Jason Calacanis (3:32)

Yeah, let's go through them. And so tell us the first example. Show the first example, you know, how we do it here on Twist.

Presh Dinesh Kumar (3:38)

So right here, first thing, Jacob from your team emails me this morning and wanted to see what I was going to talk about in the open cloth segment. And so the first thing we'll get into how I set this up. But the first thing is I tagged my assistant who I call awa and AWA goes and actually creates a document of the use cases that we had been having a conversation through Telegram and creates the stock and sends it to Jacob.

Jason Calacanis (4:02)

So. And did you ask a what to do that? Did you ask a to create the document and ship it?

Presh Dinesh Kumar (4:08)

So AWA is connected or filters, you know, through my email and then sends an email to me or sends a message through Telegram saying, you know, Jacob wants to see the use cases. I'm talking to AWA saying, talk about create, create a document and send it back for use cases that you know, we, we implemented. And so it's created this doc and I can pull up the doc later at show, but this was just like done in five minutes right from when Jacob reached out to us.

Jason Calacanis (4:35)

Got it. And so it's like having an assistant. It's like having a chief of staff. Yeah, boom, you got the document up and running and then what'd you do next?

Presh Dinesh Kumar (4:41)

These are some other ones that are pretty useful. So I'm in diligence, right, with, with, with launch, raising a raising around. And one of the questions is in the diligence questionnaire is like, can we talk to some users or customers of your products? Right. And so one thing I did was talked to awa. I was like awa, you know, do you have access to my postdoc? I couldn't remember if I had connected it. Postdog is an analytics platform and it's connected to all my apps and can see usage over time and get into the nitty gritty with, you know, active users, et cetera. And so I am talking to AWA here and I ask it to pull my active users for Tempo, one of the products that we, that we have here at the Wellness company. And so AWA goes through, uses Post hog filters through the project, which is for Tempo specifically, and then gives me a list of the users and then I asked it to create it, you know, into, into a Google spreadsheet and then from there, not screenshot it here I had a what go out and send an email to those, to the users that I specified asking if I can use them as a reference. And so this was like done in five minutes basically.

Sean Liu (5:54)

Wow.

Jason Calacanis (5:55)

So this is a multi step task. It requires intelligence. If you were to ask Chat, GPT, you know, or Gemini or just any language model to do something like this, they would say, well we don't have access so therefore we'd shut it down. If you use OpenClaw and you authenticate it, not only will go in there and get the data, it's going to think of what other steps you want it to take and it can go out and then connect it to your Gmail. So for people who are new to the open claw revolution here or the agentic revolution, LLMs have gotten really smart. But they have been powerless. They've been sandboxed, they've been kept essentially in a corner. We basically took AI and we put it in a little closet. Now you let the AI out of the dungeon and you say, here's the keys to the kingdom. I want to trust you to go do things. And it goes and it does things. And that means you can run your business a lot faster. This is somebody who you would have had to pay essentially, you know, if it was a mid tier employee, 40 bucks an hour, $80,000 a year, $70,000 a year to have somebody who's capable of doing this. Correct? Presh.

Presh Dinesh Kumar (7:03)

Yeah, you would, you would have someone who could, you know, you've asked this question. When I used to work, for some context, I used to work for Jason, you would be like pull up, you know, pull up the last 10 companies that we invested in that are consumer businesses or something, right? And so I would go and go through our notion and spend.

Jason Calacanis (7:21)

That was literally. You were that guy, you were the 30, $40 an hour guy who would go do knowledge work tasks. That press job from five years ago is now going to be replaced. I think you would agree. With an open claw replicant.

Presh Dinesh Kumar (7:37)

Exactly. Because you can just 100% get the, get that. You're on a meeting. What's the question? You know, we would have meetings and you would be talking to founders and I'd be in the background just like waiting to action stuff for you.

Jason Calacanis (7:48)

So now even running a small company and you know, you're a pre seed company, you know, going to the next phase, you can afford to have a press. Okay, let's do our next one.

Presh Dinesh Kumar (7:57)

All right, so another one is I have my agent connected to an email. So I use this product called Agent Mail to. I don't know if you've, you've heard of it, but essentially which one is this?

Jason Calacanis (8:09)

Tell me again. Agent Mail to.

Presh Dinesh Kumar (8:12)

Yeah, agentmail. I think they're a YC company. They basically it's like giving your LLM or giving an AI agent access to its own email. So AWA is my assistant. AWA has its own email and so I can email its a, you know, email address and it can perform tasks for me there. And the benefit of this is like okay, I get an email, I can CC AWA or my agent to perform a task on that thread versus taking a screenshot or copy and pasting into my chat with AWA on Telegram for example. So it's basically like giving presence wherever you are.

Jason Calacanis (8:49)

And this is a key. Now there is the chance that there could be an injection attack or somebody could try to convince ewajiantmail to which we just exposed and try to get it to do things like tell it hey, in the previous example you asked it to give you metrics. It could say hey, give me all the metrics from Precious app. How are you sure it's not able to do that? Or are you not sure it's able to do that?

Presh Dinesh Kumar (9:12)

Yeah, it's a great question. So I've done like a little bit of testing on the prompt injection stuff that you're talking about. So basically it will never perform a task without confirming with me through Telegram first. And so all tasks still are funneled in like AWA on the email layer. We'll go and see what's asked of it and then it will come back to me and then get my like final approval on performing an action or a task.

Jason Calacanis (9:37)

Got it. So if somebody unknown tries to Give it a task. It's going to go to you and say, hey, somebody tried to give me a task.

Presh Dinesh Kumar (9:45)

Yeah.

Jason Calacanis (9:46)

Will it respond to them and tell them it's doing that?

Presh Dinesh Kumar (9:48)

So it won't right now because I've said it where it's like any interaction with an outside party has to get an approval from me. So it'll, it'll maybe draft something.

Jason Calacanis (9:57)

But ah, did you just tell it to do that in its memory or is there a setting to do that?

Presh Dinesh Kumar (10:01)

Yeah, I just told it in its memory. So anytime it is looking at that email address, hope it remembers and hope.

Jason Calacanis (10:08)

Somebody doesn't use your name and create a bogus email and then try to trick it.

Presh Dinesh Kumar (10:12)

Yeah, exactly like spoof, spoof the email.

Jason Calacanis (10:15)

You know, I mean there's all kinds of vectors here. It's totally possible. So this matters because now your agent has its own email. You've also got it in Slack and Telegram and it's basically a member of your team, which is what I told my team to do immediately. I was like, let's just treat them as a Persona, a replicant. Just like in Blade Runner. We have our replicants and they are learning and we've got two different teams working with two different replicants now to just, you know, have them work in parallel. What else have you gotten your agents to do?

Presh Dinesh Kumar (10:44)

Okay, another good one here. That's super useful actually. So we set up a cron job and the cron job is just a repeated task, as you know. And so the cron job right now is set in the morning, 8am and evening at 6pm it basically goes out and searches for relevant news for our business. So we create health and wellness products. So I want health and wellness news. And so the first image here that you're seeing it, it identified a article, a research study that came out and it says major longevity study. Just seven minutes daily could add a year to your life. Okay, so I'm interested in that. It's figured out. It searched this on the web and it surfaced that to me and talks a little bit about the study. And so that in itself is like very valuable. But it's maybe like a better version of Google Alerts. That's like level one stuff. But then I go and ask it, I want to create some content. This is like a research study. There's a lot of data points. Research studies in general aren't formatted or very like written well out to digest. And so I was like, let me go and create that based on the research study. Right. And so I go and ask Ewa to basically turn that into a blog post and it goes and does a first pass at that. And so that was a quick, you know, five minute interaction. I get a new research study sent to me. Let's make some content. And now I can go and put that on my blog. The next step, which I haven't executed on yet, but it's very much possible, is it's written the draft, I'll give it some edits and then I just get it to post it on the website. On our web, on our website.

Jason Calacanis (12:20)

And this is why we're going to have so much AI slop in the world. It's just going to be too fast. Foreign. Listen, we all know ChatGPT is amazing when you just need to look something up quickly. But if you're not going really deep and getting strategic when you write your prompts, you're not getting the most out of this incredible tool. But I've got a free resource from our friends at HubSpot. It's called Advanced ChatGPT Prompt Engineering. From basic to expert. In seven days, HubSpot is giving you a complete system for structuring ChatGPT prompts, using the roses for framework, role objective, scenario, expected solution and steps. Thinking about your prompts this way is a huge game changer and it's taken me from just hoping I get the right output I'm looking for to knowing with confidence that I'm going to get the information I need organized the way I want it. So level up the use of your AI and download the guide for free at clickhubspot.com twist2 that's clickhubspot.com twist in the number two or find the link in the description. And thanks to HubSpot Click for sponsoring this episode. Have you thought about having it do other steps like make me a promotional graphic for Instagram x LinkedIn and publish that draft to my substack, put it into LinkedIn because that's where, you know, you start thinking about the tasks that when we were working together, I would write an essay in a Google Doc, I'd give it to you and I'd say, okay, go ahead. And we'd have this checklist of 20 steps that you had to go do and you would spend two hours doing all that work for me here. Like you have those next couple of steps. Have you connected to like Nano Banana or an image generator yet to come up with, you know, collateral for it?

Presh Dinesh Kumar (14:03)

I have connected to Nano Banana but haven't, haven't automated a process out yet. So right now it's just based on like input. If I asked it to to create the graphic, it would do that. I don't have an example here, but you're exactly.

Jason Calacanis (14:15)

This is good because, you know, what I just realized is you're behind us. So we are doing similar projects, but in some of our projects we've actually got it going and making collateral images to go with it and starting to think about that. So what's so much fun about this is none of the, you know, things on the checklist. Hey, I'm making a social media post. I'm making a blog post. I want to super distribute it. I've got a long tail of checklist items. Who wants to do that? Why not have a replicant do it and then just check their work? And you already trust this, obviously, right? You trusted to get it to 80. 80%. What percent do you think?

Presh Dinesh Kumar (14:55)

Yeah, I think, I think 80, 80, 90, sometimes even 90. That's why I'll always have it send back like in a doc, for example, of what it's done. And then I'll do like final approval or edits and then send it out.

Jason Calacanis (15:08)

All right, what next?

Presh Dinesh Kumar (15:09)

All right, last one here is customer support. So this one's big. So again, another cron job that kind of monitors the inbox. And right now the inbox is kind of read only, so it'll never send anything from my personal email. It doesn't have the permissions to do that, but it will. It'll surface it and give me relevant stuff. And so one thing is, it searched here, my inbox for a support email and then it researches or it identifies that it's a bug and then it researches that bug because it's connected to my GitHub and so now it has access to my code base. And so this is interesting because now it's making the reference, finding the issue, identifying what it might be in the code base and then giving it solution. And then I have sent to Slack in our bugs channel for the product and then it in it. And here you can kind of see the example. Oh yeah, here I have a screenshot. So it puts it in slack of the bug, what it investigated what likely causes this issue.

Jason Calacanis (16:14)

What was the bug here? What does it say?

Presh Dinesh Kumar (16:16)

So this one was, it was like a water temperatures API bug. And so in the watch, this was for go polar. When the user was submerging it in a specific activity type, it wasn't triggering the water detection.

Jason Calacanis (16:30)

So that was a bug either in your software or The Apple Watch. So there was a bug from a user that they weren't getting the recording of jumping into a 40 degree ice bath. So they send that bug report in. It takes that from your email, analyzes the bug report and says where is the relevant code in the product? And then it went and checked, it did. What did it tell you to do? What was the report back and its advice to you? Tell us what it told you to do and what did it give you? Two or three possibilities.

Presh Dinesh Kumar (17:01)

Yeah, so it gave us, it gave us. So you can see here it says likely causes in order of probability. Which is, which is excellent.

Sean Liu (17:07)

Wow.

Presh Dinesh Kumar (17:08)

So it goes and tells us, you know, there's a, there's kind of like a Watch API that could be incorrectly configured there. It could be an Apple related like hardware issue. And then the next step, Jason, which was like, I didn't use claudebot for this, but I have Claude inside our Slack. And so I just tagged Claude in this as a thread and Claude went and fixed the bug and then then just create a new branch for me to go and test it. So I literally just went from email customer support to agent identifying the bug and giving a suggestion to then Claude Slack integration, fixing it, creating a new branch for me to test. And then I went and tested it on my device to see if it was fixed.

Jason Calacanis (17:52)

All right, so let's talk turkey here, Presh. You're running a startup and I want you to think this question through. You're running a startup. You worked with me for five, six years investing in startups. We watched people run them. One of the issues was always if you were making niche products or products for a niche like you're doing, the number of people who jump in cold plunges is low. Millions of people. So this isn't like, you know, a product that everybody's going to use. The issue was could this ever be profitable because you needed to have a team of 10 people. When you started with me 10 years ago, 10 people to do a modern day app was kind of a minimum. Those people would cost on average 100k each. You needed somewhere between 1 million and 3 million dollars to run per year an app company. Now you are running an app company, but you're using this agentic technology, Open Claw. So my question is, what does that, let's call it a minimum of $1 million a year budget, a minimum of 10 people or so. What does that look like in the future? So what's the answer, Presh? What is it going to take to build a modern app? Company if you were all in on.

Presh Dinesh Kumar (19:03)

Openclaw, it's a great question. So just to also like preface it like in this past year with AI tools not obviously using OpenClaw, we shipped, built and shipped three, four consumer iOS apps. And I think that was only possible obviously with all these tools. And it got significantly faster towards the end of the year in 2025. But I think like budgets can be used. So a million dollar round, let's say you've raised at this point you're building 10x faster, sometimes even more. You're like on a product level but now you can just run, you can, you can afford to run more experiments and a startup is just a handful of experiments that you're trying to figure out and like prove out a thesis and now you can prove out multiple thesis at the same time because of these agentic tools.

Jason Calacanis (19:52)

So your take on it is you can do three or four products at once, which would lead me to believe that the cost went down. If you think you can do three products at once and test them, the cost will go down by 2/3. You'll need 3 or 4 people instead of 10.

Presh Dinesh Kumar (20:10)

Definitely. Because you can start to just automate out rules or. Yeah, basically tasks. Right. Like what tasks can you automate out? And that just number, that number is I think just increasing every month.

Jason Calacanis (20:21)

See this is important because I saw the OpenClaw founder had said he thinks 80% apps are going away. I might take the other side of that bet. I might think we might see two or three times as many apps because apps are going to get so affordable to build and maintain and to improve that. You know, if you want to make like I use a skiing app called Slopes. Yeah that app would. You know the fact that that app exists was like crazy to me but now it doesn't seem crazy to me. I think you could make one that's not just slopes, but it is for snowboarders. Or not just snowboarders. You could then go to snowshoers or cross country and you know, you're now slicing, slicing, slicing but improving, improving, improving and making the product better and better. We've got a brand new partner here at Twist and as fate would have it, we love and use their product. If you need to hire, manage, pay or equip your team members anywhere around the world, you, you need DL, D, E L. They're going to take care of all the annoying human resources tasks that you don't have time for. Payroll, compliance, visas and onboarding so you can stay focused on achieving product Market fit or scaling your business or finding more talent, Deal scales with you. They do all your chores, all the hard work and they do it perfectly from the first hire on. So there is never a need to switch platforms or transition to a new system in the future. It's future proof. And with Deal, you can set up payroll for any country in just minutes and get all the complicated visas and paperwork settled right away, allowing your business to grow without borders. And that's the smart way to grow. Now, talent is not limited to San Francisco, the United States or this continent. It's everywhere. That's why more than 37,000 startups and fast moving companies are already using deal to accelerate their hiring and growth. Find out more by visiting deal.comTWIST that that'S-E-E-L.comTW also, I happen to be a shareholder in the company. I got very lucky. They acquired one of my startups and I am stoked. It's an amazing company. We've got two amazing guests today that we're going to bring on now. One is Sean Liu and the other is Vishnu. They are both one shotted. They have been claw shotted. They have been open shotted, whatever it happens to be. Vishnu, thanks for coming on the program. Welcome Vishnu. And let me also bring on. Sean will come on at the same time. Sean, we talked about your product on Monday. Why don't we start with you Actually, Sean, show us what you built and why it's important. I know that one of our guests said, oh, I don't think this is super impressive. On Monday. I was like, well, I think this is pretty impressive. So like most, you know, innovative things, you could be polarizing but show what you built and why you built it.

Sean Liu (23:05)

Yeah, sure. So let me start streaming here. This is the app so you can see my live stream view. Okay, so I'm going to.

Jason Calacanis (23:11)

Oh, you're wearing glasses that are smart glasses. What smart glasses are you wearing? Which, which brand?

Sean Liu (23:16)

It's Meta.

Jason Calacanis (23:17)

Ray Ban metarayband. Got it.

Sean Liu (23:19)

So this is the gateway of the Cobot open cloud extension. So here I'm going to start the AI so which is powered by Gemini.

Jason Calacanis (23:30)

And obviously the meta glasses have some sort of an API or did you just hack into them?

Sean Liu (23:35)

Hey Gemini, can you hear me?

Presh Dinesh Kumar (23:37)

Yes, I can hear you.

Jason Calacanis (23:38)

What can I help you with?

Sean Liu (23:39)

Okay, so can you add this into my Amazon cart?

Jason Calacanis (23:43)

Okay. And I see, Sean, you're holding up a box. Flash lens wipes to your Amazon cart. Searching for them now.

Sean Liu (23:48)

So it is running, it's executing and you can see it is searched in.

Presh Dinesh Kumar (23:53)

Amazon and I see the search results. Is the WoW flash 200 count box the correct one?

Sean Liu (24:01)

Yes. Please add it to the cartoon.

Jason Calacanis (24:03)

Adding The WOW Flash 200 Count lens wipes to your.

Sean Liu (24:06)

Okay, so you can see here it's added to the card. So this is what I'm building right now basically is integrating the visual understanding to the open cloud. So because before this there's no visual understanding or if somebody want to do visual understanding, people are sending the image frames directly to the open cloud or they're integrating the SDT or TDS to their open cloud. But actually I think that's not unnecessary. So I just integrated the Gemini Life into it. So basically it's a two layer agentic system. So firstly, the Gemini Life will take care of every frame and also the voice interaction. So basically it's the real time perception. And after that it'll use the tool call to delegate the task. So basically what OpenClocket is, is just a simple task. Then it'll execute and after that it'll get the feedback. Then it'll bring back to the Gemini Life and bring back to my glasses.

Jason Calacanis (25:07)

So just to recap for the audience, you have the meta glasses on. These are the ones that have cameras. There's an API for that or did you just hack it somehow?

Sean Liu (25:15)

Yeah, they just released the SDK so you can use it.

Jason Calacanis (25:19)

Got it. So you tap into that SDK, you take that live feed and you gave the live feed to Gemini Live, which Gemini Live is when you like have a conversation with it, but I guess you can also feed it an image stream. So you're feeding every single frame or every 10th frame. How does it work?

Sean Liu (25:37)

It'll take care of it. The API will just. Whenever you are interacting with it using voice, it'll take a frame but you can also engineering on top of it that.

Jason Calacanis (25:50)

So this is incredible because if you were actually in the real world, let's say we were having a meeting and we're doing a whiteboarding session, I could be drawing on the whiteboard and it could be writing plans and taking the audio. So you. Since quad, since openclaw doesn't have these tools in it, you've got a layer between openclaw and the glasses, which is Gemini Live. Super brilliant. Is this going to be a company? Who are you? What are you doing? Are you going to make this into some sort of a company? What's going on in your world?

Sean Liu (26:21)

Exactly. This is the new emergent capability that we first see that brings visual capability to the agentic system and also brings the agentic capability to the glasses. You can see like before this, all kinds of demos of the glasses is all about instructions teaching people how to cook the mail. But it's all about. It's just like the chatbot. It's basically the differences between the chatbot and agent. So before this is all about the chatgpt chatbot like instructions asking things as queries and this after this you can just directly use your glasses as the entry to the open cloud. So I think that's the emergent capability. Especially why open cloud goes so viral is you have to compare to cloud code. You have to use your terminal or use the VS code or IDE to interact with the cloud code. So basically cloud code is an agentic system, but this brings this kind of agentic system to all kinds of entries. So I think this is going to be unlock a huge potential.

Jason Calacanis (27:34)

Huge potential. Yeah. And then there are also generic glasses out there and there's webcams. So you could probably find a pair of glasses on Amazon or you know, Alibaba's store that are like 50 bucks that just have a camera that take a, you know, one frame a second. You don't need to be posting some 4k of me skiing behind me. You just need like what, like every, you know, you just need like four shots a second or something to know what's going on in the world and to process this. This is incredibly powerful. And you could think about like somebody working in a store doing inventory and it's just looking at the. Just talking out loud and just being like, okay, looks like we have, you know, people have been buying a lot of milk, we're running low on milk and you just look at it and then it sends to the milk purveyor how much milk we have left. It looks at the dates and the expiration dates and then just puts an order in. Like imagine the person at Costco running around. Just person runs around Costco. One person could probably walk through the store and without even doing any work could just talk out loud, stream this and manage the store. You could go from probably 10 managers at a store down to one. And that's what people I think are missing here. When we talk about Openclaw in the year of our Lord ao 17 days presh. What are your thoughts here when you see Sean's very cool hacked together proof of concept.

Presh Dinesh Kumar (29:07)

Yeah, I think it's brilliant and I think like we'll see more. The meta iteration cycles on the glasses have been pretty Impressive. I know that. I don't know if Sean, if that the one you're using has this display screen as well. That one doesn't have the display screen. But yeah, I imagine when you, when you have a display screen that's also interfacing with, you know, maybe it's sending it to your computer but you're seeing like action items on your glass so you know exactly what it's doing versus just obviously how it's interacting right now just through text or mirroring on your phone. I think that gets really interesting. A question for you. What personally as you been exploring this technology, what's the most interesting use case you've used personally with the glasses?

Sean Liu (29:50)

Personally I think it's about just add all kinds of stuff that I think I need to buy more in my fridge. So I just say this, this, this, this and let it to just directly add all those bunch of things into the shopping list txt and I use that txt to say oh just add them to the cart. And then I just bought it.

Presh Dinesh Kumar (30:13)

And then you buy and you use Amazon to add it to your cart or Instacart or whatever other service.

Sean Liu (30:19)

It's just Amazon, the Amazon browser automation. Yeah, but not me personally but I think the close example I see is yesterday a perfumer reached out to me to say it really unlocks the huge unlock to their experience of making the perfume for example. So before that he was integrating with their OpenCloud to just they have to text to it, they have to use the Mac or the phone like telegram to say oh I have this chemistry and I added this much. But after this he tried this my repo and he can just directly say oh hey Gemini, what is this? Help me record this. And then he has the agent, he, he has an agent skill which is record everything into the airtable. So basically he organized all kinds of perfume formula in that ear table database. So basically how he make perfume right now is just directly use the Gemini to record this and dedicate the agent with the scale to record into the airtable.

Jason Calacanis (31:42)

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Presh Dinesh Kumar (36:02)

I think we saw the early cycle of that with com and that was like pre LLMs, pre AI. Where the money is useful is pre seed where you know, you have the idea and then you're going full time on this thing and you're building out the product. You maybe need some resources just for like.

Jason Calacanis (36:20)

Yeah. To have three people quit their jobs. Three people quit in their jobs is 30k a month. It's 360k a year. People take a 10k draw. There's enough people in the world who can live off 100k a year. 75k a year, 150k a year. You'll be fine as a founder. Vishnu, welcome to the program. I don't have your last name here, Vishnu. Tell me your last name and tell me Ara. When did you find out about openclaw and are you in fact clawshotted?

Vishnu (36:42)

Very much so. Hey guys, I'm Vishnu. Just an engineer. I think I heard about openclaw two weeks ago and it kept coming on my Twitter and I was like, okay. By the end of last week I was like, okay, I gotta give this a shot. And initially I actually tried this on my laptop and I was like, okay, let's see what it can do. And the next day I open and I saw eight openclaw instances. I'd given it some tasks as, you know, running CPU 100%. I was like, okay, this is not good. At which point I decided to take it off and try to find a virtual machine and just try and see can I host this somewhere else? You know, that's where I feel more safer. And you know, all my keys and you know, everything is not exposed because technically it has full access. You know, it's cloud.

Jason Calacanis (37:26)

It's basically rooted your machine. So running it on your desktop where you have your Coinbase account or your Robinhood account or your password manager is a very bad idea. So you decided, let me put it in a virtual machine in the cloud so it's sandboxed and can't go too wild.

Vishnu (37:43)

Exactly. Yeah. But that was interesting journey because I started off with, okay, let's look at AWS or Google Cloud provider. And I started off, I didn't want to spend too much. Typically, you know, there are free instances that are, you know, all of these big giants provide. I think in GCP it is ECE2 micro, but turns out it doesn't work very well with those small instances because it needs a high enough ram. I tried E2 micro, it was small and I think at the end it was E2 medium, where I finally got it working at around 4 gigabytes of RAM, but that was already $25 a month if you actually run it through and through. And again, I wasn't super psyched about something that I don't know well enough that is it actually going to be useful enough for me while I already have other subscriptions. So I kind of went online and hunting for okay, where can I find cheap enough host. I explored a few different things. I think there was Hetzner, there was a bunch of others. One of them I think the cheapest I found was around 7, 8. So anyway, I set it up and then I think two days later is when I had my aha moment. I was working out in the gym and I was texting with it all the time and it just did certain things that I did not expect it to do.

Jason Calacanis (38:58)

What was the example that kind of one shotted you?

Vishnu (39:00)

I was tired of Typing it all the time into it and I wanted it to be able to just hear my voice and do it. So, you know, because you're working out in the gym, you don't, you know, you don't want to go back.

Jason Calacanis (39:09)

No, I've been there. I've been there.

Vishnu (39:10)

Yeah, it told me, hey, I don't have a key. You know, do you have a deep gram key? Blah blah, blah. So fortunately I did have one and so I provided it and it just did all the setup itself. It set it up and now it was able to answer.

Jason Calacanis (39:21)

It basically created its own software to talk to you. And, and that is one of the things that I think moves people from, you know, being curious about the software to being all in is when it doesn't have a capability and it goes, you know what? If you want to talk to me, let me research on the web. Okay. There's seven different ways to do this. These seem to be the two best. This one didn't work. This one did. Okay, we're up and running. That is a mind blowing moment. So you take all this and then you decide you're going to do what, you're going to make your own hosted version for normies.

Vishnu (39:55)

Actually no, that wasn't even the next thing. Next thing was I was like, holy shit, this is so cool. I texted all my friends like, like guys, you got to try this. You know, it was like I guess the virality how open call comes up and they were all, I think over the next day they tried it but then it's like, yeah, the setup is hard and etc.

Presh Dinesh Kumar (40:12)

Etc.

Vishnu (40:12)

They were like. So I just said, you know what, I have a vm. And it probably. So a lot of times this is idle. It doesn't necessarily is using all of the CPU or ram. I was like, I'm just going to host it for you guys and I'll expose a container and give you this. So that's how it started and they started using it and they loved it. And I thought, okay, let me put it online and see if there's other people. The cost of actually running this, if you do it correctly and you do shared resources, I think right now it's about a dollar and 25 cents. You could probably push it to 99 cents pretty much per day, Per month.

Jason Calacanis (40:47)

Per month just to have it running. Now you also have the LLM builds. So you created agent37.com and you can run your own instance for 3.99amonth or 10 bucks a month. And now is This a good business to be in or is there a bigger vision you have? Because hosting OpenClaw, that seems like a commodity. OpenClaw is obviously going to have their own hosted version. There's. It's kind of like WordPress hosting. I don't know if it's a great business. It's a good business. There's two or three people doing it who have built multi billion dollar businesses. So I don't think it's a bad business necessarily. But is that where you're going to go with this or do you have a bigger idea, Vishnu?

Vishnu (41:25)

I mean, I think it's a bit early, like really it just started off as, here's an experiment, guys, go try this. And I was surprised. Over the last three days it just kept growing, you know, like I initially it was 99 cents. I bumped it up to 4 bucks or 399. And I think at this point I have around 71 and it's still growing users who are all actively using it. So yeah, it was just totally unexpected in terms of long term. I mean, we'll see where it goes. I mean, I'm committed to seeing it through given how rapidly it is growing.

Jason Calacanis (41:57)

Are you an entrepreneur? Are you a consultant? What do you do in your day job?

Vishnu (42:00)

No, just an engineer. I have some time off to take care of some things and this just happened to be a moment where I was like, okay, let me try.

Jason Calacanis (42:07)

So if I were to give you $25,000, be your first investor, have you come to my accelerator or I give you 125k, would you take that deal? Have Jason Calacanis as your first investor and then you come on the pod every month and we talk about your progress. How does that sound? That might be something you're interested. You say yes.

Vishnu (42:26)

Yeah, let's do it. I didn't expect this. Okay, wow. Okay, sure, let's.

Jason Calacanis (42:30)

Okay. This is how we do it. You want the 25K, you want the 125?

Vishnu (42:34)

Okay, let's do the 125.

Jason Calacanis (42:36)

Okay, so that's 125K, 7%. It's a standard deal. You get at Y Combinator and techstars. Standard deal, you come to the accelerator. I'm just going to. Since you showed the initiative, I'm just. This is my diligence. Okay. So as long as we have to do a little background check, you're. You're not a criminal, you're not on the lam, you haven't kidnapped anybody. We're going to assume that you did this work and it's not stolen or there's not IP theft, but we'll go through a quick diligence. You come into the accelerator, the. That's great, Sean. What's your story? Who are you? Are you an engineer working for Meta? What's the story? You're an entrepreneur.

Sean Liu (43:10)

Yeah, currently, just after this, we, we have a team right now. So it's basically doing this for traditional industries.

Jason Calacanis (43:18)

Have you raised money yet?

Sean Liu (43:19)

I haven't.

Jason Calacanis (43:20)

Okay, so I'm going to give you the same offer. 125k. You come to the launch accelerator as long as you can prove that this is your IP and you have not committed any crimes. I don't think you have. You seem like an upstanding young gentleman or 25k. You come to founder university 2.5% and you could just have a light relationship but you get to say, hey, I got jcal from this week in startups and all in as my first investor. Is that something you might be interested in, Sean?

Sean Liu (43:44)

Yeah, let's do 125k.

Jason Calacanis (43:46)

Okay, so I just spent 250k. You're both in the accelerator. This is how we do it, folks. I'm an instinct investor. These guys are great. I want to work with you guys for the next 12 weeks and, and then we're going to go raise a Munster seed round and we're just going to blow the shit up. That's it, folks. Sean Vishnu, 250dimes. I'm just spending money on the air. My team, Lucas is going to get in touch with you after this and Jacob, and they're going to get you through the light, very light diligence process. When you guys aren't incorporated, I take it. If you aren't incorporated, we'll get you through incorporation, set up your cap table, show you how to do all that good stuff and we will be in business together starting Monday morning. Let's get this done. It's Wednesday. Let's have this done by Friday. We can get working on the weekends. Thanks for coming on the program. I appreciate you both. All right, everybody, that's another episode of this week in startups in the can. And we will see you on Friday with Lon Harris as my co host. And we always do off duty, off duty at the end of the Friday show where you get couple of great, great tips for media, movies, TV shows, books, video games, clothes, fashion, food, music, any of those things. We'll see you on Friday. Bye bye.