1 00:00:01,129 --> 00:00:04,460 S1: Welcome to Unsupervised Learning, a security, AI and meaning focused 2 00:00:04,460 --> 00:00:07,340 S1: podcast that looks at how best to thrive as humans 3 00:00:07,340 --> 00:00:11,570 S1: in a post AI world. It combines original ideas, analysis, 4 00:00:11,570 --> 00:00:14,810 S1: and mental models to bring not just the news, but 5 00:00:14,810 --> 00:00:22,400 S1: why it matters and how to respond. All right, welcome 6 00:00:22,400 --> 00:00:25,310 S1: to unsupervised learning. This is Daniel Miessler. All right. So 7 00:00:25,310 --> 00:00:28,370 S1: here's what's up this week gearing up for Las Vegas. 8 00:00:28,400 --> 00:00:31,000 S1: Going to be there for 11 days. Assuming I don't 9 00:00:31,000 --> 00:00:33,129 S1: get sick after like four of those. And if you 10 00:00:33,130 --> 00:00:35,620 S1: see me, please, uh, please know that I'm a bit, uh, 11 00:00:35,620 --> 00:00:39,220 S1: shy and awkward, a bit, uh, like 20 to 60% 12 00:00:39,220 --> 00:00:41,379 S1: of the time. I mean, sometimes it's just not there 13 00:00:41,380 --> 00:00:44,050 S1: at all, and sometimes it's pretty bad. So just, uh, 14 00:00:44,050 --> 00:00:47,920 S1: say hi. Anyway, I love seeing people, and, uh, it's 15 00:00:47,920 --> 00:00:50,229 S1: good training for me. Need to get better at this. Anyway, 16 00:00:50,229 --> 00:00:53,760 S1: watch a number of videos last night, I believe about 17 00:00:53,760 --> 00:00:57,060 S1: people losing their jobs and starting like a YouTube channel, 18 00:00:57,060 --> 00:01:00,150 S1: and like just trying to branch out and maybe start 19 00:01:00,150 --> 00:01:03,000 S1: a new career. And it was extremely sad. And I 20 00:01:03,000 --> 00:01:05,429 S1: watched like two of these. And of course the feed 21 00:01:05,430 --> 00:01:08,039 S1: showed me like ten more. And I ended up just 22 00:01:08,040 --> 00:01:10,140 S1: watching these videos for like an hour and a half, 23 00:01:10,140 --> 00:01:12,750 S1: two hours. And it was extremely sad. I feel like 24 00:01:12,750 --> 00:01:16,949 S1: people are seeing the ground shift underneath their feet and 25 00:01:16,950 --> 00:01:20,750 S1: they're like, why does nobody want me, right? This one 26 00:01:20,750 --> 00:01:23,360 S1: particular one was really sad because he was like, I 27 00:01:23,360 --> 00:01:25,700 S1: thought I was a good product manager. And then after 28 00:01:25,700 --> 00:01:29,449 S1: ten and a half months of interviewing and getting all 29 00:01:29,450 --> 00:01:32,090 S1: the way through interviews and getting to the very end, 30 00:01:32,090 --> 00:01:35,690 S1: and then not being given an offer or being given 31 00:01:35,690 --> 00:01:38,330 S1: an offer and then being ghosted after ten and a 32 00:01:38,330 --> 00:01:40,610 S1: half months, I no longer believe in myself. I no 33 00:01:40,610 --> 00:01:43,420 S1: longer believe I'm a good product manager, and it was 34 00:01:43,420 --> 00:01:47,170 S1: just devastating to hear this. And he was being so like, 35 00:01:47,170 --> 00:01:50,080 S1: just open and honest and vulnerable. And it was just 36 00:01:50,080 --> 00:01:52,840 S1: it was hard to watch and just felt very human. 37 00:01:52,840 --> 00:01:55,570 S1: And this is essentially why I'm doing what I'm doing 38 00:01:55,570 --> 00:01:58,990 S1: now is to basically try to help people who are 39 00:01:58,990 --> 00:02:01,930 S1: in that situation and try to hopefully explain what I 40 00:02:01,930 --> 00:02:06,500 S1: see coming in terms of like, you shouldn't judge yourself 41 00:02:06,500 --> 00:02:11,179 S1: based on what these companies think about your skills and 42 00:02:11,180 --> 00:02:14,270 S1: what they think about your worth. Um, they do not 43 00:02:14,270 --> 00:02:16,970 S1: determine your worth. And this is why we need something 44 00:02:16,970 --> 00:02:22,670 S1: like human 3.0 is because human 3.0 is how to 45 00:02:22,669 --> 00:02:27,470 S1: measure yourself. Okay, you in your entirety. Full spectrum is 46 00:02:27,470 --> 00:02:30,400 S1: how you should be thinking about yourself, not in terms 47 00:02:30,400 --> 00:02:34,480 S1: of do you have these particular product management skills that 48 00:02:34,480 --> 00:02:37,690 S1: this particular type of startup needs, or this particular type 49 00:02:37,690 --> 00:02:40,450 S1: of company needs? That is a very old world way 50 00:02:40,450 --> 00:02:43,420 S1: of thinking about things. And I don't fault anyone for 51 00:02:43,419 --> 00:02:45,610 S1: thinking that way because you're trying to get a job, 52 00:02:45,610 --> 00:02:47,800 S1: you're trying to pay bills, right? So I understand that. 53 00:02:47,800 --> 00:02:51,220 S1: I'm just saying that society as a whole is stuck 54 00:02:51,220 --> 00:02:53,850 S1: in the past in seeing the world in this way, 55 00:02:53,850 --> 00:02:58,020 S1: and we need to move beyond it, especially because AI 56 00:02:58,020 --> 00:03:00,480 S1: is going to accelerate this whole process where they're going 57 00:03:00,480 --> 00:03:03,180 S1: to see more and more people as expendable in this way, 58 00:03:03,180 --> 00:03:05,220 S1: the same way that this guy was fired in ten 59 00:03:05,220 --> 00:03:07,919 S1: and a half months later, he still can't get a job. 60 00:03:07,919 --> 00:03:10,590 S1: That's going to keep happening to more and more people, 61 00:03:10,590 --> 00:03:14,190 S1: and we need a solution. So I would say be 62 00:03:14,190 --> 00:03:18,260 S1: kind to people. Everyone is hurting in some kind of way, 63 00:03:18,260 --> 00:03:22,280 S1: and especially right now. And when you look at our politics, 64 00:03:22,280 --> 00:03:25,310 S1: I see a lot of the current situation as a 65 00:03:25,310 --> 00:03:29,570 S1: symptom of people being hurt and people being afraid. And 66 00:03:29,570 --> 00:03:33,260 S1: I think that makes people more mean and less empathic 67 00:03:33,260 --> 00:03:35,570 S1: than they otherwise would be. And I got this great 68 00:03:35,570 --> 00:03:38,540 S1: quote here. Kindness is a language which the deaf can 69 00:03:38,540 --> 00:03:41,840 S1: hear and the blind can see by Mark Twain. All right. 70 00:03:41,840 --> 00:03:44,870 S1: so I got my best argument for why I will 71 00:03:44,870 --> 00:03:48,110 S1: have an extraordinary effect on economy and jobs. And if 72 00:03:48,110 --> 00:03:51,110 S1: you have any friends who are still skeptical about AI, 73 00:03:51,140 --> 00:03:54,710 S1: I recommend sending them this. It's called, uh, if we've 74 00:03:54,710 --> 00:03:57,710 S1: been thinking about AI all wrong created a video for 75 00:03:57,710 --> 00:04:02,060 S1: a substrate. It's, uh, one of my best performing videos, actually. Uh, 76 00:04:02,060 --> 00:04:05,060 S1: and if you prefer videos to long articles and you 77 00:04:05,060 --> 00:04:07,980 S1: still want to learn about substrates, recommend checking that out. 78 00:04:07,980 --> 00:04:12,780 S1: Security Knowbe4 accidentally hired a North Korean state actor who 79 00:04:12,780 --> 00:04:15,510 S1: tried to install info stealing malware on their device. The 80 00:04:15,510 --> 00:04:20,280 S1: craziest thing about this whole North Korea stuff and how 81 00:04:20,279 --> 00:04:24,690 S1: they're like breaking into these companies, they're not really breaking in. 82 00:04:24,690 --> 00:04:27,960 S1: They're applying they're applying to these companies and getting hired. 83 00:04:27,960 --> 00:04:30,890 S1: And it really reminds me of this Key and Peele 84 00:04:30,890 --> 00:04:33,289 S1: skit where the guy's like, yeah, so what we're going 85 00:04:33,290 --> 00:04:36,349 S1: to do is we're going to sneak in. I think 86 00:04:36,350 --> 00:04:38,870 S1: I told this joke before, but he's like, what we're 87 00:04:38,870 --> 00:04:41,300 S1: going to do is we're going to sneak in, we're 88 00:04:41,300 --> 00:04:44,420 S1: going to get hired. And then when they give us work, 89 00:04:44,420 --> 00:04:47,659 S1: we're going to do the work, and then they're going 90 00:04:47,660 --> 00:04:52,430 S1: to deposit money in our bank accounts every two weeks. 91 00:04:53,300 --> 00:04:56,830 S1: And his buddy looks at him and he goes, dude, 92 00:04:56,830 --> 00:05:02,080 S1: you're talking about a fucking job. That's what a job is. 93 00:05:02,110 --> 00:05:04,360 S1: And that was the end of the skit. And that's 94 00:05:04,360 --> 00:05:09,070 S1: literally what these guys are doing. They are applying to jobs, 95 00:05:09,070 --> 00:05:12,190 S1: getting hired and doing the work. The trick is they 96 00:05:12,190 --> 00:05:15,310 S1: just take their regular salaries, and I'm not sure what 97 00:05:15,310 --> 00:05:18,070 S1: percentage is actually doing the work versus doing the work 98 00:05:18,070 --> 00:05:22,320 S1: poorly versus doing the work, plus trying to hack them additionally. 99 00:05:22,320 --> 00:05:24,750 S1: Not sure about that exactly. I'm not sure anyone is, 100 00:05:24,750 --> 00:05:28,020 S1: but here's what I do know. Uh, they're taking the 101 00:05:28,020 --> 00:05:31,500 S1: money from the salaries and putting it back into the 102 00:05:31,500 --> 00:05:34,589 S1: government to use for like the missile program or whatever 103 00:05:34,589 --> 00:05:38,190 S1: other shenanigans North Korea is doing. So they're they're literally 104 00:05:38,190 --> 00:05:43,600 S1: deploying it people into the world, into advanced economies to 105 00:05:43,600 --> 00:05:46,989 S1: get actual jobs, to earn salaries, to put into the 106 00:05:46,990 --> 00:05:51,430 S1: missile program. That is trippy to me. That is completely insane. 107 00:05:51,430 --> 00:05:55,870 S1: All right. Next one GitHub's repository design flaw allows indefinite 108 00:05:55,870 --> 00:05:59,500 S1: access to data from deleted and private repositories. This is 109 00:05:59,500 --> 00:06:02,680 S1: a piece from Truffle Security. A plane's GPS was jammed 110 00:06:02,680 --> 00:06:06,070 S1: on a commercial transatlantic route for the first time, raising 111 00:06:06,070 --> 00:06:08,609 S1: fears that thousands of other flights could be at risk 112 00:06:08,610 --> 00:06:12,990 S1: of deliberate hacking. This was between Madrid and Toronto, thanks 113 00:06:12,990 --> 00:06:17,250 S1: to teens for subscribing or not subscribing for sponsoring, and 114 00:06:17,250 --> 00:06:21,480 S1: there's been a 400% increase in GPS spoofing incidents, so 115 00:06:21,480 --> 00:06:24,150 S1: this one is related to the previous one, affecting around 116 00:06:24,150 --> 00:06:30,980 S1: 900 flights daily. And this is from Opsa Group. And um, yeah, 117 00:06:30,980 --> 00:06:35,120 S1: spike is causing major safety concerns, especially with systems like e.g. 118 00:06:35,600 --> 00:06:39,350 S1: AWS becoming unreliable and there are a number of people 119 00:06:39,350 --> 00:06:43,040 S1: looking at like, why is this happening? What are the implications? Yeah, 120 00:06:43,040 --> 00:06:46,640 S1: I'm wondering what the other options are for Nav if 121 00:06:46,640 --> 00:06:50,419 S1: there's like secondary or tertiary systems that we can use 122 00:06:50,420 --> 00:06:54,940 S1: when a jam happens or blocks against the jamming, like 123 00:06:54,940 --> 00:06:57,340 S1: a couple of different options. The other thing is, if 124 00:06:57,339 --> 00:07:01,450 S1: there's six miles up or whatever and the jamming is happening, 125 00:07:01,450 --> 00:07:03,940 S1: is that coming from inside the house? Like, is that 126 00:07:03,940 --> 00:07:06,310 S1: someone with a jammer on the plane? Is that someone 127 00:07:06,339 --> 00:07:08,200 S1: on the ground? I mean, these are kind of the 128 00:07:08,200 --> 00:07:10,570 S1: only options. Someone on the ground is shooting it up 129 00:07:10,570 --> 00:07:14,020 S1: at the plane, which seems pretty hard. Another plane coming 130 00:07:14,020 --> 00:07:17,020 S1: along with it and shooting it. That seems very hard 131 00:07:17,020 --> 00:07:21,780 S1: and also very expensive. Or they're in orbit, uh, and, uh, 132 00:07:21,780 --> 00:07:24,510 S1: they would be moving very fast if they were in orbit. 133 00:07:24,510 --> 00:07:26,970 S1: So and they're shooting down I mean, I'm trying to 134 00:07:26,970 --> 00:07:29,940 S1: list all the options other than aliens. And, uh, it 135 00:07:29,940 --> 00:07:33,180 S1: seems like someone on the plane is more likely so 136 00:07:33,180 --> 00:07:36,900 S1: out of those. But, uh, I don't know. I have 137 00:07:36,900 --> 00:07:39,660 S1: no insider knowledge on this one yet. Uh, Francis, high 138 00:07:39,660 --> 00:07:43,190 S1: speed rail traffic got disrupted due to what officials are 139 00:07:43,190 --> 00:07:47,420 S1: calling malicious acts just before the Olympic ceremony. Thanks to 140 00:07:47,420 --> 00:07:50,780 S1: nudge security for sponsoring, Google has decided not to phase 141 00:07:50,780 --> 00:07:53,660 S1: out third party cookies in Chrome and will instead offer 142 00:07:53,660 --> 00:07:59,180 S1: users more control over how the user's cookies are used. And, uh, I, 143 00:07:59,210 --> 00:08:02,510 S1: for one, am absolutely shocked that this company that makes 144 00:08:02,510 --> 00:08:05,270 S1: most of its money on advertising and is really bad 145 00:08:05,270 --> 00:08:09,290 S1: at rolling out products, cancelled a thing that's really won, 146 00:08:09,290 --> 00:08:14,180 S1: difficult to do, and too would negatively affect advertisers and 147 00:08:14,180 --> 00:08:18,350 S1: therefore their main source of income. An evaluation of Amazon 148 00:08:18,350 --> 00:08:22,910 S1: GuardDuty reveals limited coverage and high costs, with significant latency 149 00:08:22,910 --> 00:08:26,690 S1: in detecting attacks like S3 ransomware. Amazon. When it does 150 00:08:26,690 --> 00:08:29,420 S1: things like this, it's a little bit similar to Cloudflare, 151 00:08:29,420 --> 00:08:31,990 S1: where they just kind of like do an initial version 152 00:08:31,990 --> 00:08:33,880 S1: of the tool. It kind of works a little bit, 153 00:08:33,880 --> 00:08:35,679 S1: and then if you adopt it, you just kind of 154 00:08:35,679 --> 00:08:38,109 S1: hope it gets better over time. And basically what a 155 00:08:38,110 --> 00:08:40,780 S1: number of these articles are basically saying is that, yeah, 156 00:08:40,780 --> 00:08:44,230 S1: they don't necessarily always catch up. It depends on the 157 00:08:44,230 --> 00:08:47,140 S1: product team, it depends on luck. And sometimes if you 158 00:08:47,140 --> 00:08:49,150 S1: lump your eggs in with that basket, like it just 159 00:08:49,150 --> 00:08:52,270 S1: goes nowhere and other times it becomes a full fledged product. 160 00:08:52,270 --> 00:08:56,270 S1: So other examples of this GuardDuty the WAF product, there's 161 00:08:56,270 --> 00:08:59,510 S1: a million different ones. Google's reCAPTCHA is showing its age 162 00:08:59,510 --> 00:09:04,850 S1: and is harvesting user information with labor worth billions, while 163 00:09:04,850 --> 00:09:09,020 S1: being almost universally disliked and vulnerable to bots. The Senate 164 00:09:09,020 --> 00:09:14,480 S1: unanimously passed the Defiance Act, letting victims of non-consensual intimate 165 00:09:14,480 --> 00:09:18,470 S1: images created by AI sue their creators for damages and 166 00:09:18,470 --> 00:09:22,179 S1: then get up to 150 K to 250 K if 167 00:09:22,179 --> 00:09:27,460 S1: linked to assault, stalking or harassment. A US Commerce Department 168 00:09:27,460 --> 00:09:30,939 S1: or the US Commerce Department says shipments of high performance 169 00:09:30,940 --> 00:09:34,480 S1: processors from China and Hong Kong to Russia have dropped 170 00:09:34,480 --> 00:09:37,780 S1: by 20%, but Hong Kong is still the main hub. 171 00:09:37,809 --> 00:09:42,550 S1: AI and tech whiz turned down $23 billion in acquisition 172 00:09:42,550 --> 00:09:46,490 S1: from alphabet and is instead aiming for an IPO. I 173 00:09:46,490 --> 00:09:49,160 S1: thought I did a deeper comment on this. I think 174 00:09:49,160 --> 00:09:51,469 S1: I do a deeper comment on this later. Yeah. Oh, 175 00:09:51,470 --> 00:09:53,810 S1: here it is. Yeah. My thoughts are basically that they 176 00:09:53,809 --> 00:09:57,080 S1: knew they could get more money for this. That's number one. 177 00:09:57,080 --> 00:10:00,020 S1: But two is they basically knew that if they got 178 00:10:00,020 --> 00:10:02,690 S1: bought they would get that $23 billion, which is a 179 00:10:02,690 --> 00:10:06,470 S1: decent amount. And the problem is it would most likely 180 00:10:06,470 --> 00:10:10,360 S1: end up in the Google graveyard, so who knows what 181 00:10:10,360 --> 00:10:14,439 S1: percentage of that was. Maybe that description is online somewhere. 182 00:10:14,470 --> 00:10:17,380 S1: AI is replacing jobs in the video game industry with 183 00:10:17,380 --> 00:10:22,540 S1: major companies like Activision using generative AI tools for concept art, 184 00:10:22,540 --> 00:10:27,370 S1: and they're saying an estimated 10,500 people lost their jobs 185 00:10:27,370 --> 00:10:30,070 S1: in 2023. I don't know if that's all from art. 186 00:10:30,070 --> 00:10:32,850 S1: I imagine a lot of it is from art. But yeah, 187 00:10:32,850 --> 00:10:35,190 S1: I got a bunch of buddies in this industry and 188 00:10:35,190 --> 00:10:39,270 S1: they're saying very similar things. Lots of different things in 189 00:10:39,270 --> 00:10:43,470 S1: the creative workflow are vulnerable. I would say. A new 190 00:10:43,470 --> 00:10:46,860 S1: study shows that while generative AI like ChatGPT makes individual 191 00:10:46,860 --> 00:10:49,439 S1: stories more creative and engaging, it also makes them more 192 00:10:49,440 --> 00:10:52,230 S1: similar to each other. And I've heard a lot of 193 00:10:52,230 --> 00:10:54,510 S1: criticisms of AI and like, oh, it's going to do 194 00:10:54,510 --> 00:10:56,900 S1: this bad thing or that bad thing, but this one 195 00:10:56,900 --> 00:11:00,890 S1: sounds quite realistic. So basically, we need to engineer in 196 00:11:00,890 --> 00:11:06,800 S1: mechanisms for exposure to alternative frames of reality, different models, 197 00:11:06,800 --> 00:11:11,630 S1: different ways of viewing reality to avoid people consolidating and 198 00:11:11,630 --> 00:11:15,890 S1: following like one AI powered groupthink. Or maybe it's like 199 00:11:15,890 --> 00:11:19,190 S1: 20 AI powered group thinks, but it's like controlling the 200 00:11:19,190 --> 00:11:23,319 S1: narrative of how everyone's thinking about certain things. Because something 201 00:11:23,320 --> 00:11:26,080 S1: was generated by AI, it went viral. Maybe it was 202 00:11:26,080 --> 00:11:28,929 S1: pretty cool or whatever, so it goes viral, and then 203 00:11:28,929 --> 00:11:32,080 S1: that just becomes a thing that everyone's thinking. I've actually 204 00:11:32,080 --> 00:11:35,859 S1: been really worried about this for myself. It's one of 205 00:11:35,860 --> 00:11:38,290 S1: the main things I want to use ADR for. And 206 00:11:38,290 --> 00:11:41,260 S1: I've been talking with Joseph Thacker about this as well. 207 00:11:41,260 --> 00:11:43,630 S1: It's like, imagine that you have in your brain at 208 00:11:43,630 --> 00:11:48,630 S1: any given moment a number of ideas, and I definitely 209 00:11:48,630 --> 00:11:51,179 S1: have this. I have multiple ideas brewing all the time. 210 00:11:51,179 --> 00:11:54,929 S1: So what percentage of those are like super new that 211 00:11:54,929 --> 00:11:58,410 S1: I came up with myself? What percentage of those are 212 00:11:58,410 --> 00:12:02,400 S1: like mostly derivative? And how many of those are just 213 00:12:02,400 --> 00:12:06,570 S1: seeds planted by some random add that I didn't even understand? 214 00:12:06,570 --> 00:12:09,270 S1: And like this is completely opaque to me. Yet I 215 00:12:09,270 --> 00:12:12,900 S1: think that these 19 ideas are like mine, and so 216 00:12:12,900 --> 00:12:15,900 S1: I'm going out and sharing them with other people, and 217 00:12:15,900 --> 00:12:18,900 S1: then the same phenomenon happens to them. And then the 218 00:12:18,900 --> 00:12:21,750 S1: question is how many of those came from AI? Then 219 00:12:21,750 --> 00:12:24,870 S1: the question becomes how many came from a government or 220 00:12:24,870 --> 00:12:28,440 S1: some corporation where they injected these things into the world 221 00:12:28,440 --> 00:12:31,920 S1: with AI, and now these memes are spreading all over, 222 00:12:31,920 --> 00:12:35,290 S1: and pretty soon it's in all the articles, it's in 223 00:12:35,290 --> 00:12:38,620 S1: all the news, it's in all the top podcasts. And 224 00:12:38,620 --> 00:12:41,980 S1: essentially what you have is like a soft version, lower 225 00:12:41,980 --> 00:12:47,860 S1: case version of propaganda, mind control and the steering of narratives. 226 00:12:47,860 --> 00:12:50,319 S1: So I think it's going to be super essential for 227 00:12:50,320 --> 00:12:53,620 S1: people who care about free thinking, want to be able 228 00:12:53,620 --> 00:12:59,250 S1: to disconnect and sort of read books slowly, think slowly, 229 00:12:59,250 --> 00:13:03,209 S1: come up with, you know, old style thinking, old style 230 00:13:03,210 --> 00:13:05,850 S1: critical thinking. That's going to be one thing. But the 231 00:13:05,850 --> 00:13:09,599 S1: other thing is I want my Da to monitor the 232 00:13:09,600 --> 00:13:13,020 S1: ideas that I'm talking about and that I'm writing about, 233 00:13:13,020 --> 00:13:18,179 S1: monitor those things, and then also say, hey, guess what? 234 00:13:18,179 --> 00:13:22,120 S1: So I saw this idea get released into the world 235 00:13:22,120 --> 00:13:26,260 S1: by this. Whatever. This shady group over here, that was 236 00:13:26,260 --> 00:13:29,890 S1: three and a half weeks ago. They then started broadcasting 237 00:13:29,890 --> 00:13:33,189 S1: it on these 19 different networks. Those got picked up 238 00:13:33,190 --> 00:13:37,990 S1: by these 347 other networks, and that became one of 239 00:13:37,990 --> 00:13:41,800 S1: the top 14 top ideas over the last two weeks. 240 00:13:41,800 --> 00:13:45,390 S1: And you are now over here writing about it. So 241 00:13:45,390 --> 00:13:47,609 S1: I just want to let you know, as my as 242 00:13:47,610 --> 00:13:51,630 S1: your friend, that you are being influenced by this campaign 243 00:13:51,630 --> 00:13:54,120 S1: that was launched by this group three and a half 244 00:13:54,120 --> 00:13:56,729 S1: weeks ago. That is a service that we all need. 245 00:13:56,730 --> 00:13:59,070 S1: That is a service I'm going to build for myself, 246 00:13:59,070 --> 00:14:02,520 S1: because that's going to be part of this overall mechanization 247 00:14:02,520 --> 00:14:05,670 S1: involving substrate pipelines, a whole bunch of other stuff that's 248 00:14:05,670 --> 00:14:09,859 S1: basically monitoring not just the world, but also myself and 249 00:14:09,860 --> 00:14:12,140 S1: seeing like how I'm being influenced. That's like one of 250 00:14:12,140 --> 00:14:14,990 S1: the use cases. All right. Switzerland has passed a law 251 00:14:14,990 --> 00:14:18,860 S1: requiring all public sector agencies to use open source software 252 00:14:18,860 --> 00:14:24,620 S1: and open source, any code that they develop. I love this, but, uh, 253 00:14:24,620 --> 00:14:27,620 S1: open doesn't mean secure by itself. That's one thing. The 254 00:14:27,620 --> 00:14:29,660 S1: other thing is you got to find a way to 255 00:14:29,660 --> 00:14:32,330 S1: support the stuff, which you could just hire consultants or 256 00:14:32,330 --> 00:14:36,610 S1: companies or whatever, but or train internally, but definitely have 257 00:14:36,610 --> 00:14:40,600 S1: to worry about support. Govloop super cool platform for AI 258 00:14:40,600 --> 00:14:44,350 S1: workflows doesn't quite work, as far as I could tell. 259 00:14:44,350 --> 00:14:47,140 S1: And if you're like a founder there or something, feel 260 00:14:47,140 --> 00:14:49,180 S1: free to reach out and let me know. And, uh, 261 00:14:49,180 --> 00:14:52,240 S1: happy to correct that. But that's just my current impression. 262 00:14:52,240 --> 00:14:56,170 S1: But I am extremely excited about it and I think 263 00:14:56,170 --> 00:14:58,800 S1: they're doing great stuff. The guy is amazing. Reminds me 264 00:14:58,800 --> 00:15:01,770 S1: of Yahoo pipes. Definitely want to check it out. Alphabet's 265 00:15:01,770 --> 00:15:05,130 S1: putting another $5 billion into Waymo. It's basically looking like 266 00:15:05,130 --> 00:15:08,790 S1: Waymo versus Tesla for self-driving taxis. But the approaches are 267 00:15:08,790 --> 00:15:12,720 S1: very different. Waymo basically needs to train for a very 268 00:15:12,720 --> 00:15:16,170 S1: long time in a city before it works, because they 269 00:15:16,170 --> 00:15:18,180 S1: kind of work out all the bugs just by driving 270 00:15:18,180 --> 00:15:21,090 S1: all the streets for a very long time. And, and 271 00:15:21,110 --> 00:15:23,390 S1: they sort of ramp that up. And Tesla is like 272 00:15:23,390 --> 00:15:25,640 S1: typical Elon. He's like, no, I'm just going to make 273 00:15:25,640 --> 00:15:27,050 S1: the thing. It's going to be awesome. It's going to 274 00:15:27,050 --> 00:15:30,830 S1: work perfectly. I'm announcing it soon. But you have to 275 00:15:30,830 --> 00:15:33,800 S1: realize that he's not always right, right. He said full 276 00:15:33,800 --> 00:15:36,020 S1: Self-Driving was going to be pretty easy and we would 277 00:15:36,020 --> 00:15:39,080 S1: have it years ago. And now it's 2024 and it's 278 00:15:39,080 --> 00:15:42,350 S1: just now getting good. So something to think about there 279 00:15:42,350 --> 00:15:48,250 S1: between the two different approaches, Joe Procopio argues that tech 280 00:15:48,250 --> 00:15:50,770 S1: companies are struggling to find good employees because they focus 281 00:15:50,770 --> 00:15:54,820 S1: too much on creds and not enough on skills. I 282 00:15:54,820 --> 00:15:58,240 S1: like it. I think this is exactly the shift that's happening. 283 00:15:58,240 --> 00:16:02,080 S1: He basically suggests that companies should prioritize practical experience and 284 00:16:02,080 --> 00:16:06,220 S1: problem solving abilities over degrees and certifications. That's exactly what's 285 00:16:06,220 --> 00:16:09,220 S1: going to happen with something like Astra, which is a 286 00:16:09,240 --> 00:16:11,790 S1: fake thing. But this this article that I wrote that 287 00:16:11,790 --> 00:16:15,750 S1: rates things in and gives scores, basically does a gauntlet 288 00:16:15,780 --> 00:16:18,720 S1: of tests and then gives you like these comprehensive, really 289 00:16:18,720 --> 00:16:22,080 S1: deep scores. That's essentially how that's going to be solved. 290 00:16:22,080 --> 00:16:24,360 S1: Apple just launched a beta version of Apple Maps for 291 00:16:24,360 --> 00:16:27,720 S1: the web. Humans. Did I talk about ChatGPT? I thought 292 00:16:27,720 --> 00:16:29,970 S1: I did I take that out? Where did that go? 293 00:16:29,970 --> 00:16:33,980 S1: Oh very strange. Yeah, there's a new chat, GPT, a 294 00:16:33,980 --> 00:16:39,290 S1: search web chat, GPT that basically competes with Perplexity and 295 00:16:39,290 --> 00:16:45,830 S1: Bing and it's it's out, but it's, uh, waitlisted. Um, so, uh, 296 00:16:46,010 --> 00:16:48,320 S1: I don't know. I haven't checked to see if I 297 00:16:48,320 --> 00:16:51,140 S1: have access to that one yet, but, uh, looking forward 298 00:16:51,140 --> 00:16:54,380 S1: to that one. Humans. Wall Street Journal explores why the 299 00:16:54,380 --> 00:16:59,820 S1: US birth rate is declining, citing economic uncertainty, career priorities 300 00:16:59,820 --> 00:17:03,990 S1: and lifestyle choices. The idea that UBI reduces the need 301 00:17:03,990 --> 00:17:06,990 S1: to work isn't new, but recent studies show that it 302 00:17:06,990 --> 00:17:10,260 S1: does not lead to better jobs or more education. I 303 00:17:10,260 --> 00:17:12,390 S1: think the issue here is that certain people will spend 304 00:17:12,390 --> 00:17:15,450 S1: free time and money to better themselves, and certain people won't, 305 00:17:15,450 --> 00:17:18,240 S1: and it's not clear why that is. It's not clear 306 00:17:18,240 --> 00:17:22,399 S1: what the actual like factor is, but the way forward 307 00:17:22,400 --> 00:17:24,530 S1: is basically trying to figure out what that is and 308 00:17:24,530 --> 00:17:28,100 S1: not believing in fairy tales like giving away free money 309 00:17:28,100 --> 00:17:31,460 S1: will make everyone ambitious. It does not. And this reminds 310 00:17:31,460 --> 00:17:34,700 S1: me of a lesson that's very similar that I learned 311 00:17:34,700 --> 00:17:38,600 S1: like 20 years of hiring people, which is exposing people 312 00:17:38,600 --> 00:17:42,710 S1: to training and encouragement makes the best people stand out, 313 00:17:42,710 --> 00:17:45,590 S1: but it does not turn everyone into the best people. 314 00:17:45,590 --> 00:17:48,910 S1: And I've known this because I tried. Because I tried 315 00:17:48,910 --> 00:17:52,389 S1: for like ten years with cohort after cohort where I 316 00:17:52,390 --> 00:17:54,970 S1: would just be like, oh, well, my training wasn't good enough. 317 00:17:54,970 --> 00:17:57,639 S1: That's why they didn't get it, or they were distracted 318 00:17:57,640 --> 00:18:00,010 S1: or they were busy or whatever. So I just keep 319 00:18:00,010 --> 00:18:03,460 S1: hitting them and keep dumping hours and hours and hours 320 00:18:03,460 --> 00:18:05,859 S1: and more training and more training and send them to 321 00:18:05,859 --> 00:18:09,130 S1: more classes. And you come back to them and you're like, hey, 322 00:18:09,130 --> 00:18:11,580 S1: what have you done? They're like, I don't know. I 323 00:18:11,580 --> 00:18:15,300 S1: didn't do anything. What should I do? And you're like, uh, no, no. 324 00:18:15,390 --> 00:18:17,490 S1: So you've you've got to figure out what you want 325 00:18:17,490 --> 00:18:19,649 S1: to do. You've got to I've given you all the training, 326 00:18:19,650 --> 00:18:22,740 S1: I've given you all the stuff and all the books, 327 00:18:22,740 --> 00:18:24,720 S1: and I gave you all the demos. I told you 328 00:18:24,720 --> 00:18:27,000 S1: how to set up the demos. I showed you how 329 00:18:27,000 --> 00:18:30,000 S1: to set up the tech, and, um, have you done 330 00:18:30,000 --> 00:18:32,639 S1: any of that? And they're like, no, what should I 331 00:18:32,640 --> 00:18:35,180 S1: do first? I don't I don't know how to do 332 00:18:35,180 --> 00:18:37,790 S1: any of that. Can you show me? And you're like, well, 333 00:18:37,790 --> 00:18:39,920 S1: I showed you four times. You want me to show 334 00:18:39,920 --> 00:18:41,480 S1: you a fifth time? Okay, sure. I'll show you a 335 00:18:41,480 --> 00:18:43,850 S1: fifth time. Show them a fifth time. Come back two 336 00:18:43,850 --> 00:18:47,960 S1: weeks later. Hey, how's it going? Going with what? With 337 00:18:47,960 --> 00:18:50,300 S1: the stuff we talked about last time. And they're like, yeah, 338 00:18:50,300 --> 00:18:52,310 S1: it never got around to that. Like, I couldn't find 339 00:18:52,430 --> 00:18:55,130 S1: this one driver. Like there was a driver download, but 340 00:18:55,130 --> 00:18:58,690 S1: the driver download didn't work. It's like, okay, well, did 341 00:18:58,690 --> 00:19:01,030 S1: you research that? No. How would I do that? Can 342 00:19:01,030 --> 00:19:04,869 S1: you help me that that ends up being like 50 343 00:19:04,869 --> 00:19:09,639 S1: to 80% of everyone. Here's the alternative. Here's the alternate 344 00:19:09,640 --> 00:19:14,440 S1: version of a star. Okay? You're like, hey, um, hey, everyone. 345 00:19:14,440 --> 00:19:17,920 S1: So check this out. This is a class about. And 346 00:19:17,920 --> 00:19:20,080 S1: you see a hand go up. Hey, look, I read 347 00:19:20,080 --> 00:19:23,520 S1: the whole document. Um, I looked forward in the class. 348 00:19:23,520 --> 00:19:26,189 S1: I've got this thing running, but there's an error. Um, 349 00:19:26,369 --> 00:19:29,400 S1: so I fixed it, and I stood up a different version, 350 00:19:29,400 --> 00:19:32,010 S1: and it's running. Can I show you? And you're like, okay, 351 00:19:32,010 --> 00:19:35,340 S1: so you basically read everything, jumped ahead in the class, 352 00:19:35,340 --> 00:19:38,280 S1: built something. It didn't work, but you fixed it. Now 353 00:19:38,280 --> 00:19:40,380 S1: you're running a different version and you want to show 354 00:19:40,380 --> 00:19:42,180 S1: it to me now. But I just started my first 355 00:19:42,180 --> 00:19:45,090 S1: sentence of the class and they're like, oh yeah, sorry. Yeah. 356 00:19:45,090 --> 00:19:48,950 S1: Please continue. Sorry. I was just excited. The difference between 357 00:19:48,950 --> 00:19:53,390 S1: these two things are so insane. Okay. And what I've 358 00:19:53,390 --> 00:19:55,970 S1: learned after all these years is that if you're trying 359 00:19:55,970 --> 00:19:59,570 S1: to build a star team and you're trying to build 360 00:19:59,570 --> 00:20:02,870 S1: like a group of the best people, what you want 361 00:20:02,869 --> 00:20:06,919 S1: to do is throw help and throw training and throw 362 00:20:06,920 --> 00:20:11,889 S1: encouragement to a massive group of people. and not judge, 363 00:20:11,890 --> 00:20:16,180 S1: not prejudge, not do anything. You have no idea. You 364 00:20:16,180 --> 00:20:19,210 S1: have no idea. It's not people who wear yellow shirts. 365 00:20:19,210 --> 00:20:23,260 S1: It's not people from Idaho. It's not people from Mississippi. 366 00:20:23,290 --> 00:20:27,190 S1: You have no idea what the criteria are like background, 367 00:20:27,190 --> 00:20:31,659 S1: ethnic group, you education, background. Are they educated? Do they 368 00:20:31,660 --> 00:20:34,909 S1: have a PhD? Do they have two master's degrees? Are 369 00:20:34,910 --> 00:20:38,000 S1: they still in high school? You don't know. And that's 370 00:20:38,000 --> 00:20:40,790 S1: the smartest thing you could possibly realize is that you 371 00:20:40,790 --> 00:20:43,880 S1: have no idea. You have no idea who's going to 372 00:20:43,880 --> 00:20:48,740 S1: just be kind of, like, complete, completely inert, and just 373 00:20:48,740 --> 00:20:52,640 S1: unable to learn and unwilling to learn and unable to 374 00:20:52,640 --> 00:20:56,840 S1: absorb any amount of effort that you give versus some 375 00:20:56,840 --> 00:21:01,360 S1: random person who doesn't think much of themselves and are 376 00:21:01,359 --> 00:21:07,810 S1: unassuming and quiet and they just absolute sponge. Absolute murder. 377 00:21:07,810 --> 00:21:12,250 S1: Like the the craziest, best IT person you've ever found. 378 00:21:12,250 --> 00:21:14,800 S1: And suddenly they just can't shut up about everything. I 379 00:21:14,800 --> 00:21:18,100 S1: was like, oh, and I did this and I read everything, um, 380 00:21:18,100 --> 00:21:20,800 S1: all those books that you recommended. And then I went 381 00:21:20,800 --> 00:21:23,310 S1: and downloaded this and I oh, but I changed it. 382 00:21:23,310 --> 00:21:25,320 S1: And I want to show you this thing, and you're 383 00:21:25,320 --> 00:21:28,500 S1: just like, Holy crap. They were sitting right next to 384 00:21:28,500 --> 00:21:30,810 S1: the other person who I gave the same exact training. 385 00:21:30,810 --> 00:21:35,250 S1: This is a lesson, incredibly powerful lesson. And to bring 386 00:21:35,250 --> 00:21:38,400 S1: it back to this article, I believe the same thing 387 00:21:38,400 --> 00:21:41,399 S1: is true with UBI. The same thing is true with 388 00:21:41,400 --> 00:21:45,450 S1: social programs. The same thing is true with all these 389 00:21:45,450 --> 00:21:49,459 S1: different things. So the whole trick and we're going to 390 00:21:49,460 --> 00:21:52,070 S1: get a little broad here. The whole trick is to 391 00:21:52,070 --> 00:21:56,270 S1: make sure that no one's at a disadvantage, okay. Because trauma, 392 00:21:56,270 --> 00:22:02,359 S1: because they're hungry, because they can't afford coverage. Right. Because 393 00:22:02,359 --> 00:22:06,470 S1: there are stars all throughout this entire population, this these 394 00:22:06,470 --> 00:22:09,950 S1: 8 billion people that we have, there are stars who 395 00:22:09,980 --> 00:22:12,250 S1: you will never know if they're a star, because they 396 00:22:12,250 --> 00:22:15,640 S1: can't make it to your class because they're working seven 397 00:22:15,640 --> 00:22:19,840 S1: jobs and they got an ant and a dog and 398 00:22:19,840 --> 00:22:23,530 S1: a and a parent and a girlfriend or a boyfriend 399 00:22:23,530 --> 00:22:27,520 S1: who is sick. And so they're over here juggling and 400 00:22:27,520 --> 00:22:31,540 S1: they're another Einstein, they're another von Neumann. And you will. 401 00:22:31,540 --> 00:22:31,780 S2: Never. 402 00:22:31,780 --> 00:22:35,260 S1: Know because they got screwed. They got screwed by luck. 403 00:22:35,260 --> 00:22:38,230 S1: So the goal and now we're going really broad. The 404 00:22:38,230 --> 00:22:41,020 S1: goal is to build a society in which nobody is 405 00:22:41,020 --> 00:22:43,900 S1: screwed in that way. And when they do get messed 406 00:22:43,900 --> 00:22:47,050 S1: over in that way, we help them. We help them 407 00:22:47,050 --> 00:22:50,890 S1: get back into this, remove the obstacles so that everyone 408 00:22:50,890 --> 00:22:53,619 S1: has the opportunity to be one of these stars to 409 00:22:53,619 --> 00:22:57,669 S1: stand out. But we should not think that everyone who 410 00:22:57,670 --> 00:23:02,840 S1: gets exposed to the opportunities is necessarily going to be 411 00:23:02,840 --> 00:23:06,230 S1: one of those stand out people. So if we see variation, 412 00:23:06,230 --> 00:23:08,510 S1: which is probably going to look a lot like a 413 00:23:08,510 --> 00:23:11,090 S1: bell curve, right, you're going to have most people in 414 00:23:11,090 --> 00:23:14,390 S1: the middle. Um, when you see that variation, you should 415 00:23:14,390 --> 00:23:17,390 S1: be like, yep, that's exactly what we expect to see. 416 00:23:17,390 --> 00:23:19,700 S1: Most people in the middle, some people who don't want 417 00:23:19,700 --> 00:23:22,550 S1: to do anything despite any training, and some people who 418 00:23:22,550 --> 00:23:26,050 S1: could just hear the first word and write a book. Right? 419 00:23:26,050 --> 00:23:30,580 S1: That's just expected. Now, I would say that there's some 420 00:23:30,580 --> 00:23:33,010 S1: nuance here in the sense that, like, okay, if we 421 00:23:33,010 --> 00:23:37,810 S1: can identify that grit and self-discipline and I don't know, 422 00:23:37,810 --> 00:23:41,020 S1: something that we could try to put into culture, something 423 00:23:41,020 --> 00:23:44,169 S1: we could try to put into education systems, something that 424 00:23:44,170 --> 00:23:46,840 S1: we could try to put into training, something we could 425 00:23:46,840 --> 00:23:50,740 S1: try to put into parental education if we could find 426 00:23:50,740 --> 00:23:53,619 S1: those tokens and just like, make sure they're part of 427 00:23:53,619 --> 00:23:58,270 S1: the training and that helps lift everyone or some number 428 00:23:58,270 --> 00:24:01,240 S1: of people, that'll be fantastic. But it has to start 429 00:24:01,240 --> 00:24:05,440 S1: with realizing there's going to be a distribution and that's 430 00:24:05,440 --> 00:24:09,609 S1: going to be based on meritocracy, which is good. However, 431 00:24:09,640 --> 00:24:14,110 S1: the whole purpose of society in liberal society or liberal 432 00:24:14,260 --> 00:24:18,640 S1: liberal approach to building a society is making sure that 433 00:24:18,640 --> 00:24:25,090 S1: everyone can enter into the meritocracy funnel without disadvantage. That's 434 00:24:25,090 --> 00:24:27,460 S1: that's the goal. That's the trick. And I'm going to 435 00:24:27,460 --> 00:24:31,870 S1: actually try to instantiate this using substrate, by the way. 436 00:24:31,869 --> 00:24:34,449 S1: All right. That was a oh goodness. That was a 437 00:24:34,450 --> 00:24:38,340 S1: massive diversion. Uh, Southwest is ditching its open seating policy 438 00:24:38,340 --> 00:24:43,080 S1: after 50 years to boost profits and meet customer preferences. 80% 439 00:24:43,080 --> 00:24:46,590 S1: of their flyers preferred assigned seats, 80% is a lot. 440 00:24:46,619 --> 00:24:52,020 S1: The Senate's version of 2025, NDAA, does not include the 441 00:24:52,020 --> 00:24:55,680 S1: countering CCP Drones Act. Happy and sad about this I 442 00:24:55,680 --> 00:25:00,359 S1: love DJI drones, DJI drones, but I feel like we 443 00:25:00,359 --> 00:25:04,650 S1: need to go without them because they're so good that 444 00:25:04,920 --> 00:25:09,300 S1: there aren't many alternatives. And, uh, Chinese companies have the 445 00:25:09,300 --> 00:25:12,000 S1: ability to be just taken over by the CCP at 446 00:25:12,000 --> 00:25:15,780 S1: any given moment if it's advantageous to them. So I 447 00:25:15,780 --> 00:25:19,560 S1: don't like that. Same reason I don't like TikTok. As 448 00:25:19,560 --> 00:25:24,280 S1: you might expect from an ex-military security person, nearly 40% 449 00:25:24,280 --> 00:25:28,389 S1: of Americans are stressed out about making ends meet 40%, 450 00:25:28,390 --> 00:25:32,140 S1: up from 28% in 2021. And this is a very 451 00:25:32,140 --> 00:25:36,040 S1: similar number to during the Great Recession, which I believe 452 00:25:36,040 --> 00:25:41,530 S1: they're talking about 2028, I'm sorry, 2008 US economic growth 453 00:25:41,530 --> 00:25:47,770 S1: hits 2.8% weight loss. Drugs like Ozempic, mounjaro and Wegovy 454 00:25:47,770 --> 00:25:51,030 S1: are causing people to spend less on groceries. I heard 455 00:25:51,030 --> 00:25:55,020 S1: it's also making planes lighter and use less fuel. So 456 00:25:55,020 --> 00:25:59,310 S1: Wegovy is a climate change product. They should try to 457 00:25:59,310 --> 00:26:04,590 S1: get some funding for that. Subsidies or something? A new antibiotic. 458 00:26:04,920 --> 00:26:10,440 S1: Oh my goodness. A new antibiotic from the University of 459 00:26:10,440 --> 00:26:15,149 S1: Illinois-chicago Chicago disrupts two different cellular targets, making it 100 460 00:26:15,150 --> 00:26:19,609 S1: million times harder for bacteria to evolve resistance. One dose 461 00:26:19,609 --> 00:26:23,180 S1: of a new nasal spray treatment clears toxic tau proteins 462 00:26:23,180 --> 00:26:26,570 S1: from brain cells. And one thing to remember about these 463 00:26:26,570 --> 00:26:29,540 S1: I'm going to keep sharing these studies like I am. 464 00:26:29,690 --> 00:26:31,520 S1: That's the thing I'm going to do. I'm going to 465 00:26:31,520 --> 00:26:36,310 S1: have more and more scrutiny because part of the pipeline's 466 00:26:36,400 --> 00:26:38,859 S1: workflow and substrate and all of that, I'm going to 467 00:26:38,859 --> 00:26:42,100 S1: have these things being tested and going through pipelines. It's 468 00:26:42,100 --> 00:26:45,820 S1: going to be awesome. But you still have to realize 469 00:26:45,820 --> 00:26:48,490 S1: the bigger the finding, the more you should wait for 470 00:26:48,490 --> 00:26:51,910 S1: supporting studies. And in my mind, the stuff is not 471 00:26:51,910 --> 00:26:56,260 S1: actually truly real until the drug is available to normal people, 472 00:26:56,260 --> 00:26:59,260 S1: which means it's already been rigorously tested. And of course 473 00:26:59,260 --> 00:27:02,460 S1: it will be constantly tested afterwards as well in in 474 00:27:02,460 --> 00:27:05,850 S1: the larger population. So that to me is like the 475 00:27:05,850 --> 00:27:09,119 S1: real stuff. But I still like watching this. I always 476 00:27:09,119 --> 00:27:14,940 S1: thought that this tau protein stuff was ripe for some 477 00:27:14,940 --> 00:27:19,740 S1: sort of chemical solution, some sort of, you know, uh, 478 00:27:19,740 --> 00:27:24,090 S1: medicinal solution. Uh, of course, that was just intuition. I 479 00:27:24,090 --> 00:27:27,260 S1: have no expertise there, but it just seems like something 480 00:27:27,260 --> 00:27:30,320 S1: that people can grab onto once they realize the mechanism. 481 00:27:30,320 --> 00:27:33,890 S1: Liberals and conservatives are both prone to conspiracy theories. They 482 00:27:33,890 --> 00:27:37,520 S1: just prefer different ones. Hendrik Carlson talks about how generating 483 00:27:37,520 --> 00:27:40,310 S1: interesting ideas is like building a muscle. He says the 484 00:27:40,310 --> 00:27:42,740 S1: more you write and think deeply, the better you get 485 00:27:42,740 --> 00:27:47,810 S1: at coming up with new and meaningful thoughts. 100% agree ideas. 486 00:27:47,810 --> 00:27:51,040 S1: Zuckerberg is arguing that China is going to steal weights anyway, 487 00:27:51,040 --> 00:27:53,020 S1: so there's no way to stop that. So you might 488 00:27:53,020 --> 00:27:57,400 S1: as well develop advanced AI as open source. Okay, not 489 00:27:57,400 --> 00:27:59,500 S1: really okay. But I think I wrote about that somewhere, 490 00:27:59,500 --> 00:28:02,500 S1: so I'm not going to go into that. Search GPT 491 00:28:02,500 --> 00:28:04,960 S1: oh that's where I put it yeah. Search GPT in 492 00:28:04,960 --> 00:28:09,760 S1: discovery here I use obsidian Jason Pepper Hepler talks about 493 00:28:09,760 --> 00:28:13,310 S1: how he uses obsidian for notes. In the beginning was 494 00:28:13,310 --> 00:28:17,540 S1: The Command Line, Neal Stephenson's classic essay on operating systems, 495 00:28:17,540 --> 00:28:21,200 S1: my obsidian note taking workflow. It's on my mind. So 496 00:28:21,200 --> 00:28:23,450 S1: that's why I got multiple things in here on it 497 00:28:23,450 --> 00:28:28,310 S1: bash one liners, data chain. Unstructured data management for AI projects. 498 00:28:28,310 --> 00:28:32,330 S1: Llama agent stack. This one's cool. I JSON has a cool, um, 499 00:28:32,330 --> 00:28:35,209 S1: video on this. You should check out on YouTube. Open 500 00:28:35,210 --> 00:28:41,260 S1: world exploration in Minecraft clod engineer Lakhera low latency AI 501 00:28:41,260 --> 00:28:46,120 S1: application firewall GPT four Captcha Bypass Flow Analyzer a tool 502 00:28:46,120 --> 00:28:51,100 S1: for understanding OAuth two grant flows with support for Oidc 503 00:28:51,100 --> 00:28:54,580 S1: and Jwts Jots. I don't know how you're supposed to 504 00:28:54,580 --> 00:28:58,990 S1: pronounce that JWT s, I bet you about 5050 people 505 00:28:58,990 --> 00:29:03,480 S1: do that jot versus JWT bash, simple curses, simple bash 506 00:29:03,480 --> 00:29:07,680 S1: library to create terminal interfaces and the recommendation of the week. 507 00:29:07,680 --> 00:29:10,410 S1: I'm going to try to do something for the next 508 00:29:10,410 --> 00:29:13,410 S1: several months. Kind of do this already, but I'm going 509 00:29:13,410 --> 00:29:17,310 S1: to try to do it even more going into this election. 510 00:29:17,310 --> 00:29:20,760 S1: When someone labels me as super liberal, I'm going to 511 00:29:20,760 --> 00:29:25,340 S1: say something nice about their conservative views. I'm going to 512 00:29:25,340 --> 00:29:30,080 S1: humanize them. When somebody labels me as super conservative, I'm 513 00:29:30,080 --> 00:29:32,959 S1: going to say something nice about their liberal views. I'm 514 00:29:32,960 --> 00:29:35,930 S1: going to humanize them. Try this and see if it 515 00:29:35,930 --> 00:29:38,210 S1: opens up the conversation at all. I learned how to 516 00:29:38,210 --> 00:29:41,750 S1: do this from Jonathan Hite's book, The Righteous Mind, by 517 00:29:41,750 --> 00:29:44,540 S1: the way, and this was probably 6 or 7 years 518 00:29:44,540 --> 00:29:50,410 S1: ago now. This book brought me to the center politically. 519 00:29:50,410 --> 00:29:53,470 S1: It brought me to the center, specifically what it did 520 00:29:53,500 --> 00:29:59,290 S1: because I was already pretty far left. I've always been left, um, 521 00:29:59,290 --> 00:30:04,090 S1: always voted Democrat. I've only been a Democrat, I guess, uh, 522 00:30:04,090 --> 00:30:07,630 S1: technically and legally my whole life. Uh, although I wouldn't 523 00:30:07,630 --> 00:30:10,150 S1: say I'm a Democrat, I would say I've always been 524 00:30:10,150 --> 00:30:13,770 S1: liberal my whole life. Uh, but technically, yes. Democrat. Um. 525 00:30:13,770 --> 00:30:17,130 S1: This book. Well, let me just continue on that. Born 526 00:30:17,130 --> 00:30:19,710 S1: and raised in the San Francisco Bay area, which grew 527 00:30:19,710 --> 00:30:24,270 S1: up with super liberal hippie parents, and I, as a result, 528 00:30:24,270 --> 00:30:28,530 S1: have always seen conservatives. Or I was trained to see 529 00:30:28,530 --> 00:30:34,050 S1: conservatives as, uh, holding progress back in, in a negative way. 530 00:30:34,050 --> 00:30:37,080 S1: So not just conservative, but conservative in a bad way. 531 00:30:37,310 --> 00:30:41,210 S1: In other words, conservative was bad and basically just viewed 532 00:30:41,210 --> 00:30:45,320 S1: them poorly. What this book did was show me this 533 00:30:45,320 --> 00:30:47,180 S1: is the I think the main premise from the book, 534 00:30:47,180 --> 00:30:50,270 S1: which I'm I'm happy for him that I remember this 535 00:30:50,270 --> 00:30:54,020 S1: so clearly. I think the main premise is that both 536 00:30:54,020 --> 00:30:59,450 S1: sides value things very closely, very dearly, um, in a 537 00:30:59,450 --> 00:31:02,830 S1: sacred way. But they are different things. So it's not 538 00:31:02,830 --> 00:31:05,350 S1: that one is going for good things and one is 539 00:31:05,350 --> 00:31:07,450 S1: going for bad things. It's that both are going for 540 00:31:07,450 --> 00:31:10,330 S1: good things, but they're different good things. So the whole 541 00:31:10,330 --> 00:31:12,430 S1: trick to being in the centre and the whole trick 542 00:31:12,430 --> 00:31:18,430 S1: to moving progress forward is realizing that people are just different. Okay? 543 00:31:18,430 --> 00:31:21,160 S1: And there there are extremes to this that make it 544 00:31:21,160 --> 00:31:24,700 S1: not true. But in general, you should look for this 545 00:31:24,700 --> 00:31:28,770 S1: common ground and realize that there being a good person, um, 546 00:31:28,980 --> 00:31:31,920 S1: in their way, and you're being a good person and 547 00:31:31,920 --> 00:31:34,620 S1: your way, and you're going to try to find, uh, 548 00:31:34,620 --> 00:31:38,700 S1: Harmony there. Now, in the case of extreme sides of 549 00:31:38,700 --> 00:31:41,760 S1: left and right, you can argue that, yeah, their good 550 00:31:41,760 --> 00:31:44,160 S1: is actually bad for the whole world. So, like, you 551 00:31:44,160 --> 00:31:48,090 S1: don't want to support that. So understand there are situations 552 00:31:48,090 --> 00:31:51,340 S1: like that. But in general, we would all be better 553 00:31:51,340 --> 00:31:55,510 S1: off by reading this book and realizing this, this nuance, 554 00:31:55,510 --> 00:31:59,200 S1: which might help bring you towards the center and produce 555 00:31:59,200 --> 00:32:02,530 S1: better conversation. And the aphorism of the week, the highest 556 00:32:02,530 --> 00:32:06,670 S1: form of knowledge is empathy. The highest form of knowledge 557 00:32:06,670 --> 00:32:12,010 S1: is empathy. Bill Bullard. Unsupervised learning is produced and edited 558 00:32:12,010 --> 00:32:16,440 S1: by Daniel Meisler on a Neumann U87 AI microphone using Hindenburg. 559 00:32:16,920 --> 00:32:19,740 S1: Intro and outro music is by Zomby with the Y, 560 00:32:20,460 --> 00:32:22,710 S1: and to get the text and links from this episode, 561 00:32:22,710 --> 00:32:24,990 S1: sign up for the newsletter version of the show at 562 00:32:24,990 --> 00:32:29,700 S1: Daniel meisler.com/newsletter. We'll see you next time.