A (6:16)

Yeah. So a few things I just want to talk about. One thing is when you showed that Orgo screen, you had five machines running. So what's interesting is I've got my Mac Mini going. I've got one instance using something like this is cool because you can have multiple instances going. You can see them all in one screen in one view. That's really cool. That was an aha moment for me.

B (6:45)

Yeah, absolutely. Yeah. It's like everyone as far as where you deploy your main opencl, you can see here I. I starred the main one. And so that's like you could have that wherever. But what people don't realize is openclaw can spawn sub agents. And I think this is gonna be huge for people who start like right now we're at the phase of having one openclaw. It's gonna happen quickly. You're already seeing on it on Twitter memes about like, oh, what if you have a 10 Mac minis or all the Mac studios are being bought out, so this is happening fast. Or you're going to want 10 open clause, you know, 100 open clause. Right now you can have one open claw and just have it spawn up to, I think eight sub agents and each sub agent could have its own computer. And you could do this. Like this is why this is like kind of where Orgo shines is you can spin up multiple computers for, you know, each individual sub agent of Your open clause. And in this case here, I had It Looking on Upwork for actual things that we could automate with OpenClaw is like a little hack here as far as, like, well, I want to make money with open cloud. Well, oh, I don't know any. Any business that I can reach out to that could automate stuff for. A great place to start is upwork, because there's jobs on upwork that are literally posted. They're asking you, they're like, I want to pay 500,000, 1500, 3000, $20,000 for this AI workflow. And you can. I spawned sub. It went viral on Twitter. I spawned subagents to go find all these jobs and then build out little demos for each of them. And then we pick the best one and okay, let's apply for that proposal with that. So that's a little tidbit there on.

A (8:22)

Upwork and all of that, which is kind of hilarious because, I mean, upwork, you know, is designed for human beings to complete work. Right. It's not designed for machines, let alone multiple machines, to complete work. But I mean, as long as the quality of work is good, customer is going to be happy, right?

B (8:44)

Yeah. And I think it's good to treat it as a starting point. If you can save time preparing a proposal for a job on upwork, it's like, what is that worth? And if you could do just 100x volume, what is that worth? So there's a couple things on that of the parallelization of work with OpenClass. So there's could you have 10 open clause working on a given task, and it breaks up that tasks into 10 subtasks. And so each open clause does one of those subtasks. That's one way of having parallelization, but another way is to have 10 open clause working on the same task, just 10 different instances of it. And that was kind of like what I was doing here. It's, you know, four different instances of the one open claw, you know, or four different open clauses doing the same thing of looking up different jobs on upwork that they could apply to. So that's kind of an interesting topic there. But yeah. So as far as OpenClaw goes, it's a huge opportunity. I just want to throw this in here. Andreessen Horowitz talks about computer use agents. I view openclaw as a computer use agent. You're giving an agent a computer and it's able to use that computer. It's a computer use agent. But that's half the story. The other half of the story is for it to be able to click around, actually operate a graphical user interface on legacy softwares and systems. And so you can imagine like in here, this automation I built, this is navigating a legacy platform for this client that doesn't have any clean APIs and it's able to click in, download reports and actually be the universal API to be able to solve problems that you couldn't previously solve without computer use agents. So I think there's a of lot a huge opportunity here. And Andreessen Horowitz, they talk about it and they say we believe that the properly to properly verticalize computer use agents and assist companies in adopting it will be a major area of exploration for startups. And this is like this, to me this screams openclaw. You know, can you create a vertical use case for OpenClaw for a business and actually assist that company in adopting it? I think that's, that's the huge opportunity here. So going back to this workspace we have set up for you with all the people setting up Open Claw, you could set it up as easy as I invite Greg to this workspace, I create him a new computer. We could just do it now. Open Claw two, I select how much ram. Let's do, let's do eight gigs, launch that, open that up and then all we need now to get you set up is to get the, let me get the CURL command for OpenClaw. I copied that from their website. And then as this computer loads, we'll be able to then literally just paste it into the terminal and once I see the interface pop up, boom. Okay, so now I'm going to hit enter and now we're off to the races installing openclaw. So it's as easy as that. And I think this in and of itself is like a workspace where you can invite people and get them set up with openclaw or Claude code. I think this is a huge opportunity as well of like just there are executives right now that are like reaching out to me, like law firms, insurance companies, they're like, can I just pay you to teach me how to use this stuff? So that's its own whole thing as well as far as like, if you're savvy enough to even know how to install OpenClaw and get it set up in the first place. I just think there's a huge opportunity around just helping executives, businesses adopt it. So yeah, you can see it's as easy as this to get set up. And then as far as like what specific Things can we automate with openclaw? There's a couple. It takes a little bit of a design thinking approach. So when you go into a business, let's say you find a project on Upwork and you want to automate that with OpenCloud, let's say you go into a business and you're talking to the executive, the decision maker and it's clear that they have things that need to be automated. Well, as far as like design thinking goes, like, you need to have a clear way of like first mapping all the different possibilities. You can see here. This is something I did in the past of like, there's all these different things that you could automate and you want to map them off. Two very simple metrics. What is the value that we can create by automating this thing and what's the relative effort, cost and time? And so we ultimately want to start with, okay, we want to automate things with openclaw that are high value and low effort, cost and time. And that's like your low hanging fruit. And so you start there. And so like for this client, this was like, this was that, this was like, okay, we're looking at products on this website, we're downloading them, we're parsing all the information. That's the low hanging fruit. So start with the design thinking approach of like, okay, simplest, fastest to deploy and then you need to map out the systems design around. How is this thing going to be automated, right? So for this client, she's like, okay, I send an email to a client of hers, right? She sends an email to a client and she has a presentation link with all these products, okay. And all those products, she needs to look all of them up and get all the information on them and then upload them into Zoho. So then your next step after identifying the opportunity is to literally like map this out. I use figma, you could use whatever, but map out the actual workflow process of like, okay, step one, step two, step three, what is this automation going to look like? Tip to tail so that we can do the whole thing. Because with OpenCloud computer use now you can do that. You could do things from tip to tail. It's not like it used to be where you'd have to go into a website and click a button and then it'd be able to do some 50% of the whole thing. But then you'd have to copy that and paste that somewhere else and do it on your own. We can do it tip to tail. So quick recap install OpenClaw into a computer, identify the next, identify the low hanging fruit opportunities, the highest value opportunities and then begin to map out what that even looks like to begin with.

A (15:23)

Couldn't you, you know, sort of this is meta, but couldn't you use OpenClaw to actually do some of the prioritization on the automations? And actually, I mean you as a human being did the figma, but couldn't you actually just use the Open Claw or cloud code or something like that to, to help you with that? So for example, like if you go back to the, the figma, like you could walk into a business and basically say hey, I want to figure out what we can automate here. And you do customer interviews with different people on the team. You, you record those customer interviews, you get the transcripts, you upload the transcripts and then you say hey, based on that then you're like, you can actually say, you give this as a reference image, basically say like hey, I want to figure out which automation opportunities have the highest amount of value, lowest amount of effort, cost and time. Give me the top three and then create figma. And I think there's like a figma MCP even that you can use and you can say like hey, like can you map this thing out based on these customer transcripts? Does that make, does that make sense or am I. Oh absolutely, yeah.

B (16:41)

No that's, that's, that's the way to do it. Like I, whenever I do any kind of call with a client or cut like potential customer. Oh my gosh. Gemini for, for Google Meet is amazing. You just have it take, take all the notes and then actually that's, that's how I even got. Cause I don't know about, I don't know about you Greg, but sometimes when you're in these calls you kind of this industry, you're in a new industry, you're helping this customer, you don't understand their domain expertise, the lingo. And so you got to go back and like okay, what was it that they said? And so have the granola or Gemini notes or whatever and then literally ask it to like okay, what's the step by step workflow? And then map it out. It just helps me to map it out visually. But you could literally ask it, yeah, like you said based on this transcript, what is the automation workflow look like step by step.

A (17:33)

And if you don't want to use figma, you can also even say do output in mermaid code and then you can use the mermaid code and insert that into an Excalidraw or a TLDraw or something like that. So a little pro tip there.

B (17:51)

Nice, nice. Yeah, yeah, and the Figma MCP is pretty cool too. Yeah, definitely check that out. So then once you figure out what the workflow is, right, this is where you have to actually be able to know, okay, how much can I really ask? How much can I just say to openclaw right now, hey, build this, hey, build this thing. And you just describe the workflow versus genuinely using something like Claude code to build out what that workflow would look like. With Python APIs, a genuine automation pipeline and process that your open claw can actually just trigger upon whenever it's contextually relevant. For instance, this whole pipeline here of going to these websites, looking at this product information, downloading the information, parsing it, uploading it to Zoho. The trigger of all of that is the open claw being cc'd in an email. And it's seeing that email, it has a link that is relevant for this type of workflow to be triggered. So that is like the thing that's like the listening event that OpenClaw can do with like a cron job that it sets up to just like, okay, listen for this trigger. And then once that trigger starts, it can then activate the whole Python script, workflow, automation, everything that you would need downstream of that. So you're not relying too much on open clause like abilities in and of itself. You're more so creating specialized AI workers underneath the open claw that it can call individually, if that makes sense.

A (19:33)

You know, you talked earlier about sub agents. I think a lot of people are confused about what is a subagent versus a task and stuff like that. Can you just clearly explain that?

B (19:46)

Yeah, so subagents are. There's a couple ways to view them, right? So in the context of. And the reason I say there's a couple ways to view them is because there's a couple ways of using them. So in the context of like OpenClaw, you can ask it to spin up five research sub agents that all go and research some given task. And like I said earlier, you could have it parallelize that task across, splitting it up across each subagent, or having each subagent actually go and do the same task across five different instances. But the next thing around subagents is that you can actually, like you said, maybe think of them in terms of skills. So if you're familiar with anthropic skills, you can have these specialized instructions and rules along with actual code that you can provide to your agent for it to be able to go and do a given task. And this is really nice because it gives you a very more powerful general purpose agent that can do many of your specific nuanced tasks across various domains. But the thing is, it's like I want my general agent to be freed up and to more so just be the orchestrator. And what if the general agent, this one, right, the one I have start here, can just call a sub agent like worker number four here to do a given skill that you have created. So if your skill is that it goes on Twitter and finds the most viral ideas and it bookmarks them rather than having your main agent do that, and now you can't talk to your main agent for the next 20 minutes because it's working on that, can it call that skill into a sub agent and have the sub agent do that? That, I think is where things get really interesting. And in terms of the context of deploying open clause for businesses, I would think of everything that you have in terms of an AI automation opportunity around workflows, skills, tasks, et cetera. I would actually just create that as its own specific sub agent with its own skill that your openclaw could then call. Does that make sense?

A (22:03)

It does, it does.

B (22:04)

Nice.

A (22:05)

It's, it's been, you know, I think the basic idea is like, you know, in layman's terms, it's as soon as you have your openclaw instance, you know, do something, they're busy, you know, it's like they've got a mug of hot coffee. And so. And your job is you want to leverage this as much as possible. So you don't want your agent to hold a hot coffee. So if you ask it to move this desk into this area, it says, no, I'm holding a cup of hot coffee, I can't do that. So what sub agents do is it basically creates leverage for your open claw. And it basically says, okay, you're going to create a set of sub agents who are going to be good at XYZ thing. And that way it frees up your main agent to, as you say, orchestrate to basically be the manager of the sub agents. And what that can mean is looking at quality of work, it can mean checking for certain things and stuff like that.

B (23:20)

Exactly, exactly. I think that's going to be huge when you start working with these businesses and customers who want things to be automated. Once you show them what's possible, their eyes light up, they get all these ideas themselves. These are high agency people. They come up with creative ideas that they want to start implementing. Then what you realize is there's just a huge list of things that can be automated and they're excited about that. And so actually the ability to, okay, first solve a vertical specific workflow for a customer, and then that opening up their mind and then them being like, oh, I wonder if I could, could I text this thing? And it does this. This is kind of where, like, the whole open call moment is really powerful. It's like, it's the assistant, like capability. It's the, you know, I have it, I have it here. It's like a lot of people might get confused about why is it that openclaw is so special. It's the ability that has its own computer. It's running 24. 7. You can text it and you can schedule tasks. And really, if we just removed openclaw from this card here and you just called this a really good employee, it would just make sense, be like, oh, works 24. Seven, can code, can schedule tasks. I can text it. And they have their own computer. So I think that's kind of why OpenClaw is exciting for a lot of people. And if it's not, if some people think it's overhyped, you have to kind of look at the whole picture, I think, and then you're able to really gauge it.

A (24:54)

So I'm certainly bought in on this idea that it could be a really good employee. I think there's also cases where people aren't setting up their open claw in the right way, where it ends up being a bad employee. And I think, you know, that's sort of like the issue with that is, you know, sometimes you have a bad employee because the manager, the coach essentially is not doing a good job at giving the right context at the right time. So I think, you know, do you have any tips and tricks around besides spinning up sub agents? Like, how could people listening to this if they want to go after this opportunity of essentially verticalize open claws and automating some of these flows? How could people actually take their open claw from a bad employee to a good employee?

B (25:51)

Yeah, I think it comes down to maybe we should walk through. So I know you have. I was actually looking at this so idea browser. So for everyone, if you don't know, Greg has this amazing product idea browser. And I love this trend. This idea today. TikTok trend tool that catches viral waves before they peak. I was actually looking at this this morning. I was like, wow, this is actually something that you can maybe Turn into a skill for an open cloth to create a specialized skill around this. Let's just do a live. Let's see. I genuinely don't know. How far can we get? Can we build this out now? Let's see if I copy all of that. And now I'm here in the open claw that, that we set up for you. And you can see it just got set up. It's. I just told it. Hey, I'm Greg Eisenberg. So we're getting started. That's it. And let's just say I want to build a, a specialized skill to be able to do the following. And I'm just going to paste that entire idea, browser idea and I'm going to ask it as far as like creating these automations, creating workflows or doing anything with openclaw, my number one tip is always ask it to ask you questions. So what do you need from me to be able to build this out? Let's create a plan. And so a lot of people need to remember openclaw is like almost a little bit of a wrapper around Claude code in a way. So let's see. Okay, cool. This is a big vision, Greg. I like it. Let's break down a realistic build as an openclaw skill. So now it's saying, okay, I need data access, I need the scope, I need the niche focus that'll shape what we build. A lean skill. So the first thing that we can maybe do is here in like Orgo, we have this playground mode and you can ask the playground agent here to do things in this computer, in this computer environment. So one thing I might test first is like, can we get this agent to even just spin up TikTok and just scroll TikTok and identify what is on the for you page of TikTok? So let's like maybe start there. Does that sound good?

A (28:18)

Yeah, absolutely.

B (28:20)

Let's do that. Open Firefox, go to TikTok, scroll. I'm going to say scroll TikTok. Looking for what the most common videos are on the for your page. Give me a summary. So let's see how it's able to do this. So this is using our playground mode and boom, opens up Firefox. It's going to go to TikTok. And for those listening, I'm just going to talk through a little bit about what this agent in our playground is doing. It's visually interacting with the screen. It's, you know, clicking into Firefox, it's opening up the browser. Now it's typing in TikTok.com it's going there. And let's see.

A (29:20)

This is always such a magical experience, just watching a computer navigate the web like a human being.

B (29:28)

It's amazing. You know, this is. Yeah, there it goes. It's on the homepage. And now it's going to actually scroll TikTok. It's going to probably take a screenshot of this, get the context of based off that screenshot of what the video is about. You could even see it hashtags, movie hashtag for you page. So it's going to be able to infer a lot of things. Boom, it scrolls, it scrolls, it gets a pop up, it's going to close out the pop up. Um, but on your point, Greg, Dario Amadeh, the CEO of Anthropic, he just had a podcast with Dora Kesh and it came out a couple days ago. And you know what's really interesting? What he said in that, in that, in that podcast. He said, he said this idea of his around this data center full of brilliant, you know, scientists and Nobel prize winners. Essentially his concept of what AGI will be like. He says the constraint to getting there is computer use agents. The ability to have an AI that can operate a computer like you and I can, but better, you know, interact with the visual interface, also be able to do things under the hood, kind of like Claude code. This is the constraint, he said. And I mean, it makes perfect sense if it can do anything that you and I can do on a computer. That seems like it can go pretty far. Openclaw is like a chatgpt moment, I think for this kind of idea of computer use as far as building these computer use agents out. You can see this is clearly working. So we know this is possible. You can build. A lot of people might get confused with Orgo when they come to our site, they see computers for agents. They're like, what does that mean? What? Well, I think they get it now. It's like, okay, you want your cloud bot to have its own computer, but also we provide, and I'll show this, we provide the doc in our docs. Like we actually provide the programmatic APIs so that you can create custom computer use agents that do a given task very well. So you can bring any model you can get. Kimi 2.5 is like the super cheap, you know, Chinese model. It's very good at computer use and you can give it the ability to click, drag, scroll, type in the keyboard, spin up a computer and you can create specialized, really fast performance, low cost. Computer use agents using our docs. And I just say that to say like, as far as you know, this, this thing that we're doing here, creating a skill around scrolling TikTok. I think that's how we can, we can actually give it a shot. It's. Let's just. We see this is working now let me just grab the orgodocs and let's start building this skill out. If you didn't see here, I'm at the orgodocs, I click this thing called llmsfull. Txt. This is like all the instructions for the LLM to be able to build on top of Orgo. And what I'm going to do is I'm going to wait for probably I'm going to wait for this to finish its task and then I'll be able to tell it. But while this goes. Greg, you have any thoughts?

A (32:42)

Well, just sort of a thing I was thinking about is it sounds like whenever you're trying to do a new automation, you start by thinking about what is a lightweight skill that I should create. Is that correct?

B (32:57)

Right, exactly. What's the mvp?

A (33:00)

Yeah. So you start with a lightweight skill, you test it and then from there you're probably like, hey, here's what went wrong, here's what could be better. That sort of thing.

B (33:13)

Right, exactly. Just fine tuning, debug. I think the design thinking process around all of this is super important of like, okay, if I want to build a car, maybe the first thing I do isn't to build the frame of the car or to build the whole body of the car. That's not the first, the first thing I should do if I want to build a car. Well, why do I want to build a car? Well, I want to build a car to go from point A to point B. Oh, okay. So really maybe I should start by building a skateboard. How can we accomplish the task to get the dream out, the dream outcome? And sometimes that means like starting with something that's completely different than the end state. So this is done here. I think it interrupted itself, probably a context thing. But now we can actually what we can do. This is all live. So you're seeing this in real time. You can literally. There's a couple ways to go about this. You can actually install Claude code into this computer and have it build out the automation in here. Or we can just ask this agent to do it for us. So let's see, I want to build a computer use agent that does this exact thing, but more programmatically using The Orgo API docs and I paste that here. What do we need to get started? And we send that off. All right, here we go. So we need an Orgo API key. Here's the architecture of what we'll build. Okay, this looks good. Orgo key, anthropic key. It has all the code here. Cool. You're looking at a TikTok video. Extract the username, video description, the category, the appropriate like count. Boom, boom, boom. Okay, cool. So I have all these things already. And don't worry, I'm going to delete these keys so I'm not worried about leaking or anything. I'm going to copy my Orgo API key, I'm going to paste it in there and I'm going to say argo API key. I also have my anthropic key. Let me grab that. I'm going to paste that here. Can we build this out? And so now we're going to have our playground mode build out this computer use agent to be able to go do this thing that we just tested out. We know it works, we know we can do it. Let's turn it into something that could be more programmatic, kind of like a skill. And let's give it to claudebot so it always has access to it.

A (35:58)

The dream, the dream.

B (36:03)

It's literally any idea you have, you can just build it.

A (36:10)

And that's sort of the arbitrage opportunity. Right. Especially like in our world, of course, we're so used to this now, even though it's only been like two months. But the opportunity is the vast majority of people on this planet and businesses would love to have better automation and would love to have computer use agents working for them, AKA really good employees working for them, but they don't know how. And so I think. Which is cool that you're showing us some of the best practices on how to do it.

B (36:50)

Exactly. And I think this is also. I can imagine the whole audience of this podcast, we're all pretty tech savvy. We know how to do things like vibe code and play around with cloud code and be able to do these things. And we take it for granted in terms of what that value is worth as far as OpenClaw and the opportunity around that. OpenClaw started going viral on Twitter around two to three weeks ago. Only now is it starting and I'm starting to see it's starting to go viral on TikTok a little more mainstream. So, yeah, I just mean people are catching on and a lot of people still need help with getting up and running on this type of stuff. And what you might think is, oh, I mean I have a basic understanding of openclaw and claudebot and Claude code and you might underwrite that. A lot of people find that valuable. So being able to help businesses adopt it or just people in general, I think it's super good opportunity.

A (37:53)

Yeah, I think my only advice for people would be to focus. Like don't be everything to everyone. Like don't, don't help any. Every business. Help real estate agents for example. Or like pick a vertical that maybe you have some unfair advantage for some particular reason. And that unfair advantage doesn't necessarily mean you have, you have 20 years of experience. It might mean just that you want to build something for real estate agents because your mom was a real estate agent. So you know the customer. Right. So I think that's the way to do this.

B (38:36)

Absolutely. Yeah. It's whatever, you know, that's your advantage. And I mean, I guess as far as like some. Maybe this doesn't apply to everyone. Obviously if you're in this industry, then go for it. But probably things to avoid, things that have a lot of red tape like healthcare, finance. I recommend maybe starting something like you said you could do even manufacturing or there's a lot of distributorships out there, they distribute merchandise just like the demo I was showing earlier of that computer usage agent working. So yeah, I think as far as doing what you know and starting there and then the market will tell you, the market will pull you into specific verticals. You'll start seeing ways that as you build these specific. This is a great point. As you build out these specific workflows around these verticals of like, let's say you do the manufacturing thing and you do manufacturing for doors. Over time you're going to have a workflow for almost like every kind of thing you could imagine in that industry. And if you have the agents all built out, can you imagine you have a workspace and you invite some new company into this workspace for automations, for manufacturing, for doors, like luxury doors, and you invite them and they have all these AI employees in the workspace in Orgo set up ready to go. You just see them all here and they're all ready to go. It's like you feel like you just hired not a person, but a team. I think that's a very near future. In fact, I don't think there's anything stopping us from having that right now. It's all about just who's going to go out there and put in the work to actually do that. And if you do, I think it's pretty clear.

A (40:30)

Yeah. That's why I truly believe that, you know, agents are the new SaaS. Like, you know what I mean? So, yes, I agree with the vision you painted. I think that in the past we created software that we would sell to these businesses and then they would have people actually press the buttons, touch the knobs to make it useful. Now you're not going to create software and invite them to the software. You're going to create agents and you're going to invite them to the agents, and then the agents are going to do work that creates value for these companies. So that's the mindset shift. And, you know, it's only recently, actually, that people have been, you know, I think over the last, like, two weeks, I would say two, three weeks that people have been like on X talking about this, how agents are the new SaaS. But I do think that, like, over. You're gonna see over the next two to three months, like, some really big winners and you're gonna start to see it work. So I'm excited for people listening because I think that this is the type of audience that will act on some of this stuff and it'll be interesting to see what happens.

B (41:54)

Yeah, yeah. I think it was it Sam Altman that just said, you know, every company is turning into an API company.

A (42:02)

Yeah.

B (42:03)

And that's interesting because interfaces are, in a sense, dying in that way of like, you know, you won't interact. Like, the ultimate interface for whatever reason seems to be chat and text message. And it's happened twice now of like, okay, the chatgpt moment was a chat box, and now it's the open claw moment, which is like a text message or telegram. So it's like, okay, chat has happened twice and that just means. Okay, so we just want a way for our agents to be able to use all the tools that we use. And we don't really care about how it does it. It just needs to be able to do it and it runs in the background and does it under the hood. So here, okay, I started, I built. You could see that this agent in the playground built out the. The TikTokAgent py inside the computer. So now I asked OpenClaw, hey, I built a TikTok Agent py in your desktop. Can you take a look? And it says, okay, I've read it. Here's what I see. There's a skeleton for using Orgo plus Anthropics API. Go ahead, Greg.

A (43:14)

No, you Keep going.

B (43:16)

And it was, and it was, you know, all, all the actual TikTok logic, trend detection, all this stuff. It's like, okay, maybe we should build that out. And it's like, oh, you got the API keys hard coded, et cetera, et cetera. So let's just say let's use this script, build on top of it or whatever you need to do and let's spin up a orgo VM inside of the Greg Eisenberg workspace to accomplish this automation with TikTok. Let's demo it. Just working. We can spawn a subagent and VM in this workspace and let me just give it an API key just in case it needs that.

A (44:12)

What really blows my mind about this whole thing is that, you know, I was going to say we're building a business in a very short amount of time, but it's really like we're building an asset. Like the amount of assets that people are going to be building using tools like this is going to be crazy, right?

B (44:35)

Yeah, it's insane. Honestly it comes down to like, honestly it comes down to taste now good ideas because if you have a good idea, you could just build it and yeah, there's going to be like, oh my goodness, what's going to happen with all of these assets? Like you said that people are just going to build and build and build. There's going to be so many assets. I think. Is this what we mean when we talk about the, the abundance that AI will bring of solving all these problems?

A (45:09)

Yeah. Well, I think what ends up happening is unfortunately there are going to be more and more layoffs as AI helps with productivity. At the same time, I think there's going to be a renaissance, the golden age of entrepreneurship and people creating assets products like this one person businesses. And that's how I see it playing out.

B (45:38)

Yeah, we don't know officially, but maybe it's already happened with Peter Steinberger, the creator of OpenClaw. He just officially announced he's joining OpenAI. I think it was just him who built OpenClaw. How much did he get acqui hired for? I think it was a lot, so that's cool. I think this is the best time to be a builder tinkerer, to get creative. I'm excited to see what people build with OpenClaw and computer use agents in general of like there's so many things, whether it's a super fast chess computer use agent or if it's something that's genuinely driving an outcome in your business. I just think there's so many things that can be built. Playing around with these tools, getting familiar, learning how to leverage them. Yes, AI is going to be replacing a lot of jobs, but also it's going to enable a lot of people to do things that they've never been able to build before. And now they can do it. Yeah, here it is. Okay, so you can see it spun up this computer, TikTok trend hunter. And my screen's just refreshing. Let me just click into it and I could just tab back and forth a little bit to see. Okay, VM is up, it's opening Firefox. Let me wait for the agent loop to start. Here we go. So it spun up its own computer. This is insane, Greg. It spun up its own computer. OpenClouded, and now it's using its own Python script that it just made just now. We took it from Idea browser and now it's gonna go do this thing that we just built out. I don't know how long this took. Last like 10 minutes, kind of just, you know, in between we're talking and having our coffee. It's like, this is insane. I don't know. You gotta. It makes me. It gets me giddy. It's like. And it's gonna figure this out. It's like, you know, it's gonna debug, like, okay, what's going on? Why am I on the ads.TikTok.com let me reroute myself. I'm sure it's gonna figure all this out, but it's just cooking.

A (47:55)

Crazy, dude. Crazy. Anything else you want to cover before we head out?

B (48:04)

Yeah, I think as far as other things to cover, I mean, I just want people to start thinking about these tools. Yes, once again, they're great personal assistants, but if you start thinking of Openclaw as an N8N or you start thinking of it as a Lindy AI of people. There are real businesses right now. We can go to upwork right now and find jobs that are being posted around. Things like this is what you do. You just go to upwork. Upwork's great because you get to see what the market's asking for. You just type in Robotic Process Automation, this old outdated way of programmatically automating tasks that is clunky and it breaks and it's not intelligent. If the button isn't in the exact UI space that you delegated it to, it won't work. And you go, here, look. Posted yesterday, Android RPA Automation. Posted yesterday, Automation pipeline for client Upload. You go here, you could Just do. Let's find $500. $1,000. $5,000. Let's look at all these projects. Okay, this one, Boom, right here. $1,000 budget. I'm looking for experienced automation engineer to build desktop automation computer use for my software business. We sell a specialized dynamic PDF. You take all this context, give it to OpenCloud, give it to Claud code. How much of it can you build out as a demo based off of this context alone? Send a proposal, you have your first customer right here. $1,000. Get some case studies, leverage that maybe go deeper into this industry that this person's in. Start building out specialized workflows using cloudbot openclaw for all the different vertical use cases in that create a workspace of it. I think that's where we're at right now. And I'm excited by this. Um, yeah, I guess we'll just see where it goes and. Yeah. Okay. This needs a little debugging as to be expected. We spent 10 minutes on it, but I think you get the gist. And yeah, I'm excited to see what everyone builds.

A (50:22)

From your lips to God's ears, baby. I think your approach makes complete sense. It's the exact approach I would use. Totally recommend it. People get your hands dirty, get tinkering. I'm excited for what you build. Nick doesn't do a lot of podcasts. I think I was only able to find him do one livestream before. So show him some love in the comment section, like the video. Show him some love. And Nick, I hope you come back on share more use cases. I'm going to be sharing more use cases than that. I'm using that. I haven't done too many publicly. I'm going to be sharing more of my use cases both on virtual machines and on my own Mac Mini for my open Claw stuff. So get ready for that, folks. And Nick, wait, is there anything else before we go that you want to share?

B (51:24)

I just want to share with you, Greg. You don't know this, but I've been a long time follower. This is my YouTube Rewind 2025 top 0.5%. So if you're watching this and you love Greg's podcast, you love his videos, you get building on top of it. You put cool stuff out there, you know, you join your. You join your top YouTube channel on your. On your rewind of the year.

A (51:51)

I love it. I love it. Nick, you're a legend. You got to come back on. You're one of us. You're one of us. So I appreciate that.

B (52:00)

Thank you, Greg. Thank you for having me.