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 episode 472. Hope your week is going well.
S1: Updates on this side I've got an RSS feed which
S1: I encourage you to go sign up for. RSS is
S1: a real thing and it's awesome. And I wish it
S1: didn't get knocked off the radar by Google Reader going under,
S1: but I use Feedly. A lot of people use Netnewswire.
S1: Whatever your RSS reader is, go check it out. It's
S1: just slash feed RSS, I think at the end of
S1: the URL. Thanks for all the cooking advice. Around 500
S1: people responded with experience, encouragement, recommendations. It was just insane.
S1: So really appreciate all that. I tried windsurf, a lot
S1: of people are talking about it. I basically try all
S1: these tools that people talk about. I've been using Klein
S1: for a while. I was using cursor before that and
S1: I keep trying cursor every time they have a big
S1: release or when windsurf has a big release. I'll try
S1: it again. The stuff is changing so fast that I
S1: just constantly try different stuff. But Klein has been my
S1: go to. I feel like it manages the functionality of
S1: Lmms better and the UI. I just like better, so
S1: that's what I've been using. It is just an extension
S1: into VS code. I wish it was actually a full editor,
S1: just like cursor and windsurf are. So Klein people. If
S1: you're listening, please make an editor. And my personal AI
S1: infrastructure is getting absolutely insane right now. I'm going to
S1: have something to show on this soon. I'm going to
S1: do a demo of like how this all works together,
S1: but basically it's one main agent and then all my
S1: personal services are sitting behind it that do different things,
S1: and they're all able to access different services that do
S1: output as well. So there's like you send the request
S1: into the agent, the agent, if I tell it, send me,
S1: you know, test a website and then send me an
S1: email for it, it'll do the two different services. One
S1: is to test the website and the other one is
S1: to send an email. And again it just naturally parsed that.
S1: And I just sent that from my phone. Right. So
S1: the whole idea is I'm just using my regular tech infrastructure,
S1: which is my iPhone, and I send a request into
S1: my agent, and this is what it's going to be
S1: in the future too. I'm just sort of hacking it
S1: together with my own services. Pretty soon it'll just be
S1: built into Google and built into the iPhone and stuff
S1: like that. But right now I'm just making it happen
S1: through like shortcuts and a bunch of AI services, but
S1: the overall effect is quite awesome and you have to
S1: see this thing actually working. It's very much in line
S1: with what I've been talking about. Uh, in the predictable
S1: AI post, which is this link here where it's all going,
S1: put out a concise explanation of model context protocol, MCP
S1: servers and why everyone's excited about them. So I got
S1: a full thread on that and you should go check out. And, uh,
S1: I'm enjoying the fact that this is starting to happen,
S1: specifically the MCP server, because in that predictable AI post,
S1: which actually comes from the book in 2016, I talked
S1: about the API ification of everything and specifically how businesses
S1: would all become APIs. So this is text. From that,
S1: most software businesses will become algorithms presented to others through
S1: their business daemons. Many traditional businesses will continue to become
S1: software businesses. So basically, Andreessen talked about software as eating
S1: the world. I think AI is going to eat the world.
S1: Like that's pretty. Everyone's saying that it doesn't really mean anything.
S1: What I think is actually eating the world is APIs.
S1: And the fact that AI is what's going to interact
S1: with all these APIs. The primary interface for doing anything
S1: is going to be APIs, because it's the AI that's
S1: parsing them, right? What humans need to look at a
S1: website or a list of services, or a menu of
S1: food or whatever. What humans need for that is way
S1: different than what a computer needs. A computer can parse
S1: an API or an MCP service directory, you know, basically
S1: instantly and know exactly how to use it. So if
S1: you if you think about SEO, if you think about UI, UX,
S1: if you think about advertisements, if you think about websites,
S1: if you think about News media, all of that. It's
S1: all right now focused on presenting that to humans. The
S1: fundamental change that's happening right now is that's all switching
S1: away from being primarily designed for humans to being primarily
S1: designed for AI. Primarily, these interfaces will be for AI
S1: to find a business, AI to find a service AI
S1: to rank those services. Right. So examples will include and
S1: this is keep in mind this text right here. This
S1: is from 2016 when I wrote that stupid book which
S1: you probably don't need to read anymore because it's all
S1: in that, that post and it's got pictures which took
S1: me like hours to make. So it's a better version
S1: of the book that blog post is. But the key
S1: point here is I was talking about this in 2016,
S1: so I'm looking for a little like, I don't know,
S1: somebody to send me a pat on the back. That
S1: was kind of the whole purpose of writing that stupid
S1: book was to get this stuff locked in, because I
S1: saw this happening so clearly, even though that was way
S1: before this AI stuff popped. Right. So what is 22
S1: the end of 22 versus the end of 2016? That's
S1: six years. Six years. I basically said, we're going to
S1: have digital assistants, which are AI powered. I talked about
S1: the AI component right there in there. It's going to
S1: know everything about us. We're going to talk to our Da.
S1: Our Da is going to read all the services. Every
S1: human is going to have a demon. Not every human,
S1: but most humans. A lot of a lot of humans
S1: are going to have demons. But most importantly, all the
S1: businesses have demons. We talk to our Da. The Da
S1: infers what we probably want. We don't even need to
S1: talk to it. It's the one interacting with the world, right?
S1: And there's a millions upon millions of other DA's out
S1: there working for their principles. Some are working for businesses,
S1: some are working for individuals, some are working for organizations, entities, Whatever.
S1: But the point is, they're the ones doing the hard
S1: work of parsing all these millions of demons. Right. So examples,
S1: finding gifts customized to the exact individual organizing person. Perfect
S1: vacation based on the four people going, navigating all the
S1: logistics and the best way possible of that four person vacation.
S1: Determining the current mood of a person or location based
S1: on what is known about them. Finding the optimal route
S1: from one place to another. Determining the best way to
S1: charge a customer based on evolving competition, logistics, and conditions
S1: on the ground. All these tasks will be services available online.
S1: There will be several or thousands of competitors in any
S1: particular space. There will even be services that consume and
S1: rate those services and present their output through their own demons. Right.
S1: So that's another demon. So my digital assistant, my personal
S1: I will be using those services to find the best service,
S1: to organize a trip or to play the perfect song
S1: or to do basically anything. All these SaaS services, all
S1: these B2C services companies. Forget a human going to the
S1: website and clicking around. That's all done. That is all done.
S1: Our Das will be doing that for us through their demons,
S1: through their business demons, which maybe MCP is that, and
S1: that's where we are. This is essentially what MSPs are
S1: finally starting to happen. MSPs are a way to take
S1: any application and turn it into globally available services. That's
S1: why this MCP thing is so exciting. It's taking any
S1: application and presenting it to I. So I like what
S1: we're doing now is we're taking IDs and we're loading
S1: in these marketplaces. So we have the ability to manually
S1: choose these things. But pretty soon our IDs and more importantly,
S1: our digital assistants are just going to there's going to
S1: be natural, like Vetted, high quality lists of these MSPs
S1: of these demons out there, and they're going to be
S1: super high quality. They're going to be rated for quality.
S1: They're going to be rated for uptime. They're going to
S1: be rated for, you know, how good the results are
S1: and everything. So my Da is just going to be
S1: constantly using all of those, right. So when I'm like
S1: hey I've got sniffles. It's going to be find the best, uh, pharmacy,
S1: find the best medication, do all the research, um, find
S1: the best delivery thing and within, you know, like six minutes,
S1: somebody drops it off on my porch, right? It's it's
S1: going to be that simple because it made 400 different
S1: requests to multiple different demons to be able to do
S1: the research and figure that out and find the best one.
S1: Turns out I paid like a tiny amount of money
S1: for that, or whatever the the amount was. Probably won't
S1: even be a human delivery. It'll be like a Waymo delivery. Anyway,
S1: bottom bottom line is we all get digital assistance. Everything
S1: gets an API, including people, objects and businesses. Our Das
S1: are the ones interacting with the world on our behalf,
S1: because there will be billions of APIs or demons out
S1: there to interact with, and our Das will. Ultimately, this
S1: is the AR piece, which is not quite happening yet,
S1: but everyone's building it. It will display in our are
S1: the best version of of the world to look at, right?
S1: If we're in a security situation, it will turn to
S1: like a security filter for in like a dating situation,
S1: it will turn into like a dating filter. If we're
S1: in like a library, it'll like. Overlay interesting topics based
S1: on the books that we're currently looking at. It'll be like, oh,
S1: you know, Jason really liked this book and Chris really
S1: liked that book or whatever. Or, hey, you were reading
S1: something about this. This book will go into it in
S1: more detail, right? So contextual display of information within your AR.
S1: Everyone's currently building Das MCH and lots of other projects
S1: are APIs for everything, and lots of people are working
S1: on the AR stuff as well, like meta and I'm
S1: sure Apple eventually. All right. Security, cyber security critical PHP, CGI,
S1: remote code execution vulnerability is being mass exploited against windows systems.
S1: It was originally a bunch of Japanese targets. Now it's gone.
S1: Global help Net Security put together a list of 28
S1: cyber security positions across the US. President is nominated Sean Plinky,
S1: Plonky veteran with experience at US Cyber Command in the
S1: previous Trump administration to head Cisa. So Cisa has a
S1: new leader. Malicious poisoning of AI narratives. So TechCrunch put
S1: out a report that Russian propaganda was there was a
S1: basically a a network called Pravda, which is a Russian
S1: propaganda network, is flooding the web with fake news about,
S1: I assume it's like, uh, Russian narratives around Ukraine and
S1: a whole bunch of stuff that just Russia wants everyone
S1: to believe. This is crazy. This is an actual AI
S1: poisoning attack. Real world because everyone was worried about you.
S1: Poison the training data. Oh, but it's like, well, how
S1: are you going to break into the company and poison
S1: the training data? Turns out these bots, like OpenAI, like anthropic,
S1: they're all constantly parsing the internet. You don't have to
S1: poison the original pre-training stuff. Well, actually, that's being poisoned
S1: as well, because every time you do a pre-training, you
S1: have to re harvest from the internet. If you change
S1: the internet, then you have poisoned it, right? So, for example,
S1: if this this group, Pravda is trying to change the
S1: narrative around something. So let's say this company, Pravda successfully
S1: saturates the entire world with the idea that Ukraine attacked first. Okay.
S1: Ukraine came over the border and killed a bunch of
S1: innocent people. And what Russia did was they did a counterattack.
S1: And this is purely defensive. I'm not saying that's that's
S1: what the propaganda said. Let's just take it as an example. Well,
S1: if they put out millions of articles like that across
S1: the entire internet, and then you parse the internet and
S1: you put it into your training data for the next
S1: version of sonnet or the next version of, uh, you know,
S1: O4 or GPT five or whatever it is. Well, how
S1: is the thing? How was I going to know that's
S1: not real? It's it's parsing the internet to figure out
S1: what's real. So this is an actual AI data poisoning attack.
S1: Ten leading chat bots were tested. Chatbots were tested for
S1: susceptibility to Russian disinformation. Pravda published 3.6 million misleading articles
S1: just in 2024, 3.6 million misleading articles and news Knausgaard's
S1: analysis found that the chatbots collectively repeated false information a
S1: third of the time. That's insane. Real world AI data poisoning.
S1: So it used to be about SEO poisoning. Now it's
S1: about flooding the internet with marketing or propaganda that will
S1: get you picked up by up by bots. Now I
S1: say marketing and propaganda. It's the same thing, right? Let's
S1: say you have something. You have a an endpoint product
S1: that's nowhere near as good as CrowdStrike, but you hire
S1: Pravda and you blast out to the internet that, you know,
S1: secure point is way, way better than CrowdStrike. And all
S1: the bots parse this thing. And a third of the time,
S1: Secure Point comes back as better than CrowdStrike. That would
S1: be massive. I mean, that that would get you promoted
S1: on a marketing team, because guess what? The new search
S1: engine is actually AI. A lot, a lot of people
S1: are actually asking AI to compare products and to come
S1: back because. Because it knows that the AI is read
S1: everything and is is summarizing the results of all of that. Well,
S1: that changes that changes significantly when now everyone's going to
S1: switch to this. So, so all these all these LLM
S1: providers are going to have to figure out a way
S1: to filter out poisoning. I don't see an easy way
S1: to do it. You're going to need a lot of
S1: really smart I just to do the data cleansing for
S1: this type of poisoning. All right. Poland's space agency Polska
S1: has taken its systems offline after detecting unauthorized network access. And, uh.
S1: And they're blaming Russian cyber campaigns against the country. Japanese
S1: telecom giant NTT got breached and exposed corporate data for
S1: around 18,000 companies. AI is targeting of enemies. US is
S1: using AI to scan social media and revoke visas of
S1: foreign students it believes support Hamas or other terrorist groups.
S1: The reason I think this is worrying because this this
S1: might be a good use of it, right? But the
S1: problem is, once you use it once and everyone's like, oh,
S1: that's okay. I mean, there's a saying that says first
S1: they come for so-and-so, then they come for so-and-so, and
S1: you say it's okay, and then they come for so-and-so,
S1: and you say, oh, that's still okay because it's not me.
S1: But eventually they come for you. And I'm not saying
S1: that's going to happen. I'm just saying whenever you have
S1: a thing that's like, hey, we're looking for people like
S1: this because they're bad and the people are like, well,
S1: that's okay. And everyone like allows that type of Gestapo behavior.
S1: It's slippery, very slippery, and it's way worse when it
S1: would have taken a lot of research in the past
S1: to actually go and find these people. Right. You got
S1: you got to parse all these videos. You got to
S1: you got to, you know, go through all the blog posts,
S1: you got to find people You basically have to hire
S1: a bunch of researchers, researchers to do this. Well, what
S1: happens if you just spin up a really powerful AI
S1: with a bunch of tools and say, hey, go parse
S1: the internet for the last two years or during this
S1: six month period and find everybody who in the crowd
S1: was waving a Hamas flag, or in the crowd who
S1: was waving whatever flag for the thing that I currently
S1: don't like. And this could be a left leaning thing.
S1: This could be a right leaning thing. Doesn't matter. You
S1: could target an enemy and basically go after them. That
S1: is that is very serious stuff. And the better the
S1: AI gets, the better it's the faster it's going to
S1: be able to do that. I mean, you could be like, look, hey,
S1: we got a Swat team that's ready to go find
S1: me the best possible enemy of me and my friends,
S1: and find me a reason in all of their in
S1: the last six months to go after them. And boom,
S1: you send the Swat team like that. That's a very
S1: powerful weapon, and you have to be very careful who
S1: is actually wielding that thing. DOJ charged 12 Chinese nationals
S1: for state backed hacking operations, including two government officers, eight
S1: employees of a hacking company called I-soon and a few
S1: members of APT 27. Microsoft Threat Intelligence says China linked
S1: Silk Typhoon hacking group is shifting from compromise to actually
S1: going after it. Supply chains as an entry point to
S1: corporate networks, national security. Russia and China and probably others
S1: are targeting Doge fired US federal workers with special focus
S1: on those with security clearances. So basically, a bunch of
S1: people with clearances who know things get laid off and
S1: they're going after them, especially if they're angry, right? That's
S1: a lot of vulnerability. China announced another 7.2% increase to
S1: its military budget, taking it to $245 $45 billion a year.
S1: The Polish president, Andrzej Duda, is pushing NATO, NATO members
S1: to increase defense spending to 3% of GDP. Yeah. China
S1: is trying to bump it by another 7.2%, and we're
S1: trying to get, uh, NATO members to 3%. Oliver Carroll
S1: reports that the US has cut off critical Himars, intelligence, Himars,
S1: Himars intelligence to Ukraine, reducing their ability to defend against
S1: Russian forces. I so manas broke the internet last week.
S1: Big hype is, uh, it's kind of like AGI similar. Um,
S1: they didn't say it was AGI. The video that they
S1: put out was like, hey, it's leaning towards AGI. It's
S1: a little bit like AGI. We're heading in that direction.
S1: But it's basically like Claude or OpenAI, whatever they have
S1: behind the thing. But it's just way smarter. It's doing
S1: a lot more manual work that that would have needed
S1: to be done by a human in the past. And
S1: it's very much in line with what I was talking
S1: about the other day where, um, I don't think that
S1: we need smarter models, actually, to get to AGI. I
S1: think we just need better orchestration of tooling and more
S1: working memory for the models we already have. And it
S1: seems to me like madness is just is just that. Right?
S1: It's probably Claude underneath. And a lot of people have been, uh,
S1: you know, basically hypothesizing that that's the case. We don't
S1: know for sure, but it could just be Claude with
S1: better orchestration. Really want people to think deeply about this
S1: general intelligence competence might be already possible, given how smart
S1: something like O1 Pro or Sonnet 37 already are. It
S1: already has the intelligence to understand stuff like millions or
S1: billions of questions that you could ask it. It's smart
S1: enough to generalize allies answers, or you can give it
S1: situations like brand new situation and based on everything it
S1: knows about these other things, it can give you the
S1: direction or a solution, you know, for that new problem
S1: that you have. That's generalization, right? There might be smart enough.
S1: The problem is its eyes and its hands and it
S1: getting confused after doing too many things because of context windows. Right.
S1: And just overall coordination and orchestration of the thing that
S1: it's doing to try to accomplish a task. Seems like
S1: Manas did that better, but I think that's all that's
S1: needed is that orchestration. So the point is people are like, well,
S1: AI is not getting smart enough fast enough to actually
S1: move us towards AGI. We don't need that. We don't
S1: need smarter models. We need larger context windows, more tools
S1: for the agents to have and better orchestration. That by itself,
S1: I think gets us there to AGI. And I've got
S1: another post, which yeah, I'm not going to pull it up,
S1: but I've got another post that basically talks about exactly
S1: what AGI is going to look like under my definition,
S1: which is, um, my, my definition is basically can it
S1: do the work of a knowledge worker making $80,000 in
S1: the year 2022? And I say 22 because that was
S1: like pre AI or whatever. So the marketing for the
S1: first AGI is going to be something like welcome to
S1: new hire. New hire will join your team on Monday morning.
S1: The day that they start they will do the onboarding.
S1: They will read all the slack messages. They will read
S1: all the documentation. They will meet with their manager. They
S1: will take direction. They will reach out to the team
S1: members and introduce themselves, and they will start to do
S1: the work that they are assigned. And most importantly, if
S1: that work changes because of a reorg, or because they
S1: bought some new company or whatever. They get new tasks,
S1: new goals, new work they have to do. They will
S1: discard their previous stuff. They will learn the new stuff,
S1: they will read the new docs, they will take new direction.
S1: They will learn what they have to, and they'll start
S1: doing that work. And if their manager is like, hey,
S1: you're doing pretty good here, but you need to improve
S1: on this just like a human. They'll be like, okay, thanks, boss.
S1: I'll make that adjustment. I'll make sure you get a
S1: status report every day and whatever it is they were
S1: asked for this company, new hire, which is not a
S1: real company, by the way, but it's going to be
S1: something exactly like this. That's how they're going to market it.
S1: Treat it exactly like a regular employee. That is going
S1: to be the moment, in my opinion, that we've reached AGI,
S1: because if that tool, by the way, there's going to
S1: be a bunch of tools that say they can do
S1: this and then people are going to implement them and
S1: they're going to be super stupid. They're going to be
S1: more waste than it's worth. It's just not going to work.
S1: But when this happens and let's say it's this company
S1: called New Hire. Um, when that happens, let's say new
S1: hire takes off and it just it goes slow for
S1: a second and all of a sudden everyone starts saying,
S1: oh my God, I employed ten new hires, and they're
S1: doing the work of like 50 regular employees. And I
S1: just increased my subscription because I'm growing my business faster
S1: than ever. I employed my new hires in marketing. I
S1: employed them in sales, I employed them in administration. I
S1: employed them in security administration in my SOC. Whatever the
S1: moment that happens, I'm calling that the AGI moment. I
S1: believe that is the actual AGI moment, because there is
S1: no better definition of general intelligence than humans. That's what
S1: we're basing this whole entire thing on, is humans ability
S1: to generalize, generalize when it's working. Right. The fact that
S1: you could change up context. Say, hey, that's not your
S1: job anymore. Here's your new job. That's the best definition
S1: we have is a knowledge worker constantly dealing with change,
S1: constantly dealing with one offs and asks and adjustments. So
S1: that's the perfect definition also for actual AGI artificial general intelligence.
S1: And if it could do the job of an average
S1: knowledge worker I would say that hits the bar. So
S1: that's why I'm using it for a definition. And I'm
S1: saying that when a product comes out that can do
S1: this and it's actually doing it, that will be the moment.
S1: And my prediction is late 2025 at the earliest. This
S1: is my modified prediction. My overall prediction, which I said
S1: in 23 was 25 to 28. So my modified prediction
S1: is late 25 to early 27. So it's still right
S1: there inside that window. But I think it's getting pretty
S1: close and things like manis are starting to show signs
S1: of it. All right. New model q w q 32
S1: billion parameters. This thing is outperforming or performing just as
S1: good as 671 billion parameter deep seek 32 billion versus
S1: 671 billion. And they are right on par. This is
S1: the absolute most impressive local model I've ever used. I
S1: test out all these big ones when they come out.
S1: I've got right in my other room. I've got two
S1: 49 seconds over there and I fire them up and
S1: I test this thing. This I mean, it performs honestly
S1: very similar to like a cloud or an OpenAI, um,
S1: like an O one or an O one mini. It's really,
S1: really good. Um, I used it with some, uh, story
S1: writing stuff. I'm trying to write a fiction story about some,
S1: like an eye feature, getting pretty excited about that, but
S1: I was using it to help me write, and it's
S1: doing things that no previous model could possibly do. Um,
S1: just vast jumps in quality in the output that I'm getting.
S1: So really excited about that. You should go check it out.
S1: You could run it with a llama llama run q
S1: w q. That's all you have to do. It'll download it,
S1: it'll run it, and you could test it out. And
S1: it's a thinking model built with reinforcement learning, which means
S1: you're going to see the thinking instructions as part of
S1: the output before it starts outputting the real thing. Myo
S1: addresses eye hallucinations, so doctors are very upset and worried
S1: about hallucinations for good reason because it's life and death
S1: for them. Right? So reverse Rag is a technique that
S1: they're using to basically figure out the sources for the
S1: data that they return. So they don't just take hallucinations
S1: or they don't just take outputs and assume it's good.
S1: What they do is they take each particular one and
S1: they go and research it and find sources to it.
S1: And then they say, okay, this is now trustworthy. And
S1: I've been talking about this for, I don't know, six
S1: months or now or however long. I did a talk
S1: about this at some conference. And basically what we're talking
S1: about here is a pipeline of trust that has to
S1: happen for high trust output. So if you're in medical
S1: or you're in military or you're in, uh, law or something,
S1: if you're in something where results actually matter, you have
S1: to have a system that comes after the AI output,
S1: which is like a series of steps for validation, and
S1: only after it goes through those steps and gets a
S1: green check mark. Then you can trust the thing, right?
S1: I mean, humans need that, by the way. I mean,
S1: humans don't put out stuff that's trustworthy, right? So I
S1: think AI is going to be actually much better at
S1: doing this than, uh, most human doctors. You still don't
S1: get that. Like the doctor touch and the doctor intuition
S1: and the the human aspect. So it's it's not better
S1: in all ways, but in terms of, like, putting out
S1: the best possible diagnosis and stuff like that, I think
S1: it's going to be fantastic. So kudos to Mayo for
S1: doing that. Tyler Cowen told Dwarkesh Patel that he predicts
S1: AI will only boost economic growth by 0.5% per year,
S1: half a percent. I massively disagree with this and so
S1: does Dwarkesh. Black Forest Labs, also known as replicate, is
S1: dominating image generation. I'm using this myself. Um, I've been
S1: using it in conjunction more with Midjourney. Midjourney is like
S1: my go to, but I've been using a lot more
S1: replicate and it is absolutely fantastic. Larry Page is working
S1: on a new AI company that uses artificial intelligence to
S1: design and manufacture optimized products. Ben Buchanan, Biden's AI advisor,
S1: says Washington now believes AGI is possible and realizes they're
S1: competing with tech companies for talent to regulate it. Nirvana
S1: raised 80 million for the AI based trucking insurance platform
S1: that uses telematics and 20 billion miles of driving data
S1: to create better policies for drivers. And I've got a
S1: post here on real time insurance premiums from 2016. Yeah,
S1: I've been talking about this for a while. 2016 essentially, again,
S1: everything with AI comes down to context. So if I'm
S1: wearing a microphone and a camera and I give access
S1: to that, there's like that progressive thing. Remember that progressive
S1: box you put in your car and it can see
S1: how you're driving? Well, imagine that it could see that
S1: you're cussing or yelling at your spouse or kicking garbage
S1: cans or, um, you just came from the bar. And
S1: it knows that because it has the cameras on and monitoring. Well,
S1: guess what? Your insurance premiums might be getting modified in
S1: real time or close to real time. Or you might
S1: be like, hey, if you get in this car right now,
S1: having come from, you know, oceanians, then, um, you your
S1: policy will be instantly cancelled, right? It's going to be
S1: things like that. Um, all possible because of AI and context.
S1: Simon Willison taught a Nykaa 2025 workshop on advanced web
S1: scraping techniques. Really, really cool talk. You have to check
S1: that one out. Okay, McDonald's rolling out a massive AI
S1: upgrade for its 43,000 locations, including AI, drive through smart
S1: kitchen equipment and generative AI Virtual Manager, one of the
S1: biggest visible AI rollouts so far. Can't wait to see
S1: how this works. I heard somebody say, I don't know
S1: who because I don't. I wouldn't eat there because it's
S1: really bad. But Domino's and Carl's Jr has really good AI.
S1: So I heard technology. Apple is pushing back. It's more
S1: personalized Siri features. So basically, the big unified Siri that
S1: has access to all the context is being pushed back.
S1: And a lot of people are speculating that this is
S1: because of security issues, prompt injection against that entire context,
S1: which what do you think is going to happen when
S1: your assistant that you call up with this keyword can
S1: do anything across your entire context, including all of your applications,
S1: all your health data and everything and your interface into
S1: that is is the English language and other languages as well,
S1: but especially English. That is a massive attack surface to
S1: go after. And I think Apple, rightly so, is being
S1: very careful about that. So they push that back. One
S1: thing to keep in mind though, is they already rolled
S1: out tons of Apple intelligence. I already use, uh, my
S1: Siri to actually, um, do live queries because it's connected
S1: to ChatGPT. So don't think that we're waiting for this
S1: thing to happen. And you don't have Apple intelligence before that.
S1: It's already out there. Like they already rolled out like
S1: dozens of these features, including, most importantly, the ChatGPT one.
S1: And the fact that if you hold down your camera button,
S1: the new camera button on the side, you can have
S1: ChatGPT identify things with the camera. So all this to say,
S1: Apple intelligence is like already live and already working. It's
S1: just this unified big context, one that they haven't enabled
S1: yet because of the security concern. Waymo is expanding its
S1: self-driving car service in Palo Alto and some surrounding peninsula areas.
S1: Cannot wait to get it around here. Waymo is just
S1: just unbelievably good. New Zealand appears to be managing its
S1: $16 billion health budget. Using a single Excel spreadsheet probably
S1: makes Doge pretty happy. Apple is evidently planning a major
S1: redesign of iOS, iPadOS, and macOS for the fall. Can't
S1: wait to see what that looks like. I'll be running
S1: it on day one because I'm stupid. Tesla's global sales
S1: are tanking pretty hard right now with significant drops across Germany,
S1: Australia and China. Yeah, you do a Hitler salute and
S1: sales fall in Germany. Big surprise. New Mac studio has 32.
S1: This is the M3 ultra 32 CPU cores and enough
S1: unified memory, 512GB to load the 600 billion parameter models.
S1: I was going to get one, but I already have
S1: my eye box. I'm going to skip this generation, maybe
S1: even another one, because I already have an M2 ultra.
S1: Not worth it to bump up to the 512 especially.
S1: That's like a $15,000 desktop discovery. A machine learning engineer
S1: named Jasper Gillie quit his job because he believes AI
S1: will automate his entire role by the end of 2025.
S1: Keep in mind, this is an extremely highly paid machine
S1: learning engineer, and he's saying his job is going to
S1: be replaced by the end of 2025. Robin Moffat argues
S1: we should or we shouldn't, just write the perfectly structured
S1: blog post, but also share our messy struggles and solutions
S1: that other people need, too. I like this. It's like
S1: learning in public. Show your work, you know. Feel free
S1: to be vulnerable and imperfect online, I think is, uh.
S1: It's endearing. Hopefully so since I've said edit like nine
S1: times in this recording already. Small docs a project around
S1: writing small docs instead of giant ones that are hard
S1: to follow. Jamie Rumbelow created a slick hybrid between databases,
S1: programming and UIs. Make scripts more user friendly without needing
S1: a full web app. And basically the pitch there was
S1: that the database, the UI and the app are kind
S1: of unified. They're all the same thing. Astro and vim,
S1: one of the better vim distros. I do prefer lazy vim,
S1: although I hate the name. Reminds me of the dummies books,
S1: which I could never get myself to buy. Too much self-respect.
S1: Interview DB lets you access crowdsourced tech interview questions from
S1: the community. Alexander Solzhenitsyn on the future of the West.
S1: This was a crazy, crazy piece of text there. Seneschal MCP.
S1: MCP by a brilliant Ull member named Matt, who, um,
S1: wrote an MCP that helps Llms access standardized health data
S1: from the sky. Senegal. Senegal. I keep thinking, Senussi. What
S1: am I thinking of? Senegal. API. So basically, this is
S1: an NCP that makes your health data available so that
S1: you can have local agents or any kind of agent
S1: really make your health data available to an app. So
S1: if you're building like a full life encompassing, you know,
S1: management app with like workout routines, health, fitness, food, diet, money,
S1: all that stuff. This is an API. This is an
S1: MCP that presents the health side of it. Very very
S1: cool from UL. Member Matt Self interview AI tool that
S1: runs invisibly during technical interviews, helping you with coding questions
S1: while your screen is shared. Also known as cheating. So
S1: this is what you have to watch out for if
S1: you're doing interviews. Piano FM generates endless beautiful AI piano music.
S1: I listened for a while. It was not repetitive and
S1: it sounded. I'm not a classical music expert or a
S1: piano expert, and it sounded pretty normal to me. It
S1: sounded like regular piano, whatever that's worth. Matrix hacking game.
S1: Someone built a matrix themed AI hacking game lets users
S1: learn about prompt engineering and security by defeating an Agent
S1: Smith AI character. Send new app connect your iPhone camera
S1: to multi-modal Llms for real time visual intelligence made with
S1: zero VC money and cursor. This is the type of
S1: thing that could have gone for, could have raised, you know,
S1: $20 million and been sold for $100 million maybe a
S1: couple of years ago. And this person just built it
S1: with cursor with no money whatsoever. Since Feedback Dev built
S1: an AI that reads the emotional vibe of your product,
S1: social media mentions and gives you instant feedback. And somebody
S1: posted their cursor rules, which I thought were quite good.
S1: All right. This is the end of the standard edition
S1: of the podcast, which includes just the news items for
S1: the week to get the rest of the episode, which
S1: includes my analysis, the discovery section that has all of
S1: the coolest tools and articles. I found this week, the
S1: recommendation of the week and the aphorism of the week.
S1: Please consider becoming a member. As a member, you'll get
S1: lots of different things from access to our extraordinary community
S1: of over a thousand brilliant and kind people in industries
S1: like cybersecurity, AI, technology and the humanities. You also get
S1: access to the UL Book Club, dedicated member content and events,
S1: and lots, lots more. Plus, you'll get a dedicated podcast
S1: feed you can put in your client that gets you
S1: the full member edition of this podcast. To become a
S1: member and get all of that, just head over to
S1: Daniel Comm Slash upgrade. That's Daniel miessler.com/upgrade. We'll see you
S1: next time. Unsupervised learning is produced on Hindenburg Pro using
S1: an SM seven B microphone. A video version of the
S1: podcast is available on the Unsupervised Learning YouTube channel, and
S1: the text version with full links and notes is available
S1: at Daniel Mysa.com slash newsletter. We'll see you next time.