was a new notation, for representing, computation and mathematical thinking. This is from his, Turing Award, presentation paper, and you don't know APL, it won't look very familiar, but what he's showing here is he's, proving some, characteristics of, the inner product. In his new notation. And one of the really interesting things about this if you ever get into APL, and I strongly recommend it, is it turns out that this generalizes in a much deeper way than normal mathematical nomenclature and that the inner product in APL can actually is, is an operator that can combine any two functions. It's not necessarily multiplication in addition, And so suddenly, he's proved a whole class of features about a whole class of functions, many of which never been looked at by a mathematician before, just through notation. And this can go a really long way. Some of you might have seen this very famous single line of code, in APL, which is a complete implementation of Conway's gain of life. So, here in this video, that, life function is being applied over these two characters and off it goes. Again, it's the same thing. Right? Iverson was passionate about creating this connection, between between the human and and supporting the human's thinking notation as a tool of thought. Perhaps most mind blowingly, Brett Victor who spent a couple of years as he described being a hermit living on a train. And he came out the end of those two years of hermithood, having built the most extraordinary and inspiring array of real world demos showing about how to understand climate, how to understand electricity, how to understand, how to how to build games, how to build graphics, how to understand waveforms, and he shared this all with the world. This is his coding environment whereas he changes it graphically. This is his amazing game playing demo where he actually created a time machine for his code. Haven't seen this, please watch everything Brett Victor's done because it's incredibly inspiring, and all of it you'll see it's all of it's the same thing. It's creating this connection. Between the the human and the computer that they're working with so that they can craft This is this is all effortful craft that he is supporting. Chris Lattner, this is his playground system. He's created a whole amazing hierarchy, from from LLVM playing, Swift, Playground, MLIR, Mojo, you know, at every level trying to improve this ability for humans to to connect to and work with their computers. My argument is that actually we're still on this chain. We we we can continue working in in along this history from the mother of demos in a really deep and powerful way. AI is a marvelous way to connect more deeply with our computers. And achieve this fullest representation of humanity. So this has actually been kind of my mission for the last thirty years and very dramatically for the last ten, and at Answered AI, it's all of my focus This idea that we should be seeking to augment human creativity, not to replace it. It's interesting to see hopefully this resonates with you, but also hopefully you see this is almost never what you actually see as being marketed when somebody's trying to sell you a piece of AI. It's like it's it's gonna summarize this for you. It's gonna write this for you. It's gonna do this for you. It's all about being done for you. So, I wanna quickly demo something we've built. To give you a sense of what it looks like to have a tool that that is specifically designed for this augmenting human creativity, and understanding. Indeed. And so I'm gonna do it. I'm gonna just show you a couple examples of actual dialogues I've gone through with the help of AI in the last three days. So one was I wanted to learn about recursive language models. So probably a lot of you know about recursive language models. I've been kind of taking over the world. So I, we've got this system called Solve It. And, so I should show you. Solveitsolve.com. And I loaded the paper, recursive language models paper into Solvit, you could just read it in the normal way, piece at a time, but make sure you understand it. And so here, I looked at figure one, I was like, well I don't know what this is or this is or this is. And so, normally, I might just skip over it. But here, I can just say, hey, what's this figure? And it tells me, like, okay, these are the three evals that the RLM authors are doing here. And and interesting is like why? It's going from a constant to a linear to a quadratic complexity. Which is not actually captured in the original paper. So, that's really helpful information. Now, I don't work very well at an abstract level. I need things to be concrete, I could ask for an example of each task, and this is much easier than going into the papers and trying to dig them out and so forth. And so, here I've getting little examples. It's like, okay, I get it. So I always tell people, don't move on. When you're learning a new thing or working on something until you you get it. So I was like, okay, I get it. So, then there's another figure, where they describe it and I didn't fully understand this figure, to be honest, at first. And so I just it it tells me what the the key pieces are. It's very handy. So I I when I'm reading this, something like, okay, I want to push beyond this. I wanna understand it but try and go a bit past it. I'm thinking like, okay, I wonder if we can replicate, all of the features of an RLM right now. So I kind of had a hypothesis about how to do that and I kind of checked with the AI, like, I think we can try this ourselves. And it clarified some key points about, where sub agents fit. And, as it turns out Solvent has a sub agent. So I said, yeah, you've got a sub agent. Why don't you try it? So in this case, it spawns an agent, and tells it to use Python, to solve the complex square root. And we can also write code here. Right? So I can then try it myself. And I can compare. I'm like, oh, cool. Okay. So I kind of confirmed. I'm in an environment where I can actually do the same things that the article did. So, another thing I mentioned as I read papers is often it's very to skip over citations. Alright? But here, it says, well, here's recent recent work. Alright. Well, okay. I don't know any of these, so I shouldn't keep moving. So I said, like, stop. Can you please go and read all those papers for me and tell me basically what they are and why they're here? I can decide whether to click on those links, read them for myself, and I would have kind of test my understanding at this point. So anyway, to skip ahead a little bit, I'm thinking like, okay, let's let's try it. So, they've got their table of results here. For four different tasks. So I'm kind of thinking, like, I'm not sure that this thing called an IRA may even exist. It just feels like a normal tool loop that happens to have particular tools in it. Nice thing to have the same tools right now, so I think we can be an IRLM, can't we? So I said, let's try it. It. Let's try this code QA thing. I've never heard of this before. It tells me where to find the data. And so I start So it writes some code for me. Which actually didn't work, but then we can it can help me debug it, and then I can test it, and I then I could experiment and look at this data carefully, make sure I understand what this eval is and how it works. And then I just tell it like, okay. Solve it. Go, you know, go ahead and just solve this right now. And so this is one of the things in one of the emails. It goes ahead and tries to do it, and it says, I think the answer's b, and the check oh, it is b. No. No. Tell me how you did that. And then, okay, let's create another eval. And, answer c, yeah, answer c. And so, this is very interesting. So, I did this for a few different, tasks including the hardest, quadratic tasks. So I downloaded another of these data sets, went through them, and, yeah, discovered that solve it correctly solved every one of the tasks. So, And so I've now not only do I understand IRLM, I've reimplemented it. This is all in the space of a couple of hours, and in fact discovered that this is a much more powerful platform than even IRLM is. Another example was I was looking at, Julia Evans, who's a fantastic writer, always really interesting and she, I'm very I'm not a fan of tailwind, and I was keen to see how she had done this year, this article called moving away from tailwind. Which had a particular structure. This was the structure she had and I decided, okay, I wanna go through her blog post. So I loaded the blog post, as you can see, and to solve it, and I started reading it. And, again, so the bread is me asking questions as I go through the article. And so she said she used something called tailwind preflight. As her starting point for her styles, and I was like, well, are there other options? So I went and grabbed it, And actually, one of the cool things about Solvit is it's because it's in a browser, I've got actual styles, HTML, here. So I'm actually modifying my environment as I go. And so I can see, see this happening, and then I can try it out. So I'm trying out layers. I've never really I've never done anything with layers before, so make sure I understand how they work. But, again, I'm actually using them. So before I actually used RLM, you know, rebuilt RLM inside dialogue. Here I am rebuilding Julia's styles inside a dialogue. She had a section about components. And again, same thing, I started creating the components myself as you can see, with the help of AI to make sure I understand So here I've got a badge component, for example. And, yeah, this is all real. Right? I'm creating code. I'm seeing it actually, running. Components, then very interested in colors, I kind of had some ideas about how to create a new color framework. So I kind of bit of a discussion as you see with the AI. Again, the AI, didn't write it for me. Right? I kind of came up with this idea of what I think I wanted to do. And then I, tried it. And you can see I've created my own new color palette that I'm very happy with and I thought, oh cool. I'll try and map those to kind of semantics now. Like, okay, which should danger be? So, like, it helps me show me what it looks like, different colors on different backgrounds. Inverted ones, It helps me create these full swatches I can see how my color palette looks. Did a similar thing with font sizes. I had an idea of how I wanted to create nice topography. So again, kind of write the code,