Screensharing Kevin Rose's AI Workflow/New App

Today's episode is a showand tell episode with none other than Kevin Rose. He takes us through his entire AI workflow and lets us in and a new product that he's developed that he hasn't shared anywhere and we learn about how he thinks about building new products, cloud code, versel, all the tools he uses. So it's super super fascinating. Kevin is just one of those iconic entrepreneurs and to have an inside look into his AI workflow and how he's building products in this AI age is absolutely fascinating. It got my creative juices flowing. I think it will get yours too. And if you stick around to the end of the episode, you will understand how to build products in the AI age, how to think about it, and what tools to be using. Enjoy the episode. We got the one and only Kevin Rose on the podcast. I'm super excited to have him. He has been going down the AI rabbit hole for a while now. And I wanted Kevin to come on just to share, you know, what are the tools he's using, uh, some of the AI workflows that he has. And he's just going to screen share. Kevin, by the end of the this episode, what do you think people are gonna get out of it? Like, if they stick around? >> Yeah. Well, I certainly first, thanks for having me on. It's I love the content and everything you're producing. It's so important right now, especially with things moving so fast. Um but I think at the end of it you will see that things that are technically out of bounds for you like things that you just think that you cannot do are very much possible as a solo engineer solo designer and now we're finally at the point where I'm not even calling it slop anymore. Like they call the AI slop or whatever it may be or however you want to look at the code. It's damn good and it's getting better by the week. And um I think that you'll just I hope that you'll be inspired to go build something amazing because I'm going to show you something that is a little mad sciency weird and all over the place. And you'll get to see kind of a a raw version of my brain and how deep I go on some of this stuff. Um, but I think that's the beauty of it is is is this idea that we can go build anything and often times when we do it ends up being a a little bit of a I don't know a sandbox that can be a little too big and messy and then you have to refine it back to something that's actually usable. So I think that the future engineer and the future developer, the future product builder here, it's not going to be uh what you build as much as what you don't build, if that kind of makes sense. because it's going to be so easy to build anything and everything. Um to pair it back to something that's really usable, I think is going to be the a real skill. >> Yeah. The hard part is the clarity. Like how do you get the clarity to know what to build? >> Uh at least that's that's that's what I've been struggling with. >> Yeah. And then that's actually rolls right into the the project that I've been kind of working on just for fun. And um yeah, so I'm excited. >> All right, let's get into it. Okay. So, we can I'll show you kind of this, you know, quote unquote vibecoded or let's just call it coded project and I'll tell you the inspiration and then um we'll get into some of the the kind of dirty details and how it functions and what's possible as someone that kind of walked into this uh thinking I actually just asking myself can I pull this off? So, um let me go ahead and do a little screen share here. All right. You should be able to see uh TechMe up there, right? >> Yep. >> Okay. Awesome. So, first thing and and hope we don't uh in the edit. This has to make it in. I have nothing but a huge amount of respect for Gabe and what he's created in TechMe. Uh what I'm building today is not meant to be a competitor. I don't plan on launching it in its like form. It was mainly uh a personal curiosity of can I build something that's on par or better than TechMe by myself and call it like a week and see what that would look like. And one of the nice things about TechMe is for those that don't know or for those that are lightly familiar with it, it's been around for a long time. Gab's been building software since I was back in 2004, which is crazy. And we were both kind of, you know, experimenting in early social news and what that meant. And what you see here is an aggregator um that pulls from RSS and other sources. I don't know all the sources that he pulls from, but then also pulls from social media as well. And when you gather momentum, whether it be multiple um news stories coming in or multiple people tweeting about something or talking about it socially, that is considered signal and then that is considered used uh as part of, you know, ranking uh and showing you what's prominent here. And so what what um what you're seeing is actually visually the higher an object is here, the more weight it carries with a user because if you come in here and you scroll down to the bottom and you see something with very little tweets on it, it doesn't nearly carry as much impact as, you know, something with 15 or 20 different X posts underneath it. And so I like what he's doing here. I think it's a it's a really cool way to say, you know, maybe you might recognize some names, especially in tech, given how small the ecosystem is, you can probably look through this list and say, "Oh, I know these the two or three people. If they're talking about it, what are they saying? And might I just hover over and see what their comment was about a particular story?" So, a lot to love here. I mean, Gab's done a a great job at at creating this, but I um have a slightly different area of interest. A lot of this is big tech news and I kind of wanted to dive into like more of what's going on in AI because AI is moving just so fast. You know, how can I slice slice this in really interesting ways to find my version of this and this is stuff that we're playing around with at DIG with the reboot as well. So it kind of goes handinhand with some of the kind of exploration that we're doing in a lot of these different areas. And so my job these days um you know with Alexis O'hane on at at Dig is we have Justin as a CEO who runs kind of like the day-to-day and and builds out you know the Reddit competitor version of Dig. And then we have, you know, me that I'm kind of more in the labs area where I'm pushing on the edges of ideas and saying what might we do and does any of this make sense to roll back into the main product. And then Alexis is kind of just a overarching, you know, great ideas. So much depth of knowledge around, you know, both micro and macro communities and how they work and how they scale and what to do and not do and some of the missing tools and that's kind of how it all comes together. So, long story short, this is what I built. Um, so first thing I want to show you is that um there's going to be some errors here because it's been a minute since I have actually uh done the whole um actually touch this codebase. But what you see here is I have 63 sources of information coming in. And so these are from just your classic RSS feeds. Yes, RSS still does exist. Uh, a lot of sites do break RSS and so there are actually ways to go and add in um kind of scrapers that will create dynamic RSS for you even though RSS is no longer around. Um, but you can see here um it's a bunch of different uh RSS uh uh sources that are coming in here. Some of them are Reddit, some of Techrunch, but all largely uh techreated. So, because this hasn't been run in a while, we've got a lot of jobs that have to run in the background here to go and rec crawl all of this. So, I haven't run this project in a few days. And so, it's essentially going out and hitting all these different uh RSS feeds, pulling in and ingesting all that information, bringing it into the into the engine, and then expanding upon. And there's orchestrators and there's ways to actually go in and and resolve certain chunks of information and find out more about individual stories. And I'll get into that in a second, but we're playing a little bit of catch-up here. So, if the news stories for some reason don't look exactly fresh fresh, you'll know why. But they they'll be pretty damn close here in a couple minutes. It just needs to go and uh and do this this catch up. So, um going back to the RSS feeds here. So, okay, great. We've got sources. Now, what do we do? Well, sources lead to articles. And um when we go over to articles here, you can see now it's starting to pull in some of these articles in real time. So, we've got Mac Rumors 13 minutes ago. And then we've got a little status check here. And the status check is really what happens once the article is pulled in to the system like how do we process that article? How do we figure out what to do with that article? So, you know, 21 25 minutes ago, 29 minutes ago, The Verge 9 to5. And so we're pulling in and uh saving all of this in a Postgress database. And then we're also going in and doing things like figuring out who the authors are. And this comes down to author reputation. And so we can start look taking a look at different authors that are here. And I can click on you know any of these authors and actually um you know see what articles they've actually contributed as well. Um so you know this individual uh author here and what all of the articles that they published. Um, but what's more interesting here is actually what we're doing with these articles. So, what happens is is as I start to pull in these articles, and this is where it gets pretty crazy because, you know, I didn't have this um I I didn't really know I understood technically how to pull this off in terms of the the tools that would be required, but I couldn't write the code to do it. And so once we go in and we take an individual article, let me just get into an actual um an article itself here. See if that will take me into the actual that might pop me out to Okay, here we go. So here's a article right that now that just came in talks about the new air tags that just got announced and um this came in from Mac Rumors. It's got an ID associated with it. So I was setting all this up with in the database. It has a cluster membership associated with it which we can get into in a little bit but also it has a pipeline status and so what happens is is it comes in via RSS I hit I framely and I framely gives me back additional metadata about it so like title description um sometimes it can pull in even additional kind of deeper context about it article data things like that and I want to store all of that that's really important for me to have that rich data and then I can see that Gemini has been run against it as well which we can get into in for uh in a minute for as to the reason why. And so the winner has been resolved, the TLDDR has been generated and the embedding has been done. So the winner is basically saying, okay, there are a bunch of different factors here that might come in. Like for example, if I'm pulling this in with RSS, I might not get a description there because RSS sometimes is like kind of truncated. I might not get a really good description. But if I go and I hit I framely, then I might actually get a longer and better description. or if I go and hit firecrawl, I actually might get the full actual paragraph of or the full body of the article. So what we do is we have a judge that looks at it and says, "Okay, which is the best of these three that came in and pick a winner and the winner is the one that actually gets stored to the database." So that's what winner means. And we do that on a variety of different things. We I don't know why we say we. I I do that. I'm the one that actually ever coded this thing. Um so basically we can see here that for this particular uh story it did not have I think we got a failure. We're coming from Mac rumors. The winner was actually uh Gemini. So Gemini is used as a last resort. So you can see here fire crawl returned a 403. We had a hard air there. Um I framely we had some kind of low quality signals. We got a successful crawl but we had low quality signals. And then RSS um good it's used as a fallback. Good amount of characters here. We can see what the actual uh summary of that content was when we got ours out of RSS. But as you can see here, it's actually a pretty crappy RSS poll. There's not a whole lot of data there. You know, it just doesn't look that well or it doesn't look that good. So what we did is we hit Gemini and said, "Hey, Gemini, turn on your ground truth. Turn on your search. Go out and figure out what is like what is this actually about?" Right? And often times they won't come out and say this, but they kind of crawl the article and give you back what the article actually says. They might reward it a little bit, but it's largely what the article says. And I can prove that because they can you can do that with Reddit articles and things like that where most all everything will fail because Red's so hardcore about blocking it. Does that Greg does that makes sense so far? >> That does make sense. I just have one question around like why did you choose firecrawl and I framely especially and can you just explain what th what they do for people who aren't familiar with those services? >> Yeah, for sure. So, um let's go ahead and um take the actual article here and so uh I'll show this tab instead. And so you can see this is the Mac Rumors um article right >> now. If I take this Mac Rumors article and I copy and paste it and then I go into I framely here, I can paste in a URL, any URL. And obviously I'm doing this via API, but I just want to show you how it works. And I hit check URL. And what it's going to do is it's going to give me this this card back. So it's almost like those cards that you see on on X, you know, where you get a piece of rich media, you get the best possible image, get a beautiful title, nice little description here. Sometimes it's a little bit longer. And then I can just jump in here and look at the JSON and see like okay what did we get here and kind of expand out and say did we get a high quality logo like you know like I can look for and parse through all of this and try and find highquality data and then also I mean it's called I frame largely because if there is an embedded piece of media you also get all of those embed codes as well. So you get all of it. So, I can actually take, and the reason I like Gemini is because they're tied in with uh YouTube, obviously, and I can get transcripts back from full YouTube um uh videos, which is great for vector embeddings when I want to figure out what it's actually about. Okay, does that make sense? >> Crystal clear. >> Okay. And so, you can consider Firecrawl to be a very similar thing. It is a little bit more on the crawling side. So, a little bit more fine-tuned around crawling. they have some AI um uh aspects to it as well where AI will actually try and go out and figure out how the best way to kind of scrape the content. And then uh yeah, lastly with Firecrawl, I think it's just they have some stealth modes that you can turn on. So some of these news sources, they get really picky about what what they're allowed to be crawled and so you can kind of like hide and turn on stealth mode and then get to the actual data. And it's not my in my intention is not to like, you know, get around their ads or do something like evil here. It's really just to figure where's the signal and and to start to cluster these things together. So, back to and by the way, the working title for this is my my little thing that I code on nights and weekends. It's called nylon. It's my little incubator that I I work on. So, that's uh that's why you see nylon up here in the corner. Um, all right. So, scrolling back down, we can see that. Okay. So, here's the resolve content I framely. We got the picture. We got the author, we got the summary, and here's the main content from Gemini. So, we use that. Gemini won here. I framely won the summary. Gemini won the actual U main content. And then what I hit is I hit GPT5 mini largely because it's fast as hell and cheap and you don't it's very it's quite smart. Uh, Anthropic has a handful of models that also fall in this camp. I mean they all do you know it's really at the end of the day uh because I'm using uh vector embeddings uh from um you know OpenAI it's just when I have one model provider I just stick with it and unless I really need to bounce around which I did for the actual Gemini crawl and understanding of other things. So I try to keep it as simple as possible but sometimes you need multiple models and I will say Verscell's AI gateway is a great way to kind of code once and just flip a model on on the go. So, um, highly recommend checking out Versela AI gateway as a way to quickly swap models, uh, rather than having to recode it. All right. So, I want a TLDDR. That's important. I just want that human readable so you can see that. So, I want a TLDDR that is a vector TLDDR. And so, for people that don't don't know, um, vector embeddings are really interesting. I had never worked with them before. I'd heard about them. I understood technically how they function. But the point is that if you take a keywordrich and kind of deep understanding piece of content, you can create mathematical representations of that content and embed these with open AI and store them in Postgress by using a vector extensions. They're actually stored in your database as those pieces of math. And when you apply some of these clustering algorithms on top of them, they get really good at nuanced information where keyword search would completely fall down. So the old school ways back in the day in 2004 when I launched, you know, dig as a social news site, if you search for, you know, Apple um releases, whatever, it's it's find it's looking the word Apple. was looking for release and it's looking for whatever it was back then that they were doing like a iPod or something, right? And it would it just does it based on just can I find that text in the database and if so show me back the article. The beautiful thing about what we have today with our understanding of linguistics and around using vector embeds and algorithms on top of that is that you can say there is a difference even though they're both have the same type of keywords but there is a huge difference between Apple sues Google and Google sues Apple and that is impossible to do with keyword search because you're not understanding at a deep level what's going on here, right? So anyway, this is a very rich purposely rich uh longer form version of the TLDDR used just for for uh vector embeddings. And then I also wanted to create some key points here that we can use to feed into other models later when we're comparing the difference between articles when we see multiple articles starting to get clustered together. And then I don't use this, but I asked AI like, "Hey, write me like a spicier title, like a title that people might click on more, find more interesting, and just to kind of rewrite the title." And and Techbeam does this, too. Like when you go to the front page of Techbeam, it's not the title of the article, it's like actually what their editors chose to write. So, I just wanted to see how this looked. And then I wanted to put it in one of uh three different categories. um tech core and a couple other categories largely because there's a lot of stuff that come through these tech feeds especially when you add in like Forbes and some of these others where it's like you know does not relate to actual core tech or AI are the things I care about and I just wanted to put them in a bin and so here's the embedding I'm using the large model uh from OpenAI uh you can see when it was generated when it was done and then uh just some information about making sure that we don't rec crawl the article and have all that so that's done Okay, any questions so far? We got one article into the system. >> Uh, no, keep going. Keep going. >> Okay, so now let's talk about clusters. So clusters are well actually let me show show off one other thing. So how does this actually work? So how does that all that work on the back end, right? And there's a couple different ways that you could do this. You could say I want to kind of um write this in Nex.js and I want to have this just be a function. And if you want to get a little bit more fancy, you could say, "Okay, I'm using Superbase, so I'm going to throw on uh some cron jobs there and fire off these things, and I don't know if they fail or, you know, there's there's a lot of um it gets a little dicey here because often times things bad things can happen like you can have a RSS feed that gets blocked temporarily. You could have timeouts, you could have a model that actually doesn't complete. Um, and so I need durability around a lot of this stuff, right? And so what I do for that, actually, let me just do a screen share again, is for the durability side, okay, I use a service called uh, trigger.dev. And so I like trigger.dev because what it does is it allows me to create um these functions and they're all TypeScript that live in the cloud and that they are fired off either when I call them from my app or at a certain cadence. And so there are um these orchestrators that will go in here. You can see the expansion orchestrator that's when it go wants to go in and expand a story and actually see you know more more information about it. You can see clustering different uh things here. Here's my firecrawler. Here's my Gemini. Here's my I framely. So, anytime I fire something off to be enriched, I actually create this new little micro instance that goes out and runs on its own. You can see here the ones that are executing. See the little spinners here. So, these are all kind of running in real time. And then you can see the compute uh charge to go off and execute these and then how many milliseconds they they took. Now the thing that's interesting here is um you actually get to see the whole chain in which everything went down here. So you can see okay I was looking to kind of resolve a story ID uh locked in candidates I got you know one picture one summary u did it resolve the winners yes Gemini was the winner here uh it was published at and then it it finished the whole process. The nice thing about this is that if something fails, I get retries for free. And so I will automatically, you know, if an AI tlddr for a vector embedding, which is very important as bu we're building clusters, fails once, fails twice, it will continue to retry as, you know, and automatically spin up these instances and try again. uh and then will report back to me via Sentry or any other type of monitoring software that I have on the back end that oh I had a failure why did this actually happen right and so I like that because a couple things if I'm developing this locally and I don't have this on production my data continues to be enriched in the database and so I can continue to develop before I actually deploy to production so when I'm running this locally like I'm showing you here it's like it's it's if it's functioning and continuing to build things out. There's a few things that I are still tied locally that I need to catch up on uh to fire this back up because I just don't want to be burning through cash for for no reason if I'm not using it. But um yeah, so does that make sense? >> Yeah. I also think if I remember correctly, trigger.dev is open source, so we like that. I also think it's relatively cheap. >> Oh yeah, it's really inexpensive. like it's something like 10 15 bucks a month for 50 tasks or something last I checked. >> It's well I mean I I think we look when we looked on there we were actually seeing the per per I mean I'm running thousands and thousands of these and you know yeah it's like under $100 a month and that's I mean when I say thousands I mean like per day. >> Yeah. >> So it's it's it's not it obviously if they're going to be super you you can choose your instance type like with anything else. These are really lightweight non-computational kind of tasks to do. Some people use trigger for things like um you know using ffmpeg to do encoding and and you know things of that nature that are going to require a larger instance are obviously going to be more more pricey. So it really depends on like everything in in cloud on on the workload. Right. >> Totally. Yeah. I just don't want people to see this and be like oh my god this is probably thousands of dollars a month. It's like shockingly inexpensive for what it is. Yeah. And the other thing I was going to tell you is that um Verscell now has something called workflows that they've launched in beta that are basically it's for free and it is trigger.dev for free. Um but it's part of the the Verscell ecosystem if you're in in into that ecosystem. >> But um they're kind of these it's a way to monitor, retry and have these long working tasks as you know with edge functions like that's been the challenge, right? like you don't really get a whole hell of a lot of time uh and and things get stale. So anyway, this is nice. Uh I like it. It's um yeah, you're right though. It is an extra expense, but um I think you can it is relatively inexpensive for this type of task. Um all right, so back to the clusters. So now what we've we've done is we've run an algorithm on top of that. We've got all the vector embeds and we're starting to build clusters. So as you can see here, uh this is today. A I don't know that everything's caught up. It looks like we've enriched Oh, yeah. We're close. 2284 of 2,288 stories have been so 99.8% has been enriched in the last 24 hours, meaning that they've gone through that entire pipeline of that uh ones that were more or less failures that we actually had to reach out to Gemini and and get some extended data from them. 353 stories were done there. And you can see here we're starting to get some actual weight. So 105 sources reported on this EU investigation into X over Grock uh uh generated sexual images. Nvidia invests uh two billion into debtridden coreweave 47 stories and you're starting to get something that looks kind of like a tech memeish type feed. And then obviously you can slice and dice this in a variety of different ways. But this is where it gets even crazier. So I promised you crazy stuff. This is where we go down the rabbit hole. So let's just take this idea of um coreweave and Nvidia investing uh 2 billion. So we'll click on that cluster and now we have 47 uh stories uh nine were done via RSS and 38 discovered. So what does that mean? That means that when I see enough signal and I consider that three or more RSS stories talking about the same thing, I then hit search APIs. You could hit Brave Braves uh search API. Um, I use a Tavly search API and you can say go out and find me other stories outside of my RSS uh, ecosystem that may match this and bring them in. So, I can expand the scope and see is this a broader story that has more context that I'm just not seeing because of my finite set of RSS articles. Does that make sense? >> Absolutely. >> Okay. So, and then I'm looking at there's average distance between articles and similarities. This is all part of this this algorithm right here that I'm using for the clustering. Um, so first story started four hours ago, last minutes was 13 minutes minutes ago. So then I created something called the gravity engine. And so this is kind of like a a editorial type score and then it has actually it votes on it on how important this story is generally speaking. And I rate these in a few different buckets here like the impact uh another one called this it's gravity which we can get into the confidence ratio that this is pertains to technology and the things that I'm interested in and then this is all the stories that it's evaluated and then here's a little matrix here that I've had built out where you can see u you have an impact and then you have gravity and it's in the high high area here viral potential and how bubble size like how big this will eventually get how many people will you talk about it within tech is 60% % uh early trend like a growing trend is 75% intellectual gravity is 86% and impact is 88%. And so a total uh editorial vote of 85%. And then we can get into the uh the the rubric here which is uh so these are the impact uh dimensions. So x-axis drivers are industry impact. So it's 90%. So the massive $2 billion investment solidifies corewave's position as a key neo cloud provider intensifying competition with hypers scale scalers. Critically Nvidia is also launching its Vera CPU as a standalone product directly challenging Intel and AMD in the data center market. Uh consumer impact low 10%. This is negligible for consumers. So rated that actionability do I need to actually do anything here? Is this for a general audience? Can I take action? 30%. uh said some action for investors maybe but that's kind of it and then the risk and urgency like do I need to act on this which would be like okay there's a 911 iOS patch that you need to apply to your phone or something right that would be pegged at like 99% risk and urgency um and then the the Y uh drivers here are intellectual uh gravity drivers which is how novel is this which is very important to me because I want high and I'm not I haven't fine- tuned this in because it's saying how novel is for today. But in reality, what I want is novelty is applied against the longer horizon timeline because one of the things that's really fun is if you go back and you look at the first time uh on uh hacker news when Bitcoin was mentioned, everyone was like, "Ah, this is stupid blah blah blah." But if if I would have found that in here, the novelty would have been off the charts. And that's important to me because oftent times the largest things that we do in tech and that we see evolve that turn into these blockbuster things over time seem very silly when we first hear about them or so odd or different. And that's the signal as both a builder and investor that I want to find as early as I possibly can. Right? So that that's that's one that I care about deeply. uh technical depth um you know second order potential uh builder relevance this was important to me on the AI side I want to know how uh important like as a someone that is building in tech how much should I pay attention to this uh entertainment value which uh didn't have a lot of that and then I've got some cross cutting signals here signal to noise ratio viral potential and early trend detection as well um and then I added in some other uh like I could these judges that just sit here and kind of walk through this which is you know what's the PR fluff risk like how much of this is just a PR thing because a lot of this can be you know we'll see 20 or 30 and I can see this it's it's really crazy um you can tell I can detect when something is a paid sponsorship even when sometimes people aren't calling it out as such which is like really scary and illegal because I will see I can detect the similarity and distance difference between the vectors of the published content between the news articles and they're all released with plus or minus an hour of each other and they're all t they're all hitting the same major key points and it's just like AI reworking the paid sponsorship and I'm just like it's pretty effed up like you know and I'm like starting to find this stuff you know it's just like wow so this is this is what I do at night Greg I'm I'm I'm embarrassed but this is what I do at night >> so just a question on this whole this whole feature because like when I didn't expect it to be this full-fledged like onpoint like is this something that you sketched out on paper and built it or are you like working with >> you know AI as your co-founder to kind of help you come up with this like walk me through the product management piece of this. Yeah, I mean I I I I'm very much someone that that builds based on gut instinct and for me 99.9% of the features that I create and and don't get me wrong there's a lot of stuff I would cut out here now you know because I would consider like ah I didn't like that should probably cut that out like source distribution is a great one like we can get into but um I started was like okay let's crawl RSS okay let's just do that let's throw images up let's get as much rich data as we Let's do a very basic clustering algorithm even before we put in vector embeds. What does that look like? How does it feel? And then I just was like, well, what is TechMine not doing that I I really care about? And and that led to my personal curiosity around novelty of of objects. How can I detect these trends before they become big? You know, all these things where I was just like, I'm not seeing that anywhere else, so I should just build it, right? And so it's one feature at a time. So what you're seeing here actually is like I would say each of these things that you're seeing is probably a day or two of just being like well let's see if this will work and what it looks like and then putting it and then you know kind of using in today's tools what I would do is I would just you know use compound engineering on cloud code and do a workflow and say this is the idea that I have um actually I probably would well it depends on how big of idea. If it's a minor feature tweak, I would just do it that way. If it's something bigger that I needed to flesh out that had technology that I didn't know um what the right choice was, then I would use AI is more of a sparring partner on that side. So, I'll give you a great example. There are probably 10 competing clustering algorithms that you can use for news. And hell, if I know which one's the best, right? And so, I actually took the top two that it recommended for what I was trying to do. And you you don't what you see here at the top of this URL is you see clusters V2. And the reason it says V2 is because the second one ended up being better than the first one. The first one no longer exists. And so it's a lot of kind of like just going down that rabbit hole and saying, "Well, let's let's try these things out." And and sometimes I would immediately realize that was the wrong direction and just actually just uncommit that last whole GitHub reo repo or that commit and and PR that I spent four hours on and just chunk it and throw it away all together and be like well that was four hours lost but at the same time I want to learn something new and that's that's all of that's all of building. All building is is failure after failure and just and that and failure is awesome because it's just admitting that you've learned something new. So many people beat themselves up over failure and I I don't see it that way. I just see it as like that's failure is like it's the best part. That means the next time it's going to be a little bit better, you know? So anyway, >> I'm really interested in the this whole concept of like uh testing products on synthetic audiences. I don't know if you saw this, but you know, a few weeks ago, I think Toby from Shopify launched a feature where it was like you launch your e-commerce store, but like based on synthetic AI audiences, here's how they would perform. Here's how your conversion rate would look like. Here's the products that they would click into based on these personas. And I think that's like that's sort of the direction we're probably going to head in. So before you like publish something, you know, before you publish an ad, you have some certainty that it's going to work. Before you publish, >> right, >> uh an article, you know that, you know, people are going to be clicking into it, >> right? Yeah. I I I think there is so many domains where that makes a lot of sense. Um and and Toby's obviously just a freaking genius and so brilliant. >> Um what I do that's slightly different and I would I'd be all for that type of thing, but is I build for myself. I think we're entering into this era of of personal software, right? Like if there's a workout app that you don't like because the the buttons placement doesn't doesn't do it for you or it doesn't track one key core metric, like you just build your own, right? And like that's going to be the norm in, you know, if it isn't already, it's going to be the norm in like six months from now, right? And then the question is, how many people are there like you that also care about said thing, right? And so when I'm building this particular slice of the news or the industry and we haven't even gotten into the understanding of who's touching things because I think one of the things that um that that has done really well in tech is that social touch of like you know when Mark Andre touches something with a tweet or what you know how much more credibility and weight does that add to it even more so than Bloomberg or Wall Street Journal or business insider writing about something right so that's yet another thing that needs to be baked in here as well But then, you know, if I enjoy it, the nice thing about this whole thing is this was, you know, maybe $300 in AI credits or something, you know, to go build this whole thing. And I could stand this up, cash the crap out of it, meaning like so that it's performant and and just put it out there and everyone would be like, "Okay, if I'm also into the geeky things that Kevin is, I will use this, too." And that might be a thousand people, might be 100,000 people, no one knows. But also at the same time, like that's okay. Like if you if you have 500 people that really love what you've created, I get a lot of value and joy out of that. And it's like we we think in numbers now on the internet in terms of, you know, millions and billions of people. But in reality, if you like went outside your house and there were 500 people standing like cheering you on, you'd be like, I'm the biggest rock star in the freaking world, right? So we lose this perspective of what it means, what success means and and so I just I hope that we can all agree and and just realize it doesn't have to we don't have to swing for the fences. So yeah, so I I just you know that's I think what I'm so excited about this is it's democratizing code quality or coding for everyone. And I was a computer science uh major. I dropped out and I didn't know why and I was really slow and I could understand the core concepts but I didn't know why I couldn't just do it as fast as my my my everyone I was working with in my my class and just six months ago I found out that I have something called aphantasia which is this inability to have a mind's eye so when like people close their eyes they and they say picture an apple um or like you know the famous one of like when you're trying to go to sleep like like sheep keep jumping over a fence or some like that. I always thought they were joking, you know? I didn't know that you could actually close your eyes and envision things. And so because of that, you know, things like how to handle proper syntax and on on on and code and like all the things I was trying to beat into my brain, the retention just wasn't there. Somehow the core concepts stick with me and the and the creativity. I've always had that in abundance, which is great. But uh but yeah and now like the AI will fill in the deficiencies wherever they are for you which is just beautiful. >> So I also dropped out of CS school uh only had three classes left and I was the same way in the sense that for me I wasn't uh I couldn't get the last 10%. So, like I couldn't get the code to compile because there was like I had I had 92% of it there, but there was, you know, an integer missing here, a variable missing there. >> And what's cool is nowadays, you know, of course, it's nice to know that stuff, but if you're just trying to get something out the door and to get feedback from people, you know, let Claude Code figure that out for you. >> Yeah. Exactly. And the other thing too I I I I think that is lost on a lot of people that I see on social media around what you know quote unquote vibe coding means is they say well great and this has been the complaint for a while vibe coding is buggy it's not performant it'll fall over under weight blah blah blah I would argue those are great problems to have the hardest thing to do is to find something that somebody actually wants to use right like that's the hard problem if I have something that I vibe coded and I launch it and if it crashes under the weight of 50,000 people beating my door down because it's the the next best thing, I guarantee you I can find you engineers to work on that and scale it, right? And so I I I don't think that should be a reason why we we kind of like I like it because you get more shots on goal, right? Like I don't have to look at the code. It's not because like you, yes, I can jump into a component in Typescript and and be like, "Okay, I I kind of see what it's doing here." You know, I can we can we can do that. It's slow, but I can do that. But that's not the point. I don't care right now. I'd rather see actual humans using it and saying, "Yeah, Kevin, this is so cool. I want you to formalize this a little bit more. Make sure it does scale. And if if that comes in the form of usage, then I'll find the find the right engineers to to take my my kind of scribble code and and make it real and performant. And and honestly, compound engineering has already been amazing and that it's it's finding a bunch of stuff and and making things more performant for me on the fly. >> So for this particular project, you're going to put it up like success. I'm just trying to think like success. Success looks like what? >> Well, it doesn't look like anything. I might never launch this. It's like I I might put together a little one pager that's just like the best AI stories and shown by the things that I care about like novelty and impact or whatever it may be or you know there's just I I realize that you've got Product Hunt which is kind of people that have already launched things. You've got um you know TechMe which is fantastic at overarching big news you know coreweave $2 billion like that's a techme story all day long right and then you've got X which is just a flood of stuff that's coming at us so hot and heavy and it's just like okay well where do I decide to spend my time like we were just talking about this before the the podcast uh started you're like oh have you play with this you play that and I'm like ah you know cuz It's like you get so there's so much coming at you. I want this thing to eventually, if it ever sees the light of day, what I wanted to do is to say, Kevin, this is important to you because it maps to you. these very important people have touched it and said it's worth your time and it's passed that threshold to where now you should go install it, play, have fun and learn about it because otherwise I'm just going to be and sadly because of my ADHD I'm going to be bouncing around too much to even get anything done. So that's that's my hope. But yeah, that's so you can see there's a lot of other features we can get in here that we won't have to. But it's um you know it it this is me playing to see cuz a perfect product here actually would be to cut 90% of these features and just find the 10% that really mean something to me and a lot of people and launch it as a standalone single page website. Right? And that's what all but this is the messiness of it all. And I I just want to show people that like for me what I do is I put everything on the table. All the stuff on the table. All the stuff that I would never show anybody like the the the distance and similarity scores between two stories. Maybe I would show that. But it get it all out there and then decide to cut and and go in there with that kind of director or editor's cut and start making and start trimming trimming trimming trimming down to where you eventually get something that's really usable and useful to to folks. One feature which I would love to see on something like this would be I'm just thinking out loud like if I was a PM on this product I would be like what is the mechanic to bring people back? Uh so if you go to go to ideabrowser.com >> so I built this uh it started off as like a lead magnet actually. Um >> yeah I've I've seen this by the way. This is like a very famous thing now that you built. >> And so basically the idea was the like the first the lead magnet started off as here's a database of 30 ideas I would build today. >> Yeah. >> And then you know it was like put in your email to get the access to the database. And then it was like okay how about what we do to make it more fun is almost like you remember group on let's do like instead of a product a day an idea of the day and then the mechanic is the email every single day you know we get a 50% open rate people open up the email >> and it's grown you know very very fast through that way so I wonder you know if I'm building what you're building n you know nylon if it does see the light of day it's like what is the mechanic to get people back to the website. >> Yeah, I mean in in my mind the at least for um for for news in general, it is relevance to the to the end user. You know, if if if it is discovering stuff that you are missing or you have overlooked and it's saving you time and energy because it's saying, you know, it's presented in a visual way like I'll give you an example. if you were on a conference call today and you had you know uh let's call it like just like the some leaning minds in tech if Sam Alman and Mark Andre and like a handful of other people in AI said hey um you know Greg I think you should go check this out and play with it there's a good chance by that afternoon you would be like installing it and and messing around with it right and so visually I would have to say there here are a thousand signals that came in today, how can I show you the five things that have launched or that are in beta that are in GitHub that are worth your time and map to your interests as well, right? So, I would probably go in and pull your last 100 or 500 exposts and and look at how you interact and and and create, you know, vector representations of who you are as an individual and then also try and get a little a little bit more custom so that it maps to you. So if you're very much into robotics, you would see a bunch of amazing kind of robotics information being presented to you across a variety of different fronts. So you know, you would see it both in terms of things that are being talked about on X and also things that are being talked about on Product Hunt or things that are be talked about on Hacker News or, you know, Reddit or any number of sources of the new dig or you name it. So I don't know. I I I really don't I think at the end of the day for a consumer app um you know it has to come down to am I finding something on useful from this site or service that I don't see anywhere else and is it helping me save time and energy uh and the answer may be no and then guess what it's it cost me $500 and I flush it down the drain and onto the next thing >> you know like I I've got another one I could show you if we have time don't have time do we have two minutes >> yeah let's do Right. >> Okay. So, this one is is like December 15th. So, December uh 15th of so 12 years ago, I posted this idea that um you could have a blog where you can actually see the person in the background in real time, but it's blurred out, but it gives you a sense of kind of presence that they were actually there. And I was I'm still kind of enamored with this idea especially because we're entering into a world like there me there I am like inputting in uh information or typing as I'm typing but we're we're we'reing entering into this world where we don't even know if there's another human on the other side of it. I think that's just going to get worse and worse over time. And so you know I like fullon built this in uh clawed code and now I have it you know completely running and I can I can just show it to you real quick. Uh, let's see here. Just pull it up. Um, >> while you're pulling that up, it just it strikes me that there's a bunch of good ideas from, for example, 12 years ago that could be recreated today. >> Oh, 100%. >> Right. >> Yeah. I mean, there were things that were just either so much of this is just right idea at the right time, you know, and and some of it was not technically possible. these crazy ideas that we had or some of it is even richer now that we can bolt on AI and make it you know cooler and different in some unique way. So there are a lot of things that I feel will see the light of day again but slightly modified uh because it the cost to do so is is is next to nothing you know which is which is great. So, what wasn't possible back then was real-time video compression uh in the browser. And the reason I say that is uh the prototype I built 12 years ago, the issue would have been that you uploaded raw video to the site and then you blurred it after the fact and which meant a user could go trim away the CSS and actually see that person in a very awkward kind of position or whatever and like maybe an environment where they were like looking for a little bit of blur privacy. And so now this is like all done uh in real time. So what you're seeing now is it's actually recording me. It's so like hello world. Um this is a test. And then I can just kind of move my arms around like this a little bit. Get a little movement and and please like this is the V.01 alpha of this whole thing. Um and then hit submit. And it's doing some horrible broken math here. But um you see like that's actually me in the background and that's not me now. That is the blurred version of me. And um it's a horrible interface. I would I would never release this interface. But see that was me when I was waving my hands uh a second ago. But I could do all of the compression now on uh client side so that I do preserve privacy and put this up in the background. And so you can imagine these little slivers and this little visibility into people's world as they're kind of blogging. And in my head, I'm like, "Okay, well, maybe this should just be my be my blog and I'll release this and I'll just open source it and give it away and there doesn't need to be a business model because this is just fun, you know?" Like, so like that's that's what I love about the time we're living in, man. It's the best. >> You just have fun. >> You can just you could just put out things and and sometimes though, it's sort of interesting. It's like when when there's not um when you just put out projects for fun somehow, I don't know why, but they end up being the the the the projects that could end up becoming the biggest businesses. >> You are 1,000% right. It is the weirdest thing that I do not know how to explain. >> Like when I when I did dig back in 2004, it was just like I just want to see if people can vote on things and what it looks like when the best stuff hits the homepage. And then, you know, a year and a half later, there's 38 million people a month using the site. When I made zero, the intermittent fasting app, I was like, I just want a way to track my fast and like have a silly little timer. And then, you know, the company's doing double digits, millions of dollars in revenue off of a little like timer app with some other content on there. And I'm just like, how is this even the these were just for fun. These were, you know, and it's it's very it's very strange how that works. So, uh, before we wrap up, you're you're working on DIG and you're kind of incubating projects. Like, I'm listening to you and I'm like, how can I work with Kevin? I'm sure people are listening to this being like, this this sounds cool. Like, how could people support get to work with you? >> Yeah, I think there's a couple different ways. I appreciate you saying that. There's I have a um I'm sitting in an office right now that's completely empty and so I have an incubator here in LA, a studio that I'm going to kind of open up and there's just going to be free desks for people to come in that are jamming on really cool stuff. So if you know at reply me or DM me on X if you you're building really cool AI stuff and you even if you're just coming through LA and just want to come in and jam and it and the point is like let's just compare notes and talk about what's cool over lunch and like you know if you need an office you need a room to take a call you got it for an hour like no big deal right no fees or any type of like I don't want to charge for desks or anything like that so I'm going to surround myself with those types of people here in in Venice uh out in LA and I want to do that. Um, so please like hit me up and let's let's figure out a way to connect if you're building really cool stuff. Um, and then you know I'm a venture capitalist still over at True Ventures. And part of what I think I think VC is going to evolve dramatically over the next couple of years because I I believe that people don't need to raise capital and oftentimes most ideas especially like just great lifestyle businesses that get to you know two, four, five, 10 million in revenue like own that 100%. Don't sell it to VCs. I'm not supposed to be saying that because I'm a VC. And so, but like don't do it. And so, but that's why I hope people will realize that like that that's why I there is a time and place for VC. Like when you really get to scale and you're like, damn, I need I need a couple million bucks to hire because the growth is so outrageous, you know, like and and that's that's kind of what I do and that's why I want to play. Also, I think VCs, the the era of VCs just being these like MBAs that sit there and like try to tell you how to run your business, I think is so boring to me. I want a VC that is playing, that's building alongside me, that's like pressure testing my ideas, that's a thought partner on this stuff. And so that's kind of like why I don't even want to call I hate the even word venture capital. I think it's like evil. It feels evil these days. Um I just if I if if someone does need first of all I try to talk everyone out of taking money and if they do need money especially hardware companies need a lot of money um then they should find someone that's also building and that's built stuff at scale not just because somebody has a a great a great pedigree or you know uh yeah does that make any sense at all? It does. And and I just want to add like I'm the first person to be like, you know, people listen to me on the pod know that I think that you should your MVP, your business, you know, you probably don't need venture to start. Um, and there's some businesses that shouldn't raise venture. But I also think that in this world where you can build software, MVP it, uh, if you want to build hardware, you're like, you build this cool piece of software, it starts to take off and you're like, you know, oh, there's this logical extension to hardware, you're probably going to need to raise money. >> Yeah, 100%. We we invested in a company called Sandbar that's doing this AI ring. I don't know if you've seen the prototypes for it. >> Yeah. >> Um, but it's it's really cool. And, you know, they I can't remember how much we put in. it was close to 10 million or something like that. But it turns out to do the tooling to do to to go and you know get that to scale like that's those those are real dollars still required to to pull that off. >> Totally. Kevin, it's been an absolute treat having you on the pod. Um I hope you come back on again and show and tell more because you're up to some really cool stuff. You're a legend and uh I'm going to take you up on that. I'm next time I'm in LA I'm going to pull through. Yeah. >> Done. like you got an office here, so come come hang. And and thanks for having me on. I appreciate it. And thanks for all the work you've been doing in this space, man. It's like I I know we don't talk that often except for like the random DMs and stuff, but I see you all over X and the the content and stuff that you're putting out. It's absolutely fantastic. >> Thanks, man. Just trying to create some signal out in the world of noise. >> It's great. Well, maybe the Nylon app will uh identify that and put it in front of more people. >> Exactly. That's what I'm hoping. All right. Take care, Kevin. Take care.