Latent Space
Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0
The AI Founder Gene: Being Early, Building Fast, and Believing in Greatness — with Sharif Shameem of Lexica

The AI Founder Gene: Being Early, Building Fast, and Believing in Greatness — with Sharif Shameem of Lexica

Ep. 11: How to be early to everything, build ambitious things, and learn to train your own models. Plus: The future of Agents and why you should sequence your genome!

Thanks to the over 42,000 latent space explorers who checked out our Replit episode! We are hosting/attending a couple more events in SF and NYC this month. See you if in town! was introduced to the world 24 hours after the release of Stable Diffusion as a search engine for prompts, gaining instant product-market fit as a world discovering generative AI also found they needed to learn prompting by example.

Lexica is now 8 months old, serving 5B image searches/day, and just shipped V3 of Lexica Aperture, their own text-to-image model! Sharif Shameem breaks his podcast hiatus with us for an exclusive interview covering his journey building everything with AI!

The conversation is nominally about Sharif’s journey through his three startups VectorDash, Debuild, and now Lexica, but really a deeper introspection into what it takes to be a top founder in the fastest moving tech startup scene (possibly ever) of AI. We hope you enjoy this conversation as much as we did!

Recorded at the beautiful StudioPod studios in San Francisco

Full transcript is below the fold. We would really appreciate if you shared our pod with friends on Twitter, LinkedIn, Mastodon, Bluesky, or your social media poison of choice!


  • [00:00] Introducing Sharif

  • [02:00] VectorDash

  • [05:00] The GPT3 Moment and Building Debuild

  • [09:00] Stable Diffusion and Lexica

  • [11:00] Lexica’s Launch & How it Works

  • [15:00] Being Chronically Early

  • [16:00] From Search to Custom Models

  • [17:00] AI Grant Learnings

  • [19:30] The Text to Image Illuminati?

  • [20:30] How to Learn to Train Models

  • [24:00] The future of Agents and Human Intervention

  • [29:30] GPT4 and Multimodality

  • [33:30] Sharif’s Startup Manual

  • [38:30] Lexica Aperture V1/2/3

  • [40:00] Request for AI Startup - LLM Tools

  • [41:00] Sequencing your Genome

  • [42:00] Believe in Doing Great Things

  • [44:30] Lightning Round

Show Notes


Alessio: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO on Residence at Decibel Partners. I'm joined by my co-host Wix, writer and editor of Latent Space. And today we have Sharish Amin. Welcome to the studio. 

Sharif: Awesome. Thanks for the invite.

Swyx: Really glad to have you. 

[00:00] Introducing Sharif

Swyx: You've been a dream guest, actually, since we started drafting guest lists for this pod. So glad we could finally make this happen. So what I like to do is usually introduce people, offer their LinkedIn, and then prompt you for what's not on your LinkedIn. And to get a little bit of the person behind the awesome projects. So you graduated University of Maryland in CS. 

Sharif: So I actually didn't graduate, but I did study. 

Swyx: You did not graduate. You dropped out. 

Sharif: I did drop out. 

Swyx: What was the decision behind dropping out? 

Sharif: So first of all, I wasn't doing too well in any of my classes. I was working on a side project that took up most of my time. Then I spoke to this guy who ended up being one of our investors. And he was like, actually, I ended up dropping out. I did YC. And my company didn't end up working out. And I returned to school and graduated along with my friends. I was like, oh, it's actually a reversible decision. And that was like that. And then I read this book called The Case Against Education by Brian Kaplan. So those two things kind of sealed the deal for me on dropping out. 

Swyx: Are you still on hiatus? Could you still theoretically go back? 

Sharif: Theoretically, probably. Yeah. Still on indefinite leave. 

Swyx: Then you did some work at Mitra? 

Sharif: Mitra, yeah. So they're lesser known. So they're technically like an FFRDC, a federally funded research and development center. So they're kind of like a large government contractor, but nonprofit. Yeah, I did some computer vision work there as well. 

[02:00] VectorDash

Swyx: But it seems like you always have an independent founder bone in you. Because then you started working on VectorDash, which is distributed GPUs. 

Sharif: Yes. Yeah. So VectorDash was a really fun project that we ended up working on for a while. So while I was at Mitra, I had a friend who was mining Ethereum. This was, I think, 2016 or 2017. Oh my God. Yeah. And he was mining on his NVIDIA 1080Ti, making around like five or six dollars a day. And I was trying to train a character recurrent neural network, like a character RNN on my iMessage text messages to make it like a chatbot. Because I was just curious if I could do it. Because iMessage stores all your past messages from years ago in a SQL database, which is pretty nifty. But I wanted to train it. And I needed a GPU. And it was, I think, $60 to $80 for a T4 on AWS, which is really slow compared to a 1080Ti. If you normalize the cost and performance versus the 1080Ti when someone's mining Ethereum, it's like a 20x difference. So I was like, hey, his name was Alex. Alex, I'll give you like 10 bucks if you let me borrow your 1080Ti for a week. I'll give you 10 bucks per day. And it was like 70 bucks. And I used it to train my model. And it worked great. The model was really bad, but the whole trade worked really great. I got a really high performance GPU to train my model on. He got much more than he was making by mining Ethereum. So we had this idea. I was like, hey, what if we built this marketplace where people could rent their GPUs where they're mining cryptocurrency and machine learning researchers could just rent them out and pay a lot cheaper than they would pay AWS. And it worked pretty well. We launched in a few months. We had over 120,000 NVIDIA GPUs on the platform. And then we were the cheapest GPU cloud provider for like a solid year or so. You could rent a pretty solid GPU for like 20 cents an hour. And cryptocurrency miners were making more than they would make mining crypto because this was after the Ethereum crash. And yeah, it was pretty cool. It just turns out that a lot of our customers were college students and researchers who didn't have much money. And they weren't necessarily the best customers to have as a business. Startups had a ton of credits and larger companies were like, actually, we don't really trust you with our data, which makes sense. Yeah, we ended up pivoting that to becoming a cloud GPU provider for video games. So we would stream games from our GPUs. Oftentimes, like many were located just a few blocks away from you because we had the lowest latency of any cloud GPU provider, even lower than like AWS and sometimes Cloudflare. And we decided to build a cloud gaming platform where you could pretty much play your own games on the GPU and then stream it back to your Mac or PC. 

Swyx: So Stadia before Stadia. 

Sharif: Yeah, Stadia before Stadia. It's like a year or so before Stadia. 

Swtx: Wow. Weren't you jealous of, I mean, I don't know, it sounds like Stadia could have bought you or Google could have bought you for Stadia and that never happened? 

Sharif: It never happened. Yeah, it didn't end up working out for a few reasons. The biggest thing was internet bandwidth. So a lot of the hosts, the GPU hosts had lots of GPUs, but average upload bandwidth in the United States is only 35 megabits per second, I think. And like a 4K stream needs like a minimum of 15 to 20 megabits per second. So you could really only utilize one of those GPUs, even if they had like 60 or 100. 

[05:00] The GPT3 Moment and Building Debuild

Swyx: And then you went to debuild July 2020, is the date that I have. I'm actually kind of just curious, like what was your GPT-3 aha moment? When were you like GPT-3-pilled? 

Sharif: Okay, so I first heard about it because I was also working on another chatbot. So this was like after, like everything ties back to this chatbot I'm trying to make. This was after working on VectorDash. I was just like hacking on random projects. I wanted to make the chatbot using not really GPT-2, but rather just like it would be pre-programmed. It was pretty much you would give it a goal and then it would ask you throughout the week how much progress you're making to that goal. So take your unstructured response, usually a reply to a text message, and then it would like, plot it for you in like a table and you could see your progress over time. It could be for running or tracking calories. But I wanted to use GPT-3 to make it seem more natural because I remember someone on Bookface, which is still YC's internal forum. They posted and they were like, OpenAI just released AGI and it's GPT-3. I asked it like a bunch of logic puzzles and it solved them all perfectly. And I was like, what? How's no one else talking about this? Like this is either like the greatest thing ever that everyone is missing or like it's not that good. So like I tweeted out if anyone could get me access to it. A few hours later, Greg Brockman responded. 

Swyx: He is everywhere. 

Sharif: He's great. Yeah, he's on top of things. And yeah, by that afternoon, I was like messing around with the API and I was like, wow, this is incredible. You could chat with fake people or people that have passed away. You could like, I remember the first conversation I did was this is a chat with Steve Jobs and it was like, interviewer, hi. What are you up to today on Steve? And then like you could talk to Steve Jobs and it was somewhat plausible. Oh, the thing that really blew my mind was I tried to generate code with it. So I'd write the function for a JavaScript header or the header for a JavaScript function. And it would complete the rest of the function. I was like, whoa, does this code actually work? Like I copied it and ran it and it worked. And I tried it again. I gave more complex things and like I kind of understood where it would break, which was like if it was like something, like if it was something you couldn't easily describe in a sentence and like contain all the logic for in a single sentence. So I wanted to build a way where I could visually test whether these functions were actually working. And what I was doing was like I was generating the code in the playground, copying it into my VS code editor, running it and then reloading the react development page. And I was like, okay, cool. That works. So I was like, wait, let me just put this all in like the same page so I can just compile in the browser, run it in the browser and then submit it to the API in the browser as well. So I did that. And it was really just like a simple loop where you just type in the prompt. It would generate the code and then compile it directly in the browser. And it showed you the response. And I did this for like very basic JSX react components. I mean, it worked. It was pretty mind blowing. I remember staying up all night, like working on it. And it was like the coolest thing I'd ever worked on at the time so far. Yeah. And then I was like so mind blowing that no one was talking about this whole GPT three thing. I was like, why is this not on everyone's minds? So I recorded a quick 30 second demo and I posted on Twitter and like I go to bed after staying awake for like 20 hours straight. When I wake up the next morning and I had like 20,000 likes and like 100,000 people had viewed it. I was like, oh, this is so cool. And then I just kept putting demos out for like the next week. And yeah, that was like my GPT three spark moment. 

Swyx: And you got featured in like Fast Company, MIT Tech Review, you know, a bunch of stuff, right? 

Sharif: Yeah. Yeah. I think a lot of it was just like the API had been there for like a month prior already. 

Swyx: Not everyone had access. 

Sharif: That's true. Not everyone had access. 

Swyx: So you just had the gumption to tweet it out. And obviously, Greg, you know, on top of things as always. 

Sharif: Yeah. Yeah. I think it also makes a lot of sense when you kind of share things in a way that's easily consumable for people to understand. Whereas if you had shown a terminal screenshot of a generating code, that'd be pretty compelling. But whereas seeing it get rendered and compiled directly in front of you, there's a lot more interesting. There's also that human aspect to it where you want to relate things to the end user, not just like no one really cares about evals. When you can create a much more compelling demo explaining how it does on certain tasks. 

[09:00] Stable Diffusion and Lexica

Swyx: Okay. We'll round it out soon. But in 2022, you moved from Debuild to Lexica, which was the search engine. I assume this was inspired by stable diffusion, but I can get the history there a little bit. 

Sharif: Yeah. So I was still working on Debuild. We were growing at like a modest pace and I was in the stable... 

Swyx: I was on the signup list. I never got off. 

Sharif: Oh yeah. Well, we'll get you off. It's not getting many updates anymore, but yeah, I was in the stable diffusion discord and I was in it for like many hours a day. It was just like the most exciting thing I'd ever done in a discord. It was so cool. Like people were generating so many images, but I didn't really know how to write prompts and people were like writing really complicated things. They would be like, like a modern home training on our station by Greg Rutkowski, like a 4k Unreal Engine. It's like that there's no way that actually makes the images look better. But everyone was just kind of copying everyone else's prompts and like changing like the first few words. 

Swyx: Yeah. Yeah. 

Sharif: So I was like using the discord search bar and it was really bad because it showed like five images at a time. And I was like, you know what? I could build a much better interface for this. So I ended up scraping the entire discord. It was like 10 million images. I put them in a database and I just pretty much built a very basic search engine where you could just type for type a word and then it returned all the prompts that had that word. And I built the entire website for it in like 20, in like about two days. And we shipped it the day I shipped it the day after the stable diffusion weights were open sourced. So about 24 hours later and it kind of took off in a way that I never would have expected. Like I thought it'd be this cool utility that like hardcore stable diffusion users would find useful. But it turns out that almost anyone who mentioned stable diffusion would also kind of mention Lexica in conjunction with it. I think it's because it was like it captured the zeitgeist in an easy to share way where it's like this URL and there's this gallery and you can search. Whereas running the model locally was a lot harder. You'd have to like to deploy it on your own GPU and like set up your own environment and like do all that stuff. 

Swyx: Oh, my takeaway. I have two more to add to the reasons why Lexica works at the time. One is lower latency is all you need. So in other words, instead of waiting a minute for your image, you could just search and find stuff that other people have done. That's good. And then two is everyone knew how to search already, but people didn't know how to prompt. So you were the bridge. 

Sharif: That's true. Yeah. You would get a lot better looking images by typing a one word prompt versus prompting for that one word. Yeah. 

Swyx: Yeah. That is interesting. 

[11:00] Lexica’s Explosion at Launch

Alessio: The numbers kind of speak for themselves, right? Like 24 hours post launch, 51,000 queries, like 2.2 terabytes in bandwidth. Going back to the bandwidth problem that you have before, like you would have definitely run into that. Day two, you doubled that. It's like 111,000 queries, four and a half terabytes in bandwidth, 22 million images served. So it's pretty crazy. 

Sharif: Yeah. I think we're, we're doing like over 5 billion images served per month now. It's like, yeah, that's, it's pretty crazy how much things have changed since then. 

Swyx: Yeah. I'm still showing people like today, even today, you know, it's been a few months now. This is where you start to learn image prompting because they don't know. 

Sharif: Yeah, it is interesting. And I, it's weird because I didn't really think it would be a company. I thought it would just be like a cool utility or like a cool tool that I would use for myself. And I really was just building it for myself just because I didn't want to use the Discord search bar. But yeah, it was interesting that a lot of other people found it pretty useful as well. 

[11:00] How Lexica Works

Swyx: So there's a lot of things that you release in a short amount of time. The God mode search was kind of like, obviously the first thing, I guess, like maybe to talk about some of the underlying technology you're using clip to kind of find, you know, go from image to like description and then let people search it. Maybe talk a little bit about what it takes to actually make the search magic happen. 

Sharif: Yeah. So the original search was just using Postgres' full text search and it would only search the text contents of the prompt. But I was inspired by another website called Same Energy, where like a visual search engine. It's really cool. Do you know what happened to that guy? I don't. 

Swyx: He released it and then he disappeared from the internet. 

Sharif: I don't know what happened to him, but I'm sure he's working on something really cool. He also worked on like Tabnine, which was like the very first version of Copilot or like even before Copilot was Copilot. But yeah, inspired by that, I thought like being able to search images by their semantics. The contents of the image was really interesting. So I pretty much decided to create a search index on the clip embeddings, the clip image embeddings of all the images. And when you would search it, we would just do KNN search on pretty much the image embedding index. I mean, we had way too many embeddings to store on like a regular database. So we had to end up using FAISS, which is a Facebook library for really fast KNN search and embedding search. That was pretty fun to set up. It actually runs only on CPUs, which is really cool. It's super efficient. You compute the embeddings on GPUs, but like you can serve it all on like an eight core server and it's really, really fast. Once we released the semantic search on the clip embeddings, people were using the search way more. And you could do other cool things. You could do like similar image search where if you found like a specific image you liked, you could upload it and it would show you relevant images as well. 

Swyx: And then right after that, you raised your seed money from AI grant, NetFreedman, then Gross. 

Sharif: Yeah, we raised about $5 million from Daniel Gross. And then we also participated in AI grant. That was pretty cool. That was kind of the inflection point. Not much before that point, Lexic was kind of still a side project. And I told myself that I would focus on it full time or I'd consider focusing on it full time if we had broke like a million users. I was like, oh, that's gonna be like years away for sure. And then we ended up doing that in like the first week and a half. I was like, okay, there's something here. And it was kind of that like deal was like growing like pretty slowly and like pretty linearly. And then Lexica was just like this thing that just kept going up and up and up. And I was so confused. I was like, man, people really like looking at pictures. This is crazy. Yeah. And then we decided to pivot the entire company and just focus on Lexica full time at that point. And then we raised our seed round. 

[15:00] Being Chronically Early

Swyx: Yeah. So one thing that you casually dropped out, the one that slip, you said you were working on Lexica before the launch of Stable Diffusion such that you were able to launch Lexica one day after Stable Diffusion. 

Sharif: Yeah.

Swyx: How did you get so early into Stable Diffusion? Cause I didn't hear about it. 

Sharif: Oh, that's a good question. I, where did I first hear about Stable Diffusion? I'm not entirely sure. It must've been like somewhere on Twitter or something. That changed your life. Yeah, it was great. And I got into the discord cause I'd used Dolly too before, but, um, there were a lot of restrictions in place where you can generate human faces at the time. You can do that now. But when I first got access to it, like you couldn't do any faces. It was like, there were like a, the list of adjectives you couldn't use was quite long. Like I had a friend from Pakistan and it can generate anything with the word Pakistan in it for some reason. But Stable Diffusion was like kind of the exact opposite where there were like very, very few rules. So that was really, really fun and interesting, especially seeing the chaos of like a bunch of other people also using it right in front of you. That was just so much fun. And I just wanted to do something with it. I thought it was honestly really fun. 

Swyx: Oh, well, I was just trying to get tips on how to be early on things. Cause you're pretty consistently early to things, right? You were Stadia before Stadia. Um, and then obviously you were on. 

Sharif: Well, Stadia is kind of shut down now. So I don't know if being early to that was a good one. 

Swyx: Um, I think like, you know, just being consistently early to things that, uh, you know, have a lot of potential, like one of them is going to work out and you know, then that's how you got Lexica. 

[16:00] From Search to Custom Models

Alessio: How did you decide to go from search to running your own models for a generation? 

Sharif: That's a good question. So we kind of realized that the way people were using Lexica was they would have Lexica open in one tab and then in another tab, they'd have a Stable Diffusion interface. It would be like either a discord or like a local run interface, like the automatic radio UI, um, or something else. I just, I would watch people use it and they would like all tabs back and forth between Lexica and their other UI. And they would like to scroll through Lexica, click on the prompt, click on an image, copy the prompt, and then paste it and maybe change a word or two. And I was like, this should really kind of just be all within Lexica. Like, it'd be so cool if you could just click a button in Lexica and get an editor and generate your images. And I found myself also doing the all tab thing, or it was really frustrating. I was like, man, this is kind of tedious. Like I really wish it was much simpler. So we just built generations directly within Lexica. Um, so we do, we deployed it on, I don't remember when we first launched, I think it was November, December. And yeah, people love generating directly within it. 

[17:00] AI Grant Learnings

Swyx: I was also thinking that this was coming out of AI grants where, you know, I think, um, yeah, I was like a very special program. I was just wondering if you learned anything from, you know, that special week where everyone was in town. 

Sharif: Yeah, that was a great week. I loved it. 

Swyx: Yeah. Bring us, bring us in a little bit. Cause it was awesome. There. 

Sharif: Oh, sure. Yeah. It's really, really cool. Like all the founders in AI grants are like fantastic people. And so I think the main takeaway from the AI grant was like, you have this massive overhang in compute or in capabilities in terms of like these latest AI models, but to the average person, there's really not that many products that are that cool or useful to them. Like the latest one that has hit the zeitgeist was chat GPT, which used arguably the same GPT three model, but like RLHF, but you could have arguably built like a decent chat GPT product just using the original GPT three model. But no one really did it. Now there were some restrictions in place and opening. I like to slowly release them over the few months or years after they release the original API. But the core premise behind AI grants is that there are way more capabilities than there are products. So focus on building really compelling products and get people to use them. And like to focus less on things like hitting state of the art on evals and more on getting users to use something. 

Swyx: Make something people want.

Sharif: Exactly. 

Host: Yeah, we did an episode on LLM benchmarks and we kind of talked about how the benchmarks kind of constrain what people work on, because if your model is not going to do well, unlike the well-known benchmarks, it's not going to get as much interest and like funding. So going at it from a product lens is cool. 

[19:30] The Text to Image Illuminati?

Swyx: My hypothesis when I was seeing the sequence of events for AI grants and then for Lexica Aperture was that you had some kind of magical dinner with Emad and David Holtz. And then they taught you the secrets of training your own model. Is that how it happens? 

Sharif: No, there's no secret dinner. The Illuminati of text to image. We did not have a meeting. I mean, even if we did, I wouldn't tell you. But it really boils down to just having good data. If you think about diffusion models, really the only thing they do is learn a distribution of data. So if you have high quality data, learn that high quality distribution. Or if you have low quality data, it will learn to generate images that look like they're from that distribution. So really it boils down to the data and the amount of data you have and that quality of that data, which means a lot of the work in training high quality models, at least diffusion models, is not really in the model architecture, but rather just filtering the data in a way that makes sense. So for Lexica, we do a lot of aesthetic scoring on images and we use the rankings we get from our website because we get tens of millions of people visiting it every month. So we can capture a lot of rankings. Oh, this person liked this image when they saw this one right next to it. Therefore, they probably preferred this one over that. You can do pairwise ranking to rank images and then compute like ELO scores. You can also just train aesthetic models to learn to classify a model, whether or not someone will like it or whether or not it's like, rank it on a scale of like one to ten, for example. So we mostly use a lot of the traffic we get from Lexica and use that to kind of filter our data sets and use that to train better aesthetic models. 

[20:30] How to Learn to Train Models

Swyx: You had been a machine learning engineer before. You've been more of an infrastructure guy. To build, you were more of a prompt engineer with a bit of web design. This was the first time that you were basically training your own model. What was the wrap up like? You know, not to give away any secret sauce, but I think a lot of people who are traditional software engineers are feeling a lot of, I don't know, fear when encountering these kinds of domains. 

Sharif: Yeah, I think it makes a lot of sense. And to be fair, I didn't have much experience training massive models at this scale before I did it. A lot of times it's really just like, in the same way when you're first learning to program, you would just take the problem you're having, Google it, and go through the stack overflow post. And then you figure it out, but ultimately you will get to the answer. It might take you a lot longer than someone who's experienced, but I think there are enough resources out there where it's possible to learn how to do these things. Either just reading through GitHub issues for relevant models. 

Swyx: Oh God. 

Sharif: Yeah. It's really just like, you might be slower, but it's definitely still possible. And there are really great courses out there. The Fast AI course is fantastic. There's the deep learning book, which is great for fundamentals. And then Andrej Karpathy's online courses are also excellent, especially for language modeling. You might be a bit slower for the first few months, but ultimately I think if you have the programming skills, you'll catch up pretty quickly. It's not like this magical dark science that only three people in the world know how to do well. Probably was like 10 years ago, but now it's becoming much more open. You have open source collectives like Eleuther and LAION, where they like to share the details of their large scale training runs. So you can learn from a lot of those people. 

Swyx: Yeah. I think what is different for programmers is having to estimate significant costs upfront before they hit run. Because it's not a thing that you normally consider when you're coding, but yeah, like burning through your credits is a fear that people have. 

Sharif: Yeah, that does make sense. In that case, like fine tuning larger models gets you really, really far. Even using things like low rank adaptation to fine tune, where you can like fine tune much more efficiently on a single GPU. Yeah, I think people are underestimating how far you can really get just using open source models. I mean, before Lexica, I was working on Debuild and we were using the GP3 API, but I was also like really impressed at how well you could get open source models to run by just like using the API, collecting enough samples from like real world user feedback or real world user data using your product. And then just fine tuning the smaller open source models on those examples. And now you have a model that's pretty much state of the art for your specific domain. Whereas the runtime cost is like 10 times or even 100 times cheaper than using an API. 

Swyx: And was that like GPT-J or are you talking BERT? 

Sharif: I remember we tried GPT-J, but I think FLAN-T5 was like the best model we were able to use for that use case. FLAN-T5 is awesome. If you can, like if your prompt is small enough, it's pretty great. And I'm sure there are much better open source models now. Like Vicuna, which is like the GPT-4 variant of like Lama fine tuned on like GPT-4 outputs. Yeah, they're just going to get better and they're going to get better much, much faster. 

Swyx: Yeah. We're just talking in a previous episode to the creator of Dolly, Mike Conover, which is actually commercially usable instead of Vicuna, which is a research project. 

Sharif: Oh, wow. Yeah, that's pretty cool. 

[24:00] Why No Agents?

Alessio: I know you mentioned being early. Obviously, agents are one of the hot things here. In 2021, you had this, please buy me AirPods, like a demo that you tweeted with the GPT-3 API. Obviously, one of the things about being early in this space, you can only do one thing at a time, right? And you had one tweet recently where you said you hoped that that demo would open Pandora's box for a bunch of weird GPT agents. But all we got were docs powered by GPT. Can you maybe talk a little bit about, you know, things that you wish you would see or, you know, in the last few, last few weeks, we've had, you know, Hugging GPT, Baby AGI, Auto GPT, all these different kind of like agent projects that maybe now are getting closer to the, what did you say, 50% of internet traffic being skips of GPT agents. What are you most excited about, about these projects and what's coming? 

Sharif: Yeah, so we wanted a way for users to be able to paste in a link for the documentation page for a specific API, and then describe how to call that API. And then the way we would need to pretty much do that for Debuild was we wondered if we could get an agent to browse the docs page, read through it, summarize it, and then maybe even do things like create an API key and register it for that user. To do that, we needed a way for the agent to read the web page and interact with it. So I spent about a day working on that demo where we just took the web page, serialized it into a more compact form that fit within the 2048 token limit of like GPT-3 at the time. And then just decide what action to do. And then it would, if the page was too long, it would break it down into chunks. And then you would have like a sub prompt, decide on which chunk had the best action. And then at the top node, you would just pretty much take that action and then run it in a loop. It was really, really expensive. I think that one 60 second demo cost like a hundred bucks or something, but it was wildly impractical. But you could clearly see that agents were going to be a thing, especially ones that could read and write and take actions on the internet. It was just prohibitively expensive at the time. And the context limit was way too small. But yeah, I think it seems like a lot of people are taking it more seriously now, mostly because GPT-4 is way more capable. The context limit's like four times larger at 8,000 tokens, soon 32,000. And I think the only problem that's left to solve is finding a really good representation for a webpage that allows it to be consumed by a text only model. So some examples are like, you could just take all the text and pass it in, but that's probably too long. You could take all the interactive only elements like buttons and inputs, but then you miss a lot of the relevant context. There are some interesting examples, which I really like is you could run the webpage or you could run the browser in a terminal based browser. So there are some browsers that run in your terminal, which serialize everything into text. And what you can do is just take that frame from that terminal based browser and pass that directly to the model. And it's like a really, really good representation of the webpage because they do things where for graphical elements, they kind of render it using ASCII blocks. But for text, they render it as actual text. So you could just remove all the weird graphical elements, just keep all the text. And that works surprisingly well. And then there are other problems to solve, which is how do you get the model to take an action? So for example, if you have a booking page and there's like a calendar and there are 30 days on the calendar, how do you get it to specify which button to press? It could say 30, and you can match string based and like find the 30. But for example, what if it's like a list of friends in Facebook and trying to delete a friend? There might be like 30 delete buttons. How do you specify which one to click on? The model might say like, oh, click on the one for like Mark. But then you'd have to figure out the delete button in relation to Mark. And there are some ways to solve this. One is there's a cool Chrome extension called Vimium, which lets you use Vim in your Chrome browser. And what you do is you can press F and over every interactive element, it gives you like a character or two characters. Or if you type those two characters, it presses that button or it opens or focuses on that input. So you could combine a lot of these ideas and then get a really good representation of the web browser in text, and then also give the model a really, really good way to control the browser as well. And I think those two are the core part of the problem. The reasoning ability is definitely there. If a model can score in the top 10% on the bar exam, it can definitely browse a web page. It's really just how do you represent text to the model and how do you get the model to perform actions back on the web page? Really, it's just an engineering problem. 

Swyx: I have one doubt, which I'd love your thoughts on. How do you get the model to pause when it doesn't have enough information and ask you for additional information because you under specified your original request? 

Sharif: This is interesting. I think the only way to do this is to have a corpus where your training data is like these sessions of agents browsing the web. And you have to pretty much figure out where the ones that went wrong or the agents that went wrong, or did they go wrong and just replace it with, hey, I need some help. And then if you were to fine tune a larger model on that data set, you would pretty much get them to say, hey, I need help on the instances where they didn't know what to do next. Or if you're using a closed source model like GPT-4, you could probably tell it if you're uncertain about what to do next, ask the user for help. And it probably would be pretty good at that. I've had to write a lot of integration tests in my engineering days and like the dome. 

Alessio: They might be over. Yeah, I hope so. I hope so. I don't want to, I don't want to deal with that anymore. I, yeah, I don't want to write them the old way. Yeah. But I'm just thinking like, you know, we had the robots, the TXT for like crawlers. Like I can definitely see the DOM being reshaped a little bit in terms of accessibility. Like sometimes you have to write expats that are like so long just to get to a button. Like there should be a better way to do it. And maybe this will drive the change, you know, making it easier for these models to interact with your website. 

Sharif: There is the Chrome accessibility tree, which is used by screen readers, but a lot of times it's missing a lot of, a lot of useful information. But like in a perfect world, everything would be perfectly annotated for screen readers and we could just use that. That's not the case. 

[29:30] GPT4 and Multimodality

Swyx: GPT-4 multimodal, has your buddy, Greg, and do you think that that would solve essentially browser agents or desktop agents? 

Sharif: Greg has not come through yet, unfortunately. But it would make things a lot easier, especially for graphically heavy web pages. So for example, you were using Yelp and like using the map view, it would make a lot of sense to use something like that versus a text based input. Where, how do you serialize a map into text? It's kind of hard to do that. So for more complex web pages, that would make it a lot easier. You get a lot more context to the model. I mean, it seems like that multimodal input is very dense in the sense that it can read text and it can read it really, really well. So you could probably give it like a PDF and it would be able to extract all the text and summarize it. So if it can do that, it could probably do anything on any webpage. 

Swyx: Yeah. And given that you have some experience integrating Clip with language models, how would you describe how different GPT-4 is compared to that stuff? 

Sharif: Yeah. Clip is entirely different in the sense that it's really just good at putting images and text into the same latent space. And really the only thing that's useful for is similarity and clustering. 

Swyx: Like literally the same energy, right? 

Sharif: Yeah. 

Swyx: Yeah. And then there's Blip and Blip2. I don't know if you like those. 

Sharif: Yeah. Blip2 is a lot better. There's actually a new project called, I think, Mini GPT-4. 

Swyx: Yes. It was just out today. 

Sharif: Oh, nice. Yeah. It's really cool. It's actually really good. I think that one is based on the Lama model, but yeah, that's, that's like another. 

Host: It's Blip plus Lama, right? So they, they're like running through Blip and then have Lama ask your, interpret your questions so that you do visual QA. 

Sharif: Oh, that's cool. That's really clever. Yeah. Ensemble models are really useful. 

Host: Well, so I was trying to articulate, cause that was, that's, there's two things people are talking about today. You have to like, you know, the moment you wake up, you open Hacker News and go like, all right, what's, what's the new thing today? One is Red Pajama. And then the other one is Mini GPT-4. So I was trying to articulate like, why is this not GPT-4? Like what is missing? And my only conclusion was it just doesn't do OCR yet. But I wonder if there's anything core to this concept of multimodality that you have to train these things together. Like what does one model doing all these things do that is separate from an ensemble of models that you just kind of duct tape together? 

Sharif: It's a good question. This is pretty related to interoperability. Like how do we understand that? Or how, how do we, why do models trained on different modalities within the same model perform better than two models perform or train separately? I can kind of see why that is the case. Like, it's kind of hard to articulate, but when you have two different models, you get the reasoning abilities of a language model, but also like the text or the vision understanding of something like Clip. Whereas Clip clearly lacks the reasoning abilities, but if you could somehow just put them both in the same model, you get the best of both worlds. There were even cases where I think the vision version of GPT-4 scored higher on some tests than the text only version. So like there might even be some additional learning from images as well. 

Swyx: Oh yeah. Well, uh, the easy answer for that was there was some chart in the test. That wasn't translated. Oh, when I read that, I was like, Oh yeah. Okay. That makes sense. 

Sharif: That makes sense. I thought it'd just be like, it sees more of the world. Therefore it has more tokens. 

Swyx: So my equivalent of this is I think it's a well-known fact that adding code to a language model training corpus increases its ability to do language, not just with code. So, the diversity of datasets that represent some kind of internal logic and code is obviously very internally logically consistent, helps the language model learn some internal structure. Which I think, so, you know, my ultimate test for GPT-4 is to show the image of like, you know, is this a pipe and ask it if it's a pipe or not and see what it does. 

Sharif: Interesting. That is pretty cool. Yeah. Or just give it a screenshot of your like VS code editor and ask it to fix the bug. Yeah. That'd be pretty wild if it could do that. 

Swyx: That would be adult AGI. That would be, that would be the grownup form of AGI. 

[33:30] Sharif’s Startup Manual

Swyx: On your website, you have this, um, startup manual where you give a bunch of advice. This is fun. One of them was that you should be shipping to production like every two days, every other day. This seems like a great time to do it because things change every other day. But maybe, yeah, tell some of our listeners a little bit more about how you got to some of these heuristics and you obviously build different projects and you iterate it on a lot of things. Yeah. Do you want to reference this? 

Sharif: Um, sure. Yeah, I'll take a look at it. 

Swyx: And we'll put this in the show notes, but I just wanted you to have the opportunity to riff on this, this list, because I think it's a very good list. And what, which one of them helped you for Lexica, if there's anything, anything interesting. 

Sharif: So this list is, it's pretty funny. It's mostly just like me yelling at myself based on all the mistakes I've made in the past and me trying to not make them again. Yeah. Yeah. So I, the first one is like, I think the most important one is like, try when you're building a product, try to build the smallest possible version. And I mean, for Lexica, it was literally a, literally one screen in the react app where a post-process database, and it just showed you like images. And I don't even know if the first version had search. Like I think it did, but I'm not sure. Like, I think it was really just like a grid of images that were randomized, but yeah, don't build the absolute smallest thing that can be considered a useful application and ship it for Lexica. That was, it helps me write better prompts. That's pretty useful. It's not that useful, but it's good enough. Don't fall into the trap of intellectual indulgence with over-engineering. I think that's a pretty important one for myself. And also anyone working on new things, there's often times you fall into the trap of like thinking you need to add more and more things when in reality, like the moment it's useful, you should probably get in the hands of your users and they'll kind of set the roadmap for you. I know this has been said millions of times prior, but just, I think it's really, really important. And I think if I'd spent like two months working on Lexica, adding a bunch of features, it wouldn't have been anywhere as popular as it was if I had just released the really, really boiled down version alongside the stable diffusion release. Yeah. And then there are a few more like product development doesn't start until you launch. Think of your initial product as a means to get your users to talk to you. It's also related to the first point where you really just want people using something as quickly as you can get that to happen. And then a few more are pretty interesting. Create a product people love before you focus on growth. If your users are spontaneously telling other people to use your product, then you've built something people love. 

Swyx: So this is pretty, it sounds like you've internalized Paul Graham's stuff a lot. Yeah. Because I think he said stuff like that. 

Sharif: A lot of these are just probably me taking notes from books I found really interesting or like PG essays that were really relevant at the time. And then just trying to not forget them. I should probably read this list again. There's some pretty personalized advice for me here. Oh yeah. One of my favorite ones is, um, don't worry if what you're building doesn't sound like a business. Nobody thought Facebook would be a $500 billion company. It's easy to come up with a business model. Once you've made something people want, you can even make pretty web forms and turn that into a 200 person company. And then if you click the link, it's to LinkedIn for type form, which is now, uh, I think they're like an 800 person company or something like that. So they've grown quite a bit. There you go. Yeah. Pretty web forms are pretty good business, even though it doesn't sound like it. Yeah. It's worth a billion dollars. 

[38:30] Lexica Aperture V1/2/3

Swyx: One way I would like to tie that to the history of Lexica, which we didn't go over, which was just walk us through like Aperture V1, V2, V3, uh, which you just released last week. And how maybe some of those principles helped you in that journey.

Sharif: Yeah. So, um, V1 was us trying to create a very photorealistic version of our model of Sable to Fusion. Uh, V1 actually didn't turn out to be that popular. It turns out people loved not generating. Your marketing tweets were popular. They were quite popular. So I think at the time you couldn't get Sable to Fusion to generate like photorealistic images that were consistent with your prompt that well. It was more so like you were sampling from this distribution of images and you could slightly pick where you sampled from using your prompt. This was mostly just because the clip text encoder is not the best text encoder. If you use a real language model, like T5, you get much better results. Like the T5 XXL model is like a hundred times larger than the clip text encoder for Sable to Fusion 1.5. So you could kind of steer it into like the general direction, but for more complex prompts, it just didn't work. So a lot of our users actually complained that they preferred the 1.5, Sable to Fusion 1.5 model over the Aperture model. And it was just because a lot of people were using it to create like parts and like really weird abstract looking pictures that didn't really work well with the photorealistic model trained solely on images. And then for V2, we kind of took that into consideration and then just trained it more on a lot of the art images on Lexica. So we took a lot of images that were on Lexica that were art, used that to train aesthetic models that ranked art really well, and then filtered larger sets to train V2. And then V3 is kind of just like an improved version of that with much more data. I'm really glad we didn't spend too much time on V1. I think we spent about one month working on it, which is a lot of time, but a lot of the things we learned were useful for training future versions. 

Swyx: How do you version them? Like where do you decide, okay, this is V2, this is V3? 

Sharif: The versions are kind of weird where you can't really use semantic versions because like if you have a small update, you usually just make that like V2. Versions are kind of used for different base models, I'd say. So if you have each of the versions were a different base model, but we've done like fine tunes of the same version and then just release an update without incrementing the version. But I think when there's like a clear change between running the same prompt on a model and you get a different image, that should probably be a different version. 

[40:00] Request for AI Startup - LLM Tools

Alessio: So the startup manual was the more you can actually do these things today to make it better. And then you have a whole future page that has tips from, you know, what the series successor is going to be like to like why everyone's genome should be sequenced. There's a lot of cool stuff in there. Why do we need to develop stimulants with shorter half-lives so that we can sleep better. Maybe talk a bit about, you know, when you're a founder, you need to be focused, right? So sometimes there's a lot of things you cannot build. And I feel like this page is a bit of a collection of these. Like, yeah. Are there any of these things that you're like, if I were not building Lexica today, this is like a very interesting thing. 

Sharif: Oh man. Yeah. There's a ton of things that I want to build. I mean, off the top of my head, the most exciting one would be better tools for language models. And I mean, not tools that help us use language models, but rather tools for the language models themselves. So things like giving them access to browsers, giving them access to things like payments and credit cards, giving them access to like credit cards, giving them things like access to like real world robots. So like, it'd be cool if you could have a Boston dynamic spot powered by a language model reasoning module and you would like to do things for you, like go and pick up your order, stuff like that. Entirely autonomously given like high level commands. That'd be like number one thing if I wasn't working on Lexica. 

[40:00] Sequencing your Genome

And then there's some other interesting things like genomics I find really cool. Like there's some pretty cool things you can do with consumer genomics. So you can export your genome from 23andMe as a text file, like literally a text file of your entire genome. And there is another tool called Prometheus, I think, where you upload your 23andMe text file genome and then they kind of map specific SNPs that you have in your genome to studies that have been done on those SNPs. And it tells you really, really useful things about yourself. Like, for example, I have the SNP for this thing called delayed sleep phase disorder, which makes me go to sleep about three hours later than the general population. So like I used to always be a night owl and I never knew why. But after using Prometheus it pretty much tells you, oh, you have the specific genome for specific SNP for DSPS. It's like a really tiny percentage of the population. And it's like something you should probably know about. And there's a bunch of other things. It tells you your likelihood for getting certain diseases, for certain cancers, oftentimes, like even weird personality traits. There's one for like, I have one of the SNPs for increased risk taking and optimism, which is pretty weird. That's an actual thing. Like, I don't know how. This is the founder gene. You should sequence everybody. It's pretty cool. And it's like, it's like $10 for Prometheus and like 70 bucks for 23andMe. And it explains to you how your body works and like the things that are different from you or different from the general population. Wow. Highly recommend everyone do it. Like if you're, if you're concerned about privacy, just purchase a 23andMe kit with a fake name. You don't have to use your real name. I didn't use my real name. 

Swyx: It's just my genes. Worst you can do is clone me. It ties in with what you were talking about with, you know, we want the future to be like this. And like people are building uninspired B2B SaaS apps and you and I had an exchange about this. 

[42:00] Believe in Doing Great Things

How can we get more people to believe they can do great things? 

Sharif: That's a good question. And I like a lot of the things I've been working on with GP3. It has been like trying to solve this by getting people to think about more interesting ideas. I don't really know. I think one is just like the low effort version of this is just putting out really compelling demos and getting people inspired. And then the higher effort version is like actually building the products yourself and getting people to like realize this is even possible in the first place. Like I think the baby AGI project and like the GPT Asian projects on GitHub are like in practice today, they're not super useful, but I think they're doing an excellent job of getting people incredibly inspired for what can be possible with language models as agents. And also the Stanford paper where they had like the mini version of Sims. Yeah. That one was incredible. That was awesome. 

Swyx: It was adorable. Did you see the part where they invented day drinking? 

Sharif: Oh, they did? 

Swyx: Yeah. You're not supposed to go to these bars in the afternoon, but they were like, we're going to go anyway. Nice. 

Sharif: That's awesome. Yeah. I think we need more stuff like that. That one paper is probably going to inspire a whole bunch of teams to work on stuff similar to that. 

Swyx: And that's great. I can't wait for NPCs to actually be something that you talk to in a game and, you know, have their own lives and you can check in and, you know, they would have their own personalities as well. 

Sharif: Yeah. I was so kind of off topic. But I was playing the last of us part two and the NPCs in that game are really, really good. Where if you like, point a gun at them and they'll beg for their life and like, please, I have a family. And like when you kill people in the game, they're like, oh my God, you shot Alice. Like they're just NPCs, but they refer to each other by their names and like they plead for their lives. And this is just using regular conditional rules on NPC behavior. Imagine how much better it'd be if it was like a small GPT-4 agent running in every NPC and they had the agency to make decisions and plead for their lives. And I don't know, you feel way more guilty playing that game. 

Alessio: I'm scared it's going to be too good. I played a lot of hours of Fallout. So I feel like if the NPCs were a lot better, you would spend a lot more time playing the game. Yeah. 

[44:30] Lightning Round

Let's jump into lightning round. First question is your favorite AI product. 

Sharif: Favorite AI product. The one I use the most is probably ChatGPT. The one I'm most excited about is, it's actually a company in AI grants. They're working on a version of VS code. That's like an entirely AI powered cursor, yeah. Cursor where you would like to give it a prompt and like to iterate on your code, not by writing code, but rather by just describing the changes you want to make. And it's tightly integrated into the editor itself. So it's not just another plugin. 

Swyx: Would you, as a founder of a low code prompting-to-code company that pivoted, would you advise them to explore some things or stay away from some things? Like what's your learning there that you would give to them?

Sharif: I would focus on one specific type of code. So if I'm building a local tool, I would try to not focus too much on appealing developers. Whereas if I was building an alternative to VS code, I would focus solely on developers. So in that, I think they're doing a pretty good job focusing on developers. 

Swyx: Are you using Cursor right now? 

Sharif: I've used it a bit. I haven't converted fully, but I really want to. Okay. It's getting better really, really fast. Yeah. Um, I can see myself switching over sometime this year if they continue improving it. 

Swyx: Hot tip for, for ChatGPT, people always say, you know, they love ChatGPT. Biggest upgrade to my life right now is the, I forked a menu bar app I found on GitHub and now I just have it running in a menu bar app and I just do command shift G and it pops it up as a single use thing. And there's no latency because it just always is live. And I just type, type in the thing I want and then it just goes away after I'm done. 

Sharif: Wow. That's cool. Big upgrade. I'm going to install that. That's cool. 

Alessio: Second question. What is something you thought would take much longer, but it's already here? Like what, what's your acceleration update? 

Sharif: Ooh, um, it would take much longer, but it's already here. This is your question. Yeah, I know. I wasn't prepared. Um, so I think it would probably be kind of, I would say text to video. 

Swyx: Yeah. What's going on with that? 

Sharif: I think within this year, uh, by the end of this year, we'll have like the jump between like the original DALL-E one to like something like mid journey. Like we're going to see that leap in text to video within the span of this year. Um, it's not already here yet. So I guess the thing that surprised me the most was probably the multi-modality of GPT four in the fact that it can technically see things, which is pretty insane. 

Swyx: Yeah. Is text to video something that Aperture would be interested in? 

Sharif: Uh, it's something we're thinking about, but it's still pretty early. 

Swyx: There was one project with a hand, um, animation with human poses. It was also coming out of Facebook. I thought that was a very nice way to accomplish text to video while having a high degree of control. I forget the name of that project. It was like, I think it was like drawing anything. 

Swyx: Yeah. It sounds familiar. Well, you already answered a year from now. What will people be most surprised by? Um, and maybe the, uh, the usual requests for startup, you know, what's one thing you will pay for if someone built it? 

Sharif: One thing I would pay for if someone built it. Um, so many things, honestly, I would probably really like, um, like I really want people to build more, uh, tools for language models, like useful tools, give them access to Chrome. And I want to be able to give it a task. And then just, it goes off and spins up a hundred agents that perform that task. And like, sure. Like 80 of them might fail, but like 20 of them might kind of succeed. That's all you really need. And they're agents. You can spin up thousands of them. It doesn't really matter. Like a lot of large numbers are on your side. So that'd be, I would pay a lot of money for that. Even if it was capable of only doing really basic tasks, like signing up for a SAS tool and booking a call or something. If you could do even more things where it could have handled the email, uh, thread and like get the person on the other end to like do something where like, I don't even have to like book the demo. They just give me access to it. That'd be great. Yeah. More, more. Like really weird language model tools would be really fun.

Swyx: Like our chat, GPT plugins, a step in the right direction, or are you envisioning something else? 

Sharif: I think GPT, chat GPT plugins are great, but they seem to only have right-only access right now. I also want them to have, I want these like theoretical agents to have right access to the world too. So they should be able to perform actions on web browsers, have their own email inbox, and have their own credit card with their own balance. Like take it, send emails to people that might be useful in achieving their goal. Ask them for help. Be able to like sign up and register for accounts on tools and services and be able to like to use graphical user interfaces really, really well. And also like to phone home if they need help. 

Swyx: You just had virtual employees. You want to give them a Brex card, right? 

Sharif: I wouldn't be surprised if, a year from now there was Brex GPT or it's like Brex cards for your GPT agents. 

Swyx: I mean, okay. I'm excited by this. Yeah. Kind of want to build it. 

Sharif: You should. Yeah. 

Alessio: Well, just to wrap up, we always have like one big takeaway for people, like, you know, to display on a signboard for everyone to see what is the big message to everybody. 

Sharif: Yeah. I think the big message to everybody is you might think that a lot of the time the ideas you have have already been done by someone. And that may be the case, but a lot of the time the ideas you have are actually pretty unique and no one's ever tried them before. So if you have weird and interesting ideas, you should actually go out and just do them and make the thing and then share that with the world. Cause I feel like we need more people building weird ideas and less people building like better GPT search for your documentation. 

Host: There are like 10 of those in the recent OST patch. Well, thank you so much. You've been hugely inspiring and excited to see where Lexica goes next. 

Sharif: Appreciate it. Thanks for having me.

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Latent Space
Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0
The podcast by and for AI Engineers! In 2023, over 1 million visitors came to Latent Space to hear about news, papers and interviews in Software 3.0.
We cover Foundation Models changing every domain in Code Generation, Multimodality, AI Agents, GPU Infra and more, directly from the founders, builders, and thinkers involved in pushing the cutting edge. Striving to give you both the definitive take on the Current Thing down to the first introduction to the tech you'll be using in the next 3 months! We break news and exclusive interviews from OpenAI, tiny (George Hotz), Databricks/MosaicML (Jon Frankle), Modular (Chris Lattner), (Jeremy Howard), et al.
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