This Week in Startups - Is Nvidia overpriced? Plus, price targets, GodMode demo, and more! | E1795

Episode Date: August 22, 2023

This Week in Startups is brought to you by… Embroker. The Embroker Startup Insurance Program helps startups secure the most important types of insurance at a lower cost and with less hassle. Save up to 20% off of traditional insurance today at Embroker.com/twist. While you’re there, get an extra 10% off using offer code TWIST. MasterClass. Learn from the world’s best minds - anytime, anywhere, and at your own pace. Get 15% off an annual membership to MasterClass at masterclass.com/startups Roots. Invest in the only real estate investment trust that creates wealth for you and its residents at investwithroots.com/twist * Today’s show: Sunny Madra joins Jason to discuss Nvidia’s upcoming earnings and if the company is overvalued (1:08). Then, Sunny demos more AI tools, including an incredible AI dubbing product (16:43)! * Time stamps:(00:00) Sunny Madra joins Jason (1:08) Is Nvidia overvalued? (6:05) Leveraging hardware gains (9:46) Embroker - Use code TWIST to get an extra 10% off insurance at https://Embroker.com/twist (11:02) Nvidia’s price targets and why Jason would not Jay Trade it (16:43) Sunny demos rask.ai (18:50) AI voice dubbing (26:18) MasterClass - Get 15% off an annual membership at https://masterclass.com/startups (27:51) Sunny demos a reconstructed Pink Floyd song from brain recordings (33:28) Sunny demos GodMode (37:43) Roots - Head to investwithroots.com/TWIST to sign up and start investing today! (39:20) Sunny demos Pluto.fi (46:34) Content theft and Sunny demos PlayHT (55:47) Sunny’s X experiment * Follow Sunny: https://twitter.com/sundeep Check out : https://app.rask.ai/ Check out GodMode: https://github.com/smol-ai/GodMode/releases/tag/v1.0.0-beta.2 Check out the Pink Floyd brain recording: https://news.berkeley.edu/2023/08/15/releases-20230811 Check out Pluto.fi: https://www.pluto.fi/ Check out PlayHT: https://play.ht/ * Read LAUNCH Fund 4 Deal Memo: https://www.launch.co/four Apply for Funding: https://www.launch.co/apply Buy ANGEL: https://www.angelthebook.com Great recent interviews: Steve Huffman, Brian Chesky, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland, PrayingForExits, Jenny Lefcourt Check out Jason’s suite of newsletters: https://substack.com/@calacanis * Follow Jason: Twitter: https://twitter.com/jason Instagram: https://www.instagram.com/jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis * Follow TWiST: Substack: https://twistartups.substack.com Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin * Subscribe to the Founder University Podcast: https://www.founder.university/podcast

The fact that they would even consider doing round-tripping, where, you know, defined as we invest in this company, that company buys compute from us. So the money goes into their bank account and then comes back to our bank account, even if it's not part of the deal. Yeah, that's, that's Feghzi. It's, you know, like something's wrong there. This week in Startups is brought to you by, and Broker's Startup Insurance Program helps startups secure the most important types of insurance at a lower cost and with, less hassle. Save up to 20% off of traditional insurance today atembroker.com slash twist. While you're there, get an extra 10% off using offer code twist. Masterclass. Learn from the world's best minds. Anytime, anywhere, and at your own pace. Get 15% off an annual membership to Masterclass at

masterclass.com slash startups. And Roots. Invest in the only real estate investment trust that creates wealth for you and its residents at invest with roots.com slash twist. Hey, everybody. Welcome to this week in startups. It's Monday. So Sondip Madra is with us from definitive intelligence. And we are going to break down everything that happened in AI over the past week and what

we're going to see this week going forward. You know, this AI thing never stops. In videos reporting their earnings, that's going to be a blowout, it seems. They just keep selling more and more of this hardware. But I was thinking about Nvidia specifically. And I was wondering, and I wanted to ask you, if they are going to have a durable franchise similar to, and a monopoly is really what I'm saying by durable franchise,

I don't want Lena Khan to get in here. So LenaCon translation, monopoly. Are they going to have a monopoly in this space on building AI supercomputers to just use an incorrect term here? Or is their margin and their revenue and customers going to inspire all the other people making chips and platforms to go get it? What do you think? Because Google became an enduring franchise with 90% market share and search and search advertising. And then you look at Apple. Didn't think that's kind of a duopoly people say.

But if you look at the actual revenue and profits, Android's make no profit. 90% I think of the profits from smartphones go to Apple. So it's essentially a de facto monopoly. What do you think? Invidia? Is it a monopoly or is it a at-risk franchise? It's the latter. And look, I don't want to call it at-risk. I think Nvidia is going to have an incredible business. But, you know, the reason we're seeing this explosion today is a function of, you know, they're way more demand than supply of Nvidia chips. Secondly, what really leads to that is the tools and frameworks that exist for at-scale training and ad-scale inference are primarily built around the Nvidia infrastructure.

But we're already starting to see that break. You know, Carpathie had a really, really awesome tweet last week where he did a bunch of inference on his MacBook. And what we're going to see is the frameworks adapt in a couple of different ways. One, for at-scale training, we'll see framework start to pop up that can be used on, you know, other silicon. That's already going to start to happen. The Nvidia one is leading, Kuda, I believe, right? But others are going to get there really, really fast. I also think what we're going to see is inference move to different workloads.

In fact, you know, there's a, here you go, right? So he was running 16 tokens a second on his MacBook, right? And so, and you can, you know, he kind of maps out the difference. and where things are going to go and everything else. And so... Take a look at this chart. Can you explain this chart to the listeners? It looks like there's a bunch of red dots,

a bunch of blue dots, and an average line going up. But what is this chart explaining to us? Yeah, basically it's showing like, you know, flops, right? Which is like operations per second, right? And correlated against like memory bandwidth in this particular case. And what it's showing is that, you know,

obviously, Nvidia is the clear leader and top into the right in this chart at, you know, A100, which is kind of, you know, they're world class. But what you can see on the right hand side in terms of like memory bandwidth and cost, right? And you know how much these things cost. So if you just scroll, like if you can just scroll the thing on the right hand side right there, yeah, exactly.

I'll just down a bit right there. And time is on the bottom axis and then the up and down access is. Is scaling. Yeah, it's like just normalized scaling, exactly, right? And so what you can see here is, you know, the memory bandwidth and the flops out of an A100. But then a MacBook M2, which is just a commercial CPU on your device. Yeah, I got my MacBook Air, which is the greatest laptop ever made.

I literally bought it. The second I started using it, it was so good. It was better than my M1 MacBook Pro. And I had the giant one, but it was so heavy. And I just gave that to my assistant. And then I literally went and bought my wife one because they're so great. Yeah. And what's the interesting thing here is when you're taking on this category of problems,

there's two things you have to think about, which is compute and memory bandwidth. And what he specifically calls out here is, although the compute is 200x more, which you know, you think is a problem, I'll explain why it's not. The memory bandwidth is only 20x more. So it's, you know, sort of within an order of magnitude. And you can scale compute by just going broad, you know, by pushing the problem out to more and more devices, like sort of like a set-y-at-home type thing. You have 10 laptops and then this would go down.

Exactly. Memory bandwidth, you can't scale that way because memory bandwidth needs to be, you know, the memory needs to be very close to the CPU, right? And so you probably remember this from your days of building machines. So, but the fact that you can see that one is within an order of magnitude and the other is like two orders of magnitude, but it's solvable. I think we're going to see solutions to this problem very, very quickly.

Right. So, and this is what people have been doing since the beginning of server farms, which is throw hardware at it. Yes. You know, one chip can't do it. Well, what if I had five chips? And that's what I think we're going to see. And also, what jobs are you doing? It might turn out that 90% of work, it doesn't matter if it takes one second or 10 seconds or one second or even 200 seconds.

If I'm building a model and I want to refresh it every hour and it takes 200 minutes, I could have a rolling refresh going on, you know, and taking the new data. And what's that going to cost me? So it's amazing that we're even talking about Silicon right now in 2020. 23, but we do see this happen every decade or two. Something bandwidth gets clogged because movies come to the internet. We're here, you know, language models, cripple, uh, compute power or outstrip compute power. It's great. People have been looking for ways to leverage the amount of bandwidth we have and to

leverage the amount of CPU we have. It used to be like only people who could really use up the CPU where people making, using video games or like Pixar making, movies. A person doing, you know, email, surfing the web, can't even use a fraction of what's happening on your laptop. So much so that those chips were designed in large part to increase battery life. That's how little compute matter to the average user. That battery life was a more important metric. You literally come into the store and they tell you 20 hours of battery life, you're more interested in that than you are in the memory and the CPU. 99 out of 100 cases. Exactly. So I wonder if video editing will work. Nick, will video editing work on the Apple MacBook Air M2 with a lot of memory in it?

Yes, absolutely. SSD drive? Yes. So this idea that people would buy that Mac Pro Studio for five or six grand or the Mac Pro Tower for 10 grand, what video editing difference would you see between a Macbook? Like IMAX, faster rendering of like large, massive files, IMAX. 4K stuff. So if you're editing this pod or you're editing all in, when you hit the export button,

the difference would be one minute versus 10? No, probably... 10 versus 20? Probably 10 versus 20, 25, I think. It'll double the time. Got it. Yeah, and we've already seen that from the, you know, I'd say the 2018, 2019 MacBook, the show is exporting two times as fast from there, maybe faster.

How long does it take to do export an hour or two-hour show? I'm curious. Our show is about 15 to 20 minutes. depending on how much like graphical stuff. More than I would think. Yeah, wow. It's actually more than I thought it would take. For video, for audio, it takes about, you know, a minute.

Hence that. Yeah. So, yeah, I wonder when video is going to catch up here. And I wonder what the fastest you could do if you spend 10,000 or 20,000 on a computer because you get it done in one minute or no. Yeah, well, it depends on the compression. So we compress the video pretty heavily so people can download it on Spotify, right? So the more compression you're using, the longer it typically takes.

If you're using a pretty light compression, It'll be a larger file, but it'll be quicker. It's very interesting. The two things that are, three things that are CPU heavy right now in the world. Video, video, video games, and language models. Yeah, those are GPU heavy specific, not CPU. They're GPU heavy. Yeah.

And when I say CPU and GPU, I just think we just put it all into the bucket of compute now, right? It's trending in that direction. Yeah, I think, you know, calling them GPUs is, like, confusing at this point because they're used, like, we're not using them for graphics. using it for matrix math. Listen, I work with super early stage companies at launch, like literally year zero. They haven't even incorporated yet. And then we hit the Series A. People have thousands of dollars in MRR.

And maybe they've only raised a couple of hundred thousand before that Series A. And they don't have their insurance set up. And in fact, we recently had a great startup that didn't have DNO. And we had to really stop everything because they were having board meetings. They were making massive decisions. There were legal issues. And they didn't have the basic D&O insurance that protects directors and officers. So we send them right to Embroker.

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That's eM-B-R-O-K-E-R dot com slash twist and use the code twist for 10% off. Okay, let's get back to this amazing episode. All right, well, listen, InVidio's price targets just keeps going up. People say 800 bucks, 780 bucks. It's nuts. The warning I will say here on this one,

one, J-Cal is if you remember the late 90s, there was a boom in optical or networking chips, let's put it that way. And I started my career in that space. And what happened was, it was a category of companies, like one specific one, maybe a bunch of folks that are from that generation, remember it called JDS Uniface. I don't know if you remember it, but it was like one that made optical transceivers for, you know, fiber optics. And what ended up happening is when they couldn't meet supply, you'd have to basically, if you wanted like 500 transceivers, you'd have to order like 5,000 from them and you'd get 500. So their stock went through the roof because it's a huge, you know, deluge of orders.

Orders. And then ultimately the orders get fulfilled. And then, you know, Cisco in early or 2001 or two, they basically all that supply showed up. And then had to write down like $5 billion of inventory because they bought way too much. And so one of the things I fear in this era right now is there's this mad grab and everyone is going to everyone asking for compute. And so there's, you know, there's a weird. dynamic, and you've talked about it before, where there's companies doing these weird trades of like, oh, you know, take an investment from us, the user, exactly. And, you know, sometimes

I fear that there's a lot more kind of artificial demand than actual demand created through a lot of these deals that are out there. So I just say, you know, look, I'd be an incredible company. They're doing great, but the demand may not be as big as we think it is. But yeah, I'm not J-trading it. I'm not J-trading it. I'd rather J-trade the folks. that people think InVIDIA is going to run over, but they have great management teams. So if TSM or Intel or AMD think they're going to make progress in this area, or Intel,

I don't know, who are leading candidates or new companies, I would rather make the bet on those, which are undervalued right now, but that have great management teams and great revenue and reasonable PEs, because when you're investing in companies, your entry price and the valuation matters. The P.E. of Nvidia right now is like 230,

240, depending on the day, you know, which is crazy. Like high growth companies are typically 30 PE ratio. So, yeah. Be careful, folks. Be careful out there. I really do like your insight, though, about the bulk ordering and the shenanigans going on with the round-tripping. Potentially, I'm going to put the word potentially in there. I don't want to insinuate that people are breaking any laws here.

But the fact that they would even consider doing round-tripping, where, you know, defined as we invest in this company, that company buys. compute from us. So the money goes into their bank account and then comes back to our bank account, even if it's not part of the deal. Yeah, that's Fagasy. It's, uh, you know, like something's wrong there. Jason, can I ask a theoretical question for you about the, yes, you may, producer Nick, um, Nvidia, uh, thesis. So AMD is up 68% year to date. Intel is up 22% year to date, sort of on the entire broad spectrum float of the AI market, right? And the market returning. And the market returning as well.

Yeah. Do you think that should eliminate them from a J-trade just due to them being sort of kind of already a little bit more overvalue than they would have been otherwise? I feel like the whole market's gotten a little bit ahead of itself. Those names could also have gotten ahead of themselves. and I think you'd have to sharpen your pencil in that analysis. Look at the price earnings ratio.

Look at the growth. And then look at the product roadmap. And do you trust that management team? How long have they been working together? You know, and the management team matters. Say what you will about Dara. He's not, he's no TK. I would rather Travis be running the company, obviously.

I think the company would have a greater stock price if he was. But, you know, he did get it to profitability. He did create stability. And it would have been just two different looking companies. One would have been Amazon under Bezos, and one would have been, you know, Amazon under Andy Jassy. And I have a professional operator versus a visionary. So you get two different valuations there. So, yeah, I would sharpen your pencils and look at it.

How long the management team has been working together, product roadmap, you know, customers buying stuff. And then what's the financial fundamentals? Yeah. But on the flip side, Jensen and team have done a great job. You can't take anything away from Jensen and the products they've made. I watched them go from being like this, you know, dorky video game to dorky crypto to like, what's the point of all this? I thought for sure with the crypto collapse, like that company was toast.

You know, and then you look at all of a sudden this customer race that has more money than they know what to do with Apple, Google, Amazon, Facebook, you know, and then the investment community. And listen, even sovereign wealth funds, the UAE and Abu Dhabi are making their own Falcon language models. The Saudis supposedly stopped buying Ferraris and yachts, and now they're stocking up on H-100s, like literally. Martin Screlli wears an H-100 around his neck with the gold chain. I mean, this has become a status symbol, H-100s. And just this morning, the UK Prime Minister said they are investing 100 million pounds into via AMD and Intel compute. Oh, interesting.

Fantastic. Okay. So there's the ground. There's your fundamentals, as you know, have the industry is growing. This is not a drill. All this computer, I believe, is going to get used. Maybe people are overpaying for it. Maybe they'll have extra capacity. Who knows? But, you know, there are a lot of jobs going to these servers. So let's talk about how this infrastructure is getting leverage. What do you got for demo? It's demo time, everybody. Type in this week and startups on YouTube and go subscribe to the channel. Hit the alert bell. You get alerts when we post a new episode.

Awesome. So one of the things, one of the themes I'm just trying to build here is the evolution and usability of these models. And even just in the short while that we've been doing AI demos, we're starting to see an evolution and advancement in that place. And so the latest one here, Jason, is one where it's called RAS. That AI. And what they make it, it's basically a GUI interface to convert models from one language to another language.

and so I'll play the original. Sony, I created like a strung together clip of these if you want me to hear it and play it. Oh, yeah, let's do that. Please. Let's really do that. All right. So you got a whizzy wig interface.

Anybody can do it. The URL is once more. Rask.a. RASK. RASK. Yeah, exactly. And it's terrible. It's super straightforward.

You basically, you know, there we go. Pay 100 bucks a month. Here you go. All right. So here's a picture of me in a TikTok vertical format, making a comment about it. That's something. So let's play me.

The thing I love most about this is like, we have this Oppenheimer film comes out, all of this energy at the same time of people who are just really stoked, do material science, to do basic science, to figure out big problems. This is Jason in Spanish and Spanish. Okay, here we go. A Spanish. What most me like this is like, we have this movie of Oppenheimer,

and this energy at the same time of people who are really entusiasmed for a science of the materials, to do science of basic, to resolve grand problems. And this is Jason in Hindi. Oh, I was waiting for this. Hindi.

What I was the thing is the most most important is the idea that Oppenheimer's this film one in a time in a lot that's in a lot, who, in the vastom of big samuasiaum to find out of

building to bringi briban to get to beaute. Hmm. So, the question here is, do I start doing this as a business opening up a YouTube channel on YouTube Español? I think there is a separate like

login and everything. D.S or something, yeah. Okay. Yes, whatever. Do I take the time to do that? Translate all this, do the expense, post it, and hope that a business emerges and that an audience emerges.

Or do I just wait another two years before every video on YouTube just like it has captions is going to have the ability to dub it? Because that's what's going to happen, isn't it? Like these tools exist now. Yeah. One is about capturing the audience today.

You know, if you are trying to grow your audience as a creator, these are barriers to that. And I think even Mr. Beast, right, Jimmy talks about one of the things that was frustrating for that is people were ripping off his videos and just dubbing them and re-uploading them and, you know, profiting to it. And so now you can, you know, Jimmy now has large infrastructure, but, you know, you can think about it if you're a soul opener or a small, company and you want to get your word out there or you're just you're a creator. Think about it, you know, those, I did four of those and four different languages. I didn't even send the Greek. I did Greek as well and just paying, you know, homage to your history, JCal. And, you know, I did them in like a minute each.

Like, and that goes to, you know, combining the conversation before, the speed, the UI, like how fast everything is evolving. It's incredible. Do we have a clip of Mr. Beast in another language? Like, is he actually doing that? Yeah, he's doing that now officially. I wonder if he's doing it with AI or he's hiring people to dub because at his level, he could just hire people to dub it and just give him a thousand bucks to be his voice or whatever,

or a production company probably cost, I would say probably cost a thousand, two thousand of a video. So he talked about this in the Samir and Colin pod. There's some places where he's got hired actors that he actually went and met with. So he talked about the whole experience that it's weird because he used to listening to them. And then, so that's like their primary and the bigger. markets and then the other ones they're starting to leverage AI. Great. Yeah. I mean, this is something I've been thinking about. You know, you have to look and be cutthroat about your time and your effort. Would this be a good use of our time? Well, we're not

investing in companies, you know, in other non-English speaking countries right now because we don't know how to invest in those countries as well as the domestic folks, right? So China, India, Japan, you know, we're going to be at, we're going to get our butts kicked by the investors there who understand culture, understand regulation, understand founders, understand society, and products in the history of the products going to market there. We'll never compete. So, you know, I would be afraid almost to put this week in startups in Japanese and have too many Japanese founders contacting me because I can't work in Japan right now. I don't have like the infrastructure set up there. So while this is great that it's happening, I wonder, about, you know, the scale you need to have. Now, if you're Uber or Airbnb, and you want to do an update to all your hosts, and you've got some new regulations or something where you just want to have the CEO,

you know, give an inspirational message or do a Q&A, man, this would be incredible to just do a quick, get it out your host in every language, in every country you operate in. So I see a very big corporate value here. But on the first one, I disagree with you a little bit of, A little bit J-Cal because think about, you know, Twitter now does

the creator payments. And if all you're doing is English, the only thing you can get paid in is English, right? Because you're kind of lose out on everybody else from all those languages. And so, you know, would your, and I saw you posted something about a creator payment you got. Yeah, it's weird.

I didn't even know I was in the program, I guess. They selected some folks. Yeah, you have some friends there. I don't know. I met somebody who works there. Yeah, exactly. Employee. So, yeah.

I mean, it's, yeah, so I guess your point is free money. Why wouldn't you pick up the free money laying all over the ground? And I do think that there's a possibility of that. And so the question is, how much would this cost to do an hour? Because when I got quoted this, you know, doing an hour of my podcast, I think it was $500 to $1,000 to use actors to dub it. And then I think the AI people were like, it was $100 or $200 an hour to do this. I wonder what they're at now, which means it's going to be a dollar next year. So what they've gone to here is, um,

what it looks like is about $100 for 100 minutes a month is what they're asking for, and then a dollar a minute. So a dollar a minute means a 75 minute podcast is 75 bucks. Not bad, but that's pro-language. That's about what it cost to transcribe a podcast before all the new tools, by the way. Like five years ago, the best transcriptions were about a dollar a minute. If you wanted to do a transcription, add a manila with somebody that English has a second language who doesn't understand, you know, the names or the organizations you're talking about

and will have some mistakes in it, a raw transcript, in other words, it was a dollar a minute. Then it became free on YouTube. They got there first. Now, if you use Temi, Otter, or Descript, it's free and included, Nick? It's part of the subscription package, the standard subscription package basically for Descript. Yeah, and they're incredibly fast. You get some number of minutes? Yeah, it's.

They do have a cap on it? Yeah. Yeah, I think we pay like 120 a month for all-in features and there's like no cap. Yeah. Yeah. Incredible. Do you want to see, by the way, the Mr. Beast dubbing feature? It's pretty impressive.

I just looked it up. It looks like YouTube is testing with a couple of thousand creators. I believe you have to upload your own dubs. I'm not sure if they, Mr. Bees uses AI for it, but let me know if you could just hear this in English. The latest video is a great one. You have 30 seconds to get across these three hurdles. Can you guys hear that? Yep.

Okay. They have 30 seconds again, course. So it's pretty impressive. They YouTube in the settings tab has a audio track dub, and you could switch from English to Spanish to Vietnamese to French. It's like Netflix. Yeah. This is the French.

Vietnamese. No, that's a human. Yeah, totally. Wait a second. They have humans doing this? Well, like I said, I listen like a computer. No, no. So I listen to, like I said, a, like I said,

Colin and some of your podcast where they, he talked about that for these main languages, because he went and met these folks that do it. And not only for him, but for his, his, you know, co-hosts,

right? So they've got characters that act as all of them. Yeah. But Google is abstracting this for the top level creators, which makes sense because Google wants to grow in those markets. So why not take the top 10 creators and then offer this for free for them? Yeah. Probably costs them,

you know, and so that means Google is hiring voice actors to do this. outsourcing it to some third party company to do it. It's pretty crazy when you think about it. Or Mr. Bees uploaded his dump. Mr. Beast uploaded his dub. Yeah, it looks like they're... Oh, I see.

So they put it on Mr. Bees. That makes more sense. Yeah. Yeah. Got it. So that would be the equivalent of like Netflix, you know, buying a movie from, you know,

an independent studio. And then the studio has dubbed it and they put it up there. And then when it gets bought by, you know, whatever, some other regions, you know, the Chinese version of a,

Netflix, they can upload those same dubs there. Brilliant. We all have self-doubt. I can promise you if there's a hundred people in a room, a hundred people have some level of insecurity. You can't have any self-doubt about your commitment and conviction to do this. You might have self-doubt about your ability to succeed, to raise the money, to attract the right people. Those are two different issues. What an incredible clip. Self-doubt. We all have it as founders, as human beings. and the voice you just heard. One of my favorite business leaders,

I've been trying to get him on this weekend startups forever. Somebody help me out here. Starbucks CEO, Howard Schultz. Now, I may not have gotten Howard Shultz to come on this week in startups, but you know who did get him? Masterclass. And all of the lessons he learned are sitting there waiting for you at master class. In addition, you can get Chris Voss on negotiations,

Kim Scott, friend of the pod on Radical Kander, Bob Iger on leadership, and so many more. Think about if you get one. insight, just one. And it changes the course of your startup, your life, your career. I bet you there's 10 incredible insights that will change your life in each course. All of these legends are sitting there. Gordon Ramsey on cooking, Serena Williams on tennis, Steph Curry on shooting, and so much more, just waiting there for you. Get unlimited access to every class. And right now, as a twist listener, you can get 15% off when you go to masterclass.com slash startups. That's

masterclass.com slash startups for 15% off an annual membership masterclass.com slash startups. All right. Let's go on to what's this Pink Floyd one. Okay, yeah, this is interesting. So let me just cue this one up. I love Pink Floyd. Yeah, I know.

I've been listening to Dark Side of the Mule, Dark Side of the Mule. If you know, you know. Mule. Yeah, Dark Side of the Mule. It's a cover band that does a live version on New Year's of Dark Side of the Mule. It's a live album by government mule. And they just cover. like the great songs by Pink Floyd.

Pretty dope. Yeah. So I want to give Rowan credit again for this one because, you know, he found it. And I went and read the article. But basically, uh, high level, what they did was, uh, researchers at Berkeley. And, you know, we'll include links to the stuff for folks. But, but they're able to do, shout out to Berkeley. What they're able to do was, uh, implant some electrodes into a person's brain that had,

you know, some issues regarding, uh, speech. And they were able to recreate a song. through a person's neural activity associated with the song. And so I'm just going to play this here. Hopefully the audio comes through. So the original song. This is short, 30 seconds. Yeah.

Sweet. Sweet. Okay, sweet. Okay. And now this is the recreation. Wait a second. Wait, what do you mean recreation? Who's recreating that?

an AI. So here's the article. Sorry, I got a lot of windows open here. So an AI created the brainwave version of that song so I could experience it? No, this is not so you could experience it. So these are the electrodes that were hooked into the person's brain.

It's a skull cap. Yeah. Yeah. Well, no, but these are actually on the brain. It's not like, so they call out that it's not, you can't read people's minds with this. But if you actually can hook up into the brain an AI basically can recreate audio from brain waves

of you recalling how you're hearing the song. And so think about this from a speech perspective. Oh, I see. That was the output of my brain. That was the output of your brain. Yes. Oh. So it's like me humming. Yes.

And it, yeah, AI. Oh, wow. Well, that's really weird. What's the application of that? I can replay my experiences. What if you can't speak? What if you have a speech amendment? So that I could.

have my computer, I could read a poem and the computer AI would figure out what my brainwaves were saying and then you could actually hear me read a poem or sing a song. Wow. It sounds like it's underwater. Yeah, it sounds like it's underwater or over like a phone, which is exactly how like a lot of these things start out. Markoni sending telegraphs and it's going to evolve from here. You know, last week we didn't get to talk about it, but like, you know, there was like a negative, a Pete article out there of like an AI being used to listen to keystrokes. Did you see this one? Yes, explain that to everybody.

Yeah. Yeah, so basically someone had used, you know, one, some form of AI, I don't know particularly which one it was, to use an audio of you typing in your keystrokes to basically determine what you were typing. And, you know, the idea is that, again, pattern recognition, that's all these things are about, right, is, you know, understanding pattern. So, you know, I guess when we type, we follow certain patterns.

and if you give enough of that sample data to an AI, it can just recreate whatever you're typing between telling the difference between how fast you type ER versus ET versus EP. Exactly, exactly, because the speed from the distance, those characters and all that matter. And so that was like sort of a maybe- I wonder if it needs training data from an individual

or if it has training data from just, you know, 100 hours of people typing the most common words. I think it's a ladder. Okay. Yeah. Wow. So then what could happen is if, and I think this probably already exists in the CIA, FBI, Musad, everything, KGB, that they have ways to do this already. Because they also have ways to look at your phone.

And when you use your fingerprint on your keypad, you could look on an angle or with certain, you know, chemicals, figure out where you press the button most often from the oil on your finger. To figure out your code. And so here you could test this. So the researchers used a 2021 MacBook Pro to test their concept, a laptop that features a keyboard identical in switch design to their models from the last two years and potentially those in the future, typing on 36 keys 25 times each to train their model on the waveforms associated with each key. So that, to me, indicates it's not just the typing of,

it's not just the typing of like common words, like the, it might be that certain keyboards give off a certain sound for Q-W-E-R-T-Y, and each one of those might be slightly different? That's wild. Or it could be the acoustics, because if you had the speaker in the right spot, it was stationary, the time for the Q versus, say, the P, opposite ends left and right of your keyboard, could come in at a different angle. Because there was a company called Shot Spotter.

This was a really cool idea. I didn't invest in it. but it's active in a lot of locations. I think they had Oakland. They put microphones all over a community. When a gun goes off, they figure out, and they triangulate where the, within like 100 feet, where the gunshot was from.

Really powerful. And then they just send the cars right there. Cops go right to that location. Very powerful. Okay, what's this God mode? What's God mode? Yeah. So this one I think you'd really like,

let me just pull this up as well. So I'll pull up the GitHub project so, you know, folks can, folks can see where this is from. So let me share this as well. So it's GitHub. GitHub is a repository of code. And people share their code.

Exactly. Code repository. And so it's by the developer that created small AI, which is a bunch of AI enhancements to help people be more productive. And this God mode project is super interesting. And I'm going to just pull it up here in a different share now, which, give me a second,

I'll pull it up. So this means by default, it's open source. There's some sort of license here. But anybody can take it and fork it, create their own program, and build on it. This is the power of open source and these code repositories.

Exactly. And so what this is, it's like a browser window that has all of the major AI is running in like a phone mode. And so, you know, and I didn't log into, you know,

Po here, but like basically you have chat GPT on the left here. you have Claude here, you have the Bing here, Perplexity and Poe. And it lets you basically, if you're working on a problem, just try it across the different AI.

How do I get this on my desktop? Yeah, so you can just go to that GitHub URL and you can download. There's a DMG, like a Mac DMG file that you can download and install. So, yeah, it's just there. I'll pull it up to show you there.

And I think, yeah, I thought you'd really like it because you can just go there and download. You just cut in case from one window to the other and I have a 50-inch monitor. I have one of those giant ones. It's like a little form of,

it's like, yeah, it's like a little form of, like I, you know, reminding me a tweet deck a little bit. Yeah, so you just go here. Yeah. There's a URL in the, in the window here. And yeah, you download this DMG and you're off to the races. You just got to sign in to all the different use cases and that's it.

Love it. Yeah, so download it, try it. Have all your AIs, you know, instead of being enhanced by one AI, be enhanced by five of them. what I would like to have happen is I'd like an AI on top of the AIs. So I'd like a meta AI that I could say it to once. It would look at the five different versions and then it would look for differences between them.

And I'd say, which one do you think is the best answer? If I asked it to look for family offices that I could meet with to be LPs and launch run for four, it would say, you know, here are the, you know, we came up with 117 unique ones. here I d-dupe the list and giving you the whatever yeah and here's the links you know it's pretty great idea so and this is where the reinforcement learning

as these data lakes and these LMs start to emerge and become refined right and we're still in year zero here I was like year one of this you know by year three of this these things are to be very sophisticated there will be AI's talking to AI's AIs managing other AIs.

Baby GPs, you know, managing a process that you say, hey, I just want to know all of the important new restaurants in my neighborhood within a, you know, and these are my three favorite foods. Just read the reviews and bring me the best reviews and tell me where I should eat next. And it's going to go out your personal AI, maybe it's Claude, maybe it's another one, and it's going to give those searches to multiple AIs, consolidate the results, clean them up, and then maybe in put citations, hey, Bard found this new sushi place.

You love sushi, and these are the three best dishes. Here are the reviews of it. Boom. And then when I talk to mine, I just say, give me five reservations for when I'm at Lake Tahoe next week. I want to eat dinner. I want all of them to be on the early side between 5 and 630 and make them all for five people, boom.

Yep. And it just doesn't. We've done this in definitive where, you know, we have an automated data science agent that has like 50 or 60 personas. of different things that data scientists try. And basically, that's what it actually does for that particular use case. So this is already here.

We're going to see it sooner than later. Oh, wow. Hey, everybody. Today I'm joined by Roots CEO, Dan, welcome to the show. Thanks for having me, Jason. Tell everybody here in the audience, what is Roots and what makes it different

than the other real estate investing platforms? I'm a complete neophyte. Roots is a reet with a little twist. Sorry, how to do it. We are the first real estate. portfolio that we know of that builds wealth for both our investors and our residents. And we've created a unique win-win model that creates partners and not tenants. How does the resident get their equity? Do they put in a hundred bucks just like I might as an

investor or is it blended into their rent? What we do is we do not take security deposits at Roots. Instead, our residents fund a Roots wealth building account and invest in the REIT. And so from day one, when they move into one of our homes, they're actually an investor just like me or you would be an investor. The second step of our program is what we call live in it like you own it. And so if they pay rent on time for three months in a row, and then they'd be a good neighbor, so like no noise complaints. And then the last thing we want them to do is a quick walk-through video of the inside and outside of their unit. We're actually going to give them a rebate of rent of usually around $150 into their investment account.

So just for being a great partner with us, they're going to make $600 extra on top of the growth of the fund for helping us take care of the asset. Head to investwithroots.com slash twist to sign up and start investing today. That's invest with roots, no spaces, no dashes. Dot com slash twist to sign up today. The user interfaces haven't been built out yet.

Users haven't been trained on how to do it. It just reminds me of like the early days of PCs, DOS, Windows. where you're just, you know, some of the stuff can be done. It's just a little bit of work to figure out how to do it. Hey, you built a little search project for everybody's favorite social network. Can you show that? Oh. Do you send me?

Yeah, let me finish my demos and pull that up next. Yeah, but, you know, kind of leveraging AI there. Yeah, a little secret, yeah, we'll pull that up as well. Let me do this last demo and then pull that up as well for you. Awesome. Yeah. This one I think you'd like. It's kind of related to, you know, day trading.

So this is called Pluto.Fi. And so it's a finance chat. It's a chat bot that's been built and customized around. You know, we talked about this. Everyone's saying, oh, these are just chat GPT wrappers. Well, no, like this is when you take a generative eye endpoint and build something around it, this is quite powerful. And so in this particular case, you know, I've logged in and I started and say,

you know, tell me something about, give me a chart about Ford and it did. And then it gave me a little history. And then it said, hey, create an automation for me that that buys Ford when it drops by 5% and it creates that automation. This thing has its own platform for basically putting money in so you can buy and sell stocks. And so it's really, really powerful,

right? And, um, you know, they've, yeah, I was really, really impressed by it. They have an AI co-pilot.

They have AI charts. They have sort of, you know, all the different use cases of automations you can create. And, um, I was very impressed by it. You can chat about a company.

Yeah. Yeah, well, you think about, you know, Robin Hood did to E-Trade, what E-Trade did to Charles Schwab. You used to have to call Trash Robb on the phone. E-Trade made a webpage. Robin Hood made it an app, and now Pluto's making it AI-based. Every time a new platform comes out and your technology comes out, you can ask the question, why now, and you can create a new service based on that new platform. And right now your choices are ARVR or, I guess cloud computing is kind of over.

Everything that could be in the cloud is in the cloud. So it's AI or ARVR, the two new platforms that you could build for. Yeah. And so obvious Y Now's. The Y Now and this is really well. And I think, you know,

J-Cal, you should give it a little try for J-trading and see. I would love to, yeah. You know, and it gives you a little analysis here. It tells you how Ford is, you know, doing over the last year

compared to everything else. And just, I thought it was really well done, you know, I was thinking about, you know, it's getting towards the end of the year. And so I was thinking about doing,

because I did so well on my J-trades, I didn't sell anything because I was like, this is all going up. I got in at the right price. There's no reason to sell here. But I got my ass kicked by Disney and one or two others, like Stitch Fix, I think, didn't work out great.

And what was the other one? Warner Brothers maybe didn't work out great, but Disney kind of killed me. And so I was thinking, you know what, maybe I take some losses here, take the losses for the end of the year taxes, get some losses on my taxes, and then I would redeploy that

into one of the winners and start consolidating down. into my winning position. I wonder if we gave it your portfolio how it would perform to those use cases. Yeah, no, that's what I would like to do is give it my portfolio and just start asking you questions like,

you know, tell me about the debt of each of these companies and then tell me the debt ratio to their earnings. Give me the debt ratio to, like, so if you did that right now, if you asked it, tell me about the debt to earnings or debt to revenue ratios of Disney and Warner Brothers. and just see what it comes up with because, you know, you can do that on something like Y charts, which is pretty nice, or Yahoo's coming out with, which is different than Y charts

as an independent company. Yahoo's coming out with a new, has a beta testing, a new charting tool that adds, like, a lot of the features that Y charts got ahead of them on that are really available on Bloomberg terminals and other, like, more sophisticated paid stuff. And just knowing what, you know, being able to quickly figure that stuff out and then say, hey, what other companies look like this would be interesting. So what are the companies that is similar? You said, tell me, I'm just trying it here.

What is the debt to yearly revenue ratio of Disney? Okay. I mean, this is a question, yeah. Yearly revenue ratio of Disney. That's it? I'm Disney. Yeah, sure. I'll start with Disney.

I mean, this is going to confuse the heck out of it. I don't think it's going to get a rent. I think it's going to. I mean. And then, Nick, if you could do this on white charts, maybe. That would be pretty cool if we have a wide rate. my charts account still. We don't.

So, yeah, this is interesting because it's going through. Do it on any charting software. It's definitely going through like an AGI style process here, which is gathering its thoughts, trying to figure out how it's going to get there. And it's kind of, you know, let's see. It's kind of cute here. Let's peek on the curtain, shall we?

No, I'm not looking for you to be cute. Disney debt to yearly revenue ratio is a bit like a roller coaster ride at Disney. What is this? What are they doing? Yeah. It's elucidating. Yeah. Has ups and downs and down.

Currently, Disney debt to equity ratio is 0.48 and is the debt to asset ratio is between two, three. Simpler terms, every dollar of equity, Disney has about 48 cents in debt. For every dollar assets, it has 23 cents in debt. That's interesting. So if you thought about it like a mortgage or something or, you know, your own revenue to your mortgage ratio, that's how it is. So their market cap would be dollar of equity. Disney has 48 cents in debt. So if I own a million dollars worth of Disney,

Disney shares. Disney has mortgage 48 cents of that. According to this, if this is correct. But they must have put in something here, be cheeky, and make jokes. Yeah. So I don't like that. Just a note, I'm not here to,

for comedy. If I want comedy, I go to Netflix and watch Chappelle or whatever. So no bueno on the comedy. Let's just get dollars and cents here. And remember, it's a small world that for a girl. What are they doing over there? A famously joking. A famously jokey bunch, the Wall Street traders, famously joking. Yeah, I mean, if you want a post or you want to troll me on my trade, I'm here for it.

But I don't want you to make, yeah, you should, that's what I want. I want to be able to tell that AI to question every trade I make and give me the other side of it, to steal man my trade, and then to straw man it, criticize it, whatever. you know, every possible analysis, like Ventriss AI is doing. Just give me every possible analysis of my trade. Did I make a good trade or not? Who's with me? Who's against me?

What are their arguments, right? Because that's what I do with Uber. You know, I keep holding my shares because the press says stupid stuff like Uber will never be profitable on CNBC, on everything. You know, they get these guys on the show or in the incubator. Yeah, for sure. I like to be. So they, you know, keep doing that.

And then you watch the analysts and they're like, yeah, you know, Here's how this gets to profitability eventually in the same way Amazon did. I'm like, I would feel pretty good if I was a private investor in Amazon and I held my public shares forever. And here we are, you know, Uber, you know, having its first profitable quarter. So the analysts tend to. Oh, go ahead, Jacob. I'll finish. Sorry.

I got one surprise for you. Then I'll show you. No, no, I got a surprise for you. All right. Nice. Sorry, let me to cut you out because I want to get through these. I think, so, Jacob, you'll remember, this should look familiar for you. And I can make the font bigger here.

Okay. Okay. The book has a single gold. Oh, this is my book. Yes. Well, this is a big problem right now is that people have started training. Open AI has been hit with this. Yep. There are authors and there are on the web stolen books, right? You know, you go to Pirate Day or any of these bit torrents, whatever. People around the world still books. You know, it's like in the West, it's like, well, why would I steal a book? I can get it for 10 bucks or whatever, audio book, candles. But, you know, if you were in China or. you can't afford it and you're in an emerging market, of course you're going to steal it. So all of these have been ripped and are on the web in PDF form. If you want to search for this, you can just search for my book and try to find a PDF. I actually just, I needed it quickly.

I owned it. Yeah, somebody ripped it. But I'm going to play this for you. Honestly, I mean, it's so cheap to buy a book compared to the value that I think that's, they've, they've priced it so piracy in the United States or the West doesn't even make sense. Like, who wants to go find it and then use a PDF instead of use it in your Kindle player? Yeah. Kind of like what happened with music.

Like, why would I even bother? But now what happened was all of those since they're on the web, the folks at the claim is folks at like OpenAI, and we'll see if this is knowingly or just part of the generalized crawl, looked for PDFs to train their data. So you could say like, oh, just find every PDF on the web that you find in Google drives. And so one of the great ways hacking is going on right now or piracy is if you want to look for something, you do site like docs, dot, dot Google. or drive.gov.com. So people, if you're looking for a movie and you were looking for Blade Runner, you just do Blade Runner site, colon, do docs.gov.com, and you will find, like, I just did it, and I found the Blade Runner script. It's a second result on Google. I'm sorry to expose, like, hacking techniques. This is a pretty obvious one that most people don't know. So here's how to be a

hacker and find pirate stuff. Sorry. Type in Blade Runner and then do site, colon, in docks.google.com. And you can do this for Dropbox or other public things. And you can also do file type, I think, in the operators in your Google search. But here you see, Blade Runner the Final Cut. That's my version. Blade Runner, Esper Retirement Edition. This is Esper Retirement Edition.

This one says no preview available. I'm not sure why. But here, Blade Runner Final Cut, I can just download it. I can literally download Blade Runner right now for free. And here's the Blade Runner movie script. So if they pointed, if OpenAI or anybody else doing language models just did script, script, scropt, docks, Google.com, they could just take every script from Hollywood and do this. Yeah.

Or they could take an IMDB database, scrape the IMDB, get all that data, and then say take every movie name, then do it the search, movie name, site, colon, docs, whatever, and then put that into my natural language model. And we don't know for sure, but like something like this has happened, J. Kell, because if you go into Open AI and ask it to write you like a modern science, Seinfeld episode, it can do a really good job. Yes. And, you know, we know how it happened. I just showed you. Yeah, yeah. I just do.

Keep talking. Yeah. Yeah. And so that's where we have to get to. That's why open source models are so important because, you know, everyone will understand the, the training data that was used in them because everything is made available. Yeah. So, I mean, you just go here and you can start finding all different scripts, you know.

Yep. Yeah. Friends is a tough one to do because, or Seinfeld. Yeah. TV show. scripts season one see oh sims i mean it's just it's going to happen everything so the problem is there you have to do a little bit of refinement but you'll find it pretty easily yeah i think actually what you have to put in here is that it's a PDF so if you put PDF in then it will probably find

you do file type yeah file type colon PDF yeah yeah all right here we go file type colon PDF or now they're filtering it out now they're like google knows you're trying something bad it's like telling you no. Well, then you'd just use any other search engine. Or there isn't, there is a, correct me if I'm wrong, there's an open crawl. So there is an open source project to crawl the entire web and anybody can use open crawl. What is it called? Common crawl.

Common crawl. Yeah, yeah. Who has access to common crawl? You can just go download it anyway. Yeah. But what is common crawl? Who did, you know, like, it's a nonprofit. I guess you can donate to it?

Yeah, it's a nonprofit. it and it's, I believe it's related to like the internet archive. Oh, yeah, it's Rich Crenta. Yeah, I remember this.

Yeah. Yeah, I know these guys. Rich Crento was doing, he was doing his own like search engine thing. Yeah. Oh, and Gil is, Elbas is on the,

he did apply it semantics, which was ad cent bought by Google. It became ad sense. Yeah, yeah, yeah. We should have Gil back on the pot. He's been on the pod. So this is fascinating. So what they do,

common crawl is, I'm going to read what they do. Everyone should have the opportunity to indulge your carize, analyze the world, and pursue brilliant ideas. Small startups and even individuals can now access high quality crawl data that was previously only available to large search engine corporations for more information on the corpus, get the skill to get starting. Our Google group is Active Hub. So what's really interesting about this is, you know, they started this before people

were doing AI. And, you know, other people who were doing search engines or wanted, like, you know, data about the web could use this. but it was a very small use case. Now, the use case is caught up. Commonfrawl.org. That's it. Amazing.

Look at this. Wow. And I guess they do it. Look, and they have all the images of it like from 2014 on. Oh, yeah. So you could do really interesting.

Yeah. So if they do this monthly, what's really great about this is you could compare May June, 2023 to, you know, pre-GPT, you know, go to 2019. And man, wow. that would be super powerful to look at the differences. All right, go ahead.

What, anything else? Well, I just, I just wanted to play this for you, get your take, because I've been refining this voice of J-Cal a little bit. Well,

what about your little project? I'm going to show that. Let me just play this one sentence. Let's do that. God, you wouldn't take any credit for your work. A C-minus student from Brooklyn, before Brooklyn was cool,

clawed his way into the tech industry, got lucky seven times and counting, and made tens of millions of dollars. Yum. Yum. That's it. I mean, it's 90% of the way there. If you put this on my website and did my blog post or my substack or substack built something

like this in after and it just trained your data, like this is a great substack feature. It's a great Twitter feature. Like, why not just have Twitter read my tweets out loud in my voice? It's killer. It's killer. You know, and what's interesting about how great that quality is, I use something called Speechify. Speechify lets me send it a PDF.

So I'll send it like a long read, you know, an FT article, a New Yorker article. So I can then listen to it when, you know, I'm going on a hike out here or skiing or something like that. So before I go out skiing, I may take three or four long reads, put them into Speechify and then play them in a playlist on Speechify. And I can pick Barack Obama's voice or Gwyneth Paltrow did a thing with them. And she approved them. So I listened to Gipo, big fan of the Allent Pod coming to the Allens Summit, by the way, talk about Goop and celebrity and business and everything life. Should be a great talk. So they haven't.

Pretty good. I would love to see that reading. That feature should be built into your browser. So that's essentially what Speechify does for me is it builds it into my browser. Big fan of Speechify. I know shout out to the speechify team. They wanted to do my voice as one of the default voices. I will totally do that.

Nick, let's see if you can get in touch with the Speechify guy and see if we can get that going again. I want to be one of the voices. It'd be like Barack Obama. They call it Mr. President. But then they have a picture of a cartoon character. of a black president, you know, it's like, I think this is Barack Obama and sounds just like Barack Obama. Yeah.

So great. Awesome. Because, you know, they don't want authors to read their audio book. Did you know that? Do you know why? No, see, I didn't know that because I really enjoyed Bill Clinton's own read of his book. If it's somebody that iconic, yes, who's a great speaker. Okay.

Most authors can't get through it. It's too hard to read 50 or 100,000 words. in three or four days. I did it in two and a half days. They begged me not to do it. I told them I'm a podcaster. People want to hear my voice, not like some professional voice.

And they're like, you're wrong, you're wrong, you're wrong. I said, what's the real reason? They said, the real reason is most people suck. And then they quit after two or three days

and then we have to pay for that. And I said, I'll pay for it if I suck. You can bill me. And if I suck, just say, JCal, you suck. And then I'll take your word for it.

So at any point, you can say, this sucks. And I'll literally pay for the studio time. that got burnt. And they're like, not necessarily, we'll give a shot,

gave a shot. They were like, you're great. Awesome. But yeah, this is amazing. All right. But you were playing with something.

I know that you have an affinity for the website, Twitter slash X. Yep. Yeah. Like to post X, I understand. Yep.

Yeah, I do. And, uh, the search on Twitter has sucked since day one. They had to buy a company called some eyes back in the day to add search.

And then search never got better. Like literally, how long has Twitter been around now? It's 18 years, 17 years. the search has always been horrible. Yeah, 17 years. So 17 years. It's never been good.

It's never been a priority. And they've got the greatest data set in the world and search sucks. It's like, well, what are you doing? Well, now there's a new proprietor and I know he's working on it. But you decided you would work on it. Yeah, you know, we spend a lot of time organizing data and understanding data. And, you know, this URL is not openly available. So this is something that we built like for a demo, which is basically, yeah, for definitive.

And the idea is like, look, in the work that we're doing and helping enterprise organized data, we took some initiative. We bought the API access, and we basically built a natural language search. And so just some examples here of, you know, obviously, you know, last week there was some stuff going on with, you know, Barstool Sports and Dave Portnoy. He bought it back for a dollar. And, you know, if you want to find out what's going on, you want to just go into Twitter and type what's going on between Barstool Sports and Dave Portnoy, then you want to get sort of the summaries there.

So there's a language model trained against the search results, or you get the search result, then you have the language model analyze it? This is what's called a semantic search that's built for Twitter data. Yeah, exactly. And it leverages some of things that we built and understand around like LLMs and just also vector databases. And so how bad is crime in San Francisco right now?

It's just natural language, you know, what's hot in LLMs? And so, you know, I actually use this when I'm doing research for the pod. I'm trying to figure out, like, hey, what's out there and I can kind of find interesting things. This is amazing. Type in,

who are the Knicks going to trade for? Okay. I wonder if it will give me, because that's what I'm always looking for next Twitter. I don't know, some highlights, work out videos, whatever.

I just want to care about the trade market. So I'd love to have, like, that trade running all the time. Let's see if there's any, if it could semantically figure that out. Let's see what comes back here. Oh, look.

Oh, here we go. They made flexible after signing Josh Hart, Right. There's not a lot. So this isn't the full Twitter data set. This is not the full Twitter data set. There's only, exactly.

So, but you know, the idea is that you want a baseball search this way without kind of doing the broken search that we do today. Well, somebody clip this and send it to Elon. We'll put it on Twitter. We'll put it on X and send it to Elon. All right, everybody. This has been an amazing episode of this week and service. We'll see you all next time.

Thanks, Sunny.