TECH006: Open-Source AI That Protects Your Privacy w/ Mark Suman (Tech Podcast)

23:54And we've seen the open models catch up a ton in that timeframe. There were like 50 % is good. Then 75 % now they're in the 90 % range. You have a coding model, Quen3 Coder, which is scoring just as good as some of the proprietary models on programming in some areas, not all areas. And we're seeing a point where the benchmarks, however you want to define that, are really getting similar. And then it comes down to just using it and seeing how it behaves for you. And really, most people don't need to have that extra 3 % in their model to really get a lot of value out of it. And then the other thing we're seeing is you bring up GPT-5 and arguably GPT-5 is an incremental increase over GPT-4.

24:38And a lot of people complained and wanted them to go back to GPT-4 and make it available again. And some people have done introspection into the routing technology behind GPT-5 and think that there's actually still a lot of GPT-4 just under the hood that's helping to power version 5. And so you look at that and you say, okay, maybe their progress has slowed down just a little bit. And then also they open sourced GPT OSS, which is really just kind of 4.0 under the hood. And so you're seeing that open source from their standpoint is starting to catch up. And then you have this whole market dynamic of the Chinese models.

25:11You have DeepSeek, you have Quinn, you have these other ones. I'm blanking on the other one right now. But you have these other ones that are coming up. And in order for them to compete with these big proprietary models, they're going to open first. And they're trying to be as good so that they can compete. So I think we are seeing this world where the open source catches up just enough that it becomes just as valuable to a regular person, mainstream person than these other models. What's the business model for them to go open source like that? That's the challenge that I'm continuing to struggle with.

25:41What's the incentive for them to go that route? How are they going to make money doing that? It's a good question. One that I'm still trying to figure out. I think that if you are China and you don't have access to chips or you don't have access to some of these things, then maybe you're trying to build this model that you just want to spread around the world. Really, I guess one thing that I've come down to is if ideologically, you want your view of the world to be out there and used by everyone. And you know that the American models are not going to have your viewpoint. And so you're going to build that.

26:15And the best way to get out there is just give it away for free. So that could be one thing. The difference with the open source and running the open models is you can see exactly what's going on. And so you can build around that and build around that propaganda. So I don't have a good answer for you on what's the business model, other than it seems like if you can't compete from a proprietary standpoint, then you go open and you spread your message far and wide and try to catch up that way. Do we get to a point where these large language models start peeking out and it just doesn't make sense to train them with even more, you know, they're plowing so much energy into these latest models.

26:54Is there a point where they get like peaked out, call it two years from now, and there's no longer this race to just build an even larger compression of all the information on the internet? Because that's basically what we're doing, right? Yeah. It's hard to guess five, 10 years from now, even two years from now, because AI is moving so fast. But I think what we start to see is a slowdown of the general models. And then we start to see more specific models. The first thing we're seeing is with coding, right? People want to program. And very quickly, we've seen that a model that is fine-tuned at just software engineering is performing generally better.

27:29And so we're seeing a divergence there. And I think we start to see medical field and legal field and all these other different industries come up with their specific models. I know there's people working on therapy ones. And then we have these general models that act as routers in the front. So you come up to it and you start talking to it and it pulls in as specialists. And we can really go deep and dive deep when we have those specific models. So I think that might be the next thing is how do we have specialization? And then how do we have these general models kind of wheel and deal and be the general contractor, if you will?

28:04How do you think through stitching all these different models together? So when a person creates an account, walk us through the process of creating an account at trymaple.ai, which is the website that you've built in order for people to run their own AI locally. Walk us through what that is. And then more importantly, how are you stitching the different models together to provide the experience that the user has? Help us understand that. Sure. Yeah. So first off with Maple, right now we're only in the cloud. We want to provide local stuff as well. It's like a hybrid local cloud where all your data is encrypted locally first on your device.

28:40And we use a private key. Coming from the Bitcoin world, we understand the power of a private key. Same with Nostra, right? And so we apply that here. So you're chatting with AI locally on your phone, it encrypts it, and then sends it to the cloud for processing. In the cloud, a secure enclave uses your private key, decrypts it, gives it to the AI, it comes with a response, re-encrypts it with your private key and sends it back to you. So we are not in the middle. We can't see anything going across the wires. Only the secure enclave sees the personal data, but you can go look at the source code and see that there's nothing going on there.

29:13So how do we tailor that experience? Right now, we just give you a model picker and you're having to choose. And we have a lot of users telling us they get almost like low key anxiety from like trying to pick which one's the best model that I should use right now. So part of our big 2.0 push that we have coming up is we want to build something that helps guide the user and say like, I am in big brain thinking mode right now. So I'm going to click on this thing and it's going to drop me into a model that helps me do that. Or I'm just in quick trivia mode. I want to look up something. So it's going to drop me in there.

29:42In the beginning, it'll be like just an easy picker to do for the user. But then we'll switch over to an auto mode where they just chat and it knows what to do. And you just put a simple classifier in front of it. So it looks at your prompt and it can quickly determine itself what should be used, just like you would. You would say, I'm in this mode. Here's what I'm thinking. And you would pick that. Well, it'll do that for you. So we want to get more automatic with that. but always provide these advanced features that people can turn back on and be more selective if they want to. Love that. The next thing that I personally want to see, so with OpenAI, I can, you know, I'm having a chat and I'll say, this is really important.

30:18I want you to remember this for other context windows and other discussions that I have with you, right? It then it compresses whatever that is. It puts it into like this, my understanding of Preston Memory Bank. Are you guys working on something like this? Because I think this is something that people really want to have, but they want to have it in a private way. I know I personally want to have this in a private way. I hate every time I do this, but I also find myself going back to similar conversations where that context is really important. And I hate just, I have to type it in every time I'm in a new context window.

30:50So is this something you guys are working on? And where do you see the roadmap for something like this, if ever? Yeah, definitely. What you just described there are two different implementations that help out with the same thing. So one way that the ChatGPD does it is they have these custom GPTs where you can set up this thing and it has a lot of the context pre-built into it. It's basically like you typed in a system prompt with all the stuff that you want. And for people listening who maybe aren't super into AI, a system prompt is basically the instructions that you give to the model, you give to the AI to say, hey, when I talk to you, I want you to be in this personality or this frame of mind or as this character as I'm talking to you.

31:31So you can get silly and you can say, I want you to talk like a pirate to me. So every time you chat with it, it's going to talk like a pirate. That's the extreme example. But more nuanced, you can be like, hey, I am going to have a legal discussion with you right now. So I want you to be a lawyer. I want you to be a contract lawyer. And I want you to have these qualities about you. So to me, that's one part of what you just described. And we definitely do want to do that. We have a system prompt you can edit. We show you what the system prompt is. We want to have multiple in the future where you can customize those and maybe a dropdown or something.

32:01Say, hey, I'm in legal mode right now. Switch into that mode. The other aspect you just described is the memory side of it. And we are definitely working on that. We're going to have an open source memory component to it. We don't know exactly which direction we're going to go yet with it, but it's going to be something where you will see, okay, here's everything that the AI has learned about you. And AI memory is really fascinating because I like to view it as you're sitting down with a biographer, right? Say you're Steve Jobs and you want to have everybody know about your life so you get the best person out there to write biographies.

32:34That's what's happening with you every day as you're using ChatGPT or any AI product. It is sitting down and trying to learn everything he can about you. Here's how he thinks. Here are his childhood memories, yada, yada, yada. The difference is in a proprietary system, you don't get to read that biography. You don't really get to see what's in there. They will show you an interface that says, oh, here's the things we know about you and we'll even let you delete it. But there's no guarantee that that's actually happening, right? If you delete that thing out of there, it probably still remembers it, but it's just like, oh, we'll tell them that we're not going to use it, but we could use it if we want to.

33:05What we want to build is a truly sovereign AI memory where you can go in and see what we remember about you. Not we, what the system remembers about you. And then you can edit it, you can add to it, and then that will get pulled into future chats. And so with those two combined, the system prompt personality thing, that's more proactive. You can say, I want you in this mode. Whereas the memory side is more passive. It's like, hey, this is my context about me. So use it selectively as you see fit. Let's take a quick break and hear from today's sponsors. Curious about online trading, but haven't taken the first step yet?

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36:31Whether you're a fast-growing startup or a global enterprise, Vanta is here to help you automate your security and compliance and earn, improve trust. Get started today at vanta.com slash tip. That's V-A-N-T-A dot com slash tip. All right, back to the show. For me, the ladder there where it's really kind of understanding your past and just understanding the essence of who you are and what it is that you want is super powerful and useful as I continue to interact with this. I'm curious what the challenges are from an engineering standpoint to put something like that together? Because it seems like it could dominate as you go into a new context window, talking about a brand new topic, that when it has this memory that it's pulling from to be customized responses, that it could potentially dominate the next conversation that has nothing to do with the memories that you're asking it to hold.

37:31How do you think through the problem? And I'm just kind of curious, is that going to be a really difficult feature to kind to roll out as you continue to build this out. Yeah, absolutely. That is one of the biggest challenges with AI memory right now is that it overweights and overemphasizes something that you give it. Because you think about your brain, you've been storing up memories for decades in your brain. And you know, without realizing it, you know when to selectively pull on something and when to not pull on something and use it in the decision that you're trying to make. Whereas AI, high, maybe it has right now, maybe it has like two pages of information about you.

38:06And so that one memory you tell it is suddenly like one of the most important things it knows about you. So it's going to like overly push it into the things that you're using. So we're trying to figure out how do we downweight that and how we not have it influence everything. I think a lot of that comes down to you have to get really good at annotating information when it goes in and say, okay, this little memory you have, this memory is really focused around finances, or this is focused around health or being outside mountain biking or something, right? And you want to classify that. So when you're having a chat, it knows, hey, I can totally ignore that right now because it has nothing to do with this topic.

38:42Rather than like, oh, you're mountain biking today. Hey, you should bring along your financial advisor and have a good chat about your IRA or something like that would be really dumb. So we do have to figure that out. And I think it goes back to that verifiability thing is I want to know what the AI is doing and which memories it's suppressing and why, rather than a closed, opaque system that is going to do that, make that decision for me. Because maybe that memory is really important and relevant right now to something we're talking about, but it is choosing to not make it relevant for whatever reason.

39:13Could be accidental, could be profit-driven, could be nefarious. One of the things that I've heard more recently is that inference is where is going to be the competitive mode moving forward in the coming five to 10 years. So for people listening, you have, and correct me if I'm wrong here, Mark, if I'm not describing this correctly, but when you think about AI, you've got the training of the model itself, which we were talking about earlier with some large language models. And then you have the inference, which is using the trained model that you spent all this energy and poured into all these GPUs that compress all of it into a single large language model.

39:48The inference is using that trained model to generate the answers that you're getting back. And every time you prompt, you ask a question and it goes into this model, this happens every time you prompt it. It requires GPU memory and you have to run it through that entire model and then it pops out the answer. And this is the inference process. And what I've heard is that in the future, the speed of that inference and then the cost to do it as efficiently as possible, but still give you the high quality answer is where the competitive edge is going to have the winners and the losers. I'm curious if you agree with this.

40:25And then if you do, how do you think about that inference piece with your company and being able to provide an efficient and quality, fast response to people as they're doing this in an open weight, decentralized kind of way? Yeah. So inference versus training, the costs involved and the power involved are very different, right? So the training part costs, I don't know what the exact numbers are. Let's just say 10x. It might be bigger, it might be smaller. But yeah, it's like it takes 10x the amount of resources to train something. Just like as a human, it takes all this time to train you over decades for you to live life and learn all these things.

41:01And then eventually you can sit down and have a fruitful conversation like we are right now, right? And so it's easier for us to have this conversation than it was for us to learn everything we learned up to this point so that we are capable of having this conversation. So inference should be viewed that way. Now you're ready to have a chat. And I see that. What's the moat? What's the unique thing that's going to be competitive? And that is just the user experience. And so these apps that we're building on top of the inference are going to be the competitive moat and what different qualities they have.

41:31And we're already seeing that with ChatGPT and some of these others. They're trying to build apps on top of their inference layer now that really pull people in. The latest is the Sora video app that's pulling people in and trying to make it more engaging. As far as inference goes, I think that just only comes down in cost over time. And even though we're going to get bigger models, we're going to build chips that are more efficient for processing those models. Apple, even though they don't have the right AI solution yet, according to the market, they have built these chips in every single device that are just highly specialized at processing these models.

42:04So one thing that we're looking at with Maple is doing a hybrid approach. where you actually have smaller local models that run incredibly fast and are extremely cheap to run because they're just running on your spare cycles on your device. And they will do a bunch of the initial processing and on some of the most sensitive information. And they will come up with the most efficient prompt to give to the cloud model. So you might go in and bang out this massive prompt, paste in a whole PDF of information. And then the local model will crunch that all and say, okay, this is all good and dandy, but really what I need to pass on is a smaller chunk.

42:39It'll pass it on to the cloud, that'll get processed on the more expensive servers and then come back to you. And I think in a model like that, inference continues just to drive into the ground as far as price goes and gets faster as well. And so we end up with a better user experience. And so the people that can consult that kind of user experience are going to have a better moat, be better as a competitive advantage. Yeah. I recently read that XAI has custom ASICs that they're building just to improve the speed of the inference. And I guess it's 10 to 20 times faster than some of the best GPUs on the market right now, just because they custom made it for that specific task, which is fascinating.

43:18And it also just, I guess it leans into this idea that I think it is challenging to compete with some of these larger, like XAI, it's going to be a bloodbath of competition to compete against because they're going to be able to provide such quick and efficient and quality responses because they're going out and doing things like this that are very capital intensive. Like I'm just looking at this whole space and I'm curious your thoughts on just how expensive some of this stuff is. And, you know, we saw the thing with open AI and NVIDIA and help me out with the other Oracle. Yeah. And that Broadcom is in the mix too.

43:59Yeah, the numbers are crazy, Mark. Absolutely nuts. How do you see that kind of resolving itself? And like, where is that going? Is it just going to keep going more? Yeah, it's crazy. I mean, I saw somebody comment over the last 24 hours about the whole Broadcom and OpenAI thing, where it's like, hey, OpenAI, we want to buy all these chips from you, but we don't have the money to pay for them. And so they basically say, let's do a press release together. Broadcom stock goes up. Now their market cap has gone up$150 billion. And it's like, boom, there's your money that you needed. So we'll loan you our market cap basically to help you out.

44:35So it's kind of this crazy thing, a lot of money being tossed around. Where does this resolve? Oh, man, I wish I had a crystal ball to understand. But I think that we are going to have big players out there. We're going to have these people who are building these big, massive mainstream solutions. And I also think that you look at the government contracts that have come to these major models, right? They gave 200 million to XAI, 200 million to OpenAI, to Anthropic. I think Meta got that too. I can't remember. So there are bigger things at play here with Department of Defense and other governments around the world.

45:08So I think that there is going to be a need for large-scale systems like that. There's also a need for other people to build out systems. And I think there's a world where they all exist together. I don't think this is going to be a race to where there's going to be one winner take all. Because really, there are so many different ways to approach intelligence in this life. And there are so many different avenues and so many different needs that Grok's not going to be able to solve them all. Chagipity is not going to solve them all. And so I don't know where all the money resolves. I think we're definitely going to have a bubble at some point that's going to pop.

45:40It's a view of very similar to the internet. And so we're going to have all these companies that overinvest, and then there's going to be a retraction and a retracement back, and the winners are going to remain. So I don't have a perfect answer for you on that, but I just think that there will be some overinvestment, but I don't think it's going to pop and go away. There's too many benefits. People are seeing too much productivity from AI, too much value coming out of it, that it's going to survive. It's just which people and which companies will remain standing. It's hilarious because it's almost similar to like a meme coin pump of them raising capital and then transmute the common stock pump into capital, but then invest into the hardware.

46:22I'm sure you've seen the charts of like OpenAI goes over to NVIDIA and they give money to Oracle and it's all circular. It's this weird thing going on. It's crazy. What's one of the most challenging things for you right now building out this business? Well, we're trying to keep up and we're trying to get feature parity with arguably one of the biggest companies in the world right now. And so OpenAI and others, they have billions of dollars to throw at designers, at engineers, and trying to build the best user experience possible. So in order for us to get people to care about privacy, we have to give them a tool that's as convenient, as usable as Chachapiti, as a baseline.

47:01So that's really the biggest challenge right now. That's where we're racing is we're trying to figure out how do we pick off the most important features because we can't build them all right now. It's just meeting another person. We can't build them all right now. And so let's selectively pick the most important ones, get those to feature parity, and then keep building from there and keep growing. So we're going to be raising some money soon, hopefully, and that'll help us hire a few more people. But even then, we're never going to match these bigger companies as far as team size. But we're using AI against them.

47:29We're using AI to help us build faster than we could. And we only launched back in January and we're getting ready to do our 2.0 launch probably next month. And it's come a long way just in those last nine months. And so the next nine months, next year are going to see drastic changes to what we're building in the positive direction. That's the crazy part, the reflexivity of the AI itself as it's getting better. You just get more and more powerful. And in a way, having maybe a smaller team, you're able to just kind of focus in on those features that are most important. The things that I'm seeing on the programming front, especially with the Google model, seems to be almost unfathomable what it's one-shotting with a prompt.

48:14Can you just kind of help us understand what's transpired just in the past year with respect to your ability to program and write code? So there is a lot of salesmanship going on when it comes to the vibe coding space. And so a lot of progress has been made. And there are definitely stories where people go on and say, I wrote a few sentences and it gave me an entire app that I can use. And those are great, I think, for proof of concept. We've definitely seen a lot of great things in that space. But getting an app that you wrote, which is one paragraph of text into production that millions of users can use that has covered all the edge cases and stuff, that's a totally different story.

48:55So I definitely see a lot of memes where it's like, oh, and software engineers are so cooked. But really what I think the great power is, is software engineers using AI and accelerating their abilities. So that's what we're seeing. I don't want to cast, throw shade at the companies that are doing Vibe coding for people who are not software engineers, because what I see is a huge value add right there. Being a software engineer myself for decades, I get approached all the time. Someone's like, I've got a great idea for an app. I want to build this. Can you please go build this? And they'll draw on a little piece of paper.

49:26They'll maybe build a PowerPoint presentation, but they try to give me requirements. And I can see very quickly that the requirements haven't thought of everything or the idea just is off base. So where Vibe Coding comes in now is they can take that and instead of coming to someone like me, they can give it to an AI. It can build the proof of concept, they can play with it and they can say, oh, this is a piece of garbage, or this is a great idea. Let me iterate a bit on this. And so now when you go to approach someone to build it for you, you've got this really refined proof of concept that conveys your idea and has thought through a lot of the initial things.

49:58And then for us, we've taken AI and built it into all sorts of parts of our process. So we're using tools locally, where we are running coding environments, IDEs locally with something like Cloud Code. We've tried out Codex from ChatGPT. We're using Factory right now also as a new hotness that's come on. So we're using all of these. We also use Maple. We've got Quinn 3 Coder, where we got that plugged into IDE. So we're using all these tools. And then when we check in code to GitHub, do a pull request, we have two other AIs that hop in there as code review agents. And so they're both reviewing the code and they come from two different models, two different companies.

50:34And so they give a different perspective. And so they drop in their comments and say, hey, this line of code is maybe you should think through this more, there's a potential bug here, that kind of stuff. And then we go in and we say, hey, Claude, respond to these comments from the whole request. And so you have these agents that are really helping out. But ultimately, in the end, we are the ones reviewing the final say on the code. And we might say, hey, we don't like the approach they all took. So let's get in there and bang things out a little bit differently. But truth be told, I think probably like 90 % of our code, maybe 95 % of our code is written by AI with the human in there directing it, guiding it, inspecting it, and making sure that it comes out correctly.

51:11Would you say that your time has been multiplied 10x? What used to take you 10 hours you can do in one hour now? Yeah, I haven't measured it specifically, but that's definitely the vibe that I feel. I look at what we've built in the last nine months and launched. And we have a sizable number of users now, a sizable amount of revenue coming in. And I think about trying to do that before. I've been part of multiple startups. I look at another startup that I was with and it took a lot longer just to get the product to market. It took almost an entire year of just understanding and writing initial versions and then throwing those away and doing different versions to where now we're just, yeah, we're so accelerated.

51:50Like I said, it's just two of us. And so if we were doing this prior to AI, we probably would have had to have two more people, three more people to get to this point. So we're definitely seeing an acceleration. Yeah. On the home hardware, running this locally? Like right now you're saying you're doing it in the cloud. You send the encrypted data over to the cloud. It then processes it. It sends it back encrypted. But for people, let's say 10 years from now, I'm curious how you see the home market for running your own server. I know as a Bitcoiner, I run my own node. There seems to be some synergy there for people that are do-it-yourselfers that like to do these types of things.

52:30But is this going to become something that's almost like a heater in your home or like any type of appliance that's in your house? Are people going to have their own data that's stored locally, that's run with some type of ASIC or GPU that's a specialized piece of hardware so that we can not give up our data? Is that where this is kind of moving? Do you see that trend going there? Or do you think that that's still a very hard ask to ever expect out of people that might not have the technical competence or desire for privacy? What are your thoughts? Yeah. I would love a world where everybody had their own home server plugged in and it's doing all this for them.

53:11So 10 years from now, I could see the technology catching up to where we can make really easy plug and play. You get this little box, you plug it in, you connect it to your Wi-Fi or plug it in via ethernet. And now everything you do on your phone, everything you do on your computer, everything you do on your watch or if we're in glasses, whatever device is the input device and the output. It's talking to your home server and doing everything locally. I think we will definitely be there from a hardware perspective and from a user experience perspective. That'll be possible. The difference is, will people do it?

53:42I don't know. I mean, there's definitely incentive to keep the cloud model running. And if you want to look at a parallel to this, you can look at email, where we all have the capability to run an email server at home. We can host our own server, we can be totally sovereign and have total control over it. But we don't. We still just go on to Google Workspaces, give it our domain name, and now our email is run by Google. Because it's just so dang convenient and they handle all of the DevOps and IT headaches that would come along with running our own email server at home. So I would love to think that that's the future is home AI stuff.

54:18And maybe it could be. Maybe this is finally the line in the sand where it's like, you can have our emails, but you can't have our brains. Our brains need to live at home. And I hope that's the delineation that people are not willing to give up that aspect of them. So I just don't know, but we will definitely technically be there. It'll be possible. Last question I have for you. You mentioned Nostr earlier. There's a lot of opinions as to what in the world Nostr even is. But one of the talking points that's constantly shared is just, it's an identity layer. And so you're talking about having a private key, public key pair to ensure that the encryption is actually being conducted between the cloud and your request coming from your phone or your computer.

55:01Do you see NOSTER as playing an important role because that's an inherent feature of the NOSTER protocol, which is this public key, private key relationship that you can sign anything with? So I see it all coming back to that word verifiable. And that's really the power of NOSTER is verifying that this communication truly came from me. Me, yeah. Now, right? And so that's the private key, public key promise that we have. And whether or not it's Noster as an open source brand that ends up being the thing, I think that concept is what's important. And so being able to say, hey, this little piece of memory that went into my AI, that's signed with my private key.

55:43And so I know that I'm the one who put it there and that it came from me. And so I think that's where verifiability takes us. And so that could be online communications. I post something, I want to sign our maple builds and have those signed with a private key. So I want to integrate this throughout the entire process And I think that's really where it shines. Whether or not it becomes a replacement for Twitter, that remains to be seen. Right now it's a niche protocol, but I think the power is beyond that. People who are looking at it as a Twitter clone are not seen far enough in the future, where it's really all about verifying that this communication came from the person that they said they are.

56:20Wow. Any other parting comments or things that you think are important for the audience to know about what you're doing? Sure. I think what you should view like AI is as a toolbox, right? So you have a toolbox, you have different tools to use for different things. So I would say I'm not asking people to throw away ChachiPT. I'm not asking them to throw away these other services. Instead, I'm asking them to add Maple into their toolbox. And that way, when you are talking with ChachiPT and you're like, I don't really like the fact that I'm sharing my children's name and personal information about them, you can switch over to Maple and you You can have the exact same conversation with models that are as powerful or like 95 % of the way there, powerful enough for you.

57:01And it's very refreshing. You get this refreshing feeling knowing that this is just a private room with you and an AI and nobody else is listening. Nobody else is recording that information and it's not being sold to anybody. You're not being influenced in any way. And so I would just say, go to trymaple.ai, get the free account. You can upgrade if you want to support us, whatever, but grab it and just have that extra tool in your toolbox and play around with it and start to see where it takes you and what you gain from that. Mark, I think you're working on one of the most important things in the world right now, truly.

57:34I wish you all the best and I can't wait to try it out myself. We'll have a link in the show notes for people, but it's trymaple.ai if you want to go to the website and try it out. And this stuff is so important. I think it's only going to get more important and hats off to you for what you're building. And I really appreciate you taking the time to come on the show for the conversation. Definitely. Thank you, Preston. Appreciate it. Thank you for listening to TIP. Make sure to follow Infinite Tech on your favorite podcast app and never miss out on our episodes. To access our show notes and courses, go to theinvestorspodcast.com.

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