Episode Summary
In this episode of My Space, host Manav sits down with Dawson Chen, a 20-year-old entrepreneur and founder of Trimarten. Dawson is on a mission to build "Martin," a deeply integrated AI assistant inspired by iconic assistants like Jarvis from Iron Man. Unlike current voice assistants such as Siri, Martin aims to deliver powerful and proactive support by performing real actions—like sending emails, texts, scheduling events, and even making phone calls—across a wide range of platforms and productivity tools.
Dawson shares his journey, from attending Yale and ultimately dropping out to pursue his passion for building impactful software. He reflects on his early projects, including Campus Guide (an "Airbnb for college tours") and Flick (a social friend-making app), before diving into why current assistants fall short and how Martin is different. The conversation explores the technical challenges of creating a reliable, real-time voice interface, data privacy and security, and the vision for making AI assistants as helpful and trustworthy as a human personal assistant.
The episode also touches on startup life, advice for young founders, and a candid discussion about the challenges and opportunities ahead for AI in personal productivity. Dawson’s story is an inspiring look into the future of hands-free productivity, and listeners are invited to try Martin for themselves at trymartin.com.
Transcript
Manav [00:00:00]:
Martin. It's like an AI agent.
Dawson Chen [00:00:02]:
You can call Martin over the phone. You can have Martin call your friends and talk to them over the phone.
Manav [00:00:06]:
What was the process like getting into Yale?
Dawson Chen [00:00:09]:
I went to sort of like a competitive school so we all like worked pretty hard.
Manav [00:00:12]:
Why did you decide to drop off?
Dawson Chen [00:00:13]:
I was pretty bored of my classes by the end and I really wanted to do something bigger and do it full time.
Manav [00:00:17]:
Do you believe in a decade from now we'll be completely hands free?
Dawson Chen [00:00:20]:
The movie Her. In that movie everyone's only using audio.
Manav [00:00:24]:
What's an advice you would give to 20 year old founder?
Dawson Chen [00:00:27]:
You need to build your confidence and your faith. For me, the best way to build it was.
Manav [00:00:37]:
Today on the show. We have Dawson Chen and he's building Jarvis. His company is called Trimarten. But if you've seen Iron man and you might have seen Jarvis in that movie, he's building the real life version of that in reality. So I'm really excited to have you on the show. Dawson. How are you doing today?
Dawson Chen [00:00:53]:
I'm doing great. Yeah. Thanks for having me.
Manav [00:00:54]:
Can you give a little like short intro about you, like what you were doing before? How old are you and what are you working on now?
Dawson Chen [00:01:01]:
I'm Dawson, I'm 20. Before starting Martin, I was a student at Yale. Last summer I dropped out to start Martin with some friends and since then for the past year we've been just doing a lot of product work and trying to make it basically a better Siri with a large language model and deeper integrations. It's a lot of product work and we recently did some launches that we're really happy about and we want to share with as many people as we can. Now we were in beta for a long time for the better part of the first year that we started. Recently we started opening up the product for everyone to try and we want to get everyone's feedback.
Manav [00:01:31]:
Martin, in simple words, it's like an AI agent where you can talk to it. If I tell it to send an email to someone or send a text to someone, it'll actually do it. A lot of the time we try it with Siri, but it doesn't do it.
Dawson Chen [00:01:43]:
The problem with Siri is it's very shallowly integrated with a bunch of Apple products. What we want to do is integrate with all of your productivity apps and do really deep integrations. So for example, when we integrate with phone, like your phone calling, you can call Martin over the phone. You can have Martin call your friends and talk to them over the phone, and you can have them call strangers and ask them questions or book a reservation, stuff like that. So for each integration we do, we try to make it really deep. For Calendar, we make it so that you can view, add, edit, and delete events just with, like, voice commands. You can also text Martin to delete or add events, and you can have Martin text your contacts and schedule full events with them. So every integration we do is super deep. And we also aren't limited to one platform. So we have, like, Apple Calendar and Google Calendar, and we have, like, Gmail and Outlook coming up. And you can not only text Martin, you can also speak to him in our interface, you can call him and you can WhatsApp him and you can email him. So we try to be really well integrated, and that's sort of our specialty.
Manav [00:02:32]:
That's really amazing. How did you come up with the name Martin?
Dawson Chen [00:02:35]:
Martin is the name of my robotics mentor in high school. And we sort of really liked his voice. So the Martin voice is somewhat based on his voice, and he's a great guy. He's really charismatic. If I were to choose someone to be my mentor or the guy I would hang out with every single day, that would be the voice I choose.
Manav [00:02:51]:
Yeah. The cool thing was every other company chooses a female voice, Siri or Alexa, and I think you were the first. Something different. I like that.
Dawson Chen [00:03:00]:
Yeah. I think there is some subconscious bias towards your assistant being female, but we kind of wanted to be the opposite of that.
Manav [00:03:06]:
Interesting. I think one suggestion I have for your next model would be Alfred. You could call it Alfred because it's like the Batman reference. You know, that would be cool. So, okay, before Martin, like, what were you doing? Is this your first ever software product or you built something else before?
Dawson Chen [00:03:20]:
I grew up hacking together a bunch of, like, websites and apps. Like in middle school and high school, with just my friends, we built a bunch of software for a school. Like a bell schedule that you could, like, track your classes on. It was like this big team at our high school. And then in college, I built a couple of, you can call them, startup projects, a lot of side projects. So one was called called Campus Guide. It was basically like Airbnb for college tours. So you could, like, book a tour with a college student and they would meet you on campus and then give you a full tour for like, $45 an hour.
Manav [00:03:47]:
Interesting. Does it still exist?
Dawson Chen [00:03:48]:
The website's still up and a bunch of my friends are still, like, doing it. If people are, like, looking for college tours, they should check it out. I also launched this social app called Flick. You would get assigned a new friend within your like a mile of your location every morning and then you have to take a photo with that friend in the next 24 hours. Built a social app also.
Manav [00:04:04]:
That's really good. It's like breaking all the social anxiety.
Dawson Chen [00:04:07]:
Yeah, it was like something I wanted during wanted was to hang out with friends and make some money on the side. So both of these products were built for myself also during because I only went to school for one year, I was pretty bored of my classes by the end and I really wanted to do something bigger and do it full time. So I was the perfect opportunity to start.
Manav [00:04:25]:
Martin, what was the process like getting into Yale? Did your past projects play a role into getting into Yale or was it mainly just like giving sat?
Dawson Chen [00:04:34]:
I'm not sure if my hacker projects really played a role.
Manav [00:04:37]:
It was the test, right? It was your resume, your, your grades.
Dawson Chen [00:04:41]:
Yeah, I went to sort of like a competitive school in the Bay Area, so we all like worked pretty hard and we did a lot of like, yeah, just a lot of like academic stuff.
Manav [00:04:48]:
You're in Yale and do you find it boring? Do you find it useful? Like, why did you decide to drop out? Because most people are dying to get into universities like that.
Dawson Chen [00:04:56]:
I really enjoyed my one year at Yale, but I feel like by the end I felt just like a dire need for more adventure. I feel like first semester of college for anyone is like this huge like adventure. But then somehow really quickly it became like kind of dull. I could sort of imagine my next three years in college and what that would feel like. A lot of recruiting and a lot of classes and sort of the same things over and over again. And I wanted something new. And when I came home from college like last summer after my freshman year, I was like telling everyone, like, I'm going to drop out, I'm going to drop out. I just like need some sort of excuse to drop out. And then when we got funded for the startup, it was sort of a no brainer for me.
Manav [00:05:30]:
Yeah, I mean you did a good job like coming up with the idea for this. Like, I want to get into your thought process of coming up with an idea for Martin. Were you like scratching your own itch? Was this something that you were thinking about for a long time? Like, how did you decide to do it?
Dawson Chen [00:05:43]:
Well, I've been a Siri power user for a long time.
Manav [00:05:46]:
That's rare.
Dawson Chen [00:05:46]:
By the way, everyone who I talk to, like never even use Siri to Do like half the things that I use it for.
Manav [00:05:51]:
What's an example?
Dawson Chen [00:05:52]:
I use Siri to open my Uber app whenever I like. When I go outside and I'm in a rush, like, Siri open Uber. I use Siri to open apps and I use it to like set alarms every night. Now I type to Siri to set up alarms when I don't want to talk at night. And I also out and about. I use it to like make a phone call. It's like I use it so much despite it being really bad. Throughout my high school, I think I was like, really frustrated with how bad it is, but I still used it because I just like, I love voice as an interface and I find really cool. That was sort of like the start of the idea. We were like, well, there's this large language model thing now. This was one year ago, so it's still pretty new. And it seemed like a great opportunity to push the state of the art forward a little bit. And there were a lot of better voice models, like better text to speech and better speech to text. So we thought, why don't we just chain these things together? No one has really done a good job of it and then integrated with as many things as possible. We were also frustrated that Siri is really limited in its number of integrations. All it can really do is help you move through your iPhone a little bit faster. But it doesn't have your email, it doesn't have your Google Calendar, it doesn't have the ability to have full conversations with other people. It doesn't have like Slack or Trello or Notion, any of these things.
Manav [00:06:51]:
Why do you think is. What's the reasoning behind that? Do you think Apple has been hesitant to make it more. Is there like a privacy concern? What would be the reasoning behind that?
Dawson Chen [00:07:01]:
Yeah, I think it is like a data and a privacy concern for Apple, probably. And I mean, same for Google Assistant and Amazon. Like all of their Assistant products are limited to their own platform. And I think it's just a lot of risk for them if they were to like branch out. They have like an ads business in addition to like a SaaS business. So for them it's a lot more risky if you have like data problems because there's an ads part of business and there's a lot of like murky waters with advertisers relationships for them. They're always stuck in their own ecosystem. And what we can do is like, we can take just get the best possible integrations for the customer and whatever that looks like, we're just going to build it.
Manav [00:07:31]:
And Martin, Right now it's iOS and Android both.
Dawson Chen [00:07:34]:
We're not on Android yet, but we have like, for iOS users, we try to give them like Google products and Apple products and Microsoft products for the integrations.
Manav [00:07:41]:
So what's the business model? Is it like ChatGPT where you pay $20 a month and pretty much this voice assistant is active all the time?
Dawson Chen [00:07:48]:
We're still pretty early in our exploration of the business model, but right now we charge $30 a month with a free trial.
Manav [00:07:54]:
And what's like the traction been so far?
Dawson Chen [00:07:56]:
It's been good. We haven't launched for that long. So still early days of iterating with the first few hundred paying users.
Manav [00:08:02]:
What was your journey? Raising fundraising. And how did you get into yc?
Dawson Chen [00:08:06]:
Yeah, we did YC last summer. I mean, the YC application's pretty short. I feel like anyone can apply to YC and it's the same process for everyone. You just apply and you have one interview. And then we did raise. We haven't made the announcement yet, of course, of any further fundraising, so I won't talk about that yet.
Manav [00:08:19]:
Okay. All right, what model are you using? Are you using GPT4? And also like, are you exploring other models coming out by Llama like Gemini? I want to hear your thoughts on that.
Dawson Chen [00:08:29]:
Our philosophy with foundational models is we're just going to use the best one for any use case, irrespective of price or who owns it, at least for now. So we use a mix of GPT4O and Claude. Right now we have like a lot of AI problems, problems for AIs to tackle that are somewhat different. Like, we want Martin to be able to anticipate what actions you might want to take today when he syncs with you in the morning. A lot of users do a morning sync with Martin. They just chat with him for 10 minutes in the morning and Martin should come to that meeting with an agenda of like, I think this person might want to work out today or I see this thing on their calendar. So let me suggest that he can do this. Like, maybe I should check with him about this reschedule that happened in one of his emails that he just received. Anticipating needs is one like sort of core AI problem. So we use different models for that. Compared to like generating natural responses and compared to like which tools to use based on your command, we just use like the best foundational model we can find. And we take the same approach for speech to text, voice activity detection and all Those things.
Manav [00:09:22]:
How do you even compare a model? How do you compare GPT4 to Gemini to Llama? How do you test the difference in results from each model?
Dawson Chen [00:09:29]:
It's all about benchmarks. So if you just integrate it and then test it with users, it's still really hard to tell. And it takes a long time to determine which one's better. So we have a massive testing suite of all of our function calls and example cases that users can talk about with the assistant. And so whenever we change anything about the model, whether it's a prompt or a chain of thought, we always do, like a lot of thorough testing with a ton of test cases. For example, just the function of schedule reminder. For me, you could say it in a bunch of different ways. You could say, text me tomorrow morning to remind me to do this or ping me 9pm to go to the gym. Stuff like that. There's so many ways to say it. So we have hundreds of example queries that we test to make sure that our model can pick up schedule a reminder. So that's just one example. And we do that same thing for calendar and for reading emails, searching through your inbox, stuff like that.
Manav [00:10:15]:
That's a really hard problem. I think one thing I struggle with personally is I take a lot of notes. I'm always notes. I usually talk to Siri. I'm like, take this note and it's always incorrect. Like, most of the times it's like it comes out incorrect. It might be my accent, I don't know what it is. But I think that's one thing I really want to use Martin for is like taking notes and setting reminders. Especially when I'm reading a book and I see something interesting, I don't want to, like, distract myself by taking out a notebook and write it. I could just keep reading, but I could be like, hey, Martin, like, take this notes for me. So can you talk a little bit about note taking actually?
Dawson Chen [00:10:46]:
Like, if you were to take notes, where would you want them to be stored?
Manav [00:10:48]:
Like, how do you want Apple Notes?
Dawson Chen [00:10:49]:
Apple Notes.
Manav [00:10:50]:
Okay, because I tried notion, I've tried many other apps, apps, but I always keep coming back to Apple Notes.
Dawson Chen [00:10:55]:
We have like, I mean, our main note taking feature right now is probably like most of our users, when they talk about note taking, they usually send it to themselves in an email. That's the most common way we definitely can integrate with more notes apps, though. A couple of our, like, integrations that are coming up are like Google Docs, so we want them to be able to like take notes on a doc and then just email that doc to someone or to like just text the link of that doc to the user. We also want to integrate with something like Slack where you could like take notes and then put it into Slack. Apple Notes. I'm not sure if you can interview with it, but if you can, then we'll definitely do it. I think for now we haven't gone into note taking as much as we eventually probably will.
Manav [00:11:29]:
Right now the action items are more like make a phone call or play the podcast or call this person, text that person, set a reminder.
Dawson Chen [00:11:36]:
Yeah, the. The two main categories that we've started in are scheduling and communications. So scheduling is like reminders and then managing your Google Calendar and your Apple Calendar and then communication ties into that pretty well. Because the biggest communication use case is have Martin text someone on your behalf and coordinate back and forth with that person. So you can give Martin like a mission and then he'll reach out to this person in your contacts from his own phone number and then he can talk back and forth with that person to coordinate the thing and then during that conversation he has access to your calendars. That connects really well with our scheduling use case. So those are the two main ones right now. And we're just about to launch phone calling. Actually we launched a beta version yesterday, but we're going to push out having Martin call other people on your behalf very soon. So this is sort of like tying together the last part of our communications mvp. After this, we're going to work more on email things. So we're going to make Martin able to coordinate and communicate over email in addition to texting and phone calling. And then soon after that, I think we will eventually get into this like note taking, slash documents information management area, which will include like Slack and Google Docs integrations.
Manav [00:12:35]:
Yeah, I have a friend named Chris, he's building SimpleTalk AI, which is like a AI cold calling company. An AI customer service like Cold Calling Company. I think the biggest challenge they were facing was latency. The thing sounding too robotic. How are you going to deal with that?
Dawson Chen [00:12:49]:
I think our latency is pretty good. We worked really hard on it when we first started because when we started like summer of 20, there weren't that many voice APIs that existed. Basically end to end, you send audio to an AI and it gives you audio back. So we built our own voice pipeline and from the beginning we knew latency was top three problems in the whole voice pipeline. So we've got it down pretty good. I think now if you were to call Martin over the phone, the latency is probably definitely under a second. Typically.
Manav [00:13:14]:
That's amazing. I feel like a lot of people will use that feature, especially booking a restaurant. I don't want to waste 10 minutes booking a restaurant. I could be like, hey, Martin, can you please call this restaurant, make a reservation for 8pm and how will I get a confirmation back if I do that?
Dawson Chen [00:13:28]:
So you can track the call in the app. You can track, like, there's a summary of the call and we'll probably put you, give you a transcript as well. We haven't added like, playing back the audio, but we could do that too, if users really want it. So, yeah, you'll see the transcript and then you'll see, like, when the call started, when it finished, and then if you wanted to, you can call that person yourself too, or contact that person yourself.
Manav [00:13:45]:
Do you believe, like, in a decade from now it'll be like, we'll be completely hands free with just, just talking? I don't. Do you think that's going to happen or.
Dawson Chen [00:13:51]:
No, I don't think, like, visual interfaces are going anywhere. I think audio has been, like, really poorly done in the past decade. It'll definitely become, like, a lot better. You know, like, movie, her, like in that movie. Like, everyone's only using audio. And I find that a little hard to believe because there are some things that it's just so much better if you're, like, typing, if you're coding, I'd much rather type and like, speak my code. So I think that's why we're not just voice. You can text Martin via SMS, via WhatsApp, can also email Martin depending on the scenario. Like, let's say you got an email with like, 10 upcoming, like, events, 10, like, Zoom calls. There's a convention coming up and there are 10 events during the day. If you get that in an email, you can just forward it to Martin and say, put it. Put this on my calendar and remind me five minutes before each event. And he'll like, set up 20 events, 20 calendar events and like 20 reminders for you all in one email. If you were to do that with voice, it would take forever.
Manav [00:14:35]:
That's amazing. So what's the ultimate vision with Martin? Like, where do you want to go with it?
Dawson Chen [00:14:40]:
I mean, the dream is basically the same dream that I think Siri and Bixby and like, Google Assistant all set out with, which is to make like a personal and a proactive AI assistant. And I think the key to that is, like, great Integrations with everything. Like, the assistant needs to be able to use your own productivity apps as good as, like a virtual assistant, like a real person. And then it needs to know you as well as a virtual assistant would like, know all your preferences, personal practice, and then well integrated. I think if you have these two things, you can make like a dream personal assistant. And then that would just be like.
Manav [00:15:07]:
Jarvis, I had a personal assistant. And one thing a personal assistant has is a memory. And I want to touch, I want you to talk a little bit about the data memory and like the privacy because each person will be sharing a lot of personal information with Martin. How do you make sure that doesn't go haywire?
Dawson Chen [00:15:23]:
That's really important to us. And we're a small startup, so we don't have that much bandwidth to like, build our own infrastructure and stuff. But we do make sure we take a lot of precautions for what we can do. So we make sure we find too many models on, like, personal data. If we do train models in the future, it won't be on any of our customers data that they share with Martin. And if we do ever train any models, it'll be on anonymized data. Every person's Martin is like an individual instance. So Martins don't share memory across each other. And this is like one of our principles. Like, we want to make sure that if you're talking to your Martin, like, this data will not be used to train the model to influence, like, another person's Martin. So everyone's Martin is like totally separate. And then we also do like the basic, like, data encryption and security measures. So we have gone through, like, CASA appliance for California and we make sure that all the integrations that we make, we go through all the compliance that Google wants from us or Microsoft wants from us.
Manav [00:16:10]:
That's a lot of work. What other companies are you competing with? I haven't seen any, like, other players in this space.
Dawson Chen [00:16:16]:
I think most of the other, like, startups in the space are just as early as we are. It's a really hard product to build and I think it's just like hardcore engineering work. Like there's no way around it. You just have to build a great experience. And then the existing players are like, you know, Siri and Google Assistant. They have a pretty bad track record. I really hope, just as a user that they get a lot better, but I have, like, low expectations based on getting disappointed. Like, year after year.
Manav [00:16:36]:
There's this general myth that voice assistants are usually listening to you when you're not talking to them. You know, like how people say, oh, Amazon knew what I wanted, what I wanted to buy and now he's recommending to me. Do you think that's a myth? Because you're an engineer, so you probably understand that question much better.
Dawson Chen [00:16:50]:
Well, some of them like, like Siri has to listen for hey Siri. So it must be listening at all times. But what they're doing with the data, I have no idea. They have to be listening if they're listening for hey Siri. Because if you say hey Siri, like your phone's off, it'll turn on.
Manav [00:17:01]:
So in this case, will we be saying hey Martin?
Dawson Chen [00:17:04]:
Right now we're, we've kept Martin. It doesn't listen when it's off. You have to open the app or we have shortcuts set up so you can like, we have one shortcut which is, can double tap the back of your phone and then Martin will turn on. Or there's one where like you can also hold down the action button. If you have an iPhone 15 and it'll turn on Martin, you can set up a shortcut that's like, hey Siri, get me Martin. And then Martin will show up. So we don't listen unless you activated him with some, some action.
Manav [00:17:25]:
That's super cool. And right now Hobig is a team.
Dawson Chen [00:17:28]:
It's very small. We're running super lean. It's just me and two others. Yeah, we're going to stay lean for a long time, I think.
Manav [00:17:32]:
Amazing. And when is this going to be like, for the masses? I'm guessing right now you're beta testing with users. Is this like available for the masses?
Dawson Chen [00:17:39]:
Yeah, it's available on the App Store and you can go to trymartin.com to get an access code. If you want to get an access code, you'll have a seven day free trial and you can log into the app with the access code and set up all your integrations so it's public right now. Most of our power users are like fanatic about productivity and really like to optimize their workflows. A lot of developers. But we hope that as time goes on we make the interface more and more intuitive so that my grandparents can also use it.
Manav [00:18:02]:
Yeah, I think that's, I was thinking about that like I would be a power user but I've seen a lot of older people like only users voice assistant, like even sending a text, they don't want to type because they have vision issues or something like that. And they always like use Voice assistant. So I can, I can see this product becoming, like, really sticky and, you know, like something that people, like, really would rely on. Because I don't think people rely on Siri.
Dawson Chen [00:18:23]:
I mean, most voice assistants are not reliable at all. Like, they're not. Not reliable enough for, like, people who aren't super tech savvy. Yeah. And to be fair, like, I don't think Martin is there now either. If you give Martin to someone who has never used like a voice assistant or has never used, like an AI, if they never use ChatGPT, I think they might be a little bit confused. We're trying to make our onboarding process, like, as smooth as possible. We work really hard on that. It is still a ways away and I think it takes people who. Programmers or people who have used a bunch of AI tools. Those people tend to enjoy our product the most and they get the most out of it. And these people also give us really good feedback. So hopefully we can make the product even better for them too.
Manav [00:18:58]:
Everyone go try martin.com and let Dawson know your feedback on what you think about the product.
Dawson Chen [00:19:04]:
Yeah. In fact, you can tell Martin during a conversation to notify his developers about something and it'll just like, text me.
Manav [00:19:10]:
That's amazing. One thing I wanted to ask is, like, if, for example, if you were not working on Martin, what other, like, areas or industries or what other ideas are you, like, excited about? For anyone listening who's probably, like, looking.
Dawson Chen [00:19:22]:
For an idea, I don't really have a good answer. I feel like I don't have, like, any backup plan. Like, there's no other thing that I really want to work on right now. Yeah, I think if I worked on Martin for another, like, five years, I might have a better answer. I think for now I just, like, there's so many things to do for this, like, personal AI productivity space that I haven't really thought out of any other spaces. But there's a lot of work to do here. So I think there should be more people working on personal productivity for AI. It's such an obvious idea, but I think so many people shy away from it because they think big companies will do it or something like that.
Manav [00:19:50]:
It is quite complex when you actually go into the trenches and see how you have to comply with so many privacy issues and it is not something easy to build. So I think you're onto something here. What other books, movies would you recommend other people to read if they want to become more technical or, or want to figure out, like, what to work on or have you read anything or watched anything that was life changing?
Dawson Chen [00:20:13]:
Well, the first entrepreneurship book I read was I think hatching Twitter. It was too tactical for my liking. Looking back, I felt like it didn't really inspire me to be a founder. I think what definitely inspired me was reading a bunch of hologram essays when I was at Yale. That's what made me want to apply to yc. I just started reading his entire blog and every single essay was so good. Especially from back in 4, 2010. Whenever someone tells me they're interested in starting a startup or they want to learn more about startups, I would just read some early program essays.
Manav [00:20:39]:
What's an advice you would give to 20 year old founder in a university right now who wants to start a new startup?
Dawson Chen [00:20:46]:
I think college is like primarily the place where you go and find your identity in addition to sort of people you want to be around. I certainly got more of that out of college than any other academic thing. It is quite evident to me that when you are in college you become the average of your friend group in college. So don't make up your mind too quickly about who you want to be because. Because you think something's too hard to achieve. The thing I hear the most frequently from my peers who want to start a startup is they don't think they're ready or they think they need like five or ten more years to be ready. Everyone has their own internal clock of when they feel ready and you need to build your confidence and your faith somehow. And for me the best way to build it was to launch some projects. I launched my campus tours website, my social app and then I read some hologram essays and I think that gave me all the confidence that I needed.
Manav [00:21:30]:
Everyone go to trymartin.com and try the product and you'll be amazed.
Dawson Chen [00:21:35]:
Yeah, I think the best way to experience Martin and to experience what we've made is just try it out. I mean there's like so many things you can do with the product and I think if you're interested in like productivity and like you've always wanted a Jarvis like I had like growing up, it's a really magical product to try. Yeah. And let me know any feedback. Just let Martin to ping me.
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