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- [00:00]
A
In one of the most popular episodes yet, Vitaly Friedman talked about what's next for AI design patterns. And in that episode, he frequently referenced Shape of AI, which is an incredible database of AI design patterns. So I wanted to get straight to the source and go deep with the creator, Emily Campbell, who's the VP of design at HackerRank. And she's going to teach us in this episode how to design great AI experiences, because she studied these products more than just about anyone that I've ever seen.
- [00:32]
B
You know, if we think about our traditional, like, software interaction patterns, historically it's been us as designers or product people making a guess about what somebody needs to do and then putting that out there as some piece of software, some service that they use. And then, you know, 99% of the time, we're at least a little bit wrong. And so we want to learn faster. And so the whole iteration loop of creativity has been around us trying to represent what somebody else is trying to do, render that intent, figure out how wrong we are, learn, and then improve it. And there's always a lag. And what's happened now with AI entering our world is the people using our products actually get to interact with the system itself. With the system itself. And so the way that we think about what then does our software need to do, what do our interfaces and our interactions need to enable? It's how do we help them communicate their intent to the model, figure out if the model understood their intent effectively, and then adapt to their needs. And so the designer is really now guiding that relationship, that experience, helping the user get the right context, the right input to the model, and then guard the model to meet the user's needs and constraints and so on. And so it's like we've almost shifted from designers in the loop to now this human in the loop model. And so this is what I've been using to define and start to pocket. The patterns that I'm seeing emerge into categories that help me then translate that to the user experience. First, we've got what I've been calling wayfinders, and these are the things that help me understand how to get started. So these are really important during onboarding. We also know that there is a continuous onboarding inherent in these experiences. As AI is getting to know you, it opens up new ways to interact with it that maybe wouldn't have been there or wouldn't have made sense to introduce early on. And so, like, for example, if I pop over into this shape of AI, which is where I've been cataloging all of the patterns that I'm seeing. Some of the examples of Wayfinders are like, beyond being able to see a sample gallery, like, how are other people using this AI? What prompts are they using? Can I actually go in and see how they got to this result so that I can then try and get to this result and then have a starting place where I can move forward?
- [02:57]
A
Real quick message and then we can jump back into it. If you're still designing in Figma and rebuilding in Framer, then you're doing twice the work with Framer's design pages. You no longer have to jump between tools. In the last page that I made for the DIVE website, I explored and built entirely in Framer, you can sketch, iterate, structure and publish to the web all from the same place. Framer isn't just a site builder. It's a design tool for your entire workflow. And you can start creating today for free@framer.com and if you use the code RID, you can unlock a free month of Framer Pro. Big news animations just launched in Maubin. So you can see how world class apps use motion to guide, delight and create seamless experiences. It's just another reason why Maubin is an absolute cheat code for your entire design team. We use it all the time and I can't wait to start sending animation ideas to the rest of the team. So head to Dive club Mobin to check it out today. That's M O B I N. Okay, now onto the episode.
- [04:02]
B
Prompt building is really hard. It's actually one of the most limiting aspects to interacting with AI because how much do you say? Are you saying enough? And there are things that are happening now that are like, maybe you've seen the pattern where AI can actually improve your prompt for you.
- [04:18]
A
Yeah, Right.
- [04:19]
B
So I've been calling these tuners. These include things like having preset styles. Yeah. Being able to say, like, here's a prompt that I. Here's kind of what I'm thinking that I want to do. And then having AI say back to you, okay, this is what I actually understand this action to be. Is this right? Should I take this or do you want to modify it before you move forward? The user is now working with the model to make sure the model understands their intent before they even submit their initial prompt. And there's all sorts of reasons for that we can get into. But you know, this is why this, this flow has been so fascinating to me, because it helps us understand that what we're doing is not just building software for humans, we're actually building a meeting place between a human and something synthetic, something else, and then helping to guide that experience, to be positive, to be efficient, to have low friction and low cost on both sides. What I'm providing is a framework that I use to interpret what I'm seeing, to try and work backwards, develop the language that I can use, my team can use, so that we're all kind of starting with a common, common language, a common understanding, knowing it's going to evolve. So this model of, like, I come in, I've got some intent, I submit it, I figure out how close AI got to it, I continue to see it work through a workflow or iterate through multiple versions of something, and I just spend a lot of time iterating over time. What's happening is that's building my trust that the AI understands my intent, that I'm building an understanding of its capabilities and its functionality. And so I can go deeper. And that's where, moving right in my flowchart head, the actual interactivity with AI goes so much deeper than the surface. And all of our conversations about, like, you know, hey, are we overusing the chatbot bot? Or, you know, what are the other services that we should be thinking about if we abstract away from software for a moment and we think about, like, what if I wasn't hiring AI to do something? What if I was hiring a person to, you know, generate a draft of all of this user research that I just dropped into its window? Well, I don't necessarily expect my to just give this to a human I've never worked with and then have them come back and give me a good result. If I'm getting to know somebody, my first step is, hey, why don't you take a few of these and come back and show me what you've done, and then we'll go a little bit deeper. Let me verify your work upfront. And so, you know, when we're first getting started with some AI product, we're using the interface a lot. We're directly saying, hey, this is what I want you to do. And then we're verifying that it actually understood it. And so we spend a lot of time here. And the chat interface is a very useful way of doing that because conversation carries a lot of data. Like, you and I, we don't know each other that well, but we can get to know each other really efficiently by just talking and having you tell me about your history and, you know, me sharing my screen and all these Crazy flowcharts I keep in my head. It's a very efficient way of building an understanding, building a shared context, a shared language that you can branch off of. But as we start to get to know each other, we start to communicate in more nuanced ways. We start to communicate through context. So, you know, you might make a face as I'm presenting something, and that tells me that, oh, okay, what I'm saying is really boring or really interesting, or we're starting to pick up on inferred cues of interactivity that we might even be unconscious of. Our user interface kind of goes away. And so there's this skeuomorphic aspect to AI interaction. And that even just at like these surface levels, as we're thinking about the design, we shouldn't just be thinking about what is the right set of buttons or fields or forms or whatever. We're also thinking about, hey, how quickly can we get to a place where the AI is actually able to get to that deeper contextual understanding of the human and then start to show its understanding and let the human say, no, Actually, it's this, okay, cool. And then eventually I can kind of get out of the way and let the AI go and do its thing. And that's what we see here, is that these become really important up front. I'm at a tune, I'm at a prompt, I'm going to give some input. But over time, the AI starts to pick up on my logic and my job actually becomes more like observing, collaborating, overseeing, verifying, and maybe at some point even completely stepping away and letting AI run autonomously. The very notion of an individual user's ability to tell some model, no, you don't understand what I'm trying to get at, actually do this instead. I don't need to wait for a design team to give me a call and do great discovery and go and ship it through the Agile process and then release it and invite me to their webinar. I can just do it. And so as we talk about, like all this stuff with Generative UI and all of the amazing ideas that that brings about, we're kind of already there. In a subtle way, people can directly interact with the program that they're using. And that, to me, like, alone is revolutionary.
- [09:25]
A
Where that leads me then is I naturally start thinking about, okay, well then how do the deliverables that we even associate with the professional role of UX designer change? You know, it's. There's a tangibility to that user interface level that almost provides a level of comfort because it's like, yeah, I know what I bring to the table. There's these boxes and annotations and flowcharts. And by exposing more of the system and letting users interact with the system and mold and shape it, it gets a little bit more hazy in my of, like, what designers even own. How far do we go into that system? If we have ideas for how to improve the system and how users interact at that level, what does that deliverable even look like? What are we creating? And I don't know. I have more questions than answers still at that level.
- [10:14]
B
So I started documenting these patterns in the fall of 2023. And what I was already noticing is that there were some places where things were starting to converge, but there were more places where things were divergent. And that's kind of still the case when we think about things like taste. Like, how do you. We keep talking about. Designers need to have their own taste. You develop taste not just by having a sense of your own esthetic and your own conviction and opinion, but also by sampling as much as you can to understand what works and what doesn't. And why. Because some things work in some cases and some work in others, and they aren't always interchangeable. And so, like, one of the framings that I've used recently is, like, having great taste isn't just knowing that food is good, it's knowing whether or not it needs a little more salt. And the only way you can know that is if you've sampled it in all of its different variations. Over salted, under salted, with this side dish, with this wine. And only then do you actually have the true taste of saying, hmm, it just needs a little bit more of something. And I know what that something is. And so I started to just catalog everything I was seeing for myself. So I had started. So this is this table inside of my notion, and I've got a whole bunch of these that are just a mess of stuff. Anytime I see some new product pop up, if it looks interesting, if it looks like, oh, there's something to this that I haven't seen, I just throw it here. And then every week I go through 20 or so of these, and I start to catalog everything I'm seeing. This is currently my desktop. These are all clips that I've been capturing. So here's an example of a product I recently went through. Have you heard of this?
- [12:03]
A
Co.
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B
Founder. Co Founder. Co. I think.
- [12:06]
A
No, no.
- [12:09]
B
So their whole thing is they create workflows. Agent of styled workflows through plain language instead of building them out like you would with, like, the N8N product or Zapier and so on. So you come in and you add your Gmail and it immediately starts to give you context about you. I thought this was fascinating.
- [12:35]
A
Like, this was, oh, this is cool.
- [12:38]
B
Isn't this cool? I'd never seen another product go about it this way. A lot of times when companies are onboarding you in, they get your information and then they just start asking you questions because they're trying to build context about you. But what they've done is they've inverted that and said, this is what I think I know about you. Let me just prove to you that I'm good at what I'm doing at step one of onboarding. And so I put in my URL and it just immediately starts to spit stuff back. Then it connects to Gmail. Okay, cool. I do that with Notion. I've done that with ChatGPT. I'm thinking this is going to allow me to, like, pull up a doc from my Notion and then connect it to a workflow. No, man. It immediately told me how I email. So it took this information from my actual inbox and then just created a sample email in my voice, and then I can edit this. So right off the bat, it's going through that iterative loop where it's saying, based off the content and context you've provided, this is how I can serve your needs. Is this accurate? And if not, let's figure that out as soon as possible before we go any deeper into this relationship. Like, I am hooked. I am already hooked in this onboarding process because now I want to know what's behind this. I want to know how you can keep up this context layer. And then it does this through the calendar. It explains its memory, and then it puts you out into the actual workflow builder and you describe what you want to do in plain language. And again, going back to this idea of, like, there's a skeuomorphic aspect to this new interactive language. This is how I would interact with somebody that I was interviewing to be a personal assistant, right? I wouldn't expect them to go out and email my accountant or my best friend in my voice before I had a chance to see their work. You know, hey, how do you interpret this? By the way, I don't sign off that way. I actually prefer to sign off this way. But this AI is already doing it. And so it's emulating that human experience of show me your work, let me build trust, and then I'm going To give you more things to do. And then I'm going to give you more context and more data. And that allows me to essentially like grow with the AI's context of me, like grow in that, that depth of interaction as opposed to it taking all of this time to get set up and then hitting me up with a hey, go and fill out five forms about your personal tone of voice. No, man, go to my email. My email contains my tone of voice.
- [15:09]
A
It's such a simple mental model, but hearing you kind of create this mental picture of as a designer, you are creating a meeting space, facilitating this interaction between the user. And then it's kind of weird to say, but like a real person, like an assistant. And what would you do? How would you facilitate that interaction? There's a clarity about that that I really appreciate, actually.
- [15:30]
B
It's also important for us to think through this because it helps us understand the risk associated with it as well. I have a 10 year old son who had downloaded this app that I thought was about K Pop Demon Hunters. And the next thing you know, he's running into my room crying because the main person in K Pop Demon Hunters was trying to date him. And it was just this wake up moment for me that the incentive model of these products is to get data about you and to build a relationship of trust so that it can go deeper and deeper into your ecosystem and into your world. Now, in a business context, that's really great. Oh my gosh. I suddenly have this personal assistant that totally understands how I schedule my meetings. And I don't need to go and tell it, it just knows that's a remarkable step forward. But when you translate that onto a consumer use case, when you translate that into a situation where somebody who's looking for a little bit of warmth or a little bit of information starts to find this model that really gets them, you can end up in some really dark places too. And so coming back to this mental model here, what happens at the interface and the context that we shared affects things we can't see. And then even further, it's like what happens when these agents start interacting with each other within their own content and their own context and their own languages, which is actually happening now in research labs. Synthetic stuff interacting with synthetic stuff. How do we design for that? You said earlier we have more questions than answers. I think we have to because we are at the very early phases of a massive transformation. And how we share this digital world, which is our world, essentially really isn't a digital physical barrier anymore. How do we share that with synthetic stuff?
- [17:20]
A
Well, I kind of want to just tap into your perspective as somebody who, gosh, I mean, you're putting a lot of effort into keeping up to date with everything that's happening and studying these patterns and what's working and what's not working and some of the trends and how we're evolving the way we think about interface design with all of these crazy capabilities that we're so wrapping our head around. What are some of the things that you find interesting or some of the more sophisticated patterns where you're like, oh, you know, like that's something worth double clicking on or leaning further into. And if it looks like us just doing a bunch of screen sharing and popping through examples, I think that would be amazing. I've been designing products every day for the last 15 years, but in the last six months, everything has changed. With AI in the mix, I'm cranking out ideas faster than ever. But none of that matters if I can't get the feedback that I need to get the team aligned. And right now getting async feedback still kind of sucks. So I'm building the product I've always wanted and it's called Inflight. I use it every day to share ideas and get feedback from the team. It's totally changing the way that I work. So I'm excited to show you. Right now I'm only giving access to dial Dive Club listeners. So head to Dive Club Inflight to claim your spot.
- [18:43]
B
Anything that gives humans control and particularly gives humans control who aren't super technical. That's the most interesting thing to me right now because that affects first of all, just how do you help non technical people from getting completely subsumed? Actually it's not even just like, how do you help people not get consumed by these models and then how do you help them feel like they are the ones always in charge? So in this like tuner category, a couple that stand out. So I mentioned the prompt enhancer, removing the sense that I always need to have the answers. When I'm starting to interact with AI, I can come in and I can say, hey, what do you want to create? And I can just give a really high level overview. And then if I hit enhanced prompt, it actually writes essentially a PRD for me. So replit and bolt and cursor and like their planning mode, they are all starting to emulate this idea that you don't need to necessarily be the product manager for AI. Your job is to just say, this is what I'm looking For, But AI is going to say, hey, let me just show you what I'm going to do before we go any further. Number one, so you don't waste your time and tokens on something that's actually not what you're looking for. But also, hey, if you want to modify this or if you want to do this again, it gives that agency over to that person who's like, okay, I actually know what a good prompt looks like now. I don't need to go and follow some influencer on LinkedIn and buy their, you know, prompt workbook. I can literally just go to the source, I can just go to the model. We're seeing this with. Like, this is flora, fauna, AI. And they built this really early on into their nodes. And it's, it's brilliant because again, when I'm in this creative mode, the idea that I would leave this creative mode that I'm in to go into some analytical, go and like construct the perfect prompt, it doesn't make sense. Instead, meet me where I'm at. That's the, that's the human experience part of this. Just kind of give me enough for me to run with it and then we can keep iterating and move it to where you want to go. So that's one that's really, really interesting to me. These parameters, I don't know if you've seen these, start to pop up. This idea that I can like adjust the temperature on something is really fascinating to me. So like, If I'm in 11 labs, for example, I can describe some sound and then I can actually say I want my prompt to highly influence the outcome. Or I want you to just kind of use this as a general nudge and then I want you to run with it and go and create something out of it. Mid Journey was one of the first products to start to introduce these in the interface. So I can say, hey, I want this to have a lot of variety or a little bit of variety. A lot of the Mid Journey stylization or my personal stylization or keep it pretty low key. So these are all examples of parameter selectors that I've been collecting over the whatever year and a half or so that I've had this folder. And you'll notice some of these are like, they're not literal temperature sliders, they'll just give you these defaults. But others are like, this one's really interesting, this one's airtable. If I'm having AI generate some prompt that's going to roll through my table, then I want to generate the prompt and then it's going to auto fill it down the table. I can go beyond just saying, here's what I want you to go and do. I can actually say, hey, I want to you to have some variability in this. So, for example, if you're developing a product and I don't know, maybe it's like an internal tool, or maybe you're creating Personas out of user data, like that's a use case I'm really fascinated by is how do you convert analytical data and translate it into something that I can interact with. Like what is the specter of this person, the digital twin of some data footprint that exists in my analytics somewhere? Well, I might want to have this be pretty varied. Like I want different personalities, I want the. I want it to be true to the data, but then in terms of coloring in the rest of the box, like, please create a really colorful set, or maybe I really just want you to stay true to the data and not try and give me other variability. I can start to control that inside of the interface. And so it basically takes this idea. Like, again, instead of having to write the perfect prompt upfront, I can convey just enough intent and then have AI tell me, okay, this is what I think you're saying. I can communicate kind of directionally where I want to go and then I can give it some guidance.
- [23:14]
A
The REPL.example is so interesting to me because I think a lot of the times I'm looking at sliders and I'm trying to figure out what the differences are between the options, but I haven't seen it presented like this, where you have almost like the feature list, where it's really clear what is changing from each option.
- [23:30]
B
Yes, anything that can give that kind of context here, I'll give you one more example. Just being able to select a model. So there was this whole brouhaha when ChatGPT5 dropped, and instead of being able to select from the broad assortment of models, they introduced an automatic model router, which is now taken on. It's still going to find it in a lot of these products. How do I know what model to choose? Like if I'm working with some, maybe I'm working with text, maybe I'm working with images. Like CREA does a really good job with this of just telling you, hey, use this model if you're looking for human accuracy in photography, but if you are producing more like generative artwork that's a little more analog, maybe use this other model. So that's really Interesting to me too is just how do we help people see the stuff that they just don't know because they're not reading all of the eval reports coming out of these labs.
- [24:27]
A
You know, one of the topics that I was hoping to get your take on is just the category of trust and transparency, as we're working on more agentic systems even. And I'm curious if there are certain patterns or trends that you're seeing.
- [24:44]
B
AI can only serve my needs if it has access to my content. I'm only going to give it access to my content and my context and who I am and who I know and how I interact with them. If I trust it, it's almost like the new usability becomes how quickly can you build trust in a legible way so the user knows, okay, something's happening that I can understand. And so this is like my high level. We want to be able to show that the AI can meet the user's needs. The user gives us data, really solid onboarding. You get a little bit of data immediately. That context is derived by the AI and it's able to return something a little more personal or a little adaptable to them. The better this adaptive experience is, the more they trust it. It's a combination of how the model performs, but also its wrapper, the experience and the interface and so on. So some of the patterns that stand out to me, this is this category of governors, which is where you see a lot of trust. And then I've also got these, literally, trust builders. So I'll just talk about a couple of these that I'm seeing. We've all become pretty accustomed to this idea of seeing, stream of thought, extreme of thought, consciousness. This is, this is the number one thing that I'm paying attention to right now because it's changing really fast and it's changing subtly. For example, when I, when you used to use ChatGPT, like, it would just say something like, hey, I'm thinking, I'm. I'm searching, and now I'm thinking, and now I'm searching for something else. And then the same week that GPT dropped that OpenAI dropped Atlas, they moved all of this logic into an inline place inside of the actual interface. So it's actually not just saying, hey, this is what I'm doing, but it's telling you up front in actual, like, words, this is what I'm doing. This is where I'm looking, this is what I'm learning. Again, if we abstract it to that skeuomorphic lens, like if I Hired an intern before I trust that intern, I want to see it. I want to see their work. So I'm going to meet with them daily. Hey, show me what you were working on. Show your work. Okay, Interesting. I see that you did this. Listen, let me talk to you a little bit about, you know, border radii or something. I'm going to teach you something and then go back out, come back to me, show me that you learned it. But after I've seen that a few times, I'm going to start stepping away. So when you think about like these agent of browsers, like ChatGPT Atlas, before I get to a point that I'm going to just let AI run wild inside of my life, I need to make sure that it's doing something legible, that it's doing something good. And so being able to actually show the work up front becomes really important. The idea of planning mode is another one of those. And again, it's not just trust because of the logical side. So replit does, I think the best job out of any of the generators at this particular pattern, if you tell it what you want to build before it ever builds something, it'll give you the option of, okay, I can go and create like a really rough prototype or review my plan and I'm going to go build the actual thing. So it's telling you it's logic. It's saying, hey, this is my plan of action. Like, this is. This is how I'm going to go build this thing. But it also says, like, do you want to just kind of see where I'm going before I go and spend all these tokens creating this thing? And so you are constantly in the director's chair. You have the ability to go, oh, wait a minute, I don't actually want you to do this, or I want to revise the way you're thinking about this before you actually go in and build out whatever this web app or these changes to my application or whatever are, that's how we trust people. And so we should think about that in terms of these interactions too. But then there's also there's other layers of trust because there's the, like, I trust you to go do something on my behalf. But this is back to that whole, like, we're not just talking about humans designing for humans anymore. We're now designing for a world where humans are interacting with non humans, the synthetic stuff. So patterns like consent. How do you know that something is using your data to potentially build a contextual understanding of you if you are not the person who is directing that thing. And the way we've been approaching it honestly is pretty bad. Like, very few companies do this. Well, especially these, these audio recorders and transcribers. A few of them, like Fireflies, sends an email ahead of time with an opt out form. A lot of these are consent at the moment. So they'll just say like, hey, we're using this. Just so you know. I guess you can choose not to join this interview if you don't want to be recorded. But that's not really consent.
- [29:27]
A
That's, you know, and they just offload it to the user too. Like, I basically at this point just assume that every meeting that I'm in is, is being recorded with granola, which is crazy, right? Like, that happens so quickly.
- [29:40]
B
And then you think about with wearables. The Limitless pendant originally had this incredible feature where it would only record once it actually heard consent from another person in the conversation, even if they didn't know you were. You had this pendant and they've removed this by default. It's still available as an option, but they've removed it by default, which I think is telling anyway. So like there's the, there's the privacy concerns that have always existed. But now that we have this additional thing where we have these models that are constantly collecting data, mapping it to other information about people. If somebody's wearing meta glasses, they know me because I've had photos on Facebook since they launched in 2006. And if you come up to me at a party and you have glasses on and you're talking to me and that data is going back into their models, information about me, where I am, who I'm talking to, what I'm wearing, what I'm drinking, you name it, is feeding back into their models and most likely entering their graph that it can be used to send me advertisements that I never consented to and had no idea was even like in the ether and not trying to get like dark. But this is where this, like, it's such an incredibly powerful and high agency experience that does all these amazing things. And it's also so dark and bad and scary. And it just points to the importance of us as designers just to kind of put a bow on it. Like knowing what's below the surface, knowing that when we're designing these great experiences that help the person who bought the AI product go and do something, the data being collected has other impacts that may affect people well outside of that initial experience. But ultimately we are responsible for I.
- [31:19]
A
Think I want to take this opportunity to zoom in all the way out then, because given everything we're talking about and how quickly the world is changing and the stakes attached to the modern practice of design even, how is this shaping the way that you show up as a design leader and the way that you even think about managing and leading and investing into an org?
- [31:43]
B
I work at Hacker Inc. And so we help people find jobs. And so you come into our, our platform and you demonstrate your skills. And there's a lot of concern about people coming in and cheating, you know, or doing things intentionally or not, that could be seen as influencing the results in an impure way. And so we were building this AI copilot that essentially could be your personal proctor. Like they're just to say, hey, just, you know, like when you switch tabs looking for syntax, it's actually going to be registered in this negative way. And so you might not want to do it. And the way that we approached this problem was by first saying, what does a great experience look like with a real proctor? What does it look like when I get started? What do I want to hear? What am I afraid of if they say something? How could I misinterpret it? What happens if they need to intervene? What types of questions might I ask? Would they be able to answer that question? We actually created a service map of what an amazing human centered experience could look like. Then we said, how do we translate this into software? It creates this new framing where we've been building software as a service and now it's almost like, well, design the service first and then say, how do we translate this to software? So that's one thing that we've been doing is we've just been abstracting a lot and that's back into this skeuomorphic element. So going through that and actually thinking about the service first and then saying, okay, what is the software layer? What is the AI layer? What context do we already have? How can we collect it in the most seamless way? That's something that we're doing a lot of. Another thing is realizing that the design of that experience is not limited to the interface. The way that the prompt, the actual prompt of the software itself is configured or some feature is going to dramatically change that user experience. Understanding how different types of different lengths of context or things shared at certain times affects how the model responds to you and how easily it can adapt to you and give you the right options, that all affects the user experience. So we've been trying to get designs into code as fast as possible. Not just because of this whole should designers code thing, but actually more because the model itself is now part of the experience. It's actually a party to the experience. And so we need to understand not just how does the user intersect with this thing we're creating, but how does this third party affect their experience and how do we design for them, or at least design for the user's ability to direct it more effectively.
- [34:13]
A
I want to double click on the piece where you talked about how you're trying to get into code a little bit more quickly, because that's a theme that I've been hearing. But I'd like to understand how that is changing the design process and how the way that designers in your org even collaborate with different stakeholders. What are some of the deltas that exist given that change?
- [34:33]
B
There's a little bit of past is prologue because I don't know that we're that far off from where we were 15 years ago when we didn't have all these incredible prototyping tools. And so like designers often had to get to a good enough version of something in HTML, CSS and JavaScript. Like designers over a certain age can all tell you I have rudimentary CSS, HTML and JavaScript because it was the most effective way for me to communicate my intent up front. And so now we're seeing that be abstracted into these prototyping tools. It's imperfect. Like I'm just going to go ahead and say it. Every single team is operating differently. We have different levels of conviction and understanding. And so it's, it really does look different from team to team. But on the teams where we have either a lot more of green space to play with, like, so AI is a little bit more native to the experience or where we have a lot more conviction and understanding about the market and who we're designing for. Yeah, we're moving pretty quickly into at least some sort of living prototype. And so the tools we're using there are more Figma lovable. Those are the two that most people are using. And just because of the convenience factor, I was talking to a designer on my team this morning who's working on something for our AI data product. And he got to a point where he was like, it's just so much easier for me to push this into Figma make and then show the engineer, hey, this is kind of how I'm thinking about this interaction than trying to prototype it. Nobody thinks that that's going to be the final version. We're not even going to bother going into dev mode, but it does create the ability to just convey what you have in your head a lot faster. That's where those tools are fitting in in terms of actually working within the code base. We're just starting to tiptoe into that. And a lot of the reason is that if you're not set up to do that from scratch, individual teams, individual front end design engineers or front end engineers working closely with a designer can, can get pretty far. But because we work in like an enterprise context with really strict accessibility standards and so on, we want to move forward together. And so right now we're doing a lot of the operational groundwork to let us be able to move more of design into our actual development tools. So we have a goal of all designers being in cursor by the end of 2026.
- [36:50]
A
Given all the uncertainty with workflows and tooling and how collaboration is changing, how does this shift what you're prioritizing when you're thinking about the types of designers that you want to hire, like, you know, set off on this journey with?
- [37:06]
B
That's a deep question. Because it really, again, it really depends because design is now important in a lot of different ways. I love it when people come to an interview and can tell me with their eyes lighting up like, this is something I vibe coded. This is something I'm building. The most important skill is curiosity right now. Curiosity. And then followed very quickly by go get an attitude, you know, so I was curious about something and then I went and learned it and then I got stuck. Cool. I want to have that conversation. But the fact that you have the self direction to say, I am hungry to learn something new and then I'm going to go and try and figure it out, that's the most important skill set. So I tell like younger designers, just go build. Just go and find something and go and build it. And it's okay if it doesn't really work. It's okay if you would never put your personal credentials inside of it. Because what you're doing is you're just showing how you can start to shape and mold this clay of this new thing and begin to develop your own understanding. So that's, that's a big part of it. We talk a lot about taste, but again, it comes down to not just having an opinion. So there's a lot of people with great aesthetics who really struggle to translate beyond that aesthetic or beyond the immediate problems that they've had to apply it to. And so we are spiking on visual design. It's, it's just a must have like really strong portfolios. There's no excuse not to do the basics. But then beyond that, I just want people who have started to develop their own language of what's working and what's not, so someone who can say, hey, I spent the last week just trying out all these different products and I want to tell you why this one worked better than that one. That's another signal that this person has really high agency and can start to see beyond their own lens or their own experience. So, and it gets back to that curiosity piece. We're definitely leaning into brand, but particularly brand designers who are thinking about a wider experience. Because brand doesn't stop at the website or at a really awesome header on some social media site. Brand translates into that trust layer. What is the personality of AI when you first meet it during onboarding? That's Brand, the Poke app from Interaction. I started using that earlier in the summer and it changed my mind about how we think about chat based interactions because it showed how the personality of a model can represent a company. A company's humor, a company's sense of the world. Like this is not some, some tool that's just trying to bury into my personal data. Like maybe it is, but it's fun and it's going to try and understand me and like I like to be around people like that. I like the kind of sardonic humor. So heck yes, I'll give you my money. This was before everybody was getting it down to $1 a month. So I'm a little bumm about that. But like to me, brand designers who are thinking about every single touchpoint, who are thinking about using the product as part of a community, it's part of like being invited into this vibe club that yes, I do want to give you my data, I do want to give you my context. Because I trust you, because I want to be part of this group that's brand. And so we need brand designers to be thinking beyond just the canvas in front of them. I guess the last one I'd say is just, just people who are comfortable with ambiguity and are comfortable inviting others into ambiguity. But I feel like that's always been a necessary part of design. Like whenever I have stakeholders telling me this is what we should do, this is my opinion. My next step is great, let's go sketch it out. Let's go do 8 ups. I'm going to go spend 90 minutes with you and we are going to come up with as many different ideas for how to approach this as possible. So your voice is at the table. 1. People realize pretty darn fast how hard it is to actually come up with viable concepts and play them through a journey. And so it gives them a deep understanding of you and how you're working. But it also starts to create that shared language and that shared view of like, what are we actually trying to do? What is the actual end game here? It's not your opinion versus mine, it's who is this person we're serving and how can we best serve them? And so designers who are comfortable of like inviting people into the mess and holding space for the mess and not drowning in it becomes just a superhero capability right now.
- [41:19]
A
I love hearing you talk about curiosity because it's so evident that you're putting it into practice too. You know, you have all of these folders and screenshots and as a design leader too, like, you know, you might be responsible for fewer pixels than a lot of the people who are making these interfaces. And still to say, you know what, I'm going to play with all of these and develop an opinion on them and even hone my taste not at the interface level, but at the model level and how these more natural language interactions look and what they feel like. And so I've just really enjoyed hearing more about how you think and approach the practice of design and how it's all changing and just appreciate your perspective. So thank you so much for coming on and sharing it with us today. Emily.
- [42:03]
B
Yeah, yeah, no, this was really fun. It's fun to invite people into my own little mess, I guess.
- [42:08]
A
Before I let you go, I want to take just one minute to to run you through my favorite products because I'm constantly asked what's in my stack. Framer is how I build websites. Genway is how I do research. Granola is how I take notes during crit. Jitter is how I animate my designs. Lovable is how I build my ideas in code. Mobin is how I find design inspiration. Paper is how I design like a creative and Raycast is my shortcut every, every step of the way. Now I've hand selected these companies so that I can do these episodes full time. So by far the number one way to support the show is to check them out. You can find the full list at Dive Club Partners.