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S1: Unsupervised learning is a podcast about trends and ideas in cybersecurity,
S1: national security, AI, technology, and society, and how best to
S1: upgrade ourselves to be ready for what's coming. All right.
S1: Welcome to Unsupervised Learning. This is Daniel Miessler, and I'm
S1: building AI to upgrade humans. Episode 466. Hope your week
S1: is starting off better than Nvidia's did, which is probably
S1: pretty easy to do. Went to a phenomenal offensive security
S1: and AI conference sponsored by Rob Ragan. Not really sponsored
S1: by him, but put on by him. Had lots of
S1: cool sponsors for different things. But anyway, it was a
S1: whole day of hacking. Basically, it was a hackathon fundamentally,
S1: but it was also just a great networking event for anybody,
S1: kind of at the intersection of AI and security in
S1: San Francisco. And it was just just great. Rob did
S1: a great job. Kind of a nerd observation. Far too
S1: many people don't know how to use name drop. If
S1: you take your iPhone and set it next to another
S1: one's iPhone, someone else's iPhone at the top. Like if
S1: you just touch, you don't even have to touch, but
S1: you get close enough. It's NFC based and you could
S1: transfer your contact. Every time I do this, people are like,
S1: what have you done? Like, they can't believe it. It's
S1: got like this little wobbly like liquid effect. It's like
S1: the coolest thing ever. And it's how I like to
S1: exchange contacts with people who have iPhones. And yeah, I
S1: don't know why more people aren't doing it or don't
S1: even know the feature exists. And this is in San Francisco,
S1: by the way. This is like the center mass of
S1: Apple in the center of mass of like the tech scene.
S1: And people in San Francisco don't know how to do
S1: this thing. They don't know that it exists. Really glad
S1: I bought a bunch of TSMC last week because it
S1: just massively crashed. Uh, yeah, that's that's awesome. All good though.
S1: Playing the long game. I think it's fine. We'll actually
S1: talk about that a little bit later. I just finished
S1: reading a book for UL. Book club. Picture of Dorian gray.
S1: Lots to say about that. That's a whole nother talk show.
S1: But one thing I will say is that read classics.
S1: You need to read classics. They are more dense with
S1: value than almost any other type of book, probably any
S1: other type of book. And every time I read one,
S1: I'm like, how many of these are just sitting there
S1: full of this much knowledge, and I'm not reading them. Infuriating.
S1: So luckily we have UL Book Club to prod us
S1: into doing that. Here's what we do. In UL Book
S1: Club we oscillate between nonfiction, fiction, nonfiction classic. Nonfiction fiction, nonfiction. Classic.
S1: So what we're doing is half nonfiction and half or
S1: half nonfiction, one quarter classic and one quarter fiction. And
S1: that has been a good mix for us. And we've
S1: been doing this since like 2016 or something. So a
S1: long time this book club has been going. This week's
S1: discovery section is really good. I make some tweaks to
S1: make it even better. So this is kind of the
S1: first instance of that. Had a great conversation with Faisal Khan,
S1: a GRC guy over at Vanta, and that was a
S1: sponsored interview And fantastic. Last couple of sponsored interviews have
S1: just been really, really good. So that was fun. I'm
S1: going to jump into security Sonicwall vulnerability being actively exploited.
S1: Really nasty vulnerability in their SMA 1000 appliances. And it
S1: is being used in the wild and it is a
S1: 9.8 on the Richter scale. My buddy Sam Curry and
S1: Shubham Shah found that they could remotely unlock, start and
S1: track Subarus. So a couple of the goats of Bounty
S1: and yeah, found a really nasty one. And it looks
S1: like the network that they're using actually involves other manufacturers
S1: as well. So it's not just Subaru, but I think
S1: the main effect was on the Subaru stuff and got
S1: a thread here on the downsides of everyone getting a
S1: coding assistant, where I say one of the biggest impacts
S1: of AI that goes kind of unnoticed is that we're
S1: about to see an explosion of poorly built applications, specifically
S1: applications built completely By eye with no thought of security whatsoever.
S1: And the reason I a little bit of like opening
S1: the kimono here. The reason I noticed this, or just
S1: wanted to think about it, is because I'm doing it.
S1: I see myself being sloppy. Now I'm going and cleaning up.
S1: In most cases. But but there are certain cases where like,
S1: it's not really publicly exposed. I'm not too worried about it.
S1: Like it's only running local. I am moving so fast
S1: that I can see myself being sloppy. There's another thing
S1: that is really seductive about AI coding that you really
S1: have to watch out for, which is the better it gets,
S1: the more it encourages you to just kind of trust
S1: it and just kind of go with it. And this
S1: is bad because it's bad for a number of reasons. One,
S1: it's bad for the security reason because what you don't understand.
S1: You can't secure. Okay. But another reason is it just
S1: stops you from actually getting under the covers and actually
S1: doing the mental work of figuring out how the components
S1: should be laid out. So the better the A's get,
S1: what you'll do is you'll just drop a a stupid
S1: paragraph into your AI agent in a coding tool like Klein,
S1: or like cursor, or like windsurf or something. This is
S1: a starting point that so many people are doing right now.
S1: It's not my recommended way to start, and it's not
S1: the way I start when I really want to build
S1: a good application. If I want to build a really
S1: good application, I actually start with a structured prompt, which
S1: is a little bit like a PRD document, and you
S1: have that in the directory, and then you tell the
S1: agent to go look at that thing, or you put
S1: it in the place where it automatically parses it, like
S1: in the cursor rules file, for file, for example, and
S1: that's the way you do it properly. But what most
S1: people are doing, I would guess, because I even find
S1: myself doing this as well, is they're just babbling, typing,
S1: or even dictating, oh, I want an app that does
S1: this and oh, it's going to be like this. Oh,
S1: and make sure it looks cool. And oh, and by
S1: the way, like it should have this thing, but I
S1: want the button on the left instead of the right.
S1: It's just this long paragraph of babble garbage. And then
S1: they press enter and then the thing starts making stuff.
S1: Now it could be making the most horrendously insecure things.
S1: It could be making things that are not really scrutable
S1: or understandable, like they split the logic across multiple pieces.
S1: So it's not like it's not readable, it's not understandable.
S1: And there's a tendency for people because they want to
S1: move fast. And again, when I say people, I mean me.
S1: And I think the situation is what? Much worse than
S1: me because I'm actually guarding against this actively. So I
S1: think it's way worse for just the general public. And
S1: it's already bad for me. But it's like what you
S1: end up with is a thing that kind of works.
S1: Which is why I use this image here, um, which
S1: my team actually came up with. Angela came up with this, and, uh,
S1: it's like you end up with this monstrosity that can, like,
S1: fall over so easily. It's just total, absolute garbage. And
S1: then you're like, uh, no, not like that. No, no.
S1: Change that one thing. And then after like 20, 30, 50,
S1: 100 iterations of this thing over the course of like
S1: an hour and a half, like if you're doing something
S1: else on the side, but you come back and check
S1: on its progress. Okay, maybe you made something, but one
S1: can you edit it? Can you? Do you understand it?
S1: Do you understand all the technologies that you use to
S1: build that thing? Probably not. Did it use proper Technique
S1: and best practices to build the stuff? Probably not. It depends. There's.
S1: Wide variation there, but probably not. And finally, like okay,
S1: for all the configs, did you use cloud services? Okay cool.
S1: Where are the API keys? Are the configs readable? Like
S1: oh did you use cloud storage? Are those buckets locked down?
S1: All these things are like the faster we get at
S1: using these AI tools, and the more people get in
S1: who aren't actual coders who never coded anything beforehand, this
S1: problem is 100 times worse for that. So bottom line is,
S1: there is so much good that's going to come out
S1: of AI. And I would say it's still more good
S1: is going to come out of AI. Because I would
S1: say creation is more important than security. That would be
S1: my my thing. And which might be strange to hear
S1: from a security person, but I honestly believe that's true.
S1: I would, I want secure creation. That's what I want. But, um,
S1: if I had to choose. Everything is secure and nothing happens.
S1: I would rather have insecure creation. So I think that's
S1: more important for humans. But bottom line is there are
S1: externalities to. These AI tools, severe externalities to these AI tools.
S1: The better they get, the more they coax you into.
S1: Not looking under the covers. And it's nasty under the
S1: covers with a lot of these AI tools. Crater of
S1: curl just announced they're completely abandoning Cvss scoring because it's
S1: fundamentally broken for wide, widely used open source projects. And
S1: this is specifically Daniel Stenberg explaining how Cisa recently marked
S1: a low severity vulnerability as a critical with A91. And
S1: he's basically saying it should not have been A91. It
S1: should have been a low. So all this talk has
S1: really been flying around for a long time, and they
S1: kind of made some updates with the latest version of CSS.
S1: But I think ultimately the problem is that these static
S1: things are not made to be looked at constantly. They're
S1: not made to look at the actual risk of your
S1: org and your codebase and all the context around that,
S1: combined with the risk of the vulnerability. And then to
S1: compare those two. And this is probably going to surprise you,
S1: but I think I might be a good application here.
S1: You've never heard that from me that I might be useful.
S1: But the idea is that what you what CVS, CVS
S1: and other tools like this are bad at is they
S1: are they're static. Right. You put some dumb information there
S1: about like, oh how exposed am I or whatever. That's
S1: not really the way to find out how exposed you are.
S1: The real way to do that is to pull in
S1: real time context. So the future of all of this
S1: is like super obvious. It's basically context plus intelligence of
S1: combining a vulnerability with your actual like risk posture. And
S1: that's what's going to determine like what your current risk is.
S1: That's that's just the reality of what's coming. And so
S1: it could be that, you know, there's nothing particularly wrong
S1: with Cvss. It's it's always been like this. The previous
S1: versions were, you know, worse. And then there's other systems
S1: and they also have their problems. But ultimately it comes
S1: down to this one thing, which is what is the
S1: state of the world? What exactly is the vuln and
S1: how does it relate to me? And if you want
S1: to do that properly in the old days, that's that's
S1: like sending security engineers to go do research, to study to,
S1: you know, look at a whole bunch of docs, figure
S1: out where we have this thing installed. We're talking about
S1: asset management. We're talking about nightmares. I mean, you can
S1: spend weeks looking at one phone and seeing how vulnerable
S1: you are to it. If you want to have a
S1: good assessment, right. So the whole point of this is
S1: that you're going to be able to do that much
S1: faster now. And I think that's ultimately what replaces systems
S1: like Cvss and the change. Healthcare ransomware attack is now
S1: officially largest healthcare breach in the US 190 people affected.
S1: Now Swedish authorities just grabbed a ship they think could
S1: an underwater internet cable between Sweden and Latvia. This is
S1: after multiple similar incidents and a lot of them are
S1: tying back to Russia. All right, the big story deep
S1: in AI and tech. So Nvidia loses $600 billion after
S1: deep sea AI breakthrough. And the stock market overall on
S1: Monday lost $1 trillion. So it was the single biggest
S1: day of a market loss in history, and it beat
S1: the record previously from also from Nvidia. But um, basically
S1: here's what happened. This is what it came down to like.
S1: Nvidia had been a darling of AI hype because they're
S1: the GPU leaders. Much of the future hope of making
S1: money from AI has been embodied by Nvidia. The idea
S1: is that GPUs rule the AI world and Nvidia rules
S1: the GPU world. And implicit in that assumption is that
S1: Nvidia chips are scarce and necessary and expensive. That means
S1: anyone who wants to be a leader will have to
S1: have lots of Nvidia chips. So deep tech just blew
S1: that out of the water because they produced something that
S1: should have cost them like tens of billions, but they
S1: did it for $5.6 million. They basically found workarounds that
S1: allowed them to get a lot more performance for less resources.
S1: And it freaked everyone out. Mostly investors, but it like
S1: it messed with the entire stock market because the stock
S1: market is largely pushed by tech. So basically less necessary
S1: equals less valuable. My analysis is so what. Right. If anything,
S1: deep seek is nothing but exciting because we're getting more
S1: AI for less resources and we need way more AI.
S1: And I've got a whole video which I'll link up here,
S1: but it's like, how much do we need? Way, way
S1: more than we have. And I keep doing this thing
S1: where I'm like, oh, we only have 0.000 whatever, ten
S1: zeros and A19. We're only at that percentage of how
S1: much AI that we need. But I don't know that percentage.
S1: I'm making that up. I'm trying to show lots of
S1: zeros to prove it's like a tiny fraction of 1%.
S1: It's a made up number. It's everyone should obviously know that.
S1: But the point is we're just getting started. And in
S1: that video, I lay this all out. I lay out
S1: what types of things we as a society are going
S1: to want to do with AI, and why that requires
S1: so much processing, so much inference, so much intelligence to
S1: be able to do that for basically every human problem.
S1: And when I say human problem, I mean that means
S1: also business problems and stuff like that. Personal problems, everything. So, uh,
S1: I think we're very confused about this whole thing. Um,
S1: and the advantage that Deep Seek found is an example
S1: of what I've been calling slack in the rope. And
S1: I've been talking about this with my friend Jai Patel
S1: for a very long time, and he's, like, one of
S1: the smartest AI people that I know. And I basically
S1: said back in like 23, I'm like, look there. There
S1: is so much slack here that's going to allow people
S1: to jump way ahead, and it's going to jump them
S1: ahead of the of the line, even above people who've
S1: been grinding. So, for example, you spend billions of dollars,
S1: let's say we're at parity between like two competitors and
S1: somebody spends a billions of dollars and they jump up
S1: by 20%, but somebody figures out, hey, wait a minute.
S1: Like if you just double the size of the alphabet
S1: and you add some special characters to the mix or whatever,
S1: it actually jumps up by 600%. Isn't that weird? And
S1: then everyone starts doing that, and then everyone collectively jumps 600%. Well,
S1: what happened to the last six months where competitor B
S1: just spent $4 billion trying to go up by 20%?
S1: My argument to GI was basically, there are so many
S1: of those big jumps completely hidden from us because we
S1: have no idea how any of this stuff works. So
S1: this China team just found one of these things they
S1: just found through a number of things with like. Mixture
S1: of experts. And they've got papers detailing everything they did.
S1: But it's like, hey, look, we just did this. And
S1: it's like, now, now it's obvious to everyone, everyone's going
S1: to go copy that. That's another big jump. I think
S1: there is hundreds of times the amount of performance, like
S1: laying on the floor, laying, sitting on the table ready
S1: for the taking. They just they have to be discovered.
S1: And I think as the technology changes, there's a new algorithm,
S1: there's new transformers that we use, maybe a completely different
S1: type of AI. Every time we go into this like unknown.
S1: It's a new set of things, a new set of
S1: tricks or slack in the rope just sitting around to
S1: be discovered. And my point is that those pieces of slack,
S1: those tricks that allow you to jump forward, and I
S1: don't want to want to demean with the word trick.
S1: Someone mentioned that. Like when I say trick, I just
S1: mean in retrospect it'll appear obvious. It doesn't mean it
S1: doesn't take extreme genius and hard work and discipline to
S1: actually discover it. So I just want to make that clear.
S1: But the point is, we're going to be able to
S1: just keep jumping massively. And I think a very large
S1: percent of those jumps are going to come from these
S1: sorts of tricks or slack in the rope type effects.
S1: And that's what I think we had with, uh, with
S1: deep Post-training is perhaps the most powerful category of these tricks.
S1: This is what I said back in August of last year.
S1: The most powerful category of these tricks is like teaching
S1: a giant alien brain how to be smart when it
S1: had tremendous potential before, but no direction. And again, I'm
S1: saying there's lots of these different ones, but kind of
S1: playing off this thing from 2024, I'm going to say
S1: this in a different way, and this is kind of
S1: a casual way of me thinking about this, which, uh,
S1: I feel like GI doesn't really like when I do
S1: this because it's not perfectly accurate. But one way I'm
S1: casually thinking about this is that there are now two
S1: steps here the intelligence, which is the model, and then
S1: the wisdom, which is the reinforcement learning. So this is
S1: kind of where a lot of this slack has come from,
S1: is the fact that the model itself is not super
S1: intelligence by itself. You actually have to tell it what
S1: constitutes intelligence or wisdom or smarts or usefulness. And that's
S1: after the fact with the. And more generally, the reinforcement learning.
S1: So it's almost like intelligence is the size of the brain.
S1: And reinforcement learning is the life experience. And like I said,
S1: it's not technically true. But I think it's it's pretty powerful.
S1: So bottom line is I think the the market reaction
S1: to all of this is very wrong. Well, I've got
S1: another piece in here about the total tam of I.
S1: In fact, I'm just going to jump to that right
S1: now because I don't think I mentioned it anywhere else.
S1: Let's see here. Oh, this is really cool from Andre.
S1: Move 37 is the word of the day. It's when
S1: an AI trained via the trial and error process of
S1: reinforcement learning discovers actions that are new, surprising and secretly brilliant.
S1: So this is in reference to Lee Soto losing in
S1: go to an AI and it was, uh, it was
S1: moved 37 that made everyone freak out. So I thought
S1: that was a cool concept, but, um. Yeah, if I
S1: go here, here we go. The total Tam for AI
S1: is a combination of two primary components. The total cost
S1: of human workforces, and the amount of money that current
S1: and future companies will pay to start ten x or
S1: 1000 X their business. So we're talking about hundreds of
S1: trillions of dollars. So if you go back to this,
S1: this is why I think, uh, the whole Nvidia deep
S1: sea thing is very mistaken. The Tam and I did
S1: a deep sea analysis, actually, of this very tentative, very non-scientific,
S1: but it came up with like 80 to $120 trillion
S1: as being the Tam for AI over the next ten years.
S1: So I don't know. Again, it's better if you do
S1: like one of the, uh, the new Google Deep research
S1: projects to actually feed it that thing and then give
S1: that to deep Seac or O1 or O3 or whenever
S1: it comes out. Um, that would be a better way
S1: to get those numbers and have it fully explained the numbers.
S1: But either way, it's trillions upon trillions of dollars. So
S1: that's why I think this whole thing is overblown and mistaken.
S1: And that's why my attitude is very much so. What,
S1: Because the market has gone from being foolish to overvalue
S1: Nvidia to being foolish to undervalue it. Right. It's because
S1: there is just so much to go, so much room
S1: before we get to anything like a ceiling. And here's
S1: where I talk about that. Right. So what happens to
S1: Nvidia or any other part of the stack doesn't matter
S1: much at all, because we're still at whatever percentage of
S1: the amount of AI that we need in the world.
S1: Doesn't matter how we get there, it's not predictable. Could
S1: be ARM processors, GPUs, some brand new thing. Doesn't matter.
S1: I think Nvidia and TSMC and all these people are
S1: going to continue to rise because again, we're at the
S1: tiniest little sliver. The question is who is going to
S1: move into the next space. Can Nvidia pivot? I believe
S1: they can. If they had to I'm not sure they
S1: have to. First of all, Deep Seek was built on Nvidia.
S1: If they had better Nvidia deep seek would be better.
S1: Like it's like more Nvidia still makes everything better, right?
S1: And I'm not trying to just go pro Nvidia because
S1: I'm invested. I actually got a decent amount out of Nvidia.
S1: I want to say, um, about two weeks before this,
S1: because I made the choice to move more money into
S1: places that would benefit from AI, and not necessarily just
S1: the roads and bridges. Happened to get lucky there because
S1: I pulled some money out before it went down. But, um,
S1: I very much I did buy the dip a little bit,
S1: and I very much would have liked to buy even
S1: more at the dip of Nvidia, because I think it's
S1: still massively going up along with everything else. It's like,
S1: I don't think they're particularly perfectly, uh, set to be
S1: the ones. Everyone's going to be the one if you're
S1: smart about it and you have a good leader, which
S1: Jensen is. All right. Enough about that. So OpenAI launched
S1: a preview of operator which can navigate web browsers just
S1: like a human. I think we need more generalized agents.
S1: I didn't like the whole App Store vibe to it.
S1: Google just dropped a massive update to Gemini. This is
S1: like one of the sleepers. Honestly, Gemini is one of
S1: the sleepers of this whole competition. Google has been just
S1: doing insanely good for the last six months 12 months.
S1: I use the the flash 2.0 Pro. I think it
S1: is for a lot of analysis where I need really
S1: good haystack performance over like 2 million tokens. So when
S1: I do rag searches across like all my content, for example,
S1: and I get back like 50 documents that hit on
S1: the rag and this is over like 3000 posts, 3200
S1: posts since going like back to 1999. So if I
S1: have a big giant piece of content like that and
S1: I don't want to miss like the The nuggets or
S1: the or the, you know, the needles inside of this
S1: massive haystack. I send it over to Google and it
S1: does a better job of of finding the haystack. Plus,
S1: I mean, just brute force. It has 2 million tokens
S1: as opposed to 200,000 for like sonic anthropic build citations
S1: API to combat hallucinations. That's really cool. Uh, Google just
S1: dropped another billion dollars into anthropic. Very interesting. So they're
S1: not only doing their own play. They're also betting against
S1: OpenAI with anthropic. I don't know if they're in OpenAI
S1: as well. But anyway, they're investing in anthropic, which I
S1: thought was really interesting. Leaked memo from Apple says they're
S1: focusing completely on rebuilding series infrastructure and improving their existing
S1: AI models this year, which, yeah, it's the AI chief
S1: talking about it. So obviously they're talking about AI. Well,
S1: overall startup funding has dropped significantly since 2021, seed rounds
S1: are actually getting bigger. This is interesting. My friend, uh, Mike, um,
S1: has a really, really cool podcast on this, uh, which
S1: you could check in the show notes. His name is
S1: Mike Privett, and you should absolutely go check out his stuff. Uh,
S1: Colorado police are now giving away free AirTags and the
S1: tile trackers to help prevent vehicle theft in their community.
S1: A ring camera in Canada. This is insane. Caught the
S1: exact moment of a meteorite smashing into a basically a
S1: walkway in front of their house. So they just came
S1: out and just like passed that that location and you
S1: hear like this really loud crash and it hit tiles
S1: on the walkway. And I, I don't understand how it didn't, like,
S1: blow out the tile and create a little hole in
S1: the ground. But anyway, they picked it up. It was
S1: an actual meteor and it just like blew up against
S1: the tile. I don't know, That had to be going
S1: thousands of miles per hour, right? It's terminal velocity, I guess.
S1: Is it hundreds of miles an hour? I don't know,
S1: but had to be going fast. It definitely would have
S1: killed someone if it hit him, I think. Anyway, I
S1: don't know why it didn't crack the, uh, the stone
S1: on the ground. A new Harvard study shows we should
S1: be taking blood pressure readings while lying down instead of sitting.
S1: And Hans Zimmer is apparently in talks with Saudi Arabia
S1: to remake their national anthem. And a pre-mortem is basically
S1: where you imagine your product has already failed, and you
S1: work backwards to figure out why, and you do this
S1: before you even start the project. That's brilliant. Reminds me
S1: of the PR system inside of Amazon, which I learned
S1: a lot because we use that at Apple. And uh, yeah,
S1: four components of top AI model ecosystems. It's in the
S1: ideas section related heavily to this deep tech stuff. And
S1: for discovery, Klein is my new go to for doing
S1: AI stuff. It is an extension built into standard VS
S1: code and actually like it more than using cursor, which
S1: is like more integrated and everything. Klein is more conversational.
S1: It's more following my thread. It's just seems more intelligent
S1: and more like cohesive in the way that it answers things. Um,
S1: and again, it's I'll show you the website. Yeah. Klein bot.
S1: Thoughtful AI coder. I don't know why it's better, but honestly,
S1: it feels better to me. I take this pain right here.
S1: I actually move it over to the right. That's how
S1: I have it going. Developer created a brilliant trap for
S1: web scrapers by using specifically crafted CSS selectors. So anti
S1: scraper trap I recommend you try out deep seek. One
S1: thing about deep seek that is really, really cool is that,
S1: uh you when it responds, it just starts talking out
S1: loud and you just see it. It's very similar to
S1: O1 in that way. You just see it. That's the
S1: whole reason it's doing so well in the benchmarks. That's
S1: the reason it's controversial because it's very much acting like O1.
S1: But bottom line is you see it having a conversation
S1: with itself and it goes back and forth and it's like, yeah,
S1: but this is really cool. Yeah. But have you thought
S1: of this? Actually that could be wrong. Let me think
S1: about this. And it does that for a number of
S1: paragraphs before it actually gives you an answer. I recommend
S1: you try it out with Olama. It's really easy to install.
S1: And magenta NVMe is one of the I first, uh,
S1: vim tools that I'm trying to use. Ultimately, I'd like
S1: to get out of VSCode and have all my AI stuff.
S1: Something like Klein inside of vim, but it's hard to
S1: do that in the terminal, which is why I'm like
S1: stuck in VSCode and not happy about it. Ben Thompson
S1: had a pretty cool deep seq FAQ, FAQ, and Lane
S1: Changes dropped. A cool new tool that lets you do
S1: deep web research. This is the thing I was talking
S1: about before completely using a hosted LMS. I've not messed
S1: with this one yet, but I cannot wait to. Um.
S1: Somebody created a simple service that converts WordPress blogs to
S1: Hugo static sites. Highly recommend. Anyone building a website goes
S1: static these days and basically keep your markdown. Your markdown
S1: is your website that is your content. You can make
S1: a rag out of it. It is just text and
S1: basically any place you want to bring your blog to
S1: give them the markdown and have it turn it into
S1: a nice website that you like. You like the aesthetic of.
S1: So it's content aesthetic and don't mix the two and
S1: don't have it where it's like the platform owns the
S1: mixture because then you're screwed. Um, and I've had this
S1: happen to me multiple times. I just had it happen again,
S1: getting my Getting my content out of beehive, which I love.
S1: Beehive for my newsletter, but it's not my favorite place
S1: to run an actual website. Philips Hue bulbs getting motion sensing.
S1: I really hope this is true and I can't wait
S1: to get it and play with it. Be nice to
S1: have all different hue things basically. Also be motion sensors.
S1: How to say no as a product manager. This is great.
S1: This is great. I think it just refreshes. Yeah it
S1: just refreshes. Let's not do that. So I'm hitting refresh.
S1: Can we revisit this after the next sprint refresh. We
S1: need to ensure alignment before moving forward. Refresh. This could
S1: be part of a larger initiative, but let's hold off
S1: for now. This is really good. It's really good. Uh,
S1: and recommendation of the week. Remember that AI is not
S1: AI stocks. AI is not the survival of AI companies
S1: that did marketing in 2023 and 2024 A's. Tam is
S1: the replacement of human labor and the magnification of GDP
S1: that can come from millions or billions of people becoming
S1: a founder, builder or creator. That is the ball to
S1: watch and everything else is noise. And the aphorism of
S1: the week is to be completely cured of newspapers spend
S1: a year reading the paper from the previous week to
S1: be completely cured of newspapers. Spend a year reading the
S1: newspaper from the previous week. Nassim Nicholas Taleb. Unsupervised learning
S1: is produced on Hindenburg Pro using an SM seven B microphone.
S1: A video version of the podcast is available on the
S1: Unsupervised Learning YouTube channel, and the text version with full
S1: links and notes is available at Daniel. Com slash newsletter.
S1: We'll see you next time.