I think we've all noticed tools like vZero getting pretty good at generative UI and creating good-looking things as well as Claude code being able to let us run things more complicated locally and build on those things so I think the the thing that comes out of this is designers product people and engineers all building together and I'm really excited about that because I've never loved the the divides and between these things so this really lets us get rid of in my mind get rid of mock-ups get rid of the click-through prototypes and all the hand-wringing about whether the thing that we're building is worth the engineering effort so as we as we go into this it's time for us to jump in and feel the material that we're working with and see what emerges so I'll give you a super quick overview of flat files AI stack this is not an official diagram but it's how I see it and we migrate data big if you need to move a lot of data between systems frequently you use our developer platform and since we're a developer platform LMs are good at writing code makes it the perfect place for a lot of AI at the bottom here we have our customers flat file applications that they deploy to our infrastructure then there's this like real-time context which is the data and the validation outcome so what are the errors and warnings and things that are in that that data that dirty data and then our AI agents the tools they have and the jobs that they can run and then what gets shown to users so I see it as four buckets here there's more there's invisible so it's kind of like the ghost in the machine almost called called that ghost ambient so it's kind of happening in the space but you're not directly working with it in line so it's actually in your work in your workflow and then conversational the ones that were I guess all arguing about I think that's what I learned being here at this conference here's an example of invisible so when you start if you sign up for a flat file we go in the background we take your email address you find the company you work for we look it up and in the background the AI agents are writing a flat file application so they're writing code and essentially sending you up a demo that is perfect for your use case so if you come in you're from an HR company you're gonna get an HR demo and while that's running you don't need to know that AI is working on it so that I'd say is like it's working in the background here's something working more ambiently it's a very initial take on this but you can see there's something an agent analyzing the data in the background this is a tool actually I lead this team for AI transformation and you can see the little sparkles pop up on the columns when it finds opportunities to fix it so that's ambient this is in line so you're busy working in the data and the AI is able you're able to use the AI I'm directly in line here to fix the data these agents are writing code that then gets run on this data set so you could have a million rows and 50 columns or whatever you want and that code will run really fast which is pretty cool and then finally the conversational ones we're all used to so this is build mode it's the no code low code agentic system that writes flat file apps now so before you would probably have to have had a engineer at the company building these applications now it can all be built up so that's pretty cool um and that's that's kind of the the general surfaces I think about um I listened to um Amanda Askell um from Anthropic talking to Lex Friedman about um building um Claude's character and in that moment I realized I'd been doing something a little silly I'd been giving engineers feedback on our agents like oh it shouldn't start saying this and it shouldn't use these words and and why should it do this and I realized I was I was doing like I would do design copy right I was I was I'm in my my my normal instinct and when I heard her talk I realized I needed to go from controlling um to being a character coach and and and actually building out um the the nature that I wanted so this is a v0 I hope have you most of you used v0 from Vercel before um yeah um so this is a v0 I built one of my early ones and it was um I called it a chat tuner it doesn't look like much but that wasn't the focus um but I could essentially um put our orchestrators so the system prompt for our ai orchestrator for build mode um in here and then I can modify it I can say what is it like if I tell Claude to be more friendly versus more balanced versus more concise what what does more cautious mean to this model um and the point of me showing this is just to say like the design of the final thing is always a tempting thing to design to um but now we can actually go and build tools to help us to design that um and this brings me to like uh I have like three themes um the first theme which is feeling the material I'm a woodworker so you'll have to forgive the analogies to physical material but if you're going to uh design something with a physical material you have to feel it right you have to what are the properties of it and you need to understand it and so I feel like before with design we were kind of looking at everything through like layers right mock-ups and prototypes and and kind of trying to see what was going to work and what wasn't what we need to do now is go feel the material feel feel how these models work my new north star is like creating an environment for these llms to shine right what's what's this form factor that can help them nail their assignment stay aligned and grow as the models get better right that's that's my new goal um we're basically anything we do with an llm I feel like we're putting it in a box um and that's you also hear people say that llms are like interns like oh it's an intern with a phd and so I try think now if you're putting an intern with a phd in a box like it better be a good box um and so we need to put effort in um this was a conversation we were having about what tools does this uh co-worker this new form factor this new model like what tools do we give it when it shows up for work um and I got fixated on this idea of cursors I was like oh what happens if we just had a mouse or a trackpad I'm a trackpad person um so that's probably controversial but uh essentially what happens if we gave the AI um those tools and so I I created this v0 and they moved it into cursor um and I was like well I work in design tools a lot so I don't migrate a lot of data so this is the best place for me to feel this right to feel this material so I created a canvas um and I could give it orders and be like hey and honestly I was I was very enthusiastic about this um for like a few seconds it felt like I was touching the AGI a little bit but I also very quickly started feeling like I was putting a Formula One driver in a Prius it just it felt like I was constraining it and controlling it um it could only move one thing at a time um but so so learning from that um was uh something like this was also um a v0 um that I used Claude code on eventually and this is a new uh product that we're working on which brings like the all the stuff we've learned about uh migrating data to consumers to let them work on their data um but you can see the AI is is operating in the space um and it's it's got presence and so it's it's able to read multiple files while writing into another one um it's not like me who can only focus on one thing at a time even though I think I can focus on more um it's not true and so this is us moving from determinism to inference and figuring out what this material feels like and so um that's feeling the material right like working with the model getting it into your space understanding how it feels to work alongside of what's it capable of and then the form factors that we're putting on them actually actually you can now go build it and play with it um and feel it um the next material analogy I have which is finding the grain once you've got the characteristics of the material you understand it usually the piece of material that you're building with and you're creating with might have its own characteristics and so as we're creating these form factors uh finding the grain is about feeling it out where is it smooth and rough um where is it weak where is it strong um and we'll have to remain humble here because um things are going to change and are changing so quickly that whatever we rebuild is going to most likely need to be rebuilt this was an example of that build mode agent I asked it to do one thing which was enable the auto map plugin so it's just automatically maps data from the source data to the target data and I get a wall of text and it's not bad because this went and I saved me probably a week of work um I didn't have to have a product manager write a prd send it to an engineer get the in the road map get the engineer to write it qa this was all just done right all that code was written but the noise gets in the way and so this was a v0 of of kind of rethinking the tool ux what could it be like and so um the way I thought about this was if you're designing for a if you're if you're going to a co-worker and you're going to do something complicated for them and you want to communicate you think okay I'm going to choose my words carefully I'm going to communicate visually I'm going to stop and check whether whether it's right and so I wanted this to feel similar and so you can see here split personal details it's visually telling you what's doing saying hey is this right then it's saying I'm aligned I took a snapshot you can roll back I'm holding you accountable you approved this and then telling you what you can do next we also wanted to it to be able to express itself so if something went wrong and I'm shaking its head and a little bit of frustration which is probably what the user is feeling too when something goes wrong and then finally it can back off when it gets something wrong and sort of say okay I'm handing control back over to you and that's a lot more intro that feels a lot better and it felt like we had found the grain and found the right place to put this this material with this and so what's really cool about this one is that as we're implementing it we've realized that it can you can fit in other places so not just in conversational flow it can fit in line and this is going to be in our kind of like inline transform functionality really soon so I think like as we as we find a new technology and work with it we run the risk of just automating the tedious things and I was so excited about those previous two talks because there's kind of like some emergence in there right like something interesting that we wouldn't be able to do before and I'm most excited about those things like what what emerges from from playing we stopped playing for a few years um when we were kind of got the internet and we were like really excited and css3 came out and then like html5 we were playing a lot um now I feel like we're all playing again and so that's really exciting for me um this is an example of me playing um I created this v0 and I I we've been in search of this characteristic of an agent that is that feels forward leaning and what I mean by that is it's an agent that's curious and it's excitable um but it likes getting done um and it's very focused um so not going crazy right like we've all seen the lms kind of go too far when you give it a task and that doesn't feel good um so here I dropped a json file and a csv file um and the agent decided um you know what would be good to do is combine those two things because the data look pretty similar um and so here we can see it's it's combined the the file the two files into one um that's a good thing that it did I didn't have to ask it to do that um it picked up on it um and then after that it wrote a report so it told us what it was doing said hey I found some duplicates this is probably what you need to do next and so it built up context um and I was actually just trying to play with Claude 4 here and feel the material and kind of see how it would be but I realized um I'd kind of come across this nature that we were after um it made some suggestions and generated slide deck which I I asked it for um so within just dropping two files um it's it's done something emergent um and now we're baking this into our our new product called obvious which is coming soon another one was we had this idea of giving our agents a knowledge base so all the customer calls we'd had with them were all recorded and transcribed like most of ours are um and we had documentation from the customer and so we put it in into knowledge base and then when we analyzed all of this customer data we surfaced up um suggestions based off that I was fully expecting better suggestions I was fully expecting more suggestions got those but then here um the the agent decided I can't fix this but I know how to fix it and so I'm going to tell you how to fix it and so it suggests here that the user actually goes to HR and gets them to generate the missing employee IDs and what emerged here was something I wasn't expecting maybe you look at this and say that makes a lot of obvious sense but to me I wasn't expecting it to be able to help the human to go and do the job um where it couldn't um so that was really exciting I don't think I would be able to get to that without um playing and and being curious um and then the last thing I want to talk a little bit about is eyes on the future and we all have our eyes on the future because how can you not there's always something new now um with models um so I I like to think about it as like what's your pelican on a bicycle um and one of my pelican on a bicycles is order complete I'm super excited about it's probably a bad idea um actually to use an LLM for this but I'm like I want to make an order complete that is backed by an LLM and so this one has a hundred suggestions for fixing some data um and it's kind of like a bake-off um between these two things I'm yet to find um a model that is both very fast and very good at this problem um but this is a a benchmark or something that I've created just for myself to be able to feel the materials um that we're getting and so I think about that for my design practice now like what are the things I care about and can I like design into the future and start to think about the form factors I want and then build an application that can actually test that so yeah that's all I have for you today I I'm very excited to see all the new form factors um that we build um with our new tools thank you