All right. Hey, everyone. My name is Sharif. I'll be talking to you about demos and why I think demos are probably the most important thing in the world right now. I'm the founder of Lexica. We're working on generative models, specifically image models. But I kind of want to just talk to you about something a bit more than just models themselves, even more than demos.

I kind of just want to talk to you about curiosity. There's a famous French mathematician, Poincaré, he said, at the moment when I put my foot on the step, the idea came to me. He was working on this really, really esoteric field of mathematics called fusion functions. And he was stuck on this problem for weeks at a time.

He didn't really know how to make any progress at all. And he was boarding a bus one day. And suddenly, it kind of just all came to him. He went from not knowing at all to having a full understanding of the problem. He said something along the lines of, the role of this unconscious work in mathematical invention appears to me as incontestable.

I'm going to make one main argument to you guys today. And that's going to be that curiosity is the main force for how we pull ideas from the future into the present. And when we have these subconscious patterns that our brains recognize, they kind of surface as a feeling.

And this feeling is what we know as curiosity. So I'm going to present you with a few demos I've worked on over the years. And they've each followed a specific pattern, where initially, I had this really great idea. I thought it was fantastic that it was going to change everything.

And then you kind of get to implementing the specific idea. And you realize it's actually not possible at all. And then like, through sheer effort and like determination, you somehow find a way to make things work, even though you're working with models that have maybe a context length of like 2000 tokens.

And then once you get to work, and you feel this like really good sense of pride and joy, and I think the most important thing about good demos is that they're kind of a way of exploring what's possible with these models. I'm, I kind of see these models as not necessarily things you can kind of understand fully without interacting with them.

And I think the way you can best interact with them is by making really, really interesting demos. And the way you make interesting demos is just by following your curiosity. So this is from 2020. This was when GPT-3 was released. This was pretty mind-blowing for me. And I was surprised no one was really talking about this.

GPT-3, for those of you who remember, had a context length of 2000 tokens. It cost, I think, $75 per million output tokens. And yeah, you had to get specific permissions from OpenAI before you shared anything about the model. You couldn't ship a chat app because that violated the terms of service, but it was a really magical time.

It felt like you had this new tool in this like, in this toolkit of computing, and you could do so many things with it. And I think what was really important about this demo is that it inspired people that you could actually do things with software. And I think the way you get really impressive ideas is actually not by doing anything special.

I think each person has their own unique kind of context window, the things you've seen and experienced. And I just so happened to watch a talk by Brett Victor before making this where he came up with this principle that creators need an immediate feedback with what they're creating. And I was really tired of copying and pasting code into my editor and then compiling it and then like running it and see what would happen.

So I decided to just put like a JSX compiler in the browser, and it just felt different. It felt kind of magical in a way. And now today, like Claude's system prompt is 25,000 tokens, which is kind of funny. Here's another similar demo. This one's a bit more interactive.

So you can kind of describe what you want. And then because the context windows were so small, it couldn't actually generate the entire application in a single prompt. You actually had to do three parallel prompts and then join them in the background. This is really simple, just asking for a Google homepage.

And then it generates three different components for it. But yeah, this is just, I think, one of the ways you can express your curiosity. You kind of look at the world around you and what you've experienced. And you kind of synthesize new ideas. And you get this subconscious feeling pulling you in a direction.

And I think the demo is the best way to kind of express that feeling to the world. Here's another more interesting one. This was 2021. I think the context lengths now expanded from 2,000 tokens to about 4,000 tokens. So we could do a bit more with this. I was kind of curious if these models had any sense of agency.

So I decided to give it a really simple objective of buying me AirPods in Chrome. And if you were to just dump a web page into the browser, into the context window, it just wouldn't work. Like the Walmart.com shopping page would be like 24,000 tokens. It's just impossible. So I was actually a bit frustrated that I couldn't get it to work for a few days.

So I wrote a custom HTML parser that would parse a web page into its core essence, which was able to fit it into the tiny context window of GPT-3 in 2021. It definitely failed spectacularly. It got distracted with the terms of service. But I think it was more so just interesting that we discovered that these models pre-trained on web text had this sense of agency kind of internal in their weights.

We kind of take that for granted now, but 2021 was a very different time. Here's a more recent demo from a friend of mine, Farza. He's using Gemini 2.5 Pro here today. So we're still discovering new capabilities. Here he's making a basketball shot tracker where he's just putting in a video of him playing basketball, asking it to provide feedback as if Michael Jordan was watching his gameplay.

And I think this is a really great demo because it inspires people to realize that you can actually make video-first experiences with Gemini 2.5 Pro. Before this, it was kind of like, oh, you can have it watch your screen and it'll give you feedback on your code. But there's so much more we can do.

And I think one of the main reasons I find demos really, really interesting is that they inspire possibility. So much so that there's probably so much low-hanging fruit today in these models that if you were to halt all capabilities, if you kept all the weights frozen, didn't do a single backpropagation, I think you could build really amazing products for the next 10 years, keeping everything constant.

And I think the way you do that is just by building these demos and following your curiosity. So I have this really famous quote by Richard Hamming where he says, "In science, if you know what you're doing, you should not be doing it. Engineering, if you know what you're doing, you should not be doing it." So traditional engineering is very teleological.

It's very goal-oriented, very purpose-driven. But I think AI engineering is a bit different. I think AI engineering is actually a bit more -- it's a bit closer to excavating. You're looking for new capabilities hidden within these models. And your toolkit is a demo. Your curiosity is kind of your flashlight guiding you to where the interesting bits of the models are.

And the way you discover what's possible is just by making things. And what's really, really interesting is that even the researchers today at labs like OpenAI and Anthropic actually don't have a full understanding of the capabilities of these models. I've had OpenAI researchers show me or tell me that they didn't even know GPT-3 could do this -- could browse the web or that it could generate fully-functioning React components, which was pretty interesting.

This is pretty funny. Charles Darwin was famous for coming up with a theory of evolution, but little known fact, he actually spent eight years studying barnacles, like the things on the sides of ships and piers and docks. He spent eight years studying barnacles, so much so that people thought he was going crazy before he published evolution.

In the moment, you wouldn't have known that it was important, though, but the barnacle studying taught him that evolution was correct and it was kind of indisputable evidence for his theory. In the moment, you actually don't know what is actually work versus play. What you're doing might feel like it's useless.

It might feel like it's leading nowhere. But sometimes you need to study barnacles for eight years before you can publish evolution. So I think we're in this really strange moment right now in 2025. These models can do amazing things. There's tons of them. Their context windows have now expanded from 2000 to maybe a million tokens or so.

And I think demos are the way we explore what's possible. It's the way we expand the search space and kind of see what we can do with these capabilities. And I think it's not something you can predict ahead of time. It's kind of like crossing a foggy pond. You kind of take one stone.

You kind of step on a stone and then see where it leads. If it leads somewhere interesting, you can keep going. But if it doesn't, you can always backtrack and go a different way. You'll never be able to plan your route across the pond ahead of time. You just kind of have to take the first step.

I came across this really interesting tweet. And I really like it. It's because Anthropic really markets Claude as kind of a coding model or like a general reasoning model. But it's like trying to sell an intergalactic spaceship as a toaster because one of its surfaces gets hot every once in a while.

And I think this is a really, really good way of thinking of these models. There are so much capabilities latent in them that we kind of only focus on the immediate and the obvious. But good demos reveal really interesting capabilities, mainly through exploration and play. And I think uncertainty is at the core of being an AI engineer.

If you know what you're doing, you're kind of doing it wrong. And I think if you're uncertain and you're kind of just exploring, you'll you'll lead down interesting, you'll find yourself being led down interesting paths. Yeah, in subconsciously, you notice these patterns because you've worked with things that no one else has worked with before.

Your life is unique to you, your context window is unique to you, and no one else has that same shared context window. So when you come across an idea in your head, oftentimes, you're one of the only people to ever have that idea. And I think you'd be doing yourself a great injustice if you never actually tried to make that idea a reality.

So I'm going to close with this slide. One of the greatest computing papers ever written was Man Machine Symbiosis by Licklider in the 1960s. And the epitome of technology at the time were vacuum tube computers and punch cards. If you wanted to write a program, it would probably take a few hours, maybe even days to run.

Meanwhile, today, we have Cloud Opus 4 on our computers. It's actually kind of insane. And I think Licklider genuinely would have killed someone to have an hour with the tools we have today. And like, I'm not even joking, I think it's kind of important, so much so that I feel like today we have a moral obligation to do him justice and everyone else in the field that came before us.

Not only to just follow your curiosity, but to share what you explore with the world. Because by sharing your demos, you kind of share what's possible with these models. And I think that's how we move the field forward. And yeah, that's really it. Your unique perspective shouldn't be wasted.

And I think you have a moral responsibility to share them with the world. Thank you. All right, we've got some time for questions. Does anybody have any? None? Cool. Thank you, guys. Appreciate it. We're doing really good for time. I was going to ask you if you had like other demos that you wanted to show us, because I liked seeing the 2020 versions of things.

I have a few more, actually. Do you want me to pull them out? We've got eight minutes. All right, let's do it. You might as well. As long as they don't use Wi-Fi, because that's the running joke of this conference. I think I might have a few downloaded. Let me check.

For the people in the room, I did try that basketball one, except I tried to apply it to running, and it works really well. And it pretty much gave the same feedback that my $600 a month running coach would give me. And I thought, wait, I think I can cancel this.

Did it give you like pretty good advice on your gait? It gave me not just the gait per step. And so that's something that my coach would never be able to do. What I couldn't get to figure out is how it had the little arrow on top of the head.

But if I had 20 more minutes, I probably could have. Yeah, here's a pretty cool demo. I think was also from 2020. Let's see if it's playing. Oh, it's not. Let me -- oh, it is. Yeah, this was about a few weeks after the GP3 API came out. And I think the way I came across the API was really funny.

Someone had said to me, you have to try this out. OpenAI has created AGI and it's here available today. And no one's really talking about it. And I was like, okay, let's see what this is about. And I quickly realized I could actually write code. But writing code in the text interface was not really the best way to do it.

So you actually hook it up to an API, put a compiler in the browser, and you get this like nice back and forth visual interface. We kind of take this granted for today with tools like cursor. We can kind of like chat with your code in the sidebar. But in 2020, this felt really, really different than what anything was possible.

Here, I'm like working on a like a really, really basic like banking app where you just ask it to add $3 or subtract another $5. It was pretty funny because like the bugs were really bad. You could actually -- there's a button where you could give away all your money.

And if you were in debt, it would just like negate it and make you -- make your balance go to zero again. But this really was, I think, the start of vibe coding. And it really needed a lot of people to take these models, not only as like language models, but kind of reasoning engines.

Yeah. And I think -- I think the way to think about these models is really that like they're these really, really intelligent in a way beings, which sounds kind of weird to say out loud. But that's like the mental model I have for them. And you kind of hook them up to these different apparatuses.

And they can kind of work them. And you kind of like instill these tools with a sense of like purpose and agency. Yeah, I really just hope a lot more people are inspired to work on demos, because the capabilities we have today are really impressive. And you'd be really doing a disservice by not just like building something really fun and simple and sharing it with the world.

Yeah. Tim over there? Yeah. Yes, it was the base model. We didn't have an -- we didn't have an instruct model until about a year later. So what -- it was essentially similar to base models we have today, where you give it a prefix and it just completes it.

So you prompt engineer it with a few examples, and that's usually good enough. Any other demos you've got there? I can go into the archive. Do you want to go through your entire desktop while you're at it? Let me close this one. I have a few, but I don't -- do we have time?

We have five minutes. You've got five minutes. Let's see if I can find anything. Okay. Let me try something really quick. Do you have time? Yeah, sure. Because it's my first time I've come to a conference where the subconscious is making a talk, which I found just glorious. How did the concept, or what was your thought process that led up to a presentation like this?

Personally, I've never thought about doing demos in the world. I'm like, now do it. Okay. But maybe you could tell us, like, how this -- Sure, yeah. I think a lot of it was just kind of introspecting on why I made these demos in the first place. A large part of it came from a sense of frustration that we have these really powerful models today, and no one really knows what they're capable of doing.

And I think I examined it a bit further, and it did feel like a sense of moral obligation. You have these pioneers of computing from the 60s and 70s and 80s, like Licklider and Alan Kay and whatnot. And they came up with these grand ideas with the computers they had available to them.

They just couldn't make it possible. And I look at what we have today, and it's kind of like we're spoiled by so many amazing pieces of technology, and we're kind of just making the same things all over again. But really, I think if you look back at, like, what people were writing about in the 60s and 70s, there's a whole goldmine of ideas there that we can revisit and actually make possible today.

In the man-machine symbiosis paper, Licklider talks about an assistant that knows everything you're working on and has, like, perfect context and can help you with anything immediately. And here we have, like, ChatGPT, where every time you want to talk to it, you press "New Chat," and it has no memory of what you've talked about beforehand, minus, like, a few basic facts.

And I think it's really just -- it really boils down to wanting to kind of do the ideas that these pioneers came up with justice beforehand. Thank you. Three minutes, so did you have something loaded up? I'm looking. Yeah, this was an old GPT-3 demo where the idea was, how do you get these models to solve very large and ambitious problems?

It was called Multivac. And the idea was, you can't really fit everything into a 2000 context window, 2000 token context window. So what you do is you essentially break down the problems into more digestible sub-problems, and you have this kind of visual interface to help you see where things are going.

So you can give it some really ambitious problem, like, how do you solve climate change? And it might come up with things like, convince more people to go vegetarian or build climate or build wind turbines and, like, install more solar panels. And then you can click on each of the sub-ideas and it kind of breaks it down even further.

Yeah, I think one of the core ideas behind this was, like, these models are a lot more than just text completion models, but I think they can be useful as, like, very helpful reasoning assistance, specifically at solving big problems. So much so that they could come up with ideas on their own one day and hopefully be really useful thought partners.

Yeah, I mean, looking at it now, it's pretty rudimentary, but maybe someone should make a new version of this with, like, Opus Max. Yeah. I mean, someone here should do it. I think that'd be pretty cool. Yeah, that's about it, guys. Thanks.