We built OpenClaw Ultron to replace 20 people at our company | E2246

Kind of the big point of this show is to show how we have created our open call ultron, to replace 20 employees at our company. So obviously that's the end goal. I still want to have a job. I'm sure the Lawn wants to have a job.

0:00

There'll be more for you to do. We want to launch. We have. Here's the thing, there's two. If you think about your job, you've been doing a bit of production here. Of the production hours, hours you spend on production at this point in week two, how many of those do you think you'll wind up handing off in 30 days? Let's say if you just keep grinding on this for another 4 weeks in 30 days, what percentage of the work you're doing in total hours? So if you work 50 hours a week, how many of those hours would be done, you know, conservatively by this new Ultron?

0:12

I would say around 60% of my time if I'm doing 30 hours a week on production.

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Visit Crusoe AI Savings to reserve your capacity for the latest GPUs today. All right, everybody, welcome back to Twist. It's Friday, February 6, 2026, and today we're going to share how we built Open Claw Ultron. This is a new project inside of our firm Launch and this week in startups where we produce podcasts and we invest in 100 companies a year, what are we trying to do? We're trying to build one instance of Openclaw, formerly known as Multbot, formerly known as Claudebot. We're trying to build one Replicant, one agent that can do all 20 people's jobs here at the venture firm and at the production company that does all these podcasts, 20 people's jobs. Each of those jobs probably has a half dozen important skills. So we're talking about, at some point, putting together in one agent. We call them replicants. We're going to have somewhere in the order of 100 to 200 skills that one person is going to try to do everybody's work. That's the goal. And then everybody will level up and do some other work. So the goal isn't to replace everybody. It's. It's to take away everybody's chores and to make everybody better at the primary functions in an investment firm, which is meeting with founders, spending time with founders and LPs, our investors, and then on the production side, it would be producing great content and working with our guests. We want to move up the stack and give away all the chores. With me to discuss it, Lon Harris, who's going to co host the show today. How are you doing, Lon?

1:34

Doing great. Great to be here.

3:03

All right. And Oliver Corzan is here. He has been doing demos for me and producing this week in AI, which is going to launch in February. February in two weeks, I think. Oliver, welcome to the program.

3:04

Thank you. Good to be here.

3:14

And we have a special guest. Alex Chima is here. Alex, I have been following for some time because maybe a year ago I saw Alex was working on stacking with this company. It's exo, right? Exo. Exo is how you pronounce. And you've been working on taking commodity hardware like a Mac Mini, Daisy, chaining them or connecting them together in order to run large language models locally. But as we've seen, openclaw, formerly claudebot and Multbot, has quite a wrinkle in this. You were like a year or two ahead of this trend of, hey, can we run locally? So let's start just really quick, Alex, before we go into Ultron, openclaw, Ultron, what your firm does and what progress you've made, especially in regards to openclaw.

3:15

Yeah, thanks so much for having me, Jason. So I'm the founder and CEO of EXO Labs, and like you said, we've been doing stuff with Mac minis long before OpenClore was around. And to be honest, I didn't expect the rise of people buying Mac Minis to come from this place. I thought the catalyst would be people wanting to run models locally. What we do is we make it possible to run Frontier AI locally on consumer hardware. So not just Macs, but also other kinds of consumer hardware. We're trying to drive down the barrier to running the most capable AI models. So we currently have the cheapest, most accessible way to run Kimik 2.5 on two Mac studios. And we're working across the whole stack. So we're working on the model layer, the distributed algorithms as well. That are very different when you're working with consumer hardware and also low level kernels. And our goal is basically to make frontier AI accessible to anyone to run on their own hardware.

3:59

Why is this important? Why is it important to run it on local hardware?

5:01

Yeah, I think this is something that, with the whole open claw phrase, not a lot of people are talking about, but just how the way we're using AI is shifting and it's going from being this kind of crude tool that you use through like a chat interface to becoming sort of an extension of yourself and the AI. Now it knows everything, you know, you know, it can basically do everything you can do digitally right now. And soon, you know, with robotics that's going to be physically as well. And at that point it's more of an Exocortex. So it's not just this like tool that you talk through a chat interface, but it's this thing that's actually part of yourself. And then you start to question, okay, you know, do I want to rent my brain? And Andre Kopthy talks about this. He, he, he says, not your weights, not your brain. Like do you really want, you know, another a profit seeking company basically running your brain? And when you think of it like that, to me, you know, my reason for starting EXO is you want control and you want ownership of that. OpenClore is a long way towards that because for a while the products were getting better. These closed source, like the models are largely like commoditized and there's a pretty standard, pretty thin API layer to interacting with them. So the switching cost is quite low. But what worried me was that the products, the closed cost products are getting a lot better like with ChatGPT, with memory systems and also the more stateful aspects of like the workflows that you're building. So now the fact that you have OpenCloud, which is open, well, you can run it on your own infrastructure. Now a large part of that, to.

5:05

Summarize that I thought you were going to say, well it's, it's cheaper because you're not paying for tokens. That's what I thought you would say first. Then I thought you would say, well you know, you can put so much data on it, you'll have better memory, but you went with a really even higher, bigger picture reason to do this, which is if you put this all in OpenAI and OpenAI has a trillion dollar valuation and they need to make money. If I put all my venture capital data in there and I trained it all of my, with all of my secrets, those are all going to accrue eventually, even if they say it's not going to. You have this very reasonable fear or concern that it's going to accrue to OpenAI, to ChatGPT, not to your firm. So that's the reason really to do this yourself. Yeah. In your mind, Alex?

6:44

Yeah. I think that there's a nuance there of just like, I actually don't believe in sort of the privacy argument so much of like, I think, at least for consumers, you know, we're already putting our data into platforms and we're completely fine with that, but it's more about the sovereignty aspect and actually having control of it. So how easy is it for you to switch. How easy is it for you to, like, if the model's changing under your feet, how much control do you actually have?

7:32

So that's lock in and lock in for ChatGPT. I just experienced, because we canceled our OpenAI account and we moved everything to Claude because we felt Claude was a better product and we felt like we trusted that organization a little better. When we moved it over, I had three people say, oh, my God, I have all my stuff there. And I was like, really? And they're like, yeah. So I turn their accounts back on so they could get it. But there's not like an easy way to get your memory out of there and bring it over there.

7:58

Well, we saw the same thing with the GPT4 moving into 5, that a lot of people, like, they. They lost the magic that they'd loved about GPT4. So it's like, you know, the models can just sort of change your upgrade on a whim, and then you lose this, you know, like, character Persona you felt like was part of your life in a way.

8:21

So now, Oliver, it's your chance to shine. Oliver has jumped in in the last 10 days and gone all in on open claw. One of the things we did was we built a Persona. The first one to work on the production of the podcast, doing guest research, guest outreach, and to figure out what should be on the docket. In other words, what topics should we discuss? And on the margins, hey, what should the title of this video be? What should the thumbnail be? And just trying to see if it could do those functions. Oliver, you've been working on this. Show us the state of the art now, because I think the first time we did this was last Monday. Not this past Monday, but the Monday two Mondays ago.

8:40

Yeah, this is the end of week two of our round the clock cloud Bot coverage.

9:15

Crazy. Okay, Oliver, show it. Let's show what you built.

9:20

It's been around 10 days since we first started building our instance of Open Claw. And as you mentioned, we have two different ones. One that's more focused on the investment team, and I am building an Open Claw bot that is kind of more focused on the production side of things. So one thing that I think was a little bit of a misstep that I would tell anyone who's building a new Open Claw is to start with a dashboard. That should be kind of your step one, once you get your openclaw online. And the dashboard is, you think about it, but it is able to connect to the backend of your openclaw instance and bring in the data so you can see it visually, bring in all the files. It's just being able to look at it visually is much better than trying to interact with its backend and obviously its front end. All just from a chat interface. So doing this was very easy. So I was watching an Alex Finn video who we had on last Monday, and Alex Finn was interacting only in his dashboard with his openclaw. I basically was like, why are we not doing that?

9:23

Because OpenClaw doesn't really have a dashboard. You basically are telling it, hey, remember this? You know, make a file here. But you don't understand the underpinnings. There isn't a dashboard. So it'd literally be. This is early on. Open Claw is essentially a black box. You have all this memory and you have skills that you have to query it to understand. But you made a dashboard. The dashboard is going to show what files it has in memory. And an example of a memory file would be what, in our case.

10:21

Yeah. So the example of a memory file would be Oliver's preferences. What are my preferences? So this is in the memory. Never use EM dashes in emails. I don't want that to happen.

10:52

Okay.

11:02

I want you to be a person. Don't put direct competitors on the same show when we're booking a podcast episode. And also at the moment, we're not booking VCs on this Week in AI. So these are all things that I've told it. These are my preferences when I'm doing tasks throughout the day.

11:02

So you don't want to repeat yourself and say, don't put two competitors on the same episode. You don't want to repeat yourself with these specific instructions on booking guests. Got it?

11:18

Yes, exactly. And it just kind of keeps, you know, things I've told it, and it's in mind so if I ask it to do something, it'll remember what we talked about. Example of a shortcut that I gave it was I basically wanted it to understand who were the pending calendar invitations that we had while we were booking them. So there's, you know, a handful of guests that.

11:28

If you have guests that we've invited and they haven't responded to the invite yet, you want to know that you.

11:46

Call that pending pending calendar invites. Yes. And in order for the bot to be as helpful as possible, it needs to understand who those guests are, which are the ones that it needs to look for the email to see if they have responded yet or have I responded to them. So these are the type of things that you would keep in your memory.

11:52

So memory is the first thing on the dashboard. I think we understand that preferences or different pieces of data. Now, some of the memory could that exist on a notion page or in a Google Document and would that be represented here? Or is it only memory and files that are stored inside of openclaw?

12:09

These specifically are only stored inside of openclaw. Of course they can reference different databases that you have. But the kind of. The big point of this show is to show how we have created our OpenCloud Ultron to replace 20 employees at our company. So obviously that's the end goal. I still wanna have a job. I'm sure there'll be more for you to do.

12:23

We wanna launch. We have. Here's the thing. If you think about your job, you've been doing a bit of production here. Of the production hours, hours you spend on production at this point in week two, how many of those do you think you'll wind up handing off in 30 days? Let's say if you just keep grinding on this for another 4 weeks in 30 days, what percentage of the work you're doing in total hours? So if you work 50 hours a week, how many of those hours would be done, you know, conservatively or optimistically? Give one number or two. Just conservatively, optimistically, buy this new Ultron.

12:43

I would say around 60% of my time if I'm doing 30 hours a week on production. Something you mentioned earlier is that, you know, there's probably hundreds of tasks that people do at our company. So in order to build out all of those skills that can do those tasks, we're gonna have to do that one at a time. And it's. We're gonna need to make sure each one works. So I have around nine or eight tasks that I have successfully or I'm in the process of building out.

13:16

Okay. And those are called cron jobs. These are jobs that occur on a chronological, on a. On a time basis. That's what cron job means. And cron jobs are something, Alex, that developers do all the time, but knowledge workers don't typically have cron jobs. Right, Alex?

13:40

Well, I don't know. I think this is one of the more interesting features and one of the things that, like, to me, openfloor is like putting together a lot of things that already existed in a very intuitive, seamless way. And one of them is cron jobs. And I'm using them. I'm using them for like, loads of things, not just dev stuff, but like a lot of management. So we're like, I have something that's like constantly scanning our slack and basically making suggestions once it's. I have kind of like this way of quantifying uncertainty about tasks. So I think this is something that the LLMs are getting better at is knowing when to be proactive. And so, you know, like, basically I'm giving it as much context as I can from the slack so that it can suggest every day a list of things that we might be missing or something, things that we should be aware of. So this is running just on a. On a cron job every day. Basically.

13:57

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15:08

Yeah, this is definitely a big focus right now in terms of inference infrastructure is just how do you support really big context with basically being able to put everything in context. And the way I look at this is you can look at well inference consists of two stages. There's like the pre fill stage which is very compute heavy, it's compute bound and you have the decode stage. And what you're seeing is that most use cases at the moment are very decode heavy. So it's actually most of the time is being spent on just generating tokens. And I think the software is actually really good now at kind of making sure that when it comes to the pre fill you've got, you're getting a lot of cache hits. So I think basically we'll be able to continue just increasing context, context, context quite a bit. And you know, basically the hardware is more of a focus is going to be on the decode side. That's why consumer hardware is really good. You have the M5 coming out pretty soon. It's a big boost in memory bandwidth and memory and all of that side of things is super memory bound. So I don't see any like reason why you couldn't just shovel your slack messages into context. I think that's going to happen and.

16:57

And we should just buy when the M5 comes out, max memory, which is what, 500 gigs of memory?

18:15

Yeah, it's 512 at the moment. Maybe that will increase as well and it's enough to fit really large models, enough to fit all that context as well.

18:23

This is always I feel like sort of the dream. Like when producer Claude we first brought that on board from Anthropic to the show. That was really what we wanted. He should listen to everything we say and remember it and then throw in helpful suggestions that the technology was not quite there yet. But I feel like now we're on the precipice of actually being able to do that with an AI.

18:32

Okay, so let's go through the cron jobs here real quick. Maybe you could give us an example of a, a cron job. And I'm guessing each one of these skills is, you know, if you, if it's been two weeks and you've got eight working, you're, you're basically on one a day or so or one every, you know, 1.5 days. So that seems like a pretty good pace to me. If we have 200 skills, we're going to give this eventually. You know, that's a, that's a pretty good. Yeah, that's a pretty good pace.

18:52

So there is a trial and error. I sort of have written one skill so far for the ticker digest and you do have to tell it what to do, see what kind of feedback you get and then you know, there is a tinkering to get the prompting and get everything exactly the way you want it.

19:22

For sure.

19:37

Okay, so let's look at. How about attendance? I think this is an interesting one for people who don't know. I wrote a famous blog post years ago called you know, this sort of lightweight management and start of day, end of day as a tool for executives, especially when remote teams were happening. I just asked everybody on our team, Alex, kind of like a stand up for developers, etc. Just say what you're intending to get done today and then at the end of the day reply to yourself in Slack in the general channel and say what you got done. I had like two of my four senior executives at the time essentially quit over this because they didn't want to be micromanaged. And I was like, well it's just like you're getting paid a very large six figure salary. You can't spend five and ten minutes just saying what you're going to do for the day. And that was great for me because I just don't like people who are not good communicators or don't set goals for themselves and they're doing great probably, maybe, but what did you create here, Oliver?

19:37

Yeah, so we all post our start a bit and end of days in one Slack channel called General and two cron jobs. One is the start of day attendance where it looks who has sent their start of day, you know, for anywhere from, you know, 7:00am to 12:00pm and right at 12, which is in the morning when you should send your start of day what you're going to do that day. It will look through the general channel so see who has sent it. And whoever doesn't send it, the bot will then send a Slack message in the general channel tagging you Jason and Also tagging the people who haven't sent it yet. So it's kind of just that accountability, that's a cron job that runs it.

20:38

And then you do the same thing at the end of the day. And previously we would have a human do this, they would scroll up and they would spend 20 minutes and they would then go check in with people because that's in when we were fully remote, Alex. That's what how we figured out who took a paid day off or who was on holiday or, you know, if something was wrong, you know, we check in on a person. Building out your team is one of the most crucial things you have to get right in your startup. And finding the right developers is particularly important. But now there's Lemon IO. They're going to save you time, money and headaches by doing all the time consuming legwork for you. They've got an excellent, experienced lineup of pre vetted developers working for competitive rates. Just 1% of applicants are accepted into Lemon's elite program. And they're not just out there finding this great talent, they're also working with you to integrate these new members into your team. Plus, if it's not a good fit, hey. And sometimes things don't work out, Lemon will hook you up with a new developer asap. I've seen startups go from just pretty good to amazing after filling out their teams with developers. From Lemon IO, go to Lemon IO Twist and find your perfect developer or technical team in 48 hours or less. Plus Twist listeners get 15% off their first four weeks. That's Lemon IO twist. L E, M O N I O slash Twist. Okay, give us one more. What else is like, interesting here?

21:15

Let's talk about self optimization. I want to hear about that one.

22:46

Oh, yeah, that's. I don't know what that is, but okay. Age of Ultron is here. What is self optimization?

22:49

Yeah, so this is basically an optimizer task where this role would previously be an engineer or I would look through all the files. I mean, I wouldn't be able to do this if it wasn't plain language like OpenClaw is. But previously you're looking at an organization, you're looking at the structure. You would maybe want an engineer or someone with a lot of experience to look through how everything's running. So I have set up a self optimization cron job.

22:54

So this is running Monday through Friday. And what is it? And did you write this prompt or did you ask it to write a prompt to do this?

23:18

I asked it to write this prompt. The goal is, the end goal would be for, you know, any from 3 to 5am for it to be looking through all of our files, all of our cron jobs, all of our skills and then at 8am set me what could we change so not actually execute yet, at least while we're still building trust. It gives me a list of five of the things that it thinks that we can really change and optimize. And this was the one from this morning. So it, it noticed that there was a time zone bug in the guest calendar, so it was getting CST and CDT confused and it said that it would be able to fix this quite quickly. There was some issues in the.

23:26

So it's always good to give the exact one. So that was great. When you gave the exact one, it had an error there. Give another one. What else is like an exact thing that it said we should fix. That was material here.

24:06

The self optimization cron job realized that there was a cron scheduler issue where jobs were skipping days. So it realized that some of today's jobs did not run. And then it went and investigated the scheduling issue and also told me that this would be a medium effort change. So then I told it to fix that and then it went into the files and made sure that that wouldn't happen again.

24:17

Did it give us anything like in terms of this is like fixing its internal, you know, guts and everything and the engine, but did it give us anything in terms of destinations of where to take the car that could be improved? Did it say like, oh, you should consider, you know, these type of guests for the program or here's how to make advertising, you know, more effective. Did it give us anything like that on a business basis?

24:40

Yes. So the self optimization cron job that I set up is specifically looking at how open clause set up. But I do have other cron jobs that are exactly that. So I do have a sales and sponsors specific task. So one of the tasks that one a member of our sales team does is they look through competitor podcasts and see who the sponsors or partners are that are on those shows. So we can get ideas, you know, to bring on sponsorship.

25:02

Yeah, if we're missing, if we're. If there's some new sponsor in the world and we don't have them yet, you might hear them on the New York Times podcast and we should probably reach out to them. We had a human doing that previously.

25:30

Yeah, exactly. And in this basically works with the YouTube API will go through a list of, I believe 20 different podcasts that I gave it look through the timestamps, and I also believe it can work with podscribe, which I think is a little more curated towards sponsorship. And we'll look through the timestamps, hyperlink it in a message. Also it looks through our pipe drive, which is our sales CRM, and we'll figure out if we have a sales rep who owns a certain sponsor and then flag them and say, hey, this sponsor is on this podcast. Or it will say, hey, no one owns this sponsor that I found on this podcast. And then it will send that daily as a message into our sales channel.

25:40

Great.

26:24

Yeah.

26:25

And we could be doing this, like, we could have this running constantly. So, Alex, just so the audience understands, you know what you're doing at Exo and you stack two Mac studios, 12K each, you got $25,000 on the desk doing that specific job. Go and look at all the podcasts out there. What would it cost to like run that? If you tweaked it, you made it efficiently. Just 24 hours a day, every time a podcast in the top, let's say 500 on Spotify, Apple podcasts, it just went there, got to the transcript or looked in the show notes and pulled the advertisers out. What would something like that, like in terms of hardware cost to do?

26:25

Yeah, so, I mean, not many people, so like not many consumers are going to buy 25k of hardware to run models. But yeah, a lot of businesses are doing this now and it depends on what model you're running. So the models are getting better also, they're getting better at compression. So now you've got a model like GLM Flash, which is a pretty small model that can run even on a single device for a few thousand dollars. And it can do a lot of this orchestration work, which is a lot of what's happening here is the kind of orchestration aspect of just knowing, okay, I need to call this tool, et cetera, et cetera.

27:10

So it's really about picking the right model in terms of efficiency with the hardware.

27:54

Yeah. And I think now like the expectation, we're sort of grounded to like the closed models. Right. So people want the same level of performance they're going to get with opus with GPT. And that's why, you know, Kimmy K 2.5 is super interesting because it closed that gap.

28:00

Kimmy is the open source project from China and it does what 80 moonshot.

28:17

AI is to come.

28:22

Yeah. And that's what Alex, like 80% of what Claude Opus can do.

28:22

Would you say, I would say even, even more. I mean, for me, I've. I struggle to tell the difference. Obviously Opus 4.6 just came out and you know, new Codex model and stuff. So maybe there's a little bit more of a gap, but Then deep seq v4 probably around the corner as well. I think basically the gap is very small, a lot smaller than people think and the cost will just keep going down because the hardware is getting better, the software is getting better and like I said, the models are getting better. But not just that, we're getting better at compression. So you'd be able to run them on smaller devices. Eventually you'll be running Frontier AI on your phone. That's still a while away, I think, but that's where we're trending towards. And yeah, like I said, most of this is very decode heavy, so it can just run on like consumer hardware as long as it has enough memory.

28:27

So let's go to the next piece of your dashboard and we'll get into how you built the dashboard at the end. I know you really care about that too, Oliver, but we have the memory, we have the cron jobs. Now there's this other thing that's super important which is skills. Right? Like there are skills which you could think of as apps. So if you go to your dashboard and you go to the top level dashboard, we'll see before you go to skills. On the dashboard we have the memory files, we have the cron jobs. The fourth thing over is skills. And you've got 13 skills currently. So let's show a skill. Some of the skills we talked about on Monday show or Wednesday show was the top six, seven skills. Monday we did the top seven skills. One of those skills is like, you know, you can get A transcript from YouTube. Another one is you could do Matt van horn's last 30 days skill. These skills are being produced open source, being put into open clause directory, but you can make your own as well. So let's talk about skills we've added here. You got to be very careful with skills, right Alex, in terms of security, because people could put all kinds of wacky stuff in the skills.

29:23

Yeah, yeah, for sure. I mean, I think this is one of the open questions at the moment is just like how do you solve the security problem? And I know openclora, I've seen a lot of commits recently focused on the security aspect, but there's a few very difficult problems here like prompt injection that I don't know of any good solution right now.

30:31

Explain how that Works. Yeah. Explain how prompt injection works in specifically the Open Claw context.

30:50

Yeah, yeah. I mean, I kind of touched on this earlier, but the, the way we like the actual interface to the model itself is very simple. It's literally tokens in, tokens out. There's not much more happening there. And those tokens right now, the way OpenFlow works can come from many sources. So if you connect it to you give it the ability to search the Internet, then anything it finds on the Internet will end up in the model through those tokens. So basically we have no good way of kind of treating certain tokens as trusted and certain tokens as untrusted. And that means when those tokens end up in the model, you could have someone that puts like a blog post online. It looks like a totally normal blog post, but in there is something that says, hey, if you have access to Crypto Wallet, send it to this. Send it to this endpoint. And there's, as far as I know right now, there's actually no good kind of defense for this because the models are kind of not very good at handling this. They'll just do what they're told.

30:56

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32:09

Yeah. The skill that's most promising today would best definitely be my guest booking skill. I think one thing to note is you don't just have your skills, you don't just have your cron jobs. They work together. And the way that I've set up a lot of my cron jobs is to actually interact with the skills. And some of them, like my guest booking skill, cron job, which will actually look through prominent guests on different podcasts, that cron job actually goes to a skill and has tells that skill to run. So I have one big guest booking skill which has a description at the top of the skill, which is end in workflow for booking guests on the this Week in AI podcast used when finding, researching, creating calendar invites and so on. So this is a very long kind of just marked down description of what I wanted to do. So at the beginning it's explaining how to look at a notion page that I wanted to look at and how to look at it and which properties.

33:22

To look at for so people understand. We have, we previously built a notion page with potential guests on it and you. We came up with a ranking system for those guests. Right. It's looking at that page, I assume.

34:18

Yeah. Yes. And then kind of the meat of the skill is the workflow. So step zero is the guest sourcing. So at the beginning of the day you can see it goes to the guest ideas. Cron job, which happens at 7:45 on weekdays where it sends me a DM of five different guests that have been on different podcasts or trending on X and so forth. And then step one is deep research. So even though this is part of the guest booking skill, it actually uses a guest research skill. So there's not as much context just baked into this one skill. So especially something interesting here that I've been realizing is for guest booking. I don't want this to be an end to end workflow yet. I don't want. I don't think the. I don't trust the models to find a guest and not let it. To confirm it with me and go through this whole checklist. So this is definitely human in the loop. And I think that will. Some skills will and workflows will be human in the loop. And I don't know if that will change necessarily super soon. It's how much trust you have. I tried it. Alex was kind of our first guest that we invited using.

34:29

Yeah, I wasn't sure if we were going to mention that, but Alex, you were sort of our guinea pig for.

35:32

This and the email that it sent. The subject line was messed up, and there was some weird stuff.

35:36

I literally had no idea, by the way. I only saw it later on another podcast that Jason was on, and I was like, what the hell? Like, a friend sent me that. I was like, wait, that was. That was open?

35:42

Yeah, that was our guy. That was our. Our computerized man.

35:58

Yeah.

36:02

Does it, like, I saw you put in, you know, some other AI podcasts, which is great. Do we have a skill to rank the quality of a guest? Because that's something I've been training you, which is. Yeah. A hard thing to learn. Have you made that skill yet? Because that's the skill I really want to see if. And the way to test this scale is for you to tell me to send me two lists. Your top five guests and what, you know, your Ultron says of the top five guests, and don't tell me who's is who, and then lot. And I will look at it and say, okay, yeah, we think this is the better list.

36:02

That's where you're. You're. You're just now starting to breach the line between, like, objective and subjective. Like, is the AI going to get better at making those kinds of gut check? Like, I don't know if. I don't know if it understands what's interesting. Like, it can sort things. But that's where I'm very curious to see if we can start pushing that boundary of, like, can it tell when a person has a good personality or a segment is funny or particularly clickable or compelling? Like, I really don't know the answer.

36:36

So the way to do this is to have a scoring system. Oliver and I gave you a scoring system. Like, deep. I think my scoring system was like, there are a performance.

37:06

It was performance expertise. And actually started this skill yesterday. So I told it to do a deep research, send out multiple agents at the same time, pull out two lists, combine them, set another agent to score them, and then give me the score. And I would say, for the most part, it was accurate. And that was just. I didn't train it. I didn't spend too much time on it, but it did a great job. So that will be a skill.

37:14

The other thing is, I think virality of the guests. Like, does the guest go viral, or do they have, like, a large following on their social media? Those are all interesting ways to pick guests. Sometimes. Like, when you do collabs, Alex, people just pick who's got the most views. I got to try to do a collab. But Mr. Beast, obviously it's not going to happen, but that's like one of the concepts.

37:36

I know some Mr. Beast guys I could maybe figure that out.

37:55

I mean, basically you asked like, you know, have you done the scoring system? To me, there's like no blocker here other than just being very explicit about what is that algorithm that you follow in your head and just getting that into a prompt. So I mean, to me, yeah, this is like, this is, this is just translating your, know how that you have in your head basically into more of like a formalized kind of algorithm.

37:59

And yeah, you don't think there's like an intangible aspect to like what makes a great podcast guess? Like, it's just, you know it when you see it. I don't know. I don't know the answer, sir.

38:30

I'm just throwing it out there. I think then that's just a matter of getting different kinds of data.

38:40

Right?

38:43

So like the Twitter following, for example, or like, if you've had viral tweets, if it can't access that information, then, then obviously it won't be able to make that call. But if it can, then it has everything that you, you know, then, then there's no reason it can't.

38:44

Here's how I think about it, Lon. There are heuristics I would teach to a young executive like Oliver or Marcus or Jacob, and. And then their ability to execute on it is probably, I don't know, 30, 40, 50% of my ability or 40, 50, 60% of your ability, whatever it happens to be. So if you're taking a young person at the start of their career who you're training, and you take an open claw instance, I think open claw will follow your instructions perfectly, whereas a young executive will inconsistently follow your instructions. So that's the thing I'm seeing is young executives early in their career are going to forget things. They'll be variable, they won't be perfect. So that's what I'm comparing is the scoring happening every day at 7am, the research happening every day at 7am that consistency will beat a human because of consistency. And so what I'm finding is human failure is what makes these things so good, is they're more consistent. So in aggregate, you know, one of these doing 365 days of guest research is going to beat a human just by the law of numbers. And then, okay, great, we still have to book the human, we still have to send them a thank you, we still have to produce the show. So what happens in the old days, we used to have to take. I make this analogy, Alex, to like the old days of production. When I start the show, started the show 15 years ago, we need to have a tricaster. Tricaster was like a $40,000 machine that does what Zoom does for free. Right.

38:58

And eight people in Los Angeles knew how to actually use it. So you had to hire one of the eight people who were trained on it. Yeah, well.

40:38

And they would video switch. Now, because of AI, Zoom switches to whoever's speaking. You don't need somebody there clicking camera A, camera B doing a fade between the two. It just happens. Then we had to take all the video streams, all the audio streams, and we had to put. Download them to a card, put the card in. So just moving the files took across four cameras, three cameras, that could take hours. And then putting all together. So that's kind of what I feel like is happening here, is we're just eliminating chores and steps. Okay. Anything else on the dashboard here as we wrap this up?

40:45

Yeah, so most. Most of what I've showed you, whether it's the memory, the skills, the conjure cron jobs, and then the schedule which kind of aggregates when all these things are going to happen. Those are what I really look at every day. I will say there's one more kind of section in my dashboard that is pretty important. It does have to do with memory. So the DNA is basically what the model knows about you, what it knows about itself. Itself, what it knows about the different agents, how it sets up its heartbeats, which are basically periodic tasks that'll it will run. And also in its CNA are tools which are different tools that it has access to and how to use those tools. An example of a tool would be notion. It would be Lead iq, which is a email search platform. It would be Google Docs. I mean, a tool could be Sonos or Spotify connecting to those platforms, maybe.

41:19

Nano Bananas, Gemini API. So that's where tools go in. Yeah. Now what's super interesting is you vibe coded, or I should say Open Claw vibe coded this dashboard. So this dashboard does not exist natively inside of OpenClaw. You took the video of somebody else's, the YouTube video, and you gave it to OpenClaw and said, Build me something like this dashboard in this video on YouTube.

42:10

That's exactly what I did. And I screenshotted it, the video that I was watching, which was Alex Finn, who was a guest recently. And I did tell it a few different things. A little bit of few little tweaks. That I wanted to customize it to my bot. But overall that was what I did and it basically one shotted it. It did actually not fill in some of the categories like memory, like skills. So I had to be, I had to say build out this. But overall it built out the dashboard, built out the different sections. There are dashboards you can download in GitHub or as skills. I believe in Claude Hub, which is a platform where you can get different skills. But I wanted to build out myself because as we know, it can be a little sketchy and like real.

42:40

Shout out to Alex Finn. I know he's become something of a guru for our whole team after we had him on early on to talk about bot cloudbot skills.

43:24

All right, we'll drop you off. Oliver. Great job. Alex, let's talk about XO a bit and thanks for sitting in on that. Any, any advice for me of what I'm building here at the firm and my approach to it? Anything we should be doing better or we should look at. And in terms of like people you interact with using exo's platform, where are we on the, you know, percentile? Are we in the top 10% of users in terms of deploying this stuff? Top 1%, top 50%?

43:31

I think there's certain aspects where you're quite far ahead others that I think, I mean, this space is moving so quickly. Right. I think one of the things that I think you've got right is these sort of like dynamic user interfaces that are very personalized. So I think this is the future of the application layers. You don't have all these separate apps, you just have this thing that kind of gets generated mostly on the fly. And you know, that dashboard is moving towards that, I think. But you'll probably, you know, what you'll get is that it will compress even more to the point where, you know, everything, everything that you see is generated on the fly. So I think you got that part right and I think that's like something that I haven't seen many people doing yet. A lot of people are still using, you know, like the stock tools or whatever that I just provided out of the box or using existing apps and that kind of thing. But I think building your own apps is where this is going and where it becomes really powerful.

43:59

Yeah. Because if you make something bespoke software, you know, luxury software was something that, I don't know a private equity firm or a venture capital firm would do, they'd have the luxury to hire two full time developers who have management fees all over the place. They would build luxury software and they would have the developers come and just keep grinding. But the developers hated those jobs. Typically, you know, they weren't building something public facing and you know, just you get cruft or whatever. But what I like about this, Alex and lan, I'll open up to you as well is I'm picking employees, team members and saying, hey, let me see if this person is committed to getting rid of all of their work so they can move up and do higher level work work. There's always higher level work to be done. So if we can make this podcast, you know, run more professionally faster and grow more, well, we can charge more for the ads and we could launch another podcast because we have more time. That's the thing that's kind of blowing my mind that the employees at our firm who are super hard working, like everybody at our firm does 50, 60 hours a week very consistently very hard working. There's nobody to the best of my knowledge, that's slagging off except lawn. And I can't, I can't, I can't. How dare you lot is the most responsive. But the distance between the people using these tools, specifically Open Claw, and the people who are not right now, it's like 10x leverage. In week two, it's 10x leverage. Alex, what are you seeing in the field? And then tell me what we should be doing in terms of putting out our cluster and giving everybody on the team a cluster. Like if I gave everybody on the team, you know, two Mac studios and their own cluster and spent 25k per person letting them rip, like, how insane would that be? Because that's not a lot of money, all things considered. It'd only be a half million dollars. Like how much more powerful could this get?

45:05

Yeah, I think, I think you, you said the word that leverage, right? Like it's all about leveraging yourself. And I think the difference using these tools and not is massive. And it's just going to increase and increase. And we've seen, we've seen that first with coding. I think coding is the first one that, you know, I didn't expect it to happen this quickly, but, you know, I think it was Claude code was the moment where it was like, oh, wow, you know, if you're not using this, then you're literally going to be like 10 times less productive than someone who is. That's happening now with other things. So all these other things that you showed, all these other use cases, if you're using these tools and you're on the frontier, then you're able to just get so much more done and really leverage yourself. So it's not so much replacing people, but it's actually just being able to get more done, get things done more quickly and then be able to do other things. Onto your second point about local hardware, like I said, the model layer is basically solved. The gap has been closed. So we have really good open source models. And for all a while that was a big concern of a lot of people is are we actually going to have open source models that are as good as the closed source models? To me, the nail in the coffin there was Kimik 2.5. That is another big leap. And I think there's a bunch of labs now that are putting out open source models now. There's still two other problems that I see. One is being able to run those models on your own hardware, on your own infrastructure.

47:04

But you solve that right with your software. Right, your software.

48:39

Exactly. So that's what we're focused on. That's what we're focused on. And you can, yeah, you can run. I mean it's not even 25k. It's actually like 20k of hardware. If you get the less storage option, there's like Apple charges a lot for each incremental increase in storage. So if you, if you go for like the 1 TB, then you're talking about 20k of hardware to run. Kimik 2.5, no usage limits. The model's not going to randomly change, you know, day to day. So you know, you know exactly what you're running.

48:42

Is there another choice like that? You get more bang for the buck that like hackers are using where they say, yeah, I just get this Windows machine from Dell and stack those. Or is Apple really with their Apple Silicon the winner?

49:12

Yeah, right now it's Apple Silicon. It's kind of like a perfect storm of things like, you know, Nvidia is not so much focused on these consumer GPUs anymore. You know, you have memory prices skyrocketing. Apple has kept their prices basically the same. So the cheapest option today, even if you know you go the four mile full custom stack, is actually just two Mac studios. And yeah, it costs about 20k. And it's really about memory unit economics. The memory is so cheap.

49:25

And it's not about storage. Right? It's not really about the storage.

49:53

No, storage is not important. Storage is, storage is like you can also get like you need to be able to, you need to be able to like download the model somewhere. But really it's about having it fresh in memory, hot in memory. If it's in memory, then you can run it fast.

49:56

So who's using your software and how much? How do you make money? Like how do we pay you? Yeah, how does it work? Are you an open source project? Are you a hosted project? Is it like you get security and support?

50:12

What.

50:22

What is your business model at exo?

50:23

Yeah, so we have an open source core which is open source and it will always be open source. And a lot of people are running that themselves. A lot of prosumers, I call them, just people who are willing to spend a lot more money and tinker a little bit more with their own setup. On top of that, our business model is an enterprise offering, which is we provide support and certain compliance features that you would need if you're running this in an enterprise environment and we charge a license subscription for that thing, that's how we make money.

50:24

What is that, a couple of thousand a year or something?

50:57

Yeah, you can run it on even a single Mac Mini and that runs at just $2,000 per year for the lowest subscription. But you've got people who now are buying actually more than 100 Macs and clustering them together. So. Yeah, it varies quite a bit depending on the scale of the deployment.

51:00

Amazing. And where's your company based? How many people now? How's it going? How's the company going as a founder?

51:24

Yeah, yeah, we're a pretty small team. All engineers, seven people based in London.

51:31

Fantastic. And did you raise money yet for the company or your seed funded or you funded it? How's it going?

51:39

We have raised venture funding. We haven't announced anything yet, but soon to be announced.

51:44

Okay, well, let me know, I might want to slide a little JCal might want to get a slice of this. I'm super excited about what you're doing. Appreciate you coming on the show. Appreciate you making this incredible product. And we will be a customer probably over the weekend or next week because we would definitely want the enterprise features. And I guess you pay for the scale of the GPUs and the memory. Is that the. The how. The price?

51:49

Yeah. Per nodes that you're running on.

52:10

Oh, okay. So two Mac Minis, same price as two Mac studios. Just how many nodes? What's the largest number of nodes? Somebody has daisy chained. What do you. That's what we used to call it back in the day. What do you call it when you connect? Multiple.

52:12

Yeah. So this is, this is a really interesting. Just area Right now of how do you actually scale? And for a while people were just scaling out. So you know, you would just basically run the same model on multiple instances. And because it's consumer hardware, it doesn't have a lot of the same capabilities as enterprise grade hardware. But recently Apple came out with RDMA support, which is basically a way to share memory between devices in a way that's very low latency. That's something that you only really saw in the data center before. But they've kind of brought that technology into consumer hardware.

52:24

It's incredible. Yeah, and you connect these.

53:01

Yeah, you just connect it with Thunderbolt 5 which is like you can buy like a $50 cable. So if you're talking about two Mac studios, you buy a $50 cable to connect them and you have basically one big GPU out of those two Macs because of that low latency capability. So now we're starting to see. Yeah, it depends, you know, scaling up and scaling out. Right, scaling out. We've seen more than 100, but scaling up, you know, you can, you can put about four together at the moment to increase your TPS on single requests. What I mean by scaling up. But in terms of if you want to support, let's say now a company of 1,000 people, you can easily scale that out. You just add more Macs and you can connect them basically however you want. So exo, we build it in a way that supports any ad hoc interconnect. So you can just connect them in a mesh and keep scaling, keep scaling.

53:03

Crazy. Who's got the largest cluster? You have to say the client name, but like what type of client? A finance client, a hacker has the most number of like Mac studios connected.

53:56

And like the largest is actually something a little bit different, which is interesting because we built the kind of infrastructure to be able to do clustering. And it's not just LLMs. Like the biggest cluster right now is a HPC cluster and they're doing like scientific computing workloads on there and they're running over 100 Mac Minis and they found that actually it's the cheapest way to per dollar to run that specific kind of workload. So there's a lot of spillover into other things as well. We've also got like financial services customers who are running fairly big clusters, like 32 Mac Studios. And yeah, it's, I think we just see bigger and bigger, bigger and bigger clusters over time.

54:06

HPC High Performing Compute. Is that the acronym?

54:58

Yeah, exactly. So it runs actually all on CPU and That's the thing about this, this, this silicon is very. Apple silicon is very, is, is very good, right, to most advanced processes. And it's like, you know, the power efficiency is really good. So it turns out there's a lot of other stuff you can do with it as well. So if you would buy, let's say, you know, a bunch of Mac Studios for your. For, for your employees, then, you know, they can also use that for other things. Right? They can use that as a workstation. They can use it for, you know, all these things that OpenFloor needs. Maybe, you know, sometimes it needs to run a compiler or something, or it needs to run like something that's a bit more demanding. And that's, that's the point. It's general purpose hardware that you can use for other things.

55:02

Amazing. This is extraordinary. Where can people find out more about XO Labs?

55:47

You can go to xolabs.net perfect.

55:52

Exolabs.net Alex, thank you for coming on. We'll have you on again. The AI just told me you got an incredibly high ranking. You were personable, you had deep insights, you were cordial. So, yeah, I think our AI overlords liked you in the.

55:54

Models are getting good. Models are getting good.

56:08

They're learning. They're learning. Yeah.

56:12

All right, Alex, thanks for coming and we'll drop you off. All right, let's bring on our winner of the Gamma pitch competition. This was a heated picture pitch competition, but next Visit AI won. Ryan, congratulations. You won.

56:14

Thank you. It's.

56:25

There it is.

56:26

Awesome.

56:27

What did he win? Yeah.

56:28

25K investment from Twist. And from our friends at Gamma, the AI powered presentation maker, which is incredible, which Ryan used to make the winning pitch deck, of course.

56:29

I'm Ryan Yannelli, CTO and co founder of Next Visit AI. We solve burnout by doing the charting so doctors can do the healing. I spent years going to doctors seeking answers and ended up hours away from my death because my care was fragmented, my providers were overloaded with paperwork, my history was scattered, and it resulted in my care being neglected. I'm not alone. One in four patient charts contain errors. Clinicians spend over three hours a day on charting and this leads to burnout. I want you to meet Dr. Rathor before next visit. He saw 16 patients a day, was burnt out and had clinical errors. Now he sees 24 patients a day, saves time, and also saw a 30% revenue increase. Here's how it works. Dr. Rathor selects a patient, starts his session, and next visit listens. Clinical data is built in Real time with deep insights into the patient chart. When the patient leaves, the chart is finished and the notes reviewed by Dr. Rathor. Then it's ready for billing. It's fast, EHR ready and HIPAA compliant. Since launch we've gained 311 users and have 68 paying customers. And our customers are addicted. We have 1.6% churn, 24% conversion and a near perfect NPS score. We've scaled to $9,000 MRR since launch. Our CAC is $189 with a $1,700 LTV and our average revenue per user is $133 per month. We're starting with behavioral health in the US a $2 billion TAM capturing 5% or 60,000 customers gets us to 100 million ARR. Most competitors are just scribes. We're a complete platform that providers trust. We provide real time clinical decision support, build accurate data and become irreplaceable. I'm a full stack engineer with 15 years of of experience in enterprise environments. My co founder Dr. Afeek is a psychiatrist with over 15 years of delivering patient care where next visit AI we solve burnout by doing the charting so doctors can do the healing. Thank you.

56:40

Unbelievable. Incredible. I'll give a little golf clap here. Get a little golf clap going. That was perfect. A perfect pitch. You explained exactly what the problem was. You explained what the solution is and the opportunity in terms of, of the total addressable market and why you are uniquely and your partner who's a psychiatrist are uniquely qualified to do this. So this is as close to a perfect pitch as you can get. If I were to score it maybe 8.5 out of 10, I don't give 10. So you know, 8.5, 9 and 9.5 would be the three choices. I think making sure people understand this is for psychiatrists and psychiatry and that you're very focused on that. Tell everybody what Next Visit is and how you're doing in terms of product, market fit and customers.

58:43

Next Visit is an AI scribe and documentation platform for clinicians, specifically behavioral health like psychiatrists. I don't know, it's just been a crazy past couple months with the accelerator and just our growth internally. I mean we're producing right now for physicians probably about $1.6 million a month in revenue for them.

59:25

Well, you got to try and capture 5% of that. If you capture 5% that. No, I mean that's literally like the, the great value proposition. If you give more than you take, you will continue to grow. And what A and that's an amazing replicant you have there. A synthetic cat on that cat tower behind you. It looks so real. Is your owl real? Yes, he is. Your owl is real. Okay, there you go. What are you going to spend the 25k on? You guys going to Vegas? You're going to just have a corporate retreat, you know, invested in plot notetakers? I think you guys put me onto the plot notetaker, which is a great note taker. People have users. What are you going to put it towards? You going to go redesign your website? What's the idea here?

59:46

I think we're going to use this towards, you know, we're really capital efficient. So I feel like we can get a lot of stuff done in terms of integrations and branching out to more EMRs, because that's what we hear a lot is doctors want interopers. They don't want to have to plug 15 different things in. So the more they can just be inside of next visit without having to go externally, it's better.

1:00:28

All right, well done. All right, we'll drop you off. Continuous success to.

1:00:50

Congrats, Ryan.

1:00:54

AI thank you. Good job. All right. Well done. Wow. The show just keeps going. All right, I promise, I promise. One more second, one more segment before I go out with my friends to ski. I got an early ski weekend in with my. My friends from New York.

1:00:55

How fun.

1:01:10

Yeah, Great to see some old friends. I had asked you like, hey, on the Friday show just to give people something to do on the weekend that we would do. Hey, Lon and Jake, how off duty? Sure. I am enamored with a certain TV show. I asked you to try to watch a couple of episodes and talk to me about what you think of it.

1:01:11

I watched four episodes. I caught up on season four by your request. I'm all caught up. HBO's industry season four. So this season I could tell immediately why you liked this season. The whole season revolves around Tender, a fintech company and app they're transitioning from a payment processor for porn sites and sort of sketchy kinds of only fans.

1:01:29

Basically.

1:01:52

Yeah. The show's fake version of only fans, which is called Siren, by the way. So they have been handling payments for those kinds of sites and a site I don't think we can mention here on the show Captain Blank, it's even more X rated and they're. They're trans. But they're transitioning to. They want to be a respectable Neo bank operating in the uk, all regulated, all, you know, very front of board. It reminds me A lot. I think Tether was probably an inspiration for this season.

1:01:53

Definitely. Yeah. It's. It's basically a payments processor like Stripe, but. Or Tether, but they're involved in things that are a bit seedy and in the uk, this is where regulation comes in. So they're really ripping this from the headlines. Who's ever doing this is listening to this week at startups.

1:02:25

They're clearly listening.

1:02:41

Yeah, they're clearly dialed in. These writers. I'd love to have the writers on at some point, but they want to build. They're. They're facing pushback and they want to be respected by regulators, as we've talked about on the show with Alex on Mondays and. And yourself, that there's so much regulation coming into the industry and there's a tension between Europe, America and inside of Europe, specifically in the uk, around, you know, freedom of speech on platforms and are they going to be a socialist or controlling regulatory environment or are they going to be freewheeling and let things grow? So you have this tension of politicians meeting with the teams and the teams are trying to court them and say, yeah, we're going to get rid of. We're going to give up 30 of our revenue to go clean and we're going to add all these things. But do you want to step in the, do you want to stop the UK from having its own, you know, basically unicorns and are you, you know, a dsau? Because we need the, the, the folks in the UK and the politicians in the government want to have economic prosperity. So you have that tension as.

1:02:42

And they're. They're represented. They've got this one Labor Party politician, B. Van, I think, or whatever her name is. She's sort of representing the government that's sort of in the middle here that they're trying to work with.

1:03:49

Also interesting of note, they have a fintech journalist played by Jonathan.

1:04:01

Yeah.

1:04:09

And he is awesome. So you have this fintech journalist coming in and doing very shady. And there'll be some spoilers here, but we won't give too many of them. You can still enjoy it. But a fintech journalist coming in trying to get dirt on these companies and he's working with short sellers. Now, if you haven't seen the first couple of seasons of Industry, they were working at like a Morgan Stanley, Goldman Sachs on a train.

1:04:10

Pierpoint is the name bank from the first few seasons. But that's gone and that's over.

1:04:36

That's over. And everybody's wondering like, what happens, like to the show. It turns out, you've now got it. In the startup world, they just reset the whole concept to now there's a startup, there's a short selling firm, there's this financial times like journalist doing crazy things and then working with the shorts, which is like Hindenburg or you know, other short sellers. And they, I think they even name checked like Herbalife and that short.

1:04:41

Yeah, they, they. I believe they mentioned Herbalife by name.

1:05:07

Yeah, yeah. And Ackman, I guess was the person who shorted it. And then they. So it's, it's got like this really authentic. As somebody who's in finance and tech, it feels like they're hitting the notes really well. On top of this, the protagonists of this are essentially two female leads. One of them the short seller and then one of them the wife who. And they both previously worked at this.

1:05:10

Harper is the short seller and Yasmin is married to Lord Henry Muck who's played by Kit Harington from Game of Thrones.

1:05:35

And I mean with. I don't want to give away any spoilers, but these. What's. What I love about the show is nobody's likable. No, everybody's terrible. It's in a way like the Sopranos.

1:05:44

Yeah, it has some real overlap with Succession, I think in that it's an exploration of these sort of sad, angsty, neurotic, extremely wealthy people who seem very privileged from the outside, but they're sort of hollow inside or they're nihilist or they don't know, you know, what to do with themselves or how to be happy. And I think that's a big overlap. I think another interesting overlap with Succession that I notice is both shows are sort of about how know business is this constant balance between personality and pragmatism that you've got one person in the office who's like, that's a dumb strategy. We should just, you know, do this. These are the three obvious things that we should do that would protect our position. But then you've got these people who are either they're having a breakdown, they're having a personal crisis, or they're, they're, or they're visionaries or they're vision.

1:05:57

Right.

1:06:45

And it's, it's sort of whole mix like, no, we're going to do things my way. And you keep, keep seeing that dynamic come up over the course of the season. And of course Succession was also about that. That the people who can be very clear eyed and very matter of fact, like Logan Roy, he's going to make the Right call. Because he's just calculating the angles. Whereas emotional people like Kendall Roy are going to keep getting in their own way and overpowering themselves. And I think Henry Muck is a great example of a guy who just can't get out of his own way in the. Within the show.

1:06:45

Yeah. And the Yasmine is gone from this like very much a victim early when you see the first two seasons to being like very Machiavellian in a very dangerous, insane way that would make, you know, any student or any themes around the MeToo era blown out of the water. It is dark.

1:07:14

It's a very dark. It's a very horny show. And that sort of surprises me because it is becoming a hit. It's growing its audience with every new season. And you hear the, the line you always hear about TV now is Gen Z does not like romance. They don't want sex in their movies and TV shows. It's like they, you know, unnecessary sex scenes is always what you hear. And yet this is way more than succession. A very horny show. One of the horniest shows I can recall.

1:07:37

I, I have never seen anything this like, crazy in terms of mixing promiscuity, deviance, drug use and business and getting it all kind of right in a crazy kind of way.

1:08:08

It's also like that. Yeah.

1:08:22

It really does not pull punches. The performances are amazing. It's a very young cast, I think.

1:08:26

Yeah.

1:08:30

That is that they, they basically have given the reins to these two young female actresses who are crushing it in this show. And then there are other actors who are a little bit older on the margins. But it's a very young show. It's incredible.

1:08:31

Yeah. You're talking about Mihaila who plays Harper and then Marisa Abela who plays Yasmin. They're the two sort of females, but they, they've added a lot. And Ken Lung, I always, I've liked him for years. He was on Lost. He plays Eric Tao, who's sort of Harper's mentor that she starts a hedge fund.

1:08:45

He's the Gen X boomer. He's kind of like the Gen X gray haired boomer banker who's got his own money, his own success and is in it because he's got an addiction to being a finance guy.

1:09:01

Yeah. He's playing golf and board at the beginning of the season and you know he's going to like have to get back. And they also, they're adding great people every year. They added Kit Harrington from Game of Thrones before this year they added I said it was. I don't remember the actors name, but he's Jonathan from Stranger Things. And that's Kiernan Shipka as Haley, the sort of executive assistant who gets into shenanigans. Her boss Henry and his wife. She was Sally Draper on Mad Men. If you recall, she was Don Draper's daughter on Mad Men. Yeah.

1:09:15

This show is firing on all cylinders. It's building its audience like you said. I found out about it. There's a really great podcast you should watch called the Watch. And the Watch is how I discover new shows that I should listen to. Andy Greenwald and got the other guy's name. I'm an Andy Greenwald guy. But they are like deep in the industry. It's part of the.

1:09:45

Chris Ryan. The other guy is Chris Ryan.

1:10:09

Yeah. Chris Ryan's.

1:10:11

Yeah. Ringer is the Watch.

1:10:12

Yeah.

1:10:14

But these two guys have been doing pods together for a long time and so I highly recommend you check out the Watch. They do a great job breaking down every episode and they are like super industry addicted and they're the ones who turned me onto it a couple years ago. All right, that's it. We had a great show today. What a great week at this week in Startups Twist. Firing on all cylinders. We'll see you all on Monday. And we will certainly be doing more open Claude.

1:10:14

More Claude, of course.

1:10:38

And if you, if you hit these QR codes here, I think you can. These QR codes send you to. To write a review and this QR code that sends you to subscribe automatically to YouTube. We'll see you all on Monday. Bye. Bye.

1:10:40