:34,730 S1: I'm just saying that society as a whole is stuck 64 00:03:34,730 --> 00:03:37,370 S1: in the past in seeing the world in this way, 65 00:03:37,370 --> 00:03:41,510 S1: and we need to move beyond it, especially because AI 66 00:03:41,510 --> 00:03:43,990 S1: is going to accelerate this whole process where they're going 67 00:03:43,990 --> 00:03:46,690 S1: to see more and more people as expendable in this way, 68 00:03:46,690 --> 00:03:48,700 S1: the same way that this guy was fired in ten 69 00:03:48,700 --> 00:03:51,400 S1: and a half months later, he still can't get a job. 70 00:03:51,400 --> 00:03:54,100 S1: That's going to keep happening to more and more people, 71 00:03:54,100 --> 00:03:57,700 S1: and we need a solution. So I would say be 72 00:03:57,700 --> 00:04:01,750 S1: kind to people. Everyone is hurting in some kind of way, 73 00:04:01,750 --> 00:04:05,800 S1: and especially right now. And when you look at our politics, 74 00:04:05,800 --> 00:04:08,800 S1: I see a lot of the current situation as a 75 00:04:08,800 --> 00:04:13,090 S1: symptom of people being hurt and people being afraid. And 76 00:04:13,090 --> 00:04:16,779 S1: I think that makes people more mean and less empathic 77 00:04:16,779 --> 00:04:19,089 S1: than they otherwise would be. And I got this great 78 00:04:19,089 --> 00:04:22,060 S1: quote here. Kindness is a language which the deaf can 79 00:04:22,060 --> 00:04:25,330 S1: hear and the blind can see by Mark Twain. All right. 80 00:04:25,330 --> 00:04:28,360 S1: So I got my best argument for why I will 81 00:04:28,360 --> 00:04:31,609 S1: have an extraordinary effect on economy and jobs. And if 82 00:04:31,610 --> 00:04:34,610 S1: you have any friends who are still skeptical about AI, 83 00:04:34,640 --> 00:04:38,179 S1: I recommend sending them this. It's called, uh. If we've 84 00:04:38,180 --> 00:04:42,560 S1: been thinking about AI all wrong, created a video for substrate. It's, uh, 85 00:04:42,560 --> 00:04:46,159 S1: one of my best performing videos, actually. Uh, and if 86 00:04:46,160 --> 00:04:48,859 S1: you prefer videos to long articles and you still want 87 00:04:48,860 --> 00:04:53,450 S1: to learn about substrates, recommend checking that out. Security Knowbe4 88 00:04:53,450 --> 00:04:56,620 S1: accidentally hired a North Korean state actor who tried to 89 00:04:56,620 --> 00:04:59,860 S1: install info stealing malware on their device. The craziest thing 90 00:04:59,860 --> 00:05:04,780 S1: about this whole North Korea stuff and how they're like 91 00:05:04,900 --> 00:05:08,380 S1: breaking into these companies, they're not really breaking in. They're 92 00:05:08,380 --> 00:05:11,710 S1: applying they're applying to these companies and getting hired. And 93 00:05:11,710 --> 00:05:14,800 S1: it really reminds me of this Key and Peele skit 94 00:05:14,800 --> 00:05:16,869 S1: where the guy's like, yeah, so what we're going to 95 00:05:16,870 --> 00:05:19,950 S1: do is we're going to sneak in. I think I 96 00:05:19,950 --> 00:05:22,560 S1: told this joke before, but he's like, what we're going 97 00:05:22,560 --> 00:05:24,989 S1: to do is we're going to sneak in, we're going 98 00:05:24,990 --> 00:05:27,900 S1: to get hired. And then when they give us work, 99 00:05:27,900 --> 00:05:31,169 S1: we're going to do the work, and then they're going 100 00:05:31,170 --> 00:05:35,940 S1: to deposit money in our bank accounts every two weeks. 101 00:05:36,839 --> 00:05:40,320 S1: And his buddy looks at him and he goes, dude, 102 00:05:40,320 --> 00:05:45,580 S1: you're talking about a fucking job. That's what a job is. 103 00:05:45,610 --> 00:05:47,860 S1: And that was the end of the skit. And that's 104 00:05:47,860 --> 00:05:52,570 S1: literally what these guys are doing. They are applying to jobs, 105 00:05:52,570 --> 00:05:55,690 S1: getting hired and doing the work. The trick is they 106 00:05:55,690 --> 00:05:58,810 S1: just take their regular salaries, and I'm not sure what 107 00:05:58,810 --> 00:06:01,570 S1: percentage is actually doing the work versus doing the work 108 00:06:01,570 --> 00:06:05,830 S1: poorly versus doing the work, plus trying to hack them additionally. 109 00:06:05,830 --> 00:06:08,250 S1: Not sure about that exactly. I'm not sure anyone is, 110 00:06:08,250 --> 00:06:11,520 S1: but here's what I do know. Uh, they're taking the 111 00:06:11,520 --> 00:06:15,000 S1: money from the salaries and putting it back into the 112 00:06:15,000 --> 00:06:18,089 S1: government to use for like the missile program or whatever 113 00:06:18,089 --> 00:06:21,719 S1: other shenanigans North Korea is doing. So they're they're literally 114 00:06:21,720 --> 00:06:27,120 S1: deploying it people into the world, into advanced economies to 115 00:06:27,120 --> 00:06:30,510 S1: get actual jobs, to earn salaries, to put into the 116 00:06:30,510 --> 00:06:34,909 S1: missile program. That is trippy to me. That is completely insane. 117 00:06:34,940 --> 00:06:39,350 S1: All right. Next one GitHub's repository design flaw allows indefinite 118 00:06:39,350 --> 00:06:43,010 S1: access to data from deleted and private repositories. This is 119 00:06:43,010 --> 00:06:46,190 S1: a piece from Truffle Security. A plane's GPS was jammed 120 00:06:46,190 --> 00:06:49,550 S1: on a commercial transatlantic route for the first time, raising 121 00:06:49,550 --> 00:06:52,130 S1: fears that thousands of other flights could be at risk 122 00:06:52,130 --> 00:06:56,470 S1: of deliberate hacking. This was between Madrid and Toronto, thanks 123 00:06:56,470 --> 00:07:00,760 S1: to teens for subscribing or not subscribing for sponsoring, and 124 00:07:00,760 --> 00:07:04,960 S1: there's been a 400% increase in GPS spoofing incidents, so 125 00:07:04,960 --> 00:07:07,659 S1: this one is related to the previous one affecting around 126 00:07:07,660 --> 00:07:12,730 S1: 900 flights daily. And this is from ops Dot Group. 127 00:07:12,730 --> 00:07:17,200 S1: And um, yeah, spike is causing major safety concerns, especially 128 00:07:17,200 --> 00:07:22,080 S1: with systems like e.g. AWS becoming unreliable and there are 129 00:07:22,080 --> 00:07:24,990 S1: a number of people looking at like, why is this happening? 130 00:07:24,990 --> 00:07:28,230 S1: What are the implications? Yeah, I'm wondering what the other 131 00:07:28,230 --> 00:07:32,670 S1: options are for Nav if there's like secondary or tertiary 132 00:07:32,670 --> 00:07:36,210 S1: systems that we can use when a jam happens or 133 00:07:36,210 --> 00:07:39,570 S1: blocks against the jamming, like a couple of different options. 134 00:07:39,570 --> 00:07:42,390 S1: The other thing is if there's six miles up or 135 00:07:42,410 --> 00:07:45,890 S1: whatever and the jamming is happening, is that coming from 136 00:07:45,890 --> 00:07:48,410 S1: inside the house? Like, is that someone with a jammer 137 00:07:48,410 --> 00:07:50,960 S1: on the plane? Is that someone on the ground? I mean, 138 00:07:50,960 --> 00:07:52,820 S1: these are kind of the only options. Someone on the 139 00:07:52,820 --> 00:07:55,370 S1: ground is shooting it up at the plane, which seems 140 00:07:55,370 --> 00:07:58,880 S1: pretty hard. Another plane coming along with it and shooting it. 141 00:07:58,880 --> 00:08:02,990 S1: That seems very hard and also very expensive. Or they're 142 00:08:02,990 --> 00:08:06,980 S1: in orbit, uh, and they would be moving very fast 143 00:08:06,980 --> 00:08:09,760 S1: if they were in orbit. So and they're shooting down 144 00:08:09,760 --> 00:08:11,650 S1: I mean, I'm trying to list all the options other 145 00:08:11,650 --> 00:08:14,800 S1: than aliens. And, uh, it seems like someone on the 146 00:08:14,800 --> 00:08:18,850 S1: plane is more likely so out of those. But, uh, 147 00:08:18,850 --> 00:08:21,820 S1: I don't know. I have no insider knowledge on this 148 00:08:21,820 --> 00:08:25,480 S1: one yet. Uh, Francis, high speed rail traffic got disrupted 149 00:08:25,480 --> 00:08:29,080 S1: due to what officials are calling malicious acts just before 150 00:08:29,080 --> 00:08:32,950 S1: the Olympic ceremony, thanks to nudge Security for sponsoring, Google 151 00:08:32,950 --> 00:08:35,679 S1: has decided not to phase out third party cookies in 152 00:08:35,679 --> 00:08:38,890 S1: Chrome and will instead offer users more control over how 153 00:08:38,890 --> 00:08:43,300 S1: the user's cookies are used. And I, for one, am 154 00:08:43,300 --> 00:08:46,630 S1: absolutely shocked that this company that makes most of its 155 00:08:46,630 --> 00:08:49,360 S1: money on advertising and is really bad at rolling out 156 00:08:49,360 --> 00:08:53,679 S1: products canceled a thing that's really one difficult to do 157 00:08:53,679 --> 00:08:59,270 S1: and two would negatively affect advertisers and therefore their main 158 00:08:59,270 --> 00:09:03,349 S1: source of income. An evaluation of Amazon GuardDuty reveals limited 159 00:09:03,350 --> 00:09:07,550 S1: coverage and high costs with significant latency in detecting attacks 160 00:09:07,550 --> 00:09:10,910 S1: like S3 ransomware. Amazon. When it does things like this, 161 00:09:10,940 --> 00:09:13,610 S1: it's a little bit similar to Cloudflare, where they just 162 00:09:13,610 --> 00:09:16,130 S1: kind of like do an initial version of the tool. 163 00:09:16,130 --> 00:09:17,900 S1: It kind of works a little bit, and then if 164 00:09:17,900 --> 00:09:19,690 S1: you adopt it, you just kind of hope it gets 165 00:09:19,690 --> 00:09:22,089 S1: better over time. And basically what a number of these 166 00:09:22,090 --> 00:09:25,810 S1: articles are basically saying is that, yeah, they don't necessarily 167 00:09:25,809 --> 00:09:28,480 S1: always catch up. It depends on the product team, it 168 00:09:28,480 --> 00:09:31,210 S1: depends on luck. And sometimes if you lump your eggs 169 00:09:31,210 --> 00:09:33,520 S1: in with that basket, like it just goes nowhere and 170 00:09:33,520 --> 00:09:36,520 S1: other times it becomes a full fledged product. So other 171 00:09:36,520 --> 00:09:40,180 S1: examples of this GuardDuty, the WAF product, there's a million 172 00:09:40,179 --> 00:09:43,640 S1: different ones. Google's reCAPTCHA is showing its age and is 173 00:09:43,640 --> 00:09:49,040 S1: harvesting user information with labor worth billions, while being almost 174 00:09:49,040 --> 00:09:54,020 S1: universally disliked and vulnerable to bots. The Senate unanimously passed 175 00:09:54,020 --> 00:09:59,000 S1: the Defiance Act, letting victims of non-consensual intimate images created 176 00:09:59,000 --> 00:10:02,270 S1: by AI sue their creators for damages and then get 177 00:10:02,270 --> 00:10:07,160 S1: up to 150 K to 250 K if linked to assault, 178 00:10:07,220 --> 00:10:12,170 S1: stalking or harassment. A US Commerce Department or the US 179 00:10:12,170 --> 00:10:15,950 S1: Commerce Department says shipments of high performance processors from China 180 00:10:15,950 --> 00:10:19,520 S1: and Hong Kong to Russia have dropped by 20%, but 181 00:10:19,520 --> 00:10:22,490 S1: Hong Kong is still the main hub. AI and tech 182 00:10:22,490 --> 00:10:27,230 S1: whiz turned down $23 billion in acquisition from alphabet and 183 00:10:27,230 --> 00:10:30,560 S1: is instead aiming for an IPO. I thought I did 184 00:10:30,559 --> 00:10:32,970 S1: a deeper comment on this. I think I do a 185 00:10:32,970 --> 00:10:35,699 S1: deeper comment on this later. Yeah. Oh, here it is. Yeah. 186 00:10:35,700 --> 00:10:38,490 S1: My thoughts are basically that they knew they could get 187 00:10:38,490 --> 00:10:41,640 S1: more money for this. That's number one. But two is 188 00:10:41,670 --> 00:10:44,490 S1: they basically knew that if they got bought they would 189 00:10:44,490 --> 00:10:47,579 S1: get that 23 billion, which is a decent amount. And 190 00:10:47,580 --> 00:10:50,579 S1: the problem is it would most likely end up in 191 00:10:50,580 --> 00:10:55,380 S1: the Google graveyard. So who knows what percentage of that was. 192 00:10:55,550 --> 00:10:59,270 S1: Maybe that description is online somewhere. AI is replacing jobs 193 00:10:59,270 --> 00:11:02,750 S1: in the video game industry with major companies like Activision 194 00:11:03,080 --> 00:11:07,429 S1: using generative AI tools for concept art, and they're saying, uh, 195 00:11:07,429 --> 00:11:12,199 S1: estimated 10,500 people lost their jobs in 2023. I don't 196 00:11:12,200 --> 00:11:14,750 S1: know if that's all from art. I imagine a lot 197 00:11:14,750 --> 00:11:16,579 S1: of it is from art. But, uh, yeah, I got 198 00:11:16,580 --> 00:11:19,700 S1: a bunch of buddies in this industry and they're saying, uh, 199 00:11:19,700 --> 00:11:23,320 S1: very similar things. Lots of different things in the creative 200 00:11:23,320 --> 00:11:27,460 S1: workflow are vulnerable, I would say. A new study shows 201 00:11:27,460 --> 00:11:30,940 S1: that while generative AI like ChatGPT makes individual stories more 202 00:11:30,940 --> 00:11:33,579 S1: creative and engaging, it also makes them more similar to 203 00:11:33,580 --> 00:11:36,730 S1: each other. And I've heard a lot of criticisms of 204 00:11:36,730 --> 00:11:38,410 S1: AI and like, oh, it's going to do this bad 205 00:11:38,410 --> 00:11:42,160 S1: thing or that bad thing, but this one sounds quite realistic. 206 00:11:42,160 --> 00:11:46,800 S1: So basically we need to engineer in mechanisms for exposure 207 00:11:46,800 --> 00:11:51,240 S1: to alternative frames of reality. Different models, different ways of 208 00:11:51,240 --> 00:11:57,420 S1: viewing reality to avoid people consolidating and following like one 209 00:11:57,420 --> 00:12:01,200 S1: AI powered groupthink. Or maybe it's like 20 AI powered groupthink, 210 00:12:01,200 --> 00:12:04,320 S1: but it's like controlling the narrative of how everyone's thinking 211 00:12:04,320 --> 00:12:08,420 S1: about certain things. Because something was generated by AI, it 212 00:12:08,420 --> 00:12:11,000 S1: went viral. Maybe it was pretty cool or whatever. So 213 00:12:11,000 --> 00:12:13,520 S1: it goes viral, and then that just becomes a thing 214 00:12:13,520 --> 00:12:17,720 S1: that everyone's thinking. I've actually been really worried about this 215 00:12:17,720 --> 00:12:20,270 S1: for myself. It's one of the main things I want 216 00:12:20,270 --> 00:12:23,480 S1: to use ADR for. And I've been talking with Joseph 217 00:12:23,480 --> 00:12:26,240 S1: Thacker about this as well. It's like, imagine that you 218 00:12:26,240 --> 00:12:29,240 S1: have in your brain at any given moment a number 219 00:12:29,240 --> 00:12:33,130 S1: of ideas, and I definitely have this. I have multiple 220 00:12:33,130 --> 00:12:36,309 S1: ideas brewing all the time. So what percentage of those 221 00:12:36,309 --> 00:12:39,760 S1: are like super new that I came up with myself? 222 00:12:39,760 --> 00:12:44,589 S1: What percentage of those are like mostly derivative? And how 223 00:12:44,590 --> 00:12:48,310 S1: many of those are just seeds planted by some random 224 00:12:48,309 --> 00:12:50,770 S1: add that I didn't even understand? And like this is 225 00:12:50,770 --> 00:12:54,040 S1: completely opaque to me. Yet I think that these 19 226 00:12:54,040 --> 00:12:57,390 S1: ideas are like mine. And so I'm going out and 227 00:12:57,390 --> 00:13:00,570 S1: sharing them with other people, and then the same phenomenon 228 00:13:00,570 --> 00:13:03,330 S1: happens to them. And then the question is how many 229 00:13:03,330 --> 00:13:06,240 S1: of those came from I? Then the question becomes how 230 00:13:06,240 --> 00:13:10,170 S1: many came from a government or some corporation where they 231 00:13:10,170 --> 00:13:13,560 S1: injected these things into the world with AI. And now 232 00:13:13,559 --> 00:13:16,890 S1: these memes are spreading all over, and pretty soon it's 233 00:13:16,890 --> 00:13:20,160 S1: in all the articles, it's in all the news, it's 234 00:13:20,160 --> 00:13:23,229 S1: in all the top podcasts. And essentially what you have 235 00:13:23,230 --> 00:13:27,939 S1: is like a soft version, lower case version of propaganda, 236 00:13:27,940 --> 00:13:31,990 S1: mind control and the steering of narratives. So I think 237 00:13:31,990 --> 00:13:34,780 S1: it's going to be super essential for people who care 238 00:13:34,780 --> 00:13:38,620 S1: about free thinking, want to be able to disconnect and 239 00:13:38,620 --> 00:13:43,630 S1: sort of read books slowly, think slowly, come up with, 240 00:13:43,630 --> 00:13:47,970 S1: you know, old style thinking, old style critical thinking. That's 241 00:13:47,970 --> 00:13:50,250 S1: going to be one thing. But the other thing is 242 00:13:50,250 --> 00:13:54,420 S1: I want my Da to monitor the ideas that I'm 243 00:13:54,420 --> 00:13:58,140 S1: talking about and that I'm writing about, monitor those things, 244 00:13:58,140 --> 00:14:03,390 S1: and then also say, hey, guess what? So I saw 245 00:14:03,390 --> 00:14:06,930 S1: this idea get released into the world by this whatever. 246 00:14:06,929 --> 00:14:10,610 S1: This shady group over here. That was three and a 247 00:14:10,610 --> 00:14:14,150 S1: half weeks ago. They then started broadcasting it on these 248 00:14:14,150 --> 00:14:18,500 S1: 19 different networks. Those got picked up by these 347 249 00:14:18,500 --> 00:14:22,640 S1: other networks, and that became one of the top 14 250 00:14:22,640 --> 00:14:26,600 S1: top ideas over the last two weeks. And you are 251 00:14:26,600 --> 00:14:29,390 S1: now over here writing about it. So I just want 252 00:14:29,390 --> 00:14:33,010 S1: to let you know as my as your friend that 253 00:14:33,010 --> 00:14:35,920 S1: you are being influenced by this campaign that was launched 254 00:14:35,920 --> 00:14:38,680 S1: by this group three and a half weeks ago. That 255 00:14:38,680 --> 00:14:40,750 S1: is a service that we all need. That is a 256 00:14:40,750 --> 00:14:43,180 S1: service I'm going to build for myself, because that's going 257 00:14:43,180 --> 00:14:47,920 S1: to be part of this overall mechanization involving substrate pipelines, 258 00:14:47,920 --> 00:14:51,610 S1: a whole bunch of other stuff that's basically monitoring not 259 00:14:51,610 --> 00:14:54,010 S1: just the world, but also myself and seeing like how 260 00:14:54,010 --> 00:14:56,620 S1: I'm being influenced. That's like one of the use cases. 261 00:14:56,620 --> 00:14:59,610 S1: All right. Switzerland has passed a law requiring all public 262 00:14:59,610 --> 00:15:04,680 S1: sector agencies to use open source software and open source, 263 00:15:04,680 --> 00:15:08,130 S1: any code that they develop. I love this, but, uh, 264 00:15:08,130 --> 00:15:11,130 S1: open doesn't mean secure by itself. That's one thing. The 265 00:15:11,130 --> 00:15:13,170 S1: other thing is you got to find a way to 266 00:15:13,170 --> 00:15:15,810 S1: support the stuff, which you could just hire consultants or 267 00:15:15,810 --> 00:15:20,100 S1: companies or whatever, but or trained internally, but definitely have 268 00:15:20,100 --> 00:15:24,080 S1: to worry about support Govloop super cool platform for AI 269 00:15:24,080 --> 00:15:27,860 S1: workflows doesn't quite work as far as I could tell. 270 00:15:27,860 --> 00:15:30,650 S1: And if you're like a founder there or something, feel 271 00:15:30,650 --> 00:15:32,690 S1: free to reach out and let me know. And, uh, 272 00:15:32,690 --> 00:15:35,750 S1: happy to correct that. But that's just my current impression. 273 00:15:35,750 --> 00:15:39,650 S1: But I am extremely excited about it and I think 274 00:15:39,650 --> 00:15:42,290 S1: they're doing great stuff. The guy is amazing. Reminds me 275 00:15:42,290 --> 00:15:45,280 S1: of Yahoo pipes. Definitely want to check it out. Alphabet's 276 00:15:45,280 --> 00:15:48,640 S1: putting another 5 billion into Waymo. It's basically looking like 277 00:15:48,640 --> 00:15:52,270 S1: Waymo versus Tesla for self-driving taxis. But the approaches are 278 00:15:52,270 --> 00:15:56,230 S1: very different. Waymo basically needs to train for a very 279 00:15:56,230 --> 00:15:59,680 S1: long time in a city before it works, because they 280 00:15:59,680 --> 00:16:01,690 S1: kind of work out all the bugs just by driving 281 00:16:01,690 --> 00:16:04,600 S1: all the streets for a very long time. And, and 282 00:16:04,600 --> 00:16:06,880 S1: they sort of ramp that up. And Tesla is like 283 00:16:06,880 --> 00:16:09,160 S1: typical Elon. He's like, no, I'm just going to make 284 00:16:09,160 --> 00:16:10,530 S1: the thing. It's going to be awesome. It's going to 285 00:16:10,530 --> 00:16:14,340 S1: work perfectly. I'm announcing it soon. But you have to 286 00:16:14,340 --> 00:16:17,310 S1: realize that he's not always right, right. He said full 287 00:16:17,310 --> 00:16:19,500 S1: Self-Driving was going to be pretty easy and we would 288 00:16:19,500 --> 00:16:22,560 S1: have it years ago. And now it's 2024 and it's 289 00:16:22,560 --> 00:16:25,830 S1: just now getting good. So something to think about there. 290 00:16:25,830 --> 00:16:31,739 S1: Between the two different approaches, Joe Procopio argues that tech 291 00:16:31,740 --> 00:16:34,280 S1: companies are struggling to find good employees because they focus 292 00:16:34,280 --> 00:16:38,300 S1: too much on creds and not enough on skills. I 293 00:16:38,300 --> 00:16:41,750 S1: like it. I think this is exactly the shift that's happening. 294 00:16:41,750 --> 00:16:45,590 S1: He basically suggests that companies should prioritize practical experience and 295 00:16:45,590 --> 00:16:49,729 S1: problem solving abilities over degrees and certifications. That's exactly what's 296 00:16:49,730 --> 00:16:52,730 S1: going to happen with something like Astra, which is a 297 00:16:52,730 --> 00:16:55,280 S1: fake thing. But this this article that I wrote that 298 00:16:55,280 --> 00:16:59,250 S1: rates things in and gives scores, basically does a gauntlet 299 00:16:59,280 --> 00:17:02,220 S1: of tests and then gives you like these comprehensive, really 300 00:17:02,220 --> 00:17:05,580 S1: deep scores. That's essentially how that's going to be solved. 301 00:17:05,580 --> 00:17:07,860 S1: Apple just launched a beta version of Apple Maps for 302 00:17:07,859 --> 00:17:11,220 S1: the web. Humans. Did I talk about ChatGPT? I thought 303 00:17:11,220 --> 00:17:13,470 S1: I did I take that out? Where did that go? 304 00:17:13,470 --> 00:17:18,180 S1: Oh very strange. Yeah, there's a new ChatGPT a search 305 00:17:18,180 --> 00:17:24,530 S1: web ChatGPT that basically competes with Perplexity and Bing and 306 00:17:24,530 --> 00:17:30,109 S1: it's it's out, but it's waitlisted. Um, so, uh, I 307 00:17:30,109 --> 00:17:31,939 S1: don't know. I haven't checked to see if I have 308 00:17:31,940 --> 00:17:34,760 S1: access to that one yet, but, uh, looking forward to 309 00:17:34,760 --> 00:17:38,180 S1: that one. Humans. Wall Street Journal explores why the US 310 00:17:38,180 --> 00:17:43,520 S1: birth rate is declining, citing economic uncertainty, career priorities and 311 00:17:43,520 --> 00:17:47,620 S1: lifestyle choices. The idea that UBI reduces the need to 312 00:17:47,619 --> 00:17:50,920 S1: work isn't new, but recent studies show that it does 313 00:17:50,920 --> 00:17:54,010 S1: not lead to better jobs or more education. I think 314 00:17:54,010 --> 00:17:56,110 S1: the issue here is that certain people will spend free 315 00:17:56,109 --> 00:17:58,960 S1: time and money to better themselves, and certain people won't, 316 00:17:58,960 --> 00:18:01,750 S1: and it's not clear why that is. It's not clear 317 00:18:01,750 --> 00:18:05,889 S1: what the actual like factor is, but the way forward 318 00:18:05,890 --> 00:18:08,020 S1: is basically trying to figure out what that is and 319 00:18:08,020 --> 00:18:11,580 S1: not believing in fairy tales like giving away free money 320 00:18:11,580 --> 00:18:14,970 S1: will make everyone ambitious. It does not. And this reminds 321 00:18:14,970 --> 00:18:18,210 S1: me of a lesson that's very similar that I learned 322 00:18:18,210 --> 00:18:22,110 S1: like 20 years of hiring people, which is exposing people 323 00:18:22,109 --> 00:18:26,190 S1: to training and encouragement makes the best people stand out, 324 00:18:26,190 --> 00:18:29,100 S1: but it does not turn everyone into the best people. 325 00:18:29,100 --> 00:18:32,400 S1: And I've known this because I tried because I tried 326 00:18:32,400 --> 00:18:35,899 S1: for like ten years with cohort after cohort where I 327 00:18:35,900 --> 00:18:38,450 S1: would just be like, oh, well, my training wasn't good enough. 328 00:18:38,450 --> 00:18:41,119 S1: That's why they didn't get it, or they were distracted 329 00:18:41,119 --> 00:18:43,520 S1: or they were busy or whatever. So I just keep 330 00:18:43,520 --> 00:18:46,970 S1: hitting them and keep dumping hours and hours and hours 331 00:18:46,970 --> 00:18:49,580 S1: and more training and more training. Send them to more 332 00:18:49,580 --> 00:18:52,640 S1: classes and you come back to them and you're like, hey, 333 00:18:52,640 --> 00:18:55,070 S1: what have you done? They're like, I don't know. I 334 00:18:55,070 --> 00:18:58,810 S1: didn't do anything. What should I do? And you're like, uh, no, no. 335 00:18:58,900 --> 00:19:01,000 S1: So you've you've got to figure out what you want 336 00:19:01,000 --> 00:19:03,129 S1: to do. You've got to I've given you all the training, 337 00:19:03,130 --> 00:19:06,250 S1: I've given you all the stuff and all the books, 338 00:19:06,250 --> 00:19:08,199 S1: and I gave you all the demos. I told you 339 00:19:08,200 --> 00:19:10,479 S1: how to set up the demos. I showed you how 340 00:19:10,480 --> 00:19:13,510 S1: to set up the tech, and, um, have you done 341 00:19:13,510 --> 00:19:16,119 S1: any of that? And they're like, no, what should I 342 00:19:16,119 --> 00:19:18,670 S1: do first? I don't I don't know how to do 343 00:19:18,670 --> 00:19:21,280 S1: any of that. Can you show me? And you're like, well, 344 00:19:21,280 --> 00:19:23,430 S1: I showed you four times. You want me to show 345 00:19:23,430 --> 00:19:24,990 S1: you a fifth time? Okay, sure. I'll show you a 346 00:19:24,990 --> 00:19:27,359 S1: fifth time. Show them a fifth time. Come back two 347 00:19:27,359 --> 00:19:31,020 S1: weeks later. Hey, how is it going? Going with what? 348 00:19:31,020 --> 00:19:33,810 S1: With the stuff we talked about last time. And they're like, yeah, 349 00:19:33,810 --> 00:19:35,790 S1: it never got around to that. Like, I couldn't find 350 00:19:35,940 --> 00:19:38,460 S1: this one driver. Like, there was a driver down low, 351 00:19:38,460 --> 00:19:42,030 S1: but the driver download didn't work. It's like, okay, well 352 00:19:42,030 --> 00:19:44,250 S1: did you research that? No. How would I do that? 353 00:19:44,250 --> 00:19:47,869 S1: Can you help me that That ends up being like 354 00:19:47,869 --> 00:19:52,160 S1: 50 to 80% of everyone. Here's the alternative. Here's the 355 00:19:52,310 --> 00:19:57,950 S1: alternate version of a star. Okay? You're like, hey, um, hey, everyone. 356 00:19:57,950 --> 00:20:01,430 S1: So check this out. This is a class about. And 357 00:20:01,430 --> 00:20:03,590 S1: you see a hand go up. Hey, look, I read 358 00:20:03,590 --> 00:20:07,010 S1: the whole document. Um, I looked forward in the class. 359 00:20:07,010 --> 00:20:09,710 S1: I've got this thing running, but there's an error. Um, 360 00:20:09,880 --> 00:20:12,910 S1: so I fixed it and I stood up a different version, 361 00:20:12,910 --> 00:20:15,520 S1: and it's running. Can I show you? And you're like, okay, 362 00:20:15,520 --> 00:20:18,850 S1: so you basically read everything, jumped ahead in the class, 363 00:20:18,850 --> 00:20:21,609 S1: built something. It didn't work, but you fixed it, and 364 00:20:21,609 --> 00:20:23,710 S1: now you're running a different version and you want to 365 00:20:23,710 --> 00:20:25,420 S1: show it to me now. But I just started my 366 00:20:25,420 --> 00:20:28,600 S1: first sentence of the class and they're like, oh yeah, sorry. Yeah. 367 00:20:28,600 --> 00:20:32,439 S1: Please continue. Sorry. I was just excited. The difference between 368 00:20:32,440 --> 00:20:36,900 S1: these two things are so insane. Okay? And what I've 369 00:20:36,900 --> 00:20:39,480 S1: learned after all these years is that if you're trying 370 00:20:39,480 --> 00:20:43,080 S1: to build a star team and you're trying to build 371 00:20:43,080 --> 00:20:46,380 S1: like a group of the best people, what you want 372 00:20:46,380 --> 00:20:50,430 S1: to do is throw help and throw training and throw 373 00:20:50,430 --> 00:20:55,410 S1: encouragement to a massive group of people and not judge, 374 00:20:55,410 --> 00:20:59,680 S1: not prejudge, not do anything you have no idea. You 375 00:20:59,680 --> 00:21:02,710 S1: have no idea. It's it's not people who wear yellow shirts. 376 00:21:02,710 --> 00:21:06,729 S1: It's not people from Idaho. It's not people from Mississippi. 377 00:21:06,760 --> 00:21:10,690 S1: You have no idea what the criteria are like. Background, 378 00:21:10,690 --> 00:21:15,159 S1: ethnic group, you education, background. Are they educated? Do they 379 00:21:15,160 --> 00:21:18,399 S1: have a PhD? Do they have two master's degrees? Are 380 00:21:18,400 --> 00:21:21,490 S1: they still in high school? You don't know. And that's 381 00:21:21,490 --> 00:21:24,300 S1: the smartest thing you could possibly realize is that you 382 00:21:24,300 --> 00:21:27,390 S1: have no idea. You have no idea who's going to 383 00:21:27,390 --> 00:21:32,250 S1: just be kind of like, complete, completely inert, and just 384 00:21:32,250 --> 00:21:36,120 S1: unable to learn and unwilling to learn and unable to 385 00:21:36,119 --> 00:21:40,350 S1: absorb any amount of effort that you give, versus some 386 00:21:40,350 --> 00:21:44,850 S1: random person who doesn't think much of themselves and are 387 00:21:44,850 --> 00:21:51,320 S1: unassuming and quiet, and they just absolute sponge. Absolute murder. 388 00:21:51,320 --> 00:21:55,760 S1: Like the the craziest, best IT person you've ever found. 389 00:21:55,760 --> 00:21:58,310 S1: And suddenly they just can't shut up about everything. I 390 00:21:58,310 --> 00:22:01,610 S1: was like, oh, and I did this and I read everything, um, 391 00:22:01,609 --> 00:22:04,310 S1: all those books that you recommended. And then I went 392 00:22:04,310 --> 00:22:06,800 S1: and downloaded this and I oh, but I changed it, 393 00:22:06,800 --> 00:22:08,840 S1: and I want to show you this thing, and you're 394 00:22:08,840 --> 00:22:12,010 S1: just like, Holy crap. They were sitting right next to 395 00:22:12,010 --> 00:22:14,320 S1: the other person who I gave the same exact training. 396 00:22:14,320 --> 00:22:18,730 S1: This is a lesson, incredibly powerful lesson. And to bring 397 00:22:18,730 --> 00:22:21,910 S1: it back to this article, I believe the same thing 398 00:22:21,910 --> 00:22:24,909 S1: is true with UBI. The same thing is true with 399 00:22:24,910 --> 00:22:28,960 S1: social programs. The same thing is true with all these 400 00:22:28,960 --> 00:22:32,950 S1: different things. So the whole trick and we're going to 401 00:22:32,950 --> 00:22:35,550 S1: get a little broad here. The whole trick is to 402 00:22:35,550 --> 00:22:39,750 S1: make sure that no one's at a disadvantage, okay? Because trauma, 403 00:22:39,750 --> 00:22:45,869 S1: because they're hungry, because they can't afford coverage. Right. Because 404 00:22:45,869 --> 00:22:49,980 S1: there are stars all throughout this entire population, this these 405 00:22:49,980 --> 00:22:53,430 S1: 8 billion people that we have. There are stars who 406 00:22:53,460 --> 00:22:55,740 S1: you will never know if they're a star because they 407 00:22:55,740 --> 00:22:59,130 S1: can't make it to your class because they're working seven 408 00:22:59,130 --> 00:23:03,330 S1: jobs and they got an ant and a dog and 409 00:23:03,330 --> 00:23:07,050 S1: a and a parent and a girlfriend or a boyfriend 410 00:23:07,050 --> 00:23:11,040 S1: who is sick. And so they're over here juggling and 411 00:23:11,040 --> 00:23:15,060 S1: they're another Einstein, they're another von Neumann. And you will 412 00:23:15,060 --> 00:23:18,750 S1: never know because they got screwed. They got screwed by luck. 413 00:23:18,750 --> 00:23:21,719 S1: So the goal and now we're going really broad. The 414 00:23:21,720 --> 00:23:24,520 S1: goal is to build a society in which nobody has 415 00:23:24,520 --> 00:23:27,400 S1: screwed in that way. And when they do get messed 416 00:23:27,400 --> 00:23:30,550 S1: over in that way, we help them. We help them 417 00:23:30,550 --> 00:23:34,390 S1: get back into this, remove the obstacles so that everyone 418 00:23:34,390 --> 00:23:37,119 S1: has the opportunity to be one of these stars to 419 00:23:37,119 --> 00:23:41,170 S1: stand out. But we should not think that everyone who 420 00:23:41,170 --> 00:23:46,330 S1: gets exposed to the opportunities is necessarily going to be 421 00:23:46,330 --> 00:23:49,710 S1: one of those stand out people. So if we see variation, 422 00:23:49,710 --> 00:23:52,020 S1: which is probably going to look a lot like a 423 00:23:52,020 --> 00:23:54,600 S1: bell curve, right, you're going to have most people in 424 00:23:54,600 --> 00:23:57,900 S1: the middle, um, when you see that variation, you should 425 00:23:57,900 --> 00:24:00,900 S1: be like, yep, that's exactly what we expect to see. 426 00:24:00,900 --> 00:24:03,210 S1: Most people in the middle, some people who don't want 427 00:24:03,210 --> 00:24:06,060 S1: to do anything despite any training, and some people who 428 00:24:06,060 --> 00:24:09,570 S1: could just hear the first word and write a book. Right. 429 00:24:09,570 --> 00:24:14,090 S1: That's just expected. Now, I would say that there's some 430 00:24:14,090 --> 00:24:16,520 S1: nuance here in the sense that, like, okay, if we 431 00:24:16,520 --> 00:24:21,320 S1: can identify that grit and self-discipline and I don't know, 432 00:24:21,320 --> 00:24:24,530 S1: something that we could try to put into culture, something 433 00:24:24,530 --> 00:24:27,679 S1: we could try to put into education systems, something that 434 00:24:27,680 --> 00:24:30,350 S1: we could try to put into training, something we could 435 00:24:30,350 --> 00:24:34,189 S1: try to put into parental education if we could find 436 00:24:34,190 --> 00:24:37,050 S1: those tokens and just like, make sure they're part of 437 00:24:37,050 --> 00:24:41,700 S1: the training and that helps lift everyone or some number 438 00:24:41,700 --> 00:24:44,670 S1: of people, that'll be fantastic. But it has to start 439 00:24:44,670 --> 00:24:48,869 S1: with realizing there's going to be a distribution and that's 440 00:24:48,869 --> 00:24:53,040 S1: going to be based on meritocracy, which is good. However, 441 00:24:53,040 --> 00:24:57,570 S1: the whole purpose of society and liberal society or liberal 442 00:24:57,780 --> 00:25:02,120 S1: liberal approach to building a society is making sure that 443 00:25:02,119 --> 00:25:08,600 S1: everyone can enter into the meritocracy funnel without disadvantage. That's 444 00:25:08,600 --> 00:25:10,970 S1: that's the goal. That's the trick. And I'm going to 445 00:25:10,970 --> 00:25:15,379 S1: actually try to instantiate this using substrate, by the way. 446 00:25:15,380 --> 00:25:17,959 S1: All right. That was a oh goodness. That was a 447 00:25:17,960 --> 00:25:21,830 S1: massive diversion. Uh, southwest is ditching its open seating policy 448 00:25:21,830 --> 00:25:26,560 S1: after 50 years to boost profits and meet customer preferences. 80% 449 00:25:26,560 --> 00:25:30,100 S1: of their flyers preferred assigned seats 80% is a lot. 450 00:25:30,130 --> 00:25:35,530 S1: The Senate's version of 2025, NDAA, does not include the 451 00:25:35,530 --> 00:25:39,160 S1: countering CCP Drones Act. Happy and sad about this I 452 00:25:39,160 --> 00:25:43,840 S1: love DJI drones, DJI drones, but I feel like we 453 00:25:43,840 --> 00:25:48,149 S1: need to go without them because they're so good that 454 00:25:48,420 --> 00:25:52,800 S1: there aren't many alternatives. And, uh, Chinese companies have the 455 00:25:52,800 --> 00:25:55,500 S1: ability to be just taken over by the CCP at 456 00:25:55,500 --> 00:25:59,280 S1: any given moment, if it's advantageous to them. So I 457 00:25:59,280 --> 00:26:02,369 S1: don't like that. Same reason I don't like, uh, TikTok. 458 00:26:02,369 --> 00:26:07,800 S1: As you might expect from an ex-military security person, nearly 40% 459 00:26:07,800 --> 00:26:11,940 S1: of Americans are stressed out about making ends meet, 40%, 460 00:26:11,940 --> 00:26:15,650 S1: up from 28% in 2021. And this is a very 461 00:26:15,650 --> 00:26:19,550 S1: similar number to during the Great Recession, which I believe 462 00:26:19,550 --> 00:26:25,040 S1: they're talking about 2028, I'm sorry, 2008. US economic growth 463 00:26:25,040 --> 00:26:31,280 S1: hits 2.8% weight loss. Drugs like Ozempic, mounjaro and Wegovy 464 00:26:31,280 --> 00:26:34,520 S1: are causing people to spend less on groceries. I heard 465 00:26:34,520 --> 00:26:38,530 S1: it's also making planes lighter and use less fuel. So 466 00:26:38,530 --> 00:26:42,790 S1: Wegovy is a climate change product. They should try to 467 00:26:42,790 --> 00:26:48,070 S1: get some funding for that. Subsidies or something. A new antibiotic. 468 00:26:48,430 --> 00:26:54,400 S1: Oh my goodness. A new antibiotic from the University of Illinois, 469 00:26:54,400 --> 00:26:58,990 S1: Chicago disrupts two different cellular targets, making it 100 million 470 00:26:58,990 --> 00:27:03,239 S1: times harder for bacteria to evolve resistance. One dose of 471 00:27:03,240 --> 00:27:07,230 S1: a new nasal spray treatment clears toxic tau proteins from 472 00:27:07,230 --> 00:27:10,320 S1: brain cells. And one thing to remember about these I'm 473 00:27:10,320 --> 00:27:13,680 S1: going to keep sharing these studies like I am. That's 474 00:27:13,680 --> 00:27:15,390 S1: the thing I'm going to do. I'm going to have 475 00:27:15,390 --> 00:27:19,889 S1: more and more scrutiny because part of the pipelines, uh, 476 00:27:19,890 --> 00:27:22,379 S1: workflow and substrate and all of that, I'm going to 477 00:27:22,380 --> 00:27:25,610 S1: have these things being tested and going through pipelines. It's 478 00:27:25,609 --> 00:27:29,330 S1: going to be awesome. But you still have to realize 479 00:27:29,330 --> 00:27:32,000 S1: the bigger the finding, the more you should wait for 480 00:27:32,000 --> 00:27:35,390 S1: supporting studies. And in my mind, the stuff is not 481 00:27:35,390 --> 00:27:39,770 S1: actually truly real until the drug is available to normal people, 482 00:27:39,770 --> 00:27:42,770 S1: which means it's already been rigorously tested. And of course 483 00:27:42,770 --> 00:27:45,979 S1: it will be constantly tested afterwards as well in in 484 00:27:45,980 --> 00:27:49,360 S1: the larger population. So that to me is like the 485 00:27:49,359 --> 00:27:52,600 S1: real stuff. But I still like watching this. I always 486 00:27:52,600 --> 00:27:58,450 S1: thought that this tau protein stuff was ripe for some 487 00:27:58,450 --> 00:28:03,219 S1: sort of chemical solution, some sort of, you know, uh, 488 00:28:03,220 --> 00:28:07,600 S1: medicinal solution. Uh, of course, that was just intuition. I 489 00:28:07,600 --> 00:28:10,780 S1: have no expertise there, but it just seems like something 490 00:28:10,780 --> 00:28:13,820 S1: that people can grab on to once they realize the mechanism. 491 00:28:13,820 --> 00:28:17,390 S1: Liberals and conservatives are both prone to conspiracy theories, they 492 00:28:17,390 --> 00:28:21,020 S1: just prefer different ones. Hendrik Carlson talks about how generating 493 00:28:21,020 --> 00:28:23,810 S1: interesting ideas is like building a muscle. He says the 494 00:28:23,810 --> 00:28:26,240 S1: more you write and think deeply, the better you get 495 00:28:26,240 --> 00:28:31,340 S1: at coming up with new and meaningful thoughts. 100% agree ideas. 496 00:28:31,340 --> 00:28:34,520 S1: Zuckerberg is arguing that China is going to steal weights anyway, 497 00:28:34,520 --> 00:28:36,500 S1: so there's no way to stop that, so you might 498 00:28:36,500 --> 00:28:41,440 S1: as well develop advanced AI as open source. Okay. Not really. Okay. 499 00:28:41,440 --> 00:28:43,570 S1: But I think I wrote about that somewhere, so I'm 500 00:28:43,570 --> 00:28:46,510 S1: not going to go into that. Search GPT oh that's 501 00:28:46,510 --> 00:28:49,390 S1: where I put it yeah. Search GPT in discovery here 502 00:28:49,390 --> 00:28:53,650 S1: I use obsidian Jason Pepper Hepler talks about how he 503 00:28:53,650 --> 00:28:57,610 S1: uses obsidian for notes. In the beginning was the Command Line, 504 00:28:57,610 --> 00:29:03,720 S1: Neal Stephenson's classic essay on operating systems, my obsidian notetaking workflow. 505 00:29:03,720 --> 00:29:05,940 S1: It's on my mind. So that's why I got multiple 506 00:29:05,940 --> 00:29:09,180 S1: things in here on it bash one liners, data chain, 507 00:29:09,180 --> 00:29:13,410 S1: unstructured data management for AI projects. Llama agent stack. This 508 00:29:13,410 --> 00:29:16,830 S1: one's cool. I JSON has a cool, um, video on this. 509 00:29:16,830 --> 00:29:20,070 S1: You should check out on YouTube. Open world exploration in 510 00:29:20,070 --> 00:29:26,150 S1: Minecraft Claude engineer la Cara low latency AI application firewall 511 00:29:26,150 --> 00:29:31,010 S1: GPT four Captcha Bypass Flow Analyzer a tool for understanding 512 00:29:31,010 --> 00:29:36,860 S1: OAuth two grant flows with support for Oidc and Jwts Jots. 513 00:29:37,190 --> 00:29:39,140 S1: I don't know how you're supposed to pronounce that JWT. 514 00:29:40,460 --> 00:29:44,300 S1: I bet you about 5050 people do that jot versus 515 00:29:44,300 --> 00:29:48,410 S1: JWT bash, simple curses, simple bash library to create terminal 516 00:29:48,410 --> 00:29:52,180 S1: interfaces and the recommendation of the week. I'm going to 517 00:29:52,180 --> 00:29:55,060 S1: try to do something for the next several months. Kind 518 00:29:55,060 --> 00:29:57,430 S1: of do this already, but I'm going to try to 519 00:29:57,430 --> 00:30:01,750 S1: do it even more going into this election. When someone 520 00:30:01,750 --> 00:30:04,990 S1: labels me as super liberal, I'm going to say something 521 00:30:04,990 --> 00:30:09,670 S1: nice about their conservative views. I'm going to humanize them. 522 00:30:09,670 --> 00:30:13,800 S1: When somebody labels me as super conservative, I'm going to 523 00:30:13,800 --> 00:30:17,220 S1: say something nice about their liberal views. I'm going to 524 00:30:17,220 --> 00:30:19,980 S1: humanize them. Try this and see if it opens up 525 00:30:19,980 --> 00:30:22,200 S1: the conversation at all. I learned how to do this 526 00:30:22,200 --> 00:30:25,650 S1: from Jonathan Hite's book, The Righteous Mind, by the way, 527 00:30:25,650 --> 00:30:28,980 S1: and this was probably 6 or 7 years ago now. 528 00:30:29,010 --> 00:30:34,200 S1: This book brought me to the center politically. It brought 529 00:30:34,200 --> 00:30:37,610 S1: me to the center, specifically what it did because I 530 00:30:37,610 --> 00:30:42,800 S1: was already pretty far left. I've always been left, um, 531 00:30:42,800 --> 00:30:47,600 S1: always voted Democrat. I've only been a Democrat, I guess, uh, 532 00:30:47,600 --> 00:30:51,140 S1: technically and legally my whole life. Uh, although I wouldn't 533 00:30:51,140 --> 00:30:53,630 S1: say I'm a Democrat, I would say I've always been 534 00:30:53,630 --> 00:30:58,910 S1: liberal my whole life, but technically, yes. Democrat. Um, this book. Well, 535 00:30:58,910 --> 00:31:01,250 S1: let me just continue on that. Born and raised in 536 00:31:01,250 --> 00:31:04,000 S1: the San Francisco Bay area, which grew up with super 537 00:31:04,000 --> 00:31:08,410 S1: liberal hippie parents, and I, as a result, have always 538 00:31:08,410 --> 00:31:14,140 S1: seen conservatives, or I was trained to see conservatives as, uh, 539 00:31:14,140 --> 00:31:18,220 S1: holding progress back in, in a negative way. So not 540 00:31:18,220 --> 00:31:21,610 S1: just conservative, but conservative in a bad way. In other words, 541 00:31:21,610 --> 00:31:26,040 S1: conservative was bad and basically just viewed them poorly. What 542 00:31:26,040 --> 00:31:29,130 S1: this book did was show me this is the, I 543 00:31:29,130 --> 00:31:31,770 S1: think the main premise from the book, which I'm I'm 544 00:31:31,770 --> 00:31:34,950 S1: happy for him that I remember this so clearly. I 545 00:31:34,950 --> 00:31:39,570 S1: think the main premise is that both sides value things 546 00:31:39,570 --> 00:31:44,160 S1: very closely, very dearly, um, in a sacred way. But 547 00:31:44,160 --> 00:31:47,310 S1: they are different things. So it's not that one is 548 00:31:47,310 --> 00:31:49,850 S1: going for good things and one is going for bad things. 549 00:31:49,850 --> 00:31:52,310 S1: It's that both are going for good things, but they're 550 00:31:52,310 --> 00:31:54,739 S1: different good things. So the whole trick to being in 551 00:31:54,740 --> 00:31:58,430 S1: the centre and the whole trick to moving progress forward 552 00:31:58,430 --> 00:32:02,750 S1: is realizing that people are just different. Okay? And there 553 00:32:02,750 --> 00:32:05,300 S1: there are extremes to this that make it not true. 554 00:32:05,300 --> 00:32:08,870 S1: But in general, you should look for this common ground 555 00:32:08,870 --> 00:32:12,980 S1: and realize that there being a good person, um, in 556 00:32:12,980 --> 00:32:15,870 S1: their way and you're being a good person in your way, 557 00:32:15,870 --> 00:32:19,560 S1: and you're going to try to find, uh, Harmony there. Now, 558 00:32:19,560 --> 00:32:23,010 S1: in the case of extreme sides of left and right, 559 00:32:23,010 --> 00:32:26,130 S1: you can argue that, yeah, their good is actually bad 560 00:32:26,130 --> 00:32:27,990 S1: for the whole world. So like, you don't want to 561 00:32:27,990 --> 00:32:32,880 S1: support that. So understand there are situations like that. But 562 00:32:32,880 --> 00:32:36,090 S1: in general we would all be better off by reading 563 00:32:36,090 --> 00:32:40,400 S1: this book and realizing this, this nuance, which might help 564 00:32:40,400 --> 00:32:43,790 S1: bring you towards the center and produce better conversation. And 565 00:32:43,790 --> 00:32:47,300 S1: the aphorism of the week, the highest form of knowledge 566 00:32:47,300 --> 00:32:52,160 S1: is empathy. The highest form of knowledge is empathy. Bill Bullard. 567 00:32:53,510 --> 00:32:56,720 S1: Unsupervised learning is produced and edited by Daniel Miessler on 568 00:32:56,720 --> 00:33:01,340 S1: a Neumann U87 AI microphone using Hindenburg. Intro and outro 569 00:33:01,340 --> 00:33:04,660 S1: music is by Zomby with the Y, and to get 570 00:33:04,660 --> 00:33:06,729 S1: the text and links from this episode, sign up for 571 00:33:06,730 --> 00:33:12,370 S1: the newsletter version of the show at Daniel miessler.com/newsletter. We'll 572 00:33:12,370 --> 00:33:13,180 S1: see you next time.