AI Product Creators

Zach Hanson is an expert in artificial intelligence and machine learning product management, with experience developing AI solutions for Fortune 500 companies including IBM, Brightcove, Capital One, and Wells Fargo. He holds degrees from the College of Charleston and Johns Hopkins University. In today's episode, We discussed power of AI, Zach discusses how it aids in tasks like content parsing, summarizing, and producing video trailers. He also explores the interconnection of different AI models, and the rise of content generation through freeform speech. We discusses how AI technologies, like ChatGPT and GitHub co-pilot, can streamline creative content creation and refine stories or code. Finally, for those looking to enter the AI product management or creation space, Zach advises building something to understand core product fundamentals and getting comfortable with data. Tune in to hear Zach Hanson's insights and experiences in building Inworld AI and how you can apply these lessons to your own product. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Zach Hanson: • LinkedIn: https://www.linkedin.com/in/zachary-hanson-a1a761a3/  Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt  Transcript:- Dhaval: Hey, Zach, tell us what you've been up to. Zach: So everybody, I'm Zach Hanson. Dhaval it's great to see you again. You and I used to work together in a, past life in the AI field but What I've been up to lately is pushing for AI innovation within video, specifically at Brightcove, which is a great company, and working around how we actually build out and better experiences for our customers in the video space. Dhaval: Oh, wow. That's like the most cutting edge space in AI right now. The video and AI generated videos and all of that as we speak in April of 2023. What specifically is Brightcove's mission? And, yeah, if you could share a little bit about the, what is the product, what pain is it solving, and what are your customers, who are your customers? And then a little bit about where you are in the product space, like in the journey. Like, are you a startup? Are you, have you already found the product market fit? Are you enterprise? So yeah, any of that? So, a lot of questions, but just trying to understand the product story. Zach: So it is interesting, right? So we're a very unique company. So Brightcove's goal, to answer your first question, has become the most trusted company in streaming. So that's our goal, right? That's the big headline. That's what we're pushing and we kind of run the line of being a media company and an enterprise company. So from a competitive landscape perspective just to frame where we're at, would be the Vimeos of the world, Kaltura and some other companies that are providing OTT services to brands around the world. And Brightcove has actually been in the game for over 15 years. So to answer another piece of your question is we're a publicly traded company. We've been around for over 15 years and we've been providing these services for live streaming, for video on demand OTT for years, and it's pretty amazing. So we're one of the biggest little companies you might not have heard of. But with that comes a lot of responsibility around data because we actually ingest around one to two petabytes of video data every month. So we have an absolutely enormous catalog and data warehouse of videos, audio, all sorts of content that is being leveraged by our customers. Now, to answer one of the other questions rolled in there about our customers and some of the ones we can talk about, we help deliver video, great quality video with our encoders for the Olympics the year ago. Wow. Yeah, we've done that. We work with south by Southwest. So folks, if you've watched conference video from there, that's Brightcove under the hood masterclass for instance. So we have a huge list of really amazing clients who are doing All sorts of different things with video. And that's what we're trying to enable. Now, the other piece of your question, where are we at in the product journey? Are we a startup? Are we a mature company? And I would say from the delivery of video, we're very much a mature company. But when we start to think about machine learning and leveraging Models that are out there building our own models, we're really much more in that startup phase where we're trying to find the appropriate product market fit for the different types of models we might build or leverage the really immense amount of data we have to train models and do some really cool things. Dhaval: Wow. Thank you. Thank you so much for answering all those questions. I threw a barrage of questions at you. Wanna dive in a little bit on your last answer here on the topic of being new to AI ml. And what I wanna understand, Zach, is what is the customer trying to do when they want to use the AI ML capabilities for Brightcove? What is the thing that's going on in their head when they are like trying to use your specifically AI ML features? Zach: So this is where it's also like an interesting story because there's a lot of stuff that we're focused on from a machine learning perspective, kind of under the hood, things that our customers might not know is being powered by machine learning. So some of that has to do with encoding and how we get the video to the actual end user in a very efficient manner or in an efficient manner. As far as doing CDN optimization and making sure that the ultimate end user, which is our customer's customer, whoever's watching video. Has a great experience and that's where the bulk of our effort's been. But when you think about pain points, as we think about becoming more of a media company, when we think about enabling producers of content to be able to do some really cool things there's really this kind of crawl, walk, run approach. One is when somebody uploads content to our catalog or their catalog through Brightcove, there are sorts of metadata that should be tagged in those videos. Oftentimes people are having to do that manually time stamping stuff or putting this as a certain piece of a sub catalog within their overall experience. So we're trying to do some automation through their of automating tag management to suggest to our customers tags they might need in order to ease the burden of some of the metadata management, but then you go up the chain to content itself, the video, and we start to think about object recognition and video. We start to think about segmenting video. So you can easily cut and pull out specific elements of a video. For instance, if you were watching a soccer match or football match I grew up in the United States, so I'm a little bit more used to American football and I've become a bit more of a fan of the universal football in the years, in the past few years. But you might have an hour and a half long game and only have two goals or none. So the ability to be able to search through a video and find that really intense moment where somebody actually scores a goal, be able to rip that out really quickly and repurpose that content for marketing is very powerful. And there's a lot of startups actually playing in that space and then you have the bigger players like ourselves, Brightcove. Then you also trying to play around with segmentation of video. So it really runs the whole gamut where we've been focused mostly on backend support, leveraging ml. All the way to that kind of front facing customer content production type of use case. Dhaval: Yeah, I, that's amazing. You have, I can think of so many use cases. I was at a photo shoot video shoot event this weekend where I was hosting it, and we have like terabytes of video content that we created and now I want to create recap videos for that event. And I can imagine being able to feed something like that to your platform. Is that, am I getting it right? Would that be a potential use case? Is that how you It is parts out valuable clips. Zach: Exactly. And that is part of a potential use case that we're exploring. But this is where it goes back to being in that kind of pseudo startup space. Like with all the data we have. With the great customers that we have, there's a lot of opportunity there and we're still in that feeling out phase of saying, what are those pain points to your other question. And like the use case you just gave, that might be something at the top of mind for a lot of our customers. And that's where we're just starting to put the feelers out and understand how we might be able to build some of these things out and make sure we have the right product market fit before rolling something out to our broader customer base. Dhaval: Yeah, that's very interesting. Just like thinking for like content creators like myself. That event is an example of a use case. This podcast is an example of a use case, parsing out insights from this video, insights, and then publishing them. And then for courses that I create on product management and artificial intelligence, it's the same thing. I can imagine being able to give you a whole course and create a trailer for that. So there are a million use cases that you can be going after. Are you thinking of like any big use cases right now that you are willing to share with us, that you may wanna pursue in near future? Zach: You know, none that I want to talk about specifically for Brightcove. But there are a lot of really interesting things in that segmentation space that just interest me and that might be wrapped into something we end up doing with Brightcove. It might not, but meta just came out with a paper on Segment. Anything. Have you heard of this model that they've built? Again, it's in that segmentation space on video or, things like that. But with all the other models, stable diffusion. People are starting to piecemeal these different models together to come up with really cool use cases of really just actual

Kylan Gibbs is the CPO and Co-Founder at Inworld AI. He is the former Product Manager at DeepMind, Consultant at Bain and also Co-founder at FlowX. In today's episode, Kylan Gibbs shares his experience working in AI startups and consulting, as well as his time at DeepMind working on conversational AI and generative models. He emphasizes the importance of iterative processes, adapting to market pressures and user feedback, and the need for creativity in defining good content in the AI space. He advises aspiring product creators to focus on building something that validates their value rather than teaching others before learning. Tune in to hear Kylan's insights and experiences in building Inworld AI and how you can apply these lessons to your own product. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Kylan Gibbs: • LinkedIn: https://www.linkedin.com/in/kylangibbs  Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt  Transcript:- Dhaval: This founder and his team built a platform to create life-like characters for immersive experiences like games or books. In this show, Kylan Gibbs shares his thoughts on generative AI and how to manage products where you create immersive experiences. He also discusses his career journey. Kylen is a former product manager at DeepMind. He was also a consultant at And a co-founder at Flow X. Now he's a C P O and co-founder at In World AI. Hey Kylan, welcome to the show. Tell us about where you are in your product journey, a little bit about your product as well. Kylan: Awesome. Yeah. Thank you so much for having me. Super excited to be here. So At InWorld, we're building a creative suite of tools that allow people to build these AI characters and then integrate them into immersive experiences,  games, entertainment, enterprise experiences as well. And we started just over a year and a half ago.  And basically where we're at now is we have this studio where people can come in and actually craft their characters. We got integrations with things like Unity, unreal, where they can bring them into games, as well as Node for actually bringing these into web experiences. We've also then got sort of this arcade and, basically, which is a way to actually share these characters directly to the web. And then a suite of experiences that we're gonna be releasing this year that are self-produced. So we've got, for example, in world origins to sort of show off  the power of the product. And this is all to sort of say that this is kind of crafting that end-to-end user journey of being able to build these characters and then bring them into worlds. And basically where this has all been going is kind of setting the foundations of actually being able to not just create the characters, but, build them into experiences and deploy them scalably. And I think,  compared to a lot of the things you're seeing in generative AI right now, like it really is production ready. And we've been focused on that.  And so we've already seen  a lot of developers starting to churn out games and experiences now that are integrating in world characters. So it's super exciting to kind of see that. So, of course still early on,  I'm excited to see where it goes, but already seeing hundreds and thousands of users using it,  plus,  actually seeing live experiences is pretty magical.  Dhaval: Wow.  There's a lot there. So let me just quickly ask you a follow-up question on how you support game creators. Is that right? Or do you support all kinds of creators, like writers,  novelists, or just focus on gaming experiences at this?   Kylan: We're supporting really any type of creator. So ultimately,  we have users who are, for example, very,  well-known science fiction authors who are using their characters to iterate on experiences and potentially write new books with,  we have people actually building like AAA games and these types of experiences where you're building multiple characters and integrating them into worlds. We have entertainment companies who,  you imagine integrating these into parks or like live experiences where you're actually interacting with. And then we also have enterprises who are using these for things like,  brand representation, corporate training. So really we're open to any types of creators. Of course, we're really focused on  narrative oriented, immersive experiences, and our product is best built for that. So, you know games, narrative, entertainment, you can think about  the adaptation of movies and IP to  these experiences. And that's really  what  we're targeting. But we're really open to any types of creators, and  we're finding new ones every day.  Dhaval: Yeah,  I picked up a keyword there. Narrative oriented, immersive experiences that could be representing multiple customer segments. What is your product journey like? How do you go about defining your product capabilities with such a broad range, range that you know you could be? Kylan: I think that, so abstracting out of our specific use case for a second, like I think when you're building a developer tool, you always have two customers. You have to think, keep in mind, one is your creators, your developers, and the other is your end users. And so for us, our creator journey is really people coming in. They have ideas either their building an existing experience or they're ideating on a new one and they're using the studio and our characters to basically iterate on that and then integrate it through things like Unity and Unreal to actually bring those to users. And so when we think about success in that, it's basically: is this person able to create the character that they love and like, and kinda ultimately  represents the vision that they have and fulfills the purpose? And then are they able to integrate that and deploy that  successfully. Then you have  the actual end users, which are the people actually interacting with the characters that the developers built.  And that is really  like, is the interaction enjoyable?  Is the person you know staying around to talk with this character? Are they finding out what they need to progress through this experience?  You can imagine throughout a game you have someone that's a guide or  shopkeeper and they have to fulfill a particular role. Are they doing that successfully? And so you really have to balance the two of those. I think that's true for most developer tools, but it's kind of unique here because,  ultimately  there's a key part here, which is like, it's the generated content. So  it's almost like  we are allowing developers to create characters that are fulfilling the wants that they have for the users in the end?  And so it's always a little bit of an art and a science.  Dhaval: Yeah, there is a lot of art there, especially when it comes. The narrative, the experience, right? The character creation and then narrative and the experience.  What are some of the ways you create these characters that actually immerse themselves and are conducive to the experience?  Is there a difference in product creation? How do you actually adapt to the narrative or the experience that the creator is trying to have? Kylan: Yeah,  I guess there's two points, which is like design time and run time here. So at design time,  you could take for example,  Arah LA a large language model and generate sort of responses that are aligned with a particular character. But getting them to do that reliably and stick to a story and actually fulfill goals and actions is very difficult. So we actually allow users to come in and they can specify, for example, scenes for their story, and then the characters will actually stick to basically,  the kind of motivations and goals that they have for that specific scene.  Then we allow them to specify,  what is this character supposed to accomplish in this? How are they supposed to speak?  What types of things in the world might they be reacting to?  And all of that sort of is going towards  controlling and biasing the character behavior in a specific moment within a specific story. So they're  fulfilling what they're supposed to. And that's all kind of  the design side. When you're actually interacting with the character. Then  we introduce, for example, emotions. So the characters actually react with emotionality. We have voices that also integrate that emotionality, so the characters can actually, you know, you could hear when you upset a character, for example, and you can react to that. We then control  gestures and animations. The characters can actually react to you,  or  you can ask them to perform a particular action and they can actually act on that. And so,  that ability to actually have, and we'll be releasing this soon, and we have a new system where you can actually, for example, give a character a goal  and a series of actions that they can use to pursue that goal. And they'll autonomously pursue the goal until they've accomplished it, which is like a pretty magical thing if you think about the ability to actually create this living thing that's kind of,  pursuing goals  and motivations. But of course all of that comes back to the ability to actually drive this story or narrative forward, whether it's something like Assassins Creed or Far Cry or one of these games. Or you can think about even in an enterprise experience where you're trying to usher a user forward through sort of a brand experience.  All of it is really  the key point is that the characters are filling a specific purpose in some broader experience.  Yeah. And so we've got a huge amount of controls  that are available to enable. Dhaval: That's very interesting. You mentioned that you are able to add emotionality to a character. Wow. That blows my mind. Is there anything more you can share about how you go about doing that?  I'm personally interested in that because a few years ago I created a startup to inject emotionality in writing. An

Suman Kanuganti is the Co-Founder and CEO at Personal.ai. Previous to Personal.ai, Suman also founded Aira. Suman holds his BE degree in Engineering, MS in Robotics, MBA in Entrepreneurship, and ten patents in emerging technologies as well. Suman also founded Aira. Personal AI is a GPT implementation designed to mimic an individual's behavior and to speak like them. In today's episode, Suman shares his goal is to create AI systems that understand and replicate users' communication patterns and cognitive abilities. Suman emphasizes that their language model has time awareness, allowing it to adapt its knowledge base depending on the user's age or point in their timeline. Suman also shares his some learning lessons for AI product creators. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Suman Kanuganti: • LinkedIn: https://www.linkedin.com/in/kanugantisuman/  Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt  Transcript:- Dhaval: So today we have Suman Kanuganti on the show. Sunman is the co-founder of personal AI, a product that he will talk about and, we'll learn a lot more about where his learning lessons bear, what his learning lessons bear and all that other stuff. So yeah, let's get started. Suman, would you mind introducing yourself and tell us a little bit about your product, AI product? Suman Kanuganti: Sure Dhaval. Thanks for having me. I'm Suman Kanuganti. My background is in engineering. Over 10 years ago I started creating companies. This is my second company. Previously I built a company called Aira, A-I-R-A. My philosophy always has been how do use technology kind, solve, hard human problems. Aira was about using technology to fill the gap of missing visual information for people who are visually challenged, such as blind and low vision. And personal AI is about augmenting people's mind where memory, cognition, and our time is limited. And we would want to augment that using technology by creating a personal language model of every individual that essentially learns to behave and act and learn to be you. So that's a little bit of my background and who I am. Dhaval: Wonderful. Thank you, Suman. Thank you for that introduction. Tell us a little bit about what personal AI's narrative is like. What is the, who is your target customer? What kind of a problem, specific problem you are solving with that product? And what is the overall narrative? How is it different from your competitors? Suman Kanuganti: Yeah, totally. As individual people, like on a day-to-day basis, we create and consume a lot of information. Like, you know, a lot of experiences have a lot of conversations. But obviously, 80% of that is lost or forgotten. Our goal with personal AI is to be able to create a model. That actually learns your knowledge, your style, and your voice, more or less like to be a digital version of you. Imagine being able to surface relevant pieces of your own knowledge, on demand whenever you need it. Or imagine, you having conversations in a, chat or text message. Are people talking to you? Where relevant pieces of information when we start facing as you speak. So our intention is to be able to augment, humans with an extension of their own mind. Because one cognition is limited and then two time is also limited. You mentioned about the target market. Our goal is to actually go after everyday consumers. Our intention is to have everybody have their own personal AI. That is trusted by them. The data belongs to them. The model gets trained over a period of time and innovate kind of grows alongside with you. Unlike, public or general models that exists such as open AI, such as, Google or Alexa, which is mostly like trained on public internet of data. Personal AI is a unique model that also uses similar architecture such as GPT, but actually trains on individual person's data. And it does so stylistically, relevantly authentically to replicate as you would. So we are trying to essentially like replicate, your thought process and your mind and give you an extension of your. Dhaval: Wow. So it is adding the stylistic and tonality and the personal attributes to your, to the replica that you are building for someone which is not there in current GPT or any of those products, right? So there's a bias that. GPT has very confident answers, but doesn't necessarily align with your style or may not align with the way you communicate. And what you are saying is that not only does personal AI helps you do that, but also creates the model in the first place using your personal attributes , did I get that right Simon? Suman Kanuganti: Yeah, totally. So you will create what we refer to as a memory stack, which is essentially taking all your unstructured data that you ever have in your digital world. Let's say you're having conversations online, you are texting with people. You probably have written a bunch of different knowledge pieces out there. And then we create this memory stack, which is essentially like a digital representation of your memory vault. In other words, we basically break down this idea of structuring your data into these blocks that is associated with time. And imagine over a period of time, as you create an as you learn. Your AI technically would also be training alongside with you. So it's kind of how. Conceptually we have architecture system. Dhaval: Got it. Now, there are, if you were to dive a little bit into your products architecture or the core engine, the AI, since the audience of this show are the people who aspire to either create an AI Product. Or wanna add AI to their existing product, for their knowledge. If you were to share a little bit without getting into the confidential details about what does a product stack, what does it take to build memory stack? What does it take to build a knowledge replica, a knowledge brain summons, human brain, and human personality into something that you were doing, like what do you call that thing? The entity that. Suman Kanuganti: Yeah, I'll try to provide answers and then I'll try to provide some contrast technologies that exist out there so that we can wrap our heads around. The first thing is at the core, we are essentially an AI first company. We built an algorithm called. Personal language model. So we call it personal language model. This is in contrast or like kind of opposite in concept to a large language model. If you think of a large language model such as GPT 3 or any other open Language models that exist out there close to around like one 70 billion parameters in our case. Our language models around one 40 million parameters. It revolves around at the core individual's data and not public's data. And you can keep on adding the data to your model so that way it gets more sophisticated in regards to the purposes of your mind. You can go abroad and you can also like, go deep into specific topics itself. So yeah, at the core we build this personal language model for every individual to essentially like mimic the behavior, knowledge, and style of an individual person. And the transformer that we have developed, we call it generative Grounded Transformer. And if you think about like GPT as a generative pre-train transformer, the subtle difference of our transformer is that it is grounded in the personal data of you. And whenever I refer to personal data, is nothing bad. The memory is tag that I was explaining earlier, So every AI response that your personal language model actually generates, it has an attribution, and the attribution is nothing but attribution Back to the data or what"s? Data elements of what memories in within your stack is responsible for creating a particular response? One of the challenges for large language models is that there is no attribution, primarily because it is driven by aggregation of the data. And there is quite extensive anonymization that is involved. So technically, you cannot create that attribution and it's extremely hard to create that attribution. And our goal is exactly the opposite. We would want to have. That attribution, we want to have the ownership and we want to create that value to every individual consumer by creating their own individual model, at the foundation, it's personal language model. Dhaval: So this generated you, grounded trained model that you are referring to is that, Adaptive with human changes, human behavior changes over the lifespan of the user. Suman Kanuganti: Exactly. The transformer also has a sense of time. For example, let's say, if you're talking about AI maybe three years ago, How you refer to your transformer, how you refer to your technology, maybe different from your latest and greatest creations or thought process around your ai. So it has a time to decay component. So when you are indeed chatting with your own AI, it normally anchors around the latest and the greatest thought process of. How you would respond. However, let's say if you indeed are contextually trying to fit something from the past that happened like 10 years ago Then, we are probably talking about the first autonomous car, right? And my experience with the first autonomous car, then it will. It'll go back in time and be able to fetch that response for you. So kind of like designed to work very similar or akin to how a human mind would function. You can think about potentially being able to drop your AI at a certain period in time. Given these are small models and given as the data is going in on a day-to-day basis, there is a new version of the model. We are technically able to time travel. Your model, like let's say 2, 2020, and then when you are having the conversation, it would like replicate the information density as if you were to be functioning at that time. If that makes sense. Dhaval: That makes sense. Yeah. So with this language model the way you communicate is it's having a time sensitivity to it. It has a time awareness to it. So it,

Martin Pichlmair is the CEO of Write with LAIKA, Associate Professor at ITU Copenhagen and Co-founder of Broken Rules. He Holds an PhD degree (Department of Informatics) in Vienna University of Technology. In today's episode, Martin explains that LAIKA is designed to make AI-generated writing more accessible and user-friendly, with the AI and the user working in a tight interactive loop. Martin highlights that their product uses a "no-prompt" system, which means users don't need to be skilled in prompt engineering to get meaningful results from the AI. Instead, the software handles most of the prompt engineering behind the scenes, making it easier for users to interact with the AI. Tune in to hear Martin's insights and experiences in building LAIKA and how you can apply these lessons to your own product. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Martin Pichlmair: • LinkedIn: https://www.linkedin.com/in/martinpi/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt  Transcript:- Dhaval: welcome to the call, Martin. Thank you for joining. Tell us a little bit about your product. Martin Pichlmair: Okay, so I'm Martin. I'm the CEO of Write with LAIKA. And our product is a kind of creative writing tool that is using large language models, in our case, quite small, large language models to, support writers when they get stuck or when they need more text, that is influenced by their previous writing. Dhaval: Wow. Okay. So when the writers get stuck or when they. Interested in continuing with the style and the tone of their previous work. They can use your product including the contents, the storyline, or anything along those lines. Martin Pichlmair: Yes. How LAIKA works is that you you upload existing writing. You have when you get, for example, stuck in a murder mystery because you don't know who the murder is. Funnily, we had that case twice already with users. And then you upload what you have written before and our, system fine tunes a language model With your text and then you can prompt the model to continue writing in your voice, in your using your characters. You mentioned using scenes you have been writing about in the past and very much sounding like you. Now you can do that with your own text. Or with the text of famous writers, we have, for example Dostoevsky in there and Jane Austen in there. And a lot of, all of them, of course, dead and out of copyright writers that you can also collaborate with in a similar way by asking them how they would continue a sentence, for example. Dhaval: Wow. So it has memory and context as well as style and the personalization built into it. So is that. Large language model that's very different from Chat GPT 3, which would spit out very confident phrases very long phrases. But they're also having the same style. Is that, how is that different from the large language models? You said that you have used large language models or you have you built on top of them or like, help us a little bit on how have you built this. Martin Pichlmair: So we've built this on very small, large language models. They're still in the same architecture and come from the same family, but they're very small because that gives us the ability to fine tune them very quickly. It takes like five minutes. If you upload , a half done book, for example, takes five minutes and you get your own, we call them brains because that's a nice metaphor. Your own brain based on your writing to interact with. Now, of course it has an understanding of the context, but it's not always super, like it doesn't have an actual understanding. It can just play with probabilities of words, just like all of those language models do. Dhaval: Wow. Very cool. Let's dive a little bit into your product journey, is this your first startup? Is this your first AI product? Tell us a little bit about your background, Martin. Martin Pichlmair: So I have a weird background. I did a PhD in computer science originally at the University of Vienna, at the tech university, and then worked in academia for a couple of years. I got a little bit, I don't know I wouldn't say bored, but I wanted to do something differently. So I started a video game company and then after a year started another video game company because the first one didn't work out. It didn't work out, but it also didn't not work out. It was fine. It was just not meant to be a longer existing thing. The second one actually is still around. It's called Broken Rules and makes awesome in the games. But I'm not involved anymore because I decided at some point to go back into academia. So that's where I spent the last seven years until last year where I just realized with my partner, That we have a huge connection between what I was doing in research, which was using generative AI to create systems for video games and her background, which is writing for video games. So we sat down and, uh, started workshops during the Covid Pandemic when everyone was sitting at home. We started online workshops where we introduced writers. To, the newest possibilities in language models, using very, very clunky tools at that point. And after three or so of those workshops, we realized the workshops are always poked out, but it's really hard to work with the tools that are there. So we decided we have to make our own tool, and that became a research project that was funded by the Danish state, uh, in the beginning. With the intention of turning it into a product. And since last November, we founded a company and turned it into an actual university spin off that is based on yeah, research that is now working on a product that we will commercialize within the next month Dhaval: Very interesting background. You do have like a very traditional computer science background, making you very competent in this area. Right. So, quick question. You mentioned that you launched this in November, but you haven't commercialized. It doesn't mean that the product has not launched yet. Martin Pichlmair: Yeah. We have a wait list and we have, a data with, nearly 2000 users. So there are a lot of people using it every day, but, it's not a commercially launched yet. We're still only free for select users. Dhaval: Very cool. Is this. Is this a self-funded or have you bootstrapped this whole thing? Are you intending to, or have you raised capital? And are you intending to raise capital as you move forward? Martin Pichlmair: Well, we got some funding from Danish State again. The program that we're in that funded turning research into a product last year that was still in the context of my university has a follow up program that funds your salary basically. So we are kind of weirdly half bootstrapped. We have no investor, but we have, our salaries covered by the Danish State but a very low salary. But still, it's good enough to, know that we'll be, we'll be around for another year at least while this funding runs. we're looking for investment in the moment. It's, we are talking to a lot of VCs. It is. It just takes a while it seems. Dhaval: Yeah. how is that playing out in this current market? Like how is that, can you give us have you done this before? And if you have, like how is it compared to the current market, if you can speak to that. Martin Pichlmair: So I haven't done it before, but a good friend of mine has a very similar company, actually a very different company, but also an AI company also in Denmark. And he also has an academic background. It's otherwise very, very different because it's B2B and started out much bigger than we are. But it looks like they, the climate they saw two years ago is very different to what we have now. So I'm getting all my tips from him and half of them don't work anymore. The climate is not good in the moment. Even in the hype space of creative ai, there is a lot of chicken egg problem, happening in the sense that investors wanna see. They actually want pay traction very often .They want to see some pay traction or immense numbers in weightless users or something like they wanna have proof of actual viability very early on. But it's such a new area that you are actually creating a market. So it's very hard to say where this whole journey is going because the whole, like AI is not super new. But generative AI is really something that is only a thing since like a year or so. It's very hard to say where the journey goes in the moment and, like it could all still just be overhyped. Then I would understand the need for having paid traction, but it could also be that we are just opening, creating a completely new market here, and then a little bit of trust would be nicer than having to prove things too early. Dhaval: Yeah. Yeah. Where are you in the product stage in terms of product development? Are you close to? I know you mentioned you were ready to launch in a few months. Are you close to finishing your product development? Is is that almost there? Like, if you can share that. Cause my follow on question's gonna be on, what were your top learning lessons around creating an AI generated product? What was that experience? What was the top learning lesson there? Martin Pichlmair: So I think the whole idea of finishing a product is not really how it works with software as a service anyway, but especially in this extremely fast moving area of, AI in general, but especially generative AI where new technologies come out on a sometimes weekly basis. There's a lot of competition, but there is also just a lot of speed of development. I don't think we will ever be at the moment where we say, now we are done with this. Instead, what we are, where we are trying to get is to a point where we can say, this is our 1.0 version and we hope to be there in the month actually. And, from then on we of course continue building. and one of the main challenges to my surprise actually, and maybe that was the main learnings, is since what we're wo

Boyang Niu is the Co-Founder of Stylized. He holds Bachelor and Master Degrees in Computer Science from the University of Pennsylvania. In today's episode, We discussed his journey and vision for the AI-powered e-commerce platform. Boyang shares the long-term vision for Stylized, which is to become an asset-first e-commerce platform, simplifying the process of setting up an online store by taking care of all abstractions, from website building to SEO. Boyang emphasizes the importance of understanding one's strengths, whether it's distribution, core ML, or UX, and iterating quickly to create a high-efficacy product. Tune in to hear Boyang's insights and experiences in building Stylized and how you can apply these lessons to your own business. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Andrew: • LinkedIn: https://www.linkedin.com/in/boyang-niu/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt  Transcript:- Dhaval: This former Dropbox engineer built an AI product to create stylized professional product photos for people running e-commerce shops. Boyang is a founder of Stylized ai. He Figured out a way to leverage depth extraction, AI and 3D rendering to empower e-commerce sellers to transform their phone photos into professional assets for everyday use. One of the biggest lessons I gleaned from this conversation is how he approaches product development by focusing on areas of growth and applying the user mindset to product creation. Hey, Boyang , talk to us. Tell us about your journey. How did you come to, identify this, space? And, tell us a little bit about you first and then we can talk about the product. Boyang: Yeah, absolutely. Great to be here. Dhaval I joined Hive. So Hive was a social media company back in 2015, building like a Twitter clone. We pivoted hard to enterprise computer vision SaaS. And so that's where I first like learned about ImageNet, um, did all this AI stuff built like a trading pipeline and all of this stuff. And then after that, worked in some productivity tools at Dropbox, worked at e-commerce at Square, and then for the latest venture, I like put all those things together. And really it's about targeting the market that I care about, which is e-commerce sellers, which is a huge market, right? You get a lot of customer iterations, a lot of customers to go after. You don't have to really be scared to approach any particular one. And that's a great feeling when you're just getting off the ground, right? Because you need that iteration cycle. And so we, we. Sort of putted around me and my co-founder, looking for ideas that really resonated. And one of the things was like, oh, people want to take photos, right? People, when they sell things, they need photos of the thing. that's where buyer decisions are made, and they pay like 35, $50 per photo professionally for these images that they're putting on Shopify. And so people are coming up to us, they're being like, oh I waited like six weeks for this photo set. For, 250 photos, it cost me 10 K or 5k, whatever. And we're like oh this is interesting, right? Because, there's this new image stuff going on and maybe we could really leverage that, to make this workflow better.So that's really where we came up with the idea for, what is now Stylized, Stylized.Ai. What we're building is professional product photos, for people running e-commerce shops in under 30 second Dhaval: oh wow. So tell us a little bit about where your product is at this stage and have you launched, is it in the pre-launch stage? Is it in the wait list, stage, et cetera, et cetera. Boyang: Yep. We have launched, we are soft rolling out a launch with this is a prosumer product, right? And so we're really concentrated on the B2C motion of go to market. We're doing like organic seo. we're running a bit of ads on the side. And so this is all just to build up , a brand name and also to get really fast iteration on what the product surface is. So we have soft launch. We have about 200 customers right now. That's growing probably at a rate of, I would say, like 15 to 20 per day. Which is pretty good, right? It's only been like, I think since we opened the beta. To one and a half weeks. So we are pretty happy with that. And I think the goal for us really is to get that distribution and get in front of people into their workflows such that we get embedded. And because no one really knows, like honestly, no one really knows where the AI models are going. And so by in the next three months, someone could do some really magical stuff, right? And we want to be able to put that magic in front of customers. And to do that, you have to have a big audience. So that's our goal, right now Dhaval: That's awesome. Focusing on distribution first, that's novel. Most of the founders and product creators, they get their heads down and they start building the product and it. They spend months and months and months before even thinking of the first interaction with the customer. And as you already know, it never goes as per the plan. Right. So what is the main value proposition? What is the main customer pain point that your product solves for? Boyang: Yep. Customer pain point. I have a product. I'm trying to sell it. I have a Shopify store. I need good images. Right now my options are I get like a light box set up, which is, they can be pretty complicated, right? I need to set up a photo studio area in my house. I need to take pretty meticulous pictures. I need to then learn Photoshop and edit those just the way I want them. Or I go to a professional studio and get my photos back to me, in a couple weeks. And so that's my blocker right now. I can't get it on Shopify. So what we do is, you. An iPhone image, and as long as it's like pretty good neutral lighting, like anyone can do this. I've done this many times and I'm bad at taking photos. Right. So I do that in my product. In 30 seconds I get a virtual light box. So this is a staged, 3D rendering actually. And the technology is relatively, I wouldn't call it simple, but it pieces together a bunch of existing models, right? To render your product in 3D. And then you get to adjust whatever you want about that rendering such that you know your product is professionally lighted. You get to change all the backdrops as if you are in a product studio or in a photo studio with like different types of materials or Hey, I want this on marble or slate, or all of that stuff. But you get to do that from the comfort of your computer and the iteration time is, on the order, five seconds versus two weeks. so that's what we're going for. Dhaval: Wonderful. Yeah. I've been a product owner for e-commerce companies and that finding good stylized photographs of your product has been the biggest game changer. Like experiencing, showing people experiencing the product has been the biggest game changer. So you're solving a real pain point. You said you are in a prosumer space. If you can unpack that a little bit, why is that presumer and not just e-commerce sellers. How do you differentiate? Boyang: Yeah. So for us, the biggest differentiation is whether we are b2b, which would be selling to e-commerce platforms. And we've talked about this as well as whether we could go to Shopify and say, Hey we have an api, or we have a third party tool that. You could purchase for your sellers to make their shops more efficient. Whether that's the route or if we want to go directly to the customers themselves. So we see that as more presumer because it's self-serve one, we're just launching to anyone. You can come in, you enter your email, um, you upload a picture and boom, it's there it's free to use. You just get these premium add-ons. And that's how you are introduced to that product at first. So we're calling it prosumer mostly because all of our customers are independent shop owners, and they really get to make the decision about their own product. Dhaval: Very cool. Yeah. So your product roadmap could be either build up your distribution, get a lot of, Prosumer e-commerce, use your product, and then become down the line, become this extension or plugin for all the e-commerce outlets that are out there. Is that something you're thinking of? Boyang: Yes. I guess I won't go into too much detail, but we do have a Shopify extension that's coming out soon. As I said, we're focused really on hitting that distribution and just nailing it, getting as many customers as possible. And one of the, one of the benefits here is like we're solving one. Very individual problem, right? So it's, Hey, I need a photo. There's a very clear input, like I take a photo, there's a very clear output, I get a better photo back, right? That takes 30 seconds, 15 seconds, whatever it might be. We're solving that pain point. So it's very easy to get in front of people and say, Hey, look. This is what you're getting from us. and it's easy to onboard. And then from there, I think the strategy here is if we get many customers, we can start building up, catalog extensions. We can say, Hey, put your entire store catalog with us, like we will optimize it. Or you get better photos through all of your store. And then we can really start to leverage all of the newer AI things that we see coming out in the future. So if one day there are, very good AI models for just creating catalog webpages, we could leverage that. We could then let you know our customers create their own webpages directly from our surface. And that is really the expansion route that we're foreseeing. It's like an asset first, website. Dhaval: Very cool. So we have talked about your journey. We have talked about your product. We have talked about your potential future roadmap. What I would love to dive into now is unpacking the product stack a little bit for people, for product managers or product owners who want to either make an AI product or wanna add AI to their product. What was y

Andrew Palmer is the Co-Founder and CEO at Bertha Ai. Single Dad of a Twenties daughter He love to travel, play golf and attend WordCamps around the world. He love supporting Plugin and theme developers across the globe as it helps them get a profile and earn a living from their knowledge of WordPress, AI Content, PHP, Laravel and JavaScript. In today's episode, We discusses his journey and the development of Bertha AI, an application layer built on top of OpenAI's GPT-3. Bertha AI is designed to help website owners create and manage content efficiently. Andrew shares his advice for new entrepreneurs interested in creating AI products to start by fine-tuning their prompts and understanding what they want to achieve with AI. Tune in to hear Andrew's insights and experiences in building Bertha Ai and how you can apply these lessons to your own business. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Andrew: • LinkedIn: https://www.linkedin.com/in/andrewpalmer/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt In this episode, we cover: [00:00] Introduction to Andrew Palmer and Bertha AI [00:04:20] How Bertha AI uses GPT-3 for content creation [00:07:47] Andrew's entrepreneurial journey [00:13:58] Advice for those looking to create AI products using GPT-3 [00:17:17] Staying motivated as an entrepreneur [00:19:31] The future of Bertha AI and content industry Transcript:- Dhaval: Hey, Andrew, thank you for joining the call. I would love to hear from you on what your product is, like who does it serve, and what are the things that it solves for? Andrew: Well, Bertha.Ai solves a multitude of problems. In WordPress, for instance, we have web developers dealing with clients that really just can't get write together own content because they're not concentrating on what they should say on a website. They're concentrating on what they should say to their direct clients. They're speaking to every day on the phone or in day-to-day meetings, sales meetings, networking, that type of thing. So WordPress has a content gathering issue with Bertha.ai, when you are developing a website, the developer and all the client can use Bertha.ai to create interesting content around what the services, the particular website client provides. It can also help them write blog posts relevant to their particular industry. It can basically help you get the right words out there at the right time and much, much faster. So you're going to increase your productivity as a web developer and also if you're a client building your own website, you are going to be able to increase your productivity and get more ideas about what people actually want you to talk about within your services. So that's what Bertha.ai is all about really. Dhaval: Wonderful. Is that a content management system or is that a plugin for Wordpress? Andrew: So Bertha.ai is a plugin for WordPress, which is an application layer built on OpenAI, which everybody's hearing about the moment, probably one of the best viral campaigns out there with GPTchat. With Bertha.ai, we've got a number of modules in there that help you get your unique proposition together. You can write a full on about page. As of this week, which is January, 11th of January, so coming soon is GPTChat to Bertha.ai as well. And as of by the end of January, we'll have a Google extension out there as well. So you'll be able to use Bertha.ai everywhere. And the whole point of being able to use it everywhere is that not every website is built using WordPress. So you'll be able to use it in Shopify, Wix, those kind of proprietary website building tools. Dhaval: Wonderful. So you just answered my question that you are expanding for being a word, from being a WordPress only product to more of a open access for all types of content management systems. Andrew: Yeah, using it everywhere. So when, if you're in your Shopify site, you'll be able to just invoke Bertha .ai and write fantastic product descriptions or enhance your product descriptions. You'll also be able to get page content that is relevant to your users. So you'll, you'll ask one line question and Bertha.ai will be able to give you the answer to that question. And then you can copy and paste that, edit that to make it more human-like if you like. And then you can post that in your Shopify website or your Wix website or Squarespace, whatever you're using. Dhaval: That's wonderful idea.Tell us a little bit about where you are in your journey. When did you start first? How many customers you have? If you can share revenue. What is the split between developers versus end clients? From a business point of view, tell us or give us a few clarity on where you are in your journey. Andrew: Where we are is we're nearly a year down the line or just over a year down the line. Actually, we launched in September 2021. So, just the MVP version, and we launched that for basic WordPress plugin. So we're a year down the line. So we're way down the line. We've made the plugin faster, more accessible, easier to install. Getting the customer journey right is very important when you are building a plugin that's got a lot of things in it. There's also a learning curve as well. So we produced in just under a month, we produced 54 learning videos which are on our YouTube channel, which take you through every single module. With the hype around GPT-3, obviously lots and lots of people, you've got a million users in under three weeks, I think on GPT-3. So people are understanding how to use AI, how to ask the right questions, and obviously with images, image generation as well. So Bertha AI put image generation in there about a month ago. And that's flying. People are really kind of intrigued about how they can create unique images for their blog posts, from CVD products to computer products to any kind of product for florist, interior design, kitchen design. You can really get some great imagery there. If you want a modern kitchen design designer or a modern interior design, just ask Bertha create an image and it'll produce that image of a beautiful laid out sitting room or a house with chandeliers or whatever you like. So the point is that we came from the WordPress design or website design business. So we understand how people want to build websites. It's kind of a generic way to do that. And with the developments that we've had, we've got something like around 10,000 registered users. Some of them are website owners. In fact, I had a Zoom call with a guy today who's a website owner. They're not a developer or anything. And he wanted to use Bertha AI to better describe his products to his wider market. In fact, most of the meetings I've had with Bertha AI I love doing one on ones with our clients. And they've been with people that aren't web developers. They're the website owners. And that's the people that we really like to target. Because as I said, website owners have a real problem getting content together and writing blog posts. So Bertha helps you with all that content. But revenue terms, we're okay. We're fine. It's in profit. It's running itself. It's quite nice, we're able to expand. Development team based in UK is upon part of the development team as well. And the majority of the team are based in Kolkata in India. And they do a great job. Dhaval: So then quick question. You mentioned something very interesting. There are a couple things you mentioned. I want to glean them out. One is the customer journey. The audience for this podcast are people who are product creators and they're specifically interested in either adding AI to their existing product or they want to create an AI product. With what you just described about customer journey, what was that experience like for you? Like, understand the customer journey for your website owners. And how did you productize that? If you can share a little bit about that. Andrew: Well, it's still happening. with the WordPress plugin, we are lucky it's on the repository and you can install that directly from your WordPress dashboard. With the extension, that's going to be a harder task making sure that customer journey is OK to actually install it in a Chrome extension. We're talking about millions of users that maybe don't know how to install a crime extension from scratch because it's not going to be on the Chrome extension store for a while. It's just going to be downloaded. Once you have an account within Bertha, you'll be able to download the Chrome extension and install that. So we're building education around that about how to install a Chrome extension and maybe make it not so difficult for people to understand that it's quite easy. It takes about 30 seconds to install the Bertha extension. So it's about education, but it's also about what we want as a customer journey. There are so many extensions out there. There's so many plugins. There's so many SaaS products out there that involve a learning curve. And what we've learned is that people don't actually want to learn. So what we have to do is almost automate that process . Dhaval: I'm one of them. Andrew: Yeah, exactly. You don"t actually want to learn how to drive a car or drive a new car, let's say. So if you've got a new car and it's great, but the indicator arm is on the other side, you forget, don't you? You put your windscreen wipers on. So, There's a learning curve around everything if I really simplify it. But at the end of the day, it's up to us as product developers to make sure the customer journey is as seamless as possible to immediate use. And that's where we're having difficulty. And we've had difficulty, but we're getting better and better by that every single day. Dhaval: Now, you were a website agency website development or marketing agency, and that's how you identified this content problem that your customers were having. And that's how you ended up with this idea . Am I getting your entrepreneur

James Clift is the Founder at Durable. He is the former Founder at KarmaHire, WorkStory. VisualCV and Holopod. He building businesses since 2005. In today's episode, We discusses the benefits of large language models (LLMs) like GPT-3, emphasizing their ability to generate human-like text that can be used for a wide range of applications, including content creation for websites. He also emphasizes the significance of having a data-driven approach to fine-tune the models and make them more effective for different business categories. James shares his experience of finding partners with AI expertise, highlighting the importance of networking and being involved in communities like South Park Commons. He believes in sharing work and attracting like-minded individuals to join the venture. Tune in to hear James's insights and experiences in building Durable and how you can apply these lessons to your own business. Find the full transcript at: https://www.aiproductcreators.com/ Where to find James Clift: • LinkedIn: https://www.linkedin.com/in/jclift/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt In this episode, we cover: 00:00:00 - Introduction 00:03:50 - The story behind Durable and its AI-powered website building platform 00:07:22 - The importance of user feedback in AI model training 00:10:49 - Fine-tuning and prompt engineering for LLMs 00:11:22 - Best practices for partnering with technical teams 00:12:27 - Durable's growth, funding, and team size 00:13:20 - Remote work culture at Durable 00:13:49 - Finding AI expertise and attracting technical partners 00:15:16 - The value of sharing your work and attracting like-minded people 00:16:20 - The future vision for Durable Transcript:- Dhaval: This founder built an AI product to replaces your employer with ai. Yes. You heard that right? Your employer, James, is our guest in today's show, he shares his learning on how he built a product in a hyper-competitive market space. James Clift is a founder of durable. Durable, makes owning a business easier than having a job. He's a former founder of KarmaHire WorkStory, VisualCV and Holopod. He has been building businesses since 2005. Welcome to the call. James, tell us about your product. James Clift: Awesome. Yeah, so Durable is the fastest way to build a website on the internet. In three clicks, in 30 seconds, you can generate a business website using ai. And not only can you make a website, we've got the rest of the stack as well to operate a business, we've got a CRM, an invoicing tool a financial account. So essentially everything you need to start and grow your business in just a few click. . Dhaval: Wow, that's very powerful vision where do you sit in your market space? are you serving a specific customer segment? James Clift: Yeah, so we're totally focused on solo operators, so anyone that runs a solo business. So primarily those are service-based companies, so everything from a web designer, marketing contractor, copywriter to more traditional physical service companies like Lawn care, home Services. Plumbing contractors, skilled trades. So essentially anything where you're trading your hours for dollars or your hours for projects we're a great fit for if you're selling goods on the internet or you have a brick and mortar store, we're not the best solution there, but solo service-based companies is our primary market right now. Dhaval: How did you differentiate yourself in this crowded market space? I believe it may be high competition market space. But You have, you seem to have found reasonable amount of success based on what I have seen about you online. How did you create that differentiation for your product? James Clift: Yeah, I think there's a lot of ways to look at markets like most markets are very large on the internet, and for us it was a few things. So one is bundling, so providing all the tools you need to run your business under one login. So that was a big value add from the start. So you don't have to learn five different tools. You don't have to pay for five different subscriptions or 10 different subscriptions. It's everything you need under one platform. And then the other piece is what can you actually do 10 times better than everybody else? And for us it's the speed of actually getting a website. Out to market. So instead of taking, typically it's weeks to get a website live, if you're really good it's days. If you're really, really good, it's hours. We actually do that in minutes. So there's this order of magnitude that makes that thing faster, or that business process faster. And it actually unlocks a lot of creativity, a lot of, just makes it more fun and playful, not stressful. And I think you open up these brand new markets by just anytime there's an order of magnitude step change. Something's 10 times faster, 10 times better, 10 times more intelligent. That creates these huge opportunities. And I think AI as a platform is definitely one of them that we're seeing. yeah, I think customers are super excited about the speed, the simplicity, and then the bundling aspect of the platform as well. Dhaval: Very interesting. So you brought this up, AI and this particular discussion and discussions like. That I host are focused on people who are either interested in creating an AI product or infusing AI in their existing product. Tell us about your infusion of AI into your product. When did you decide that, was it AI first from the ground up? And if it wasn't, when did you decide to bring AI into the user? James Clift: Yeah, I think, so I've ran SaaS companies for a while now, so probably about 15 years. And, it's always, I mean, the goal of any software company is how do you make processes easier and make your products easier to use? So, the long-term vision of us and AI is really, How do we replace your employer with AI and just let you focus on your core competency. So that's your skill, right? So a lot of the time if you have a service job, you have an hourly rate that you're then getting marked up for by your employer. So, in a perfect world, you just meet that reach, that market demand. So, hey, you're a lawyer making, I don't know, call it 500 bucks an hour that your employer charges you out. You're making 200 bucks an hour. So that's a market opportunity for you to go independent and do your own thing. But what the law firm brings to you is a brand customers, some back office services. So the way we're thinking about that is what can we actually replace? And I think brand is changing a lot. Like the brand matters less, the individual matters more, and the back office piece can be solved with technology. So essentially . Everything can be automated except for the thing you're really good at, and that's really how we're thinking about ai. So from a product standpoint, we built the platform first, and then we built the AI on top of the platform because we've got a lot of features and it was always this idea of, how do you make those features easier to use, more accessible, more interesting, and just more intelligent. So the, our customers can just focus on what they're good at. so that's always been part of the strategy. Definitely it's accelerated in the last few months here with all these new technologies and APIs and libraries that have come out, and been super incredible and are moving really quickly. So definitely, the primary part of the strategy moving forward as well. Dhaval: One thing, one thing that I always hear from other product creators in the space is about finding the balance between building on top of existing AI capabilities. That other companies have created versus building your own AI capabilities? Where do you draw that line in your product? James Clift: Yeah, I think it really depends on what kind of company you want to be. Are you really good at marketing? Can you repackage these libraries and build a good user experiences around them? And you can accelerate really quickly? Are you deep technologists? in that case then you should build the underlying infrastructure layer. For us, I think the advantage, and I think. These core models are really, really powerful, but I think you have to train them on your own data sets. Otherwise you're gonna lose your competitive advantage really quickly. So if you're just re-skinning chatGPT and it's a slightly different user experience, but the same data, That's gonna be a race to the bottom pretty quickly, because everyone can do that. But if you have a user that is unique that you can build data sets around, then you can train these models to be more effective. So we're doing both, we're using the existing models, but we're also training them. Pretty specifically around our category of customers. If you think about a solo business owner, there's a set of activities that you need to do. Even from a marketing standpoint. It's okay, you've got your website. How do you optimize your seo? How do you create your ads? How do you create your marketing copy, your newsletters? Once you have customers in, like, where do you get more customers from? How do you measure your channels that are effective? When you send invoices, what is the value of that invoice? How does that tie to your accounting system and your customer database? So there's just a lot of things that we can actually build more proprietary, unique data sets around and workflows and processes that we can optimize. So I think as long as you own that customer journey and lifecycle, then you have the ability to train your model and make it better. But yeah, if you're just re-skinning an API, I think. There's some that will do well. So like re-skin the API in the category. I think one or two of those will succeed in every category because it is great tech. And if you're good at marketing and acquiring customers, there's opportunity there. But if you're the, I don't know, the 20th company to try and build a copywriting app with ai, I think that's gon

Abhi Godara is the Founder & CEO Rytr. He is also the Founder & CEO at HelpTap. Rytr is an AI writing assistant that helps you create high-quality content, in just a few seconds, at a fraction of the cost! In today's episode, We discusses the initial stages of his startup, where they utilized organic channels like LinkedIn, Facebook, and Reddit for marketing. He also discusses acquiring training data and recommends strategies depending on the domain, mentioning that GPT can work with a limited number of examples. Abhi highlights the importance of user experience in differentiating his product from competitors. Tune in to hear Abhi's insights and experiences in building Latitude and how you can apply these lessons to your own business. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Abhi Godara: • LinkedIn: https://www.linkedin.com/in/abhimanyugodara/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt In this episode, we cover: 00:00:00 - Introduction 00:03:25 - Abhi's motivation for building an AI copywriting tool 00:05:15 - Strategies for acquiring the first thousand customers 00:09:40 - Differentiating the product in a competitive market 00:12:21 - Acquiring and using training data for AI models 00:13:50 - The story behind the acquisition by Copysmith 00:15:08 - The future of AI in content creation and advice for AI creators Transcript:- Dhaval: This founder built an AI writing product that serves 4 million customers, and it got acquired in two years from Founding Date in this episode, we discuss his product development approach that differentiates his Gen AI writing product from the plethora of other gen AI writing products in the market space. We discuss his product differentiation strategy, his training, data gathering approach, and how he got his company acquired. Today my guest is Abhi Godara. He's the founder and CEO of Rytr and AI writing assistant that helps you create high quality content in just a few seconds at a fraction of the cost. Welcome to the show, Abhi tell us about your product. Where are you at with it? what's the four 11? Abhi: Right thanks Dhaval for having me. so I'm founder and CEO of Rytr one of the largest and probably the first one in the market AI writing platform. We have been there since last couple of years now. now we are serving close to 4 million customers all over the world with with close to perfect ratings pretty much on all the platforms. So it's been an amazing journey in terms of how, the platform has scaled which allows a lot of these content creators. Marketers And professionals to create really high quality copies across a range of use cases, purely through ai. So things like email writing, blog writing product description ads, you name it. Everything can be generated through our platform. Dhaval: When did you found the company? Abhi: So this was back in 2021 actually when we started working on this. although I've been in the AI space for a long time. but this idea took off only when OpenAI came to life back in 2020. So I was following that closely. And then when GPT 2 and then GPT 3 came out, and we bounced on that seemed like a great opportunity to build something like this and just to give you some background to that. Again I've been an entrepreneur for most of my career. And, when, one thing I've always found that content creation is a pain, especially when you're a small team just starting it's a fact that many startups and professionals fail because they do not possess the effective marketing and copywriting skills. While dabbling with GPT 3 on another sort of chat bot project, I realized the potential of this technology and the market it could address. And at that time we looked around and evaluated existing platforms and found the experience a bit frustrating. And decided, okay, let's give the market what it deserve. And that's how the AI writing tool was born. I think we were probably in the first six months of this technology when it came out. We launched this and yeah there is no looking back since then. From zero to almost 5 million customers now. Dhaval: Wow. 5 million customers in less than two years. Did you bootstrap this? Was this venture funded? Tell us a little bit about the financial side of the business, if you may. Abhi: Yes, absolutely. So the funny story is , it was completely bootstrap zero external financing or capital reached. We had a acquisition as well, last year now part of a bigger umbrella company called copysmith. And yeah, it was always a small team and even. As of today, we are just four people. It's a very, very small lean team. And for the first six to, I think nine months, it was just two of us, me and my co-founder, and we were just doing pretty much everything. So yeah, it's been a lean journey completely bootstrapped and even as of today we are a very small team that is focused on product and high quality customer support. Dhaval: Okay. We'll switch to gears a little bit on. Where did the AI kick in for your customer experience? Customer journey? How did you make that decision that in this point of customer journey will be infusing ai? What was that decision making process like? Abhi: Yeah, so I think the whole product itself was like, The foundation was ai, right? When GPT 3 came out, like it, it allowed people to create all kind of content and copies by just giving some examples or you can see, training data so when I played around with the technology, I could see the potential. Wow. What if I can turn it into a delightful experience for end users who can create all kinds of copies. So we did a lot of our own training data in terms of the different kind of copies that people would like to generate we trained the, the underlying sort of models which were provided to us by GPT 3 OpenAI. And then yeah, so the whole product was basically built on that technology from day one. AI was always there. It is an AI writing assistant, it's natural that AI is there. So yeah, so it was always AI first product, AI first pocket you can say. And when we launched, this was just heating up, this space was like just coming to life, I think now. AI and GPT 3 chatGPT all over the news, but maybe a couple of years back it was just a very sort of em embroiling, technology. Not many people knew about it. So yeah, but we decided, well, something like this can really make a difference. So that's how I think we bounced on it. Dhaval: Yeah. You mentioned something about you fine tuned the models that you got from open AI. For new and aspiring product creators who may or may not have deep expertise in ai, is that a preferred route? Is it easy to fine tune existing foundational large language models that you get from OpenAI? If there are any tools you could, you would share with? Abhi: I mean it to be honest with you, yeah, I think it's, uh, they've made it very easy. So it's not even a non-technical person can feed in some examples and have the AI. Produce content, which is of high quality and aligned with what the user is expecting. so it's not a highly technical of course you can fine tune to the extent that you can provide like thousands of examples with your own custom domain or maybe industry. And then the model would be like very, very customized to your needs. But , we didn't go that far and I don't think majority of the use cases need to go that far unless you're working with enterprises, I guess. so in our case it was, and this was like a couple of years back, now it is matured even further. So you can actually go in with chatGPT or any other such similar technology and with the zero short learning they're able to give you the output that, it's pretty decent. So, yeah. So I think even non-technical founders they're looking to get into the space. I think with some fair like industry experience, they should be able to train the underlying model, which doesn't require any sort of technical expertise. But if they're working with, I think, bigger clients and companies and enterprises, I think that's where maybe they would have to fine tune it a bit more. Dhaval: Wonderful. What was your biggest learning lesson in terms of finding the product market fair, especially with AI capabilities? When was that light bulb like, yeah this is happening. I know you are an AI first product, but uh, just in terms of okay, yeah, this is where we are starting to see the fit. What was that? How, what was the learning lesson there? Abhi: I think I, a lot of it was like market being at the right place at the right time as it's the case most of the times having been in the AI space for the last five years building like so I've been working on this AI chatbot tool for individuals and influencers. But then the tech wasn't there at that point, to create any sort of. Custom implementation, you would have to train tons of data and even then the responses wouldn't be anywhere close to what the user would expect. So having been through those struggles, and then suddenly when open AI release, GPT 2 GPT 3 I could see the remarkable differences in the output quality. And that's when like I said, I realized, okay, well this could be packaged into a much better, bigger product for a lot of these copywriter and marketeers. And to be honest with you, that that was, that seemed like the first, logical use case of this technology. Now you can think about a lot of other things, but at that time, I think content creation creative kind of copy generation was probably the first thing which would come to your mind, and that's how we got started with this. I think that's when it hit us. Hmm. This could actually do a lot of good things for small businesses and startups. Dhaval: Yeah. So for you it was more of a, just making sure that the, the idea and the insights that you had around being able. Use a large language model for the end users and being able to implement them. That idea and follow through itself was enough for yo

Nick Walton is the CEO of Latitude, an AI-gaming company known for creating the first-of-its-kind AI-generated text adventure, AI Dungeon. A builder at heart, he created the first version of AI Dungeon in early 2019, a revolutionary experience that had 100,000 users in its first week after being launched. Along with continuing to develop AI Dungeon, Latitude is re-imagining what games could look like with AI and is working on a platform that will enable creators to make their own unique AI powered games. In today's episode, We discusses the early stages of his startup, focusing on the challenges they faced and their approach to overcoming them. They also explored optimization techniques for model deployment and AI provider evaluation. Nick emphasizes the importance of focusing on fundamental human values and needs when developing AI-driven products, rather than novelty and short-lived appeal. Tune in to hear Nick's insights and experiences in building Latitude and how you can apply these lessons to your own business. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Nick Walton: • LinkedIn: https://www.linkedin.com/in/waltonnick/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt In this episode, we cover: [00:01:00] - Introduction to Nick and AI Dungeon [00:03:45] - Initial challenges faced by AI Dungeon [00:09:03] - Energy expenditure in the early days: dataset and fine-tuning [00:10:05] - The role of ML operations in AI Dungeon [00:11:44] - AI Dungeon: A game company that uses AI [00:12:05] - Adapting technology and unit economics [00:13:03] - Balancing user expectations and cost [00:14:02] - Optimizing operations and advice for aspiring AI entrepreneurs [00:16:23] - Tapping into instinctual user needs [00:17:43] - The future of AI in gaming Transcript:- Dhaval: Welcome to the podcast, Nick. Tell us a little bit about your product. Nick: Yeah, our main product is AI Dungeon. So it's an AI powered role play adventure game where people can jump into variety of different AI experiences and make choices that result in a fun and interesting story. So, traditional text adventures where there's a limited set of options you can really select and you go down a path that a developer pre-imagined with AI dungeon every time the story is unique written by an AI. And so you have freedom to create all kinds of different stories that the developers us would never have imagined possible. Dhaval: That's amazing so. Is this like a video game? I've never played Dungeon before. So what is this for people who have never played Dungeon? Is this a video game? Is this like a regular board game? Tell me a little bit about that. Nick: Yeah, it's like a video game like a classic text adventure game where the game shows some story of where you're at and you type actions and then there's a result. It's a little bit more towards the end of a creative sandbox than it is a really structured game with lots of mechanics. So it's more on the story roleplay side. Dhaval: Got it.Wonderful. So how do you serve your customers? Is this a mobile app? Is this a web app? Nick: Yeah, We are on mobile and on the web both. Dhaval: Wow Okay. And is there a specific segment of your customers, of video game players or other players that you address with your game? Nick: Yeah, I think for ours, we're especially targeting kind of role play gamers. So gamers who are interested in role-playing games like DnD and tabletop ones, as well as our traditional game RPGs, like Skyrim and things like that. Where you get to kind of decide who you are and what direction, and what actions you want to take in a more open environment Dhaval: wornderful Tell me a little bit about your own journey. Are you a game developer or are you a techie who got interested in games? Or are you a business person who decided to try a new idea? Tell us little bit about your personal background. Nick: Yeah, definitely the techie category. So, I come from a machine learning background. Before I ended up doing this, I spent a couple summers at self-driving car companies. I was going to go work at Aurora. And this side project that I'd been working on AI dungeon, just kind of took off on the internet and I realized there's something cool here that people are excited about. So, I pivoted from where I was going to be a founder. So I have learned lots about kind of game industry and the business side since starting Latitude. But going in, I just had the kind of tech machine learning AI background. Dhaval: And Is Latitude already generating revenue? Is that a revenue positive organization? Nick: Yeah, we just reached profitability pretty recently. Dhaval: Wow. Congratulations. That must be a big deal. So if you could share any information on your revenue or your number of users, or any capital that you may have raised that will help us understand the context of your product. Nick: Yeah. We've raised over 4 million. Since we've reached profitability, we haven't had to raise in the last little while. We've had millions of users download AI dungeon and we have a pretty active excited player base. Dhaval: Wow. Millions of users profitability. And when was the last time you raised? Nick: End of 2020. Dhaval: Wow, more than two years ago. So you are a fairly successful case study here in terms of building an AI product. I would love to dig in a little bit here in terms of what was your journey like in terms of building the product? So you said you are a techie, you have background in machine learning. Did you find some video game players, video game creators? Did you have a passion in this space yourself? How did you go about creating a business model and a product canvas for your capabilities? Nick: Yeah, it's kind of funny because I think I very much came at it from like hacker creator who got kind of surprised. And so I feel like it's taken me a while to catch up on some of these other things around understanding our product, what it is kind of game industry space. So the way it really got started was just at a hackathon. I was just playing around with the smallest version of GPT-2 had just come out. So it was a hundred million parameters, a thousand times smaller than the language models of today. But even then I could see how this kind of AI's ability to do dynamic storytelling was really fun and interesting. So I got super obsessed with it. Worked on it over the next like nine months. I would have my friends play and I'd just like watch them and see kind of what their experience was, what was challenging and what made it more interesting. And so put it on the internet, not expecting that it would really launch off and be a product. And, so I was kind of surprised of scramble after that to figure out how do we make a product out of this so that people can continue playing. Because when it first launched, it was on Google Colab. And there was so much traffic. I knew that they could not let it be there for very long. So, I wanted to get an actual app and product place where people could continue to play it and not rely on Google Colab's GPU generosity for more than a little bit. Dhaval: Wow. Colab, for people who are not aware of is like a script writing tool. Is that right? It's like one of those web editors in which you can write code. Is that true? Nick: Yeah, the thing that was cool about it is Google generously lets people use it and run on their GPUs for free. And they have paid plans as well. So it was a way that. People can run machine learning models and try them out themselves, and so that's how AI Dungeon was first distributed was just as code that would run in these Google Colab notebooks. Dhaval: Wow. So what was the journey like for you when you finalized the product and you are ready to launch it? Did you partner up with other business co-founders or are you the sole founder of this product? Tell us a little bit about your partnership. Nick: Yeah, once the game really started kicking off and I realized there was something interesting here. I pulled in my older brother who's worked at a bunch of tech startups to co-found a startup around this with me. So for the first couple months, it was just us with a couple people advising or helping out here and there. And then we raised our first round of funding and got kind of initial team together. And then we started going forward from there. So that's kind of what it looks like. Dhaval: Got It. So when you started this, you built this on top of GPT-2, which was a thousand times smaller than what we have now. And when you say a hundred million parameters I'm sorry, 10 million, did you say 10 million? Nick: Yeah. It was the smallest version of GPT-2. The very first version, the version that took off was a billion parameters. So it was about a hundred times smaller. Dhaval: Got it.Now, did you create a lot of custom capabilities on top of the foundational model or was that built in and you just used it in a clever way? Nick: Yeah, we did fine tune the model on second person kind of story adventures so that we would have an understanding of what that format looks like. Especially because there's not a huge amount of that kind of text format on the internet. And so I don't think the base models had a great understanding of what that was, especially earlier on when they weren't as smart as they are today. Dhaval:Got i t Nick: There were also a lot of things around how you manage what you feed into the model, what it comes out, how you alter the output so that it would feel more like a chat experience where the AI is telling you a story rather than stopping mid-sentence on the way Dhaval: That was also a big effort in your engineering, in your output engineering for the prompts. Is that what you're saying? Nick: Yeah. In the early days. Getting that piece right. Dhaval: Yeah.Where did you spend most energy in the early days? Fine-tuning the model, getting the data, training the model wit

Amit Gupta is the Founder at Sudowrite. He is the former Founder at Photojojo, He sold Photojojo at 2014. After selling his first company Photojojo in 2014, he started traveling, writing sci-fi, and more recently, building Sudowrite. In today's episode, We discussed the benefits of using AI to assist writers, including increasing their productivity and providing creative inspiration. He shares insights on building AI products using pre-trained models like GPT-3, as well as the challenges and costs involved in training custom models. Amit also reveals the future vision for Sudowrite, aiming to help writers create more content and monetize their intellectual property, effectively turning them into franchises. Amit shared about building on top of pre-trained models vs training custom models, the importance of personalization. Tune in for valuable insights into the exciting world of AI-powered writing tools. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Amit Gupta: • LinkedIn: https://www.linkedin.com/in/amitgupta/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt In this episode, we cover: 00:00:00 - Introduction to Amit Gupta and Sudowrite 00:02:37 - Sudowrite's capabilities and how it helps writers 00:04:12 - AI writing assistance: Assisting vs. Replacing writers 00:08:41 - Sudowrite's goal of supporting writers in their entire journey 00:09:17 - Building on top of pre-trained models vs. training custom models 00:10:58 - Fine-tuning pre-trained models for new AI products 00:11:42 - The future vision of Sudowrite: Turning individual writers into franchises 00:13:08 - Sudowrite's seed round funding and bootstrapping experience 00:14:11 - Inspiring new AI product creators to start their own projects Transcript:- Dhaval: This founder built an AI product to help writers beat creative block. He's a former writer himself, and in this episode we talk about how to approach building AI products in 2023 and beyond. Specifically, Amit shares his journey of how he launched in August of 2022 and generated a profitable company within a short amount of time with long enough runway. To allow his team and himself to chase the product vision without having to raise multiple rounds of capital. Amit Gupta and his team are building. AI writing tool called Sudowrite. He's a former founder of Photo jojo, a product that he grew to 10 million a year and sold in 2014. After taking a break for a few years, he launched Sudowrite in August of 2020. An app to help create a writers use AI to beat writer's blog. Talk to us, Amit. Tell us about your product, your market segment, your ideal users. I would love to hear about it. Amit: Sure. Our product is called Sudowrite and we're building an AI writing tool for creative writers, people who are writing novels, screenplays, any kind of creative story-based narrative content. And today our audience is those individual creators, and I think in the future, We really want to create a story engine, something that helps anyone who wants to tell a story. So that could be any of the people in the world who always thought they had a novel inside of them. It's too much work. It's too hard to get that novel out. We want to make it easier. Dhaval: Very cool. Tell us a little bit about how you got onto this idea. I know you are a very prolific writer yourself. How did you come up to wanting to solve this problem? And tell us a little bit about your background that you were willing to share with us. Amit: Sure. Yeah. I've started a couple companies in the past. The last company I started, I sold in 2014. Then I decided to take some time away from Tech. I left San Francisco, I traveled and I really decided I wasn't going to do the same thing again. I was going to try something different. So I ended up writing and I spent a few years writing fiction, which is where I met my co-founder in a writing group. And as we were writing fiction, we were bemoaning how much harder it was in running startups because, Man, you get no feedback. It's in a startup, you launch something, right away if it's working, your users will tell you, you'll see it in the numbers. And with writing, it can take weeks or even months to get a few pieces of subjective feedback on a longer form piece. So it's a very different world, much different kind of Learning curve. And we wanted to find a way to bring some of the rapid iterations and the ideation, some of the collaborative elements of working in tech or on a startup to the writing process. Dhaval: Very interesting. What was the process like for you to build your MVP? Did you start with wanting to build an AI first creative workflow, or was it writer first and then add AI to it? Tell us a little bit about how you did your MVP. Amit: Yeah, we were definitely AI first. My co-founder James started playing with GPT-3 when he came out a couple years back. A few years back. And originally we weren't planning to start a company. We were just making a tool for ourselves and for our writing group. And then we shared it with some other writers that we knew and respected. And we just got incredible feedback. So we kept tinkering with it. If I think about a year before we decide to really turn it into a company, It was for that year just a hobby project. A lot of writers that are we knew were using it. And we didn't focus on a lot of the features that most writing tools had. We focused on the things that were unique Sudowrite, the places where we could bring AI to the process and do something new. Dhaval: Very interesting. Did you build on top of chat GPT-3 or was that a private beta at that time? How did you get access to that? Tell us all the juicy details of how did you integrate AI in your workflow? And what was the product market fit experience like that like for you? Amit: Sure. So this was back in 2020. GPT-3 had just come out. It wasn't widely available. I think we DM Greg Brockman to see if we could get access and we just wanted it to play with as writers for our writers' group. So he gave everyone in the writers' group access, and started playing around with it. Eventually we started building on top of it. Dhaval: So where are you in terms of your product? Do Have you launched your MVP do you have revenue? Do you have certain number of users? Yeah. Tell us a little bit about that. Amit: Yeah, product-wise, we started building it in. I think like August or July of 2020. And we started letting people in shortly after that. We started charging people, I think last year or late the l year before, not that long ago. So it's been on the market for a bit now. And I think it's grown steadily throughout the history. As soon as we started charging for it, people started to sign up. We don't have a freemium options it's all paid. And we've really kind of seen it hit product market fit in the last couple months, or at least what we think is product market fit. It's in the past couple months where I think we've seen a lot of people signing up, a lot of people kind of entering the community, asking for things, suggesting things that are rate where like it no longer feels like we're pushing it up a hill. It feels like we're chasing the ball down the hill trying to catch up. So it's a, that's a cool feeling. It's a very quick reversal. Dhaval: Yeah. And do you have any numbers that you can share with us? The number of users you have, your revenue and daily active users or any of that data points? Cause we do have investors also listening to this. So, perhaps they can connect with you if you're willing to share that. Totally up to you. I understand that. Amit: Sure. Yeah. I don't wanna share too much, but we did share recently that we hit 500K in ARR in December, and I think we're above 750k right now. Dhaval: Wow. Wonderful. Do you have any data points around the number of users you have or market shares or rather market segments that you are going after? Any of that information? Amit: Yeah. In terms of market share, I think we obviously have hit like a very, very, very tiny fraction of the market so far. These are really the early adopters. The folks that are willing to experiment with new technology. I think there's a lot of grounds still to be covered. A lot of things, frankly, we have to improve about the product before it's really going to be something that someone who is used to a very polished writing experience is going to want to use. But today it's useful to a lot of people. And I think the market is You know all these people who consider themselves writers, but like I said, in the future, I think that almost anyone who reads has a story in them. So there's a lot of additional people we think we can bring into the market. Dhaval: Yeah, writing is such a fundamental thing, right? Once you figure out writing, you can create many other things on top of it. So I have like two questions that come to my mind. I'll ask the first question first, which are your writers using your product for instance, notion, like it's designed for short form documents, type of work, Google Docs notion word or are they using your product for writing? Like find five hundred page, a thousand. Novels or short documents? What type of short stories or long stories do you have a specific workflow in mind for writers? And I'll let you answer that. I have another question about product management. I'll get into that in a second question. Amit: Yeah. So I think the place where we differentiate is that we're really heavily focused on long form. And that means that our writers are people who are writing novels or screenplay stuff that can go up to like a hundred thousand words or even longer. That's something that's very challenging to do with today's AI models. It's something that requires a lot of kind of like work behind the scenes to condense things, summarize things to, to make it work. But I think where a lot of the value lies because it's very easy to generat

Coco Mao is the CEO & Co-Founder at OpenArt. And John is the CTO & Co-Founder of OpenArt. OpenArt is an AI-native social platform, inventing a new paradigm and tools for expression and entertainment with generative AI. In today's episode, They share how they're helping artists and designers 100x their creativity using AI-generated prompts, and their journey from ideation to launching a successful product. The also share their journey in the world of AI and how they built their successful AI-powered platform. Discover the power of leveraging pre-trained models, adapting to new technologies, and focusing on user experience to make a difference in the AI industry. Don't miss their valuable advice for non-technical founders and aspiring AI product creators. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Coco: • LinkedIn: https://www.linkedin.com/in/kechunmao/ Where to find John: • LinkedIn: https://www.linkedin.com/in/johnqiao0618/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt In this episode, we cover: 00:00:00 - Introductions and Open Art's mission 00:01:54 - Empowering creators with AI-generated image prompts 00:02:41 - The target audience and the line between empowering vs. replacing creators 00:04:13 - The importance of human creativity and AI's role in supporting it 00:05:13 - The customer journey and how AI gets infused into the user experience 00:07:48 - Business results, user growth, and revenue 00:08:53 - The journey from idea to launch and funding 00:10:28 - The journey from pre-seed to product launch 00:11:07 - The goals of their AI-powered platform 00:11:47 - Evolving audience and focus on creative workflows 00:12:28 - Product market fit lessons learned 00:13:59 - Quick product creation techniques for AI product creators 00:15:35 - Advice for non-technical founders in the AI space 00:16:35 - Upcoming API release and market focus 00:18:07 - The importance of community in AI product creation 00:19:19 - The differentiating factors for AI product creators 00:20:37 - Future vision for OpenAI and personal growth 00:22:17 - CEOs that inspire Coco and John Transcript:- Dhaval: With me, we have Coco and John. They are the founders of Open Art, and we'll start off with the question on what does your product do, what problem does it solve and who does it serve? Coco: Thank you for having us here. So in one sentence, open Art is a cutting edge platform where you can discover and generate AI art. Our website, openart.ai has been rapidly growing since our launch in August, 2022. And our long-term goal is to build an AI-powered workflow for creativity. If you think about creative tools today like Adobe Photoshop, Figma, or even Canada, they help you design or create something once you already have a rough idea, but they don't really help you come up with new ideas. You'd still go to sites like Pinterest or be hands check out other people's work to get inspired. However, generative AI can really come up with new and exciting ideas, for you and or with you like no other previous technology could before. So on our platform, we empower users to unlock their creativity with AI by one, generate images based on prompts. And second, train their own personalized AI models. Just as an example, so a fashion designer uploaded her past fashion sketches and trained her own fashion design AI model. And now she can actually immediately get hundreds of new designs waiting minutes. because these designs are based on her pastwork, but entirely new, created by ai and this magical process, really 100x her creativity. So we're really excited to let more people 100 x their creativity with the workflow we're building. Dhaval: Very interesting. So you said you launched in August, and this is a workflow product to empower creators with, creative graphic prompts. Did I get that right? Image prompt. Is that artistic prompts? Who do you serve? Like, is it, you mentioned fashion designers. Is there a specific segment within. Creative industry that you have nailed your product for. Coco: Yes, great question. To be completely honest, we're still in the process of nailing down very specific target audience. But I would say right now, we are, our audience are creative workers like artists, designers, including, like the fashion designer talk about, but creative workers who have like ideation phase. Dhaval: Very cool. Yeah. There is a common theme among the AI product founders, which is if you empower the creators, then your product will go a long way instead of if you trying to replace them. Where do you draw that line? Where do you draw the line of empowering versus actually taking over a little bit of their creativity? Where does that line stand for you? Coco: I think it depends on how people use your product because I think the way I think about it is that for for creative workers like artists or designers, like.At least our product, they can really use it as a workflow tool that really 100x their creativity. And we're constantly talking to this artist and designers how our workflow could actually help them monetize more like help them get more clients. So the way we work with them is very, very collaborative. Whereas I do think in terms of replacing artists I can see like in some use cases, for example, if you don't have artistic skills, let's say you are a writer and like you need some illustration, so perhaps before you don't have good tools to get any illustration. But now you can use our tool to make some basic illustration. However, I do think if you need high quality illustration, you still need to go to artists. So I think the AI could replace some basic work of artists, but then like they want really replace artists. Dhaval: Very interesting John: also think about the human beings, right? So the, the most important or valuable thing is our creativity. The AI just unlock the whole potential of our creativity. So instead of wisdom that maybe 5 years, 10 years, to master like your skills to draw a picture. To paint on the wall. So the AI can help you do that. The most valuable thing I think is, of course your skills is much more valuable. But the creativity is the core part of the whole thing. Dhaval: Yeah, I completely concur with you on ai supporting the creativity and creativity being the most important thing for human beings, right? One thing you said is the customer experience, where in the customer journey. Is AI prompting tell us a little bit about your customer journey and where does AI get infused, like specifically in, in the user experience? Yeah, if you can share that a little bit. And how did you go about making that decision? John: I can give you an example from one with our user on the platform. That, that guy he has a, like a blue character called Coco, , he has like a maybe 20 to 30 image of the, that character he designed and he upload to our website and use our photo booth feature to generate a model.Then one of the special feature we had on our website is like a presets we have like a presets team in our community. With Very good at writing prompt, writing prompt for the Generative AI is really hard, right? But they're super great at that. So we have a lot of presets and that guy will just by bunch of presets and apply those presets on his characters.And you can see the result are super creative. the blue character is on different off page is in different contexts in the background. And I think he really like that because he's trying to upscale and enhance the image for lots of the results. That is really impressive thing. Coco: Yeah. And I want to add one thing is that after, for example, they see so many amazing results. They usually pick the ones they like, and then they can use it as a reference sheet to rejoy it. Or they can actually, if the quality is, they're happy with the quality, they just need to do some touch up on the, on the on the image. And then they can actually, that can be their final like image. Dhaval: Wow. John: I think the guy also came back later and he also purchased lots of model and know, keep changing that factor. Dhaval: Wow. This is very interesting. The three keyword I picked up here are preset prompts and outputs, right. Is the sequence presets, and then from the presets you create prompts and then the prompt from the prompts you create final outputs. Multiple outputs. Did I get that right? The sequence. Coco: almost, I think so the step are like one you need to train your model and training process is super easy. You just upload some photos of the thing that you want the AI to learn. It could be, for example, an image of yourself or image of a character or image of even a consistent style. So you upload it and after 30 minutes. The model is trained. And and then you can purchase those presets that John mentioned, which are essentially highly curated prompts. And then those prompts will be applied on your model. And then the third step is that after those prompts are applied on your model, you can see like hundreds of amazing results immediately. Dhaval: Very cool. Wow. I fully visualize it now. Thank you so much for clarifying. Beautiful product. One quick follow up question is, you said you launched in August, 2022. Tell us a little bit about your business results. How many users you have, how many users are using your product, what is your revenue, if you're willing to share that et cetera, et cetera. Coco: Yeah I think last month we have around 800,000 visits on our platform. And yeah, and our SEO is really picking up, if you search an AI art generator, I think we're the top three results. And in terms of revenue we wouldn't be able to share the specific numbers, but then it is in the range of like tens of thousands a month. Dhaval: Wow. That's a really good MRR for someone who's, like just launched in August. Right. So six months in. Oh, thank you. Yeah. Tell us a little bit about whether you bootstrap this,

Calin Drimbau is the Co-Founder & CEO at Broadn. Broadn is personalized learning through Generative AI. In today's episode, Calin shares how he came up with the idea and how the product works, including the three layers of abstraction and the pipeline of information that flow through the product. Calin also shares insights on his financial journey and the challenges of integrating audiobooks into their data processing. Aspiring AI product creators will learn valuable lessons on how to approach AI product development and accelerate growth towards their vision of personalized learning. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Calin Drimbau:  • LinkedIn: https://www.linkedin.com/in/calindrimbau/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt In this episode, we cover: 00:00:00 - Introduction 00:01:02 - Overview of Broadn's product journey 00:02:26 - Finding the kernel of the idea and launching the product 00:05:05 - User journey and monetization plans 00:09:44 - Conceptual product architecture and data flow 00:13:30 - Building semantic search engine that feeds users valuable and summarized content 00:14:00 - Calin's background in product and his co-founder's deep expertise in machine learning 00:15:34 - Calin's financial journey and plans to raise capital for pre-seed round 00:17:58 - Challenges of integrating audiobooks into AI data processing due to copyright issues 00:19:52 - Quick experimentation and iteration using large language model APIs 00:19:52 - Importance of understanding customer problems before building AI products Transcript:- Dhaval: This founder built a whole new category of ai, product known as generative learning to help you tailor your learning based on the individual context, learning goals, and learning style. Calin Drimbau the founder of Broadn. Shares how he built his AI product using. Three layers of abstraction and have, you can follow the same type of product architecture to solve your specific workflow using AI resources that he shares. Welcome, Glenn. Tell us about yourself and your product. Calin Drimbau:  Hi, pleasure to be here. I'm Calin. I'm the founder of Broadn. We're building a new solution for learning. It's personalized learning at its best. in many ways we're defining a new category and that is generative learning. We're using generative models to be able to tailor learning based on the individual context, learning goals, and learning style. Of users. we're very excited to be building in this space and it's a pleasure to be here and have this conversation with you. Dhaval: Wow, that's like personalized learning at scale. Tell us a little bit about where you are in your product journey. Has it launched? Is it, being billed? Is it in beta? Is it still being developed? Calin Drimbau: Sure. So we've been on this journey for about a year now, and we've launched a couple of products., the first product that we launched was a product that was doing classification on, podcast content. So we would be listening and, transcribing the text from audio and then identifying topics that are being discussed in conversation and using AI and then clipping creating automatic clippings and placing all of these into a platform for learning which was in a form of a mobile app. So that was our first product, and moving away from that, in conversations with , our users, we've learned that what they wanted to do more above and beyond getting the best clips from podcasts. They wanted to navigate and explore this content by searching. so a big problem for people in the audio space is, identifying the most valuable parts of a conversation. , and they wanted to do that by search. so our newest product that we've recently launched is, A semantic search engine on top of podcast content. and happy to speak more about that. We've launched that last week on Product hunt. Dhaval: Wow. Yeah. Tell us a little more about how did you find , the kernel of the idea and, how is it doing now that you've launched it? How is it received by the audience. Calin Drimbau: Sure. So I'm a big podcast listener, and I'm a big consumer of knowledge, if you want from books, articles, YouTube. I consume a lot of information and my personal problem was that especially with audio and video content, it's not easy to navigate, this content. It's usual. It's usually presented in a linear format. So oftentimes , when I'm looking. To consume content is because I'm trying to solve a problem or it's been, it's because I'm trying to learn more about a mental model. So having had experience building machine learning products on text, I thought, why doesn't anyone do? Processing and parsing of podcast transcripts to identify and classify what's being discussed. So that was the genesis of the idea. Beyond that, I suppose once we've launched it , in the market as I've said previously, users wanted more flexibility on how they consume this knowledge the app itself was very useful in terms of like saving a lot of time and instead of having to listen to one hour or two hour podcast, they'd be able to listen to 10 clips on the specific topics that they wanted to learn more about. A lot of the content on the platform is learning content, like entrepreneurship or product or any type of lessons and clips from , podcast surfacing and talking about these topics. But then one of the constant requests that we're getting from users is I wanna have more power in my hands to be able to navigate this content. So if we take Lenny Rachitsky's content, for example which I'm a big listener, of content too, they asked for an ability to search for specific topics or episodes or experts so that instead of listening to, the two hour episode, they'd be able to zoom in and double click on exactly the specific lesson that, that the author or the guest is, highlighting in, in that episode. So that's why we built the search interface on top of podcast content. And we've actually built it. Just on top of Lenny's content as a first drop, obviously the same technology can be deployed and adapted to surface semantic search on any podcast content. And this is one of the things that we are, we're exploring but, podcast content and searching, or semantic search, if you want, is just in our view, the first or the stepping stone in term in terms of realizing our, our vision for personalized learning. Dhaval: Wow. Yeah. I would love to get into the nitty-gritty of how you build the product. We'll get into that in a second. But first I wanted to dive into the user journey. So you have two-sided market. One is the listeners and the other are the creators. Are there any other sides to your market? How do you monetize this product? Is it subscription based product? Is it advertising? Yeah. Tell us a little bit about any other sides of the market you may have, and more importantly, I'm curious about is this like a one central place where the end consumers go to search for podcast clips? Or is it a service you provide to podcast creators who want to put this interface on top of their podcast so that people can consume it more effectively? Calin Drimbau: Yeah, good question. Obviously these are things and ideas that we've been exploring for a while now, whether we pivot into one side, a two-sided marketplace, or a one-sided marketplace for , for creators. was the questions that we've, a question that we've asked ourselves At the moment. Our vision is to create a consumer platform, not a typical marketplace if you want, because we're taking all the public content that is exists out there. So everything that a creator has deployed and it's not just audio. We started with audio and we started with podcast. But we're able to process video, YouTube and we're able to process essays and any type of blog published by, by authors and creators. in terms of the, monetization, cuz you've touched on that, the big goal is for all of these mini products that we're building and validating at the moment to form. A bigger consumer product that will have a multiple set of use cases. So indeed one of the first use cases that the product will feature is the search, or semantic search, throughout a set of podcasts. But above and beyond that, the additional use cases that we're currently working on building in, into the platform are the ability for a user to define and set their learning goals. , and, the platform, it would be able to know and understand the context of the user, and it'll generate, if you want , a dynamic personalized course. For the user. Now, the way it does that is, is a little bit dissimilar to, a typical interaction with something like chatGPT where. You'd be able to prompt the interface and ask for content. And a large language model will process that request and give back some answers to you. We're doing it slightly differently in the sense that, we have micro verse of content that we are pre-selecting, and on top of which we deploy semantic search. So this is why the semantic search, is very important because within the universe of content that is created by. We're first running semantic search, and then we're deploying, summarization and other typical, generative AI techniques to surface that content for users and, and match, their learning goals, their learning style, and the context for which they're asking that. So that will be the main product that we're building. That product is currently being built. But it will take the form of consumer subscription model to answer your question on monetization as well. Yeah. Dhaval: You touched on overall conceptual flow of your product. Let's, let's, dive a little bit in there. So you mentioned microverse. And then you mentioned the ability for semantic search to be sitting on top of that. So if you are to help us understand the conceptual product architecture for the people who are product creators, the audience of this show is product creators, are product cre

Tony Beltramelli is the Co-Founder & CEO of Uizard Technologies. Uizard Technologie is a startup developing AI-powered tools to transform the way people design and build software. He work at the intersection of machine learning, design, and software engineering. Tony Beltramelli studied at IT University of Copenhagen and ETH Zurich. In today's episode, We explore the role of AI in modern product development, the significance of domain knowledge, and strategies for non-technical founders to break into the AI space. Tony shares the journey of Uizard, from its humble beginnings as an AI research project to an award-winning platform with a user community of over half a million. We dive into the world of AI and its impact on product. Tony also give advice for AI product creators. Tune in to hear Tony Beltramelli insights and experiences in building Uizard. Find the full transcript at: https://www.aiproductcreators.com/ Where to find KD Deshpande:  • LinkedIn: https://www.linkedin.com/in/koustubhadeshpande Where to find Dhaval: • Twitter: https://twitter.com/DhavalBhatt • Instagram: https://www.instagram.com/dhaval.bhatt/  • LinkedIn: https://www.linkedin.com/in/dhavalbhatt In this episode, we cover: 00:00:00 - Introduction 00:01:23 - AI's impact on product creation 00:03:32 - The shift in the design process with AI 00:06:12 - Using GPT-3 for design and product creation 00:11:13 - The importance of domain knowledge and UX 00:12:52 - Breaking into AI product creation for non-technical people 00:14:41 - The role of distribution in product success 00:15:08 - Finding initial users and iterating on feedback 00:16:13 - Building a moat around your product 00:17:22 - Tony's vision for his product and empowering non-designers 00:19:09 - Advice for AI product creators 00:20:20 - Closing thoughts Transcript:- Dhaval: This award-winning founder built an AI design product and a user community of half a million users in less than five years. In this episode, we chat about how you can gain a lasting advantage as an AI product creator. We also discuss product development philosophy for anyone who's interested in building on top of chatGPT 3. Tony Beltramelli is a founder of Wizard, spelled with a U instead of w. Uizard. Helps you build stunning mockups and prototypes in minutes. Welcome to the show, Tony. Thank you for joining our call. Tell me about your product. Tell us about where you are, how long have you been doing it, et cetera, et cetera. Tony: Hey, Dhaval, nice to meet you and thanks for having me. Yeah, so I'm the CEO and co-founder of Uizard. So if you wanna look us up online, you need to look for Uizard spelled with a U instead of a W. and we are basically building an AI powered design tool to make it easier for anyone to basically build products, interfaces for mobile app web apps, you name it. Design is pretty hard. So we we bring AI at the core to just make it easier for everyone. . Dhaval: Yeah. Design is the last frontier, right? That's the part that takes a lot of creativity, a lot of lateral thinking. What was your process, thought process like when it came to building the product and how did you infuse AI in the capability? Tony: Yeah. So I've been basically like God brought into AI doing my grad studies. I did, I've been doing like a few projects back in the days. And then in 2017, back when I was working as a data scientist I was just still tinkering around with AI and deep learning in my weekends. And actually it's one of these like research project that was laying down the foundation for the company. So the company essentially started as an AI research project, before you even become a product in a company. So it was really like we didn't have to just back it up, at the end. It was just part of the foundation. Dhaval: That's awesome, man. What was the, when you found a company, wizard, when you define that company? When was it founded? Tony: Yeah, of course. So the, kind of like part-time, weekend project that I'm talking about was something I was building back in April, 2017. But it was just honestly like a side project and we only incorporated a company officially in 2018 with my co-founder, so early 2018. It took a long time to build the product, make sure it worked, make sure to solve a real problem, iterate around a customer, and then we launched out of private beta in February 2021. It took a while.Wow. Dhaval: Did you launch it on Product Hunt or did you have a list to go from Tony: we had both. We had gathered a waiting list of folks that had signed up to our, private beta and Alpha. But eventually, of course, when we were ready to go live, we, we also did a few launches on Product Hunt. We actually won, golden Kitty Awards for best AI and machine learning product in 2021, if I remember correctly. Oh wow. Dhaval: Congratulations. And how was the launch? What was the, can you share your, can you share about a little bit about the launch, whether it was sufficient enough for you to bypass the seed round or, yeah, if you can share any of that data. Tony: Yeah, of course. So at the time we launched, we already had raised roughly 3.6 million dollar of capital. So seed it took, it takes a lot of juice to just build accompanying product. I think Figma took, what, like four years to, to launch, which is like the same story with us. And so launch, it, it didn't like it never happened, like the the launch day and then. Skyrocket, Of course, it's just new spike of launch, you just nurture your user base and it takes time to ramp up. So yeah, it took a long time to just build the awareness build the right network effect in the product to incentivize folks, to invite other folks. But yeah, it took a while. Now we've raised, what, like more than 18 million US dollars. And we have, we are serving growing community of more than half a million users. But it, it took a while to get there. Wow. Dhaval: Yeah. So that's half a million users and the whole myth about all of a sudden you are founder of product market fit and you are getting pulled and your servers are crashing. That was like a romantic story that didn't really happen for you. You had to make incremental improvements that led to finding that fit eventually. Is that what I'm hearing? That's Tony: Absolutely correct. Throughout the. Two years of beta. There was a lot of product iteration. We had to just kill features, relaunch new features, test with customers. It takes a while before you can actually measure and quantify that you have product market fit. , it would've, yeah, that's the road we took. Let's just measure that we have product market fit before we put this live on the internet. But yeah, even though we measured, we had product market fit, it's still not an overnight success. Right. It took a while to just get to the first 10 K, 20 K, 50 k, and so on and so forth. Dhaval: Yeah. That's interesting. There is a, it's a gradual process, right? It's not something that happens overnight and a lot of people have, all of a sudden you'll find the fit. . Tony: No, completely. But then when it works, you can actually really see that it works. In the past, like six months, we've acquired and served more users than we've had in the, in the first year after the launch. it compound. And when the compounding effect works, you can definitely see it. It's a no-brainer. Dhaval: Yeah. And the distribution and the right amount of, capabilities creates the pull in the market. So that's great. Thank you for sharing all of that. Let's dive into, Your, AI capabilities. Tell me how is it different than other design tools that are out there and, yeah. Tony: Yeah, so our features are honestly quite unique. You can. You can of course assume that I'm just trying to market our own product here, but, you probably won't find this anywhere else on any other product. For example, we leverage AI to just enable our users to import a screenshot. So let's say that you are a product manager at Airbnb and you want to revamp that, onboarding flow when you get new people to sign up. If you were to just open Figma, sketch, any other tool, if you don't have anything already designed, you have to start from scratch. So what we've done is that we enable you to import a screenshot of anything, and then we use AI to just recreate what's in screenshot, so you can actually then go ahead and modify it to your liking. So you overcome the white blank page problem in just a few. Drag and drop your screenshot modified. There you go. You have your design. These other places where you, we use AI as well. For example. Let's assume that you are, like me, you know what you want to build, but you're not the greatest designer. So you can tell our ai hey, you know what? This is great, but it looks pretty bad. Can you please just copy and Paste the style of, I don't know, Twitter and make it look like Twitter? And so that's also a place where, our AI can just automatically do this, pull the style of anything and then apply it to your project. . Another example would be to, you, you can brainstorm ideas on paper or on the whiteboard with your team, and then you can just snap a picture of whatever you sketched. And then our AI will transform this automatically into a design that you can then modify. So it's really all these features are, is all about like, how can you, can we just simplify the ideation flow to make people. Focus on the core value, right? I'm trying to solve a problem with this design. I don't want to get lost into the weeds of like how many pixels to the right, how many pixels to the left? Should I move that button? It really doesn't matter when it comes to ideation, and that's kind of like what we're trying to do with ai. Dhaval: Wow. I see you have a lot of differentiation within the product. One, one biggest challenge you are solving is the initial creativity block. That Some designers experience when they have a blank page and you saw that biggest problem, right? So now you

KD Deshpande is the Founder & CEO at Simplified He is the entrepreneurial product leader who founded two SaaS companies, built teams & products from grounds up, raised venture capital, and successfully sold the business through an M&A exit. Marketo acquired his last company Vessel.io. In today's episode, KD talks about how he bootstrapped his idea for the first few months and grew his product using the minimum product payable framework, which is the term that he coined, and his product development approach using OpenAI. KD also shared valuable insights on how enterprise product creators can find the sweet spot in their products and how they can leverage AI to create personalized and defensible products. KD also shared some tips on how to attract and hire the right talent for your team. Tune in to learn more about Simplified's approach to create personalized and defensible products, and some tips on how to attract and hire the right talent for your team. Where to find KD Deshpande:  • LinkedIn: https://www.linkedin.com/in/koustubhadeshpande Where to find Dhaval: • Twitter: https://twitter.com/DhavalBhatt • Instagram: https://www.instagram.com/dhaval.bhatt/  • LinkedIn: https://www.linkedin.com/in/dhavalbhatt  Transcript:- Dhaval: This founder built an AI product to replace your entire content creation workflow, including writing, design, videos, animations, and social content. KD Deshpande is a founder of Simplified A Product. He grew from zero to a million users in one year. and has continued on that growth trajectory since then. In this episode, we discuss how KD bootstrapped his idea for the first few months, and specifically how he grew his product using the minimum product payable framework, the term that he coined, and his product development approach using OpenAI. We also discussed how he recruited his initial founding team and his approach towards recruiting early-stage founder. Welcome to the show, KD. Thank you so much for joining us. Tell us a little bit about you and your AI product. KD Deshpande: Hey, thanks for having me here. I'm KD founder and CEO of simplified. Simplified is a one app for all types of content creation. It's a modern age tool where you can do more with less. We are, as a consumer, we are consuming more content then ever it's a Cambrian explosion of content creation and the tools, existing tools are slow, siloed, and we are fixing that. We are bringing the operational efficiency and truly simplifying the entire workflow from creation to distribution for creators like you who are putting a lot of efforts in creating content, inviting us to be on your podcast, but we want to make sure that we bring the efficiency in your workflow. In every content creator's workflow, that modern marketers workflow, so that way you can do more with less. Dhaval: Tell us a little bit about your market. Who do you serve? Is it sales? Is it marketing? Is it content teams in product? What is your ideal customer segment? KD Deshpande: That's a great question. if you look at like last, probably let's take the last four years. The bar for content creation is lower that with Instagram reels, Facebook, Twitter TikTok especially, they reduce the barrier to entry for anybody can be creator, anybody can be marketers. So if you look at our traditional influencers or marketers are looking like influencers, and influencers are looking like marketers. So, anybody who creates content, Anybody who help companies, businesses, to market their product, we serve them. But primarily, we help digital marketers, digital creators online, small businesses who are focused on creating content. Because content is no longer liability, content is more seen as investment because the brand, the vicinity or the people are shifting from brand vicinity to like influencer affinity. So that's kind of the target audience we are going after. Yeah. Dhaval: You mentioned something very interesting here. People are moving from brand vicinity to influencer affinity. People relate and have always related with human beings more than with brands and very interesting direction here. How does it, how does your product simplified? How does it help creators? specifically, what does it do for them? That resolves their pain points. And where what role does AI play in this? KD Deshpande: Yeah, so AI is really amazing, and last two years we have made tremendous progress, but we not officially saying, oh, we are doing ai. It's part of our DNA our AI is like Gmail Smart Compose it's there. It makes you smart, but Google never says that we are doing that by ai. So that's what simplified ai is. What we are doing is we are bringing the best technologies off the shelf as well as we are building in-house. And you, we are humanizing those. We are humanizing to a point where That marketers sitting in Utah or a marketer running a digital agency or a creator, running creator who is, like recording their next TikTok sitting in Brazil or India. All of them can use simplified to reduce, their time for creation. And then we help them optimize their workflows. Where on simplified it's a one app. Because if you remember, if you look at their workflow, probably your workflow, you have like probably five apps before your content gets ready. From step one app is just for recording other is for curation third is for publishing. Fourth is for analytics. With simplified, we, we are bringing all that workflow in under one umbrella. So that way you can reduce, we can reduce your cost We can save time, so that way you can spend that time doing something meaningful with your friends and family and save a lot more money for these businesses. Dhaval: Wow. So, that's so true. I use five different apps for creating anything I create. There is Like a place for me to research, then there's a place for me to curate. There is a place for me to distill. Then there is a place for me to create once I have distilled and then there's a space for me to edit and publish. And then there is a space for me to analyze. So seven or eight different steps. And each step has its own set of tools. Yep. , and you are saying that you are aggregating all of. Stack into one capability. KD Deshpande: Yeah. We are aggregating more than ag aggregating this stack. We are trying to simplify your workflow. Everything you need as a creator, as a marketer in your workflow. We are bringing them making it available as a part of Simplified Stack. So you just come in today, you can come in, create your you can start with ideation. So we have ai powered like GPT3 powered Writer. You can come in, and we have done a lot of fine-tuning modeling on top of it and built a really simple user experience because see as I said, that marketer or that small business owner sitting in Utah or like other places, they don't care about, GPT 3 three or ChatGPT or Stability or Dali-E. Wh what all things they need is like simple. Which is, which allows them to do more with less. So we have AI writer where which can help you create a lot of ideas. Then we have design editor which can turn your ideas in one, click those ideas into presentations, Gif memes, videos, we have templates. Then you can turn that, take that once that content is ready, plan for month or so you can start scheduling and start publishing on all the channels from Facebook, Twitter, LinkedIn, TikTok, Instagram your WordPress. From one place, you can just go from creation to delegation to distribution. All happens in one platform. So that's what the problem space we are dealing with. And under the hood, we are integrating all the AI services. So that way they are integrated into your workflow. So that way while you are doing it, the things which used to take you probably two hours, 10 hours, we are trying to reduce that in minutes, using the modern age technologies and the capabilities which we have seen, the extreme revolution in last 12 to 18 months. Dhaval: Yeah. Tell me a little bit about how is, how are you differentiating? There is a plethora. AI tools that have come out in the recent history, how are you differentiating yourself from that competition as a product owner? KD Deshpande: Yeah. to be, to give you a fact, there are probably 85 to probably 90 AI writing tools. But all these tools are build silo and where we are thinking differently is. Our tools or our stuff is all about workflow. We are thinking that persona about, of that marketer, how that creator we are living the life of that creator ourself me I'm going recording TikTok reels, trying to live and my co-founder also going through that journey. Our designers on our team are experiencing what it takes to create content. what that life of that small business owner looks like or how the modern marketing teams are working. And the, that's how we are fitting AI into their workflow instead of adding one more silo tool in their stack where they will need to copy paste this from, someplace to their day-to-day workflow. So that's the big differentiator. And second is our product is meant for teams. our whole purpose is let people collaborate. And our mission is not a build a product we are building a space where people can collaborate together , do more with less, and unleash their creativity. And creativity can come in any form. It could be ideas, audio, video, all those stuff. So that's the biggest differentiator between existing things out there and what we are building. Dhaval: Wow. So you are starting with the workflow first mindset. You're taking the existing behaviors existing workflows. You are bringing that into your product. And the second thing I heard is that you are creating a space for people to collaborate and work together. And those are two of your foundational differentiation compared to other siloed work tools that use ai. Yes. Tell us a little bit about your company's State right now. Have you bootstrapped? Are you raising capital? Have you raised capital? Yeah. Give us a lit

Yaniv Makover is the Co-founder & CEO at Anyword. He has done research in the fields of Machine Learning and Natural Language Processing. Yaniv also served as a lieutenant in the Israeli Defense Forces. Anyword's AI Copywriting Platform and also the world's first Language Optimization Platform that helps publishers and growth marketers deliver and optimize the messages they use to deliver business results across web, social, email, and ads. In today's episode, how Anyword leverages large language models like GPT-3 to personalize copy for different segments of a business's audience, providing insights and analytics on how well it will work for specific target audiences. He also highlights the importance of prompt engineering and the value of feedback loops to improve copy performance. He discusses the challenges of building an AI product, emphasizing the importance of staying focused on specific problems and being disciplined in product management to ensure the best user experience. Tune in to learn more about Anyword's approach to AI copywriting and the future of personalized copy for readers. Where to find Yaniv Makover:  • LinkedIn: https://www.linkedin.com/in/yaniv-makover-a8590b3/ Where to find Dhaval: • Twitter: https://twitter.com/DhavalBhatt • Instagram: https://www.instagram.com/dhaval.bhatt/  • LinkedIn: https://www.linkedin.com/in/dhavalbhatt  Transcript:- Dhaval: This founder built an AI copywriting product and got over a hundred thousand users in two years. In this episode, we talk about his approach to deciding where to invest your time, money, and energy, as an early stage AI founder and I learn a lot about his approach towards prompt engineering. Yaniv is the CEO and co-founder of Anyword leading AI copywriting solution designed for marketing performance. He has a Master's in Computer Science and Information Systems, and he has conducted extensive research in the fields of machine learning and natural language processing. His work optimizing ad and content channels for some of the largest publishers like New York Times, Lead him to found Anyword generative AI platform. Yaniv oversees operations across Anyword's New York, Aviv and satellite offices across the world. Dhaval: Welcome to the show. Yaniv. Tell us about your product. Yaniv Makover: Hi. Thank you for having me. Yeah, I'm co-founder & CEO of Anyword. We are in the coparating AI space. Our product primarily focus design on making copy more effective and converting more and engaging more. Dhaval: Wow. Okay. So Anyword and. how is it different than the plethora of other copywriting tools, AI-enhanced copywriting tools that are out there, at this time, or they're probably gonna come out now that there's a lot of those capabilities? Yeah. Tell me about how is it different. What's the differentiation there? Yaniv Makover: So we started out from becoming performance mindset. And I think there's one. Big problem that generative models can solve, or language, large language models can solve is basically helping you just get more content out there and high quality content removing solving for writer's block. And I think that's an interesting, huge problem to solve. For us, it's kinda like not our DNA. Our DNA is more about in our products DNA is how to make your. Copy better. So you already are a marketer. You already know what you're doing. You have a strategy. Yes, you could get ideas from ai, but this is how Anyword will work better for you for a specific audience. Or you're selling, I don't know, a sweater to somebody in the US versus other countries or a different occupation or different age or different gender. Then how, what are the best words to use for every use case and I think we use large language models to actually empower those insights or actually leverage those insights, to create ROI for our customers. I also think that when you're just a click of a button away from creating hundreds of variations of copy we thought it was a really big problem to solve. Which one are you, you're gonna publish, you can't really A/B test 1000 tweets you have to send one. And so we thought that was like the biggest problem solved. So we, early on, we focused on that. And our product, we pretty much tell you if with every copy variation that the AI generates, how will it will work and for whom and why. And if you wanna make improvements, how to do them. Dhaval: Cool. Okay. So you're taking the existing. LLMs and you are not only generating content, that's an easy problem to solve, but what you're really doing is you are helping fine-tune that content to resonate with the specific audience that your customers may have, and then fine tune the copy to increase engagement or retention with that audience is what is the primary metric that you aim for with your customers to improve? What is their North Star metric that you're helping them? Yaniv Makover: So typically it depends on the use cases of the marketing, but they'll, they'll measure lift in conversion rate or lift in engagement, and then they'll measure just ROI so if they're running ads, they'll, they should be able to see a lift in their ROI or if they're, the conversion rate on the lending page or open rates and emails. And it's pretty easy to measure easily. Just copy and see if see if it works for you. Dhaval: So do you, how do you get the information on their audience, like to fine tune the output with that highly fine-tuned output? Yeah, Yaniv Makover: so Anyword collected its own data, and basically, we have our pretty large corpus of data, performance data. And also when we, we partner with our customers, our partners, basically they have their own data sets, and then they upload in them into Anyword, and then we have, we fine-tune what we call custom models to help them predict better how their copy will do. So, for instance, just based on our data, we have an accuracy measurement of how well our model predicts performance of like copy that we already knew how well work. Somewhere around 76% depending on, on what we're testing. But if you, bring in your own data and you've actually A/B tested or just ran a copy in the past, then it goes up to 85. And that's just because you have your own audience, kinda like your own topics. I think for me, one of the most interesting parts of the space of large language models, not only they can write really well, they also understand text really well. So like five years ago, if you train a. Just lots of text and tell it, this text is good, this text is bad. We'll probably figure out that one has an emoji or an exclamation mark. But now there's a deep understanding of why text works. Like are you using, if you're missing out is that even relevant for some audiences and for some products or industries? And I think that's super exciting. So I think it wasn't possible a few years ago. And it's possible now. And I see this as kind like a. Booster and performance for marketing. Dhaval: Hundred percent. There has been a plethora of content generators using chatGPT and tools like that to create content. Dhaval: But what is still missing is the ability to fine tune that output to the specific segment of your audience and then be able to create content based on that readily, readily, as in with a click off a button. I'm sure you can stitch together a few data pipelines. Do that with existing tool suites. But what I don't see happening is the ability to just readily click of a button, say, this is my audience, this is my content. Generate some copies. Is that, am I understanding your pro product correctly? You offer that? Yaniv Makover: Yes. For every, like, while you're typing, you can even not, you can use your own copy, not even generate with ai. You'll have insights, analytics about that. Copy how well we'll do for your target. The way you defined it, what talking points work better and maybe replace them with others? And you can use your talking points that work for you while you're running ads in your emails and the talking points that work in your emails or the insights you gain from that and your landing page. And I feel like that is kind the future where there's just so much content that's gonna come out. You really know you have to know what, what resonates with your audience. Dhaval: Yeah, I imagine a world where the specific copy will be highly personalized to the reader that is consuming that information. And it could be hundreds and millions of variations of that depending on who's the reader, right? So what I am curious about is when did you. Now that I have the context of your product, let's talk a little bit about your business. Tell me about when you founded the company, and, tell me a little bit about the number of users you have amount of your revenue, et cetera. Anything you can share to provide us context on your business? Yaniv Makover: So Anyword launched March 21. And basically, it was a spinoff of the first company we founded, which called QE QE it's a SaaS platform for publishers, New York Times, CNN Washington Post helps them. Distribute their articles and their content on, on, on social platforms like Facebook. And then what we figured out there and QE works with 70% of the top media companies in the US What we saw is that some of our customers did way better than others in engagement and conversion rate, just based on their copy. And we thought, okay, we can help our other customers to, to write better copy. And then that was kind of the. What made us think about Anyword? And then the problem was much bigger. Not just copy for social posts, but you can even rewrite today a whole article for five different audiences. So you might be reading a different version of that same article, then me and the author. It's the same the same author with the same idea or message. But we'll be reading different articles that talk to us based on our. Words and familiarity and language. And I feel like that's s

Lilly Chen is the Founder & CEO of Contenda. She is the former Software Engineer at Meta. Contenda's artificial intelligence tools reimagine your content in new formats for your audience to discover, with no extra work from you. In today's episode, We talk about how they manually labeled their data and created a golden test set for how people viewed content. Lilly also explains how they use AI to transform video into written content publishable on their users' websites without any additional work. She also gives advice to product creators interested in infusing AI into their existing products. If you're interested in building AI-powered content creation or want to learn more about the benefits of using AI in your product. Tune in to hear Lilly's insights and experiences in building Contenda and how you can apply these lessons to your own business. Where to find Lilly Chen: • LinkedIn: https://www.linkedin.com/in/lillychen48 Where to find Dhaval: • Twitter: https://twitter.com/DhavalBhatt • Instagram: https://www.instagram.com/dhaval.bhatt/  • LinkedIn: https://www.linkedin.com/in/dhavalbhatt  Transcript:- Dhaval:This former meta engineer turned a hackathon product into an AI startup with 150% growth month over month. In this episode, we talk about her launch story, how she built the initial team, and her product development journey. Specifically, we discussed an interesting learning lesson around innovative approach she uses for data labeling and creating the golden data set for building your ML model. She shares a few examples of how product creators can get started with building ai. Without deep technical expertise, she also shares a tangible workflow that they can use to do so. Today my guest is Lily Chan. She's a former software engineer at Meta. She's the founder and CEO of Contenda. Contenda is an artificial intelligent tool. Reimagine your workflow in new formats for audience to discover with no extra work from you, and scale your existing technical content faster. Welcome to the call, Lilly. Thank you for joining us. Tell us about your product. Lilly:Contenda Scales, technical content marketing for developer advocates. Dhaval: Okay. What does that mean? Tell me. Tell me, that's very interesting. You got that. You nailed it. You nailed the position there. Lilly: I mean, the buzzword of the day is generative ai. We do fall in that category. Contenda uses large language models to generate technical content with a high degree of accuracy. Dhaval: Wow. Technical content is the hardest content to create of all the content categories, right? It's very easy to spit out sales copy. But technical content that's pretty challenging. So your audience is developer is that right? is a typical developer who wants to create documentation technical documentation. Is that your audience? Tell me a little more about your target user. Lilly:Our target users they're called developer advocates, so they write content for other developers. They will oftentimes do a live stream, a lecture, or a conference talk, and then they need to transform that content into a written form, such as a blog tutorial and sometimes documentation. Dhaval: Got it. Yeah. So I, as a product person, I have to write a lot of documentation based on. Product launch based on the product demo. And that's the type of a content, you help advocates with. Is that? Did I get there, right? Correct. Okay. Now, tell me where you are in that journey of your product. Is that something you have launched? Are you working on it, et ceteraLilly: When we first started building this product, we served everything through email and Google Docs. That meant that you can't sign up on our website to use the product. You just had to email me personally. We spent all of our time building the machine learning and backend infrastructure to do that. Now we have reached the point where our interest form on our website has grown 150% month over month for over the past quarter and a half. And these hundreds of people can't get on our platform unless we build one. So that's what we're currently doing, building a platform. Dhaval:Wow. Okay. So you have productized it. To your manual workflows, and right now, you are automating those workflows to create a user experience. How many users do you have? , is that, can someone use your product now? If they want to use it, how many users do you have? How much revenue do you have, et cetera? Lilly: We currently only do enterprise-level conversations. That's something that we're looking to change. We would love to get in touch more with developer advocates who have a personal brand and are thinking about using contender for their own Twitch stream or Twitter or blog, and then eventually get to a point where we can say, Hey, would you like to bring our product to work? Would you like to use it on the enterprise level? Dhaval: Wow. Okay. So would you say that you have any revenue at this point? Do you have customers that are using this at enterprise level? And, if so, like where does that sweet spotlight for you right now and in the future? Lilly: We do have a few enterprise customers. I won't disclose what each person is paying. But the total ACV value is somewhere between 50 k to 100k plus. Dhaval: Got it. Is this something that you have bootstrapped? Have you built this on your own? Have you raised a round of funding? Tell us a little bit about how you got to it. Started it. Lilly:Well, funny enough, it actually started off as a hackathon project. I was working as a full-time machine learning infrastructure engineer at Meta, and I had just flown out some friends to California for a hackathon project. That hackathon project was for Twitch streaming. It was a retention project for Twitch streamers, and our project went viral. It ended up helping a Twitch streamer break a Guinness World record for most subscribers in a month. It went viral, and a couple of news outlets picked up on the story. So investors reached out from there, and that's how we became a venture-backed business. Dhaval: Wow. So what was this hackathon project trying to do? You said something about retention for streamers. Tell me a little more there. Lilly: Right So our idea was to build something HubSpot esque, but for content creators on the individual level, Twitch streamers have really, really high churn on their subscribers, and so we wanted to build a product that could help a Twitch streamer retain subscribers over time with the project that we did. Lilly: His name was Ludwig. He's. It's currently the Guinness World Record holder for most subscribers on Twitch. We wanted to see if he spent a dollar with us, how much we could retain for him over time. And in the first month, for every dollar he spent with us, he earned a dollar 70 back, and then the following months it would drop off, 50 cents, 20 cents, so on, so forth. But overall, it was very good margins for both of us. Dhaval: Wow. So how did you do that? What was the recipe to increase the retention there? Lilly: Right. So if you are a Twitch subscriber to Ludwig, you received a notification from us that you could come and fill out this form. On this form, you would be randomly distributed into one of two groups. In one group, you received a digital hello from Ludwig, and in another group, you received a physical sticker in the mail that had a Ludwig emote on it. And we basically ran this ab test to discover that these real-life people, the people that you interacted with through the physical stickers, retained much, much higher over the period of a quarter than the other group did. Dhaval: Oh wow. So then this project went viral. You helped that streamer make more money. What happened after that? Like, did you keep the team together, and you continue to build the MVP? Did you raise the around of capital? Tell me a little more. Tell me all the juicy stuff that went into after that. Lilly:Yeah, we got our first million-dollar check shortly after that project was released. The funny thing about working with a Guinness World record holder is it only gets worse from there. The market only gets smaller, so pretty quickly, I would say by that summer, we realized this project had no legs in the venture market, and we needed to pivot. The team stayed together. But that's when we ended up discovering. Developer advocates because they were streaming on Twitch under the science and technology section. Dhaval:Got it. So you kept the team together. You used that initial funding that you got to pivot towards advocates. And that's what you're, you build a product, you got some more enterprise customers, and now you are trying to further create a UX experience around that so that you can have more, more users. Did I get that product journey right? Lilly:Absolutely nailed. Dhaval: Awesome. Wonderful. So we got the context of what you were working on. We got the context of what problem you were solving. Let's unpack how you built the initial team. What was the process like? How did you attract those people in your team? And then, before you answer that question, tell us a little bit about your background. Are you a technical co-founder? Are you a technical founder? Are you the, playing the CEO role, the business role? So tell me your role, and then tell me about how you got other people to join this interesting project with you. Lilly:I'm the CEO and founder. I have a technical background. I used to be a machine learning infrastructure engineer at Meta as well as a DevOps engineer at Rapid Seven, a public cybersecurity company, and I've been the first software engineering hire at a gaming startup. That being said, I don't have a CS degree. My background in undergrad is actually economics and math. I thought someday I might pursue a Ph.D. in economics. I'm also a high school dropout, so education, formal education-wise, I have very little. That being said, I would say my team is relatively di