S1: Welcome to Unsupervised Learning, a security, AI and meaning focused
S1: podcast that looks at how best to thrive as humans
S1: in a post AI world. It combines original ideas, analysis,
S1: and mental models to bring not just the news, but
S1: why it matters and how to respond. All right, welcome
S1: to unsupervised learning. This is Daniel Miessler. Okay. First big
S1: news that just happened is there was a release for Gemini.
S1: So there was rumor last night or, I don't know,
S1: last few days that there was going to be a
S1: big model that got released. And it turns out it
S1: was Google as opposed to anthropic, although maybe anthropic is
S1: going to do something as well. I was hoping for
S1: like an opus four or something, or at least an
S1: opus 3.5. So it turns out to have been Google
S1: and it looks like new Gemini 1.5 Pro and Flash models.
S1: So right off the bat, I mean, these seem very minor.
S1: They're minor updates to 1.5 Pro, but whatever. I'm looking
S1: for reasons to be excited. 52% reduction in output token pricing,
S1: two times higher rate limits for Gemini 1.5 flash and
S1: three times higher rate limits for Gemini 1.5 Pro. So
S1: these are really updates to 1.5 Pro. And if I
S1: go to let's see here, if I go to Kitty
S1: and I do this yeah I'm not seeing 002. Oh
S1: there we go. Yeah 20. Okay. So if I put
S1: 20in here into fabric I'm now using Gemini 1.5 Pro
S1: version two, which is the brand new one that just
S1: came out. So I guess we could do like a classic. Um,
S1: so this is the rivets video pipe to fabric SP
S1: extract wisdom DM all right, so streaming works. Seems pretty fast.
S1: See how these insights are? So first of all, it
S1: extracted a whole lot of insights. That's or no that
S1: was ideas. Now insights should be fewer. And these look
S1: really really good. So here's one thing that I love
S1: about AI right now. So if I give this same
S1: challenge to Gemini and GPT four or any of the
S1: modern ones from OpenAI or anthropic, they all come up
S1: with this one as the top quote. You can't necessarily
S1: think yourself into the answers. You have to create space
S1: for the answers to come to you. And I think
S1: that is really, really cool. And all these other quotes
S1: are just fantastic. This is like one of the videos
S1: I know the best. It's like one of my favorite
S1: videos ever. So anyway, this is the results of the
S1: capture from 1.500002, which is the brand new one. And
S1: let's look at the habits. First of all, it followed
S1: instructions really well. And by the way, the DM version
S1: of Extract Wisdom has a whole bunch of extra instructions.
S1: It's like way more chain of thought and way more
S1: prescriptive for like what I'm looking for. So that should
S1: give a pretty good advantage. And it looks like Google
S1: did really well with it. I like the way that
S1: they're saying this. Oh, you know what I want to
S1: do my other new favourite one. So let's take that
S1: one and let's do create story explanation I love this
S1: create story explanation one. Absolutely fantastic. So it basically just
S1: walks you through like you're in a conversation about whatever
S1: the input is. So they lament the decline of creativity
S1: and deep thought. In the West. They discuss the impact
S1: of technology, social media and constant barrage of information. One
S1: speaker shares her experience of deep diving into the history
S1: of the New Testament manuscripts. They discuss the importance of
S1: deep reading, embracing diverse interests. So yeah, the conversation concludes
S1: with a powerful image of the decline of the creative spirit.
S1: I mean, this is just a cool way of describing
S1: a piece of content. So I think I could do
S1: like another one. Neri Oxman so this is one of
S1: my other favorite YouTube videos ever. Yeah, look at this.
S1: Oxman believes nature possesses a wisdom beyond human intelligence. She's
S1: building a company called Oxman to explore the wisdom and
S1: augment nature with technology. Her goal is to grow products,
S1: blurring the line between made and grown. See? Check this
S1: out when I go to summarize why I love this
S1: video so much, it's hard to capture it. It's hard
S1: to remember what I love about it so much, and
S1: this does really well at that. The tech does really
S1: well at capturing what makes her so awesome and it's
S1: got advice. It's just like great. In fact, I might
S1: even get rid of this pre and post sentence. I
S1: think I might just keep that content right there. But
S1: I'm not going to do that live. But anyway. Yeah interesting.
S1: Looks like I have a little bit of a key showing.
S1: But that is not a real key. That is a
S1: HTTP Cookie header. Always like to watch my OpSec here.
S1: Going to leave a token at some point. I'm going
S1: to leave a token to see if I'm going to
S1: leave an API key out there and and monitor to
S1: see if anyone comes in on it. Exercise for the reader. Okay.
S1: So cool. So yeah, we don't have the video of
S1: Parul and Sam Altman yet. It looks like he has
S1: not dropped it yet, but that's fine. Just go back
S1: to the show. Basically some model upgrades from Gemini and
S1: they look to work quite nicely. And I just changed
S1: my default model to it just now. So I'll see
S1: how that does performance wise. Okay. Um, Thomas Rocca created
S1: a web GUI for fabric called fabric UI. Check this
S1: thing out. Look at that. And Jonathan, who's on the
S1: fabric team, is actually working on something like this as well.
S1: But look at this. You can select the model. You
S1: want to use a pretty good selection there. You put
S1: it in your API key. The guy's a hard core
S1: security guy, so I don't think he's going to be
S1: stealing keys anytime soon. Um, and then, look, you pick
S1: your pattern here, and. Yeah, you got a guy to
S1: run fabric with. And again, Jonathan on the fabric team
S1: is actually building something similar. He's building like an API
S1: server and also guy. So that's going to be really cool.
S1: But this guy, uh, Thomas Rocha. Super cool guy. I've
S1: known him for years and years. Uh, he's part of
S1: the UL community and, uh, evidently just built a UI
S1: for fabric. So that was very cool. And I just
S1: spent a whole bunch of time, like, deep cleaning on
S1: my phone, which is the new iPhone 16 Pro and deleted,
S1: probably 40 applications, phone screen cleanup, widgets, Refactors watch faces.
S1: I redid all my focus modes, which are these things
S1: up here and uh, like right here. So you got
S1: like work and personal and Do Not Disturb and all that.
S1: And all of those have individual settings which map over
S1: to the phone as well. So it's like it's basically
S1: a giant ecosystem upgrade, like computing ecosystem upgrade within the
S1: Apple ecosystem. And I do that every year, but sometimes
S1: I only do a very little. And like the last
S1: couple years, I've only done a little bit of it,
S1: and I probably spent 5 or 6 hours this year
S1: doing a massive cleanup. And the reason I mention it
S1: is because it feels really good to do that. You've
S1: probably been feeling weight weighing on you of like, oh man,
S1: these apps are nasty. Why do I have these? Do
S1: I have subscriptions that are still turned on? So all
S1: that to say, I encourage you to go do a cleanup.
S1: It will feel good. Okay, so I did an analysis
S1: of OpenAI's long form conversation with the zero one strawberry team.
S1: So this is the conversation.
S2: And I think, you know, for go players and chess
S2: players that would have come if you go to.
S1: The beginning, you see I lead the research team here
S1: at OpenAI. Look, if you see here zero one mini,
S1: as I click through, it's like, look at that.
S2: Shot of before there was the first, there was a
S2: whole bunch of we started talking to the model and
S2: people were like, wow, this is going to be doing
S2: something like that. And I think there was a certain
S2: moment in our in our training.
S1: Use it. Why? It's better than other things.
S2: Put more computers in our area than before of GPT
S2: four trained, first of all generating a long form conversation.
S2: And wow, this looks.
S1: Like fairly long.
S2: Very different than before 32nd. For me, this is the
S2: moment for.
S1: So if we, um, take the video URL and we
S1: come back here, uh, by the way, you don't have
S1: to use it anymore. You see what I'm doing here?
S1: This video is being parsed by fabric itself with the
S1: Y parameter for YouTube. Okay. So if I do that
S1: I'm going to get back the transcript like that. Um,
S1: so if I do yeah. Let's do let's do create
S1: story explanation again. And again. This is using the Pro
S1: model from Gemini. It feels a little strange summarizing an
S1: OpenAI video with Gemini, but whatever. It's not that deep. Okay, yeah,
S1: they've been working on this for a long time. It's
S1: a reasoning model, meaning it takes time to think before answering.
S1: The team had several aha moments, like, I feel like
S1: you could just read these ten bullets and have a conversational,
S1: explanatory explanation of what's in this video. And that's why
S1: I created this pattern called Create Story Explanation. It's just
S1: a way of explaining anything in a super approachable way,
S1: including like the most crazy, crazy. Okay, I'm going to
S1: do particle physics, so let's do string theory. Look at this.
S1: Imagine everything in the universe not made of little balls,
S1: but tiny vibrating strings. They vibrate at different frequencies, and
S1: those frequencies determine what kind of particle it appears to be.
S1: Look at this. That's clean. That is so clean. And
S1: it's different than then extract wisdom, which I don't even
S1: know what that would do. Yeah, that. I'm curious. Take
S1: all of string theory, basically that the model knows about
S1: and send that to extract wisdom. I very curious what
S1: this is going to do. Ideas? Holy crap. It is
S1: extracting all the ideas out of string theory insights. Okay,
S1: it's doing actual insights. Yeah. Quotes. Nothing. Facts. Nothing. Habits. Nothing. Recommendations. Nice. Okay,
S1: I'm pretty happy with this, actually. See what we get
S1: from the insights potential unified framework for understanding all fundamental
S1: forces and particles. Well, yeah, that's that's why string theory
S1: was exciting and or is still exciting. Not sure if
S1: it actually still is. Extra dimensions. A key element of
S1: string theory could reshape our understanding of the universe. See,
S1: this is really cool, right? Look at this. Look at ideas. Insights.
S1: This is really cool and powerful, but it's not as
S1: cool as this. If you're trying to explain it to
S1: just a regular person or or heck to me on
S1: any given day, I would like to hear it this way.
S1: And then if I really want to go deep on it,
S1: like then maybe extract wisdom to pull this stuff out
S1: and definitely extract wisdom if I want to extract, you know,
S1: purely the ideas or whatever, which I actually have separate
S1: patterns for those. But anyway, bottom line, the reason I
S1: got on to this is that I did an extraction
S1: of the techniques that were that they said they were
S1: using the zero one model from in this interview, they
S1: said we here's what we love doing. And they went
S1: around the room to all those different people and it
S1: was like, hey, what? What do you do? What what
S1: have you found that oh, one can do that GPT
S1: four can't. And so they came up with this list.
S1: Generate code part pass error messages for debugging. Learn complex
S1: technical subjects asking it to explain concepts without hallucinations. Brainstorm solutions. Okay. Um, okay.
S1: So I think this might be my Twitter post about it. Yeah. Yeah.
S1: So I did a thread here. I'll just walk through
S1: these real quick. So some thoughts. A lot of these
S1: takeaways are similar to GPT four sonnet that can already do.
S1: But the main theme is simply asking more of the
S1: model and expecting more out of it. And another key
S1: thing seems to be idea generation brainstorming creativity. So it's
S1: more like a creative partner. Oh, one is rather than
S1: previous models. So that was one thing they were really
S1: excited about. One of the ones was I loved the
S1: bit about asking to connect ideas. So if we go
S1: back to this list, look at this, um, ask oh
S1: one deep questions about abstract concepts to receive thoughtful, detailed answers. Um,
S1: implement projects with little prior knowledge. Oh, here we go.
S1: Organize unstructured thoughts by asking oh, one to connect ideas
S1: and identify missing elements. That is cool. I'll just open
S1: this up, make text more creative by requesting multiple alternative ideas.
S1: So again, all of these are very thinking based and
S1: very creativity based. I generate ideas for structure and content
S1: of writing. Yeah, really really cool stuff. So that's what
S1: that was about. Um, yeah. And it's in the newsletter,
S1: so you can go check it out. All right. Wrote
S1: a piece for AT&T business about AI transforming real time monitoring.
S1: So this is basically a security post about many AIS. And, uh, yeah,
S1: I think you'll like it. It's, uh, a piece I've
S1: been thinking about, so I was happy to do it
S1: for AT&T business. Who, uh, who sponsored the piece. So
S1: I'm excited to be a keynote speaker at Swiss Cyberstorm,
S1: which is a really, really cool, uh, security conference. I've
S1: been reading more about it. They're not about, like, all
S1: the latest hacks and like, oh, we we hacked this thing.
S1: Let me show you how we can break into this
S1: thing or whatever, which again, I've done a million of
S1: those talks. I'm all for them. At this stage of
S1: my career, though, it's like I'm not finding that as
S1: interesting just because I feel like it's too easy to
S1: hack things. There are lots of kids standing at the
S1: top of a stairway, and it's like, easy to walk
S1: by and push them. Why would you want to walk
S1: by and push them? The whole world is super fragile.
S1: It's a surprise that any of this works like I just.
S1: I'm not getting the same enjoyment that I used to
S1: get from breaking into things or knocking something over, you
S1: know what I mean? It's like it doesn't give me
S1: a good feeling. So the thing that's giving me a
S1: good feeling now is building and trying to figure out
S1: what the most difficult problems are in the world. Um, anyway,
S1: bottom line for this thing, this conference here keep going
S1: on tangents. Um, this conference focuses on that. It focuses
S1: on the ideas around security as opposed to like the
S1: latest hacking thing. And I like a mix of both, honestly.
S1: But I prefer more the the thoughts and the trends
S1: and stuff like that of like, where is this going?
S1: Are we moving to a place where there's no trust?
S1: How does all security change when there's no trust? What
S1: sorts of infrastructure are we going to need in that world?
S1: That kind of thing. And you could use code unsupervised
S1: learning for 15% off for your registration. And the theme
S1: for this one is AI Revolution. So yeah. Cyberstorm Swiss Cyberstorm.
S1: All right. Security Israel launched an extraordinary attack on Hezbollah
S1: using a combination of supply chain and remote triggering techniques.
S1: And I've got a whole bunch here. You know, I
S1: don't think I'm going to go into this. It's it's
S1: political and it's a bit long and we're already like
S1: pretty far into this podcast. So I think I'm going
S1: to let you go and read that one security researcher
S1: named X, y, z three VA found a catastrophic flaw
S1: in the Arc browser That lets attackers inject arbitrary code
S1: into user sessions using just a user ID. That is,
S1: that is nasty. So presumably I haven't looked deeply at
S1: this one, but it's like presumably each user has some
S1: sort of guide. And then there's infrastructure that controls like
S1: the guides. And then if you attack that infrastructure, you're
S1: now attacking the user who's using the browser if they're
S1: in as that guide. That is just so gross. So gross.
S1: All right. Hacker named Adkar 72 424 has leaked a
S1: massive database of 3.3 billion unique email addresses or claiming
S1: they're unique. And he said, yeah, it's not dupes. It's
S1: not old stuff. It's a new captcha. And evidently it's 21.8GB.
S1: We're thinking about putting this into Suckless, but that's pretty huge.
S1: Suckless is already massive as it is. Chinese scientists have
S1: figured out how to use Starlink satellites to detect stealth
S1: aircraft and drones, which are designed to dodge radar. Presumably,
S1: they're basically it's background radiation coming from the satellites. And
S1: if you look up and you detect the satellite radiation,
S1: the signals coming from the satellites, and you see things missing,
S1: you see little cutouts. Well, that's where the thing is.
S1: That's my guess. Either that or there's bounce off the
S1: thing that produces like interference, and they're detecting that interference.
S1: I'm betting it's one of those two things. Thanks to
S1: Nudge Security for sponsoring nuclei templates. Version ten is out,
S1: including new Azure Config stuff. Google is making it easier
S1: to use Passkeys by syncing them automatically via Google Password
S1: and across Chrome on windows, Mac and Linux and Android
S1: and iOS. Support is coming soon. Greynoise has been tracking
S1: mysterious noise Storms of spoofed internet traffic since January of 2020,
S1: but still remains unknown. They're still trying to figure it out,
S1: but it includes this love Ascii string in ICMP, and
S1: they might be like covert communications or DDoS coordination. Kind
S1: of interesting. That's a man that reminds me of like
S1: old school, like ham radio stuff or CB radio where
S1: like you, you hear this signal and it sounds like
S1: a pulse or something, and then you research and you
S1: find out it's like some Russian submarine somewhere, or some
S1: like Siberian radio station. But there's all this digging and
S1: this is like pre-internet. So it's like really hard to
S1: find information. Then you find out, oh, Jim. Oh, you
S1: got to talk to Jim. Jim knows about this. He's
S1: in whatever Saskatchewan, Idaho. And you talk to Jim and
S1: he's like, oh yeah, well, there's this one time. I
S1: love that idea of like digging up archaic knowledge. And
S1: I find this really interesting where it's like, Greynoise has
S1: a bunch of sensors and they're seeing this love Ascii
S1: thing and they're like, what is this? We've been tracking
S1: it since, you know, January 2020. We don't know what
S1: it is. We're trying to figure it out. I think
S1: that's cool. And thanks to Threatlocker for sponsoring the podcast.
S1: All right, AI tech. So Sam Altman just dropped an
S1: essay called The Intelligence Age. I'm going to pull this
S1: thing up. We're not going to read it. But first
S1: of all, interesting website. I mean, it's just like super
S1: got this piece of art, which is no doubt AI art,
S1: I'm guessing probably a doll image. Who knows, maybe he
S1: made it, but it's like this super basic website. It's
S1: got a date. It's IoT. Sam Altman. Com and essentially
S1: what he does. Well, actually, here's what we're going to do.
S1: Watch this. See, I just did, uh, command a and
S1: we're going to go back here and we're going to
S1: do uh, we're going to paste that in. So I
S1: did a create story explanation of the Sam Altman piece.
S1: So here we go. Society's collective intelligence has always brought
S1: us forward and I supercharges that. AI tools will help
S1: us solve hard problems. Imagine personal AI teams of virtual
S1: experts helping us create anything shared prosperity where everyone's life
S1: is better. Deep learning is the key. AI will become
S1: increasingly capable. AI assistants will handle complex tasks like coordinating
S1: medical care for us. It will help us create better
S1: AI and accelerate scientific discovery. It's going to need a
S1: lot of computing power and energy. There will be challenges.
S1: The potential benefits are immense, similar to solving climate change
S1: and even with job market shifts. AI will amplify our
S1: abilities and create new opportunities. So it's basically a rah
S1: rah for AI, which is not surprising coming from somebody
S1: running an AI company. But it was it was interesting.
S1: It was a good read. And, uh, yeah, a good
S1: summary there from Google. Gemini 1.5, Pro Dash 002 and
S1: the create Story explanation pattern in fabric. South Korea's research
S1: institute has unveiled Deja Vu AI system that analyzes CCTV
S1: footage to predict and potentially prevent crimes. Yep, basically every
S1: sci fi ever preventing crimes. Like. I'm not saying this
S1: is not possible, but I think just because it's possible
S1: doesn't mean you should run full speed, you know, carrying
S1: scissors towards that possibility. Look, a lot of good can
S1: come from anything that's even starts out a little bad.
S1: And the opposite is absolutely true as well. Like if
S1: somebody is an obvious, obvious criminal, they're walking like a criminal.
S1: What does that mean to walk like a criminal? I
S1: don't know, maybe it means like having a hoodie on
S1: and glancing suspiciously from side to side at three in
S1: the morning while carrying a crowbar in a neighborhood full
S1: of cars that get broken into at roughly 3 a.m.
S1: with crowbars. Right? That would be an example of like
S1: a high correlation where, like, you have little much less
S1: chance of bias, much less chance of jumping to conclusions.
S1: It's like yeah. Oh, and also they're limping on the
S1: right side in this particular group. Like puts all the
S1: loot in their right side or whatever. You know what
S1: I mean? It's like the more you add up the
S1: evidence or whatever from like actual data and not random
S1: things of like matching the clothing or matching the skin
S1: color or matching whatever, you know, human bias based thing,
S1: wherever you can have as much data as possible and
S1: really good analysis and retroactively and currently look at it
S1: and evaluate it and make sure you're not being like,
S1: biased in a, in a in a bad way. Sure.
S1: The problem is that's not where it will stop, okay.
S1: They will people will deploy that and it will be
S1: cool and it will be useful to so-called predict crimes.
S1: But the problem is they'll be like okay, cool. So
S1: let's open up that faucet. And now suddenly we're detecting
S1: all people who look different in the neighborhood because they're
S1: in the wrong neighborhood. Like, well, people who look like
S1: that don't ever come into this place. And then unless
S1: they're up to no good and some I could actually learn,
S1: that doesn't mean the good ones will, but there's going
S1: to be lots of bad and halfway bad eyes in
S1: addition to the really, really good ones. So I think
S1: any time you start predicting crimes and then affecting people's
S1: lives as a result of those predictions, like that's sci
S1: fi shit and it's not a good thing. Johnny IV
S1: has confirmed he's building a hardware AI device with OpenAI.
S1: This thing actually might crush the others. This is what
S1: I was talking about, because the synergy between hardware, software
S1: and aesthetics is really important in AI gadgets. Like something
S1: you're going to have on you, something you're wearing, whatever.
S1: Who gets that better than the, you know, British Apple guy?
S1: Not many people. Blackrock and Microsoft are teaming up to
S1: raise $30 billion for AI infrastructure, putting it into $100
S1: billion in investments, hopefully. And they're building data centers and
S1: energy projects primarily in the US. But this is a
S1: partnership between Blackrock, Microsoft and the UAE to build data
S1: centers and energy products projects in the US. Cool Canadian
S1: study found an AI tool can reduce unexpected deaths in
S1: hospitals by 26%. LinkedIn has quietly opted users into using
S1: their data to trade generative AI models. I think they
S1: might already be rolling this back because there was so
S1: much backlash about it, and I think in the UK
S1: they definitely said no. So I'm not sure how much
S1: that's still continuing. But they tried. They tried and they
S1: did the right thing by telling people and they freaked out.
S1: Recent study by ring rover. No ring over found that 76.5%.
S1: So 77% of recruiters preferred AI generated headshots over real ones.
S1: Pardon the drinking there or edit it out. Okay, a
S1: lot of Amazon employees are upset about the requirement to
S1: go back to five days in the office. Basically, people
S1: are freaking out. They're like, this is super lame. Whatever.
S1: I said my piece on that a million times. Sure,
S1: I'll say it again. Apple's iOS update has RCS support,
S1: which means you're going to get to do more iPhone
S1: like things with Android users with green bubbles. So typing indications, emojis,
S1: stuff like that. It's all going to be a lot
S1: more smooth now that you're on iOS 18. Apple's A16
S1: mobile processors are being produced in the US at TSMC's
S1: Arizona facility. Love this self-reliance American manufacturing push. Absolutely love it.
S1: Apple's iPhone 16 now supports wireless firmware restoration. I'm guessing
S1: Android has had this since like 2002. The Apple's Watch
S1: Apple Watch's Remote app now lets you adjust volume with
S1: Digital Crown, so it's basically like a better remote control
S1: now for your Apple TV or whatever. And somebody shares
S1: his journey of turning blog content into an e-book. Facundo Solano.
S1: Turning blog content into an e-book. Humans Rick Beato argues
S1: that music is getting worse because technology has made it
S1: too easy to produce and consume. There's a new study
S1: showing that omega three fatty fatty acids can help reduce
S1: system symptoms of anxiety and depression in mice. So if
S1: you're a mouse, huge, huge news for you. US Department
S1: of Energy is rolling out over $3 billion to fund
S1: more than two dozen battery projects across 14 states. Love it,
S1: love it, love it. Astronomers have discovered the largest black
S1: hole jets ever observed. So basically, the black hole on
S1: its axis is shooting out stuff. These are the largest
S1: jets they've ever seen, stretching 23 million light years, which
S1: is lining up 140 Milky Way galaxies. That is ridiculous.
S1: Voyager one, my favorite like friend. Like, I don't know,
S1: I'll talk to this guy like, hey, we're still watching you.
S1: You're doing good. You're doing good, buddy. You're doing good.
S1: 47 year old spacecraft cruising through space since the late 70s.
S1: Fired up thrusters that he hasn't used in decades. I
S1: right here. Meanwhile, the asphalt on our roads has to
S1: be replaced, like, every 45 minutes. They don't make them
S1: like they used to. Interesting piece in the Wall Street
S1: Journal about how pediatricians might have sparked the peanut allergy epidemic.
S1: So basically they they told parents, make sure they don't
S1: eat peanuts because they're make sure you don't eat peanuts
S1: because peanuts will give you allergies. Not eating the peanuts
S1: is what gives you the peanut allergy. I think that's
S1: what it's saying. I'm not a peanut doctor, but I'm
S1: pretty sure that's what it's saying. And I've heard this
S1: from a million places. Ridiculous. Is that my new favorite word? Probably.
S1: Ohio is directly funding private schools with taxpayer money. I'm
S1: all for more structure in schools, but I think we
S1: need to be careful about how we get there, like
S1: not with a theocracy. Hopefully, Motus is revolutionizing wildlife tracking,
S1: using lightweight radio transmitters to monitor the movements of small
S1: flying animals like birds, bats and insects. Putting a radio
S1: transmitter on an insect that's a big insect. 50,000 animals
S1: across 400 species since 2014. All right. Discovery reCAPTCHA fish.
S1: My buddy John Hammond created a fishing tool that mimics
S1: a reCAPTCHA form and steals your stuff. RGA rip grep
S1: on growth hormone basically lets you search through not only
S1: the normal stuff you could do with rip grep, because
S1: this is a wrapper for rip grep, but lets you
S1: search through PDFs, ebooks, office documents, and even compressed files.
S1: So it's got a whole bunch of extras built in
S1: so that rip grep is just way better. In fact,
S1: I need to install that like right now. Anyway, I'll
S1: do that later. All right. Nuclei templates. We talked about
S1: that one Dune shell a new take on the command
S1: line experiment experience. Aim to bring a cozy customizable feel
S1: that bash lacks dam vulnerable drone drone hacking simulator sci
S1: fi ideas. Someone compiled a massive CSV file containing every
S1: sci fi idea imaginable. Eli Burzynski Bendersky Eli Bendersky talks
S1: about building LLM powered applications in go fabric. Recently switched
S1: to go. We got a sick go developer now helping
S1: Jonathan and I out and yeah, it's going really well.
S1: That's definitely the next language I'm going to be doing.
S1: Everything in is all go. Asset node talks about their
S1: approach to recon, and Paul Graham's one pager on how
S1: to start a startup. Really like the format of this thing.
S1: Look at this thing. Pretty clean idea people. What customers want. Yeah,
S1: quite nice ideas. Don't call them LMS. Probably the biggest
S1: idea that's exploded in my mind lately is Karpathy's point
S1: about LMS being poorly named. So he basically says Transformers
S1: are general purpose computer systems and that LMS are actually
S1: sequence predictors. And crucially, it doesn't matter what the stream is.
S1: We just happen to be sending language right now. But
S1: you can actually send anything. And it actually that takes
S1: us right into the recommendation of the week. Start reframing
S1: your thinking about AI and specifically about llms away from
S1: just the next token of text. Instead, think of it
S1: as sequence prediction and then one level after that, think
S1: of it as answer prediction. So as Karpathy talks about
S1: transformer architecture works on sequences of anything languages was, you know,
S1: language was just a natural start. It works on whatever
S1: you feed it. It's just a sequence thing. So the
S1: recommendation of the week is to update your model of
S1: AI from specific text predictor to generalized answer predictor. And
S1: in fact, that's my new favorite acronym for AI. I'm
S1: going to try not seriously to try to convince people
S1: to use this gap gap generalized answer predictor. I think
S1: it's way better than LM. It doesn't mean it's going
S1: to win it. You know, the best acronym doesn't win.
S1: It's the one that people just decide to use. And
S1: the aphorism for the week people are strange. They are
S1: constantly angered by trivial things. But on a major matter
S1: like totally wasting their lives, they hardly seem to notice.
S1: People are strange. They're constantly angered by trivial things. But
S1: on a major matter like totally wasting their lives, they
S1: hardly seem to notice. Charles Bukowski. Unsupervised learning is produced
S1: and edited by Daniel Meisler on a Neumann U87 AI
S1: microphone using Hindenburg. Intro and outro music is by zombie
S1: with a Y. And to get the text and links
S1: from this episode, sign up for the newsletter version of
S1: the show at Daniel meisler.com/newsletter. We'll see you next time.