Lidiia Suslova

I’ve spent more than 15 years designing digital products - mostly fintech, SaaS, AI-powered platforms, and large operational systems. I actually started from programming when I was a kid, but very quickly became more interested in how people interact with systems than in the code itself. Even during school programming competitions I was spending more time thinking about the interface than the technical task. Which was probably not the intended goal, but apparently the direction was already obvious. Over time that evolved into product and UX design, especially systems with a lot of complexity underneath them. The bigger the system became, the more interesting the work got for me.

I work independently with product teams, mostly on fintech and SaaS products. A lot of my work today sits somewhere between UX, systems thinking, AI workflows, and design infrastructure. I’m usually involved not just in screens, but in the logic underneath them - flows, architecture, design systems, operational behavior, and how AI fits into real product workflows without making everything chaotic. Most projects I work on are fairly complex environments where usability directly affects business outcomes.

For me, design is making complicated things understandable. Not oversimplified - understandable. Good design helps people move through systems with less friction, less confusion, and more confidence. Especially in products where people are already under pressure or dealing with a lot of information. I think design is much closer to problem-solving and communication than decoration.

Complex systems. Fintech, enterprise SaaS, AI-powered products, Voice of Customer platforms, analytics systems - anything where there’s a lot happening underneath and the challenge is making it usable for normal humans. I especially enjoy products where people need to make decisions quickly and clearly. That creates interesting UX problems because the design has to reduce cognitive load instead of adding to it.

Probably the work I did in fintech during the pandemic, helping build several neobank products from zero to one. That experience changed how I think about trust, onboarding, and responsibility in product design. When someone uses a banking product for the first time, small UX decisions suddenly matter a lot more than they seem. What stayed with me most wasn’t visual design. It was the realization that interfaces genuinely affect how safe, confident, or stressed people feel while making important decisions.

A large crowdsourced SaaS platform with multiple user roles, multilingual support, payments, and a pretty complicated operational structure underneath. That project taught me something very early: the real challenge is rarely the screen itself - it’s keeping the whole system coherent while it grows. I think that project shaped a lot of how I still approach design today. I started by analyzing user behavior and pain points, then built a structured UX flow to streamline contest creation, designer participation, and winner selection. The goal was to make the process effortless while ensuring clarity at every step. Once the UX foundation was set, I designed a modern, intuitive UI that enhanced usability and engagement. This project was a deep dive into multi-language, multi-currency systems, country-specific payment options, and high-performance design — a real test of scalability and precision. After 20,000+ development hours of a team, the result was a 98% client satisfaction rate, solidifying the platform as one of the most effective ways to get custom designs online.

Right now probably AI-assisted design workflows. I work mostly in Figma, Cursor, Claude, Figma Make, Storybook, Notion, and different AI-supported systems for design pipelines and documentation. The interesting part for me isn’t AI image generation or visual experiments. It’s using AI to accelerate operational design work while still maintaining coherence across large systems. That’s a much harder problem than people think.

Usually while walking somewhere quiet with trees around me. A surprising number of design problems solve themselves outside the screen. Especially structural problems. I think constant screen exposure compresses thinking a bit. Walking helps restore perspective and lets the system reorganize itself mentally.

Structure first. Flows, user behavior, information hierarchy, edge cases, permissions, states, what happens when something fails - all of that matters much more to me initially than visual polish. Once the structure works, visual decisions become much easier and much more consistent.

Honestly, the most exciting phase is usually the messy middle. The moment where the system is still unclear, everything feels slightly chaotic, and you’re trying to figure out how all the parts connect together. That process is stressful sometimes, but also the most intellectually satisfying part of design for me.

Relief, mostly. Especially when something ships and actually works under real conditions, not just in presentations. One of my favorite moments is when developers receive a handoff and almost don’t need clarification questions because the logic is already clear inside the design itself. That’s always satisfying.

If people understand it and can use it confidently. That still feels like the core metric to me. Good design reduces confusion, supports decisions, and helps people accomplish what they came to do without unnecessary friction. If the interface constantly forces users to stop and think “wait, what is happening here?” then something failed structurally.

Clarity first. Can users immediately understand where they are, what matters, and what they should do next? Then consistency. Especially in larger systems. A lot of products look good visually but start breaking apart once you move into edge cases, operational complexity, or real-world usage. That’s usually where good systems thinking becomes visible.

I think designers have a responsibility not to manipulate people unnecessarily. Especially now, when products are increasingly optimized around attention, behavioral pressure, or dark patterns. In AI systems specifically, designers also shape who can comfortably use these tools and who gets excluded by complexity. That’s becoming a much bigger responsibility than people realize.

The field is shifting away from static screens and toward systems, behavior, and AI-assisted interactions. Products are becoming more dynamic, adaptive, and probabilistic. Interfaces increasingly react to context, user behavior, and AI-generated outputs in real time. That changes the designer’s role quite a lot. I think systems thinking and operational thinking are becoming much more important than pure visual execution.

Most of my work exists inside live digital products rather than traditional exhibitions. I usually share projects through online platforms, articles, and case studies focused on UX, AI workflows, systems thinking, and large-scale product design. I regularly publish design work on Dribbble - dribbble.com/cls and Behance - behance.net/clrscr I also write about UX, AI-assisted design workflows, and product thinking on Medium - medium.com/@lidacls

Mostly from observing real users and real operational environments. I’m much more inspired by watching how people struggle with systems than by design trends. I also read a lot outside design - psychology, behavioral science, systems thinking, biographies. Those areas influence my work more than visual references most of the time. And honestly, nature helps a lot too.

Minimal, structural, and functional. I don’t like decorative complexity very much. I prefer interfaces where visual decisions have a clear reason behind them. In complex systems, simplicity becomes less about aesthetics and more about reducing cognitive load. That naturally shaped my style over time.

Definitely. I’m Ukrainian and currently based in Baku, Azerbaijan, and I’ve worked across different regions including Eastern Europe, Southeast Asia, the Caucasus, and MENA. Working across different cultures changes how you think about users very quickly. You realize that ideas like “intuitive” or “clear” are much more culturally dependent than many designers assume. That experience made me much more careful about designing from assumptions.

Very collaboratively. I usually work closely with product, engineering, and research teams because complex systems can’t really be designed in isolation. I also stay involved beyond visual design itself - into workflows, system logic, design systems, AI integration, and implementation discussions. The goal is always reducing the gap between the design idea and the actual built product.

Hire designers who ask questions about users, workflows, and system behavior before discussing visual style. Good product design decisions usually come from understanding the operational reality of the product, not from trends or aesthetics alone. And honestly, companies should involve designers earlier. A lot of UX problems become expensive simply because design enters the process too late.

Usually it starts with research and system understanding. I try to understand: who the users are, what decisions they need to make, where friction appears, what operational pressure exists, and where the current logic breaks. After that I move into flows, architecture, and structure before visual refinement begins. AI tools help accelerate parts of the workflow now, but the structural thinking still has to happen first.

Probably my notebook, Kindle, headphones, ergonomic chair, and sketchbooks. Most of them are less about aesthetics and more about supporting deep focus and thinking clearly for long periods.

Morning walk first. Then usually a deep work block for the hardest design or systems problems before meetings start. The rest of the day depends on the project: research, flows, reviews, AI workflow experiments, team discussions, design system work, or implementation coordination. The best days are usually the ones where something suddenly clicks structurally.

Learn systems thinking early. A lot of junior designers focus mostly on visuals and trends, but real product work quickly becomes about logic, structure, tradeoffs, communication, and implementation. Also: learn how AI tools fail, not only how they impress people in demos. And learn to explain your decisions clearly. That skill becomes incredibly important later.

The positive side is that the work stays intellectually interesting. Technology changes constantly, products evolve, and there’s always a new layer of complexity to solve. The difficult part is that design is still underestimated in many business environments until problems become expensive enough to notice. You often have to prove the value of clarity after the absence of clarity already caused damage.

Design for clarity - if people don’t understand it, it doesn’t work. That rule survives almost every design discussion.

Systems thinking, communication, adaptability, research skills, and the ability to stay calm inside ambiguity. And increasingly: understanding AI workflows realistically, not just aesthetically.

Mostly Figma, Cursor, Claude, Figma Make, Storybook, Notion, and Miro. But honestly, tools change constantly now. The more important thing is whether the underlying thinking is solid enough for the tools to scale effectively.

Protecting uninterrupted focus became very important for me. Complex systems work degrades quickly under constant context switching, so I try to keep dedicated deep work time every day before communication-heavy tasks start. AI tools also help compress execution time significantly now, especially for iteration and documentation.

Depends entirely on complexity. A simple product flow can take days. Large enterprise systems with multiple roles, AI integration, and adaptive behavior can evolve for months. The interesting thing is that visual execution is often not the slowest part anymore. Structural decisions are.

“Can we simplify this?” Which is usually the correct question, honestly. But sometimes the harder conversation is understanding whether the interface is too complicated or whether the underlying system itself is too complicated. Those are very different problems.

Probably fintech during the pandemic. Building products under uncertainty, with real operational pressure and real user trust involved, changed my standards permanently. You learn very quickly that small UX decisions can have very large consequences.

Mostly fintech, SaaS, enterprise software, AI analytics, and Voice of Customer products across different regions. The common thread is usually complexity rather than industry itself.

AI-powered systems and large operational platforms. Especially environments where humans and AI are effectively collaborating inside the same workflow. That space still feels very new, very messy, and full of unsolved design problems - which makes it interesting.

Going deeper into AI design systems, operational UX, and AI-assisted product workflows. I’m especially interested in how design systems evolve when AI becomes part of the execution layer itself. There’s still a lot the industry hasn’t figured out yet there.

Both. Some phases require uninterrupted individual thinking, especially structural work. But strong collaboration with engineering, research, and product teams is essential for complex systems. The best products are rarely created in isolation.

Mostly large-scale AI-assisted systems, fintech infrastructure, Voice of Customer platforms, and design pipeline experimentation. A lot of the current work is focused less on individual screens and more on maintaining coherence across large evolving systems.

The easiest way to reach me is through my portfolios on Dribbble - dribbble.com/cls or Behance - behance.net/clrscr where I also share selected projects and design work.

Probably just this: AI is changing design workflows very quickly, but strong systems thinking matters more now, not less. Generation is becoming easier. Coherence is becoming harder. And I think that difference will define the next generation of product design work.

I’ve spent most of my career working on complex digital products across fintech, SaaS, and AI-assisted platforms. Early on, I worked on large-scale marketplace and SaaS systems, including DesignContest. That experience taught me very quickly that the difficult part of product design is rarely the interface itself. It’s the structure underneath - flows, permissions, edge cases, states, and how the system behaves once real users start pushing against it. Later I moved deeper into fintech, helping build several neobank products from zero to one during the pandemic years. Those environments were fast-moving, ambiguous, and operationally intense, which shaped how I think about trust, onboarding, clarity, and user decision-making under pressure. More recently, I led UX work for Ender Turing, a real-time conversation analytics platform operating across 24 languages. The interesting challenge there wasn’t the AI itself - it was helping supervisors process huge amounts of information quickly enough to make useful decisions in high-pressure environments. That project received a Bronze A’ Design Award in interface, interaction, and user experience design. In parallel, I’ve been working at increasing system scale - including a Voice of Customer platform for a large enterprise client in the MENA region, where I helped build the design infrastructure supporting scalable LTR/RTL adaptation across themes, breakpoints, and complex enterprise workflows. The project eventually exceeded 2.3 million layers inside a single Figma environment. Separately, I led the design and delivery of a fintech SaaS platform from zero to one in roughly six weeks using AI-assisted workflows. What interested me there wasn’t speed alone, but figuring out how to keep the product coherent across dozens of iterations without the system drifting into inconsistency. Over the last few years, my work has shifted less toward individual screens and more toward systems that can survive complexity, scale, and constant change.

It actually started with programming. I was around nine years old when I first started learning it, and during one of my early school competitions I spent more time making the interface look good than solving the actual assignment properly. Which, academically, was not a great strategy. But even back then I was much more interested in how humans interact with systems than in the code itself. What pulled me into design wasn’t aesthetics as much as communication. Interfaces shape behavior. They influence trust, stress, attention, and decision-making. That became interesting to me very early. I also naturally gravitated toward structured products - systems, flows, logic, edge cases. I never really saw design as decoration. For me it was always a problem-solving discipline with a human layer attached to it.

The biggest priority is clarity. If users don’t understand something, it doesn’t work. Most product problems I’ve seen over the years were actually clarity problems, not visual ones. Before designing anything visually, I usually need to understand the system underneath it: * how decisions flow * where complexity appears * what happens when things break * how different user roles interact * where edge cases start affecting the experience Especially now, with AI-assisted workflows becoming more common, structure matters even more. AI tools are very good at accelerating execution inside a well-defined system. Inside a messy system, they mostly accelerate confusion. I also think a lot about implementation. One of the best signals for me is when developers receive a handoff and barely ask clarification questions. That usually means the logic is already embedded into the product itself instead of living in meetings or Slack threads. Tool-wise I work mostly in Figma, increasingly with tools like Figma Make, Claude, Cursor, and AI-assisted workflows in general. But tools change constantly. The thinking behind them matters much more.

Probably relief more than excitement. The phase I enjoy most is actually the messy middle - when the system still doesn’t fully make sense yet and you’re trying to hold a lot of ambiguity together at once. I like the moment when things start stabilizing. The flows connect. The logic stops collapsing under edge cases. The product begins behaving predictably instead of fighting itself. That’s the satisfying part for me. I’m also much less emotionally attached to visual polish than many designers are. I appreciate strong visual work, obviously, but what interests me more is whether the system survives real usage without creating confusion or operational friction. The strongest feeling usually comes much later, when something ships and quietly becomes part of people’s everyday lives. Especially in fintech, where small UX decisions can directly affect trust and confidence, that responsibility feels very real.

Technical thinking shaped me a lot. I naturally approach products by asking questions like: - where does the logic break? - what happens under pressure? - how do different parts affect each other? - what fails once the product scales? That mindset pushed me toward complex systems very early. Another big influence was working in environments with incomplete information. Small teams, changing priorities, evolving products, constant ambiguity. You learn to make decisions without waiting for perfect clarity. And finally, working across different regions changed how I think about users. I’ve worked with products across Eastern Europe, Southeast Asia, the Caucasus, and MENA markets. Things people consider “intuitive” are often deeply cultural. That experience made me much more careful about assumptions.

Right now I’m very interested in how AI changes product delivery workflows - not just generation, but reliability. A lot of the industry conversation is still focused on producing screens faster. What became much more interesting to me recently is everything that happens after generation: keeping systems consistent across iterations, managing instability, reducing ambiguity, and preventing products from slowly drifting into chaos during rapid execution cycles. I’m especially interested in workflows where design systems stop behaving like static UI libraries and start acting more like operational infrastructure. As for dream projects - honestly, I’m less interested in “visionary” products and more interested in difficult ones. Products with real operational constraints, real scale, and real consequences when things go wrong.

Learn to think beyond screens as early as possible. A lot of junior designers focus heavily on visuals, animations, polished case studies, and trends. But most real product work eventually becomes about decision-making, tradeoffs, implementation, and communication. The second thing is learning how systems fail, not just how demos work. Production environments are messy. You deal with edge cases, unstable behavior, unclear requirements, iteration drift, and technical limitations all the time. And finally - communicate clearly. The ability to explain why something exists is one of the most underrated skills in product design.

Treat your design system seriously. Not as a UI kit, but as infrastructure. One thing I noticed recently while working with AI-assisted workflows is that generation is rarely the real bottleneck anymore. Consistency is. It’s relatively easy to generate a screen. It’s much harder to keep a product logically coherent after dozens or hundreds of iterations without slowly introducing contradictions into the system. The more structured the foundation is, the easier it becomes to scale products, reduce ambiguity, improve handoff quality, and work effectively with AI tools without losing control of the product itself. That changed how I think about design systems completely.

Morning walks help a lot. Ideally somewhere quiet with trees and not too much noise. A surprising amount of product thinking happens there instead of at the desk. After that I usually try to protect a few hours for deep work - complex flows, reviews, system decisions, AI workflow experiments, or anything that requires uninterrupted focus. The rest of the day depends on the project. Sometimes it’s product work in Figma. Sometimes reviewing AI-generated outputs and fixing inconsistencies. Sometimes debugging workflows after an agent decides to reinterpret half the product because of one poorly scoped prompt. That happens more often than people think.

I pay much more attention to workflow shifts than visual trends. Right now the interesting changes are operational. Tools like Figma Make, Cursor, Claude Code, and AI agents inside design environments are changing how products get built, not just how they look. At the same time, I still think fundamentals matter more than trends. Clear structure, understandable behavior, good information hierarchy, and usability age much better than visual fashion. For inspiration, I usually look at products operating under real constraints - fintech, logistics, healthcare, enterprise systems. In those environments, bad design decisions become visible very quickly.

If users understand it. That’s still the core metric for me. Good design reduces confusion and helps people make decisions with less friction and less cognitive load. I also pay close attention to whether the product remains understandable once real complexity appears - edge cases, interruptions, overloaded states, operational pressure, unusual user behavior. A system can look polished in a demo and still completely fall apart in real usage. And implementation quality matters a lot to me. A beautiful prototype that collapses during delivery isn’t successful design. Real design survives contact with production.

The strongest signal is usually developer reaction. If a handoff produces endless clarification loops, something is probably missing structurally. The best projects are the ones where developers can move confidently because the flows, logic, behaviors, and edge cases are already clearly defined inside the design itself. I also tend to test products mentally against messy conditions - weak connections, interruptions, overloaded dashboards, confused users, situations where people are stressed or in a hurry. Products rarely fail in ideal conditions. They fail in edge cases.

Probably two types of work stand out for different reasons. The first is fintech. Helping build several neobank products from zero to one during the pandemic taught me a lot about ambiguity, operational pressure, onboarding, trust, and how much responsibility sits inside seemingly small UX decisions. The second is large-scale Voice of Customer and SaaS analytics platforms. Those projects pushed me much deeper into data visualization, adaptive design, design systems, and system-level thinking at scale. The difficult part wasn’t just handling large amounts of data. It was making constantly changing information understandable and usable for people making fast decisions under pressure. Once you’re dealing with thousands of signals, filters, multilingual workflows, responsive states, and large operational ecosystems, the problem stops being “screen design” very quickly. A big part of the work became building systems that could remain coherent as complexity kept growing - across RTL and LTR behavior, adaptive layouts, responsive logic, evolving workflows, and large-scale design systems. Some of those products eventually grew to millions of layers inside a single Figma environment, which completely changes how you think about structure, consistency, and delivery. That work also received a Bronze A’ Design Award in interface, interaction, and user experience design.

Dieter Rams. Mostly because his work was disciplined. A lot of his principles are really about restraint - removing unnecessary complexity instead of adding more. I also admire teams behind products like Stripe, Deel, and Revolut. Not because they’re visually trendy, but because they make complicated systems feel manageable without oversimplifying them. That’s very difficult to do well.

I’m Ukrainian and currently based in Baku. Living and working across different regions changed how I think about products quite a lot. Different cultures have very different expectations around trust, communication, onboarding, hierarchy, even visual density. Outside work, I genuinely need nature to think clearly. A walk somewhere quiet usually solves more design problems for me than staring at a screen for another three hours. Also - I probably spend an unhealthy amount of time stress-testing AI workflows just to see where they break.

I like working with people who are direct, collaborative, and comfortable with ambiguity. Fast product environments are rarely clean or perfectly organized. Good teams are usually the ones that can keep moving while the system is still being figured out. I also care a lot about reducing friction between design and development. One of my favorite moments in a project is when engineering barely needs clarification because the structure is already clear. That requires much more than attractive screens. It requires consistency, edge-case thinking, realistic implementation awareness, and clear decision-making. I think design works best when it stays connected to reality instead of becoming presentation material.

Earlier in my career I worked on educational products and tools that helped young designers develop UX and visual communication skills. More recently, I’ve been sharing practical knowledge publicly around AI-assisted workflows, prompt structure, design systems, and operational challenges inside modern product design. I try to keep those conversations grounded in real experience rather than hype. There’s already plenty of abstract discussion around AI. What people often need more is practical information about what actually breaks, what scales, what saves time, and where the real bottlenecks appear. If sharing that helps someone avoid wasting weeks on bad workflows, that’s useful.

The most valuable part was probably articulation. Award submissions force you to explain your work clearly - what problem existed, why certain decisions were made, what constraints shaped the outcome, and what impact the system actually had. That process is useful by itself. The recognition for Ender Turingwas especially meaningful because the project was operationally complex, not just visually polished. It dealt with real-time information, large-scale conversation analysis, and people making decisions under pressure. I also appreciate the international visibility that award platforms create. Complex B2B and infrastructure-oriented design work often stays invisible outside the companies using it. Awards help bring some of that work into broader conversations around where product design is heading.

I’ve spent most of my career working on complex digital products across fintech, SaaS, and AI-assisted platforms. Early in my career I worked on large-scale marketplace and SaaS systems, which pushed me pretty quickly toward system-level thinking - flows, permissions, edge cases, responsive behavior, and information architecture. Over time the products became more operationally complex: fintech platforms, multilingual enterprise systems, Voice of Customer products, and AI-powered analytics tools working with large volumes of real-time data. A big part of my recent work has been around scalable product systems, adaptive design, and AI-assisted delivery workflows. One of the enterprise platforms I worked on eventually exceeded 2 million layers inside a single Figma environment, which completely changed how I think about consistency and structure at scale.

It actually started with programming when I was a child. Very quickly I realized I was less interested in the code itself and much more interested in how humans interact with systems. Why some interfaces feel stressful. Why others feel obvious. Why people trust one product and abandon another. What still motivates me now is the moment when something genuinely complex becomes understandable without oversimplifying it. That never gets boring.

Completely chose it. I’ve always had a mix of technical thinking and strong curiosity about human behavior, and design ended up sitting exactly between those two things. I never really saw design as decoration. For me it was always about making systems easier to navigate and decisions easier to make.

Mostly complex product systems - fintech, enterprise SaaS, AI-assisted platforms, analytics products. A lot of my recent work involves large operational systems with multiple user roles, adaptive behavior, large amounts of data, and constantly evolving product logic. What interests me most now are products where AI is deeply integrated into the workflow itself, not just added as a feature afterward.

Learn to think beyond screens as early as possible. Visual skills matter, obviously. But real product work becomes much more about structure, communication, tradeoffs, implementation, and understanding how systems behave once real users start interacting with them. And honestly - learn how things fail, not just how demos look.

A good designer can make a screen usable and visually coherent. A great designer can make an entire system stay coherent once complexity starts increasing - multiple states, user roles, edge cases, changing requirements, responsive behavior, AI-generated outputs. That’s where the real difficulty usually begins.

If users understand it. That’s still the core metric for me. Good design reduces confusion and cognitive load. It helps people move through a system confidently instead of constantly stopping to interpret what’s happening. I also pay a lot of attention to consistency. Especially in large systems, small inconsistencies create friction surprisingly fast.

Good design reduces friction in places where friction becomes expensive. In fintech it affects trust. In SaaS it affects adoption and operational efficiency. In AI systems it often determines whether people actually use the product confidently or feel overwhelmed by it. Design is not just presentation. It shapes how understandable the product becomes in everyday use.

Probably something much more personal than the products I usually work on. Maybe an interactive biographical experience or a narrative-driven digital project where people move through someone’s life instead of simply reading about it. Something slower and more emotional than enterprise software for once.

A truly AI-native product designed from the beginning around collaboration between humans and AI systems. Not AI added later as a feature, but a product where the interaction model itself is built around shared decision-making and adaptive behavior from day one. I think we’re still very early there.

Probably structure before visuals. I usually start by understanding: what decisions users need to make, where complexity appears, what happens under stress, where confusion is likely to happen. Once the structure becomes clear, the visual layer tends to become much stronger naturally.

Dieter Rams definitely. I also admire teams behind products like Stripe, Linear, and Revolut because they consistently make complicated systems feel manageable without oversimplifying them. And honestly, I draw a lot of inspiration from fields outside design too - psychology, systems thinking, behavioral science, even operational workflows.

I usually admire products that make complexity feel calm. Good enterprise systems. Strong information architecture. Interfaces where every element feels intentional and nothing exists only for decoration. I especially appreciate restraint in design. Removing unnecessary complexity is much harder than adding more features or visuals.

Probably two types of work stand out for different reasons. The first is fintech. Helping build several neobank products during their early zero-to-one stages taught me a lot about trust, onboarding, ambiguity, and designing for people making important decisions under real constraints. The second is large-scale Voice of Customer and SaaS analytics platforms. Those projects pushed me much deeper into data visualization, adaptive design, design systems, and system-level thinking. The challenge there was not individual screens anymore, but helping people navigate large amounts of information without losing clarity. One of those projects later received international recognition in interface and UX design, which was meaningful mostly because the work itself was operationally difficult, not just visually polished.

Stay close to real problems. A lot of growth comes from seeing where products actually break - confusing flows, bad handoffs, unclear systems, failed assumptions, operational chaos. I also think writing and communication skills are extremely underrated in design. The ability to explain your reasoning clearly changes everything.

Probably something connected to storytelling, psychology, or research. I’ve always been interested in how people interpret systems, narratives, and behavior patterns. That instinct exists underneath design too.

For me, design is problem-solving through structure and empathy. Empathy helps you understand what actually matters to users. Structure helps you build something that continues working once complexity increases. Both are necessary.

My mother had a huge influence on me. She’s a scientist and educator, and I think a lot of my analytical thinking comes from that environment. Beyond that, honestly - difficult projects and strong teams shaped me more than anything else. Real production environments teach very quickly.