WEBVTT - UL NO. 425: The Efficient Security Principle

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<v S1>Welcome to Unsupervised Learning, a security, AI and meaning focused

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<v S1>podcast that looks at how best to thrive as humans

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<v S1>in a post AI world. It combines original ideas, analysis,

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<v S1>and mental models to bring not just the news, but

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<v S1>why it matters and how to respond. All right. Welcome

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<v S1>to unsupervised learning. This is Daniel Missler. All right. Number 425.

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<v S1>So you all know the deal here. Tons going on

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<v S1>this week prepping for some paid talks. Going to be

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<v S1>traveling to a few different states. Honestly really excited to

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<v S1>be alive right now at this moment in human history,

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<v S1>like all the different opportunities there are that are just

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<v S1>popping off and I feel like there's always a conference,

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<v S1>there's always people. There's so many people building right now,

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<v S1>especially here in the Bay area. And I'm just excited

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<v S1>about the optimism. Like the optimism has its own gravity,

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<v S1>if that makes sense. And yeah hack build grind appreciate.

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<v S1>That's pretty much the current vibe. So I wrote this

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<v S1>piece called Efficient Security Principle. And I'm going to open

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<v S1>this up in a new tab. And it's essentially explaining

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<v S1>why a lot of security people are so upset, I think,

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<v S1>over the course of their career. So one of the

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<v S1>hardest things about being in security is just the frustration.

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<v S1>And this came from the conversation with Deno, where he

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<v S1>basically posted about raising the security minimum. And I was like, well,

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<v S1>you know, the minimum is there for a reason. And

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<v S1>I kind of wanted to capture that into a principle.

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<v S1>And ideally I would have this be one item, but

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<v S1>it was getting kind of long. So I broke it

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<v S1>into these statements. Security baseline of an offering continuous downward

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<v S1>pressure from customer excitement or reliance on a given thing.

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<v S1>And then the baseline will be set at the point

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<v S1>at which people will stop using it if it's any worse.

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<v S1>But it can't go higher than that. And then the

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<v S1>third piece of it, which these are all kind of

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<v S1>saying the same thing, is that the better the offering is,

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<v S1>or the more important it is, the lower the security

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<v S1>baseline can go without losing people. And that's really, really important.

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<v S1>So all this together basically means that the more essential

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<v S1>a thing is, and the more awesome it is, the

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<v S1>more people will put up with. And as we'll see

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<v S1>when we move through this, it's the reason you shouldn't

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<v S1>get too upset when the baseline is low. So the

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<v S1>way we know something has the right amount of security

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<v S1>is people just keep using it despite how insecure it is. Right?

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<v S1>So online companies constantly getting hacked. My friend Sasha Geller

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<v S1>basically talked about this one where he said that basically

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<v S1>the biggest example of this is actually email, where it's

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<v S1>like email gets everyone hacked. It's like the number one

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<v S1>way to get hacked is to have email. But guess what?

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<v S1>You need email. There's a reliance on it. It's like

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<v S1>our national you know, our global infrastructure of it is

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<v S1>basically emails to online banking. Everyone's getting constant fraud all

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<v S1>the time. Door locks and the internet in general is

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<v S1>pretty nasty. It's a cesspool, right? We use these things

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<v S1>anyway because the value they provide massively outweighs the security risks,

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<v S1>at least according to us in our own minds. And

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<v S1>the moment people stop using something due to security being

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<v S1>too bad, the baseline goes up. And Sasha had another

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<v S1>good point in that. SMS is actually an example of

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<v S1>this where it was deemed too nasty. And then we

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<v S1>started moving to like app based tufa. So the way

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<v S1>to actually use this is if you're a security tech professional,

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<v S1>like down in the weeds, realize that it's not that

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<v S1>level that matters at a bigger scale. So you want

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<v S1>to like uplevel your thinking and realize it's not about us.

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<v S1>It's not about the technical part of it. It's about

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<v S1>how important is the offering. And that's what actually sets

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<v S1>the baseline. And most importantly, do not take it personally.

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<v S1>Don't take it personally that the baseline is low and

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<v S1>you're showing them how to raise the baseline, but they're

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<v S1>not doing anything about it, right. That's really key. And

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<v S1>if you're a security leader, you know, you can have

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<v S1>the same sort of problem and frustration of a technical person.

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<v S1>And maybe you're also a technical leader. It doesn't really matter.

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<v S1>But the point is like you can get frustrated as well.

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<v S1>And the recommendation is first, make sure the security baseline

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<v S1>is actually where the customer thinks it is. Maybe they're

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<v S1>not complaining because they don't really know how bad it is.

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<v S1>So once those are in alignment now, now you have

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<v S1>sort of the same situation as the technical side where

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<v S1>it's like look fine, innovative ways to raise the baseline

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<v S1>without making it obvious or cause friction because people might

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<v S1>want to higher baseline, but only if it doesn't cost

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<v S1>them anything because they're only going to go as high

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<v S1>as it can go without sacrificing that experience of this

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<v S1>thing that they love. And I just think it's really powerful.

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<v S1>So yeah, only as good as it needs to be

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<v S1>to keep people from abandoning the service. And the more

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<v S1>popular or essential the offering, the lower the security can be.

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<v S1>This is in the summary here. And progress is still possible,

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<v S1>especially with policy change and regulation, because that kind of

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<v S1>like forces a baseline raise. But security experts loudly calling

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<v S1>out how low the baseline is and gesturing wildly towards

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<v S1>the solution seldom results in change. And most importantly, this

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<v S1>is kind of the big takeaway is experts struggling with

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<v S1>low security baselines should absorb the truth above and not

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<v S1>let their job satisfaction and their mental health suffer. And

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<v S1>got a bunch of notes here. But that's that's basically

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<v S1>that piece. All right. A couple of really cool patterns

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<v S1>here put into fabric this week. So the first one

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<v S1>is like it'll analyze your prose according to my favorite

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<v S1>writing book, which is The Sense of Style by Steven Pinker.

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<v S1>And here's what it actually looks like after a run.

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<v S1>This is. The analysis of me actually doing that, that essay.

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<v S1>So I put that essay through here and this is

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<v S1>the result I came back. So style analysis it's multiple

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<v S1>different styles put together positive assessment. Critical assessment I talk

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<v S1>a little bit too much about the writing itself I

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<v S1>guess is what this is saying. It gives me examples

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<v S1>of good writing from it. Things I can improve. No

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<v S1>spelling mistakes, which is cool. I'm going to be using

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<v S1>this to catch spelling from now on, and you start

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<v S1>off with a 100 score. Oh, and it gives recommendations

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<v S1>as well. But it starts off with 100 score and

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<v S1>then deducts. So I got an 85. I got an

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<v S1>85 here because it took 15 points off for these things.

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<v S1>So that's what that pattern does. And then this one

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<v S1>is extract book recommendations. So this is insane. This is

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<v S1>essentially any book you could think of that was probably

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<v S1>brought in the training of a given model. And it's

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<v S1>most that you could think of like a New York

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<v S1>Times bestseller. Any big name book that you can think

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<v S1>of is probably going to be in here. So open

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<v S1>this one up and look at it. So this is

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<v S1>a man search for Meaning by Viktor Viktor Frankl. And

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<v S1>these are the recommendations that it gave me for what

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<v S1>to do right to like improve your life. I mean

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<v S1>look at these, these these are absolutely insane. Find meaning

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<v S1>in life by dedicating yourself to a cause greater than yourself.

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<v S1>Like even the prioritization here is really, really good. Take

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<v S1>responsibility for your own life and choices. I mean, this

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<v S1>is gorgeous and this all just came from running. I

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<v S1>literally said Echo Man's search for meaning pipe into extract

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<v S1>book recommendation and this is what it produced, right? This

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<v S1>is so powerful. I'm just blown away every time we

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<v S1>make one of these things. So those are two of

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<v S1>the biggest ones that I put in. I actually put

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<v S1>in more, but those are two of the big ones.

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<v S1>So security stuff. Yeah. It looks like you can extract

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<v S1>keys from M-series chips. I'm sure they'll fix that in

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<v S1>the next iteration. After that, a lot of people are

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<v S1>saying it's not really packable and that it's kind of

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<v S1>a big problem. So and that if you do do

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<v S1>a workaround, it might affect performance. But we'll see. And

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<v S1>here's the other piece. We'll have to wait and see

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<v S1>how real world these attacks are to know if it's

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<v S1>like actually bad and the integration of drones with digitized

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<v S1>command and control systems. Yeah, this is a US war

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<v S1>fighting effort essentially, and they're calling it transformative trinity for autonomous,

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<v S1>like very dangerous drones, which saved dystopian movie writers a

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<v S1>lot of time. So that's that's good news. And yeah,

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<v S1>along the lines of this, there's never been a better

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<v S1>time to read Daniel Suarez's kill decision, which is all

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<v S1>about autonomous drones. Actually, you should check out that book,

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<v S1>a really, really good book. And Jess has a new

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<v S1>strategy for tackling AI risks. Got the PDF here. Someone

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<v S1>in China got arrested in New York for trying to

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<v S1>sell Tesla's secret battery tech to undercover agents. I'm not

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<v S1>sure if he thought he was selling it to China.

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<v S1>I couldn't quite get that from the story. Canada is

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<v S1>rethinking its ban of flipper zero, trying to go after

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<v S1>misuse instead of just banning it outright, which I'm glad

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<v S1>they listened to the feedback there. Avonte critical bug. This

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<v S1>just continues to happen. Earth crushing Kraken. Yeah, I'm not

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<v S1>sure how to pronounce that one, but basically a Abt

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<v S1>group backed by the Chinese government hit over 70 organizations,

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<v S1>mostly focused on governments, and Atlassian patched a critical SQL vulnerability.

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<v S1>So myself and a bunch of other AI builders have

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<v S1>found something really weird where anthropic smallest model haiku has

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<v S1>been performing extremely well. And this one I did. What

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<v S1>is this? So this is extract ideas. So I did

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<v S1>extract ideas on a recent YouTube video of Jonathan Haidt

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<v S1>coming on Rogan again. And if you look at these

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<v S1>results here, they are remarkably similar, similar to each other.

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<v S1>So like GPT for the first one is opus, which

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<v S1>is like the best anthropic model. The second one is

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<v S1>GPT four, which is the best OpenAI model. But this

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<v S1>is the worst. It's the third level down of anthropic models,

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<v S1>and these top level like quotes here really good. Not

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<v S1>not quotes, but findings, ideas extracted. I mean, they're not

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<v S1>obviously worse than any of the other two. And this

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<v S1>has happened to me multiple times. So I'm really impressed

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<v S1>with that. And a lot of people are reporting GPT

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<v S1>five will be out this summer. And they're pointing to

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<v S1>this interview that Sam Altman did with Lex. And but

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<v S1>what he actually said was something good would come out soon,

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<v S1>maybe like this summer. I think he might have said,

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<v S1>like this summer. And some people are saying, well, maybe

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<v S1>it'll be like a 4.5 release, but I don't think

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<v S1>it's going to be five before the end of the year.

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<v S1>That's my guess. Nvidia is patterning. Patterning. See, now you know,

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<v S1>I don't use AI to write my stuff. I wrote

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<v S1>this and that is misspelled. That's unfortunate. Yeah. Partnering with

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<v S1>Hippocratic AI to introduce AI nurses for virtual patient care.

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<v S1>So you basically looking at an iPad and it's like

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<v S1>a face telling you what to do $9 an hour.

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<v S1>It's actually an AI. Yeah. So there's a little point

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<v S1>here most of the benefit will get from AI in

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<v S1>the first few years. Won't be from doing work better

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<v S1>than good humans, right? It's going to be doing work

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<v S1>that otherwise would not have been done at all. So

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<v S1>the great examples of this are therapists. We need way

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<v S1>more therapists. We need billions of more therapists, like hundreds

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<v S1>of millions or billions of therapists, potentially. We don't want

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<v S1>to overindex on therapy, but we need so many therapists.

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<v S1>We need billions of tutors, continuous tutors, that you can

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<v S1>ask anything. Why? Well, because of this. Why? Because of this? Why?

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<v S1>How many parents get tired of that question? How about

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<v S1>somebody who doesn't get tired of that question? Right? And

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<v S1>I'm not saying parents shouldn't be there to answer those questions.

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<v S1>Of course they should. And so should teachers. But it's

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<v S1>nice when somebody is shy or introverted or they're just

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<v S1>alone and they have questions, you know, therapists, tutors and yeah,

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<v S1>we don't have enough things watching for asteroids in the sky.

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<v S1>It's extra eyes. Think of trillions of extra eyes and

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<v S1>a little bit of brainpower tied to that skin cancer screening.

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<v S1>How many people in the world have access to a

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<v S1>doctor to say, hey, look at this mole? Very few,

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<v S1>very few percentage of the entire planet have that ability.

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<v S1>But now you can with AI, right? So that's what

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<v S1>I'm excited about. US Department of Justice 16 state and

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<v S1>district attorneys, district attorney generals. Actually, I think that's supposed

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<v S1>to be pluralized attorneys general. Yeah. Lawsuit against Apple. Bottom line.

0:11:47.559 --> 0:11:52.449
<v S1>And I've got a pro Apple biased, very fanboy leaning. Biased.

0:11:52.750 --> 0:11:55.929
<v S1>I'm all about not avoiding bias, but actually just calling

0:11:55.929 --> 0:11:57.910
<v S1>it out and naming it, which I think I saw

0:11:57.910 --> 0:12:01.360
<v S1>Destiny talk about, which I thought was pretty smart. And yeah,

0:12:01.390 --> 0:12:04.750
<v S1>Apple's now making iPhones in Brazil. And Tim Cook also

0:12:04.750 --> 0:12:07.150
<v S1>just went to China to tell him everything's okay. And

0:12:07.150 --> 0:12:09.370
<v S1>don't worry. Don't worry about the fact that we're trying

0:12:09.370 --> 0:12:11.920
<v S1>to make everything not in China because you guys are crazy.

0:12:11.920 --> 0:12:14.650
<v S1>He probably didn't say that, but I did. Apple and

0:12:14.650 --> 0:12:18.250
<v S1>Tesla are losing market share in China. Meanwhile, as national

0:12:18.250 --> 0:12:21.939
<v S1>loyalty rises, and my sort of spicy take here is

0:12:22.059 --> 0:12:26.320
<v S1>one of China's greatest strengths is actually its nationalism. Right.

0:12:26.320 --> 0:12:30.160
<v S1>They have too much I would say. Um, and I

0:12:30.160 --> 0:12:32.710
<v S1>would say that the left in the US has too little.

0:12:32.710 --> 0:12:34.900
<v S1>I would say like the far right in the US

0:12:34.900 --> 0:12:37.929
<v S1>has too much. Can we can we have some medium,

0:12:37.929 --> 0:12:41.320
<v S1>can we have some, uh, some moderation, please? That would

0:12:41.320 --> 0:12:44.830
<v S1>be nice. Israel's government is reportedly running covert ops at

0:12:45.190 --> 0:12:48.790
<v S1>US universities. This turns out to be a little bit

0:12:48.790 --> 0:12:51.550
<v S1>kind of like a biased story. But that's why I

0:12:51.550 --> 0:12:53.950
<v S1>put this in here. It's like, what's the difference between

0:12:53.950 --> 0:12:59.470
<v S1>marketing and counter propaganda information operations? The difference is your perspective.

0:12:59.470 --> 0:13:02.620
<v S1>That's that's the difference. Like, we should consider ourselves always

0:13:02.620 --> 0:13:06.160
<v S1>being hit with propaganda and marketing or whatever. And like

0:13:06.160 --> 0:13:08.830
<v S1>you use the different term based on what you think

0:13:08.830 --> 0:13:11.890
<v S1>their goals are and how morally aligned you think their

0:13:11.890 --> 0:13:15.370
<v S1>goals are with, like good or bad. Measles was eliminated

0:13:15.370 --> 0:13:18.850
<v S1>in the US in 2000, and now it's back because

0:13:18.850 --> 0:13:21.640
<v S1>people don't want to take vaccines. As long Covid brain

0:13:21.640 --> 0:13:26.320
<v S1>fog could be from damaged blood vessels. That's a new study. People.

0:13:26.320 --> 0:13:28.600
<v S1>Young people are a lot less happy. And we actually

0:13:28.600 --> 0:13:30.610
<v S1>because of this, we fell out of the top 20

0:13:30.640 --> 0:13:34.030
<v S1>happiest nations in the United States. Uh, Alzheimer's might stem

0:13:34.030 --> 0:13:37.570
<v S1>from a fat buildup in brain cells. UC Berkeley professor

0:13:37.570 --> 0:13:40.180
<v S1>getting attacked for telling a student to get out of

0:13:40.179 --> 0:13:43.870
<v S1>artillery range of San Francisco and San Jose if he

0:13:43.870 --> 0:13:46.210
<v S1>wants to find a girlfriend, and he's just getting blasted

0:13:46.210 --> 0:13:49.270
<v S1>for this weather forecast. Massively improved. This is a pretty

0:13:49.270 --> 0:13:53.380
<v S1>cool seven day forecasts are now actually pretty accurate, and

0:13:53.380 --> 0:13:56.829
<v S1>four day predictions now are as accurate as one day

0:13:56.830 --> 0:14:00.790
<v S1>predictions from 30 years ago. Interesting. Yeah, four days versus

0:14:00.790 --> 0:14:03.850
<v S1>one day. You imagine the level of complexity there. Germany

0:14:03.850 --> 0:14:08.980
<v S1>legalized recreational marijuana. Blu ray is coming back because you

0:14:08.980 --> 0:14:10.720
<v S1>always have access to it. You don't have to worry

0:14:10.720 --> 0:14:13.059
<v S1>about it being pulled out of streaming. And it's still

0:14:13.059 --> 0:14:17.560
<v S1>the highest quality. Cancer rates under 50 have surged by 80%.

0:14:17.559 --> 0:14:19.600
<v S1>I think this might be in the US, but from

0:14:19.600 --> 0:14:23.620
<v S1>1990 to 2019. So that's what 30 years, basically 31

0:14:23.620 --> 0:14:27.370
<v S1>years married people are thriving way more than unmarried. According

0:14:27.400 --> 0:14:31.330
<v S1>to a ten year long Gallup study. Bidets getting way

0:14:31.330 --> 0:14:34.780
<v S1>more popular. Got some recommendations here for you. And, uh,

0:14:34.780 --> 0:14:36.910
<v S1>a new way of thinking about the economy. I don't

0:14:36.910 --> 0:14:39.790
<v S1>want to read this whole thing. Uh, just. I think

0:14:39.790 --> 0:14:42.520
<v S1>everything is starting to come down to framing for me.

0:14:42.520 --> 0:14:46.120
<v S1>It's like my universal theory. So, uh, the economy might

0:14:46.120 --> 0:14:49.630
<v S1>be a thing where if you believe in optimism, if

0:14:49.630 --> 0:14:52.960
<v S1>you become optimistic about the economy that has, like its

0:14:52.960 --> 0:14:55.870
<v S1>own gravity, it has its own weight, its own power,

0:14:55.870 --> 0:14:58.360
<v S1>because it turns into action, and then the action turns

0:14:58.360 --> 0:15:00.850
<v S1>into reality. So it's like, once again, you have this

0:15:00.850 --> 0:15:05.830
<v S1>link between understanding or framing or viewpoints that translates directly

0:15:05.830 --> 0:15:08.530
<v S1>into what actually happen, which I just find fascinating. I

0:15:08.530 --> 0:15:11.619
<v S1>think we should rethink how we use the term, uh,

0:15:11.620 --> 0:15:15.130
<v S1>the word technical, which I laid out in a tweet thread.

0:15:15.130 --> 0:15:19.590
<v S1>And this mostly affects women who. Often are the only

0:15:19.590 --> 0:15:22.560
<v S1>people at a company who could do multiple things right.

0:15:22.590 --> 0:15:26.070
<v S1>We're talking about being able to, like, navigate a business process,

0:15:26.070 --> 0:15:30.270
<v S1>get people from one place to another, actually do deeply

0:15:30.270 --> 0:15:35.310
<v S1>technical technology related work, but also deeply technical, like law

0:15:35.310 --> 0:15:39.180
<v S1>related work or business related work, other things that are

0:15:39.180 --> 0:15:43.740
<v S1>like super high end, very, very difficult stuff. And the

0:15:43.740 --> 0:15:46.230
<v S1>the system would not be able to function. Nobody could

0:15:46.230 --> 0:15:49.680
<v S1>explain things. Nobody could actually get the work done if

0:15:49.680 --> 0:15:52.560
<v S1>it wasn't for these people who are there helping. And

0:15:52.560 --> 0:15:56.790
<v S1>because it's not necessarily oriented around a piece of technology,

0:15:57.240 --> 0:16:01.800
<v S1>they get really diminished. So I think we should redefine that. Yeah,

0:16:01.800 --> 0:16:04.590
<v S1>I'm getting all the different AI devices coming in. I

0:16:04.590 --> 0:16:08.040
<v S1>really just want something that record conversations and transcribe them

0:16:08.040 --> 0:16:11.040
<v S1>and get them into my AI workflows. Going back to

0:16:11.040 --> 0:16:13.710
<v S1>original style of the new summary, which is like a

0:16:13.710 --> 0:16:16.230
<v S1>single sentence which which I did a lot up here,

0:16:16.230 --> 0:16:18.420
<v S1>rereading the Sense of style. That's why I did that

0:16:18.420 --> 0:16:21.030
<v S1>pattern really helps my writing. Every time I read it.

0:16:21.030 --> 0:16:22.890
<v S1>I try to do it every like 2 or 3 years.

0:16:22.890 --> 0:16:26.010
<v S1>Resubscribe to the Twitter API so I could do things

0:16:26.010 --> 0:16:29.100
<v S1>like this. Boom, go down here, Twitter boom. So these

0:16:29.100 --> 0:16:32.700
<v S1>are actually Twitter feeds that are in Feedly because I

0:16:32.700 --> 0:16:35.400
<v S1>have the connection there and the API key put in.

0:16:35.400 --> 0:16:37.980
<v S1>So that's pretty cool to be able to consolidate, bring

0:16:37.980 --> 0:16:40.560
<v S1>everything in. You could also do it with Twitter lists

0:16:40.560 --> 0:16:43.560
<v S1>which I use quite a bit. Yeah. Analyze pros talked

0:16:43.560 --> 0:16:46.860
<v S1>about that one and getting into the three body problem

0:16:46.860 --> 0:16:50.550
<v S1>on Netflix. Really enjoying this. The books were fantastic. Found

0:16:50.550 --> 0:16:54.780
<v S1>this service called Opus Clip. Um, Jason pointed me to it.

0:16:54.780 --> 0:16:57.660
<v S1>It's basically you take a video like this video, it'll

0:16:57.660 --> 0:17:00.300
<v S1>be output and it cuts it up into pieces. And

0:17:00.300 --> 0:17:01.710
<v S1>you're going to see some of these. You're going to

0:17:01.710 --> 0:17:03.989
<v S1>see clips from like this will go out in my

0:17:03.990 --> 0:17:06.900
<v S1>feeds and it'll be like, you know, a 32nd or

0:17:06.900 --> 0:17:10.470
<v S1>92nd or whatever little clip of a cool little section,

0:17:10.470 --> 0:17:12.570
<v S1>but it does it automatically. Normally you would send this

0:17:12.570 --> 0:17:14.850
<v S1>to like an editor, and it's really difficult because they

0:17:14.850 --> 0:17:17.160
<v S1>have to know the subject matter to know what are

0:17:17.160 --> 0:17:19.380
<v S1>the coolest parts of what you said. Well, this thing,

0:17:19.380 --> 0:17:22.680
<v S1>you don't have to do that because it's actually automatic.

0:17:22.680 --> 0:17:26.080
<v S1>You just upload it extracts like whatever, three or 5

0:17:26.080 --> 0:17:29.010
<v S1>or 10 different clips out of it. Rag Tune Tool

0:17:29.010 --> 0:17:34.140
<v S1>for tuning and Optimizing rag Pipelines by Misbah, said collects,

0:17:34.140 --> 0:17:36.750
<v S1>takes a web page, returns all the URLs, and switch

0:17:36.750 --> 0:17:39.750
<v S1>back to this view. Open dev an open source project

0:17:39.750 --> 0:17:43.470
<v S1>to clone and improve Devon Unreal Engine I mean, look

0:17:43.470 --> 0:17:49.619
<v S1>at this. This is. Come on. This is content from

0:17:49.619 --> 0:17:50.430
<v S1>Unreal Engine.

0:17:50.940 --> 0:17:52.230
<v S2>They're combing the streets.

0:17:52.230 --> 0:17:55.230
<v S1>It looks like sort of video I mean searching helps to.

0:17:55.230 --> 0:17:55.710
<v S3>How real.

0:17:57.930 --> 0:18:00.390
<v S4>If they arrest you too, they will take you to

0:18:00.390 --> 0:18:03.149
<v S4>their headquarters and you will not return.

0:18:05.920 --> 0:18:08.109
<v S5>I'm more concerned. It looks like a six foot cat.

0:18:08.650 --> 0:18:13.330
<v S5>It's got claws that can cut through. Vibranium alloy. Slightly tongue. But.

0:18:14.280 --> 0:18:18.389
<v S6>Let's just go forward. Okay. So let's boom down and

0:18:18.390 --> 0:18:21.720
<v S6>take a closer look at this environment. Yeah, sure. Look

0:18:21.720 --> 0:18:25.679
<v S6>at this. So we're talking about nanites. New adaptive tessellation feature.

0:18:25.710 --> 0:18:28.260
<v S6>Look at this. So while I let you or something.

0:18:28.260 --> 0:18:28.650
<v S1>Yeah.

0:18:29.160 --> 0:18:31.320
<v S6>There you go. For artists to create a series of

0:18:31.320 --> 0:18:34.109
<v S6>layers in the environment and then build up. So one

0:18:34.109 --> 0:18:34.560
<v S6>angle that.

0:18:34.560 --> 0:18:36.869
<v S1>We're approaching this from. This is another. Speaking of.

0:18:36.869 --> 0:18:38.910
<v S6>Set dressing. Let's go check out that.

0:18:39.570 --> 0:18:43.500
<v S1>In terms of like creating content that's completely controllable in

0:18:43.500 --> 0:18:47.580
<v S1>creative but realistic look as a skill. The secret to

0:18:47.580 --> 0:18:51.240
<v S1>a meaningful life I love this so much. A long term,

0:18:51.240 --> 0:18:55.620
<v S1>ambitious vision that pushes you to grow smarter, wealthier, and

0:18:55.619 --> 0:19:00.629
<v S1>mentally stronger. That is very concise. A guide to espouse

0:19:00.630 --> 0:19:04.470
<v S1>and use. Prompt injection and jailbreaking are not the same thing.

0:19:04.500 --> 0:19:08.430
<v S1>By Simon Wilson and Recommendation of the week. Think about

0:19:08.430 --> 0:19:12.750
<v S1>your hedonic baseline. So this is like well I'll just

0:19:12.750 --> 0:19:16.230
<v S1>keep reading. So it's thinking about my life just walking

0:19:16.230 --> 0:19:19.260
<v S1>upstairs or walking to the car. It's a stoic technique

0:19:19.260 --> 0:19:23.460
<v S1>of imagining that you're not able to do these simple things.

0:19:23.460 --> 0:19:26.730
<v S1>And as you start removing them from your life and

0:19:26.730 --> 0:19:30.840
<v S1>imagining yourself without them, you can imagine how unhappy you

0:19:30.840 --> 0:19:33.750
<v S1>would be now, like a Jedi or a monk or

0:19:33.750 --> 0:19:36.449
<v S1>something would say, well, you shouldn't care about that. You

0:19:36.450 --> 0:19:38.730
<v S1>shouldn't care about losing anything. But I like to appreciate

0:19:38.730 --> 0:19:40.920
<v S1>the things that I do have, including the ability to

0:19:40.920 --> 0:19:44.550
<v S1>walk outside in the sun and not not be assaulted

0:19:44.550 --> 0:19:47.970
<v S1>or something to breathe fresh air like all these things.

0:19:47.970 --> 0:19:52.169
<v S1>I'm trying to micro appreciate a thousand different things in

0:19:52.170 --> 0:19:55.020
<v S1>my life, which is a very stoic thing. You imagine

0:19:55.020 --> 0:19:57.689
<v S1>the things that you have, you imagine them gone, and

0:19:57.690 --> 0:20:01.350
<v S1>then you get appreciation when you realize that you still

0:20:01.350 --> 0:20:05.220
<v S1>have them. Right. So relationships, mobility, the ability to think,

0:20:05.220 --> 0:20:07.950
<v S1>being in the Bay area at this crazy moment with

0:20:07.950 --> 0:20:13.200
<v S1>AI and stuff, and it's like actively cultivate appreciation by

0:20:13.200 --> 0:20:16.710
<v S1>lowering my baseline, what is the minimum thing that I

0:20:16.710 --> 0:20:19.980
<v S1>need to be happy? And ideally that would be nothing.

0:20:19.980 --> 0:20:22.650
<v S1>And it's just something I'm thinking about. So think about

0:20:22.650 --> 0:20:26.250
<v S1>what you have and think about those things by subtraction

0:20:26.250 --> 0:20:30.000
<v S1>and the aphorism of the week. Contentment is natural wealth.

0:20:30.000 --> 0:20:35.040
<v S1>Luxury is artificial poverty, contentment is natural wealth and luxury

0:20:35.040 --> 0:20:40.050
<v S1>is artificial poverty. Socrates Unsupervised Learning is produced and edited

0:20:40.050 --> 0:20:43.740
<v S1>by Daniel Missler on a Newman 87 AI microphone using

0:20:43.740 --> 0:20:47.730
<v S1>Hindenburg intro and outro. Music is by zombie with a Y,

0:20:48.570 --> 0:20:50.760
<v S1>and to get the text and links from this episode,

0:20:50.760 --> 0:20:53.010
<v S1>sign up for the newsletter version of the show at

0:20:53.010 --> 0:20:57.649
<v S1>Daniel missler.com/newsletter. We'll see you next time.