Episode highlights
00:02:04 – Advantages of being an agency UX researcher
00:04:20 – Empathy as the core of UX research
00:08:41 – Speed vs. rigor
00:11:04 – Sense-making in working with AI-native products
00:14:34 – Traditional UX research vs. research on AI-native products
00:16:08 – AI tools Angie recommends for UX research
00:17:52 – Key skills every UX researcher needs
00:20:35 – Communicating with stakeholders
00:22:11 – Working at Metalab
00:24:01 – User trust and AI products
00:28:00 – Summing up
About our guest
Angie Amlani is an Executive Director of Research and Strategy at Metalab, the design agency behind digital products like Slack, Uber, Headspace, and Amazon. She helps product teams uncover what users need most, and turn that insight into clear, strategic decisions. With a background in systems thinking and human-centered design, Angie brings focus to the early stages of product and brand development, when direction matters most.
The identity of researchers is changing. We’re not just folks conducting research and synthesizing and producing insights, we’re also builders. There’s a real opportunity for researchers to be creatives.
Very often, teams get lost in trying to improve every single step, sprinkle a bit of AI across every individual step of the journey. Sometimes you might be better off by zooming out every now and again and looking if you can actually skip the entire bunch of steps altogether.
Podcast summary
In this episode of UXR Geeks, Petra Rajkov talks with Angie Amlani, an Executive Director of Research and Strategy at Metalab, about how UX research changes when working with AI-native products.
Angie pushes back on the idea that speed and rigor are opposites, arguing that researchers can move quickly when they narrow the decision space, while still making time for the qualitative research and sense-making that AI products require. She also explains why empathy remains central to research, especially since users are still actively developing new mental models while using AI-native products.
The conversation also looks at how AI makes sense-making more complex as researchers must compare what users did, what they believe the system did, and what the system actually did. Angie highlights the importance of looking into the metaphors users use for AI, such as seeing it as an assistant, search engine, or coworker, as this helps better understand how people use AI products.
Finally, Angie discusses ethics in AI research, from participant data and informed consent to representation, and explains why researchers increasingly need narrative fluency and systems thinking to influence product decisions.
What this means for UX teams
For UX researchers
Speed and rigor are possible when the decision space is well-defined. Stay disciplined about the scope of your research and set clear goals, and you will be able to achieve fast research without compromising its quality.
For product teams working on AI-native products
Users arrive with wildly different, evolving mental models of what your AI product is (an assistant, a search engine, or a coworker) and that framing predicts how they’ll use it and when they’ll stop trusting it. Track which metaphor your users are applying, not just their task success, since trust can break down before a usability issue ever shows up.
For UX researchers looking for a job
Narrative fluency and systems thinking are now essential for any UX researcher, so make sure you develop both. Researchers who can only operate at the task or journey-map level will find their influence narrowing as the strategic questions get bigger.
For data governance and compliance stakeholders
Session data from AI-product studies can end up in a model’s training pipeline if the study isn’t scoped carefully, so data handling needs to be defined with the client beforehand. Participants also need to be told when AI tools (transcription, clustering, summarization) are used in the research process itself — this now belongs in standard consent documentation, not as a side note.
Podcast transcript
[00:00:00] Angie Amlani: when I think about any product that I’m working on, whether it’s an AI native tool, whether it’s serving, a big organization, whether it’s a startup, I always bring it back to the people, research isn’t just academic. It’s about people’s actual lives.
[00:00:47] Petra Rajkov: Today I’m talking with Angie Amlani, who is an Executive Director of Strategy and Research at MetaLab. Before that, she has worked with impact organizations like UN Women and Rockefeller Foundation. These days, she’s deep in research for AI native products, such as Suno, Windsurf, Midjourney, where product, the user, and what people actually want from AI are taking shape at the same time.
So we are getting into what it takes to do research without losing rigor under those conditions
[00:01:30] Petra Rajkov: Hi, everyone. Welcome to the new episode of UXR Geeks. We are very happy to have you, and we have an exciting guest with us today, and it’s Angie Amlani. Welcome to the show!
[00:01:42] Angelina Amlani: Oh, I’m so glad to be here. Thanks for having me!
[00:01:44] Petra Rajkov: Yeah, and we will be talking about exciting stuff because AI is everywhere today. But you yourself, you are in the middle of it because you are working on developing AI products. And we will be talking how does research look like in this environment. But I would like to start with a hot take just to get the conversation going, maybe to hook the audience a little bit.
So is there some belief or conviction that you hold right now that most UX researchers would disagree with, or it would at least spark a good debate?
[00:02:18] Angie Amlani: Yes. absolutely. I work at a product design agency called Metalab. I think there’s a, a broader discussion with the industry. There’s always a in-house, UX researchers have more exposure, more opportunity than agency researchers, and there’s always been a bit of a battle.
I actually think that agency researchers and the unique sort of skill sets that you develop being a researcher at an agency are making folks really well positioned to, keep growing and be successful in this new world with AI. So my hot take is that agency researchers, are going to be the ones really leading sort of UXR rebuilding their identities as creators and builders, the skill sets that researchers develop at an agency really put them at advantage, for being really successful in this new world. they have a unique set of skills that I think, are gonna position them really well to succeed, amidst the doom and gloom of AI.
So I’m happy to talk more about that, but that’s my hot take for now.
[00:03:16] Petra Rajkov: It’s a strong hot take because I myself, I work as a internal researcher, and I love having the depth, having the full context and full overview. But I can see a bit of truth in what you said, because if you work for an agency, you have to be so adaptable. You have to be able to move fast from project to project, from company to company, so it’s an interesting hot take.
[00:03:53] Angie Amlani: No shade on in-house researchers the work that researchers do is incredible. But I think when you sit inside one organization, you, as you say, get incredible depth, but you also develop blind spots, and you to see your company’s way of framing problems as the only way.
When I, look at the work our teams are doing, they’re watching, the same sort of questions and challenges play out, in the AI landscape, in financial services, in healthcare, in retail, in B2B SaaS. We can start to see those patterns and contexts in a way that I think, it’s not as possible for an embedded researcher to do.
I do think it puts them at an advantage really, but…
[00:04:12] Petra Rajkov: Yeah, we shall see. But you are in the strong position to claim this because you have been doing research across a pretty wide spectrum. You have worked for nonprofits, you have worked for the big names like Google, Forbes,and now you are working on AI tools, right? how did you navigate these environments? What’s the thread that connects, all of this for you?
[00:04:34] Angie Amlani: I got my start in this industry through a bit of a unique pathway. Um, I, did my undergrad, my university degree like most people do, and had nothing to do with UX research in any way, shape, or form, which is I think a really common, thread amongst all UX researchers.but I found myself actually doing internships, through my undergrad with large nonprofits and international, organizations. And then from there, I found my first agency job, and we worked primarily with social-based organizations. and what I learned there is that
research isn’t just academic. It’s about people’s actual lives. and you learn that really good research is being able to hold multiple perspectives simultaneously, the person being served, the organization that’s doing the serving, and the systems connecting them. And I think I carried that, into my work at Metalab.
For us and even for me, when I think about any product that I’m working on, whether it’s an AI native tool, whether it’s serving, a big organization, whether it’s a startup, I always bring it back to the people, and really the, people, that we’re trying to serve.
I think my background in, in serving all different types of organizations and all different types of end users, has really shaped my ability to be flexible and dynamic in whatever sort of organization that I’m working with.
[00:05:47] Petra Rajkov: So being able to really relate to the users doesn’t matter the company you are working for, it’s the empathy that you develop.
[00:05:59] Angie Amlani: Absolutely, yeah. When you’re working in spaceswhere their user might be a refugee or a community without clean water or a donor trying to decidewhere their money can do the most good, which is where I started, you develop a really particular kind care for how you think about, the people that you’re serving or the product that, you’re working onis serving.
And I think, that sort of, like, deep ethnographic research is at the heart of what I love about UX research and I think that you can take that same type of care no matter the type of product that you are working on. And it’s actually really important, particularly now, when we’re working on AI-native projects.
[00:06:34] Petra Rajkov: Mm-hmm. When you are saying that you care, I can definitely relate because I started my, career in, support, and we were helping clients that had problems, but it was so fast – they came to us with a problem, and we were like, “Okay, solved, done, and let’s move on.” And I was missing this element of really understanding why they struggle and how we can help them more because I was caring about them.
It was just too quick for me. So then I moved to research, where we could really dig deep and understand the behavior and what’s the motivation behind that and I love it because you can really try to understand them and look at the products from their perspective with their eyes.
And I think that sense of caring, it’s so important for the researchers. If we don’t have that, then you are not able to answer the questions and read between the lines.
[00:07:27] Angie Amlani: I love that. And I, just to build on that, I feel that so much, and I think it’s such an important conversation to bubble back up to the surface of UX research right now because, so much of the conversation and rightly so, has been about optimizing. there’s, for folks particularly who work in-house, I know as well, there’s often a lot of pressure on making small tweaks that have big impact, and that’s great.but right now we’re working in a completely nascent new field.
And it’s moving so, so fast, and it is so easy to lose sight of the people at the heart of it, both in terms of what the product’s trying to do and the possible harm that the product could cause. And I’m a really big advocate right now for bringing deeply qualitative, deeply ethnographic, deeply story-driven research back to the heart of UXR and product, because without it, I think we will, we’ll go in a very dangerous and challenging direction that I think we’ve already started to go into in some different ways.
[00:08:26] Petra Rajkov: So let’s talk a little bit more about that because the speed is the factor everyone talks about right now AI is helping us in so many areas. It can help you start quicker. Some researchers are using AI for conducting research, then it is there for the synthesis analysis. But it’s not only in research, it’s everywhere. So how are you coping with this factor when we are researching AI products?
[00:08:55] Angie Amlani: Well, I think one of the benefits of being at an agency is speed is always a factor. Whether we’re working on an AI native product or not, just the nature of the type of work we do, you have to move fast. And Ito your question, speed And rigor often get framed as opposites, but I don’t think, they actually are.
What is opposed to speed is thoroughness, and rigor is about whether your method matches the decisions that the research is trying to support. But thoroughness is about how much ground you cover, and you can be rigorous and fast if you’re disciplined about scope, and you can’t be thorough and fast. But I think for us, the, first thing that we always do is narrow the decision space that we’re trying exist in, and this is particularly important for AI.
When a client says, “You know, we need to understand our users,” we push back and ask specifically what they’re going to do differently depending on what we find. and I think a tight decision lets us, run small, sharp study that actually move things forward and, often in two weeks in- instead of eight. I think, y- specifically in AI products, we are trying to slow the process down.and that feels maybe contradictory because we have AI tools now that us to move so much more quickly.
But we’re already as agency researchers in a really fast environment, and when we’re working on tools like Suno, for example, we’re introducing new paradigms to users, things that they haven’t seen before, ways of interacting with tools and products that are completely net new to them.
So there’s an adjustment period that a user has to go through, and that you have to go through as a researcher to really help them sit with something they’ve never seen before. And then there is the real sitting with, the possible harm that AI products can do in an interesting way, I think, we are our process down to do qualitative research on AI products but we have AI tools that enable us to speed up our synthesis. So we end up back in the same place, and I hate this debate about speed, because we can speed up parts of our workflow and that’s great. but answering the questions with users and really doing the sense-making is still where we need to spend our time.
[00:11:05] Petra Rajkov: So when you say sense-making, how does it look like in practice? So let’s say that you are working on a project. Where does it go different to a traditional way of doing research, and what role does sense-making play?
[00:11:18] Angie Amlani: Sense-making in traditional UX research is mostly about pattern recognition across systems and sessions, and you’re looking for where users what language they’re using, what mental models they bring. In AI native research in particular, sense-making gets harder because you’re triangulating across three moving surfaces at once. What the user did, what they thought about what the product did, and then what the product actually did, and those three things are constantly diverging. It’s a lot to pay attention to. If a user says it understood perfectly and then you look at the output and it got something wrong that they didn’t notice, there are a lot of implications for that.
So good sense-making, I think, in this space, starts with always pairing the user’s actual account of what they’re trying to do with the actual interaction. That’s really important, so a matching of one-to-one.
So for us, good sense-making comes down to a few principles, I’d say. The first is always ensuring that we’re pairing what the users say with what the system actually did. You know, the self-report alone, will mislead you in the space.
You miss errors, you blame the tool for things that it got right and vice versa. So that’s one thing, always sort of paying attention to what the system’s doing and, how the user is interpreting the system is super important. I think paying attention to… and this is a really interesting thing, the metaphors that users use when they’re interacting with AI native products, this is a really important part of the research.
How they describe what the product is is a really important part of the, sense-making and ultimately getting to good recommendations as an outcome. Are they thinking about it as a, a search engine, an assistant, a coworker? That predicts, to some extent, at least in, in my experience, how they’ll use it and when they’ll lose trust.
I think underneath all of that, there’s a real to, think about, how users are still forming mental models in, real time, and good names what we know, what’s directional, and what’s still an open question, and AI behavior is genuinely strange sometimes.
And users are still forming those mental models in real time, and so there is a need, I think, to be a little bit flexible and open in how we’re interpreting users’ interactions with AI products.
[00:14:25] Petra Rajkov: We talked about how the sense-making looks like in the traditional way of doing research and how does it look like when you are working on the AI native products. Is there anything else that is very different to the traditional way of doing research? And then on the other hand, is there something that really stays the same no matter what is going on in the world?
[00:14:47] Angie Amlani: Yeah, it’s such a good question. One of the things that I think is really different is that… I’ll give you a really good example. We’re working on a product right now that is, essentially, an agent that shops on behalf of a user, a real human. And so you use this product, the user interacts with the agent, the agent searches and scans a bunch of different types of products and indexes them in different ways.
And in this research? We have to not only consider what the user has experienced, but also what the agent is experiencing. So there’s the user to agent relationship, there’s the agent to product relationship, there’s the product to user relationship. That introduces a whole new variable to thinking about who we’re solving for.
So, I think, we have to be a lot more expansive in this type of research, and a lot more creative a lot of ways in how we’re designing studies, so there’s a lot that’s new there. What I think is the same, and you and I talked about this a little bit in our pre-session, is that I still very much believe that empathy is at the core of these experiences. We plan for research studies in the same way, we recruit for research studies in the same way. But the core and the essence of the research to me is the, same mean, there’s obviously so much else that’s different. You know, we’re not using static stimuli anymore.
Everything’s dynamic. We’re testing a lot more in dynamic environments and that requires sort of an adaptability and a flexibility to how you ask questions and how you adapt, But to me, the most important parts of research, I think, are stable.
[00:16:20] Petra Rajkov: Okay, so the core stays the same, just the way we do it, changes. And when we are talking about the way we do it, all of us are trying to find the right tools to use in our workflows and it’s inevitable that all of us will be using AI tools. Where do you yourself use AI tools?
[00:16:43] Angie Amlani: We use a lot. I would say the tool that I use the most and certainly that my team use the most is Cowork. We have our own sort of workflows and repositories that we use to create studies, to write mod guides that are all within, Cowork, but what I find myself using a lot now are tools, like Outreach, which I’m sure you’ve heard of um, maybe you haven’t, Outreach AI has really increased the surface area and breadth and, like if we talk about that sort of rigor and speed and thoroughness, the breadth of research that we can do in a really short amount of time.
So Outreach is really cool, it allows you to do qualitative research at scale basically.and the synthesis and pattern matching, that the tool can do for you is incredible. So you can, do 15 primary IDIs with users, and then if you want to scale and understand those impacts, you can put a study on Outreach, and you will get overnight, 50 to 100 video responses.
So not only can you see and view the user and the storytelling in real time, but you also get that really deep qualitative storytelling at scale, and you wake up in the morning and everything is analyzed in there for you.
[00:17:52] Petra Rajkov: So yeah, you are using all these AI tools, and the role of the researcher is evolving as well. So Are there skills that you see as required or really important to have as a researcher right now that maybe were not necessary five years?
[00:18:10] Angie Amlani: Yes, yeah. It’s such a good question. And, I think, probably my take is a little bit different because, again, I work in agency, so there are different skill sets that, that are required, in that space in general. But there are a few. The first I would say is narrative fluency. And what I mean by this is that, you know, there’s always been this debate about researchers not having space in the room. There’s always a product manager or a design director or somebody else with a really loud voice.
And what I am seeing in rooms where we’re discussing product right now is coming with a lovable prototype or an AI generated summary of how to solve a problem, and a researcher’s role in, that space now is super important. So being able to influence, being able to communicate and sort of navigate, a really opinionated landscape is super important. So there’s facilitation in there, and, I don’t mean just, like, running a workshop.
I mean the ability to hold a room through productive tension, and bring things back to the perspective of a user, to bring things back to the one or two problems that we’re trying to solve, and then have a really good plan for how to evaluate that. There’s hard skills and soft skills there, and I think that’s really important.
The second thing I think is important, particularly in the type of work that we do, is systems thinking. So the ability to really zoom out from the individual user interaction and see the broader cultural, market, technology sort of forces at play. All of those things are super important, and I think folks who can operate at different levels of their sort of thinking fidelity are going to be really important because researchers who can only operate at the level of a task or the journey map are going to find that scope is narrowing.
And the strategic questions that we’re trying to answer researchers are gonna get bigger and bigger, and if you can’t actually sort of be able to zoom in and zoom out and consider all of the different dynamics at play in making a particular insight compelling or a decision important, I think we’ll be at a really big disadvantage.
So that’s something that I look for in the folks that I hire and then on our team. Can you think at multiple orders of magnitude, and be able to zoom in and zoom out where you need?
[00:20:20] Petra Rajkov: You were talking about these, uh, strong voices in the room, right? You have the head of product, you have the designer, you have the product teams or developers that are waiting for the outcomes. For yourself, you don’t have a specific user base, you don’t have a specific problem.
So then when you are creating this report, How are you framing those insights? I understand that you have to take into consideration the business objectives. You have to use the right language. Is that, then different how you communicate with the stakeholders or how you work with stakeholders?
[00:20:53] Angie Amlani: 100%. And this is where I think maybe agency researchers are different from in-house. Like, influence is incredibly important. the ability to tell a story, the ability to shape insights in a way that feels compelling and worthwhile for stakeholders is, incredibly, incredibly important.
We’re lucky, at Metalab that we are an agency that’s filled with some of the best sort of multi and interdisciplinary thinkers, particularly in design, that I’ve ever worked with. So when we’re putting together a research report or a readout, it’s often paired with a story that’s told through design, and that is just a, I think, a really compelling way to communicate.
But this is where I think thoroughness is really important. People need to be able to stand behind their findings. And, I think AI has actually been incredible for us in our workflow because we can do so much more in the same amount of time, and we can triangulate our findings in a way that we never used to be able to with the speed at which we are working.
And if you have folks on your team who are really strong communicators who can tell a story that’s hidden in the data, it’s more compelling. So think about it from a number of different levels in our work telling that not just through words but through design and through an actual interaction and an actual sort of prototype, what an outcome could be.
Being a great storyteller and also having been really thorough in how you’ve approached the problem. So all of those things are really important, I think, in the work that we do here at an agency. Yeah.
[00:22:25] Petra Rajkov: And when you joined Metal Lab, was there something what you expected it to be? Like, did you expect the work being different, and did it hold up, or?
[00:22:35] Angie Amlani: It was a lot more similar than I thought it would be to the place that I was before. And I … think that’s because, Metalab is such an interdisciplinary place. Everybody seems to speak each other’s language, and folks that work here are really senior in what they do, but there’s like a cultural thread that’s tied across Metalab, which is that everybody’s expected to be a great communicator, and to be able to dip into other people’s lanes.
And so when I joined, you know, Metalab had a really well-established research practice. But It felt like I’d been here forever, because people don’t really work in silos. And the way that you tackle problems and sort of like digital strategy is actually, when you come back to the fundamentals, not that much different from product. And, so for me, the jump was a lot easier than I thought it would be because I moved from being a content strategist and a digital strategist to being a product strategist and UX researcher.
But all of the problems are ultimately human problems, and human and technology problems, and so you’re solving the same types of things in just a slightly different way.
[00:23:38] Petra Rajkov: So what I am hearing from you is that the empathy is still at core of no matter where you do the research. And also in this era of AI, when the speed is such a strong factor, everyone is expecting to build and ship fast, you still can do it rigorously. You don’t have to lose the quality if you are doing it right, and if you are detailed-oriented and you are holding all the guidelines.
Like, you cannot use AI to cheat yourself out of doing the good job.
[00:24:12] Angie Amlani: No, no, at all. Not at all.
[00:24:14] Petra Rajkov: Mm-hmm. is there anything else that you noticed when you are working on the AI-native products, researching AI-native products?
[00:24:23] Angie Amlani: Trust is so important. I mean, this is something that, I know previous guests on your show have talked about, and it’s certainly a big, conversation in, the space. But people come to AI-native tools with such wildly different mental models. it’s actually really hard to predict. and so everybody is starting at such a different point. And trust breaks down so, so quickly. Folks on my team and folks doing UXR here, I think, really keep that center as a big part of their focus when they’re doing the work.
They’re watching really closely the different moments when, trust starts to break down in the product. And I think it’s just become this almost like you have design principles, it’s this research principle that we have now when we’re working on AI products because they’re inherently hard to predict. and understanding how you can reinforce trust throughout a user’s journey with an AI product is one of the most important things that our designers are designing for, and thus our researchers are trying to understand. So the salience of that as sort of a research principle, that we have now on our team is really so important
[00:25:31] Petra Rajkov: Mm-hmm, yeah, so I can see that when we are developing features in UXtweak and we are talking about AI, we can see how users react to having AI there, and I think trust is such a big thing there. Like, to be transparent, to let the user know what is going on, and to be truthful is the way you build the relationship.
So I love that you said that and you shared that, and at the beginning, you also mentioned ethics in AI. Is that something that you have to really keep in mind strongly when you are researching an ethical product? Are there some risks that you define at the beginning, or, how do navigate that?
[00:26:11] Angie Amlani: I mean, it’s obviously sits on top of everything we already do as researchers. There’s informed consent, participant wellbeing, data protection, all of those things are so important. But when we run sessions on AI products, participants are often generating prompts, they’re uploading content, they’re sharing screens, and that data can end up in a model training pipelines itself if we’re not careful about how the study is set up.
So part of our job upfront Is getting really clear with the client about what happens to, session data, being transparent with participants in a way that they can actually understand. Know if a participant uploads something personal into a tool during a session they should know where that goes.
I also think, users in AI studies don’t know, what the system can and can’t do. What the data is actually drawing on or whether a human is in the loop somewhere. So we have to think really carefully, in our about how much to disclose upfront. Too much, can contaminate the behavior we’re trying to observe, and too little consent isn’t really informed.
And then obviously, the thing that, this isn’t unique to AI research, but, we think a lot about representation and who gets studied. AI products tend to work better for some users than others; language, accent, disability, cultural context, when we’re testing with voice, all of those things are so important, and we push clients really hard on sampling maybe more than we used to, even when it costs more or potentially takes longer.
That’s an ethics issue just as much as, as a methodological one. I’m trying to think, maybe one other piece is that, there’s the ethics in working with AI products, but obviously we also use AI in our research process. So if we’re using AI to transcribe or cluster or summarize, participants have a right to know that.
So that’s a part of our processes as well now to include that in our consent form. We’ve sort of built that type of disclosure into our standard documentation. So yeah, I would say underneath all of this, ethics are super, super important and when I was talking about the importance of slowing the process down, now that we’re working on these tools, that this is a big part of that.
[00:28:14] Petra Rajkov: Well, thank you for letting us look under the hood. Just to wrap it up and, to have, like, one key takeaway or one thing that our listeners could really keep in mind when they are researching and working on exciting projects, what would it be? What would be the one advice that you have for our geekers?
[00:28:36] Angie Amlani: Well, I mean, you said this, which is that the identity of researchers is changing. We’re not just, folks conducting research and synthesizing and producing insights, we’re also builders. And for me, I think about that as there’s a real for researchers to be creatives. Guest on the show talked about, you know, how vibe coding is changing the product development life cycle, but it’s also changing the opportunity that researchers have to take the next step with their insights.
We can visualize a journey map now. We can make the stories that we’re telling in research far more compelling with some of the tools… so I think the piece of advice that I would have is to see yourself really lean into the opportunities that AI tools offer you to tell your user stories better, to make the data shine and stand out and tell a story and sort of leap off the page in ways that we’ve never been able to.
So I would encourage folks to experiment, and not be afraid of the tooling.
[00:29:32] Petra Rajkov: Perfect, well, thank you so much, Angie. We actually ended the last episode with the same advice: go in, experiment, and have fun! So it’s a good advice, apparently, and I love that. So thank you so much, Angie, for being here, for talking with us, and sharing all your wisdom. And, yeah, exciting stuff ahead!
[00:29:55] Angie Amlani: So happy to be here. Thank you for having me.
[00:30:00] Petra Rajkov: Thank you for listening to UXR Geeks. If you enjoyed this episode, please follow our podcast and share it with your friends and colleagues. Your support is really what keeps us going.
If you have any tips on fantastic speakers from across the globe, feedback, or any questions, we would love to hear from you, so reach out to geekspodcast@uxtweak.com.
Special thanks goes to my colleagues, to our podcast producer, Ekaterina Novikova, our social media specialist, Daria Krasovskaya, and our audio specialist, Melissa Danisova.
And to all of you, thank you for tuning in.
💡 This podcast was brought to you by UXtweak, an all-in-one UX research tool.
