1
00:00:20,252 --> 00:00:26,452
S1: All right. Welcome to episode 496. All right, so my

2
00:00:26,452 --> 00:00:30,852
S1: video on building personal AI system using cloud code is out.

3
00:00:31,572 --> 00:00:34,332
S1: This thing has been a blocker for me in like

4
00:00:34,372 --> 00:00:38,092
S1: life and work and just it's taken so long to

5
00:00:38,092 --> 00:00:41,252
S1: get this thing out. Recording it took like 5 or

6
00:00:41,252 --> 00:00:45,732
S1: 7 takes or something. The blog post, the editing, everything

7
00:00:45,732 --> 00:00:48,292
S1: about it has been difficult. I am so glad it

8
00:00:48,292 --> 00:00:53,211
S1: is out and recommend you go check it out. Just

9
00:00:53,212 --> 00:00:59,372
S1: upgraded to a Keychron Q11 keyboard, it is a canted

10
00:01:00,532 --> 00:01:06,972
S1: and tilted. I can't remember like which words associate with

11
00:01:06,972 --> 00:01:10,772
S1: which angle, but it's basically tilted inwards because that's the

12
00:01:10,772 --> 00:01:15,652
S1: way your hands are when you type. And then it's

13
00:01:15,652 --> 00:01:20,331
S1: also tilted upwards on the inside. So it's like an

14
00:01:20,332 --> 00:01:24,412
S1: upside down V. Kind of not as extreme though. So

15
00:01:24,411 --> 00:01:26,851
S1: if you imagine you just put your hands, your elbows

16
00:01:26,852 --> 00:01:29,051
S1: by your side and you put your hands out, they're

17
00:01:29,051 --> 00:01:32,091
S1: going to be like angled at like a, an upside

18
00:01:32,092 --> 00:01:36,612
S1: down V shape, you know, slightly like a wide open

19
00:01:36,612 --> 00:01:41,371
S1: upside down V. And they're also going to be moving

20
00:01:42,172 --> 00:01:45,612
S1: well if you stick them straight out they will be

21
00:01:45,612 --> 00:01:49,651
S1: straight out. But if oftentimes you'll want to slightly bent

22
00:01:49,652 --> 00:01:54,732
S1: them inwards or, you know, uh tilt them inwards or

23
00:01:54,732 --> 00:01:59,092
S1: not tilt but um, angle them inwards. And if you

24
00:01:59,092 --> 00:02:05,132
S1: do that then of course you can um, you could

25
00:02:05,132 --> 00:02:07,612
S1: reach the keys on a regular keyboard that's lined up

26
00:02:07,612 --> 00:02:11,252
S1: right now on a regular keyboard that's lined up just

27
00:02:11,252 --> 00:02:14,412
S1: the way a traditional keyboard is. You actually have to

28
00:02:14,452 --> 00:02:19,542
S1: tilt your your wrist wrists inward, and it's just not

29
00:02:19,582 --> 00:02:23,302
S1: it's not good for you over time. I don't really

30
00:02:23,302 --> 00:02:26,182
S1: have any pain that I'm dealing with and trying to

31
00:02:26,181 --> 00:02:28,262
S1: address here. I just want it to be more natural.

32
00:02:28,262 --> 00:02:30,542
S1: I want to be able to type faster and I

33
00:02:30,542 --> 00:02:32,582
S1: don't know. I also think it looks cool. It feels

34
00:02:32,582 --> 00:02:37,462
S1: cool as well. So I like that. And then I'm

35
00:02:37,462 --> 00:02:42,262
S1: experimenting with, um, the keyboard because the keyboard, basically it

36
00:02:42,262 --> 00:02:47,942
S1: has um, it's two separate pieces. Okay. So the left

37
00:02:47,942 --> 00:02:52,822
S1: is separated from the right. That's like the most important feature. Um,

38
00:02:52,862 --> 00:02:55,862
S1: and that allows you to do this, this canting and

39
00:02:55,862 --> 00:03:00,542
S1: everything completely separate for left and right. Um, obviously you

40
00:03:00,542 --> 00:03:03,341
S1: want them to match. You want it to be symmetrical, basically.

41
00:03:03,341 --> 00:03:06,862
S1: But the idea is you can have them all the

42
00:03:06,862 --> 00:03:10,621
S1: way across, like the distance for from your elbows, uh,

43
00:03:10,621 --> 00:03:16,022
S1: one elbow to the next, which is what, you know, One,

44
00:03:16,542 --> 00:03:21,102
S1: you know, two feet, maybe something like two feet apart.

45
00:03:21,382 --> 00:03:23,502
S1: You could do that and then your hands would be

46
00:03:23,502 --> 00:03:26,181
S1: just straight out. And a lot of people who do

47
00:03:26,222 --> 00:03:30,262
S1: like the custom keyboards, they actually do that. I have

48
00:03:30,262 --> 00:03:33,901
S1: mine probably at the bottom of the keyboard. It's probably

49
00:03:33,901 --> 00:03:41,702
S1: separated by, I would say, um. Actually, I have a

50
00:03:41,702 --> 00:03:56,022
S1: tape measure. Like five six inches is how far the

51
00:03:56,022 --> 00:03:58,222
S1: bottom is and the top is only a couple of

52
00:03:58,222 --> 00:04:02,662
S1: inches separated because again, I have it, you know, canted

53
00:04:02,662 --> 00:04:06,942
S1: downwards in that way. And it's pretty cool. So the

54
00:04:06,942 --> 00:04:09,702
S1: inside of the keyboard I have elevated. So it's in

55
00:04:09,742 --> 00:04:13,422
S1: that upside down V shape. And yeah, it just feels

56
00:04:13,422 --> 00:04:18,621
S1: really natural. Feels awesome. I've tried some other more customizable

57
00:04:18,662 --> 00:04:23,382
S1: keyboards that are just like 60. They're called 40% keyboards

58
00:04:24,182 --> 00:04:26,782
S1: and those you have to use layers to be able

59
00:04:26,782 --> 00:04:29,582
S1: to get to a lot of the keys. Um, I

60
00:04:29,582 --> 00:04:34,782
S1: didn't find that very fun. Um, the muscle memory there

61
00:04:34,981 --> 00:04:38,782
S1: was not easy to grow, and I feel like there's

62
00:04:38,782 --> 00:04:41,222
S1: not a reason to get rid of that many keys

63
00:04:41,541 --> 00:04:45,382
S1: and to force you to have to do layers. I

64
00:04:45,382 --> 00:04:48,822
S1: think this is a better approach. It's a more traditional keyboard.

65
00:04:50,502 --> 00:04:54,182
S1: So I have numbers above my fingers. I still have

66
00:04:54,182 --> 00:04:59,822
S1: my spacebar on both sides. I still have number keys

67
00:04:59,822 --> 00:05:03,142
S1: in the bottom right of my right hand. So it's

68
00:05:03,142 --> 00:05:07,181
S1: like it's more classic, more traditional, but at the same

69
00:05:07,182 --> 00:05:12,022
S1: time just oriented in a more ergonomic way. So again,

70
00:05:12,062 --> 00:05:17,512
S1: it's a Keychron Q11 and I do watch too many

71
00:05:17,512 --> 00:05:22,111
S1: keyboard videos on YouTube, so this might not be my

72
00:05:22,112 --> 00:05:29,111
S1: last keyboard. I have way too many keyboards, unfortunately. Okay, um,

73
00:05:29,791 --> 00:05:33,072
S1: found a major problem with doing member essays here in

74
00:05:33,072 --> 00:05:35,952
S1: the newsletter, so I'm not actually going to do them anymore.

75
00:05:35,952 --> 00:05:39,552
S1: And the problem is very simple. It's like I'm what

76
00:05:39,552 --> 00:05:42,432
S1: I'm trying to do is orient my work and my

77
00:05:42,432 --> 00:05:46,832
S1: life around the central theme of ideas. Right? I've been

78
00:05:46,832 --> 00:05:51,032
S1: trying to do this for years now, and the central

79
00:05:51,032 --> 00:05:54,751
S1: theme of ideas is you try to have ideas first

80
00:05:54,752 --> 00:05:57,112
S1: of all, and then you try to get them out

81
00:05:57,112 --> 00:06:00,832
S1: into the world. And what I would do is I

82
00:06:00,872 --> 00:06:02,712
S1: would be writing a newsletter and I would come up

83
00:06:02,712 --> 00:06:06,551
S1: with a cool idea, and I would do a member

84
00:06:06,552 --> 00:06:09,032
S1: essay on it, and I was going to do like

85
00:06:09,032 --> 00:06:10,912
S1: one a month, but I ended up doing it like

86
00:06:10,952 --> 00:06:14,752
S1: almost every month. But the problem is, they were so

87
00:06:15,272 --> 00:06:18,672
S1: decent I thought they were halfway good a lot of times.

88
00:06:19,752 --> 00:06:23,392
S1: At least several of them were halfway decent. And I'm like, well,

89
00:06:23,392 --> 00:06:25,912
S1: that just needs to be a regular blog post. Like,

90
00:06:25,912 --> 00:06:30,432
S1: I can't, you know, just it doesn't make any sense

91
00:06:30,432 --> 00:06:33,872
S1: to put the effort into it. Have people actually enjoy it,

92
00:06:33,872 --> 00:06:37,072
S1: but it only goes out to like 1500 people or

93
00:06:37,072 --> 00:06:41,632
S1: whatever the current member count is, right? So it's like

94
00:06:43,072 --> 00:06:47,312
S1: it just it was not in harmony with me trying

95
00:06:47,312 --> 00:06:51,152
S1: to get ideas out into the world as like a

96
00:06:51,192 --> 00:06:55,952
S1: primary mission. So. So I'm not going to do that anymore. Um,

97
00:06:56,072 --> 00:06:58,551
S1: basically I'm just going to turn those into blogs, right?

98
00:06:58,592 --> 00:07:00,152
S1: So you're still going to get them. They're still going

99
00:07:00,152 --> 00:07:02,752
S1: to be in the newsletter. It's just you're not going

100
00:07:02,791 --> 00:07:05,392
S1: to have to pay for them. And instead I'm going

101
00:07:05,472 --> 00:07:11,312
S1: to basically raise the member perk that has been most

102
00:07:11,352 --> 00:07:15,032
S1: asked for and just make that the primary perk. And

103
00:07:15,032 --> 00:07:20,952
S1: basically a clearly stated explicit thing of 25 to 50%

104
00:07:20,952 --> 00:07:25,992
S1: off all UL paid content. So when human 3.0 launches,

105
00:07:25,992 --> 00:07:31,432
S1: when augmented online, the online version of augmented launches, which

106
00:07:31,432 --> 00:07:37,072
S1: will be sometime this year, sometime soon, like you're just

107
00:07:37,072 --> 00:07:40,192
S1: going to get a massive discount like hundreds of dollars

108
00:07:40,192 --> 00:07:44,112
S1: or in some cases for courses or whatever. Or, you know,

109
00:07:44,152 --> 00:07:47,872
S1: conceivably if I charge enough for a thing, it'll be

110
00:07:47,912 --> 00:07:51,632
S1: thousands of dollars off for members, which is kind of

111
00:07:51,632 --> 00:07:56,552
S1: ridiculous given that the membership is only like $100. So

112
00:07:57,232 --> 00:08:00,272
S1: I don't know. I'll figure that out later. Uh, content

113
00:08:00,272 --> 00:08:05,552
S1: is more important in the meantime. So yeah, you can

114
00:08:05,552 --> 00:08:10,482
S1: subscribe at Daniel or subscribe. My friend Monica is running

115
00:08:10,482 --> 00:08:14,242
S1: her Resilient Career Accelerator course again soon. I'd recommend you

116
00:08:14,242 --> 00:08:16,482
S1: check it out. It's a week long course. How to

117
00:08:16,522 --> 00:08:20,322
S1: build a career in security leadership. Run a security program

118
00:08:20,322 --> 00:08:24,881
S1: and generally make yourself more resilient career wise in this

119
00:08:24,922 --> 00:08:27,762
S1: AI world. And she talks about that a lot. She's

120
00:08:27,762 --> 00:08:31,602
S1: very bright, very passionate about these topics. And I'm also

121
00:08:31,602 --> 00:08:34,442
S1: going to be a guest speaker at the course that

122
00:08:34,442 --> 00:08:40,881
S1: is in November. Uh, very much in line with my

123
00:08:40,882 --> 00:08:44,242
S1: recent Westerberg shares. You probably seen some of those videos

124
00:08:44,242 --> 00:08:48,122
S1: from her. I'm loving this guy. His name is Mainstone,

125
00:08:48,122 --> 00:08:50,842
S1: or at least that's his brand or whatever. And he's

126
00:08:50,842 --> 00:08:56,842
S1: just like this. Extremely honest, like British guy. And, uh, yeah,

127
00:08:56,842 --> 00:09:00,122
S1: he wears sunglasses and talks at the camera sometimes, but

128
00:09:00,122 --> 00:09:07,602
S1: he also is a videographer, photographer, videographer. Anyway, he takes

129
00:09:07,602 --> 00:09:12,122
S1: videos of like, terrain. And so his whole theme is

130
00:09:12,162 --> 00:09:14,242
S1: like going back to basics. It's like, what are we

131
00:09:14,242 --> 00:09:17,362
S1: actually doing here? It's a very existential, like, if I

132
00:09:17,402 --> 00:09:22,962
S1: had to categorize this YouTube channel, it's an existential YouTube channel.

133
00:09:23,362 --> 00:09:28,362
S1: And he's just he's smart. He his takes on AI,

134
00:09:28,402 --> 00:09:31,202
S1: I think are pretty clean. Like some of the cleanest

135
00:09:31,202 --> 00:09:34,642
S1: that I've seen, which I find to be hilarious. Like

136
00:09:34,642 --> 00:09:37,802
S1: this guy who is I don't know, I'm not even

137
00:09:37,802 --> 00:09:45,242
S1: sure how technical he is. Um, computer wise, um, video wise,

138
00:09:45,242 --> 00:09:49,761
S1: he's like, obviously extremely technical. The guy is. Yeah, he's

139
00:09:50,162 --> 00:09:53,362
S1: he could probably be a professional, professional video guy if

140
00:09:53,362 --> 00:09:56,442
S1: he isn't already, but I'm not sure if he's like

141
00:09:56,442 --> 00:10:00,482
S1: a computer guy. I don't really know that. Um, he

142
00:10:00,482 --> 00:10:04,122
S1: mentions being broke all the time and having like, $1,500

143
00:10:04,122 --> 00:10:06,881
S1: to his name and like, trying to find work. So

144
00:10:06,881 --> 00:10:11,122
S1: I'm guessing he doesn't have or hasn't had like a

145
00:10:11,122 --> 00:10:14,202
S1: tech career. That's. I'm guessing he doesn't have that to

146
00:10:14,242 --> 00:10:18,161
S1: fall back on if he's talking about that. But in

147
00:10:18,442 --> 00:10:21,482
S1: the economy right now, who knows if that's actually the case?

148
00:10:21,562 --> 00:10:24,082
S1: Could be that he just can't find a job there.

149
00:10:25,202 --> 00:10:28,722
S1: But anyway he goes his one of his favorite things

150
00:10:28,722 --> 00:10:30,882
S1: to do is just like to disappear and just like

151
00:10:30,922 --> 00:10:34,602
S1: go far, far away. He's also really good at Lan Nev,

152
00:10:34,642 --> 00:10:37,921
S1: by the way. Um, because I'm not that good at

153
00:10:37,962 --> 00:10:41,842
S1: Lan Nev. Or at least I'd have to train up. Um,

154
00:10:41,842 --> 00:10:45,682
S1: I think he does carry, like, a satellite device and, like,

155
00:10:45,682 --> 00:10:48,242
S1: batteries and stuff. So he could, like, actually get signal

156
00:10:48,242 --> 00:10:51,761
S1: if he needed to. I think he gets, like, weather reports,

157
00:10:51,761 --> 00:10:55,322
S1: but he's going places like very few people have been.

158
00:10:56,802 --> 00:11:01,002
S1: And he, like, disappears, like in, like, random parts of

159
00:11:01,002 --> 00:11:03,881
S1: Egypt or something, or like some random mountains in, like

160
00:11:03,881 --> 00:11:07,322
S1: Alaska or whatever. Just like disappears for like seven days.

161
00:11:07,722 --> 00:11:10,722
S1: And he's doing video the whole time. He's talking the

162
00:11:10,722 --> 00:11:14,802
S1: whole time. And it's really cool to see him actually

163
00:11:14,802 --> 00:11:18,722
S1: talk to the camera when he's feeling these extreme, like

164
00:11:18,761 --> 00:11:22,602
S1: emotions of solitude and like the wisdom he's getting from

165
00:11:22,602 --> 00:11:28,322
S1: it and everything. And his voiceovers, his narrations are great. Um,

166
00:11:28,362 --> 00:11:31,802
S1: his honesty. Like, he's just like, look, I just can't

167
00:11:31,802 --> 00:11:34,882
S1: figure this out. I don't know what I'm going to do. Like,

168
00:11:34,881 --> 00:11:39,562
S1: he's just very plain and open. It's extremely refreshing, very authentic.

169
00:11:39,802 --> 00:11:44,242
S1: Highly recommend it. Especially if you're kind of thinking, you know,

170
00:11:44,322 --> 00:11:46,322
S1: what is all this tech for? I want to go

171
00:11:46,322 --> 00:11:50,602
S1: back to basics, you know, analog. It's got that sort

172
00:11:50,602 --> 00:11:53,362
S1: of vibe to it. It's got the existential vibe to it,

173
00:11:54,082 --> 00:11:57,042
S1: gorgeous scenery as well. Like some of these shots he's taking.

174
00:11:57,042 --> 00:12:05,852
S1: Just absolutely amazing stuff. So anyway, highly recommend that. We

175
00:12:05,852 --> 00:12:13,171
S1: read Epictetus, the Enchiridion for book club. And it was wonderful.

176
00:12:13,412 --> 00:12:18,772
S1: Absolutely wonderful. Um, also reminding me so the Westerburg thing.

177
00:12:19,172 --> 00:12:23,572
S1: This guy mainstone, I don't know, all my stoic studies.

178
00:12:23,612 --> 00:12:26,812
S1: A million different things have, like been bringing me and

179
00:12:26,812 --> 00:12:29,572
S1: I don't know the attribution amounts, so I can't really

180
00:12:29,572 --> 00:12:34,412
S1: properly give credit. But I've also been reading a bunch of, like,

181
00:12:34,812 --> 00:12:38,531
S1: you know, dead tree books, uh, with no tech around me.

182
00:12:39,131 --> 00:12:41,692
S1: And I feel like whenever I get an insight from

183
00:12:41,732 --> 00:12:46,292
S1: a real book, an old book with text on a page,

184
00:12:46,492 --> 00:12:49,372
S1: I feel like the inside just hits me harder than,

185
00:12:51,252 --> 00:12:54,932
S1: you know, all the cool shit that I'm getting from YouTube, right?

186
00:12:54,972 --> 00:12:58,932
S1: And I'm getting great distilled, highly produced, great stuff from YouTube.

187
00:12:59,292 --> 00:13:02,372
S1: And somehow the book stuff just hits me harder. And

188
00:13:02,372 --> 00:13:07,492
S1: I'm trying to disentangle Like, is it because it's better content?

189
00:13:07,492 --> 00:13:10,572
S1: Is it become because it's coming from a different angle

190
00:13:10,572 --> 00:13:13,292
S1: where I'm not ready for it and I'm like surprised

191
00:13:13,292 --> 00:13:18,052
S1: by it. But one of my possible frames for this

192
00:13:18,052 --> 00:13:26,572
S1: is that it's simply like. One of the models I

193
00:13:26,572 --> 00:13:30,612
S1: was thinking of is like, I heard about on Reddit somewhere,

194
00:13:30,612 --> 00:13:35,292
S1: that some people use tea bags like 12 times and

195
00:13:35,292 --> 00:13:37,652
S1: like they keep track with like a little counter of

196
00:13:37,692 --> 00:13:40,692
S1: like how many times it's like, oh, 11th time. Well,

197
00:13:40,692 --> 00:13:43,092
S1: we haven't got enough out of it. So they keep

198
00:13:43,092 --> 00:13:45,972
S1: using it. And I'm like, I wonder if the content

199
00:13:45,972 --> 00:13:49,252
S1: that I'm getting from YouTube and from like, all these,

200
00:13:49,292 --> 00:13:53,612
S1: you know, modern sources or whatever, it's just like used

201
00:13:53,652 --> 00:13:57,651
S1: tea bags like 12 times used tea bags, it's just

202
00:13:57,692 --> 00:14:02,772
S1: it's good, but it's massively diluted. Just like, not the

203
00:14:02,892 --> 00:14:05,172
S1: good stuff. And I wonder if the stuff that I'm

204
00:14:05,212 --> 00:14:09,692
S1: getting from these books. I don't know, I can't tell

205
00:14:09,732 --> 00:14:12,052
S1: if it's just a better tea leaf, or if it's

206
00:14:12,052 --> 00:14:15,612
S1: just an undiluted tea leaf, or if it's just. I'm

207
00:14:15,652 --> 00:14:20,572
S1: not used to taking tea leaves by snorting them or

208
00:14:20,692 --> 00:14:22,732
S1: shoving them into my ears like, I don't know if

209
00:14:22,732 --> 00:14:25,572
S1: it's the medium. Like I'm still trying to figure that out.

210
00:14:25,572 --> 00:14:30,652
S1: So if anyone has any ideas there. But bottom line

211
00:14:30,652 --> 00:14:35,092
S1: is I am getting extremely excited about this concept of.

212
00:14:38,612 --> 00:14:47,612
S1: Old sources. Classics, but not even classics like literature. Literature.

213
00:14:48,172 --> 00:14:57,692
S1: I'm getting so much out of the biography of. Roosevelt.

214
00:14:58,012 --> 00:15:00,372
S1: Teddy Roosevelt. I was thinking of the other one. I

215
00:15:00,412 --> 00:15:03,302
S1: get them confused. But yeah. FDR is the later one.

216
00:15:03,302 --> 00:15:08,062
S1: He was the economy thing. FDR policies and I think

217
00:15:08,062 --> 00:15:12,982
S1: New Deal and all that. But Teddy Roosevelt was much younger.

218
00:15:12,982 --> 00:15:21,702
S1: He was doing things early 1900s or actually, no, 1800s. Yeah. Um,

219
00:15:21,702 --> 00:15:26,782
S1: 18 like 76 ish or whatever. Uh, roughly around there.

220
00:15:26,782 --> 00:15:28,902
S1: I think he was in his teens, like 100 years

221
00:15:28,902 --> 00:15:33,181
S1: after the country was born or something like that. Garfield

222
00:15:33,182 --> 00:15:37,382
S1: was assassinated, uh, during that time, something that just happened

223
00:15:37,382 --> 00:15:44,462
S1: in the book. But anyway, uh, he was. Yeah. This

224
00:15:44,462 --> 00:15:48,622
S1: biography was talking about his life, and he was talking about, like, um,

225
00:15:49,342 --> 00:15:51,382
S1: so he was going through his journals. That's what the

226
00:15:51,382 --> 00:15:54,542
S1: biography is largely source based on is his journals like

227
00:15:54,582 --> 00:16:00,382
S1: Roosevelt wrote tons. And the guy was just absolutely insane, like, lie.

228
00:16:00,382 --> 00:16:03,542
S1: Completely insane. He would just. He was like a boxer,

229
00:16:03,582 --> 00:16:08,262
S1: like a fisher, like dancing, but not very well. But

230
00:16:08,262 --> 00:16:14,462
S1: he just spoke multiple languages and literature, like he just

231
00:16:14,462 --> 00:16:18,982
S1: read everything. And recitations, evidently was a huge thing at Harvard,

232
00:16:19,862 --> 00:16:23,262
S1: going from place to place, doing recitations, like, I don't know,

233
00:16:23,262 --> 00:16:28,061
S1: is that people reading literature is that people reading memorized

234
00:16:28,102 --> 00:16:33,222
S1: literature without the book. I'm not sure exactly what that is,

235
00:16:33,222 --> 00:16:38,502
S1: but I need to go research that. But. I feel

236
00:16:38,502 --> 00:16:42,422
S1: like this content I don't again, I don't know if

237
00:16:42,422 --> 00:16:45,102
S1: it's the tea leaf itself or if it's the medium,

238
00:16:45,462 --> 00:16:49,422
S1: or if it's just different and it's just a different

239
00:16:49,422 --> 00:16:53,662
S1: style with different, you know, diction and and it spawns

240
00:16:53,702 --> 00:16:56,622
S1: thoughts in the brain differently. But I just feel like

241
00:16:56,622 --> 00:17:03,022
S1: people were so sharp back then. Um, I don't know.

242
00:17:03,062 --> 00:17:07,062
S1: I think there were extremely limited in other ways as well.

243
00:17:07,542 --> 00:17:11,582
S1: So it's hard to, uh, you know, measure it on balance.

244
00:17:11,582 --> 00:17:20,142
S1: But anyway, I'm getting extraordinary creative, uh, boosts from reading

245
00:17:20,142 --> 00:17:25,662
S1: these older books. I'm reading a book right now about, um,

246
00:17:26,742 --> 00:17:33,702
S1: rhetorical figures. It absolutely insane. It's like it's rhetoric, but

247
00:17:33,702 --> 00:17:38,101
S1: specifically about something called figures in rhetoric. Uh, it's a

248
00:17:38,102 --> 00:17:42,342
S1: book about writing, and I'm just loving every single page.

249
00:17:42,342 --> 00:17:45,302
S1: I also just love how the page is written. I

250
00:17:45,302 --> 00:17:48,902
S1: love the writing itself in this book. I think it's

251
00:17:48,902 --> 00:17:52,022
S1: using like it's tricks that it's teaching me. It's using

252
00:17:52,022 --> 00:17:54,742
S1: them on me while it's teaching me. Pretty sure it's

253
00:17:54,742 --> 00:17:59,112
S1: doing that. But what I find is it's very similar.

254
00:17:59,112 --> 00:18:02,952
S1: I got to post somewhere like my favorite writers. So Hitchens,

255
00:18:03,832 --> 00:18:09,032
S1: Scott Adams, the Dilbert guy before he went crazy, although

256
00:18:09,032 --> 00:18:13,912
S1: he probably still writes well now, um, not so much

257
00:18:13,912 --> 00:18:16,592
S1: Sam Harris. I like him for a different reason, but

258
00:18:17,232 --> 00:18:22,672
S1: the writing style that I like the most, and that

259
00:18:22,672 --> 00:18:26,432
S1: I use and probably emulate quite a bit is this

260
00:18:26,432 --> 00:18:33,672
S1: alternate alteration alternation between long and short sentences? It's very powerful.

261
00:18:33,992 --> 00:18:39,351
S1: I'm seeing this happen in this book here again. Um,

262
00:18:39,352 --> 00:18:43,392
S1: Hitchens did it. It's it's in several books that I've

263
00:18:43,392 --> 00:18:46,431
S1: read about good writing, so I'm not sure what the

264
00:18:46,432 --> 00:18:48,311
S1: actual source is. I'm not sure if it's one of

265
00:18:48,311 --> 00:18:55,032
S1: these figures, these rhetorical figures. But anyway, really, really powerful stuff. Um,

266
00:18:56,832 --> 00:19:00,552
S1: and we are 700 minutes into this podcast. Um, let's

267
00:19:00,552 --> 00:19:06,192
S1: get into it. That was just the intro. Cybersecurity. Anthropic

268
00:19:06,192 --> 00:19:09,912
S1: says attackers are using models to automate full hacking operations.

269
00:19:09,912 --> 00:19:12,472
S1: So this is the thing I talked about in my

270
00:19:12,472 --> 00:19:15,472
S1: Future of Hacking video, which you should definitely go look

271
00:19:15,472 --> 00:19:18,712
S1: at if you haven't seen that. Um, and that's just

272
00:19:18,752 --> 00:19:26,872
S1: a derivation of or derivative video, a hacking focused version

273
00:19:26,872 --> 00:19:30,792
S1: of my unified entity context, which is like this unified

274
00:19:30,792 --> 00:19:34,472
S1: context for the enterprise that everything runs off of, where

275
00:19:34,472 --> 00:19:37,272
S1: the AI talks to it. And it's like, you know,

276
00:19:37,311 --> 00:19:42,832
S1: the context is all in one place. Well. Anthropic just

277
00:19:42,832 --> 00:19:45,152
S1: came out and basically said, look, we've we were tracking

278
00:19:45,152 --> 00:19:51,032
S1: this actor, and the actor basically was acting like a

279
00:19:51,032 --> 00:19:54,792
S1: giant team, like, like a team of multiple people. And

280
00:19:54,952 --> 00:20:01,552
S1: this one person accomplished what normally takes large teams, you know,

281
00:20:01,592 --> 00:20:08,152
S1: months to do. So, uh, 17 organizations in weeks using Claude.

282
00:20:08,792 --> 00:20:10,752
S1: So somehow they were getting Claude to do all this,

283
00:20:10,752 --> 00:20:14,232
S1: by the way. So they were bypassing whatever restrictions Claude

284
00:20:14,272 --> 00:20:19,591
S1: had healthcare, governments, defense contractors, even a church. It, uh,

285
00:20:19,712 --> 00:20:24,192
S1: Claude analyzed stolen data, created customized extortion plans for each victim.

286
00:20:24,752 --> 00:20:30,312
S1: North Korean agents use Claude to fake IT worker identities

287
00:20:30,311 --> 00:20:33,912
S1: at companies. They asked Claude basic questions like what is

288
00:20:33,912 --> 00:20:37,311
S1: a muffin to maintain cover? So this is one of the, uh,

289
00:20:37,512 --> 00:20:43,391
S1: prompt injection bypass techniques, or at least, you know, to

290
00:20:43,432 --> 00:20:48,232
S1: bypass the policy of, like. Hey, you seem to be suspect. Uh,

291
00:20:48,232 --> 00:20:51,831
S1: fake employees can earn high salaries. Romance scammers use Claude

292
00:20:51,832 --> 00:20:59,121
S1: as their emotionally intelligent conversation engine. That's diabolical. Chinese hackers

293
00:20:59,122 --> 00:21:04,362
S1: use Claude to guide espionage against Vietnamese telecom companies. Criminals

294
00:21:04,362 --> 00:21:08,402
S1: are building ransomware as a service. Businesses powered by AI.

295
00:21:09,282 --> 00:21:13,002
S1: So all of this is exactly what I've been talking

296
00:21:13,002 --> 00:21:16,842
S1: about here, where it's like it's their AI system against

297
00:21:16,842 --> 00:21:22,002
S1: our AI system, right? It's them defining goals and the

298
00:21:22,002 --> 00:21:25,602
S1: AI basically using all its different tools, which is essentially

299
00:21:25,602 --> 00:21:28,202
S1: what I just talked about in my personal AI video,

300
00:21:28,922 --> 00:21:32,442
S1: where I'm talking about, like, you have a central system

301
00:21:33,082 --> 00:21:35,321
S1: and you give it all these tools and you say, look,

302
00:21:35,362 --> 00:21:37,282
S1: go do this, and it picks the tool to go

303
00:21:37,282 --> 00:21:41,562
S1: do it, and it does it. And it looks like

304
00:21:41,561 --> 00:21:45,722
S1: a system probably very similar to what I've built is

305
00:21:45,722 --> 00:21:50,962
S1: being used by this attacker. And I'm sure many thousands

306
00:21:50,962 --> 00:21:54,641
S1: of others, or at least hundreds of others soon to

307
00:21:54,682 --> 00:21:57,802
S1: be thousands. And maybe, I don't know, maybe more than

308
00:21:57,802 --> 00:22:02,121
S1: that later. I just think this is the way to go, right?

309
00:22:02,402 --> 00:22:05,282
S1: Everyone's going to have an automation stack. The question is

310
00:22:05,282 --> 00:22:08,962
S1: what are they using it for? And so what do

311
00:22:08,962 --> 00:22:10,841
S1: you have to do as a defender. You've got to

312
00:22:10,842 --> 00:22:13,842
S1: have the same exact automation stack. That was my takeaway

313
00:22:13,842 --> 00:22:18,081
S1: from this. You know future of hacking is context. Video

314
00:22:18,082 --> 00:22:20,282
S1: is basically what's the answer. The answer is to have

315
00:22:20,282 --> 00:22:24,442
S1: the same thing that the attackers have. And the framing

316
00:22:24,442 --> 00:22:27,122
S1: that I have for all of this is you have

317
00:22:27,122 --> 00:22:34,002
S1: to have a, a world model of your system and

318
00:22:34,002 --> 00:22:37,082
S1: your company and your employees and your projects and your

319
00:22:37,082 --> 00:22:39,601
S1: budget and everything. You have to have a world model

320
00:22:39,602 --> 00:22:42,482
S1: of that whole thing, and you have to be able

321
00:22:42,482 --> 00:22:46,242
S1: to audit that and understand its state at any given time.

322
00:22:47,282 --> 00:22:50,282
S1: And then at the bigger picture, the whole game is.

323
00:22:53,482 --> 00:23:02,441
S1: Excuse me. Take a drink. At the bigger picture. The

324
00:23:02,442 --> 00:23:13,962
S1: ultimate game is going from current state to desired state. And. Anyway,

325
00:23:14,002 --> 00:23:17,682
S1: that's kind of a bigger topic, but the point is,

326
00:23:17,682 --> 00:23:23,321
S1: attackers that I made before is attackers will have this

327
00:23:23,922 --> 00:23:26,322
S1: world model of your company, and they will use that

328
00:23:26,321 --> 00:23:31,321
S1: to use their automation system to continuously launch attacks against you.

329
00:23:32,162 --> 00:23:34,561
S1: And basically what your logs are going to look like

330
00:23:34,762 --> 00:23:42,642
S1: is thousands of AI automation systems just hitting you constantly.

331
00:23:42,642 --> 00:23:48,841
S1: Just constantly. And if you fart or you burp or

332
00:23:48,842 --> 00:23:53,132
S1: you step out of line one second With like an

333
00:23:53,132 --> 00:23:58,332
S1: S3 bucket or a misconfiguration or, you know, a DNS

334
00:23:58,332 --> 00:24:03,012
S1: domain that you're not maintaining whatever it is that is

335
00:24:03,012 --> 00:24:06,052
S1: going to get jumped on by a whole bunch of

336
00:24:06,052 --> 00:24:12,972
S1: AI who does not sleep. Right. So your attacker is

337
00:24:12,972 --> 00:24:16,252
S1: absolutely going to jump on that, and they're going to

338
00:24:16,252 --> 00:24:19,292
S1: do it fast. And if not, one of their competitors

339
00:24:19,292 --> 00:24:21,611
S1: is going to do it. So we're not going to

340
00:24:21,612 --> 00:24:25,252
S1: have the time. So our only chance is we get

341
00:24:25,252 --> 00:24:29,811
S1: there first. Our automated AI system sees that thing pop

342
00:24:29,811 --> 00:24:34,772
S1: up first before them and goes and fixes it, or

343
00:24:34,772 --> 00:24:37,412
S1: blocks it, or does whatever, you know, puts up a

344
00:24:37,412 --> 00:24:43,851
S1: firewall rule or whatever. So this is the game. To me,

345
00:24:43,852 --> 00:24:48,732
S1: this is the security story. This is the number one

346
00:24:48,732 --> 00:24:53,331
S1: security story going forward. Everyone talks about, you know, AI

347
00:24:53,332 --> 00:24:55,851
S1: and security. Hey, what are the future trends or whatever?

348
00:24:56,492 --> 00:25:00,612
S1: This is my number one answer. Number one answer. Unified

349
00:25:00,612 --> 00:25:04,972
S1: entity context to get everything unified inside of a company.

350
00:25:05,732 --> 00:25:11,932
S1: So the future of basically company management is the founders.

351
00:25:12,811 --> 00:25:16,252
S1: The owners sit at a giant table with a bunch

352
00:25:16,252 --> 00:25:21,972
S1: of other leaders who are also extremely business savvy, creative

353
00:25:22,732 --> 00:25:27,532
S1: and and technical as well. And then there is a

354
00:25:27,532 --> 00:25:33,331
S1: number of similar minded people that are basically like wizards,

355
00:25:35,052 --> 00:25:38,372
S1: and that essentially is their IT staff. And their IT

356
00:25:38,412 --> 00:25:45,372
S1: staff isn't really IT staff. It's more like business IT liaison.

357
00:25:45,492 --> 00:25:49,812
S1: So it's like they are experts at understanding exactly what

358
00:25:49,811 --> 00:25:52,252
S1: the leaders at that table just said, because they're at

359
00:25:52,252 --> 00:25:55,132
S1: the table as well. But they're kind of like the

360
00:25:55,132 --> 00:25:59,772
S1: execution arm. And then what they do is they they're like, okay,

361
00:25:59,811 --> 00:26:03,452
S1: I just understood that I am parsing that and bringing

362
00:26:03,452 --> 00:26:07,452
S1: it into the unified entity context that is now just updated.

363
00:26:07,452 --> 00:26:11,612
S1: Our goals. Our goals have now been updated. All our

364
00:26:11,612 --> 00:26:17,332
S1: plans have been rewritten. Um, these additional details have been

365
00:26:17,332 --> 00:26:20,972
S1: written into all future iterations of all of our products.

366
00:26:21,772 --> 00:26:24,411
S1: For all the products that you just mentioned building, we

367
00:26:24,452 --> 00:26:29,571
S1: have now started specking those out and they're basically wielding

368
00:26:29,571 --> 00:26:36,972
S1: these giant teams, these giant armies of. AI systems. And

369
00:26:36,972 --> 00:26:41,651
S1: those AI systems are the planners and the speakers and the,

370
00:26:42,172 --> 00:26:44,371
S1: you know, they're going to build marketing campaigns and they're

371
00:26:44,372 --> 00:26:47,332
S1: going to build all this different stuff around that. But

372
00:26:47,332 --> 00:26:49,462
S1: it has to start at that table with the people

373
00:26:49,462 --> 00:26:54,141
S1: deciding actually what to do. Right now, of course, there's

374
00:26:54,382 --> 00:26:56,702
S1: arguments that, okay, well, in the future you don't need

375
00:26:56,742 --> 00:26:59,821
S1: them because, you know, the AI will just be smarter

376
00:26:59,821 --> 00:27:04,142
S1: than all of them. Honestly, I believe that. Right? And

377
00:27:04,142 --> 00:27:08,982
S1: that's that's the ACI story. Very possible. But that's not

378
00:27:08,982 --> 00:27:12,702
S1: a security thing. I care about right now. It's just

379
00:27:12,702 --> 00:27:16,142
S1: not tangible to me yet. Maybe in five years, maybe

380
00:27:16,142 --> 00:27:19,982
S1: ten years, maybe 20 years. And who knows, it could

381
00:27:19,982 --> 00:27:24,462
S1: be two years. But for the foreseeable future, and I

382
00:27:24,462 --> 00:27:28,621
S1: think it'll be a good amount of time, even if

383
00:27:28,662 --> 00:27:31,182
S1: ACI hits, it's going to take a while. It's going

384
00:27:31,222 --> 00:27:35,302
S1: to be that leadership team in the middle, um, with

385
00:27:35,302 --> 00:27:39,542
S1: an enforcement arm of these extremely bright people wielding giant

386
00:27:39,582 --> 00:27:46,422
S1: teams of thousands or millions of tiny little AIS doing execution.

387
00:27:47,022 --> 00:27:52,061
S1: And in terms of security, really, it's about everything. It's

388
00:27:52,061 --> 00:27:56,862
S1: continuous monitoring and continuous context management and understanding of the business.

389
00:27:56,862 --> 00:28:01,342
S1: But especially for security, you have to continuously understand the

390
00:28:01,342 --> 00:28:05,702
S1: state of what you have, which will previously have been

391
00:28:05,702 --> 00:28:09,102
S1: called asset management. But really it's like state management of

392
00:28:09,102 --> 00:28:14,222
S1: the business overall. And then once again, it's your AI

393
00:28:14,262 --> 00:28:19,662
S1: systems against their AI systems. It's just that's just reality.

394
00:28:20,182 --> 00:28:24,262
S1: How fast are yours. How well do they understand the context?

395
00:28:24,662 --> 00:28:27,302
S1: How small is the gap between a change and an

396
00:28:27,302 --> 00:28:34,302
S1: understanding of the change that is the game. Okay. Um,

397
00:28:34,742 --> 00:28:40,142
S1: anthropic shows how they detect sleeper agents hiding malicious behavior. Yeah.

398
00:28:40,182 --> 00:28:43,302
S1: Catching AI agents that pretend to be helping while secretly

399
00:28:43,302 --> 00:28:48,742
S1: planning harmful actions when deployed. Nasty FBI confirmed Salt Typhoon

400
00:28:48,782 --> 00:28:53,022
S1: breached at least 200 American companies plus firms in 80 countries.

401
00:28:54,102 --> 00:29:00,262
S1: NSA and NCIS trace the Salt Typhoon espionage campaigns to

402
00:29:00,582 --> 00:29:07,702
S1: Sichuan Jianshui Beijing. How you knew? Why would I? Why

403
00:29:07,702 --> 00:29:09,742
S1: would I choose to read these things? It's not going

404
00:29:09,782 --> 00:29:13,342
S1: to help me. I don't want to butcher people's names. Um,

405
00:29:13,342 --> 00:29:16,702
S1: but these are companies. Actually, not not people. So those

406
00:29:16,702 --> 00:29:22,822
S1: are different companies providing Chinese company providing cyber tools to

407
00:29:22,862 --> 00:29:29,022
S1: China's intelligence services. Uh, Claude will start training on personal

408
00:29:29,022 --> 00:29:31,421
S1: account data unless you opt out. So this is an

409
00:29:31,422 --> 00:29:34,902
S1: important one. They you probably seen the emails if you're

410
00:29:34,902 --> 00:29:37,662
S1: a user. Uh, they emailed everyone and basically said you

411
00:29:37,662 --> 00:29:41,422
S1: have until September 28th to opt out. I wouldn't opt

412
00:29:41,422 --> 00:29:48,112
S1: it out immediately. Um, if you don't want your data

413
00:29:48,112 --> 00:29:50,112
S1: to be trained on and if you have like a

414
00:29:50,112 --> 00:29:53,872
S1: custom like enterprise account or certain types of, you know,

415
00:29:53,912 --> 00:29:57,832
S1: paid accounts, they won't do this by default. But if

416
00:29:57,832 --> 00:30:00,112
S1: you just have a regular account, including like a pro

417
00:30:00,112 --> 00:30:03,672
S1: account or a max account or whatever, um, they will

418
00:30:03,672 --> 00:30:09,632
S1: turn this on. They will start using your content for training. Uh,

419
00:30:09,632 --> 00:30:11,671
S1: if you do not opt out. So definitely go and

420
00:30:11,672 --> 00:30:15,072
S1: do that if you want to. I'm also curious about

421
00:30:15,072 --> 00:30:19,832
S1: why everyone's freaking out about this. Don't most other AI

422
00:30:19,872 --> 00:30:23,432
S1: companies already do this? Like, I feel like this is

423
00:30:23,432 --> 00:30:26,671
S1: just pretty standard at this point. Um, what I don't

424
00:30:26,672 --> 00:30:30,432
S1: understand and what I wouldn't like is if they're like,

425
00:30:30,472 --> 00:30:34,272
S1: we don't have the ability to opt out before September 28th.

426
00:30:34,272 --> 00:30:38,672
S1: Why would they say before September 28th? I guess that means.

427
00:30:41,072 --> 00:30:43,512
S1: I guess that just means that they will turn it on.

428
00:30:44,512 --> 00:30:50,912
S1: They will turn on the use after September 28th, but

429
00:30:50,952 --> 00:30:54,272
S1: hopefully it also means that you can still opt out

430
00:30:54,272 --> 00:30:57,472
S1: after that point. I'm not sure they made that clear

431
00:30:57,592 --> 00:30:59,432
S1: in the email. I didn't see it in the email,

432
00:31:00,232 --> 00:31:02,752
S1: but hopefully that would be the case. It would be

433
00:31:02,752 --> 00:31:05,752
S1: super shitty if they were like, nope, we sent you

434
00:31:05,752 --> 00:31:07,712
S1: the thing. I told you you had to do it

435
00:31:07,712 --> 00:31:11,712
S1: before September 28th and if you didn't? Too bad. That

436
00:31:11,712 --> 00:31:14,952
S1: would be weaksauce. And the more I think about it,

437
00:31:14,952 --> 00:31:17,112
S1: that's so ridiculous. I don't think that's going to be

438
00:31:17,112 --> 00:31:23,712
S1: the case. Researchers hide malicious prompts in images that only

439
00:31:23,712 --> 00:31:29,032
S1: appear when AI systems downscale them. This is from Trail

440
00:31:29,032 --> 00:31:34,352
S1: of Bits. Really cool attack. Uh, worth checking out. DoD

441
00:31:34,512 --> 00:31:39,872
S1: uses node JS utility maintained by a Russian Yandex developer.

442
00:31:41,272 --> 00:31:45,152
S1: Supply chain Transparency. This is why I can't wait till

443
00:31:45,152 --> 00:31:49,752
S1: there's like, you know, millions upon millions of AI eyes

444
00:31:49,872 --> 00:31:54,112
S1: watching everything. Oh, here's a cool idea that, um, I'm

445
00:31:54,112 --> 00:31:56,752
S1: going to be doing a panel with my buddy Sasha's

446
00:31:56,752 --> 00:32:02,232
S1: dealer pretty soon. And by the way, sorry for my voice.

447
00:32:02,232 --> 00:32:08,072
S1: I'm just realizing because I'm trying not to sniff while

448
00:32:08,312 --> 00:32:12,192
S1: on the microphone because it's a sniffing sound. But if

449
00:32:12,192 --> 00:32:16,712
S1: I'm trying to sniff, that means I have the sniffles,

450
00:32:16,712 --> 00:32:19,872
S1: which means I probably sound a little bit nasal, so

451
00:32:19,872 --> 00:32:24,352
S1: apologize for that. Not sick, but I'm like one quarter sick.

452
00:32:24,352 --> 00:32:28,792
S1: I just don't feel bad. Anyway, uh, the idea here is,

453
00:32:29,232 --> 00:32:33,592
S1: you know how we were told many eyes sunlight is

454
00:32:33,592 --> 00:32:36,032
S1: the best disinfectant and all this. And then we have

455
00:32:36,032 --> 00:32:39,432
S1: the SSL thing, which was open source, and it was

456
00:32:39,432 --> 00:32:44,992
S1: a massive, massive vulnerability across the whole internet. Well, what

457
00:32:44,992 --> 00:32:51,832
S1: if AI is basically a revisiting of many eyes, right?

458
00:32:51,872 --> 00:32:54,552
S1: Because Many Eyes turned out not to be true. Not

459
00:32:54,552 --> 00:33:00,872
S1: to be real. Because many eyes implied that because people

460
00:33:00,872 --> 00:33:03,832
S1: could look at the code that they would, that they

461
00:33:03,832 --> 00:33:06,592
S1: actually would. Right. And that turned out not to be

462
00:33:06,592 --> 00:33:10,592
S1: the case. Nobody was looking at all these open source projects.

463
00:33:10,912 --> 00:33:14,312
S1: Just because they're open source doesn't automatically make them secure,

464
00:33:14,472 --> 00:33:17,552
S1: because that implies a second step, which is lots of

465
00:33:17,552 --> 00:33:20,552
S1: people are actually taking the time to look. Why are

466
00:33:20,552 --> 00:33:24,472
S1: they doing that? What time are they doing that in?

467
00:33:24,992 --> 00:33:28,552
S1: How are they incentivized to do that? There's no answers

468
00:33:28,552 --> 00:33:31,752
S1: to those questions, which means which is the reason that

469
00:33:31,792 --> 00:33:35,032
S1: it wasn't being done. But what if we could just

470
00:33:35,032 --> 00:33:38,392
S1: unleash like this army? Like the government can unleash an

471
00:33:38,392 --> 00:33:44,762
S1: army of millions of agents, and their job is to

472
00:33:44,802 --> 00:33:48,082
S1: literally just go and clean up all this stuff. And

473
00:33:48,562 --> 00:33:54,362
S1: funny enough, that's exactly what the AI project was about.

474
00:33:54,402 --> 00:33:58,482
S1: Guess who? It was launched by DARPA. Yes, the creators

475
00:33:58,482 --> 00:34:04,002
S1: of the interwebs. So they literally created a project that

476
00:34:04,002 --> 00:34:11,482
S1: does this exact thing. It autonomously, autonomously, using agents can

477
00:34:11,482 --> 00:34:15,042
S1: go and find bugs and fix them end to end

478
00:34:15,202 --> 00:34:23,282
S1: without breaking the software. Isn't that insane? And this is what? Trilobites.

479
00:34:23,282 --> 00:34:26,242
S1: They just won second place. This is what Michael Brown

480
00:34:26,242 --> 00:34:29,122
S1: was talking about, which I haven't interviewed, which you should

481
00:34:29,122 --> 00:34:32,722
S1: go check out. It's on YouTube. And yeah, this is

482
00:34:32,722 --> 00:34:36,642
S1: exactly what the government set out to build. And this

483
00:34:36,642 --> 00:34:40,762
S1: is like, this is many eyes. That is literally what

484
00:34:40,762 --> 00:34:42,682
S1: many eyes was supposed to be. It was supposed to

485
00:34:42,682 --> 00:34:48,562
S1: be humans. Turns out that doesn't really scale. Um, because

486
00:34:48,602 --> 00:34:52,122
S1: open source turns out to be just one guy. I

487
00:34:52,162 --> 00:34:55,882
S1: saw somebody say that recently. Actually, it wasn't a person.

488
00:34:55,882 --> 00:34:58,082
S1: I just saw it as a headline. Open source is

489
00:34:58,082 --> 00:35:01,962
S1: just one guy. Maybe it's actually in the, uh, in

490
00:35:02,002 --> 00:35:05,882
S1: the discovery section. Open source is just one guy. That

491
00:35:05,882 --> 00:35:11,802
S1: is hilarious. Uh, anyway. FBI warns about three phase phantom

492
00:35:11,802 --> 00:35:17,242
S1: hacker scam targeting seniors. Scammers use AI to analyze social

493
00:35:17,242 --> 00:35:21,442
S1: media and personalize their attacks. Oh yeah? What do you know? First,

494
00:35:21,482 --> 00:35:24,082
S1: they pose as tech support and gain remote computer access.

495
00:35:24,082 --> 00:35:28,282
S1: Then they fake bank employees, convince victims their accounts are

496
00:35:28,282 --> 00:35:34,762
S1: compromised overseas. Just so nasty, so nasty. Just going after seniors.

497
00:35:35,802 --> 00:35:38,082
S1: And it's just going to be done at scale now.

498
00:35:39,202 --> 00:35:48,282
S1: national security. Pentagon security agency admits China keeps beating their defenses. Sad.

499
00:35:48,602 --> 00:35:54,762
S1: AI Cloudflare radar tracks global AI bot traffic across the internet.

500
00:35:55,762 --> 00:36:02,202
S1: This thing is really cool looking. Yeah, it shows you

501
00:36:02,202 --> 00:36:05,162
S1: all the stats for all the crawlers and everything. Super

502
00:36:05,162 --> 00:36:10,202
S1: super cool. AI is the natural step in making computers

503
00:36:10,202 --> 00:36:15,002
S1: understand humans. AI browser agents fail at real work. Except

504
00:36:15,002 --> 00:36:21,482
S1: for one unknown tool. Yeah, this was a really interesting one.

505
00:36:21,522 --> 00:36:24,962
S1: You should go check out that one in the newsletter.

506
00:36:26,042 --> 00:36:31,842
S1: AI adoption linked to 13% job decline for young US workers. Yeah,

507
00:36:31,842 --> 00:36:34,882
S1: I saw one of my smart buddies who was like, yeah,

508
00:36:34,922 --> 00:36:38,812
S1: you know, there's no evidence that AI is affecting the

509
00:36:38,812 --> 00:36:44,092
S1: job market. I'm like, what are you reading? Like, like

510
00:36:44,132 --> 00:36:47,332
S1: the CEOs, so many founders are like, yeah, I'm not

511
00:36:47,372 --> 00:36:49,692
S1: hiring anymore. I'm looking into how I could do more

512
00:36:49,692 --> 00:36:53,532
S1: of this with AI. And it's just like dozens of

513
00:36:53,532 --> 00:36:57,092
S1: these people coming out and saying this. The job stats

514
00:36:57,132 --> 00:36:59,332
S1: are going down at the same time. And it doesn't

515
00:36:59,332 --> 00:37:04,412
S1: mean like that's necessarily proof, right? You know, it's correlation, right.

516
00:37:04,612 --> 00:37:08,732
S1: But then you just see more and more people saying, yeah,

517
00:37:08,772 --> 00:37:12,812
S1: I'm automated this, you know, I have automated this. I

518
00:37:12,812 --> 00:37:15,892
S1: was told directly that the team was being laid off

519
00:37:15,892 --> 00:37:19,332
S1: because of AI reasons. Like, there's just so much direct

520
00:37:19,332 --> 00:37:22,812
S1: evidence that we have from people, you know, which is

521
00:37:22,812 --> 00:37:26,052
S1: like anecdata you add those up and they turn into

522
00:37:26,092 --> 00:37:31,692
S1: like almost data. So 13% I think is low. I

523
00:37:31,692 --> 00:37:35,972
S1: think it's just just now starting. And yeah. And actually

524
00:37:36,092 --> 00:37:41,332
S1: I have things queued for the next newsletter that, um,

525
00:37:41,692 --> 00:37:44,372
S1: that backs that up. There's actually more studies coming out.

526
00:37:44,372 --> 00:37:48,692
S1: It's probably going to be way more than 13%. And yeah,

527
00:37:48,732 --> 00:37:52,252
S1: only getting worse is my guess. Creators are automating their

528
00:37:52,252 --> 00:37:56,372
S1: entire content workflow with AI. So you spot trending topics

529
00:37:56,372 --> 00:37:59,692
S1: with AI agents, trained Claude on your style. Clone your

530
00:37:59,692 --> 00:38:04,092
S1: voice with 11 labs, create your digital avatar with hey Jen,

531
00:38:05,812 --> 00:38:12,892
S1: generate B-roll with Vo three and runway ML. Merge everything

532
00:38:12,892 --> 00:38:19,612
S1: in Canva. Automate distribution across platforms. Complete the workflow. So

533
00:38:20,532 --> 00:38:23,492
S1: that is basically the dream, right? This is like end

534
00:38:23,492 --> 00:38:25,892
S1: to end. They're just doing everything or at least saying

535
00:38:25,892 --> 00:38:30,372
S1: that it can be done. I love this. I think

536
00:38:31,532 --> 00:38:34,132
S1: all anyone should have to do, like as a creator,

537
00:38:34,132 --> 00:38:37,292
S1: is think about their thoughts and their ideas and like

538
00:38:37,332 --> 00:38:39,812
S1: how to get them out there and like the format

539
00:38:39,812 --> 00:38:41,852
S1: and the style that they want to get them out there.

540
00:38:41,852 --> 00:38:45,692
S1: But all the busy work that isn't fun, of course,

541
00:38:45,732 --> 00:38:48,732
S1: obviously still do the fun stuff. All that stuff should

542
00:38:48,732 --> 00:38:51,132
S1: just be automated away so you could focus on the

543
00:38:51,132 --> 00:38:58,292
S1: actual creativity part. So yeah, interesting. AI coding makes me

544
00:38:58,292 --> 00:39:01,692
S1: faster but kills my flow state. So this guy Praful

545
00:39:01,692 --> 00:39:07,972
S1: is talking about how. He used to be able to

546
00:39:08,012 --> 00:39:12,812
S1: just listen to music and code, and now it's like

547
00:39:12,852 --> 00:39:16,612
S1: you're dictating or you're typing or you're going back and

548
00:39:16,612 --> 00:39:20,572
S1: forth with this AI. Uh, for a lot of the

549
00:39:20,572 --> 00:39:23,932
S1: code generation. And it's just like, it's not the same

550
00:39:23,932 --> 00:39:27,892
S1: as just listening to music and coding. And I thought

551
00:39:27,892 --> 00:39:31,732
S1: that was really, really interesting. I hadn't realized it until

552
00:39:31,972 --> 00:39:36,302
S1: he said that. And it's definitely true for me. ChatGPT

553
00:39:36,342 --> 00:39:42,582
S1: usage drops hard when summer vacation starts. Yeah. This is

554
00:39:42,582 --> 00:39:44,862
S1: sad for me because it means they're not doing full

555
00:39:44,862 --> 00:39:48,862
S1: life integration with with the stuff they're mostly using it

556
00:39:48,862 --> 00:39:51,062
S1: for like homework and research and stuff like that, which

557
00:39:51,062 --> 00:39:54,382
S1: is fine, but I would like to see it more

558
00:39:54,382 --> 00:39:59,742
S1: widely adopted. Stop calling automation AI unless it actually learns something.

559
00:40:01,102 --> 00:40:05,302
S1: I included this just because I want to include counter

560
00:40:05,582 --> 00:40:12,902
S1: opinions to my own. Um, yeah. I think it's fine

561
00:40:12,902 --> 00:40:15,742
S1: that we want employees like it's obvious that we want

562
00:40:15,742 --> 00:40:19,462
S1: employees who can learn, but that's not the main thing

563
00:40:19,462 --> 00:40:22,422
S1: that we pay employees for to learn. We the main

564
00:40:22,422 --> 00:40:31,222
S1: thing we pay employees to do is to. Execute the tasks. Right.

565
00:40:31,262 --> 00:40:33,502
S1: So I think the learning and the executing of the

566
00:40:33,502 --> 00:40:36,262
S1: tasks are two separate things, because if we had AI

567
00:40:36,302 --> 00:40:40,542
S1: that could just execute the tasks, then the learning thing

568
00:40:40,542 --> 00:40:44,782
S1: would not be as important. Um, because what's more likely

569
00:40:44,782 --> 00:40:49,062
S1: to happen is that you would simply have, you know,

570
00:40:49,102 --> 00:40:53,342
S1: separate systems that are like, okay, we detected a sufficient

571
00:40:53,342 --> 00:40:57,742
S1: change in this thing. Therefore, you know, some retraining needs to,

572
00:40:57,902 --> 00:41:01,742
S1: you know, be added, whether that's a, you know, a

573
00:41:01,742 --> 00:41:07,182
S1: fine tuning of a model or whatever it is. And then, um.

574
00:41:09,542 --> 00:41:13,622
S1: Or just prompting or context or whatever it is, the

575
00:41:13,622 --> 00:41:16,182
S1: method you're going to use to do the improvement and

576
00:41:16,182 --> 00:41:18,422
S1: then you roll it back out. And the other thing is,

577
00:41:19,182 --> 00:41:23,862
S1: as the stuff gets better, I think. A lot of

578
00:41:23,902 --> 00:41:27,182
S1: that will generalize into being the same task and maybe

579
00:41:27,182 --> 00:41:33,782
S1: not even require too much learning. Again, just keep in

580
00:41:33,782 --> 00:41:39,982
S1: mind that just keep in mind how similar problems will

581
00:41:39,982 --> 00:41:44,542
S1: start to look when eyes are seeing all the problems

582
00:41:44,542 --> 00:41:49,942
S1: for all these different companies. How different really are the

583
00:41:49,942 --> 00:41:53,902
S1: sales use cases and the marketing use cases, and the

584
00:41:53,942 --> 00:42:01,302
S1: product management problems and the writing code problems. Right? It

585
00:42:01,342 --> 00:42:05,142
S1: seems to me like the hardest part is the creating of.

586
00:42:05,142 --> 00:42:09,942
S1: Net new things. It is the true creativity. It's the

587
00:42:09,942 --> 00:42:14,342
S1: true coming up with new ideas. And guess what? That's

588
00:42:14,342 --> 00:42:17,702
S1: not what we're paying most people for. We're paying them

589
00:42:17,702 --> 00:42:22,902
S1: to execute on these other tasks, which are the other 99.99%

590
00:42:23,142 --> 00:42:29,862
S1: of knowledge work. So I think this argument about, you know,

591
00:42:29,902 --> 00:42:34,032
S1: I'm paying you to learn. And A's can't learn on

592
00:42:34,032 --> 00:42:36,832
S1: the fly. Really good. Right now. Therefore, it's not a

593
00:42:36,832 --> 00:42:42,072
S1: threat to us. That is not a good argument. DSP

594
00:42:42,112 --> 00:42:48,392
S1: turns prompt engineering from art into systematic optimization. And this

595
00:42:48,392 --> 00:42:52,832
S1: is a towards data science article. Really cool. DSP is

596
00:42:52,832 --> 00:42:58,672
S1: a really powerful framework. It's basically automation of intelligence tasks

597
00:42:59,312 --> 00:43:03,912
S1: in pipeline type structure where it's repeatable and consistent and

598
00:43:03,912 --> 00:43:07,512
S1: you can depend on it and kind of like an

599
00:43:07,512 --> 00:43:12,472
S1: enterprisey kind of na to n type of vibe technology.

600
00:43:13,192 --> 00:43:16,552
S1: Three mystery customers account for over half of Nvidia's data

601
00:43:16,592 --> 00:43:21,232
S1: center revenue. That's spooky to me as like an investor

602
00:43:21,232 --> 00:43:24,592
S1: in Nvidia, which I currently don't hold any Nvidia, but

603
00:43:25,072 --> 00:43:30,112
S1: if I were holding some, that would be scary. Like,

604
00:43:30,112 --> 00:43:32,392
S1: who are those people and can they go away? And

605
00:43:32,392 --> 00:43:38,032
S1: if so. What would happen to their stock? It's more

606
00:43:38,032 --> 00:43:41,272
S1: likely that they'll gain five other unnamed ones and they'll

607
00:43:41,272 --> 00:43:44,392
S1: go to the moon. But I don't know. It just

608
00:43:44,392 --> 00:43:46,672
S1: seems weird to me if like so, such a high

609
00:43:46,672 --> 00:43:51,192
S1: percentage of like they're good numbers are from these unknown people.

610
00:43:52,432 --> 00:43:55,752
S1: It's like one of them Satan. Like, I don't know.

611
00:43:56,272 --> 00:44:00,472
S1: I just don't feel good about it. Venture capital in trouble.

612
00:44:00,512 --> 00:44:05,112
S1: Based on SEC filing patterns, TJ Jefferson discovered that formed

613
00:44:05,152 --> 00:44:11,512
S1: filings containing fund one peaked in Q3 2022 and have

614
00:44:11,512 --> 00:44:16,672
S1: been dropping hard ever since. Pretty cool analysis there. CrowdStrike

615
00:44:16,672 --> 00:44:22,232
S1: buys On'em for $290 million to build a genetic socks.

616
00:44:23,112 --> 00:44:28,112
S1: Computer science grads are struggling to find new jobs despite demand.

617
00:44:30,632 --> 00:44:34,512
S1: Entry level tech hiring drops 25%. Who knows where they're

618
00:44:34,512 --> 00:44:37,912
S1: getting that stat from? There's a million different of these studies.

619
00:44:38,552 --> 00:44:41,232
S1: I wouldn't be surprised if it was 25%. I wouldn't

620
00:44:41,232 --> 00:44:49,792
S1: be surprised if it was 65% drop. Yeah, like many

621
00:44:49,792 --> 00:44:55,272
S1: applying to places like Chipotle while software engineer postings remain down. Oh,

622
00:44:55,272 --> 00:45:02,352
S1: here we go. Software engineer postings remain down 70%. Cognitive

623
00:45:02,352 --> 00:45:08,911
S1: load is what actually matters in software complexity. Good piece there. Humans.

624
00:45:09,432 --> 00:45:13,552
S1: South Korea bans smartphones in schools starting 2026. Great job

625
00:45:13,872 --> 00:45:19,912
S1: and great job to probably Jonathan Haidt. Therapists are secretly

626
00:45:19,912 --> 00:45:24,832
S1: using ChatGPT and clients are upset about it. Middle aged

627
00:45:24,832 --> 00:45:27,802
S1: are no longer the most miserable. Turns out it's now

628
00:45:27,962 --> 00:45:32,002
S1: younger people. Younger people are now more miserable than middle aged.

629
00:45:32,042 --> 00:45:38,762
S1: It used to be a U-shape. With younger people happy

630
00:45:38,762 --> 00:45:44,202
S1: and older, older people happy, now younger people are massively unhappy.

631
00:45:45,922 --> 00:45:50,562
S1: US retail giants are raising prices based on Trump tariffs.

632
00:45:54,122 --> 00:45:58,602
S1: Shocking everyone. A brain abscess gave Eric Markowitz the clearest

633
00:45:58,602 --> 00:46:04,002
S1: moment of consciousness he's ever experienced. Describes how facing death

634
00:46:04,002 --> 00:46:07,842
S1: from a cerebral abscess made him truly conscious for the

635
00:46:07,842 --> 00:46:12,802
S1: first time. Trade school enrollment surges as Gen Z ditches

636
00:46:12,802 --> 00:46:19,202
S1: degrees for HVAC and welding. I got an idea here.

637
00:46:21,922 --> 00:46:25,162
S1: Why go to school right now? Why go to college?

638
00:46:25,562 --> 00:46:28,082
S1: I still think you should. So I'm of two minds

639
00:46:28,082 --> 00:46:33,122
S1: of this. I'm very strong. Like polar minds here. You

640
00:46:33,122 --> 00:46:36,042
S1: should still go because of connections. You should still go

641
00:46:36,042 --> 00:46:39,722
S1: because you do learn some decent fundamentals. If you weren't

642
00:46:39,722 --> 00:46:42,082
S1: to get. If you weren't going to get them some

643
00:46:42,082 --> 00:46:45,042
S1: other way. Right. It's a rigid structure. You have to

644
00:46:45,082 --> 00:46:47,681
S1: show up to class. You have to get the grades right.

645
00:46:47,682 --> 00:46:50,682
S1: Your parents are usually pushing you. So these are like

646
00:46:50,882 --> 00:46:56,362
S1: all good reasons to go. But holy crap, compared to

647
00:46:56,722 --> 00:47:01,442
S1: having your AI build you a custom curriculum of like

648
00:47:01,442 --> 00:47:05,282
S1: the best YouTube videos to teach you computer science. And

649
00:47:05,282 --> 00:47:09,322
S1: I'm going to be talking about this a whole bunch actually,

650
00:47:09,602 --> 00:47:12,002
S1: in the next few months. I've got this whole concept

651
00:47:12,002 --> 00:47:15,642
S1: of slack in the rope and actually what a real

652
00:47:15,642 --> 00:47:18,802
S1: curriculum could look like. I'm actually going to be building

653
00:47:18,802 --> 00:47:24,802
S1: curriculums into substrate curriculums for computer science and like all

654
00:47:24,922 --> 00:47:29,482
S1: these different, um, all these different degrees and stuff. And

655
00:47:29,482 --> 00:47:31,722
S1: I'm actually going to go find some experts on this

656
00:47:32,082 --> 00:47:36,082
S1: to help build the degrees, to help build the content

657
00:47:36,082 --> 00:47:38,162
S1: that needs to be in there. And I'm also going to,

658
00:47:38,202 --> 00:47:40,602
S1: of course, look at like all the classic ones, I'm

659
00:47:40,602 --> 00:47:42,522
S1: going to look at Oxford, I'm going to look at Stanford,

660
00:47:42,522 --> 00:47:46,042
S1: I'm going to look at like all these different schools, um,

661
00:47:46,082 --> 00:47:49,042
S1: including looking at what they used to teach, look at

662
00:47:49,042 --> 00:47:52,402
S1: what the Romans used to teach, like the trivium. Right.

663
00:47:52,682 --> 00:47:56,362
S1: And basically build like these custom bespoke things. But the

664
00:47:56,362 --> 00:48:00,002
S1: point is, I bet you it's pretty easy to put

665
00:48:00,002 --> 00:48:04,922
S1: together a set of YouTube videos, um, and maybe some

666
00:48:04,922 --> 00:48:08,482
S1: custom content that is like, just gets to the heart

667
00:48:08,482 --> 00:48:11,962
S1: of it, right? Imagine learning this stuff from like, Richard

668
00:48:11,962 --> 00:48:16,922
S1: Feynman and like the best teachers you could find. And

669
00:48:16,922 --> 00:48:20,042
S1: maybe certain teachers are better for certain types of learners, right?

670
00:48:20,082 --> 00:48:24,132
S1: And that could be stacked in there. But there's just

671
00:48:24,132 --> 00:48:29,532
S1: no possible way that learning computer science in college is

672
00:48:29,532 --> 00:48:39,412
S1: anywhere close, anywhere remotely close to like optimizing the cost function.

673
00:48:39,932 --> 00:48:44,052
S1: Like nowhere close to ideal. Uh, I know I use

674
00:48:44,412 --> 00:48:50,132
S1: I learned computer science in college in a traditional four

675
00:48:50,172 --> 00:48:54,932
S1: year college, and I've seen it taught in many, many others.

676
00:48:55,212 --> 00:48:57,812
S1: And I know what the curriculum looks like. Anyone could

677
00:48:57,812 --> 00:49:01,292
S1: go look it up. They're following the same books, like

678
00:49:01,292 --> 00:49:04,132
S1: most of them are reading the same books. Most of

679
00:49:04,132 --> 00:49:09,012
S1: the lecture styles are very similar. The homework is very similar.

680
00:49:09,012 --> 00:49:13,612
S1: Like the algorithm is just so ancient for doing this. And.

681
00:49:16,012 --> 00:49:18,652
S1: Right now, at this particular moment, we're like four years

682
00:49:18,652 --> 00:49:23,572
S1: from now is like 14.5 years from now In the

683
00:49:23,572 --> 00:49:25,892
S1: sense of like how much progress is going to be made.

684
00:49:26,012 --> 00:49:30,132
S1: What is your opportunity cost for sitting in a class

685
00:49:30,132 --> 00:49:34,372
S1: for a semester, trying to learn computer science, versus having

686
00:49:34,372 --> 00:49:38,132
S1: an AI build you a custom thing and actually have

687
00:49:38,132 --> 00:49:43,052
S1: conversations with you about it? And how about this? It's like, hey,

688
00:49:43,052 --> 00:49:45,732
S1: write me some code that does this, and you're writing

689
00:49:45,732 --> 00:49:47,812
S1: the code and it's like, yeah, that's not quite right.

690
00:49:47,812 --> 00:49:51,052
S1: It's more kind of like this. And it dynamically changes

691
00:49:51,052 --> 00:49:53,372
S1: your code for you and you're like, oh, okay, cool.

692
00:49:53,852 --> 00:49:55,692
S1: Or it's like, hey, I'm not going to help you.

693
00:49:55,692 --> 00:49:58,532
S1: You go ahead and do this and see if it compiles. Um,

694
00:49:58,572 --> 00:50:00,692
S1: go ahead and do this and, you know, test out

695
00:50:00,692 --> 00:50:03,492
S1: the website. Okay. Why is it not rendering? Why is

696
00:50:03,492 --> 00:50:07,052
S1: my thing off center? Why do my code not compile

697
00:50:07,052 --> 00:50:08,892
S1: or whatever? And it's like, I don't know, what do

698
00:50:08,892 --> 00:50:11,812
S1: you think it is like a Socratic sort of tutor

699
00:50:11,852 --> 00:50:15,092
S1: type thing, except for it never gets tired and you

700
00:50:15,092 --> 00:50:18,612
S1: don't pay extra money to ask the question again. Like,

701
00:50:18,612 --> 00:50:23,692
S1: how does that even compare To like this, this older

702
00:50:23,692 --> 00:50:28,412
S1: system that I just described, it's completely ridiculous. So there's

703
00:50:28,412 --> 00:50:30,692
S1: only a matter of time before this is going to

704
00:50:30,692 --> 00:50:33,412
S1: become open source. Like I'm going to make it open source.

705
00:50:33,412 --> 00:50:37,932
S1: I'm going to make these curriculums open source. And the

706
00:50:37,932 --> 00:50:40,212
S1: part that I don't have that I don't think I

707
00:50:40,212 --> 00:50:43,252
S1: can quickly build, and I'm sure other people will and

708
00:50:43,252 --> 00:50:47,132
S1: they'll do a great job is building the tutor that

709
00:50:47,132 --> 00:50:51,292
S1: can then use that curriculum. Right. Like I have Kai here,

710
00:50:51,412 --> 00:50:54,892
S1: but Kai's not specifically trained for being a tutor. Kai

711
00:50:54,932 --> 00:50:57,572
S1: is specifically trained right now because he lives inside of

712
00:50:57,572 --> 00:51:07,812
S1: cloud code to actually be a, um. Developer, right? Kai

713
00:51:07,812 --> 00:51:11,812
S1: is currently built because of his current body and brain structure,

714
00:51:12,972 --> 00:51:15,492
S1: to be an executor of tasks, to be a writer

715
00:51:15,492 --> 00:51:18,931
S1: of code, and that sort of thing. Um, but, you know,

716
00:51:18,972 --> 00:51:21,862
S1: I could reskin him as a tutor. In fact, I'm

717
00:51:21,902 --> 00:51:24,342
S1: getting excited about it. Just thinking about it. Like having

718
00:51:24,342 --> 00:51:27,662
S1: a tutor mode or a set of commands which are

719
00:51:27,662 --> 00:51:32,182
S1: tutor based. Okay, never mind, I'm excited. I'm actually writing

720
00:51:32,182 --> 00:51:38,982
S1: this down right now. I'm making some custom commands for tutoring.

721
00:51:39,182 --> 00:51:42,462
S1: I'm really curious about this right now, pairing that together

722
00:51:42,462 --> 00:51:51,262
S1: with substrate. Oh my goodness. Tutoring commands. And then open

723
00:51:51,262 --> 00:51:56,182
S1: sourcing the whole thing free on substrate and the tutoring

724
00:51:56,222 --> 00:51:59,102
S1: to go along with it. Question is how do you

725
00:51:59,102 --> 00:52:01,902
S1: make that packageable like as a mobile app? Because it

726
00:52:01,902 --> 00:52:03,902
S1: needs to go out to lots of people who need it.

727
00:52:04,302 --> 00:52:08,472
S1: Not like the 18 people or the even the 1800

728
00:52:08,472 --> 00:52:11,742
S1: or 18,000 people who see it on YouTube or whatever,

729
00:52:11,742 --> 00:52:15,382
S1: and they use it. That's barely making a dent. And

730
00:52:15,382 --> 00:52:17,902
S1: it's helping people who already maybe don't need the help

731
00:52:17,902 --> 00:52:24,142
S1: as much. Right. So anyway, I digress. Opportunity cost for college.

732
00:52:24,382 --> 00:52:28,822
S1: It's a tough question. I still think in general better

733
00:52:28,822 --> 00:52:33,982
S1: to have a degree. But right now, at this particular moment,

734
00:52:33,982 --> 00:52:36,502
S1: I am really worried about telling somebody to go and

735
00:52:36,502 --> 00:52:39,702
S1: get a four year degree and basically shutting down their

736
00:52:39,702 --> 00:52:43,222
S1: mind the whole time. What are you doing when you're

737
00:52:43,222 --> 00:52:47,422
S1: in four years of college? You're doing the coursework. You're

738
00:52:47,422 --> 00:52:50,942
S1: trying to please that teacher. You're trying to please that curriculum.

739
00:52:50,942 --> 00:52:55,022
S1: You're trying to please that old structure of college courses

740
00:52:55,022 --> 00:53:00,942
S1: from whenever this structure started. Right. And that is an ancient,

741
00:53:01,422 --> 00:53:06,142
S1: broken structure, extremely inefficient. I'm going to give it a 3%

742
00:53:06,142 --> 00:53:11,422
S1: efficiency score compared to AI tutoring you based on like

743
00:53:11,462 --> 00:53:16,142
S1: custom match content. Custom match YouTube content? Are you kidding me?

744
00:53:16,422 --> 00:53:20,262
S1: Like no comparison? Person. I think we could get like

745
00:53:20,302 --> 00:53:23,862
S1: a less ideal is 100. I think we can get

746
00:53:23,902 --> 00:53:31,022
S1: like a 60% efficiency up from like, okay, three is

747
00:53:31,022 --> 00:53:34,102
S1: too low, up from like a 10%. I think we

748
00:53:34,102 --> 00:53:41,422
S1: can get what is that, 5X5X better. And like how

749
00:53:41,422 --> 00:53:44,342
S1: does that turn into reality. I don't know. I'm talking

750
00:53:44,342 --> 00:53:48,262
S1: about full degrees. Full understanding. Here's what I'm talking about.

751
00:53:48,702 --> 00:53:52,462
S1: Having a better grasp of computer science. A better grasp

752
00:53:52,462 --> 00:54:00,062
S1: of history a better grasp of psychology, evolutionary biology, political science.

753
00:54:00,422 --> 00:54:04,622
S1: Getting a grasp of those things in one week's time

754
00:54:06,462 --> 00:54:11,022
S1: that is better than 99.99% of all people on the

755
00:54:11,022 --> 00:54:17,502
S1: planet in one week's time. You can do that with

756
00:54:17,502 --> 00:54:23,182
S1: 20 really good YouTube videos combined with active tutoring by

757
00:54:23,182 --> 00:54:28,342
S1: your AI in a Socratic, challenging way to quiz you

758
00:54:28,342 --> 00:54:30,022
S1: to make sure you're able to move on to the

759
00:54:30,022 --> 00:54:35,022
S1: next video. Think about that. That's not even counting. We're

760
00:54:35,022 --> 00:54:39,342
S1: going to make you custom videos, right? Which is not

761
00:54:39,342 --> 00:54:43,302
S1: here yet. And still coming. Right. That that's next level.

762
00:54:43,662 --> 00:54:47,822
S1: But I think we can get massive multiples of gains

763
00:54:48,542 --> 00:54:53,102
S1: just from using the tech we already have. So like

764
00:54:53,102 --> 00:54:57,862
S1: I said violently of two minds on this whole college thing. Yes.

765
00:54:57,862 --> 00:55:01,142
S1: You should go. Holy crap. Don't turn off your brain.

766
00:55:01,142 --> 00:55:03,982
S1: You need to be doing all this AI stuff, all

767
00:55:03,982 --> 00:55:06,942
S1: this modern shit at the same time. If you're doing

768
00:55:06,942 --> 00:55:08,902
S1: it to get the degree, go do it. Get the

769
00:55:08,902 --> 00:55:15,502
S1: degree right. Especially if you're almost done. But I don't

770
00:55:15,502 --> 00:55:18,192
S1: think the people being laid off are like, nope, we

771
00:55:18,192 --> 00:55:20,672
S1: can't lay that person off. They have a four year degree.

772
00:55:20,672 --> 00:55:25,392
S1: I don't think anybody fucking cares right now, honestly. Um,

773
00:55:26,232 --> 00:55:28,712
S1: some people do, okay? Some people do. If you're trying

774
00:55:28,712 --> 00:55:31,232
S1: to go into some traditional field, I'm just telling you,

775
00:55:31,232 --> 00:55:36,392
S1: the world might be very different in four years. Like,

776
00:55:36,392 --> 00:55:42,952
S1: dramatically fucking different. So really, really think about opportunity cost.

777
00:55:42,992 --> 00:55:45,392
S1: Best of both worlds is do all the shit that

778
00:55:45,392 --> 00:55:48,792
S1: I'm talking about at the same time. Then you don't

779
00:55:48,792 --> 00:55:52,272
S1: really take a hit by getting the degree right. Plus,

780
00:55:52,512 --> 00:55:55,632
S1: ideally you're using AI to help you get your degree right.

781
00:55:55,712 --> 00:56:00,192
S1: So if it's easy, it's easy. Plus it's friendships. It's

782
00:56:00,232 --> 00:56:04,672
S1: maybe sports, it's extracurricular activities. It's social. You're making a

783
00:56:04,672 --> 00:56:07,912
S1: friend network. There's tons of benefits to college that aren't

784
00:56:07,912 --> 00:56:11,792
S1: related to the curriculum. I'm mostly criticizing the curriculum stuff.

785
00:56:13,832 --> 00:56:19,272
S1: Man can't get through these podcasts just talking. Everyone loves

786
00:56:19,272 --> 00:56:21,552
S1: it when I do this though, and go into more

787
00:56:21,552 --> 00:56:24,792
S1: detail and talk about things, but it does take a

788
00:56:24,792 --> 00:56:27,992
S1: lot of time. Um, I got to figure out how

789
00:56:27,992 --> 00:56:30,472
S1: to get it down into my old format of just like,

790
00:56:30,472 --> 00:56:33,872
S1: read the title and boom, go through, be done in

791
00:56:33,912 --> 00:56:36,272
S1: 15 minutes. Didn't I say I'm supposed to be done

792
00:56:36,272 --> 00:56:38,292
S1: in 15 minutes? Isn't it supposed to be a 15

793
00:56:38,292 --> 00:56:42,672
S1: minute podcast? Haven't had one of those in two years.

794
00:56:43,472 --> 00:56:48,512
S1: All right. Brain abscess already did that one. The feeling

795
00:56:48,512 --> 00:56:52,192
S1: your work creates matters more than the checkboxes. This is

796
00:56:52,192 --> 00:56:56,112
S1: by Mitchell Hashimoto, who also made the ghosty terminal, which

797
00:56:56,112 --> 00:56:59,912
S1: is my go to terminal. A really good article there.

798
00:57:00,352 --> 00:57:05,312
S1: Physical technical books create memory palaces that websites can't match.

799
00:57:05,352 --> 00:57:08,112
S1: I was just hanging out with my buddy Tim Leonard,

800
00:57:08,632 --> 00:57:13,912
S1: who is a I met through UL and, um, Hanging

801
00:57:13,912 --> 00:57:18,352
S1: out in Vegas, and he opens up his his logbook

802
00:57:18,352 --> 00:57:23,352
S1: that he keeps with him. And he's got like these custom,

803
00:57:23,392 --> 00:57:27,272
S1: like hand-drawn images. So, like, he takes a note or

804
00:57:27,272 --> 00:57:30,032
S1: whatever talks about a plant or a, or a thing

805
00:57:30,032 --> 00:57:31,912
S1: that he saw with his wife or whatever. He takes

806
00:57:31,912 --> 00:57:36,832
S1: these notes. He had like little pressed flowers, like he

807
00:57:36,832 --> 00:57:40,872
S1: pulled from a place. It was just brilliant, just absolutely

808
00:57:40,872 --> 00:57:45,712
S1: brilliant analog. He's got a pen, he's got paper memories.

809
00:57:47,152 --> 00:57:49,192
S1: I think you can have that with technical books as well.

810
00:57:49,192 --> 00:57:51,712
S1: And that's what this article is arguing about. It was

811
00:57:51,752 --> 00:57:59,312
S1: it was really cool. MIT and Scripps researchers develop one

812
00:57:59,312 --> 00:58:03,792
S1: shot vaccines for HIV and Covid. Really, really cool. All right.

813
00:58:03,792 --> 00:58:09,232
S1: Discovery I'm going to try not to talk for 74

814
00:58:09,232 --> 00:58:13,322
S1: minutes about each one of these. I'm going to blast through.

815
00:58:13,362 --> 00:58:18,882
S1: I'm going to try. AI continues computing's evolution towards understanding humans.

816
00:58:19,362 --> 00:58:23,362
S1: A beginner friendly jiu jitsu tutorial for non git users.

817
00:58:24,842 --> 00:58:27,722
S1: This is an alternative to git. It's called jiu jitsu.

818
00:58:27,762 --> 00:58:30,242
S1: It's supposed to be awesome, but I do not have

819
00:58:30,242 --> 00:58:34,362
S1: the time for the switching cost right now. The opportunity

820
00:58:34,362 --> 00:58:39,162
S1: cost of me trying to learn jiu jitsu right now. Um.

821
00:58:41,282 --> 00:58:44,962
S1: And migrate everything. Get off of GitHub. Does GitHub even

822
00:58:45,002 --> 00:58:49,202
S1: do jiu jitsu? I don't know. I'm guessing not. Git

823
00:58:49,242 --> 00:58:54,842
S1: is in the name. FFmpeg pages simplifies video processing with

824
00:58:54,842 --> 00:58:59,242
S1: visual commands. AI agents should use files and git instead

825
00:58:59,242 --> 00:59:06,282
S1: of context windows. Sounds pretty familiar to me with like

826
00:59:06,282 --> 00:59:10,722
S1: this whole UFC thing I just came up with. Um,

827
00:59:11,322 --> 00:59:14,602
S1: That's when I actually didn't read yet. I need to

828
00:59:14,602 --> 00:59:19,722
S1: open this tab. Because it's in the discovery section. So

829
00:59:19,722 --> 00:59:25,082
S1: I'm just kind of like sometimes kicking off cool links. Um,

830
00:59:25,482 --> 00:59:28,442
S1: that you should go check out. Single servers can handle

831
00:59:28,442 --> 00:59:32,362
S1: millions of HTTP requests per section. Dot files don't belong

832
00:59:32,362 --> 00:59:36,882
S1: in application support. Loved that one. That was so spot on.

833
00:59:38,682 --> 00:59:46,322
S1: Google lets you block AI overviews from your site. Nardin

834
00:59:46,362 --> 00:59:52,442
S1: automates bug bounty enumeration via discord, training yourself to like

835
00:59:52,442 --> 00:59:56,642
S1: things you hate. Klein runs completely offline with LM studio.

836
00:59:57,202 --> 01:00:00,122
S1: New vibe coded web app from Kathy Helps. I don't

837
01:00:00,122 --> 01:00:04,802
S1: see many of my women friends doing the AI coding entrepreneurship,

838
01:00:04,802 --> 01:00:07,002
S1: so I'm posting this here. This is a woman who

839
01:00:07,002 --> 01:00:09,802
S1: just built an app and is having super fun is

840
01:00:09,802 --> 01:00:13,722
S1: talking about it, and she's just really enjoying the whole

841
01:00:13,722 --> 01:00:16,322
S1: development process. And I just want to see a lot

842
01:00:16,322 --> 01:00:21,322
S1: more of my women friends, uh, doing this whole entrepreneurship,

843
01:00:21,362 --> 01:00:25,202
S1: like build your own business, you know, AI coding, AI

844
01:00:25,242 --> 01:00:27,682
S1: hacking thing. So I'm going to be sending this to

845
01:00:27,722 --> 01:00:31,402
S1: a bunch of my friends. Trace prompt provides tamper proof

846
01:00:31,402 --> 01:00:38,482
S1: audit trails for LLM applications. Oh, a duplicate got a

847
01:00:38,482 --> 01:00:44,122
S1: duplicate there. Claude gets a chrome extension. Physical textbooks become

848
01:00:44,162 --> 01:00:47,402
S1: unchanging memory places talked about that one. Wire prompts don't

849
01:00:47,402 --> 01:00:53,202
S1: belong in git. Uh so oops oops on that one.

850
01:00:54,642 --> 01:01:00,402
S1: Guile's or shares creative bash prompt designs and K-pop Demon

851
01:01:00,442 --> 01:01:05,322
S1: Hunters is Netflix's most watched video ever, and I still

852
01:01:05,322 --> 01:01:07,602
S1: have not watched this, but I do want to watch it.

853
01:01:08,042 --> 01:01:10,852
S1: Recommendation of the week. Start thinking about what you would

854
01:01:10,852 --> 01:01:15,932
S1: do if you had a personal assistant and what you

855
01:01:15,932 --> 01:01:20,372
S1: would give them. Scheduling, planning, collecting information, finding the best books.

856
01:01:20,412 --> 01:01:23,532
S1: Keeping people aware of what you're working on, keeping your

857
01:01:23,532 --> 01:01:29,372
S1: family synced up. Finding materials for your hobbies. Teaching yourself topics.

858
01:01:29,532 --> 01:01:32,892
S1: Start building that list because you will soon have this.

859
01:01:33,572 --> 01:01:36,732
S1: Whether it's through, like what I'm doing or a more

860
01:01:36,732 --> 01:01:40,572
S1: consumer based approach that's going to come out soon. Like,

861
01:01:40,612 --> 01:01:42,932
S1: you should start thinking about this. You want to be

862
01:01:42,932 --> 01:01:45,972
S1: able to magnify yourself in this way. Um, and I

863
01:01:45,972 --> 01:01:48,812
S1: recommend you go check out the the personal AI video

864
01:01:48,812 --> 01:01:52,932
S1: that I just put out talking about how I'm doing it. And, um,

865
01:01:54,652 --> 01:01:58,452
S1: the aphorism of the week argue for your limitations, and

866
01:01:58,452 --> 01:02:02,692
S1: sure enough, they are yours. Argue for your limitations, and

867
01:02:02,692 --> 01:02:06,052
S1: sure enough, they're yours. Richard Bach.