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S1: Welcome to Unsupervised Learning, a security, AI and meaning focused
S1: podcast that looks at how best to thrive as humans
S1: in a post AI world. It combines original ideas, analysis,
S1: and mental models to bring not just the news, but
S1: why it matters and how to respond. All right. Welcome
S1: to episode 438. This is Daniel Miessler. Okay. What do
S1: we have here? So registration for augmented v2. July 26th
S1: is now open. About half the slots are about gone.
S1: So sign up by clicking down below in the newsletter.
S1: Or you can just go to augmented dot Unsupervised learning.com
S1: and sign up. And, uh, if you become a UL member,
S1: you actually get $250 off. So inside of the chat,
S1: you can get the discount code, which is $250 off,
S1: and becoming a member is only $99 for the first year. So.
S1: You could do the math there. Let's see here. Fabric
S1: now supports Anthropic's sonnet 3.5, which is really, really good.
S1: So if you update and then you do fabric list models,
S1: it will show you that, uh, cloth 3.5 is in there.
S1: And this is the actual tag for it. Claude dash three,
S1: dash five. Sonnet dash 20240620. Uh, one thing to note, though,
S1: is sonnet and haiku. They throw a lot of copyright
S1: failures like it won't actually read podcast transcripts, which is
S1: super annoying because it's one of my favorite things to
S1: do is put long form conversations through these things and
S1: like pull out like lists of references that they mentioned
S1: if I want to order the books or whatever. So
S1: that's kind of annoying that it doesn't allow me to
S1: do that with one of the lower models. Opus does
S1: allow me to do that, and GPT four does allow
S1: me to do that, but I can't do it in
S1: sonnet or haiku. So I'm really waiting for them to
S1: either fix that with the lower models or to give
S1: me opus 3.5. I cannot wait to use that. Definitely
S1: gearing up for Vegas black Hat Defcon. Hope to see
S1: you there! I'm going to have stickers, but, uh, don't
S1: really have anything else other than stickers. Might eventually do
S1: a t shirt or something with like a superhero logo
S1: of just, uh, the UL logo, but no text or anything.
S1: So I've been feeling quite grateful for my life lately
S1: and somehow, like, super powered. Not really, because, uh, things
S1: are going well, which, uh, they're definitely going pretty well
S1: in terms of, like, the business and everything like that, but.
S1: It's more so that I feel like I'm living an
S1: authentic life. I feel like the stuff that I'm doing
S1: is all oriented in the direction, and I'm working on
S1: a like a essays and a course around this and
S1: stuff like that, but it's essentially like, do you know
S1: what you're trying to accomplish? And are you heading in
S1: that direction? And what I've been trying to do is like,
S1: hack together these systems where I say no to things
S1: that are not in the direction of these goals. And
S1: one thing that came up in a conversation with my
S1: buddy recently is, um, the cool thing about this. Oh,
S1: and I also got this from a David Perell analysis
S1: of a bunch of Seinfeld stuff. It's like fail at
S1: things that you wanted to do anyway. And it really
S1: doesn't feel like failure, right? Because I'm doing a lot
S1: of different stuff. Maybe too much sometimes, sometimes not enough.
S1: But in general, I'm doing lots of different things. And
S1: the question is, how many do you say no to?
S1: And what is your criteria for saying yes or no?
S1: And what I've come to basically figure out is that
S1: if you look at that Seinfeld thing, he basically said,
S1: fail doing it, doing what you want the way that
S1: you want. And this really resonated with me, because I
S1: think about people who are kind of chasing other people.
S1: They're chasing like a high school bully, or they're chasing
S1: like their boss or whatever, some, some competitor for some reason,
S1: and they're chasing them, and they might actually not be
S1: thinking about what really drives them, what purpose they are chasing,
S1: the thing that they actually want to do deeply. That's
S1: part of their own personal identity. Instead, they might be going, well,
S1: the thing I'm doing isn't working. The thing they're doing
S1: is working, so I'm going to copy them. Or if
S1: someone gives them advice and they're like, hey! You know,
S1: this thing that I'm doing works really, really well. You
S1: should do that. And one thing that comes to mind
S1: around this is Alex Hormozi. He is, like, obsessed with
S1: making money. Him and his wife are obsessed with making money,
S1: which I respect. They're also way younger than me. So
S1: they're like in their late 20s, early 30s. Or maybe
S1: he's like mid 30s or whatever, but he wants to
S1: crush business. And guess what? He is crushing business. So
S1: what I'm going to do is take some of that
S1: DNA from crushing business and try to sprinkle it into
S1: my stuff. But what I won't be doing is making
S1: the mistake of trying to be Alex Hormozi and switching
S1: my focus and my telescope to point towards this distant
S1: thing of like, oh, I want to make $100 million,
S1: and he doesn't want to make $100 million. He wants
S1: to be a billionaire. I guarantee you that's his goal
S1: is like $1 billion in revenue or wealth or something.
S1: So the thing is, you have to know what direction
S1: your stuff is pointing in, and then you can more
S1: easily say no to things because are they going to
S1: get you there sort of accidentally? And that's the way
S1: I've sort of oriented things, is like, I want to
S1: do things so that I can accidentally make money from them,
S1: like do stuff that you would do anyway and hook
S1: it up to a stripe page and hope it works out.
S1: And I know that's not the way to make a
S1: lot of money. And I know you should, like, go
S1: all in on a thing that's like, got a giant
S1: market and, you know, you spam the hell out of
S1: the ads or whatever, and you just try to make
S1: all your money from that. But I feel like it's
S1: just I feel like it's a bad game. And another
S1: one of my favorite things, which I've mentioned here before,
S1: is like, the worst way to lose is to win
S1: playing the wrong game. And so this is all sort
S1: of like flowing into that same thing. And I've been
S1: happy with the newsletter lately because I've just been more
S1: of my authentic self, and I feel like that's something
S1: I just want for everybody. I want everyone to be
S1: in tune with who they are and what they want,
S1: and have them actually chasing those things and talking about
S1: those things, talking about it is super important. It's like
S1: I'm really worried about people becoming invisible after AI, because
S1: AI is going to be finding the coolest stuff and
S1: the best stuff. Well, if you're not saying anything, AI
S1: is not going to find you. And also, if you're
S1: not saying anything, you might not be thinking the things enough,
S1: or you might not be in practice enough with coming
S1: up with the ideas, crystallizing them, and writing them down.
S1: Because I'm not advocating that people try to become successful authors,
S1: or they try to become successful streamers or YouTubers or
S1: anything like that for the purpose of being successful. I'm
S1: advocating that they do it because it will make them
S1: feel good to know themselves and to articulate themselves and
S1: to emote themselves. And that's why I'm obsessed with this
S1: whole thing. That's why I'm obsessed with this human 3.0
S1: and all the stuff that I'm talking about. So anyway,
S1: I hope you've noticed the difference in terms of me.
S1: But really, the thing I'm trying to do, the whole
S1: purpose of doing this for myself is to like, battle
S1: test it so I can help other people do it.
S1: All right. Stream of consciousness, collection of thoughts on US politics.
S1: Just skip it if you don't like politics. But I
S1: basically lay down a whole bunch of stuff there. Oh,
S1: this is just become a member. Yeah, yeah, become a
S1: member if you can. I would appreciate it. All right.
S1: CSA held its first. This is, uh, security stuff here
S1: or it's the new stories section. But, yeah, I always
S1: start with security. CIA held its first AI security tabletop exercise,
S1: over 50 experts, and they're looking to simulate responses to
S1: AI security incidents. I love that they're thinking about it.
S1: Extremists in the US are using AI to spread hate speech,
S1: recruit and radicalize faster than ever. And they got a
S1: couple of examples here. President Biden using using racial slurs
S1: and Emma Watson reading Mein Kampf. I kind of want
S1: to go watch these, but I also don't want to
S1: Google for them. So yeah, I do not need that
S1: garbage in my YouTube feed. The US has banned the
S1: sale of Kaspersky anti-virus software, citing security risks from Russia.
S1: And they're basically using strong language here. They're like, do
S1: not use this stuff, stop using it immediately, get off
S1: of it. And I think they have a deadline in July,
S1: and there's a new set of high security vulnerabilities in Asus.
S1: Routers let you get complete control without any user interaction.
S1: And you want to patch these immediately. And the Richter
S1: scale is 9.8. Thank you to dropzone for sponsoring Dmitri Alperovitch. Yeah.
S1: Dmitri Alperovitch why is that difficult? That's not difficult. Imagined
S1: a detailed scenario of China invading Taiwan in 2028, focusing
S1: on aerosol strategy through the rough Taiwan Strait waters. This
S1: one is fascinating to me because I don't have strong
S1: intuitions myself, I. I don't have strong expertise myself in
S1: like this whole naval warfare island attack type situation. It's
S1: just a it's not something I've ever studied. I don't
S1: know the stuff, but what's really fascinating to me is
S1: that the super, super experts on this, they disagree with
S1: each other, which reminds me a lot of the AI
S1: situation there. I do have strong intuitions and some expertise
S1: and a lot of expertise in some areas, and some
S1: expertise in others, but it's like I feel way more
S1: grounded and solid and confident in at least my intuitions
S1: on the AI side. But here I have no idea
S1: what's going on. And in both cases, the absolute best people,
S1: which we're supposed to be looking up to, they do
S1: not agree with each other and they disagree massively with
S1: each other. Tyler Cowen had someone, this woman from she
S1: was Bulgarian on his podcast recently, and it was so
S1: good and her view was completely different than so many
S1: other people. So I'm watching like Peter Zeihan. I'm listening
S1: to her. I'm listening to the Goodfellas, which is a
S1: podcast of a bunch of security and like really smart people.
S1: And I'm trying to triangulate between all these different people
S1: and basically come up with my own thoughts and opinions
S1: based on, you know, not being an expert on it.
S1: I just I find it so interesting that they can't agree. Oh,
S1: same with, um, same with Ukraine. Right. Vastly different predictions
S1: on whether or not it was going to happen in
S1: the first place, when it's going to end, vastly different predictions.
S1: And that excites me that that massively excites me because
S1: I'm like, there is signal in here somewhere. The question is,
S1: is it available second? Are people not getting the signal
S1: or are they misinterpreting it? How are the best experts wrong?
S1: And it raises the question of like, what else are
S1: the best experts wrong on? And I just like to
S1: have that constant tension and questioning in my mind as
S1: I think about everything, including the things that I. I
S1: believe the strongest US is moving to limit investments in
S1: China's AI, semiconductor and quantum computing sectors to curb China's
S1: tech advancements. And now they're looking at private equity venture
S1: capital funds. Great stuff. And then I say, yeah, cool.
S1: But we need to be building a lot more energy production.
S1: Look at this. Look at this graphic from Leopold. This
S1: is from situational awareness, which I just printed out, by
S1: the way. It's like, I don't know, two inches thick
S1: front and back. It's it's a beast. And I'm just
S1: going to keep it in my car and read it. Uh,
S1: whenever I'm parked and whatever, listening to podcasts. I won't
S1: do them both at the same time. But anyway, I'm
S1: going to keep it in the car and read through it.
S1: I've already read a whole bunch online and of course,
S1: watch the video, but. Yeah. All right. National. Yeah. Nationwide
S1: armed militia from this guy named Jake Lang, who is
S1: a January 6th writer. And they're using telegram for coordination.
S1: And this is a wired article study says staring directly
S1: at the camera during online job interviews can significantly boost
S1: your evaluation scores. And this is from University of Hiroshima
S1: AI agents and the Raz Revolution results as a service
S1: discussing the evolution from SAS to raz, emphasizing the role
S1: of AI agents. Yeah, I like this a lot. It's
S1: like abstract. This is exactly what I'm building with a
S1: project called substrate, which you're going to hear a lot
S1: about here before too long. But this is exactly what
S1: I'm building. You make a query and agents and a
S1: whole bunch of data work together in a whole bunch
S1: of steps, and the output that you get is the
S1: result of their work. So if you imagine one idea
S1: I have here, which is super exciting, is I'm going
S1: to build intelligence reports this way. So on one hand,
S1: you've got a whole bunch of sources that are maybe
S1: they're hard to find, maybe they're not, but there are
S1: many of them. And it takes some effort and curation
S1: ability to to pick the best ones. Right. You bring
S1: that together, you turn that into a giant summary or
S1: a giant context file, and you basically start producing intelligence
S1: artifacts out of this thing. And that's what I like
S1: about results as a service. Okay. You you ask the question,
S1: what is going on in Ukraine? What are the latest
S1: global trends? Where should I invest my money or whatever?
S1: And there could be hundreds of operations that happen, multiple
S1: different eyes doing multiple different things. But really, it's not
S1: even so much the AI until the end where it's
S1: like putting it together and telling the story. It starts
S1: with more legacy tech. It starts with all this collection,
S1: all this correlation and unification of this data and getting
S1: it into a thing that's ready for the for the
S1: actual AI. And having brushed up against intelligence a little
S1: bit when I was in the Army, I was attached
S1: to an intelligence group and got to see a little
S1: bit of how things work. Very little. I don't want
S1: to oversell it here, but I've also read a whole
S1: bunch about it and seen a whole bunch of this
S1: stuff in on the Intel side and also on the
S1: military side, and it comes down. Oh, I also built
S1: a program around this concept at Apple for the security org. Right.
S1: And this is a product that's still being used today
S1: inside the company, which I'm very proud of. But I
S1: use these concepts there. It's like data information intelligence. And
S1: the intelligence piece is the one where there's intelligence, human
S1: intelligence going into that effort to turn this into a narrative,
S1: into a story for decision support. So at the above intelligence,
S1: you have decision, right? Or maybe maybe it's not above,
S1: maybe it's, um, to the right or whatever. But it's
S1: like the purpose of that intelligence is to distill down.
S1: So the the general is not looking at log files. Right.
S1: Which is like data streaming in. Okay. You have information.
S1: It's like a summary of the log files. And this
S1: might not be a perfect analogy, but it moves up
S1: and up in layers of abstraction with the entire purpose
S1: of a human making a decision. Right? So if you
S1: look at results as a service, I'm thinking intelligence as
S1: a service is what I'm thinking. That's the first thing
S1: in my mind goes to there's a whole bunch of
S1: different implementations of this. Like it could be a a
S1: business decision. Do I cancel this customer? Do I refund
S1: this customer? Do I do whatever? But either way, it's
S1: a whole bunch of legacy things happening with collection and
S1: stuff like that, and IT systems and data sources and everything.
S1: And then there's this thing that traditionally human is done,
S1: or like Deborah or Chris or Raj or whoever it is,
S1: they look at all the data and they say, you
S1: know what, we are going to issue a payout for
S1: this insurance policy, and that is the decision part. And
S1: the result is a yes or no. Thumbs up, thumbs down.
S1: And that gets us to this results as a service. Right?
S1: So ideally you could just take all your different data,
S1: all your different decision points, throw that into context, throw
S1: it up to one of these services, which is one
S1: of these that I'm building as well. And in like
S1: 3 or 4 years, everyone's going to know this, and
S1: this is just going to be the way all software
S1: is built. But I'm already building this and I've already
S1: got pieces of this that work. Um, and it's it's
S1: really exciting. I mean, this is essentially the future of
S1: software is doing this. You ask a question, you include context,
S1: and you get back a result, a very high quality result.
S1: So very excited about this concept and very good, uh,
S1: piece here on medium, which I wish nothing was on medium,
S1: but whatever. All right. Sam Logan has a new post
S1: on how to get structured output from Llms, covering various
S1: frameworks and their pros and cons. Yeah, really enjoy this one.
S1: Talked about converting English to JSON and a bunch of
S1: other stuff, but yeah, enjoyed that. All right. Someone got
S1: invited to a project called Rebind where I versions of
S1: authors interact with readers about classic books. These are the
S1: really cool ideas. I just love ideas like this. Coming
S1: to life Fabian Boff from Octo Mind explains why they
S1: stopped using laying chain for their agents, highlighting its rigid,
S1: high level abstractions. So I'm going to say something extra
S1: spicy right now. A lot of these low level libraries
S1: and frameworks for doing really difficult things like Rag, uh,
S1: or agents, a lot of these agent frameworks, things like
S1: laying chain, they are extremely kludgy. If anything was like overhyped,
S1: I would say these first generation efforts at these things
S1: are massively overhyped. And it kind of goes back to
S1: this results as a service thing. Results as a service
S1: is kind of the future of this, because it's not
S1: asking you to mesh stack together these Jenga blocks of
S1: like really kludgy code. Because here's the problem. And it
S1: kind of applies to, um, to, uh, what are those called, um, notebooks.
S1: It kind of applies to notebooks as well, because notebooks
S1: like notebooks and agents and in laying chain, they make
S1: great demos, they make great demos. But when you try
S1: to take this stuff and build a production app on it,
S1: you throw real scenarios into it, you throw real data
S1: at it and it just falls over. And what universally,
S1: almost everyone I've seen, and this is definitely what I've done.
S1: You have to rebuild the entire thing from scratch using
S1: smaller parts, which I've always been obsessed with this smaller
S1: parts connected together. So it's like it it's necessary for
S1: AI as well, which is why I'm building my very own,
S1: like complete stack on this, uh, which is called substrate.
S1: And I'm basically using those components to build apps. And
S1: each one of the components is highly trusted and highly
S1: rigorous in what it takes in and processes and what
S1: it outputs. So it's dependable all the way through the pipeline.
S1: And if you don't do this and you just start
S1: piecing together laying chain and agent frameworks. And don't get
S1: me wrong, I am not like, uh, being negative about
S1: these things. Laying chain has done amazing things for AI
S1: and these agent frameworks Autogen crew AI. They're doing amazing
S1: stuff too, like no question. But as someone who's actually
S1: building this stuff, the reality is completely different when you
S1: start throwing production data at this stuff because you need consistency,
S1: you need trust in these systems. And, uh, pretty much
S1: every one that I know, they started building with these
S1: bigger blocks. And then they got a few months in
S1: and they had to tear it all down and start over,
S1: building their own individual components. And so I think that's
S1: where this is all going. Um, and eventually that's going
S1: to merge into results as a service, which is kind
S1: of like the opposite, actually. It's more like somebody did
S1: build that back end, uh, dependable infrastructure, and now you're
S1: just getting the results from it. Um, and the way
S1: to know if it works is does it work? Do
S1: you keep getting the right answer? And if you do,
S1: you'll we will, as a society, build trust in that
S1: over time. And ideally the thing will also be it
S1: will also be publishing information about its components and the
S1: sources that it used. So there'll be like some transparency there. Uh,
S1: that will be part of. The quality of the service
S1: is if you can actually inspect it and inspect like, okay,
S1: which models did you use, which data sources did you use? Right.
S1: Because if you're just like, no, trust me, because I'm
S1: awesome and you like the results and don't worry about it,
S1: for a lot of people, that won't be enough. All right?
S1: My buddy Evan Özcelik and long time Ulr, he argues
S1: that our obsession with convenience is making us less human,
S1: and he wrote this on his blog, Snake Eye Software.
S1: Solar generated a fifth of global electricity at midday on
S1: the summer solstice. Solstice? Yep, a fifth of global energy
S1: at midday. On summer solstice. That is interesting. I feel like,
S1: I don't know, I'm not an expert on energy either,
S1: but I, I really just wish we would go way heavier,
S1: way heavier on solar, build, 100 giant mega solar farms
S1: or 500 of them. And just go head to head
S1: against China on solar. Because nuclear reactors, I understand that
S1: they're really good energy and I understand they're awesome and
S1: I understand all that, but they're very complex. They are
S1: fundamentally dangerous, even though I know they're way safer than
S1: they are now. It's possible to make them safe. Here's
S1: another reason they freak people out. So there's going to
S1: be so much more friction to trying to get one
S1: or 2 or 10 of them built. And we are
S1: in a race where speed matters and time matters. So
S1: China can just build a whole bunch of those because
S1: they have nobody to ask. They're just like, move, move.
S1: We're building stuff here and everyone's like, yay, China! Like
S1: they have just this massive advantage. Well, we can we
S1: can move fast too if we do solar and it's
S1: not freaking people out. So let's just go build 500
S1: mega solar plants like Elon has been talking about forever,
S1: and just jump way ahead, way ahead of the game
S1: and still do nuclear, right? And you know where that
S1: makes sense and where we can. But don't let it
S1: slow us down. So when I'm talking about using SSH
S1: as a pseudo replacement, uh, basically you can use it
S1: to execute commands as another user. So why not use
S1: that permission model instead of pseudo? Interesting. Apple is renaming
S1: Apple ID to Apple account I like it. A group
S1: of 17 secondary schools in Southwark, London, are going smartphone
S1: free to combat the negative effects of phone use on students,
S1: and I really love that Jonathan Heights work here is
S1: spreading so fast. He put out his new book. He's
S1: been blasting it all over podcasts. And I just love
S1: that his work is catching on. And Gavin Newsom is
S1: now going to ban, uh, smartphones. Or he's trying he's
S1: trying to ban them. And likely this is coming from
S1: impact from height as well. House prices are surging again
S1: with global index up over 3% year on year. Insufficient
S1: sun exposure is now a serious public health issue, potentially
S1: causing hundreds of thousands of deaths annually in the US
S1: and Europe. Isn't this like cheese and eggs and milk
S1: and I don't know, so many, so many things. It's like,
S1: that'll kill you. Well, everyone stops and it's like, yeah,
S1: if you don't do that, that'll kill you. So I
S1: can't wait till Huberman figures this out for us. Uh,
S1: reads all the papers, uh, invites all the experts on.
S1: And I know a lot of people are starting to
S1: doubt Huberman and stuff like that, and I'm. I'm getting
S1: a little more cautious with him. I'm not sure why,
S1: but I'm getting a little more cautious with him. But
S1: I don't know anyone else who is looking at these
S1: papers and doing this great analysis. And I don't fault
S1: people for being wrong sometimes I don't, I fault people
S1: for being overconfident, never looking at themselves, never going after
S1: their own errors, never trying to self-correct, and not being
S1: analytical and pointing that lens at themselves. And I feel
S1: like Huberman is good at that. I would like to
S1: him to come back and do some episodes on, like,
S1: here's stuff I got wrong and why and why I'm
S1: going to fix that in the future. But anyway, he's
S1: the best there is right now. And what I would
S1: like to know about the sun thing is, I feel
S1: better when I go out in the sun in the morning,
S1: and I'm not sure it's only because of the light
S1: going in my eyes. I feel like there's something about
S1: walking on the ground, absorbing sunlight, and a lot of
S1: this feels very emotional. So that's why I actually want
S1: to see data. Like, I don't want this to be
S1: based on vibes, but meanwhile I'm going to do it
S1: based on vibes. Seems like a net good. Oh, in
S1: one caveat, there is, um, between 10 and 2 is
S1: like the worst time because your UV index is the worst.
S1: But like super early sun is supposedly fairly safe because
S1: the angle of incidence like coming into the atmosphere. So
S1: evidently the UV is pretty low, but the visible light
S1: I think is still much higher. I'm not sure if
S1: those are correlated, but visible light is going to, uh,
S1: set your clock, your circadian rhythm. And I feel like
S1: there's just other health benefits there, too. And anyway, what
S1: that study was basically saying is that a lot of
S1: people aren't getting any sun, they're not going outside, they're
S1: not doing anything. And I wonder how mixed that is
S1: with like other things like exercise or whatever. But obviously
S1: they try to filter that out. But the the idea
S1: is basically people aren't going outside and they're depressed. And
S1: guess what? There's probably a connection. They're having more positive
S1: experience in life is linked to lower odds of brain
S1: disorders like Alzheimer's and lower cognitive decline. And, uh, got
S1: this secret Ted Cruz funding documents slightly redacted. Cut this, um,
S1: this guy over here called Capital Press, and he's basically
S1: like a watchdog, like a one person, like journalist group monitoring,
S1: like the goings on of Congress people. And it's not in, like,
S1: a stalkery nasty way. Uh, or a legal way. I
S1: think it's more like Osint. Like what? Things are being
S1: dropped out there in the public, you know, little clues
S1: that can be hoovered up and, uh, and checked out.
S1: And this is one thing I'm really excited about. This
S1: is another thing I'm building in substrate is like an
S1: intelligence collector, uh, which will allow me to do intelligence
S1: reports like the one that I did before. And like this,
S1: this guy is doing so. Yeah. Before. Before basically, this
S1: was spy agencies, right? Journalists and spies were able to
S1: do this because they had the money and they had
S1: the training. Right. Well, I, I is cheap. It has
S1: a lot of knowledge about how to do things. It
S1: also has knowledge about how to turn those things into
S1: a story and see patterns between them. So I'm going
S1: to build something really nasty here. Really, really cool. Nasty.
S1: I mean awesome. Uh, so so I can have like
S1: a daily intelligence report of like, here's what the smartest
S1: people in the world think about this, and here's where
S1: their ideas diverge from each other. Here's how their opinions
S1: diverge from one another. Here's where their ideas point at
S1: the exact same thing. And I can do like a
S1: heat map of like, opinions and like, I can't wait
S1: to mess with this thing. All right, in our solar
S1: eclipse photo competition. Look at this thing. This thing is insane.
S1: Look at that. Look at this. That is so beautiful. Purple.
S1: It's got the solar halo. And you got a plane. Oh.
S1: Second place. Yeah. The plane one's way better. Sorry. Yeah.
S1: Look at this. Purple. Oh, man. Got some, uh, vapor
S1: trails there. Yeah. Very cool. All right. Ideas. Toddler intelligence
S1: to PhD in around four years. That's what, uh, Mira
S1: Murati just talked about on a podcast. Very interesting. I've
S1: got this thing. Uh, just don't call it AI if
S1: you're tired of hearing the word, I have a solution
S1: for you. Forget. I forget the word. Forget everything about it.
S1: And now just imagine the addition of hundreds of billions
S1: of little helpers that people and companies can use to
S1: do things like white collar work and writing and editing documents,
S1: responding to emails, doing customer service, making sales calls, etc.
S1: and taking ideas and turning those into full presentations, books
S1: or films or whatever, and millions of other tasks that
S1: otherwise would have not been done at all or would
S1: have been done slowly or poorly by humans. And we're
S1: about to have that. That's what we're about to have.
S1: We're about to have billions of those things, doing tons
S1: of stuff. I mean, just mountain loads of stuff that
S1: has never been done before or was being done worse
S1: by humans. Maybe we should call these things helpers, or
S1: maybe we should call them whatever, but they're definitely going
S1: to be very helpful, especially to employees who want to
S1: do ten x the amount of work, or 100 x
S1: the amount of work, or 1000 times the amount of
S1: work that they were doing before, and have it be
S1: done in a more consistent way or with higher quality.
S1: And remember, the bar for a human work is low.
S1: It is very low. If you've ever worked anywhere, if
S1: you've ever worked with anyone. We are fallible. We make mistakes.
S1: We are inconsistent. And if you ask a whole bunch
S1: of experts how to do something, or you have them
S1: actually do something, the quality level will be vastly different
S1: and the average worker is just not that good at
S1: their job and they don't really care that much. I mean,
S1: that's just a fact. Or I would say maybe not
S1: the average one, but like right below the average and
S1: there are millions upon millions of those workers. So we're
S1: talking about replacing that with better versions, more scalable, more
S1: consistent and more upgradable, like instantly upgradable. So that's what
S1: we're about to have billions of those. But don't call
S1: it AI. Nope, nope, don't call it AI. Because AI
S1: is annoying. It's an annoying word, so just don't use
S1: it and you'll be safe. All right. Discovery web check.
S1: Free and open source tool. Oh, this thing is so cool.
S1: This thing is so cool. Yeah, I was playing with
S1: this earlier. Watch this. All right, so first of all,
S1: you can download it and run it yourself. But they
S1: also have a web version. So look at the website websites. Cool.
S1: All right. Is this cool. This is cool. I mean,
S1: is it valuable? I don't know. But I love it.
S1: I mean, it's got so much value in here. It's
S1: just I'm not sure how I'm going to use that value.
S1: Probably parse the whole thing and ask AI to. Tell
S1: me what I need to pay attention to and prioritize
S1: it for me. Give me a turn into a task list.
S1: That's what I probably do anyway. It's really cool. Hayes
S1: Labs automated red teaming against Llms Agentic LLM Vulnerability Scanner
S1: open source tool for fuzzing and stress testing Llms with
S1: customizable rule sets. Incogni I use another one I think
S1: I use delete me is very similar to this service
S1: that removes your personal info from the web to block
S1: spam calls and protect privacy. Confusion is a muse. This
S1: is a really cool little article. Talk about. Yeah. You
S1: don't want to get rid of confusion because it it
S1: speaks to you, makes you smarter. Complexity expands to fill
S1: the space available. Mhm. Love it. The winter of content
S1: how Game of Thrones changed media driving traffic and homogenizing journalism.
S1: A new salary database created from viral videos. The internet
S1: is now for bots, not humans, and an impossibly thin
S1: fabric can cool you down over 16 degrees. I hesitate
S1: to show these things unless I can buy them, but
S1: this one was cool enough. I made an exception and
S1: the recommendation of the week. Get a copy of Meditations
S1: by Marcus Aurelius and keep it by your bed and
S1: read some before you go to sleep. Two major benefits one.
S1: You'll fall asleep faster. Two you will fall asleep feeling
S1: grateful for your life. I think that's what stoicism does
S1: for me, and I hope it does it for you
S1: as well. And the aphorism of the week inspired by love,
S1: guided by knowledge, inspired by love, guided by knowledge Bertrand Russell.
S1: Unsupervised Learning is produced and edited by Daniel Miller on
S1: a Neumann U87 AI microphone using Hindenburg. Intro and outro
S1: music is by Zomby with the Y, and to get
S1: the text and links from this episode, sign up for
S1: the newsletter version of the show at Daniel miessler.com/newsletter. We'll
S1: see you next time.