S1: All right. Welcome to episode 483. Just a quick reminder.
S1: I'm trying to get a lot more content out, and
S1: I don't want to miss regular newsletter episodes. And I
S1: want to actually get them out more closer to Monday
S1: or Tuesday, ideally Monday, so that the podcast and any
S1: videos and basically this newsletter and the podcast version of
S1: the newsletter comes out as close to Monday as possible.
S1: So I'm going to be doing a number of things
S1: to try to speed that up. But one of those
S1: things is actually having less editing on this podcast, especially
S1: when I'm doing the newsletter. I mean, I'm talking straight
S1: for 20, 30 minutes, and if I give this to
S1: an editor, it's going to take them, you know, a
S1: day or two to turn that around. Now, eventually, hopefully soon,
S1: I will be able to find all my mistakes and
S1: clean out the ums and ahs and the restatements of
S1: a sentence. Because what I'll often do is like, make
S1: a mistake in a sentence and then I'll say the
S1: word edit, and then I will say the sentence over again.
S1: So if you hear any of those or you hear me,
S1: just restate the sentence. I know it's annoying. I apologize,
S1: I do not like to hear that in podcasts I
S1: listen to. But if the podcast is high enough signal,
S1: then I'm okay. You know, taking a little bit of that.
S1: And I'm saying, just bear with me because I think
S1: it's more important that we get the content out, and
S1: it's more important that we get the ideas out, and
S1: we are just waiting for I to be able to
S1: automatically clean that up. So I will just drag and drop.
S1: It'll go clean all that up and we ship the
S1: thing and there's no delay whatsoever. So I just wanted
S1: to mention that and sort of preemptively apologize. All right.
S1: First piece here released my new video on where I
S1: think hacking is going. It's called The Future of Hacking
S1: is Context, and it is on YouTube. And I recommend
S1: you go check it out. Got a new essay on
S1: how I see AI affecting education that's on the website. Uh,
S1: new essay on AI job replacement timelines, and a new
S1: essay on my two groups of cyber AI friends where
S1: basically it's crazy. I've got one group of friends who
S1: are like, I can't believe this whole thing is a fad.
S1: This is nothing but billionaires trying to get rich. Like
S1: it's an NFT crypto scheme and it's just complete and
S1: absolute garbage. And you know, I've got one friend, Marcus Hutchins,
S1: who doesn't even believe that AI is intelligence. He just
S1: believes it's a complete scam. The entire industry is broken.
S1: It's not real intelligence. It's not real AI. And it
S1: just completely down on it. And he published a really
S1: cool essay, I think, which I linked to, which talks
S1: about why it's not real intelligence. And in the short
S1: answer or the short summary of it, is the fact
S1: that it knows so much, but it still can't invent
S1: new things. And my concise answer to this, and by
S1: the way, I'm about to debate him in public in
S1: a recorded podcast on both of our shows, and we're
S1: going to go through this in depth. But the short
S1: answer to that is your rights. It's not producing real
S1: innovation yet. But my argument is that real innovation, real
S1: net new ideas is actually a tiny fraction of the
S1: intelligence that humans use every day. You know, 99.99% of
S1: intelligence that we use every day is not inventing net
S1: new things. It's applying existing knowledge that we have existing
S1: experience that we have, using our intelligence to apply that
S1: to a new problem. And the new problem is also
S1: not a net new 99.99% problem. It's just a slightly
S1: different version of that problem. Right. If you think about
S1: an average white collar worker, if you think about an
S1: average admin or secretary person or a an average coder even. Right.
S1: Usually the problems are like very similar to problems we've
S1: solved before, right? And there are some exceptions with with
S1: coding and with high end engineering and of course, with,
S1: you know, pinnacle level science. There's lots of exceptions, but
S1: the vast majority of the intelligence that we use every
S1: single day, it's all the same stuff with different names
S1: and slightly different configurations. It's all the same stuff, right?
S1: And more importantly, teaching kids answering their questions, being a tutor,
S1: being a teacher, being a helper, you know, being able
S1: to help out inside of companies and be an assistant and,
S1: you know, make meetings happen and coordinate and summarize things
S1: and keep everyone up to date. This is what the
S1: vast majority of work is, right? And I would argue
S1: that's absolutely intelligence. It's absolutely human intelligence. And we know
S1: this because we can't automate it. That's the reason it's
S1: AI is because. If if we could automate it away,
S1: it would have already been scripted and we wouldn't actually
S1: have human employees doing that work. So if you think
S1: about the average job that someone does in an intellectual
S1: or cognitive role, like knowledge work, 99.9% of it, and
S1: I'm just making up that number to, you know, to
S1: be a little bit hyperbolic there. 99.99% of that work
S1: is extremely repetitive. It's just slightly changing the work that
S1: we do, right? It's slightly different scenarios, slightly different users,
S1: slightly different use cases. But ultimately what this comes down
S1: to is can I replace that? And I think we're
S1: already seeing that. So I think that's empirically being shown
S1: to be the case. Now Marcus's argument here is well,
S1: that's not real intelligence. That's not real AI because it's
S1: just doing the same thing over and over. And my
S1: point is, that's what millions of jobs are actually doing.
S1: That's why millions of jobs are actually going to get replaced,
S1: is because AI is going to be able to do that.
S1: 0.9 9% better and faster and much cheaper than most humans.
S1: So my whole thing with AI is that it matters
S1: because of how it impacts humans, right? Getting rid of
S1: millions of jobs affects humans. That's why I define AGI
S1: as an AI that can replace an average knowledge worker,
S1: because I don't care about AI in a theoretical sense.
S1: I don't care about it in a technical sense. I
S1: care about it mostly in how it impacts humans and
S1: human society and human meaning. So that's what our debate
S1: is going to come around. And anyway, the way I
S1: got on to this is that that's one view. It's
S1: like this very anti-ai, very negative view of like, we
S1: don't even have it. It's not real. It's all hype.
S1: It's just like crypto. It's just a bunch of billionaires
S1: trying to get rich. All these stats and benchmarks, everything
S1: being said is basically marketing from executives for these lame, stupid,
S1: temporary AI companies that are going to be gone immediately.
S1: And basically it's not going to have much job impact
S1: because it can't actually replace a human. So that's kind
S1: of their argument. I'm not lumping everything I just said
S1: into Marcuse's argument. I'll let him make his own arguments
S1: when we have that discussion. But that's one group generally now.
S1: The other group is like myself and Joseph Thacker and
S1: my friend Jason Haddox and my friend Sasha Geller. Like,
S1: we're all thinking, you know, we kind of saw where
S1: this was going, especially myself and Joseph Thacker and Sasha.
S1: It's like end of 22. We're like, oh my goodness. Right.
S1: So all the stuff I've been talking about for the last,
S1: you know, two and a half, three years is we
S1: I kind of saw the glimpses of that already happening
S1: at the end of 2022. It was kind of inevitable.
S1: And the more the more the AI started to happen,
S1: the more obvious it gets or whatever. And of course,
S1: it's part of the book. That was in 2016 as well.
S1: But the point is, one group of people just still
S1: today in mid 2025 doesn't accept it. Other another group
S1: of people like myself and a bunch of other people
S1: just like me or similar to me in mindset. They
S1: got it instantly. Or if I'm right, they got it instantly.
S1: If I'm wrong, then they were also instantly wrong, just
S1: like me. And what's fascinating to me is these are
S1: all security experts. These are all deeply technical, deeply experienced,
S1: multiple decades of experience in cybersecurity, and we have drastically
S1: different views on this. That's what's so fascinating to me, right? Why?
S1: Why do they think that way? And why do we
S1: think this way? What makes the difference between being in
S1: one group or the other? And I think the answer
S1: comes down to just how they overall view the world. Um,
S1: Marcus in particular is very, I want to say socialist,
S1: but not not socialist in a, in a bad way,
S1: socialist in a human uplifting way, which I am as well, right?
S1: I just I'm also a capitalist. So it, uh. Complex topic.
S1: I'm not going to go into that, but the point
S1: is he is very sort of anti billionaire anti-big business.
S1: He sees all this as a giant scam, a big, uh,
S1: you know, a big push to try to just, uh,
S1: remove jobs or trick people into removing jobs when it's
S1: not even real. He's just very cynical about the entire
S1: thing based on, you know, kind of being anti-capitalist, anti,
S1: you know, big business anti billionaire for sure. So I
S1: think people who have that view or I think the
S1: other view that that puts people in this category often
S1: is just being anti-change. And a lot of people in
S1: information security are actually anti-change. I didn't realize this until
S1: a few years ago, but it's like there are many
S1: people who are just like, oh, don't change that. You know,
S1: when I was growing up in the 80s or 90s,
S1: you know, the protocol worked like this and that's how
S1: it should always work and can't stand these these crazy
S1: kids with all their JavaScript and all their dynamic web
S1: apps and, you know, all the crazy APIs. Like, I
S1: just see a lot of get off my lawn type mentality.
S1: And people who have that mentality tend to be very
S1: anti-ai in this way. And then my other group of
S1: friends are like, yeah, tech changes all the time, no
S1: big deal. And they kind of have this mentality that
S1: I have, which is shepherding. So I see myself as
S1: a shepherd, you know, with like the stick and the robes.
S1: And I don't want to use sheep here because I
S1: don't consider all of humanity to be sheep, but whatever,
S1: we'll use sheep. It's not actually pertinent in this. The
S1: idea is you. It's your job to walk up ahead
S1: and see if there's danger and to, like, help steer people.
S1: Because in this metaphor, it's not superiority of the human
S1: to the inferiority of the sheep. It's more like superiority
S1: of your knowledge of the risks versus inferiority, of the
S1: knowledge of the risks. So it's my job. Switching to
S1: a military metaphor, it's my job to go out there
S1: and step on mines. It's my job to go out
S1: there and detect all the mines, and then spray paint
S1: a path on this minefield so people can walk out,
S1: walk on it without getting hurt. Right. That's the way
S1: I see it. And I see the this minefield is
S1: constantly changing because the tech is constantly changing, and we
S1: should just accept that and everything's fine, right? Change is constant.
S1: That's fine. Oh, we're going to lose all these jobs. Cool.
S1: What's next? Um, AI is going to replace a whole
S1: bunch of stuff. Cool. What's next? Let's talk about the upsides. Oh,
S1: we're all going to become way more creative. Okay, cool.
S1: Let's talk about that. Let's figure out how to uplevel
S1: and upskill. So I think that mentality versus the other
S1: anti-change mentality is the primary thing that's guiding whether or
S1: not someone is in one of these camps versus the other. So, um,
S1: that's that's essentially what that essay is about. And what
S1: I just said is like 20 times longer than the
S1: actual essay, which is like a one minute read. But
S1: that's the advantage of doing a long form podcast. All right.
S1: Let's see here. Gukesh Indian current world champion beat Magnus
S1: Carlsen in the for the first time in classical chess.
S1: And Magnus like hammer fisted the table. Crore. Super super loud.
S1: Gukesh didn't jump, so I gave him credit for that.
S1: But that was cool. Got a video of that. All right. Cybersecurity.
S1: Google patches new Chrome zero day bug in attacks Microsoft
S1: and CrowdStrike create a shared threat actor dictionary. This one's
S1: cool because some threat actors have like 15 different names
S1: for them, so they unified that. That's, I think, progress.
S1: OpenAI's O3 discovers Linux kernel zero day vulnerability I talked
S1: about this last week, but, um, yeah, this article is
S1: pretty good here. Meta plans to automate product risk assessments
S1: with AI. This is really cool. 90% of app updates
S1: they're going to do the assessments using AI. This has
S1: always been one of my favorite things. I did this
S1: very early, um, when I first started to pop, um,
S1: a filtering system for a big problem with security teams
S1: is they are the bottleneck between engineering and being able
S1: to ship and not being able to ship. So they
S1: get really mad at security about that. I love the
S1: idea of having all the context of the app that
S1: they're trying to push through and sorting it into, like buckets.
S1: You could actually use lots of different buckets. You could
S1: use like 2 or 3, but you might be able
S1: to sort it into like 15 different filtering buckets where
S1: your pipeline of security checks is vastly different based on okay,
S1: is it privacy focused? Is it customer data security focused?
S1: Is it proprietary, data focused? Like what are what's the
S1: threat model, which it could automatically do as well. It
S1: could understand your risks of the company. It can understand
S1: worst case scenarios for the company. And let's say it's
S1: sorting into 16 different granular security check types. Hopefully most
S1: of those being automated. But if it's a super high
S1: priority thing it goes into the top one. It goes
S1: into number 16. And that one is actually a manual
S1: red team pen test, which is associated with automated scans
S1: that already happened or whatever. But imagine being able to
S1: speed up 85%, 95%, 98% of projects that engineering is
S1: trying to push out for the company and have only 2%
S1: actually involve manual testing, um, or whatever. I think that's really,
S1: really powerful use case for this. And anyway, so meta
S1: is doing that or something like that for 90% of
S1: app updates using AI. Massive Asus router botnet uses persistent backdoors.
S1: Over 8000 Asus routers affected by this, and it survives updates.
S1: So if you reboot the router, the thing is still there.
S1: Russian market becomes top destination for stolen credentials. So this
S1: is the Russian market cybercrime platform. That's the name of it.
S1: And it's filling the gap left by Genesis Markets takedown
S1: DOJ takes down four major services using that were used
S1: by cyber criminals. So it's a basically like URL hiding
S1: uh infrastructure China linked hackers exploit SAP and SQL server
S1: flaws in attacks across Asia and Brazil. And this actor
S1: is called Earth. Lamia. Lamia and they're hitting multiple organizations
S1: since 2023. National security. Ukraine hides explosive drones in wooden
S1: sheds to hit parked Russian bombers. This one is absolutely insane.
S1: I'm sure you've seen stuff on it already. It's. It
S1: reminds me of what actually happened with the Israeli attacks
S1: on Hezbollah, with this ingenious, sort of like personalized little
S1: mini bombs or whatever that went after leadership. So it
S1: wasn't like giant drone strikes. It wasn't like giant bombing raids.
S1: It was these personalized little things against leadership, which, uh,
S1: a lot of people are saying, you know, tactical win here. Uh,
S1: and it was pretty effective. And I think this is
S1: similar in the sense that it's asymmetrical use of force. Um,
S1: you have Ukraine, which is out outmanned, outgunned. And they
S1: snuck these drones, uh, over a thousand miles inside of Russia.
S1: I think maybe even close to 2000. 3000 miles in
S1: some cases. Um, snuck them in, and, um, basically, they're
S1: inside of Russia. They they get all the way to
S1: the airfield. They remotely open them. They're now sitting on Russian, uh,
S1: cell networks, uh, which is what's used to activate them
S1: and control them. And they, um, they hit these Russian bombers,
S1: and these Russian bombers cannot be reproduced. Russia does not
S1: have the ability to make these bombers again. And what's
S1: interesting about this is they didn't diminish Russian bombing against Ukraine.
S1: They diminished Russian bombing capabilities completely strategically as compared to
S1: the United States as compared to China. So they significantly
S1: damaged Russia's capabilities, right? Not to mention, you know, the
S1: hundreds of thousands of actual troops they've lost. Um, but
S1: this this war is seriously, seriously harming Russia's overall strategic
S1: military capabilities. And, uh, the thing to call out here
S1: is just how much Ukraine has changed warfare using drones.
S1: And again, I implore you to go read Kill Decision
S1: by Daniel Suarez, which talks about specifically autonomous drones, which
S1: can't be taken out by GPS blockers or Em blockers
S1: because they could do everything, uh, self-sufficient inside the drone itself.
S1: China's deep network penetration signals war preparations. So former Trump
S1: adviser H.R. McMaster told lawmakers that China's extensive hacking of
S1: US infrastructure is actually them preparing for kinetic war. FBI
S1: arrests defense intelligence IT worker for Pak drop espionage. So
S1: he basically went to go do one of these, you know,
S1: spy versus spy dead drops in a park in Virginia. And, uh,
S1: the guy was actually part of DIA's internal threat division. So?
S1: So he's part of the team designed to help people
S1: find or design to find spies inside of Dia. And
S1: he was one of these spies, and he dropped this material,
S1: and it was actually dropped to an FBI agent and
S1: got all the write ups there. I Mary Meeker returns
S1: with First Trends report since 2019. Definitely go check this out.
S1: She is a beast of analysis and she's been dormant
S1: for a while. And now she's back. And this analysis
S1: is like 300 something pages. Got a decent summary here
S1: in the newsletter, but go check it out. Dwarkesh Patel
S1: has longer AGI timelines than his podcast guests. I think
S1: he's wrong about this, I think. So basically what Dwarkesh
S1: is saying is that AI is not smart enough to
S1: do continuous learning. And I think I'm not sure about this,
S1: but I think he thinks that's an IQ problem, whereas
S1: I see it as a scaffolding and system problem. It's
S1: a memory problem. It's a context size problem. It's a
S1: even more than a context size problem for the model.
S1: It's more of a context knowledge base management problem. So
S1: this is all scaffolding to me. And I think this
S1: scaffolding is going to improve as fast or faster than
S1: the IQ of the models themselves. So I think this
S1: is not as big of a hurdle as Dwarkesh is
S1: thinking it is. McKinsey says the future of work is Agentic.
S1: They released this big article talking about becoming digital workers
S1: that can think, decide and execute tasks on their own
S1: and that it's worth a read. You should go check
S1: it out. It's a it's a good article by McKinsey.
S1: The truth about AI and job loss. Neruda from meta
S1: dug into historical data to find out which AI, which
S1: jobs AI will actually eliminate, and whether there's still room
S1: for junior developers. That was a good read. Google Gemini
S1: integration with Siri could fill Apple's personal context gap. I
S1: think this analysis is wrong. I included it because it's
S1: an alternative view to my own, but I think this
S1: analysis is wrong. The problem is not Apple's access to
S1: personal data. They have the best. They've been building iOS
S1: all of this time. They have the best personal contacts
S1: on people, I believe, on the planet. The problem is
S1: having Siri prompt injected while it has access to all
S1: that context. I don't think they know how to give Siri,
S1: or whatever the super agent is going to be is
S1: probably still going to be Siri. They don't know how
S1: to give Siri access to all that data in a
S1: secure way, in a comprehensive, powerful way that is secure.
S1: I think that is what is stopping them. That's my guess.
S1: I don't know that for sure. Not talking to people
S1: on the inside about it, but that is my guess.
S1: Based on working in security at Apple for like three years.
S1: Snowflake buys crunchy data for $250 million. And this is snowflake,
S1: which is a giant data platform making an AI agents
S1: play Technology. McKinsey uses AI to automate PowerPoint creation and
S1: proposal writing. So their platform Lily now handles PowerPoint creation
S1: and proposal drafting. Over 75% of people using it monthly, internally, employees.
S1: Workday plans to rehire the same number of people they
S1: laid off, but with different skills. This is what I've
S1: been saying. They want completely different people, completely different people.
S1: Nvidia develops new AI chip for China that meets export controls.
S1: I don't think this stuff matters that much. I think
S1: if China gets less chips, they'll learn how to make
S1: more AI with less chips, and in the meantime, they're
S1: making their own chips. In fact, they just released one
S1: that performs really well. I think ultimately China is going
S1: to get everything that we have. I think ultimately, most
S1: AI advancements are happening at the software layer via what
S1: I used to call in 2023 tricks, a whole bunch
S1: of different tricks that they're using. Right. And those tricks
S1: I think get leaked, I think they get stolen. And
S1: so for example, there's an argument that's something that OpenAI
S1: was doing with oh one got stolen by deep sea
S1: and deep sea copied it. And guess what? Their R1
S1: model was nearly as good as oh one. After OpenAI
S1: spent all that money and deep sea spent way less.
S1: That's an example of a trick or a set of
S1: tricks getting stolen. I think that's going to continue to happen.
S1: And it's why not only that, the pinnacle model makers
S1: are going to stay close to each other, but also
S1: open source is going to stay close to the pinnacle
S1: model makers. So I think this whole thing just kind
S1: of churns along, chugs along, advances, slowly rolls forward with them,
S1: largely similar with them largely staying neck and neck. Not always.
S1: There's going to be these these big jumps, right? But
S1: I think that thing will be copied. What I thought
S1: was would happen, and what I think most people thought
S1: would happen is when somebody made a major advancement, like
S1: OpenAI or anthropic or someone in China. They would be
S1: a leader for six months, a year or two years.
S1: They would just be like, oh, they did something crazy,
S1: and no one can find out what it is. And
S1: they're just way ahead. And everyone's developing like independently in
S1: these silos. But it turns out so many of these
S1: researchers know each other. So many of these researchers are
S1: moving between the model maker companies. So many of them
S1: talk and share their stuff in coffee shops and in
S1: conferences and stuff. These secrets, these tricks, these scaffolding things,
S1: because a lot of this is not about the pre-training
S1: of the model. It's about what you do after Post-training
S1: where a lot of these tricks come into play and
S1: it's just being spread. It's just being leaked. It's just
S1: being shared so much that I think we're going to
S1: have a lot of parity with, with some exceptions, uh,
S1: going forward. And I think the only thing that changes
S1: this and gets really weird is when you cross through AGI,
S1: where I think that still maintains what I was saying.
S1: But when it hits ASI, that's where you're going to
S1: have these giant jumps. And I don't know necessarily. Well,
S1: no one knows what that's going to look like because
S1: it's going to be ridiculous. Computer science unemployment hits six 1%
S1: despite Major's popularity. So a lot of questions of like,
S1: is computer science still a good thing to go into? Um,
S1: I think dialectic, I think studying, um, philosophy, um, rhetoric, dialectic, um,
S1: how to think clearly, studying history, studying computer science, um,
S1: studying physics. I think these are the core things that
S1: people should actually be studying. And you should be getting
S1: your kids and your younger young ones, or even people
S1: who are advanced in their careers. This is how you retool.
S1: And this is what I'm going to be putting into
S1: the curriculum for H3 for human 3.0. Uh, this curriculum,
S1: I think, is the core sort of first principles type
S1: education that you're going to need. And of course, you
S1: can specialize in all the stuff that you want like
S1: that you're super interested in. But this core, I think,
S1: is what's going to make you resilient to all these
S1: different changes. Humans 60% of Americans have retirement savings accounts,
S1: but it's very lumpy. So 83% of people making over
S1: 100 K have retirement accounts. Only 28% under 50 K
S1: have retirement accounts. There's a massive racial gap. There's also
S1: a massive gap between college graduates and, uh, people who
S1: don't have any college. So 81% versus 39% and white
S1: versus people of color, it's 68% versus 42%. Uh, US
S1: economy contracts, contracts more than expected in Q1. It actually
S1: looks like it contracted by 0.2 percent in Q1. Younger
S1: generations less likely to develop dementia. I think this is
S1: because previous generations they would stop working at 60 or 65,
S1: and they would just be like, oh, I'm just going
S1: to play with grandkids and I don't really need to
S1: do anything. And I think when you tell your brain
S1: to shut off, it, like literally starts shutting down and
S1: you start getting dementia. That's that's me being a fake doctor.
S1: Do not listen to me. I think it's backed up
S1: by tons of data. But I think if you treat
S1: your brain and your body like you are a 22
S1: year old in terms of like you're lifting weights, you're
S1: working out, you're in the sun, you're doing cardio, you're
S1: learning new things constantly. You are executing on new cognitive
S1: tasks constantly, and you're constantly relearning and in upgrading yourself,
S1: then you are basically sending a signal to your brain
S1: and body, hey, we are 22. You better stay in shape.
S1: And me, as old as I am, that's exactly how
S1: I feel. I feel 22 all the time. Unless I'm
S1: like off routine and low energy or whatever, then I
S1: feel like my age. But, um, I would say 95%
S1: of the time I feel 22. And this is why
S1: and this is why I think there's such a big
S1: difference between dimension numbers. Uh, in and I think the
S1: number is 25% versus 15%. So what is that? That's
S1: a massive difference. Um, is just because the old mindset
S1: of retirement versus the new mindset, which is more active
S1: American versus European mindset on life. Really good piece. If
S1: you're useful, it doesn't mean you're valued. This was a
S1: good piece, uh, about economic value and how companies view us.
S1: How much coffee is too much? Really good news. About
S1: 3 to 5 cups of coffee. Uh, discovery. Run your own.
S1: I locally on your Mac Anthropic's interactive prompt engineering tutorial.
S1: Indirect prompt injection overview. My AI skeptic friends might are nuts.
S1: My AI skeptic friends are nuts. This is by another
S1: security guy who's basically saying the same thing that I
S1: was talking about in the beginning from that essay. Um,
S1: and I've got a cool, cool quote here from it. Um, quote.
S1: But the code is shitty like that of a junior developer.
S1: And his response to that. Does an intern cost $20
S1: a month? Because that's what cursor costs $20 a month.
S1: I think that's a great point. Cloud code is my computer.
S1: You to inky turns video into vocabulary flashcards, Java filters,
S1: hacker news job posts with AI powered metadata. Tensor product
S1: attention is all you need. The metamorphosis of intellect. This
S1: is a really cool book. It's an online text. You
S1: can just click the link in the newsletter and you
S1: get the full book. It's fantastic. One of my top
S1: ten sci fi books ever. Andor season two shows how
S1: insider threats actually work in real world organizations. I have
S1: not seen all of Andor one, so I need to
S1: go watch that. And two evidently it's the best Star
S1: Wars in a long time. GitHub repository of end to
S1: end workflows. Someone created a GitHub repo of tons of
S1: scraped any workflow like hundreds of them. And you could
S1: just go get them. Because they're in GitHub now. And
S1: because it's like a text configuration, you could just like
S1: paste them into N810, which by the way, is becoming
S1: one of my favorite AI automation frameworks. And the number A10,
S1: that and bedrock are my favorites and lane graph. But
S1: I'm using that last now, um, preferring to use my
S1: five year experiment with UTC. So someone is now using
S1: UTC for everything. I thought I was crazy for going
S1: to metric. This guy's using UTC book of Secret Knowledge
S1: GitHub repository. Jason Chan and Clint Gibler have a brilliant
S1: conversation in the latest TLDR sick. So my buddy Clint,
S1: who runs TLDR comm, which you should go sign up
S1: for that newsletter. Um, he had a guest post by
S1: Jason Chan, who's like a absolute security guru, actually kind
S1: of pioneered, uh, security guardrails and, um, really, um, really cool,
S1: innovative security guy who ran security at Netflix. But anyway,
S1: he wrote a piece about building a security program for TLDR,
S1: and it is extraordinary. You got to go check it out. Okay,
S1: this is the end of the standard edition of the podcast,
S1: which includes just the news items for the week to
S1: get the rest of the episode, which includes much more
S1: of my analysis, the ideas section, and the weekly member essay.
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S1: to become a member and get all that, just head
S1: over to Daniel Store.com. That's Daniel Miessler, and we'll see
S1: you next time.