← Marketing Against The Grain
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- [00:02]
A
Okay. This is an incredible time to be alive. It is now possible to create an entire AI workforce who work for you around the clock and do anything you want to do. And Kip and I are going to showcase the tool that you can use today to do this for you and give you some of the best use cases that we found of Manus AI, the very first ever autonomous agent. And oh wow, is it powerful?
- [00:47]
B
Kieran, the entire Internet is talking about Manus and Manus is another one of the growing AI startups out of China and it is really the first real, like you don't need to know how to code. You can just type in your natural language and build a multi step agent. I think this was one of our most mind blowing episodes just because it takes all the power of a model like Claude and connects it to these other apps that make it really extendable to do these more complex projects.
- [01:19]
A
Yeah, I think this is the autonomous agents chatgpt mode.
- [01:23]
B
Yes. I love that.
- [01:25]
A
This episode is how I felt like when we did the very first episode of ChatGPT and we were like, things are different. We're not sure how they are different, but we are sure they are going to change. And then over the last two years, things have rapidly changed. I feel the exact same way about Manus. Manus is the very first autonomous agent that can do real work. Now you may say to us, well, what about OpenAI operator? OpenAI operator was a fun little tool, but did not really showcase how powerful autonomous agents are going to be. And Manus is that agent. It really is the ChatGPT moment for autonomous agents and things are going to change and they're going to change very rapidly.
- [02:02]
B
Yeah. I think about my own use of Operator, Kieran. I barely use that thing. I think you barely use it too.
- [02:06]
A
I don't use it at all.
- [02:08]
B
Right. I'm going to use Manus probably multiple times a day. I've already used it like three times today. Life is pretty obvious when you start using something multiple times a day. You realize that somebody's made something good. Right. And so we're going to walk you through how you would use it and why it's actually gotten good. And one of the biggest things, Karen, is that it's getting most of the things right on the first try. It's not like we're wasting hours going back and forth trying to get to some output that's kind of okay. Right?
- [02:35]
A
Right. It's accurate, powerful, and you're really only limited by your ability to think through what work you Want done. That's the limitation is like okay, well the barrier to be in 10x more productive, 100x more productive is just me being able to think through what I want the agent to do. If you can think thoughts about work you want done, you're going to be way, way more productive.
- [02:57]
B
Yes, I want to like triple underline the point you just made. We are now with this show today we've hit the point in which the biggest barrier to AI transformation and using AI on our businesses is not the technology.
- [03:12]
A
Right.
- [03:12]
B
It is us. It is our ability to think and use the technology. It's not the technology.
- [03:18]
A
Agreed.
- [03:19]
B
So for the first example, so we get right to it so you can understand what we're talking about. I've used Manus to do what a entry level sales rep would normally do which is find companies, research those companies, then figure out what that company is really good at, really bad as it relates to like my product or service. Then figure out what product product that I am offering that could help them with those weaknesses. And then it also is going to write a call script for me to actually read through and call when I actually talk to that company. This is something that would take a traditional kind of entry level salesperson probably half an hour, an hour per company to do. And it did all of this in 35, 40 minutes and I was even inactive time. It wasn't like I was doing stuff for that 35, 40 minutes. I was off doing other things while it went and did this work. Kieran, you and I have talked a lot about in the world of AI targeting and personalization and conversion rate is kind of a solved problem. And one of the ways I wanted to test that out is I wanted to say, hey, could I write a prompt that would basically allow Manus to be my own kind of bdr. And what I said is please identify companies and contacts at those companies that might be a Strong fit for HubSpot's product offering. These companies should be growth minded, small medium sized businesses in the United States. I would like you to do the following. I said first research and identify prospective customers for HubSpot in this research, please return company name, contact name and any contact information and LinkedIn URL. Like this is pretty complicated, right?
- [04:52]
A
Right, right. The setup here for listeners, obviously we're showing the actual screen is you are giving it all the context needed to figure out how to identify the right ICP or ideal customer profile.
- [05:04]
B
Yeah, I love that. And so what we're really trying to do is give it some very precise Specific and like step by step instructions. And so I wanted it to evaluate each of these companies sales and marketing strategy, their strengths and their weaknesses. And I say sales and marketing because that's the tools HubSpot provides. We help sellers, marketers, customer service folks. And so I did sales and marketing. It identified what those sales and marketing strengths and weaknesses are by company. Right. And then I was like write a call script for each of those companies. If I was actually going to pick up the phone and call those companies, like what would I tell them? And then I was like please give me all this information in a CSV file. So that's what I requested. All that makes sense. Kieran.
- [05:45]
A
Yes.
- [05:45]
B
Any commentary on that request? This is literally what like a BDR would go and do.
- [05:50]
A
Right. And I think what you're showcasing is again you still need the domain expertise to be able to guide the agent and, and the better the prompt you give it, the better context you give it, the better you set it up for what the outcome is you want in very much the same way again you would actually manage an employee, the better your results are going to be. And that's where a lot of the skillset is going to go to is like how to manage these agentic workflows.
- [06:11]
B
Yes. And keep in mind right now I'm doing all of this for free. This isn't a free version of this product. So first of all I tried to do it a couple times, the service was too busy. So one of the challenge with startups is that they have kind of more constrained capacity. Then eventually I was able to get it to work and then here you see it basically said hey, I'm going to start going through these steps. And so it tells you the Steps here. Research HubSpot products, offerings and it's actually browsing the Internet, it's creating files, it's executing commands, it's clicking on elements on pages. Like it's amazing the level of steps involved here, like this is very complex, very multi step workflow.
- [07:00]
A
Right. It can just scrape all of the websites. Right. That in itself is pretty cool. Like you have to have all these different tools that have built to scrape websites where Manus can just go and actually in the background write that code. It uses a tool called BrowseReuse. That's how it's navigating all these websites and pulling it into the CSV for you.
- [07:18]
B
Yeah, I think that's really cool. And so this took about 35 minutes approximately to do all of these steps which if you think about it's Wild. What do you think it would take a human and ended up doing five companies? And we'll talk about that in a second. How long do you think it takes a human to find and do all this work for five companies?
- [07:35]
A
Well, I can tell you like the average BDR is going to spend the majority of their time doing these tasks and trying to research a company and trying to identify product problem fit, right? Like what is the core problems that this company is trying to solve and what are the problems that are a good map? And so this is the most time consuming part of that job.
- [07:53]
B
It takes a ton of time. And so what it actually did here Kieran, is it gave us a CSV and it found these five companies, it found the CEO at each of those five companies because I didn't specify who at those companies. If I were doing this like at real scale, like it's one of the things I would do is talk about the specific profile of person, right. And got me their contact information, their LinkedIn profile. And I think what was really interesting to me about this Kieran is the work it did to determine what were its strengths and weaknesses as it relates to the HubSpot product offering. It basically created a known taxonomy of things and whether they were good or bad at it. Right. So clear value proposition, emotional marketing approach, user generated content strategy, free trial option. Right. These are all things that were marketing strengths for these companies.
- [08:48]
A
The other thing for people to think about when they're following along here is just how disruptive of a force this is going to be.
- [08:55]
B
It's unbelievable.
- [08:56]
A
So what this agent is doing to like make sure people understand what's happening here. What Manus really is is people have heard of Cursor. Cursor is this development tool that is wrapped around Claude and other models but for the most part people are using it with Cloud 35 to code full stack apps. And the only two things that I'm really spending my time on outside of like keeping up to date with this and trying to do all of the things that I do day to day in AI. Outside of that it's really like cursor and mcps. And what this really is is a cursor like equivalent for autonomous agent, which I mean it's like a wrapper that's built around Claude 3.5 and we're going to get into some of that and then it's extended using tools. So we're going to do an episode on mcps. This model context Protoc. I'm not going to go really technical Here, all people need to know is you're able to extend these agents by giving them tools to use. Right. You can say here's a bunch of tools. And so when I give you a task you can choose whatever tools you want to use. And that's what Manus really is. It's like a wrapper around Claude. So it's built on Claude model and then it has all of these tools and then when you give it a task it's like using all these tools. I'm going to use the browser use to scrape all this website. I'm going to use my coding tool to code something, a lightweight tool that I need. And it's really incredible to see just how many tasks it can get through in such a short amount of time. And the other thing I was pretty impressed by is number one, how fast it is. Like much, much faster than I think OpenAI's operator, way faster. I'm sure they will scale it up pretty rapidly. And then the second thing is just how accurate it is. And so my original point here is how disruptive this is. And so let's say there's all these kind of data source tools and today you have all these data source providers and you would have to go and you would have to say to these data source providers, I'm looking for these kind of people in this kind of market that I can solve this problem for. You can easily extend manas by giving us more tools and then you actually have the equivalent now in that agent because it can scrape all of the stuff on the web. You give it some access to other data sources and can pull all that in. You have your own kind of like data provider. Right. And for B2B now you can build your own kind of little data provider that actually gives you all of the ideal customer profiles that you need to actually market to. And I think that's what you originally said was like I believe marketing is going to change from like a very volume centric play, how we acquire lots of things to a very like value.
- [11:08]
B
Yes.
- [11:08]
A
Get very, very targeted and how do you like really over deliver for those people to way increase your conversion rates completely.
- [11:15]
B
Because the magic here and what Manus did for free was it found these companies, found the right person at this company, then found out what the strength and weaknesses were for this company as it relates to HubSpot's products. Right. And so you have some weaknesses here. Limited lead generation tools, limited pricing, transparency, the blog organization is a weakness. Limited enterprise positioning, limited lead nurturing visibility. These are all Things that if you are selling a marketing tool, you want to understand.
- [11:46]
A
Right.
- [11:47]
B
Like, it's amazing.
- [11:48]
A
Pretty incredible.
- [11:49]
B
And the thing that really blew my mind, Kieran, is it went and it found out these weaknesses and then it went and looked at HubSpot's product pages to figure out what HubSpot products map to those weaknesses.
- [12:01]
A
And I can tell you, because we have such a broad product set, that's a hard problem. That is also a very time consuming thing for sales. And so what it picked up, when I looked through your results, it picked up things that were kind of interesting. Right. It said, hey, this company do not have a blog. This company has not posted many blog posts, so they're not leaning into content marketing.
- [12:18]
B
Yeah.
- [12:19]
A
It actually picked up that this company had a couple of 404 errors. So it's scraping their website.
- [12:25]
B
Totally.
- [12:25]
A
And actually that is just bananas. And so it's able to like take all of the website, use that as data to say, well, here's my assessment, like, here's my grade. And then based upon their weaknesses, it did a pretty good job of not only pattern matching to the right HubSpot kind of product at its first attempt.
- [12:42]
B
Yeah, this is one shot.
- [12:43]
A
And then did pricing and then created a pretty good pitch. That alone is an entire couple of days of work.
- [12:51]
B
Yeah, I mean, if you're really good at this work, what this did would have probably taken you two hours. If you're like top level and you.
- [12:58]
A
Have 35 minutes and that wasn't active.
- [13:00]
B
It'S not like I was doing things for that 35 minutes. I was doing something else.
- [13:04]
A
And that's one instance of madness.
- [13:05]
B
Exactly.
- [13:06]
A
But I want to show you this. Actually, I can show you a quick screen. There's two things I really want to show you this one, because it's just like bananas to think about. Like this is actually, I think one of the best visuals and just how wacky things are going to become. This is my point. You're showing a single agent doing a task and it looks pretty cool. What I'm showing on my screen is.
- [13:23]
B
50 instances of manus, all running different social media accounts. Right.
- [13:28]
A
All run in different social media accounts.
- [13:30]
B
Wild.
- [13:30]
A
And so there's no reason you can't have a hundred. There's no reason you can't have a company of 10,000 people that are always doing this work. It is like incredible to think about. I don't know what you felt like, but when I used OpenAI operator, I thought it was like cool. But you couldn't really grok how powerful it was going to be correct.
- [13:47]
B
It didn't feel like it had any scale or power to it.
- [13:50]
A
But this is the first time where you're like, holy smokes. If I have a bunch of these things running, you're like, the amount of work I can get done, I can.
- [13:56]
B
Do 10x100x in a day.
- [13:58]
A
The other one I want to show you, the thing I'm obsessed about is the reverse of what you're showing. So let me show what I mean by that.
- [14:03]
B
Oh, please.
- [14:04]
A
So what we think about a lot, obviously, because we're in marketing, is our ability to better market to people who want to buy B2B software. What I am really obsessed about is the B2B's buyer ability to use agents to not have to do any of the buy in themselves. Because that to me is more transformational than the reverse of that.
- [14:24]
B
Right, I couldn't agree more.
- [14:26]
A
And so this is a really good example of what I mean. So this is Manus and what I believe happens in the future is you don't touch anything that exists on the web today to actually choose software. You get it all built for you in a personalized way. So the example I'm going to show here is Manus is going to build a aggregator for rubber mats. And the thing I really care about is the price, right? So I actually want to build my own recommendation engine. My own recommendation engine, like my own kind of G2 crowd for rubber mats. And I can actually sort it based upon price. And so it's basically built this whole app. And this app now allows me to sort through the suppliers that I'm trying to find in a very customized way based upon what my core criteria is.
- [15:11]
B
This is bananas.
- [15:13]
A
So think about it. If you were buying any software, I can say, I care about ABCD and E, now build me an aggregator app that aggregates them all together and allow me to like look through it based on user reviews. I can look through it based upon whatever I want to do. And not only that, I actually think you're going to be able to get Manus to go in and actually do the trial.
- [15:32]
B
Yeah, of course you are.
- [15:33]
A
Like sign in, use the software, tell me, does it do A, B and C? And you can actually ingest that back into the app. That to me is pretty wild. Like, I can't wait to actually start to use this to try to build some product sites that I can actually choose what products I want to buy based upon a custom site built for me. I never have to touch the vendor Site only to actually complete the transaction.
- [15:58]
B
I mean, are we ready for this?
- [16:01]
A
I think this year. I don't know what you think.
- [16:03]
B
I think a decade happens in this year.
- [16:05]
A
We're only in March. It's the first time. I think things are moving too fast. I actually need someone to just, like, say, can I just finish my cursor course and build my MCP servers before you release any more stuff? Because I just can't keep up.
- [16:20]
B
You and I are. I would call us hyper learners.
- [16:22]
A
Yeah.
- [16:22]
B
Like, we're obsessive learners. And this is the first time in the last decade where I have felt like I cannot keep up.
- [16:29]
A
But the interesting thing is because your leverage goes. Last week, you and I were whatsapping and I was showing you something I built using Claude and mcps, which, again, is what Manus kind of is. It's just a way better, more sophisticated version. MCPS are, again, just tools you can give Claude and Claude can go and use. And so the example I had was I told Claude to go research marketing agency services and then build a business that could actually automate those services through AI and create the website and choose a brand and add it all to GitHub. And I showed you it did all of that.
- [16:59]
B
It was bananas.
- [17:00]
A
When we go through the episode, I'll show you what it built. So that took some amount of skill. Right. Because I actually had to, like, integrate the stuff in the.
- [17:06]
B
Yeah, you had to do some technical stuff.
- [17:08]
A
Now, Manus, you don't need to do any of that. They do it for you. So again, it continues to, like, remove where your leverage is. Like, what is my. Because you're like, what is my leverage? I can do this task. Oh, now you. Manus can just do it. I can't for everyone. Right. So, like, it's a constant. Like, where does the leverage accrue to. Is. Is, I think, a really important thing to try to figure out.
- [17:25]
B
Yeah. So to close out on the, like, the last example about, like, this BDR and prospect identification, it did write custom scripts.
- [17:33]
A
Yeah.
- [17:34]
B
Personalized to each of those companies and their strengths and weaknesses and talked about how HubSpot could accelerate their growth. I could have given some more specific instructions on the call scripts. I think it would have been much better. Again, this is like a pretty basic first shot, but it is wild, wild that this was not possible. Not even close a year ago, just literally not possible. And now it is not only possible, it's free. The only thing I will say, Kieran, before we go to another example here, is there is limits. Right. I asked it to find 100 companies, it could find five.
- [18:10]
A
Right.
- [18:11]
B
And I tried to have it find more and it was like, hey, I'm at the end of my capacity for this task. You need to start a new task. So the context window for these tasks are still smaller. One of the things you and I, when we talk to each other almost every day, we're just like, can the context windows get bigger?
- [18:27]
A
Yeah.
- [18:27]
B
Can the models get faster? I don't need them to get smarter. I need them to absorb more, remember more, and connect more with my other technology.
- [18:35]
A
They need to have bigger context windows again. Yeah. They need to connect with your own data and to be able to pull and ingest your own data really easily. I think the context vendor is a big one, right? Like it's huge. Let's say you want to really collapse a bunch of tools into Manus for your prospecting and outreach and competitor research. It's like how much that actually eventually cost you because of the amount of additional bandwidth you need. But I still think cost is going to be a solved problem for AI and so like long term it will like just be way cheaper to do this than it is today via humans.
- [19:06]
B
I completely agree. So I showed you Kieran and everybody watching, like a sales example, right? How you find and reach out to target prospects in a really interesting way. So then I was like, well, what's in a marketing example that literally any company would want to do? And so I had Mana say like, hey, review all of HubSpot.com's product pages, rewrite the copy to make an emphasis more on our three key benefits, easy, fast and unified. And the target audience for these pages is growth minded business leaders. Can you create a CSV with each page URL and updated copy? And that's very basic instructions. I could have given a much better prompt. And again, just like it couldn't find a hundred companies, it couldn't find every single product page. But if you look at what it did, it came up with five product pages and was able to return the exact request I had. It gave me the current copy. I didn't tell it to give me the current copy. Right. It inferred its ability to do that, then gave me an updated copy which I thought like definitely fit the bill for what it was trying to do. Right. And so that's a very simple example that if you're in marketing, you're going to be interested in doing because you're like, hey, I can now edit and create content at scale with way Less manual work in context. If you're just like, I don't even have to give you the URLs. It's like for most companies they don't have more than like five product pages. So that would be a simple way to do a quick update to all their product pages. And it did that in like 15 minutes.
- [20:35]
A
Right. Another example of like the kind of mundane work goes away pretty rapidly with this.
- [20:41]
B
Unbelievable.
- [20:42]
A
Once it's actually integrated into your tech stack, once it's integrated into your tools, its ability to be able to do things for you and take away a lot of the mundane work will be huge. Now it is a Chinese company, so again there is going to be a lot of limitations in the way people will want to use it. It's a Chinese company based out of Wuhan.
- [20:59]
B
Is it really based out of Wuhan?
- [21:00]
A
Yeah, I think so.
- [21:02]
B
That's wild.
- [21:02]
A
The other one I want to show you actually it's not too dissimilar from the example I give that I was playing around with with Claude. This here I thought was wild. And so this is basically a scraping the entirety of Apple's website and then cloning it. So let me give you a pretty great use case. I think this is one of the ones I'm excited myself to go play around with. So I'm a company of any type. I can now replicate any other business and just ask it to make some slight modifications and tweaks. So any website you are a fan of, you can actually just go and have it create that for you. But then say like change the brand in, change this, change that and it will adapt it to your style. Now Lovable, do a really good job of this as well. I've been using Lovable to do mock ups versus actually any kind of design tools. But again like its ability to just do this in one shot. That's the thing I was saying to you on the WhatsApp. The important thing to emphasize here when I say one shot is it can just like replicate the website. It makes no errors and you don't have to give it any revisions. Your stuff was very similar. Like it just did the thing and you didn't have to like correct it or tell it to do something differently. It just got it straight away. And that is Claude's latest model. Its ability to one shot task is pretty incredible. Like the task I give it to do research, provide services, get brand and do website all within one prompt. No revisions, no error correction. And it did it all out of the gate. And so I Think the latest cloud model with these wrapper tools are really impressive.
- [22:35]
B
The amount of just what's possible now is just insane. Right?
- [22:38]
A
It's too much.
- [22:39]
B
It is literally too much.
- [22:40]
A
The possibilities are endless. But to give our listeners, you know, a kind of tangible one that they probably do care about is recruitment. So it's actually very similar to what you did, but it's actually for recruitment. So this is an example of it basically going through and actually taking a long list of PDFs, resumes and being able to assess them and stack rank them based upon criteria you have.
- [23:01]
B
This is a good one.
- [23:02]
A
Now what I would say is a better addition to this is take those existing PDFs, ask it to scrape LinkedIn for those profiles, and then ask it to ingest any write ins or interviews that person has done externally and combine it all and provide a grade based upon like all of that data because you can actually combine it really easily. So it managed to be a really great recruitment tool. The one thing that does make me think about is if more and more companies are going to use agents to be able to do prospecting of candidates as well. You really need to have better ability to showcase your work online. You have to like, I don't know if LinkedIn need to have some sort of ways that you can showcase work in interesting ways so AI agents can pick it up and use it as part of this criteria.
- [23:46]
B
Well, I think this is a very, very important point as we're kind of wrapping up the show today is that with these big fundamental innovations like agents like Manus, like we're talking about today, it creates a second order effect where like you need all new infrastructure on the Internet, websites need to work differently, social networks need to work differently. All of these things are likely going to be reinvented over the next decade.
- [24:11]
A
Right.
- [24:12]
B
It's kind of insane.
- [24:13]
A
Yeah.
- [24:13]
B
And it might be the current companies reinventing themselves, it might be new companies coming along. We don't know the answer there, but it's clear there's going to be a bunch of reinvention because you have a.
- [24:23]
A
New user on the Internet that's basically becomes a predominant user of most of websites and apps, which is an agent.
- [24:29]
B
Correct.
- [24:29]
A
And we haven't thought through how to bill for agents. Right. Because the user is now telling the agent to do all the things it used to do, but the agent is going to work very, very differently in how it wants to consume that data. We want to end on something like I thought was also like pretty eye opening. Right. So we're going to give you a new agent, a bonus here called Anus.
- [24:46]
B
This guy's got a sense of humor.
- [24:48]
A
And so why is Anus interesting? Okay, well, what is Manus? Right. So the founder here confirmed this is true, which is it's built on entropic cloud Sonnet. Right. It's not their own foundational model. It has access to 29 tools. So again, think of that as what you're hearing as MCPS model context protocol. It allows you to extend apps and give them tools to use. This one here, browser use, is how it's doing a bunch of the browser control, all open source, and then gives it a little bit around how it communicates with different agents and then like outperforms OpenAI's deep research on a benchmark. So like it's actually pretty incredible. It can actually do deep research as well.
- [25:23]
B
Yeah.
- [25:23]
A
As well as automate all these tasks. Those two things combined are incredibly powerful. And actually OpenAI hadn't even done that, which really does show the power of combining these tools in really intuitive ways. And so the thing I want to end with was, okay, well if that stuff, none of it's their tools or apps, it's all kind of open source or available for other folks to use. What this person did was like super smart. They basically said, well, I'll just have Manus create itself.
- [25:50]
B
That is super smart.
- [25:51]
A
And so basically they had Manus create a new agent called Anus. And Anus is like Dharmesh would like this. Right. It's a pretty good Venn diagram. So Manus AI, it's a powerful AI agent. I don't know if you know this, but the invite codes were going for $20,000 online.
- [26:06]
B
It's unbelievable.
- [26:07]
A
There's a waiting list. Then there's the open source, which a lot of the tools it's using is the open source. And now you have Anus. So what is Anus? Anus is basically an open source version of itself. So they've created this agent that you can go use in GitHub now that I'm going to actually go install tonight. It's Created by Cloud 3.7. Right. So it's created by the model that it runs itself.
- [26:27]
B
Yep.
- [26:28]
A
And it basically just one shotted and rebuilt itself using open source tools.
- [26:32]
B
That's just insane. We live in this world.
- [26:35]
A
So. Right. Just try to figure out. I want everyone to realize how mind blown this is. There is a business that went viral over the weekend that has a pricing model that everyone wants to use. They want to use it. So much that there are trade in $20,000 invite codes online. But that tool is so good and Cloudsonnet 3.7 is so good at coding that it was able to replicate itself and put itself onto GitHub and now you could just use it for free.
- [27:05]
B
Wild.
- [27:06]
A
And I can't get over how mind blown that is. Like what happens if, okay, we don't need to go in this path but like what happens if I can just one shot any software and open source it? Because it's possible.
- [27:17]
B
It's just, I mean over time it'll get more and more possible. It's just we are moving to a world in which brand connection, point of view, the human aspect is going to be more and more important because the technical aspect will become more commoditized over time. I think that's where we're at.
- [27:36]
A
Darius, the CEO of Antropic said in 12 months time AI writes 100% of all code.
- [27:40]
B
That's just insane.
- [27:42]
A
People should try to wrap their head around that. It's freaking mind blowing.
- [27:45]
B
I'll be honest with you, I think this was one of the most mind blowing episodes we've ever done on the show. And I think we will look back and this will be one of the kind of tentpole moments in the history of the show and the history of AI for when like major breakthroughs have happened. And this is like the first real consumer agent that can do legit complicated work. And we showed you some great examples. Highly recommend you go and get on the waitlist and everything from Manus if you want to check it out yourself.
- [28:12]
A
And if you can't get on Manus, don't forget about anus.
- [28:16]
B
You just want to keep saying anus. It's hilarious, you know. But regardless, this is going to be the start of a wave of these type of consumer agents. Kieran, I suspect we're going to be talking more and more about this. I Suspect Claude and OpenAI will both have some version of this 100 coming in the not that distant future. And we'll be back with those updates when they happen. But my advice to everybody as we close out is you have to obsess about how you're going to get leverage for your company or your role. And these types of agents are going to be a critical part of how you're going to get way more leverage in the future than you had today. This was awesome. My head's spinning. Thanks everybody for checking out today's show. We'll see you real soon. I'm marking it's great.