S1: All right. Welcome to unsupervised learning. This is Daniel Miessler.
S1: And this is episode 482. And I want to mention
S1: real quick that there haven't been any regular podcast episodes
S1: recently on the podcast, as I'm sure you've noticed, and
S1: I'm basically rethinking how I'm approaching, uh, basically the podcast. Basically,
S1: the issue that I have is that a lot of
S1: the podcast episodes were just really long, and they're really
S1: difficult for me to actually read out, because I end
S1: up going into a whole bunch of like, extra exposition
S1: into them, and it's either too long for people or
S1: it's really exhausting for me. Well, it is too long
S1: for many people, and it's definitely exhausting for me. And
S1: the other problem is, a lot of the content that's
S1: in there is not interesting to a lot of people, right?
S1: It's a lot of detail about stuff that a lot
S1: of people don't care about. So what I've been doing
S1: is switching up to focusing on ideas that are shorter
S1: and more compact. And that's what you've been seeing in
S1: the podcast feed, and it looks like you've been enjoying
S1: those based on the number of downloads. So I'm going
S1: to continue to do that and especially focus on that
S1: because I think that's that's basically, you know, the the
S1: future of the podcast is to focus mostly on, you know,
S1: ideas and, you know, exciting things that are happening and
S1: trends and basically talking through them in depth, which is
S1: the thing that the people like from the podcast anyway.
S1: So think of it this way, the podcast will still
S1: be the same, the podcast will still be coming out,
S1: but what you'll be getting is where I would go
S1: into a topic before inside of a podcast episode. Those
S1: are now getting pulled out and turned into their own
S1: little mini segments, and those are going into the the
S1: top of the podcast at the feed level, right? And
S1: they'll have their own names and everything. The question is,
S1: do I still do this news one which I'm doing now, right?
S1: And a lot of people still want to get the
S1: news one. So I think what I'm going to do
S1: is continue to release the specific episode based ones. You know,
S1: the idea ones I just talked about, but do the
S1: audio version of this one here, where it's labeled episodes
S1: and it's actually news and it's, you know, it gives
S1: you coverage of what happened in the week before, like
S1: the podcast has always been. So I'm going to try
S1: not to go into too much depth on various ideas
S1: and topics. I probably won't be able to hold to
S1: that because I get excited, but ideally, any depth content
S1: inside of that episode should be broken out into its
S1: own episode, which will be a mini segment inside of
S1: the feed. So this is why you haven't seen the
S1: same number of specific UL labeled episodes. Is just because
S1: I'm a little bit less interested in the news stuff,
S1: and more interested in the ideas and the analysis, which is,
S1: you know, where the main push is going to be.
S1: But again, enough people are saying they want the news.
S1: So I'm going to continue to put that out in
S1: audio only right here at the same place on the podcast.
S1: All right. So with that out of the way, let's
S1: go into the episode. All right. So if you still
S1: doubt that eyes are able to reason, you need to
S1: watch this segment of two anthropic researchers talking with Dwarkesh Patel.
S1: So these are two guys that he's brought on before,
S1: and they talked about the future of AI. There was
S1: like almost a year ago, seems like five years ago
S1: or whatever, but it was really good. But this particular
S1: segment that I highlighted in the newsletter, and by the way,
S1: you won't have the newsletter handy when I'm talking when
S1: you're listening to this audio, ideally, if you could scroll
S1: through the newsletter or watch the video, pause it, listen, whatever. Um,
S1: they are definitely sisters of each other, right? The podcast
S1: versus the newsletter. It's all the same stuff. It's just
S1: the text version, but more importantly, it has the links.
S1: So I have a link in there specifically going to
S1: that part in the video where they talk about this.
S1: And the thing they're talking about is this segment research
S1: that they did. So what they did was the interpretability
S1: team from anthropic released a paper saying that they could
S1: light up. They could when they ask, uh, Claude a question,
S1: what would happen is different sections. They could see them
S1: light up. And this is parameters. The circuit that they're
S1: talking about is parameters across multiple layers of the model. Okay.
S1: So you've got like you know I don't know how
S1: many layers current models have, but it's many, many, many,
S1: many layers deep right. From left to right. Now inside
S1: of each layer there's so many parameters, right. You know,
S1: millions of parameters or whatever it is. But what they
S1: could see is what each of those lit up areas
S1: actually correspond to in terms of concepts. So they can
S1: now see that when they start to prompt somebody, uh,
S1: a model for like write a sonnet or something like that,
S1: you could see it planning, you could see it planning
S1: early in the process, you could see it thinking about
S1: where it wants to go and moving through that. The
S1: other example that he gave that was absolutely insane is
S1: there's this really rare symptom of a problem that has
S1: to do with pregnancy. And what they did was they
S1: gave the problem to Claude, and Claude immediately took the
S1: symptoms and immediately started mapping like cause and effect across
S1: multiple possible diseases. And it immediately lit up the answer, actually.
S1: But it started thinking about what would the symptoms be,
S1: which would could exclude it. Right. So it started thinking
S1: very much like a human. But but the thing that
S1: was different about it is they released a paper showing
S1: how these sections are actually lighting up. They're getting visibility
S1: into how the eye is actually thinking and how it's
S1: thinking about cause and effect and logic and conclusion. And
S1: basically like the thing that a lot of people say, oh,
S1: it's just next token prediction. This pretty much puts that
S1: to sleep. It really does because obviously it's still only
S1: doing next token prediction. You know, air quotes only. But
S1: if it's lighting up these sections and we could see
S1: it's lighting up the same sort of sections, there's probably
S1: an analog like this in our own brains. We are
S1: mapping concepts, we are mapping cause and effect in the world.
S1: And by the way, it came up with the right
S1: solution for this medical thing. And I said this to
S1: a friend of mine named Jonathan who's a cardiologist. He's like,
S1: this is exactly how I think about problems, you know,
S1: related to my industry, which is he's an active cardiologist.
S1: He's in, you know, his his office, in his practice
S1: all the time. And I just want to emphasize how
S1: incredible this is, okay. And I want you to be
S1: able to send this link and send this clip to
S1: people who keep saying, I don't really understand. And I've
S1: also got another link related to this of Ilya talking
S1: about explaining how next token prediction and true understanding can
S1: be the same and in fact are the same. And
S1: basically what he says is it turns out if you
S1: want to do proper next token prediction, you actually have
S1: to understand the world that you're talking about, okay. And
S1: that very closely maps and tracks with this segment research,
S1: where you're lighting up different parts of the model, which
S1: are interconnected, and related concepts which show a clear understanding
S1: of cause and effect. Right. So this is just really
S1: important to understand that just the fact that they're doing
S1: next token prediction doesn't mean they're not understanding next token
S1: prediction is just the output. It's just the mechanism. It's
S1: like saying to going to a poet, watching a poet,
S1: watching Shakespeare speak words on a on a stage, and
S1: then looking over at somebody and saying, he's just moving
S1: his mouth. And what happens is when his mouth moves,
S1: it vibrates sound. And the sound comes to our ears
S1: and it vibrates something in our ears. And then we
S1: hear his words like, he's not doing anything special. He's
S1: just moving his mouth and vibrating air. Yeah, you're right.
S1: You 100% got me. He's just moving his mouth and
S1: just vibrating air. That has nothing to do with what
S1: underlies it? Right. And it is the same with AI
S1: understanding and next token prediction. All right. See, you see
S1: how I get diverted here. All of that. We're on
S1: the number one bullet before we even start the newsletter. Okay.
S1: Next one. If you're part of the 23 Andme breach,
S1: which I was, you can submit a claim to receive
S1: between $500 and $1500. But you have to do it
S1: before July 14th. And I have the link here. All right.
S1: I've got a new video out. It is called Unified
S1: Entity Context. The AI stack. Everyone is building without realizing it.
S1: You've got to go see this video and make sure
S1: you like and subscribe on the channel as well. And
S1: my friend Ali is looking for a new hybrid on
S1: site position, the New York area doing back end development.
S1: She's an absolute force MIT grad, a master's in engineering
S1: and bachelor's in electrical engineering and CS three years back.
S1: End engineer experience founder Devrel experience. Lots of Golang, really
S1: strong public speaking and presentation skills and she's on social media.
S1: She explains stuff. She does education for the community. She's
S1: just fantastic. And she's a like hardcore serious MIT developer. Like,
S1: don't get it twisted. A lot of people see her.
S1: They see that she's like, has good energy and she's
S1: on social media and they assume like she's some kind of,
S1: you know, influencer who's talking about coding. No, she's not
S1: talking about coding. She's doing it. Masters from MIT. Okay.
S1: So you want to hire her very quickly if she's
S1: not already snatched up. Cybersecurity. Palo Alto their research team
S1: simulated a full ransomware attack in 25 minutes using AI agents.
S1: I continue to like, help Net Security's job posting of
S1: different cybersecurity roles. I think they put this out every
S1: week or every couple of weeks. But, um, it's pretty good.
S1: And I got the list here to latest job listings.
S1: Postman looks like it's logging secrets and environment variables. So
S1: you want to be you want to go in and
S1: change this. This is actually on by default. A lot
S1: of people are really pissed about this. So, um, you
S1: want to make sure to turn that off or, you know,
S1: find another solution if it makes you that mad. Um,
S1: O3 actually found a remote Linux kernel zero day, so
S1: it might be the first instance, although I wouldn't be
S1: surprised if it weren't of an AI model actually finding
S1: a zero day vulnerability. Cloud and GitHub MCP will leak
S1: your private GitHub repositories. You got to watch out with
S1: these MCC. You really do. There's a lot of, um,
S1: hand-waving going on with them. They're very powerful. They're very awesome.
S1: But you got to watch out. So be careful with
S1: anything you're doing that's sensitive stuff. Signal blocks, windows, screenshots
S1: to counter recall features. So recall wants to take screenshots
S1: of everything. Signal is specifically blocking that because it goes
S1: against their ethos obviously police arrest third suspect in bitcoin
S1: torture case is this guy escaped after three weeks of
S1: torture by colleagues trying to force him to reveal his
S1: Bitcoin password. And evidently he did not. My understanding from
S1: the military is if someone's good at torturing, you are
S1: going to break. The only question is when. So the
S1: fact that it went three weeks, um, probably just because
S1: it's his colleagues and not really good at the whole, uh,
S1: I guess, uh, applying pressure in that way. US intelligence
S1: is creating a unified portal for buying your personal data.
S1: So they're building a centralized system to streamline how they
S1: purchase sensitive information from brokers. So they normally would have
S1: required a court order to get this stuff. But if
S1: they buy it legally from brokers, they can do that,
S1: which means they're just now building this giant AI portal
S1: AI data lake so they can ask questions and get
S1: the data back in aggregate. I mean, we definitely knew
S1: this was going to happen, but it's still pretty scary.
S1: Coinbase hit with insider data breach, refuses $20 million ransom.
S1: So here's the craziest their overseas support agents, which means
S1: you could pay them much less money to bribe them.
S1: So there's a really cool meme on this. I don't
S1: remember how they phrased it, but my phrasing is it's
S1: cheaper to hire overseas people, which means it's also cheaper
S1: to bribe overseas people. And definitely something to think about.
S1: Czech Republic accuses China of hacking foreign ministry AI hallucination
S1: cases database launched. So someone created a database of real
S1: world hallucinations caused by AI that you EU enters vulnerability
S1: tracking race as Nvda is getting really old. So they're
S1: bringing their own competitor in there. FBI warns of AI
S1: voice impersonation campaign targeting officials. So say to watch out
S1: for that. And they're actually coming after the people who
S1: are doing it if they can find them. Amazon has
S1: a serious problem with fake supplements. Really good thread here.
S1: Indian police are attempting to read suspects minds, so they're
S1: using brain scans to detect guilt. And pretty much all
S1: the experts are like, do not do this. It's not
S1: what you think it is, but they continue to do it.
S1: Snowflake CISO shifts from shared responsibility to shared destiny, which
S1: is really just more alignment with the business, which I
S1: think is the only way to survive as a security
S1: leader right now and going forward. Russian Apt28 breaches organizations
S1: to track aid routes. So they are hacking a whole
S1: bunch of different groups to figure out who's sending support
S1: to Ukraine, obviously, so they can try to shut that off.
S1: Regeneron to acquire 23 Andme and its customer data. National
S1: security Russia mandates location tracking app for foreigners in Moscow.
S1: Every foreigner they're going to have to they're going to
S1: track their location or they're going to try to. I'm
S1: not sure how far along it is. All right. I
S1: OpenAI's Jony Ive hardware Google I o in my current assessment.
S1: So they are building this little round necklace. It looks
S1: very it looks very pretty. It looks to have a
S1: camera and a microphone on it. And Apple actually bought
S1: his little company for over $6 billion. So he now
S1: works at OpenAI for OpenAI for Sam. And I think
S1: this is moving us towards the vision that I was
S1: talking about before or have been talking about for a
S1: long time, where basically, um, your digital assistant is using
S1: this thing that you're wearing to monitor the visual in
S1: front of you. Now, Apple is talking about doing this
S1: with AirPods and actually having cameras on the AirPods. I
S1: would love to be able to see in front of
S1: me and behind me. That would make me happy because
S1: my digital assistant, whose name is probably going to be Kai. Um, well,
S1: already is kind of Kai. But when the real one
S1: comes out, transfer his soul into the new location. But anyway,
S1: I want him to be able to see you in
S1: front of me. Obviously, here, obviously monitor all the APIs
S1: that we've been talking about for everything around me. And
S1: obviously you have an earpiece, so we could talk in
S1: my ear very quietly, like, this person is lying to you.
S1: Someone's sneaking up behind you. Someone is staring at your
S1: screen from your right rear oblique, you know, uh, 8:00
S1: to your 8:00. Someone's monitoring your screen, whatever. The camera
S1: is the biggest piece here. And, um. Yeah, I'm getting
S1: worried that Apple is falling behind here. Google's I o
S1: conference was unbelievably great. They announced so many different things.
S1: They are going they're basically the monster has awoken, the
S1: sleeping giant has woken up and they're moving quick. And
S1: Apple needs to do something cool hopefully in this week. Oh,
S1: and by the way, they're renaming their operating systems. So
S1: it's going to be iOS 26 coming out in the
S1: next couple of weeks instead of iOS 19. So they're
S1: naming it for the year coming. That's upcoming just like
S1: car models. So iOS 26 coming out. Uh anthropic's. Claude,
S1: opus four shatters limits with seven hour coding marathon. So
S1: the big thing here is that, um, the agent can
S1: maintain the context of the goal for seven hours and
S1: keep working towards that goal. That's the real big update here.
S1: I've been talking for a long time about this. Uh.
S1: Scaffolding matters more than the models. Context matters more than
S1: the models. Systems matter more than the models. Having a
S1: way to track what you're trying to do and understand
S1: what you're trying to do, and understand the various steps involved,
S1: and understand where you are in terms of doing all
S1: those steps. That is the thing that's going to super
S1: power all this AI stuff. Yeah, the models get better,
S1: they get smarter, whatever. But the more important thing is
S1: how big is our context window and how powerful is
S1: the system that they are using to try to accomplish
S1: their goals? And how good can they track the state
S1: of their progress towards those? And those are the things
S1: that they keep making more and more progress in across
S1: all these different companies. And that's why we're getting these
S1: giant leaps, not just because, like the intelligence of the models,
S1: but the scaffolding of everything around it. Authors accidentally leave
S1: AI prompts in published novels. So I think these are
S1: like romance novels. And I got some examples here. But yeah,
S1: it's got like it's got like the I talking back, okay,
S1: I did this in the voice of so and So author.
S1: Hope you like it. And it's like in the actual
S1: published book on Goodreads, researchers develop RL method that predicts
S1: future outcomes. So yeah, exciting. Scary. Um, which is everything
S1: in AI tech CEOs using AI avatars to replace themselves
S1: in earning calls. So Klarna and Zoom CEOs are actually
S1: using AI versions of themselves. That's I'm not sure that's
S1: a good precedent to set, but pretty cool technology. Apple
S1: races to build smart glasses after getting behind on AI wave.
S1: So yeah, Google is building these things. Meta is building
S1: these things. Apple was starting with Vision Pro, but that's
S1: not portable. So they got to go with the glasses
S1: model because that's where Google and Meta are going. And
S1: look they got to move fast. They got to move fast.
S1: Tesla makes big push to catch Waymo in Austin robotaxi race.
S1: I can't wait to see this play out. Um I
S1: want Elon to win here. I'm mad at Ellen, so
S1: I don't kind of personally want him to win all
S1: that much right now. But I want Tesla to win
S1: because I don't want BYD to win. Um, Google is American,
S1: so I'm happy that there's a competition between Waymo and Tesla,
S1: and that makes me happy. And I can't wait to
S1: see a lot fewer drunk drivers, hopefully. Uh, because when
S1: they get into this vehicle, um, maybe it won't drive
S1: if you're drunk. Maybe, um, they just get into a
S1: back of a free vehicle because some, uh, local community
S1: service pays for any drunk person to call, and it
S1: will take them home. Stuff like that I'm really looking
S1: forward to. Plus texting and driving. Uh, someone really wants
S1: to look at that TikTok, and they happen to be driving,
S1: and that is horribly dangerous and I can't wait for
S1: those accidents to go down. YouTube introduces peak points, AI
S1: ads after emotional moments. Google commits $150 million to develop
S1: AI glasses with Warby Parker. We just talked about that one.
S1: Walmart is cutting 1500 technology jobs trying to reduce costs.
S1: Greater Manchester NHS trust rejects Palantir's national data platform. They're
S1: going to build their own in-house system. And BYD is
S1: beating Tesla in Europe for the first time, registering 7231
S1: vehicles and Tesla fell to 11th place. Humans. Tech companies
S1: cut entry level hiring by half since 2019, GPT fake
S1: consciousness performance was disturbingly convincing, so Jesse Singal asked or
S1: single asked ChatGPT to pretend to be conscious and had
S1: a very unsettling conversation with it. Uh, yeah, it was convincing,
S1: in other words. Salesforce execs says, uh, that agents should
S1: make us rethink every job. I think he's correct, but
S1: it doesn't go far enough. The right path is to
S1: constantly ask, you know, if you're a business owner, what
S1: would I do today if I started start it over,
S1: it doesn't mean you could always do that. But you
S1: should always be thinking about that and not suffering from
S1: the sunk cost fallacy. And with AI and hiring specifically,
S1: this is going to this is going to be a
S1: big deal. And I think a lot of companies are
S1: doing that. And I think this is the bigger problem
S1: with the job market is companies are basically saying, look,
S1: what if we just started over? Why do we need
S1: all these thousands of people? What are they really doing
S1: for us? Let's get back to basics. Let's only look
S1: at the core programs that we have to have, the
S1: core products we have to have. Let's cut the products
S1: we don't need. Let's cut, you know, programs we don't need.
S1: Let's cut services we don't need. Basically, I think everyone's
S1: going to be like, let's get in super thin shape,
S1: reduce everything that's not necessary. And this doesn't just mean hiring.
S1: This means focus of the business, which happens to also
S1: mean hiring. And then once they have okay, these are
S1: the core things we're going to do. How would we
S1: hire for that now. And I'm telling you the answer
S1: is going to be find really really smart. Extremely ambitious.
S1: No work balance type. People who are experts with AI.
S1: That is who people are going to be hiring now.
S1: And of course, where you have like extreme expertise, like
S1: an AI data scientist or something, then obviously they're going
S1: to go with, you know, an extreme expert there. But
S1: in general, as employees, they're going to hire crazy people
S1: who are obsessed with the company, who will work crazy
S1: hours and not ask about work life balance, who are
S1: experts with AI and who will actually have multiple AIS
S1: working for them. And that is basically what the new
S1: employees look like. That is what new employment looks like.
S1: And if you are not one of those people, I
S1: think you're in extreme danger right now. Poland is about
S1: to overtake Japan's GDP. Llms outperform paid human persuaders in
S1: both truth and deception. Claude three five was significantly better
S1: at persuading people towards both correct and incorrect answers than
S1: humans who had financial incentives. Sleep apnea pill shows striking
S1: success in large clinical trial. Yeah. Cut breathing interruptions by 50%.
S1: Denmark raises retirement age to 70 by 2040. A Guardian
S1: writer downloaded years of Alexa data and basically found their
S1: his family's soul. So 15,000 recorded conversations included his daughter
S1: using Alexa as a therapist, asking about dating ages and
S1: sleep problems, which she didn't discuss with the parents. Earth's
S1: dual high tide phenomenon turns out there's actually two high tides,
S1: one on opposite sides of the Earth. Lost dog found
S1: thanks to dead AirTag, which someone put a new battery
S1: in AirTag. And it lit up and the owner came
S1: and got it. And, uh. Tim Hartfords four decade journey
S1: with Dungeons and Dragons. So a missing teenager story in
S1: 1979 caused a moral panic, and it made D&D famous
S1: man 79. Wow. I didn't realize it was that old. Discovery.
S1: Reject jealousy and root for your friends. My buddy Joseph
S1: Thacker made a compelling case for rejecting jealousy and celebrating
S1: your friends wins. You got to go read this essay.
S1: What Sam Altman wishes someone had told him borrows from
S1: Anthropic's Claude team. I'm going to say that again. Borrows
S1: from Anthropic's Claude code team says context stuffing is way
S1: better than rag. It's funny because I've been using context
S1: stuffing as my main way of doing context. I don't
S1: like rags. I've always thought that they were janky, and
S1: it's great to hear Boris say that as well. Yeah,
S1: Ilya explaining that AI is actually do understand this. What
S1: I was talking about in the beginning inside the AI
S1: security arsenal that I've built, someone shows off 21 plus
S1: AI tools that they've created, from import table analysis to
S1: threat hunting frameworks. Paul Graham on what makes writing good.
S1: Internet Archive launches Live microfiche. Digitisation. Digitisation. Digitisation. Stream goodness. Digitisation.
S1: So basically this is a live YouTube stream of people
S1: digitizing microfiche. So they are just uploading things into the
S1: Internet Archive. And yeah, that's the stream. You're watching the
S1: internet be recorded into this giant archive. And yeah, um,
S1: it's accompanied by relaxing lo fi beats. And there's a
S1: Terraform MCP server integration tool between AI assistants. And Terraform
S1: helps you discover, explore and understand infrastructure code faster. Uh,
S1: got away here to use git as S3. Someone teaches
S1: data visualization with a bag of rocks. There's a new
S1: paradigm for psychology proposed here. Um, control systems as mental
S1: building blocks. I actually ordered this book. I can't wait
S1: to go read it. Um, the book is called The
S1: Mind and the wheel. Um. Oh, yeah, I gotta I
S1: got a go download this thing. I think it's already
S1: in my audible. Hold on one second. We're doing it live. No,
S1: there is no audible version. Okay, well, whatever. That's annoying. Um, anyway,
S1: I really want to learn this, uh, or check it out,
S1: so see if it's cool. Um, so it basically says
S1: the mind might function as a collection of control systems
S1: that regulate everything from hunger to loneliness. And, uh, yeah,
S1: I'm not sure if this is the dumbest thing ever
S1: or the coolest thing ever, but worth including for you
S1: to check out. Tycho is a new API that returns
S1: visual answers from structured data sources in response to natural
S1: language queries. So if data is trapped in databases or
S1: web crawlers, you can now go and basically get that
S1: data with natural language queries. A database of 1400 startup
S1: ideas from Hacker News and Reddit. Decibels are silly. That
S1: was a cool piece, basically talking about how the measurement
S1: doesn't make any sense whatsoever. Google Gemini advanced ads GitHub
S1: integration for code analysis, timeline views, and smart filtering on
S1: Hacker News. It's a new Hacker News interface with a
S1: lot of filtering options undetected. Tag stealth tracking for stolen items.
S1: And ideas entering into the Member edition stuff here. Ideas.
S1: So the idea of the season high agency and ideologies
S1: as tools versus values. So a lot of interest right
S1: now in what people are calling this high agency thing, which, uh,
S1: I think there was a yeah, George Mac came on
S1: Chris Williamson Modern Wisdom and I love the concept, and
S1: I think a lot of people need more of it.
S1: But the I would say most people, but not everyone, uh,
S1: we actually talked about this in book club and uh,
S1: someone in there was basically saying, look, this this might
S1: be really nasty. You know, it seems kind of elitist
S1: or like very talking about the NPC thing. And I
S1: think anything taken to an extreme can be bad. Like
S1: if you have too much capitalism, you need socialism, you
S1: have too much socialism. You might need some capitalism. Uh,
S1: too little self-esteem. You need more. But narcissism is bad.
S1: You know, Stalinism is bad. But maybe Nordic countries got
S1: something figured out. So one idea I put out there
S1: was basically a set of fundamentals, like I talked about, um,
S1: in my political post where it's like, these are my
S1: core values. This is what I believe, you know, humanity
S1: should look like. This is the ideal world that I
S1: wish we lived in. And that's one set of things.
S1: And then if someone says capitalism or whatever, some, uh,
S1: you know, Marxism or some other thing or some sort
S1: of ideology, think of the way I'm thinking of this
S1: is those are tools. They're separate from values. So are
S1: you a capitalist? No, no, what I am is a
S1: Star Trek liberal, right? I want to elevate humanity. I
S1: want humanity to be more, um, enabled. I want everyone
S1: to have equal opportunities. I want I want to raise education.
S1: I want to have everyone, you know, participating in creativity
S1: and exchanging creativity with other humans. I want technology to
S1: be in the background, not in the foreground. I want
S1: humans to be in the foreground. Right. So those are
S1: the core things that I think about kindness, helping others,
S1: exchanging value with others. Right. Uh, freedom. All these core
S1: core value things. Now, if someone hits me with, you know,
S1: high value or high agency or Marxism or whatever, I'm
S1: going to listen, right? Okay, cool. Sounds awesome. But that's
S1: a tool. That's not something that replaces one of my values, right?
S1: It's a method for getting there. So if I don't
S1: have enough socialism in my life to achieve my values,
S1: we should sprinkle some more on there. If someone sprinkles
S1: too much and I only taste socialism on my sandwich
S1: or my steak or whatever it is, well, that's too much.
S1: It went too far. And now you're actually conflicting with
S1: my values. We need some more capitalism. Cool. Let's do
S1: some more capitalism. It goes crazy, becomes like a crony capitalism.
S1: It becomes banana republic. It becomes all this stuff. Well
S1: guess what? We got too much capitalism there need to
S1: take some of that off. So the key thing is
S1: to be guided by these values and then use everything
S1: else as like these different slider bars of tools that
S1: you could use and adjust to how much you, you know,
S1: adjust to taste, with the guiding principle being not affecting
S1: the actual values of what you're trying to do. So
S1: that was the idea of the week. Um, got a
S1: simplified I security term breakdown, which, um, which I basically
S1: modified off of my buddy Joseph Thacker's list. So I
S1: alignment don't kill us. I safety don't help us do
S1: bad stuff. I security basically I appsec oh we already
S1: talked about discovery so we're good and now recommendation of
S1: the week embrace chaotic reading if that happens to you.
S1: I'm currently reading letters to a young poet, the Dharma Bums.
S1: Don't tell me I can't. Rilke's Book of Hours, the
S1: Pleasure of Finding Things out, The Peregrine, The Rise of
S1: Theodore Roosevelt, Accelerando Dominion birds, Sex and Beauty, the player
S1: of games. And they thought we were free. I'm usually
S1: reading like 2 to 3 books at a time, so
S1: this is way more than usual. Maybe more than ever.
S1: What is this? Is this, like 20 books? Like 15 books?
S1: Something like that. But I'm learning to be okay with this.
S1: And this is my recommendation. This is my ask for you.
S1: Be okay with it. If you're only reading one book,
S1: be okay with it. If you're reading 15 and you're
S1: switching between them, be okay with it. The one thing
S1: to not be okay with in my mind is not reading.
S1: So just read one book at a time or 17.
S1: Don't be frustrated with yourself, just read. And the aphorism
S1: of the week. The most important decision you make is
S1: to be in a good mood. The most important decision
S1: you make is to be in a good mood.