A Technical History of Generative Media

25:47I'll talk about a couple other architectural things just to keep it bounded with this topic. The distillation was a thing for a while. SDXL Lightning, you guys did fantastic demos of TL Draw, which we've also had on podcast. Fantastic episode. What happened to those things? How come they're not popular anymore? I think it makes for a good demo. You know, you could build real-time applications. You could build these drawing applications, things like that. But I don't think people could build applications that have user retention long term. People couldn't really build useful things with it. Let me play out what I thought was going to happen.

26:22And then you tell me why it didn't happen. Which is consistency models for drafting. It's like you use your hand to draw things and it creates the draft. Then you upscale with a real model. But that's it. Why can't it be a two-stage process instead of one stage? Yeah. And I think one thing that happened is Flux. That generation of models were not good at image-to-image when it first came out. So you need a good image-to-image model to be able to draw. Maybe it needs to be revisited around this time with some of the editing models, maybe. Like image-to-image and control-less. Flux, like STXL or control-less were very popular, where people were used to do this stuff like sketch-to-image, whatever.

27:03And with Fluxer, I think people cared less about it. One thing that I keep thinking about this is like, is this true for LLMs too? I always default to Cloud 4.1 Opus, right? Even if it's slower than Sonate, it's just like, I know I'm going to get the best quality. Exactly, that's what's happening here. Yeah, it seems like what's happening here as well. Okay, anyway, as a creator, I want fast, quick drafts and then I can refine, right? So I don't know why it didn't happen. More true for video models, right? It used to be like five minutes, four minutes for a single five second generation. Now it's mostly under a minute, but you want 10 second, five second generation.

27:40And then because the workflows of like creatives when they're working with it, they generate a ton of videos and then like pick one and then create a story around it. So when you watch these people actually generate videos, they generate like hundreds at a time and they have to like kind of sit around and wait and then like iterate on it. like the faster speeds mean a lot for for creators yeah it does the other thing i wanted to also briefly touch on before we go back to the main topics is the autoregressive models which you mentioned right like obviously i honestly i still think gemini is underrated because they were first and then but then obviously openly i did the 4.0 image gen and that was a huge thing i actually even wonder if there was a panic for you guys because obviously it's like this is soda image gen and like no one else has it it's not it's not open source you've passed through those eras so many times you know like when stopped worrying about it yeah your camera's like good stories around dolly google yeah i mean i talk about this how like when the lead two first came out as like okay open ai is so far ahead of anyone else like it's impossible for mid-journey and then people caught up within months and then stable diffusion was even maybe better or just as good as the Lee, like a couple of months later, and it was open source.

28:58So like a year later, same thing happened with Sora. Like they put out those videos and that time around, like, I think we were excited because now that people see that it's possible, like this is like actually doable, researchers get motivated and they see the hype. They see that this is possible. So they work on it. And within a couple of months, we had maybe not Sora level, but much better video models. Now we have video models that are much better than Sora. So whenever we see someone actually pushing the frontier, it's a reason for excitement because now that's possible. Other people are just going to do it within a couple of months.

29:36So we don't panic anymore. Is the fact that Entropic doesn't have an image generation model tell you anything about what the larger labs care about? It tells more about Entropic's own like personality than like the in general, like what other labs, because If you look at XAI, if you look at Meta, if you look at OpenAI, if you look at Google, they all have really good image models. Yeah, like Google, in their last announcement, they used the word generative media, by the way, which was a proud moment for us. It's a win. And a lot. And, you know, they focused on generative media as much as their new LLM models.

30:11So some labs definitely care about it, and some labs, it's not a priority for them. Look at XAI. They keep pushing like image. You're like AI slob. Yeah, I know. I know, it's crazy. waifus. In levels of interactivity, you have images, you have video, now you have Genie, this kind of like more world model. You have kind of like gaming applications of that. How far are we from like FAL getting a lot of traffic on those models? Like, is it mostly experimental today in open source? Obviously Genie is impressive, but like, it's a Google model, you know? I have a very optimistic take on this and that may be like a normal outcome.

30:44I think at worst we are going to have very capable video models that come out of world models, right? It's going to be a very controllable video model and the use cases will be similar to what video models are. You're going to create content, but you're able to control the camera angles. You're able to control the video model a lot better than what you can do today. At worst, we are going to get that from world models. And at best, I think it's very hard for anyone to predict what's going to happen. Yeah. Movies and games, like it's going to be something in the middle where you can be part of the whole movie universe that's going to be playable.

31:24So it's boundless possibilities. What's going to happen at best and how affordable is it going to be? Is this ever going to reach mainstream adoption? We'll see all that, but it's definitely technically incredibly exciting and impressive what's coming out of these labs. yeah i need to find the paper again but there was this study on like um video models and like image generation understanding physics yeah and like it could like predict the orbit of a planet but then when i actually had it draw the gravitational forces it was like completely wrong yeah you know and so i think like that's my thing with world models like i understand the creator application which is like you can create consistent world but i don't know if like the other side of people that are like hey these are like the best way to like simulate the world and like get intelligence and things like that.

32:10There's so much optimism around it too, because whenever you talk to someone who's working on robotics, they're bottlenecked by the amount of data they have. And from all these, you know, past three years of AI innovation, we've seen that whenever there's an abundance of data, that type of models actually like improve a lot. And you see, so like robotics, we expect something similar. Whenever they figure out this data problem, those models are going to get better as well. So that's why people are so optimistic. Okay, maybe this solves the robotics data problem. And it's, yeah, boundless opportunities there.

32:47And regarding the example you mentioned about gravitational forces, I think this is still the same problem as, oh, LMS can't do 9.9 plus, like 9.0. Yes, it can. You just need to train it with more data, you need to have a better tokenizer, it is the reason, whatever. It's just a matter of data scale and the underlying fundamental architectures but like i don't think it's going to change that much we just like we're going to put thousand x more data thousand x more compute and we'll get like the best physics simulators and i think this should be possible with the existing signals coming from the data just to double click on video stuff as well yeah uh you had a great slide in at aie where you're like currently 18 of files revenue comes from video models and it might be a that was february so But now it's probably over 80.

33:33No, 50%. 50? Okay. It's like over 50. Yeah, yeah. 100%. Wow. Okay. I guess editing models brought some life into the image as well. So like both of them grew, but yeah, video grew faster. Video is like pretty, pretty significant. And one of the main drivers is open source models where, you know, in February there was Hun Yuan video. I think that was like pretty good. There was Mochi from Genmo, but like the quality still wasn't there. and one from Alibaba was like insanely good model. And they released a newer version of this like a month ago, I think, or like a couple of weeks ago. And now it's getting so, so popular.

34:10And like, we can run this model, like for like 480p, like the draft mode version, we can run it like five seconds under five seconds. So people can have like instant feedback loop. And then when they want to go to 720p, like full resolution is just like 20 seconds. And we were planning to bring it down to 10 seconds. Yeah, that's amazing. And I want to double click on that. For a while, I was kind of bearish on Alibaba because they kept releasing papers with very cherry-picked. And it was like, okay, we're on GitHub. And then you go to GitHub, it's a readme. I mean, you can't see something really change.

34:42They've been releasing new image models, new video models. No, we haven't talked to them. But it seems like, and now there's competing teams inside Alibaba. One is a really good image model, but they released Quent as a competing Quent's image model. So we think one's image model is actually very, very good. If you'll do one with single frame instead of 81 frames or whatever, you get a really good text image model out of it. And this is just because of the pure amount of data that you put from the videos. So now there's Alibaba has two of the really good models from their lab. And then there's smaller labs in China that you might not hear about, but like Stepfun, they released an image editing model.

35:20Hydream, Vivo, there's all these small labs they're releasing. because I don't think training these image or editing models are that expensive. And video models might be like slightly more expensive. My guess is like training these costs like a couple million dollars, which is not that much, especially, you know, like, you know, they're probably backed by some sort of entity, you know, like other than Alibaba, you know, like there's like Stefan, whatever they probably raise a really good amount of money. So training these models will bring you a lot of attention and it's more attention that you would get releasing a subpar LLM because like LLM space has so much more competition.

35:53So just training this for like a million dollars a video model and then releasing it, I think that brings you a lot of attention. It is a hack. When you look at Hugging Face, let's look right now, I'm sure the top models are image models. Like it's, Koen image editing probably is... Probably up there, yeah. Probably up there. Yeah, number one. Number one. Hoonyeon Gamecraft. Is number three? Number four. Then you got Gemma, 270 million. ByteDance had some image stuff. ByteDance has an open source, but they have a really good team. Seed, that's their new lab. they're working on C-Dream C-Dance OmniHuman like stuff like that we have a good partnership going with them to hopefully have their models hosted in the US as well and the idea is I think that the team that they were able to assemble is very good and it's coming from their previous researches whatever ByteDance was doing really good open source stuff on like SDXL Lightning they released SDXL Lightning paper Animative Lightning so I'm pretty hopeful about them yeah first of all hopefully they don't reach out to you when they launch they just drop and then you have to rush.

36:52At this point, people reach out to us because we are the market leader. So they just reach out to us for getting distribution. Yeah, there's a Chinese platform that they always launch on first, which I forget the name of it, but you have to... We also get day zero launch with a majority of these models. So basically, I think the question is always like, you know, you are the ones making money. Stability did not make money from stable diffusion. I think the thing that Black Forest Labs did was very, very interesting in this aspect. They released three different models. Apache 2 licensed extremely distilled model, which is good for...

37:26Dev. Chanel. This is the Chanel version. This is like for four-step generations for like lower quality stuff. They released a dev model with a non-commercial license where their inference partners are, you know, like you're paying a revenue share and this is like a very good way. And then there's a pro version where you can like collaborate for hosting it. And like the revenue share is obviously different for that as well. This is, I think, a very smart choice for labs whose whole premise is releasing models. But if you're a company that is doing a product in the side, you don't necessarily need to make money from the open source models.

37:57You're doing it for getting researchers, hiring people, getting distribution, whatever. So it really depends on the company's goals. For Alibaba's case, they don't care if one model is hosted in their API. It doesn't touch Alibaba's top-line revenue, whatever one makes. So for them, it's a no-brainer to release it and get attention and maybe like get some leads to their Alibaba cloud offerings. But like in general for Black Forest Labs or companies like that, I think it's like a smart move to release like a distilled version as like fully open source and less distilled or like the actual model as non-commercial.

38:29And then partner with inference companies and stuff like that. What's the distribution of usage? So like is 80 % of your revenue like five models? Or are people really using like the long tail of all these open models outside of like the initial launch? I think like there is some power law, but not as much as you would think. And it keeps changing. That's the other part. Like it's not like only a single model that's being used a lot, like month to month. It changes a lot. This summer has been crazy. There have been like just countless amount of new video models, new image editing models. Like the leader kept changing week over week even.

39:07but if you like take a step back and look which models are being used people want to use either the best most expensive video model or they want to use like a cost efficient like good but cheap enough video model so those two models are usually like used a lot and whatever those models are it changes week over week and yeah the one good example is like fox context was released you know like on late may and kwen imuja that was released like two weeks ago and now it's like topping out context dev. It's insane how quickly these stuff transition just because there's a better quality model. And that's the value prop.

39:45You don't have to set up the infrastructure to manage flux context dev. As soon as like co-image is available, you can just switch to that with file. I mean, it seems to me that if some models are open and some models you have to pay a revenue share, you ideally want to move the people off the revenue share models into the open models, right? Like what's that dynamic? It's all price-to-date. I'm also thinking like, okay, we'll do whatever our customers are going to be successful with. We are still like early enough that these small calculations, I don't think it matters. I'd rather people actually go to production and build products with it and be successful rather than like, okay, 20 % here, 10 % there.

40:22I mean, you're doing a hundred million in revenue. Cool. I'll just ask a few more questions we had around just like the, how people really use this. Okay. I'll ask this super obvious question. Yeah. How much is not safe for work? Almost not. Negligible, yeah. You don't moderate everything. Moderation is optional, right? Moderation is optional to a level where illegal content is moderated. And we also track the non-illegal content NSFW moderation. And we haven't seen more than 1%. The models themselves are actually not generating that type of content. Some model providers, especially if you look at Blackforce Labs models, the models are not for...

41:00It's incapable of generating because it's like... Or like it's an yield in a way that is prevented. And the majority of our customer base, if you look revenue-wise, it's like enterprises are more on the higher level of stuff where some of them might be like user space, mobile applications. But for the last six months, nine months, we've been transitioning more and more to enterprise where it's less of a need for them. So what are those enterprises doing? You know, apart from building a general purpose chatbot that can generate images, maybe Canva would be a good use case. But my imagination is a bit limited beyond that.

41:32advertising seems to be absolutely like growing and if you think about it it fits very well and let's talk about video advertising so like i keep repeating this but some companies talk about oh we are gonna change hollywood filmmaking is gonna be revolutionized like i don't think it's that interesting like how many movies do you watch a year like maybe 20 25 movies how many movies in the theater you watch three four at most so if there are like thousands of movies that a year like people won't be able to watch all of these movies like there's just it's still it's a max quality exactly exactly and with advertising it's the exact opposite more content there is the different like you know ways you can create ads there is always economical value attached to it So you can create unlimited number of ads, unlimited different versions of it.

42:24And more personalized it is, more, you know, economical value there is behind it. So with ads, it fits really well to this type of technology because there's no limit what you can create. I'll tell you a side comment about a Silicon Valley trend I'm seeing, which I cannot explain, which is that all these YC startups and all these, they're spending between$10 ,000 to$70 ,000 per launch video. Yeah. In the age of generative video. like they're hiring you know actual creative directors hiring a studio hiring actors uh i was in one of them and like do you need all that when you have generative video like i think clearly roy started talking about generating videos i don't know if you guys know pj ace i think he's like the absolute killer for this stuff he uh launched like a is it a super bowl ad or something he did like a basketball playoff yeah yeah yeah nba finals right yeah he also did our like series b announcement video like we're like pretty close with him and like that it's insane that like what he's able to come up with and how viral it goes or like you know these videos where you spend like hundreds of thousands of dollars right you know like it's just like you just need to create viral content and these general media models are the best way to do it and we're still at the infancy of this right like obviously it might not be professional quality i'd still think like you know human in the loop like mixed like you know content is the way for today but in six months who would know like 12 months i think like 80 of this is going to be generated like we were like we were watching super bowl and we were like saying oh how much of this video is like ai generated it looks like ai generated like it could be right like you can't tell so like i i think at some point you know we're gonna have like 80 90 percent all that's it reminds me of uh i think um who's who's the guy uh fofer from replicate obviously he is the best inspiration for all these workflows he like lay overlaid some kind of nba realistic sort of laura on top of game footage.

44:13So you could play like NBA 2K, but it looks like a real video. I saw that, yeah. Yeah, I was like, what the hell? It's pretty cool. So maybe that's the other part of my question and I wanted to get into Comfy UI, which is how much LoRa serving is going on, right? How much custom? A lot. You know, okay. Is it like majority? That's one of the reasons. Not majority, but like, if it's like 30%, is it like the majority? And everyone trains their own LoRa's or you pick it off of like a LoRa marketplace? That's why open source works very well with image and video models, because you tap into this big LoRa ecosystem, everyone.

44:50I've never seen a closed source model that can create a good LoRa ecosystem. It just basically doesn't exist. Maybe there is mid-journey SREFs, but I don't know if you can consider them LoRa's. SREFs are just seeds, right? Conditioning, let's call it another condition, like a prompt. Yeah, and then only the open source models have these rich LoRa ecosystems, and it's extremely, extremely popular. But even for the oldest models, it brings a new life. When you see these cool LoRa's, we have still a lot of people using STXL with their own LoRa's because they're happy with the quality. It's fast enough, it's cheap enough, it's amazing.

45:27These models are not single-shutable like the language models. Even the editing models, GPT-Image 1 or FluxContext, whatever, QAnImage. if you put your face or like if you put like multiple people whatever you can't get the quality like it's gonna be like 90 there but if you train for like 1000 steps with like six to 20 images you're getting like 99 accuracy with like like the like we worked a lot on like fine tuning the right hyper parameters like writing like distributed trainers distribute optimizer stuff and with those like people can train like their lores under 30 seconds now on the platform run an inference with them in the same job and get like 99 accuracy for the same face character which is one of the biggest challenges that maybe more on the enterprise side they're facing and less on the consumer side.

46:11If you're creating AI slope, you don't really care who it looks like. But if you're actually doing a product ad, you want it to exactly look like the product. Every single pixel on the products, banner, whatever, you want it to look like that. So you train a LaCroix Laura with 20 images and then after that, you have almost a pixel-perfect model. All right, we had to train a Laura for every guest. Then we can make thumbnails. I actually think that's a very good application because it's a nice way to inject brand, but not in a strict style. And we are just entering like post training on video models.

46:45And what's that that's going to mean? Because we didn't have a good base video model that it made sense. But now we have like companies really investing into like post training on 1.2.2 or Hunyuan and like creating lip sync models on top of it, creating like different video effects, camera angles. Seems like there's a lot of possibilities with like creative data sets that people can do. I think in the next six months to a year, we are going to have a lot of like companies that are just built on post-training of open source video models. Wow. Let's talk about pipelines. So we are Confianonymous on the podcast.

47:22ConfiUI is kind of like this community that like, if you're into it, you love it. If you don't know about it, you kind of underestimate in a way, but people create all kind of crazy workflows. One, have you thought about doing pipelines? Obviously you host all the models. There's kind of this question. We do have a pipeline product called File Workflows and you can chain models together, but it's obviously less flexible than Confu where you can only chain different models outputs and not the intermediary stuff. In Confu UI, you can access the latents from one model and then pass it to a latent upscale or whatever.

47:51In our case, it's more limited, but we have a workflows product and we have a serverless Confu product where people can bring their own Confu UI workflow and run it as an API with just like posting the workflow and inputs. And let the models be served by you. Yes. So is that a bullish thing? Is that going to be commoditized by big models? The thing that we saw is as the models get better, like Confu UI was a much bigger thing or like, you know, relatively much bigger thing in two years ago, like a year ago, when the models were like, you know, one of the biggest Confu UI use case was you were like generating an ST1.5 or STXL image and you were fixing six fingers situation, you know, like you were fixing like the resolution or whatever, upscaling.

48:27Now that the models are actually so good, the Confi UI workflows are actually getting simpler for image side. For video, it's still very crazy. Like if you look at some video workflows, it's like there's like 50 nodes, whatever that you're processing. So I think this is still a matter of like how good the models are and how much extra stuff you need to do around it for majority of use cases. For artistic use cases, you still are doing like a lot of stuff. And that's like something we want to support. But like that, we don't see that happening at super scale, you know, like super scale. There's not companies that are spending like$10 million plus on running this as an API.

48:56So that doesn't seem to be happening yet, just because it's like a bit inefficient and the more existing, like it's more reliable to use an existing model than patching together 50 different things because you don't know when it's going to not work. Yeah. But it feels like for things like ads, you want to do, you know, one step, which is like maybe generate the backdrop. One step is like adding copy. Are you saying the models are so good? Chaining of models happening for sure. But I think ConfUI did very well is you can also play with the pieces of the model. Yeah, you're busy saying that's all.

49:25Yeah. So like chaining of the models, like that's what the file workflow product does. It's basically calls many different APIs back to back or in parallel and then creates the result at the end, I think. And it's very popular. We have like enterprise adoption from it, like from very big names. Yeah. Amazing. I was just going to go into the broader topics. The first thing that comes to mind was requests for startups. If you're not working on fail, but you see a lot of things in the ecosystem, right? What's the most obvious thing that people should be working on? more model companies. Go raise more money and train models.

49:56That's obviously good for FAIL. And host them on FAIL. If you're not interested in training models, but if other people are trained, that's amazing. Go raise more money. There's so much money. Or like scale AI for Imogen video models. Data collection, more prepared data sets for video models, effects, different camera angles. Everyone seems to be reinventing the wheel when it comes to collecting that data. I think it's a great opportunity for someone to come in and do this at scale. So it's really interesting because I think this is what Together AI did with Red Pajama, is they actually built a data set for language models to help people create more open language models that they can serve.

50:37So at some point, it actually might make sense for you guys to do that. The image data is a bit more finicky situation in terms of copyright stuff, whatever, but it's an interesting area. Do it in Japan. I think it requires focus it requires like this needs to be like one connected thing to what Gerkem said is image slash video RL that's an unknown unknown for us say more I can't it's like what does it look like can you RL a video model to be a world model you can right like if you consider it like it's essentially the world models are RL video models where you condition it for like you know moving around so what are the use cases for RL image and video models i don't know but that's like if i wasn't working as well that would be something that like that might be fun to explore yeah and is this is specifically for editing because the rl is for the reward is the edit or that's the thing that's that's what you should look for right like what what is the reward function like what is the interesting reward functions that you can apply on top of these base models i see interesting okay got it actually i was really asking about like if i were to build a foul wrapper startup like on top of that because Because you guys are very low level, which is fantastic.

51:47But I also want to give our listeners some ideas, if they're not going to work at that level. I think I'm going to say it again, advertising. There's so much opportunity there. And everyone's still trying to create these horizontal applications where any creative can come in and do something. But a lot more targeted to specific industries, a lot more targeted to different kinds of ad networks. There's a lot of potential there. good and then requests for models you obviously want more open models that's good for you but like any like specialization in the models like i think image image editing was a huge unlock which i didn't foresee until this year yeah where we're like oh obviously we're gonna no one's yeah i didn't like guess it was gonna go this big it's like it's insane like how popular it became and then like everyone started like catching up i was part of a group at neurops that we meet at new reps every year and like they were talking about this at the last new reps and so like it's in the air but you have to be at the researcher level and like everyone moved to video left image behind a little bit so there was like a little vacuum of research on on image but luckily people people saw it that it works very well and then they went back to it it's so much cheaper to train image models like right now like if you if you look if you want to train a sota image model i don't think it's going to cost more than a million dollars it's extremely cheap it's like a matter of data, engineering effort, cleaning.

53:08I think it's a function of data set. Image models are really, really, really affected by the data set that you use. I think one obvious thing that there's a gap in the market is like VO3 is very expensive and the way the reason why people like it is conversation, right? If you can create maybe a smaller, cheaper video model that is less capable but can do conversation and sound very well, I think there's definitely one open source one that we saw was multi-talk it was a post-trained version of one and it's like really really good for conversations but it lost the ability to generalize you know it's only like talking faces at some point versus like vo3 it could generalize and it could do scenes and whatever so i think that there needs to be like some middle ground between these two between talking faces and like you know extremely generalized video models where it's like much cheaper to run but at the same time you know you get like this conversation because it's very memetic you know people there's infinite amount of memes that you can post with this infinite amount of ads that you can do with this but you don't see a world in which you have a video model and then you have a separate maybe audio only model that can generate the audio for this is the worst question right yeah do you stitch together a whole bunch of things or do you better let people did that before v03 but what v03 gets very well is like the timing almost like totally you ask for a joke and you know the delivery and the timing and to laugh and like you know waiting right before like the joke drops like all of that is so perfectly timed i don't think when you do it separately you get it it also matches the human accent sound to the face that is talking right it's like it's an unknown challenge for like other text-to-speech models like you like it feels very natural is vo3 the best text-to-speech model it is also one of the best it is so good like i don't think any model can do what what is the emotional But I would say the counter argument is that we dub movies.

54:57So there's already, you know, obviously you can. It is also the best lip sync model. Like VO3 has the best, most accurate lip sync because it's generating very natively. There's really good lip sync models. I think they're like 95 % there, but VO3 is like 100 % there, like 99%. To me, this is like the single most bearish thing about workflows, right? And ComfyUI and all this stuff, like, because just wait for a bigger model. It's just pure bit of lesson. Yeah, we love ComfyUI. but i mean obviously like when the technology doesn't exist yet you have to stitch together things yeah so a request for engineers yeah i mean what i'm sure you're hiring right you're just 125 million hiring like we just recently crossed 40 people but like for like wow for like three months ago we were like 20 so like for the last three months we have been actually accelerating you know best kernel engineers best infrastructure engineers best product engineers best ML engineers.

55:49If you're the best at what you do, just come join us. I think it doesn't really matter what you do. Just like, we're just hiring the best self right now. Even on the go-to-market side, we are hiring account executives, customer success managers. Because we work with very large enterprises, we got to grow that side of the company as well. Yeah. On the engineering side specifically, how do you think about how many people you need? There's a whole question of like lean AI, it's like, you know, coding agent. Our performance team is like seven people. I think seven people like focusing on performance.

56:17always like some of their, some overlap with our applied ML team, which is taking these models, productionizing them, exposing new capabilities, building fine tuners. So it's like, and then helping customers adapt these models. So that team, I think we can scale to like double, triple the amount because like there's infinite amount of models and like, you know, it's better because like we're going to have more customers with more proprietary models. So just like helping them optimize it. It's just like a really good function that we have. That team scales very well because there's always like independent work that can be done.

56:45Oh, okay. So these three people are working on this new model, trying to optimize that. And it's completely independent from trying to optimize this other model. So we've been hiring a lot for that Applied ML team. Our aim for a team, you know, we're probably going to keep it lean in contrast to the Applied ML team. And the product team may be like, we want to build more higher level components where people can directly integrate to their applications. Because that's even like now. Just SDK or? SDKs, but think about with components. imagine you're an e-commerce website designer and you're not really the best component designer.

57:17So here's a virtual try-on component that you can put to your app. Stuff like that, more higher level components. And this is also coming from the fact that wipe coding has been very, very insane. We see significant... Revenue-wise, it's very small. But we see a significant amount of user adoption just coming from people who are... Just from looking at our support tickets. Maybe they need more support, but there's a lot of people who are coding these applications without that much expertise in the product building. So we want to provide them more guardrail experiences where they can integrate much easier without messing with all the other lower-level components.

57:52That's really nice care of developer experience. So crack low-level engineers. And crack high-level. Well, yeah, crack go-to-market people, crack whatever. I'm always trying to refine the definition of crack. Both of you, you lead the technical side of fail. Like, what's a really hard technical problem that if someone has the solution, they should talk to you immediately? Maybe that's the way to frame it. Write a sparse attention kernel with FB8 on Blackwell and tell, you know, like, if you can do that, come join us. We already have like a good base. Hired on the spot. Hired on the spot, you know, like stuff like that.

58:27I really like picking like all these, like some of these applied ML people, like we just picked them from discords who are working on these sort of jarrington media who are like already interested. We really have a high culture bar too where everyone in the team loves jarrington media. They're obsessed with it. They would have done this if this wasn't their job. We have this great composition. It's not a prerequisite, but it's just naturally happened where we hire these people from Discord, Twitter, Hugging Face, one of our Applied ML engineers had the number one top Hugging Face space with creative workflows, whatever.

58:58So we hired a person who was training LORAs on FAL just because they were training and posting cool LORAs. Just do cool stuff and we'll find you or you can reach out to us. That's the master builder is what I've been calling this person. Why not make it more explicit? So if I go on your career's website, right? It's like apply to Mel Engineer. It just kind of looks like any job description. I feel like there's like this question of like... It does. That's why we have to do a podcast. But I think it's not just about file. I think in general... It is more like if you know, you know, which I know is not the best way.

59:30You know, people know about file already. So it's like we haven't really cared that much, but you're absolutely right. Like we should make it more explicit. If I look at like George Rots, like on TinyGrad, you have these boundaries. It's like, hey, we'll just, if you can solve this, you should probably work here. Like, do you see? I'm adding the bounty today. Right? It's like, this seems like, hey, look, if you can write this kernel, it's like, yeah, you'll just get hired. It is also, but like one thing that we saw, even with like, there's a lot of people who are just like wipe coding stuff and reviewing those.

59:58Like, there's a limited amount of people who can review those, right? Like, so like, how can you tell it's like not a shitty kernel versus like a good kernel? Well, but then you're spending the time interviewing too, right? Yeah, so we have first line of defense with our recruiters, whatever. So there's trade-offs, but I absolutely agree. Maybe we should have a kernel bench version that you can upload your kernel, automatically evaluate the stability, performance, whatever. And then if you do, you get our email unlocked, whatever. Especially email for you. But yeah, great ideas. Come join us. Deal this.

1:00:31Awesome, guys. anything else parting thoughts yeah i love your rant so this was great yeah i'm happy to run but one is a podcast star yeah no congrats on all your success um i should also say it's fun to do karaoke with you guys yes like let's do it again both extremely technical but also like a fun crew that like and i think it's pretty hard to and rare to to see so thank you to see the good guys win awesome guys awesome

1:01:02Thank you.