This Week in Startups - Real-time AI-powered design with Krea CEO Victor Perez | E1850

Episode Date: November 17, 2023

This Week in Startups is brought to you by… Squarespace. Turn your idea into a new website! Go to http://www.Squarespace.com/TWIST for a free trial. When you’re ready to launch, use offer code TWIST to save 10% off your first purchase of a website or domain. Masterworks. The first company allowing investors exposure into the blue-chip artwork asset class. TWIST listeners can skip the waitlist by going to ⁠https://masterworks.com/twist⁠ and using promo code TWIST. Fitbod. Tired of doing the same workouts at the gym? Fitbod will build you personalized workouts that help you progress with every set. Get 25% off your subscription or try out the app for FREE when you sign up now at https://fitbod.me/twist. Today’s show: First, Jason interviews Krea CEO and Co-Founder Victor Perez, who demos Krea's creative suite, highlighting its unique blend of AI prompts and interactive design elements (2:40). Then, testRigor’s Artem Golubev breaks down how their AI-powered software helps businesses streamline QA processes (48:54). Time stamps: Time stamps: (0:00) CEO and Co-Founder of Krea.ai join Jason (2:40) Victor demos Krea’s real-time AI capabilities (8:24) Squarespace - Use offer code TWIST to save 10% off your first purchase of a website or domain at https://Squarespace.com/twist (9:22) Startup culture at the Krea house, Krea’s wide range of users, and its unique approach: enabling precise control over object placement and evolving sketches into final products (17:22) Future plans for video-to-video workflows, Victor’s background, Stable Diffusion’s capabilities, understanding the concept of 'weights' in AI (26:03) Masterworks - Skip the waitlist to invest in fine art at https://www.masterworks.com/twist (27:22) Debating copyright concerns and the state of training data in Stable Diffusion (32:20) The challenges of the startup scene in Barcelona, trying the Blueprint diet (36:32) Fitbod - Get 25% off at https://fitbod.me/twist (38:00) Artem Golubev, CEO and Co-Founder of testRigor joins Jason and explains testRigor's AI-powered QA solution (41:29) testRigor's AI approach: Streamlining software QA testing (48:54) Examining the impact of AI on dev teams, and testRigor demo * Check out Krea: http://www.krea.ai Check out testRigor: http://www.testrigor.com * Follow Victor: https://twitter.com/viccpoes https://www.linkedin.com/in/iamvictorperez Follow Artem: https://twitter.com/artemtwtr https://www.linkedin.com/in/agolubev * Thanks to our partners: (8:24) Squarespace - Use offer code TWIST to save 10% off your first purchase of a website or domain at https://Squarespace.com/twist (17:24) Masterworks - Skip the waitlist to invest in fine art at https://www.masterworks.com/twist (27:22) Fitbod - Get 25% off at https://fitbod.me/twist * Great 2023 interviews: Steve Huffman, Brian Chesky, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland Check out Jason’s suite of newsletters: https://substack.com/@calacanis * Follow Jason: Twitter: https://twitter.com/jason Instagram: https://www.instagram.com/jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis * Follow TWiST: Substack: https://twistartups.substack.com Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin * Subscribe to the Founder University Podcast: https://www.founder.university/podcast

We're getting very close to going from somebody's mind and doodling to a finished product. Is that what's happening here? That's 100% what we are trying to make. I mean, we were trying to make that happen. And so filmmakers are playing with it. Other folks are playing with it. People have had their minds blown by this. You've got hundreds of people paying 30 bucks a month for this, dozens.

Where are you at as a company? So we have a waiting list of more than 200,000 people right now. Wow. This weekend startups is brought to you by Squarespace. Turn your idea into a new website. Go to Squarespace.com slash Twist for a free trial. When you're ready to launch, use offer code Twist to save 10% off your first purchase of a website or domain.

Masterworks is the first company allowing investors exposure into the blue chip artwork asset class. Twist listeners can skip the wait list by going to masterworks.com slash twist. and FitBod. Tired of doing the same workouts at the gym? FitBod will build you personalized workouts that help you progress with every set. Get 25% off your subscription or try out the app for free when you sign up now at FitBod. me slash twist. All right, everybody, welcome back to this week in startups.

There's a ton of AI powered creative tools that are coming to market. You may have seen Canva, OpenAI, Adobe, MidGerm, obviously stable diffusion, runway, so many different products and tools to help creatives make more interesting output faster and better and even allow maybe people who aren't that creative to get creative. All these platforms run in a similar way. You enter a prompt. And then the model goes to work. You wait a couple of seconds, sometimes a little bit longer. And then it spits out an image or maybe a collection of images. And then you refine those images further with more prompts, and thus restarting the cycle, it's not ideal.

And it doesn't actually work that well unless you have a lot of creative skill and you can refine those images. It's very rare that the images come out fully baked and ready to go. But a new startup called crea.ai, that's with a K, K-R-E-A-D-A-I, is working on a real-time AI-powered creative tool set that works in the browser. It went viral on X this week. So we decided to have the CEO on. X's the website, formerly known as Twitter. Victor Perez is here today, and he's going to show us how it works.

Victor, I don't think you've done any interviews about Crea yet. So this is this week and startup's exclusive, I think. Welcome to the show. Yeah, thank you so much, Jason. I'm super happy to be here. So let's get right to it. Show me what you've built and why people are losing their minds over it. Let's go.

What we have here is a tool, by the way, this was built in one week. Right? So there was this new technology that got released. we were here at right when I'm recording in a party where we decided to make like a fun interaction with this new technology. So everybody that it was in front of the webcam would be turned into something else in real time. That's how this tool started. And after the day of the party, we realized like, holy shit, this is actually very,

very interesting. Should we build an interactive tool on top of this? And in a few days, we already hacked the whole thing together. And very, like, we divided the team. Some of us were, working on the infrastructure, on making an infrastructure that can scale, because this is kind of crazy to have thousands of people being generating images in real time. And some others, we were working on the design. So we're in a web browser, essentially, it looks like. Yeah. And on the left-hand side, there is a pink circle that is layered on top of a blue rectangle. and on the right we see what looks like a mushroom with a pink frog on it that mimics the very simple circle and square.

So explain to us what the prompt is here and how this all works. So the prompt here, it just blue mushroom in top of a pink frog. Yeah, like that's exactly like what the AI is doing is getting this initial image that we have on the left side. And it's starting it into something that looks very much realistic on the right. This is not nothing new, right? Like, we've been able to do this with models like stable diffusion before. This technique is called image to image. So this is nothing new.

What is new is that if I start moving this pink circle, you will see that the frock starts moving around in real time. Right. So this is right now giving me full control. It's giving me a whole new dimension for prompting, which is like right now I can prompt this AI model visually. And so what I could do right now is, for example, let's say that I want a, I don't know, instead of a pink frog, I want a bluebird on top of this blue mushroom.

So what I could do is get this same shape, change the color, turn it into blue, and what we should be able to see is a blue bird on top of a blue mushroom that if I start, and that's the thing like only through prompting. Right now with the stable diffusion, I would have gotten something like this, that doesn't look at all like a blue mushroom in top of a plum mushroom. in top of a bluebird. Actually, that's kind of a hard one. Let's do a bluebird on top of a blue mushroom.

That would be an easier one. So if I change the color here, like as you see what I'm doing. So you're changing the color palette, and then AI is automatically reacting to that. So by using the color palette, it's sending a prompt, I guess, saying, hey, use this color to the AI in real time.

Exactly. Yeah. It's just like you can think of it as a visual copilot where like we are used to copilot in in text that it kind of auto-completes what you want. Here like the input image is on the left side and it's kind of out-to-completeing it with this final result that looks very realistic. And so what's the back end here? What are you using? What's the language model or the image model? The image model is a version of stable diffusion that is distilled with a new technology that is called consistency. And with this model, we're able to run stable diffusion. Yeah, we're able to run a single image in 40 milliseconds, which is insane. So it will do a single image in 40 milliseconds. And so it's almost to the point where it is when you drag and drop this and move it around. So if you were to move the bluebird to the other side of the mushroom, it kind of does it, you know, whatever, five frames a second, four frames a second, something.

Yeah, that's mainly because of the all the network delays. And so how much compute power is this used? or have the stable diffusion models gotten so good that they don't require as much compute? You can run this model really on like 3090 or like some GPU that you could have on your computer right now to make it work at these speeds we are using A100s. Got it. And so if you have an A100, which costs, I think, 20 grand still, something in that range, how many people could be doing this in real time on one of those units? Right now, I think that we should be able to support from 4 to 10 people.

So in other words, you need about, let's call it, $3,000 in compute to be doing this in real time, if you would divide the GPU by the number of people using it. So that's what desktop computers cost today. It's not that big of a deal. Where is this going? What else can it do? Oh, yeah, I think that we should think of this demo as the worst it will ever be. this is the dumbest like the AI will ever be.

This is about to get way more efficient and the quality is about to get way better. So we can expect that in the who knows when because like breakthroughs are very hard to predict. But I can think that in a few months we'll be able to see these demos running on your own computer. Maybe on your M1, M2, I wouldn't be surprised if that happens. And we're kind of like setting up everything towards that.

And Apple just launched the M3. And so there are, chip set is obviously getting powerful enough to do this. And maybe next year you'll be able to do this on a desktop computer or a laptop, you think? Yeah? Yeah, like, I don't really know when it's going to happen, but it's 100% going to happen, yeah. If your landing page looks terrible, I'm out. We all know that. You see an ugly website, you skedaddle. You leave. You're done. So you need to stop selling for okay or good and start using Squarespace so you can be excellent and extraordinary. It's an out-of-the-box business solution to build beautiful websites, engage your audience, and sell anything you want.

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And when you're ready to launch, go to Squarespace.com slash twist for 10% off your first purchase of a website or domain. You guys are in a hacker house there. I see like three or four people writing code behind you. Where the heck are you?

Is this your startup here? Yeah, yeah, yeah. We all live and work in here. Are you in the Bay Area? Where are you? Yeah, yeah, we're in San Francisco. This is more like the Korea house, right? Like we have founders, founding engineer,

is also living here in a room. There are like some friends in the city that we just like set up in here so they can sleep. How long do you guys stay? How late you guys stay up, Coden? What's the vibes? This few weeks has been a madness with all this growth and like trying to scale up

all the GPUs and onboarding users. We stayed here until very late. Normally Diego, my GoFounder, stays here until 3, 4 a.m., normally. I normally go to sleep at 1. So people are grinding it out. You guys are super motivated. And so is this a company now that you've built?

Have you raised money for it? And then who are the customers that you're trying to get on board? And what are they using it for? Totally. Yeah, this is a company. We raised our Cid Brown last year. And the customers are mainly creatives. We are right now focusing in kind of a consumer product.

So the spectrum of people that get interested in this is pretty wide. Like we have from professional film directors that they are creating shots for a mood board that they want to show to their art directors, for example. We have graphic designers that are using this tool to make letters, like kind of typographies or to make all sorts of illustrations. We have 3D artists also making like kind of brainstorming or making like very low or yeah, like images that they can show to their clients to confirm if they can proceed to go and to a professional tool and actually like spend time doing it.

high resolution. And you charge 30 bucks a month for this tool, which allows you, and people don't know this, but this is how, like, Ridley Scott works. Ridley Scott, the film director, aliens,

Blade Runner, gladiator, etc. He makes these Ridley Scott like tiles, basically little drawings on the set, and then they work with the cinematographers and everybody to make it happen. I'll share my screen here because I have a couple of the demos

that were shared on Twitter. Yeah. So here is a demo somebody did, just drawing the moon, and we see the trees behind, I'm not, I can't see the prompt there. It's a little bit too small, but they put in some prompt, obviously, to make a spooky, nighttime foggy place, I'm assuming. But they start drawing. And when they're drawing, you know, very rough sketches of waves or whatever, it just makes this incredible,

uh, evocative image. And so this feels like we're getting very close to going from somebody's mind and doodling to a finished product. Is that what's happening here? That's 100% look. We were trying to make, I mean, we were trying to make that happen. Like getting a great interaction, getting a great communication with AI, so you can use it end to end from pre-idea until a product that it's 4K resolution, extremely detailed, and that you can use as a final result for whatever client you have. And so filmmakers are playing with it. Other folks are playing with it. People have had their minds blown by this. You've got hundreds of people paying 30 bucks a month for this, dozens.

Where are you at as a company? So we have a waiting list of more than 200,000 people right now. Wow. We have people paying, but right now that's actually an issue because we are still working on scaling this up. We are doing it gradually. We started rolling out in bytes during these past days, and we are doing it very slow while we monitor how all the GPU cluster handles all the requests. And yeah, we had issues because people were paying, thinking that they would get access right away. So only yesterday we got like more than $7,000 or $8,000.

of people paying. And yeah, so all that people will get access, of course, like either today or tomorrow. But yeah, like we're still not letting them pay because we want to monitor all the cluster and we want to make sure that everybody can have a good experience. Yeah, and so I could see this working for graphic designers, people building webpages, people doing illustrative work. And now you could have, you know, if you think about just journalism, Victor, in journalism, there was an art department that would do illustration.

Now you could have the journalists themselves say, hey, I'm doing this, you know, editorial about, I don't know, U.S.-China relations. I want to do an image of the U.S. as an American Eagle and China as a dragon and, you know, just start riffing and, you know, show the Pacific Ocean, whatever, put Taiwan in the middle and, you know, I'm just riffing here like a journalist might. And it could make an incredible image that would normally take an illustrator, you know, a couple of days and cost. what would probably be low thousands of dollars, and it could just be done by the journalist in 10 minutes. It seems like you're pretty close to that, huh? Yeah, yeah, we are almost there. And for people who spend a little bit of time on learning how to use these tools, I would say that we are already there. Like, people are already making things that they can publish in, like, a blog post or like in the news, et cetera. You can already get very nice quality.

Do you have any news outlets using this for illustrations or cartoons or that kind of stuff? because I was just thinking, like, the Dilbert guy, you know, he does everything digitally, Scott Adams. But now he could just talk to prompts and you could make a Dilbert AI. And with Chat Chappetee Ford, it could be making jokes too or at least giving you ideas for jokes. You could make a verticalized version of this and everybody could make Dilbert paneled cartoons for their organization or to do marketing. I mean, I guess if you allowed it and licensed it, it would be crazy. Yeah, yeah, totally. Like right now we've seen it in a couple of places.

I just talked with some guys that they were working on a project. It was not exactly journalism, but they were doing like kind of a documentary of people that were tortured by the police in Barcelona, which it was kind of a little bit crazy. And they were telling me how this tool, it was pretty interesting because they want images, right? Of these people that they were tortured, they wanted them to create the exact places where that happened. I think that the main difference of what we are doing is that you have full, like, way. more control over the final image. You can really decide this object needs to be here. This person needs to be there.

It needs to be in this position. And you can make this with very, very simple doodles. Then we'll find it with the prompt. And the AI will 100 understand what you mean. And you will be able to, yeah, to get it like with way higher quality and with a much better composition. This is a classic documentary technique. If you can't get, you know, to recreate what happened, they'll do illustrations. moving illustrations.

And there was a really great film. The kid stays in the picture about Robert Evans. I don't know. Have you ever seen it? Kid stays in the picture. You should watch it. Just an incredible documentary. But in this documentary, they will show, you know, moments in time, but they take pictures

and they kind of layer animation behind them and kind of bring things back to life that, you know, they don't have documentary footage of. And so this is a classic technique, but you, it's expensive to create animation, right? even if you're outsourcing it to China or Korea has a big animation outsourcing business. Here, you could just make incredible illustrations yourself and, yeah, fill in the blanks for people when they're trying to get a visual. And that would typically be on a documentary budget. That could be like half the budget.

A third of the budget could be making those. Maybe a third of the budget could be making those illustrations. If you're making a three or four million dollar documentary, you might spend a half million dollars, a million dollars making that art and that art direction. now, who knows, I think, could be done for close to zero. Yeah, totally. And this same idea is going to be applied everywhere from product photography to, yeah, like advertisement, gaming. I think that we will start seeing AI everywhere because it just makes sense.

You're going to be able to use this technology end to end and get the same, if not better results than the ones that you're getting right now. Okay, this is doing static images. Is there an ability to do moving images yet or loop videos? Yeah, so we've seen people recording their. screen while they move the shapes, which was very, very interesting. So we've seen people doing these kinds of kind of, even psychedelic animations, because you see like all these things changing all the time.

But there is one model that got open source a few weeks ago. It's called animate diff. And we are already thinking on ways how we could apply this technique to this video model. And once that happened, we should be able to make a pipeline that works for. from video to video, and you're able to get extremely consistent frames out of your very simple shapes. So, yeah, that should be possible very soon. So how did you get into AI? I know you went to Cornell University.

I know you published a paper on animation. But how did you get into this? And how long have you been doing it? So everything started around 2017, so six years ago. And my story is before going to university, I was very interested in artistic things, especially on music. I see that you have a guitar in there. I've been playing since I was eight years old. And I had a music band through all my teenage years.

And I was just like, yeah, recording music, making photos, making like all sorts of creative things, painting graffiti, all of these. At some point, I decided to study computer science in university. And in my third year, I got introduced to image processing. And with image processing, I started to learn a little bit more about neural networks and artificial intelligence for image processing. Very shortly, I discovered that you can not just process images and detect objects, but you can also generate. So I got introduced to things like D.C.GAN or like StyleGAN, that these are these early generative AI models for generating images very realistically. And at that moment, something clicked in my head. And I went crazy to understand everything about how they work. I started to read a lot of papers, make a lot of implementations, and use this technology as a

creatively. And I guess what I saw at that moment, it was a new kind of creative medium. Like, if I wanted to record a, I don't know, like a hip-hop beat, I needed to learn how to use this program. If I wanted to do like a 3D shape for a video, I had to use cinema 4D. And I was like in this loop over and over and over. And I think that with AI, what I saw is something that can execute your ideas for you, which I think that is the most important thing on any artistic or, on like the artistic process. I think that the really important thing is like the idea that is behind the process

and the execution is just like something that is in between and that enables you to get these ideas out there. So I think that since I saw, since I started messing around with AI, I really saw this. And that's what I've been. Stalkan was done inside of Nvidia, I think.

Yeah, yeah, it was Nvidia. I remember there was a famous Uber engineer who did, this person does not exist, which was a website that would make, a photo of a person that doesn't exist. And that kind of blew people's minds because then all of a sudden this concept of stock photography where you have stock photography models do things like, I don't know, pour a bowl of cereal, but you have to find a person and bring them to a studio and have them, you know, spend a day doing inane things like pouring a bowl of cereal or, you know, eating cereal from a bowl. Now the whole concept of stock photos is just you don't have to do that. You can just make one. And style GAN is still going, yeah? Or is it stable diffusion has just leapfrogged it so fast? Yeah, I think Gans are not a thing anymore.

I think that Stable Diffusion Jets, it was better. Like, the main difference is that it's able to do everything, whereas Gans are only good at doing a single thing. Like, you can get Gans making faces or making cars, for example, but they will only be able to do that. And there's some new research that tries to fix that, but it's not working too well. So Stable Diffusion is an open source product. There is a company called Stability AI.

that is the company by some of the people who worked on the project or created the project, and they've raised billions of dollars, and they're going for the gold there. But anybody can fork stable diffusion and do what they want with it, correct? Yes, totally, yes. It's open source. Yeah. So there are a ton of startups coming out of that space. How many people are working on that project?

And explain to the audience who are unfamiliar with open source what the activity is within stable diffusion and then how code gets committed. and what that's like right now, because this is a very unique moment in time where there's a gold rush, there's so much creativity, there's so many ideas. What's happening to the open source project of stable diffusion? Because I don't know if they've ever had a project that's had so many people who want to participate. You tell me. Yeah, I think that what is crazy about stable diffusion is that they are making accessible this thing that costs so many millions to create, right? Like Stability AI goes ahead and shares the weights of this model.

that they spend millions of dollars on GPUs in training, and now people can use it freely to do whatever they want. Once it happens, what you see is like a crazy amount of improvements. Like a year and a half, I guess, it was August. Last year, it's when the first version got released. And I remember, like, at that moment, the best thing that you could try, it was Dali. And we were all mind-blown by Dali.

And when Stable Diffusion appeared, it was not really that good. And it just took the open-source community a few weeks to get this model to a point where the results were even better than DALI. And now, of course, it's like so much better than at least the previous version of DALI. So what I think that is interesting about open source is like it shows you the power of people, of the collaborative work for making something work. Like people really, it's so crazy how we, before like all the, all the breakthroughs that came in the AI space, they were all coming from research institutes or they were all coming from

companies like Nvidia. And now it's crazy how a lot of the techniques that we are using in Korea to make the quality of the images better are coming from random people that who knows where, where they are. Sometimes they just learn how to code because of this technology, but they are so passionate, they're so driven to make this thing work, that they go ahead and make it and they make it public to the community so everybody has access. So this compounding of, yeah, like this community effort, it's what it is what it made. How many people are? what it is right now. Contributing meaningfully to the codebase

at this point, do you think? Well, there are many codebases in the end. Like, you have on the one side, hugging phase that I don't really know how many. Tens of thousands of people probably. Tens of thousands. Tens of thousands, yeah. So explain to folks who are not familiar with what weights are

and why who understands and knows the weights is important because we do hear about open AI, which some people refer to as closed AI now. Like who has the weights for their models is important. Microsoft has access to them. nobody else does is, I think, the public positioning here. So why is it so important that the weights are shared and then what are weights? How can people think about those if they don't understand that concept?

Sure. So in the end, these AI systems that are so successful like stable diffusion, they are at their core neural networks. And the way how neural networks work is they get an input, like an image. They pass this image to a set of weights or to a set of neurons. Like, it's the same idea, right? like we call weights what, because in the end they are numbers from, like they are like flowed numbers that they weigh the features that need to be generated, for example, or comprehended.

And then you get the final result. So these weights in the end is like when you train these AI systems, what you do is you, for example, in the case of image recognition, you have an image as an input, you pass it through all these weights and these weights give you an output. and if there's a car in the image and the weights end up deciding that there's a, I don't know, a dog in the image, you will be able to update all these weights so the next time they don't predict a dog and they are able to effectively create a car. But essentially, this is everything that you're doing when you're training these kinds of models. You're just like making sure that you find that combination of weights that have knowledge and that can understand the input that you have and that they can give you their right output. So when someone release the weights, you can use all these knowledge that it was extracted

with the neural network and you can generate images or generate text, do text understanding, etc. Listen, public markets can be volatile. Don't I know it? And if you're looking for a unique asset class to diversify with, let me tell you about Blue Chip Art. Blue Chip Art has historically been uncorrelated with the stock market. and Bloomberg reported that as equities dipped in 2022,

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performance, does it guarantee future results? See important disclosures at masterworks.com slash CD. So one of the big debates has been on copyrighted images or just the training data. What is the state of the training data inside of stable diffusion today? It was trained on what? And then how is it being trained as it grows? And then how do copyrights, if at all, play into that? So stable diffusion was trained with this data.

called Lion 5p. This is a dataset that comes from pairs of images and image caption that it was scraped from the internet. And it's like these kind of crazy scrapers that they go all through the internet and they get these pairs. So there's definitely copyrighted images in the datasets that it was used to train stable diffusion. And right now the situation is, well, there are like several cases going on with mid-journey,

stability AI, et cetera. there's still not a verdict of where this is going to go. But yeah, my sense is that the really important thing is how you use this technology, not how you train it. And it's equivalent with what happened a few years ago with Google Images, that they were trying to sue Google because of showing, like, the copyrighted images when you search for something on Google Images. And in the end, like, you need to answer the question of,

is this a transformative technology or is this a derivative one? If it's derivative, like you are making a copyright infringement, you're making money in the same way that the creator of that asset is making money and you cannot do that. But if you realize that you can do this, that without this technology, it's impossible from a text generating an image. In this case, you could say, there is something transformative. It was not possible before. So it's not making any copyright infringement and it should be allowed to exist. So we'll see how that case works out. In the case of the stock images, if I had a stock image library and it was used to train,

it, I would say, well, I should be able to train my own model and I should have that opportunity to then make stock images based on the library of stock images that I spent decades acquiring. There's some sympathy to that stock image library holder, yes? In the community? Or is the community just like, hey, we can do it. It's too late. We can't unpack this. What do people in the community think?

Yeah, I think that the power that this technology gained out of being trained off of all this data is superior than than like all these stock photography companies that they had all these images and copyright. For me, the really important thing is only people actually spending a lot of time on creating certain things. Like for example, imagine that

you are an artist like people. The famous, like, we all know people. Right now, the AI got so good, thanks to having seen so many images of people and that right now everybody should say this thing about in the style of people or this other thing in the style of people. And they are able to

money just out of that. I don't think that that's something positive, but I think that there's something very, very special on being able to say, okay, do this image in the style of people that mix it with the style of, I don't know, like Michelangelo. And suddenly you have something new that you were not able to have before and that it can be 100% consider something artistic and something new. For me, it's like way more important to protect like the copyright of artists than to protect like a... Yeah. So if you use, if you evoked people or you use people on the training, set, it could just say, hey, you know, you have to use the Beeple version of this, just like, you know, there might be other versions made eventually Star Wars characters, et cetera. If you want to make

Star Wars characters and you want to evoke Star Wars and Darth Vader, you just pay the license. And so the industry's got to figure that out at some point. And I think it's going to be a negotiation because what a mess. Like, what would happen if it had to be retrained? Is that even possible or you'd be starting over? If it had to take out all the Beeple's, if it had to take out getting images or whatever it was scraped on with that lion five. What would happen? Would it just set the whole thing back? No, that's kind of what Firefly is doing, for example, like Adobe.

It's training their model using Adobe stock, which is everything, it's non-copyrighted images. And, you know, like, it's going to have its use case. Like, if you just need, like, a regular image of a woman drinking a coffee next to the beach, like very basic stock image, for that is going to be, it's going to give you a great result. But if you are an artist and you want to give it like certain styles and you want to play with things that come from copyrighted images, you will realize that there's a very, very boring model and that most artists are not happy with it. Fascinating.

Well, listen, continued success on this. It's super exciting. I want to let the dev house get back to work. I can see everybody's out there having a great time. This is the spirit that built Silicon Valley and, you know, amazing for America. But you're from Spain, yeah? Right, yeah. You're from Barcelona.

You're from Barcelona. Oh, one of my favorite cities. My lord. Any city where you eat dinner at 10 or 11 p.m. and call it supper, that's my kind of city. I miss Barcelona terribly. How's the startup seen in Barcelona? Are there a lot of entrepreneurs there? I know that they've had a little bit of a challenge with the economy and what's the leg of that?

It's hard. It's hard. It's hard. It's hard. It's hard. It's hard to fire. It's hard to fire. It's like legally it's hard to create a startup.

It's not like in the U.S. that in a few days you can have your startup setup. But on the other side, there's a ton of talent. I think that in the U.S., people are better at selling themselves. Like, you find a lot of people that they are not actually that crazy good, but when you talk with them, you will think that they are like the best programmer that you've met in your life. And in Barcelona, it's more like this person thinks like they are not that good, but when you talk with them, they are actually the best programmers that you've met in your life.

So, yeah, that's kind of what I've seen. I know there's a lot of red tape there to start companies and taxes and firing people is hard, hiring people. Everything is just crazy. It's like France. And also, and if you want to raise money right now in Spain, it's extremely hard. I was talking with France that they are already doing 250K of annual requirement revenue. And they were getting valuations of $6 million. Well, if you think about that, if it was $300, that would be 20 times revenue.

Yeah. I mean, you can get $6 million probably coming out of an accelerator right now with a prototype. So, or $10K a month in revenue. So, yeah, it's, that would be on the lower side. But if they just move the company or they domicile it here and work from there and have that lifestyle, they could just become a Delaware Seagorp, which is I think what a lot of people are doing now. Yeah, yeah, that's what we recommended them. Yeah, absolutely. All right, listen, everybody, go check out Victor's Company.

They're doing amazing stuff. It's KREA.a.ai, CREA.A.I. Continued success and back to work there and order some pizzas or whatever you guys do, whatever your jam is. No pizzas. We are doing the Brian Johnson Diet here. Oh, you guys on that Brian Johnson vampire everything? You guys are just eating like a pound. What's in that?

Like a pound of... What's in that? A pound of potatoes a day. What do you eat? Yeah, yeah. So it's a... The first plate is like a broccoli, cauliflower, mushrooms and lentils and a little bit more of virgin oil and something else.

Then it's like a pudding with like a chocolate pudding with a lot of seeds and walnuts and like very, very heavy. Yeah. And finally to dinner is a salad. salad with mandarines but yeah it's like mainly

it's like a this is the blueprint diet and so how long have you been doing it and does it make you feel like a superhero or something? What does it make you feel like? I mean we're growing

like I don't know what happened but since we started with this diet like suddenly the startup went up oh you're saying the performance of your startup went up so it was a direct correlation between the blueprint diet and the productivity at your company

totally yeah I go All right, well, there you go. Forget about your lifespan, your health span, lowering your biomarkers. Also,

just get on the blueprint for performance. You write more lines of code. I just think, like, we're not going to die out of this light, right?

So that's our mode. We don't die. Yeah. I mean, that is essentially what the goal is in Silicon Valley. We're doing all this work to make all this money

to plow it into companies and experiments so we can live forever. Right now, the entire world is absolutely appalled in and at awe of us at the same time in Silicon Valley, that the central goal is to generate wealth, to create technology, to eventually live forever. I mean, just, I mean, it's kind of a joke, but if you think about what AI is doing, you're going to eventually have your blood and your markers

and your body scan, and AI is going to start looking at this, and it's going to figure stuff out. Oh, yeah. And then it's going to figure stuff out about life extension. It's going to be crazy. Yeah, yeah, I cannot wait for that. I already have my Oro ring. This is very, very helpful to track your day to day. But yeah, I'm going to wait until we have like really powerful AI to try your health. Yeah. It's coming. It's also going to help them if you had to have surgery, God forbid, because you had something in your body.

It's going to really make an incredible 3D scan and know exactly what to do. And the surgeons are going to be even more precise than eventually they'll be robotic. And all that technology is in the process of being built and then being distributed to everybody on the planet. So life expectancy could go way up. All right. Great job, Victor. And we'll see you all next time on this week and startups. Bye bye.

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FitBod can create a perfectly optimized workout for me based on these parameters. And it will do it for you, too. check it out. It's amazing. The design of this app is extraordinary. I was able to invest in it. That's how impressed I was with it. FitBod takes the guest work out of fitness. Just open the app and start making progress. You deserve it. Get 25% off your FitBOD subscription or try out the app for free. When you sign up now at FitBod.m.m.m.m.m.m.m.m.m.m. slash TWIST for 25% off. Hey everybody. Welcome back to this week in startups.

As many of you know, if you're building products out there, quality assurance QA. Testing is a massive market in software, but nobody talks about it. It's something that developers have to do. You might call them chores or a best practice. So test rigor is a startup that's building a tool that streamlines the process of software validation, aka quality assurance testing. And they help companies to use non-technical users instead. of QA engineers for testing, which cuts the cost dramatically.

And the CEO of that company is Artum Golubiv. Welcome to the program. Tell us a little bit about test rigor and maybe educate the audience on what software testing is and how AI is going to change that. Yes, absolutely. Well, first of all, let me describe the problem. As you probably know, when you're building a software, eventually you would need to test it as soon as you have paying customers to manage.

you actually don't break the functionality. Imagine that you're running Amazon.com and your customers can't purchase products. That's a disaster, and that happened before. Some companies, we were losing hundreds of millions of dollars, literally. So oftentimes you can't afford it. So you do test. Now, imagine your testing, if you do it manually,

it takes two weeks, oftentimes for smaller companies or two months on the larger ones, to be able to retest all the functionality, even if you employ tens or hundreds of people who validate that your system works correctly. That is extremely slow. People do want to speed it up to be able to move faster and automate VAR testing. Moreover, you can't even have testing done manually in 2023 because imagine whereas the security vulnerability that you need to almost immediately fix in production, you have to be able to do the release ESAP. If your testing takes two months, you can't do that.

It's a must-to-have test automation today for all companies. However, 70% of all functionality today in 2023 is tested manually. How is that possible? Isn't it a contradiction somewhere here? The problem with test automation, how people are writing tests today, is that we are hard coding how engineers wrote the product yesterday in minute details, as opposed to how it should function from an user's perspective. And of course, it changes on daily basis and all that automated testing fails instead of

telling, you know, being able to validate if does the work or not. Got it. So in an example like Amazon, I might do a search, find a product, read the product reviews, add it to my cart, then check out, pick my delivery options, pick my billing, and then, you know, order my whatever USB cables from Amazon after going through that process. In the software, the engineers might have written little tests for how that might work. However, if things change, the test. test might not change, and the test is written from a software perspective, as opposed to from the

user's perspective. So there's two ways to test this. One is to actually have a user go order to the USB cables and go through the checkout process. The other is your solution, correct? Yes. So basically, the issues you might get with automation, for example, search button now called differently. It's changed color. It moved to a different location. The input for the search it no longer quote search, it's called find the product, and so and so forth, right? And that basically, imagine you have 10,000 anti-an automated test that starts from finding that input, and they can't find it anymore. So everything breaks and there is an engineering overhead to fix it again.

So this is exactly what test trigger is fixing. It allows you to explain how you're seeing. system should function in the normal language, and test trigger would execute those instructions emulating exactly how you would do it as a human. And that brings the not only unprecedented test stability, that you can run it, and as soon as your specification is still correct, it will still work. But also, it allows non-technical people, all of those tens and hundreds of manual testers that companies already have today to be able to build test automation, mind you, about 10 to 20 times faster than even engineers could because we don't need to

write any code. We don't need to go into technical details whatsoever. You just explain how it should work and bum, it just works. Got it. So you describe, or a human describes how this human process should work. In your tool, you charge companies for doing that. and then they run your system against their product, correct? Yes.

Usually, they're so-called test environments, where people deploy and tests were released before moving it to be available to everyone. And this is where we usually run those automated tests. How is AI going to change all of this over time? Are we going to be able to just ask an AI agent, hey, here's a website. but please pretend you're a user who, and test every function here and report back what's broken and give me suggestions for how to make it better.

Is that kind of the ultimate future of this, where some AI has been given the role of a quality assurance tester and they just know what to do? Well, AI as of today can emulate what humans to. Basically, the starting point is to replacing human in their human work. And this is what we do right now, right? Instead of executing those tests manually, AI can basically execute those automatically, so much so that our customers can copy paste their manual test cases directly into our system, and our system will execute those.

So you'll just describe it in plain English. Hey, go to this website, do some searches, put some things in the basket, click checkout, and then your software runs, I guess, in a virtual desktop, where it loads different browsers and tests, hey, what does it work like in Firefox or Chrome or Microsoft Edge, whatever it is? And you can do like multiple browser tests and different speeds of computers and bandwidth. Is that part of the testing process to do that matrix of here are all the possible platforms,

browsers, and speeds that you could be interacting with on the product? There is such an option, yes. As you can imagine, if you want to run on more infrastructure, it's becoming that much more expensive. Second browser will double your cost. The third will triple and so on and so forth. But yes, of course, you can do that at the whole point of testing, making sure that your customers can use your system not only from Chrome,

but also from Safari, including Safari on IS, and from Firefox, edge, internet explorer, sometimes, and so on so forth. Fantastic. And so in terms of getting this product into customers' hands and being a startup, I know you went through Alchemy, Accelerator, or a Combinator. How do you get new customers for this product? And is the customer base ready for this sort of paradigm shift in testing? Well, we have multiple channels and the onboarding new channels.

So, of course, we started as I guess everyone, else we have some outbound. We were able to sell to people this way, and we figured out, okay, so now let's do some marketing. And marketing worked to a point where today with zero investment in marketing, we're getting more inbound business than we have from outbound. But now we also onboarding onto new channels as well. For example, in four is our customer.

It's top five largest ERP systems on the market, similar to SAP, Salesforce and such, and we have 90,000 enterprise-sized customers. So we're working with them on partnering to help their customers to test their implementations of our ERP system, and that would become an example of one of the channels we are onboarding. Now, is Microsoft also competing in the space? I know Microsoft's big on AI. They obviously have the co-pilots, Azure, the relationship with OpenAI. Are they kind of building in this kind of AI testing yet?

how do you look at the competitive landscape and why people should use you versus using maybe something from Microsoft? No, Microsoft is building some tools that we either use right now or we most probably use in the future, for example, to be able to better automate working with Microsoft products such as Word and Excel and so on so forth. Specifically, however, they do not have that system which overall, and work with any UI whatsoever, like ours, and execute these kind of stuff from plain English, very high level.

How is AI, just generally speaking, if we open up our discussion here, how is AI impacting how software is being made today? We hear about co-pilots. We hear about, hey, I'm going to put my entire environment for my company into a verticalized AI and co-pilot. So my codebase is part of the language model helping, you know, whatever, the 10th developer, 11th developer on my team get onboarded. And sometimes I need to explain maybe to the new person how the code base works and AI seems to be doing a pretty good job of that. How is AI changing just developers and dev teams operations today? And then how do you think it will

change it in the future. Current state is you probably have seen the presentation by GitHub, like literally a couple days ago. They already make engineers 55% more efficient, which is mind-boggling. However, I believe the future is AI agents that would do stuff instead of needing humans to engineer things. And TestRigger is the first example of such an AI agent where we do not generate the code. We do not need engineers.

You just write in English how it should function and test trigger will execute it for you. I can show you very quick demo if you'd like. Sure. Give us a quick demo. I'll do it on BestPy.com. We're on the Best Buy website. I see that, yeah. And this is the TestRigger suite. We're creating a test suite.

It's a set of suite usually. You're picking what operating system, what browser. You're giving it credentials to log in, I see. Yes. Yes. Pick the Chrome browser. We can generate the test. This is what you were talking about.

Hey, how do you do it autonomously? Where you go? What is the description of the test? Yes. We provided the description of the system saying that it is e-commerce website selling electronics. And it came up with suggested we selected, hey, give us the test cases. So you said, hey, this is an electronics website. give us some tests.

The test it came up with. The first one is browse electronics category and verify product listing. Second test, add Kindle to cart and validate cart contents. Third test, proceed to check out and confirm purchase success message. So these are tests that it's generating. The AI is suggesting these.

How did it get those? Is that just a language model? Are you trained a language model on what tests are typically done on a website? Or specifically at e-commerce website? So this is coming from From LLM, yes, it is suggesting some stuff. Like, this is pretty high level.

This part didn't need to be trained. It's just out of GPT4 directly. How did the language model know to suggest those three things and how accurate are those three things as tests? Well, you can modify those, right? Yep. If you find out that they are not exactly what you would expect. However, it had been trained on the full Internet,

and its internet has wide variety and library of everything around it. So you can know basic things based on the description, kind of common sense. Interesting. Where test trigger provides the largest value, and I'll show you, let's add another. This case, let's say test, adding to cart. So test adding to cart, okay. Yes, and we say things like find and select a Kindle, added to a shopping cart. So I'm trying to come up with something that you would see typically in a typical test case.

All right. So you told that, hey, find and select a Kindle to buy on Best Buy, then add it to the shopping cart, proceed to the cart, obviously clicking on the cart button, and then check the page contains Kindle. So this is all written in plain English. You don't need to have a developer do this. And then I guess it's looking at these commands and saying, hey, I don't understand. understand these commands. So let me let you map those commands. Is that what it's doing there? Yes, we know we selected. Hey, use AI to execute those commands. Do not do anything else

or just use AI directly. Whereas no specifications there are specifically. And what system is doing is we'll kick off the new server with OS that we have selected. We'll start the browser that we have selected. And then it will go step by step for the screen. And is it making a little video there for you or just taking screenshots along the way of each step? What is it doing there? It can do both. We did not select recording a video. So it's just taking a screenshot.

But it can, of course, create a video. That's amazing. So you can see here, like it decided to enter Kindle and to search and click on the search button. Yep. So you get the actual evidence of it working. Yes. So, and those are commands that it came. up with based on this prompt, there are two commands in this case, enter Kindle and click search.

Second, it basically said it's done with that prompt, so it proceeded to the next prompt, which is add to a shopping card. Here it just click on add to card. And so then we proceed to the card, it clicked on go-to card. So it got to the shopping cart out there and drum roll. You would need to confirm that it contains Kindle in this card. Fantastic. So this is pretty groundbreaking.

Now you can have a non-developer write these use cases, test them, and get the report back. Absolutely amazing. Well done. And every aspect of what we're doing in building products is being done by AI now or helped in some way. So it looks like this is going to save. people, I don't know, hundreds of hours a month, thousands of hours a year at an average e-commerce website or startup, correct? Yes, because it basically kind of common sense, right?

What's important in software testing is domain knowledge, right? So yes, AI can come up as common sense suggestions for test scenarios and so on and so forth. It can figure out based on how your system works, certain things, but only you as a domain expert would know what is truly most important, what needs to be tested now, but you might not necessarily be an engineer. You might be an expert in the product, but not necessarily an engineer.

And you shouldn't be. You should be able to just explain how the system should work and it should execute it for you. That's kind of division of labor between domain experts that are humans and just machines that would do stuff for you. Amazing. Artim, great job.

Everybody check out testrigger. com, T-E-S-T-R-I-G-O-R, and you can follow them on X, formerly known as Twitter, and you can follow our team on Twitter, A-R-T-E-M-T-R. Well done, and we'll see you all next time on this weekend startups. Bye-bye.