Vitaly Friedman

Smashing Media AG

Creator of the video library on “How To Measure UX and Design Impact”, with practical tips around UX and interface design, along with a live UX training taking place twice a year.

Video Course + Live UX Training

Creator of the video library on “Smart Interface Design Patterns”, with practical tips around UX and interface design, along with a live UX training taking place twice a year.

UX consultant and UX lead working with mid-size and large organizations on improving clarity, accessibility, performance and conversion of digital interfaces. Worked with: European Parliament, OTTO, Zalando, Marc O'Polo, Axel Springer SE, REWE Digital and many others.

UX Lead, Senior UX Designer/Consultant

Smashing Conferences

A curator of Smashing Conferences, friendly and inclusive in-person and online events for designers and developers who design and build the web. Taking place in San Francisco, New York, Freiburg, Antwerpen and Barcelona.

Curator, Creative Lead

Smashing Magazine

Vitaly Friedman, editor-in-chief of Smashing Magazine (www.smashingmagazine.com), an online magazine for professional designers and front-end engineers.

Editor-in-chief, Founder

Speaking with love and passion about the techniques and strategies that help designers and front-end engineers get their work done better, and faster.

Speaker

Freelance

Freelance Web Designer

As a UX Lead, I’ve been working with mid-size and large organizations for over 19 years, helping improve UX of complex systems, accessibility, performance and design of their digital experiences. Among many others, I’ve been working with the European Parliament, OTTO, Zalando, Marc O'Polo, Axel Springer SE, and REWE Digital.

I’m also the founder and editor-in-chief of SmashingMag, a popular online magazine and platform for designers and developers, where I help curate and organize friendly and inclusive conferences, such as SmashingConfs, and run video courses and live UX training (Smart Interface Design Patterns linked above).

I am incredibly passionate about writing, speaking, running workshops, and sharing what I’ve learned on UX. I love complex UX challenges, intricate processes, and difficult stakeholders, and my goal is to design digital experiences that people understand and can fall in love with.

If you are interested in working together, feel free to drop me an email at vitaly[@]smashingconf.com — I will do my best to help in every possible way!

UX Designer, Creative Lead, Front-End Performance Lead 

🧪 Useful Calculators For UX Research. Helpful tools and guides to estimate the right number of participants for surveys, card sorting and usability testing.

🤔 Testing isn’t about finding universal truths, but key blockers.
🚫 Scale ≠ clarity: we must know what we’re trying to learn first.
✅ Iterate with 5 people at a time: test, adjust, test again.
✅ Surveys: aim for confidence level 95%, margin of error 2–5%.
✅ With 10.000 users, you will need ≥567 answers to reduce bias.

✅ Assume the response rate of 20–30% (incl. no-show-rate).
✅ To get reliable survey results, we need to invite 2835 people.
✅ For card sorting, get 15–30+ people to sort independently.
✅ For tree testing, invite at least 25 (better: 50) participants.
✅ Nothing matters more than targeted and diverse sample.

I absolutely love Nikki Anderson’s point about big sample sizes often wrongfully viewed as the “safest” way to discover insights. They can’t fix vague goals, at times answer wrong or misleading questions, often skip the difficult part of framing the problem first. Scale sounds impressive, but it doesn’t map to clarity.

Nikki suggests to start with a decision of what we’re trying to learn first, then the question, then the method and then the sample size. But we must know — and be committed — what want to know first. And very often we don’t need statistically significant results to notice and act on critical blockers.

Research doesn’t have to be expensive or time-consuming. In the worst case, I start with 5×45 mins interviews to spot critical blockers and unmet user needs. As we run sessions, I mark critical areas and record short screen share snippets — with consent — and make them visible in the company. 

Once you’ve built enough confidence in the work that you are doing, it will be much easier to ask for bigger commitments — in fact, you might be surprised by how quickly your research work will be requested, rather than merely applied to design work.

---

Useful resources:

👥 Qualitative Sample Size Calculator, by UserInterviews
https://lnkd.in/enNhnjmZ

💸 Research Incentive Calculator
https://lnkd.in/dZim2YSq

🔘 Survey Sample Size Calculator
https://lnkd.in/e8htudk3

💯 Bonus: Design System ROI Calculator, by Knapsack
https://lnkd.in/eYCxBTGt

📱 UX Work ROI Calculator, by Paul Boag
https://lnkd.in/eAXtXWf5

🚀 UX Research Project Launch Kits
https://lnkd.in/eX2zt88x

🌲 UX Research Field Guide, by UserInterviews
https://lnkd.in/e4Ygsyuu

---

Useful articles:

How Many Participants For a UX Interview?, by Maria Rosala
https://lnkd.in/eAEq6amb

Sample-Size Recommendations, by Raluca Budiu, Kate Moran
https://lnkd.in/erRN2RsW

#ux #research

Practical insights for better UX • Running “Measure UX” and “Smart Interface Design Patterns” • Founder of SmashingMag • Speaker • Loves writing, checklists and running workshops on UX. 🍣

🔮 AI Interaction Design Patterns (https://www.shapeof.ai), a fantastic (!) living catalog of emerging design patterns, heuristics, anti-patterns and real-life examples that shape the experience of AI — from identifiers and wayfinding to prompts, tuners and trust indicators. Incredible project by incredible Emily Campbell. 👏🏼 👏🏽 👏🏾

AI experience can go way beyond a text box. One of the most underrated yet impactful patterns for AI interfaces is the ability to tune AI experiences. This could show itself as a style lenses or temperature knobs — little tools to help users generate a more personalized output easier. E.g. Risky ↔ Risk-averse, Sad ↔ Happy, Concrete ↔ Abstract, Creative ↔ Precise.

Instead of expecting large and highly detailed text prompts, we could slow people down when they prompt — e.g. with prompt constructors, prompt strength meters, presets or templates. Perhaps by defining an expected format, structure, personas, roles as checkboxes or chips — both for user input and AI responses (priming).

Another much-needed feature is scoping. Users should be able to quickly scope their inquiry to a particular domain, level of expertise, sources or even a set of videos or PDFs. We need pre-screening of sources, and proactive alignment with users. These are features that would make output much more specific without having to write a long prompt.

And: the AI output shouldn’t be bulky nor static. Users should be able to granularly iterate or revise little bits of it — e.g. by asking for sources of specific statements, or diverging from one view to another, or manipulating small parts of an image or a video. These refinements should happen not via text prompts, but contextually — acting on the relevant parts of AI outcome.

We can go way beyond a text prompt. Better results come from combining good old-fashioned design patterns such as search, filtering and sorting with AI — to first find relevant and trustworthy sources, and then generate insights from them. That’s a great way to boost accuracy and make AI more relevant to more people.

💎 Design Patterns For AI Interfaces

Prompt UX Patterns, by Sharang Sharma
https://lnkd.in/eCytfAe9

Where should AI sit in your UI?, by Sharang Sharma
https://lnkd.in/dyyMKuU9

AI UX Patterns, by Luke Bennis
https://lnkd.in/dF9AZeKZ

Design Patterns For Building Trust, by If
https://lnkd.in/eEJngtVv

AI Design Patterns Catalogue, by Maggie Appleton
https://lnkd.in/ebAp9Sb8

---

🚀 Fantastic AI Examples:

Elicit (research tables): https://elicit.com
Consensus (confidence levels): https://consensus.app/
Scispace (search + AI): https://scispace.com
v7 Labs (AI auto-fill): https://v7labs.com/
Exa (semantic grid): https://exa.ai
DeepL (translation): https://deepl.com
NotebookLM (scoping): https://notebooklm.google/

[continues in comments]

#ux #ai

🔬 How To Measure UX Research Impact (+ PDF) (https://lnkd.in/etNUJEtx), an updated framework to define and measure UX research impact across UX, business, organization, engagement, structure and reach. Wonderful work by Karin den Bouwmeester. 👏🏽

Teams often struggle showing the actual value of research, and it's often very difficult to attribute business impact to initial research initiatives. Impactful research uncovers insights that are rooted in urgent problems and severe bottlenecks.

The higher the urgency, the more visibility research initiatives will have. As Karin suggests, I would always start by exploring signals that something isn’t quite right and requires attention. 

For example:

– Significant drop-offs in the funnel
– Increased support tickets on a specific feature or flow
– Low engagement with a newly launched feature
– 2–4 star reviews citing UX issues
– High abandonment after onboarding
– Teams making assumptions without any data
– Workarounds or shortcuts reported by users
– Insights from sales and customer success teams
– Analytics showing repeated navigation loops

Karin suggests to measure UX research impact on 3 levels: Outcomes, Organizational Influence and Research Practice.

---

🔹 Level 1: Outcomes — What value does research create?

We are exploring the business value, user value and societal value that research delivers. The question is how the work helps reduce risk, increase retention and support user's goals while reducing harm or bias.

Example metrics:
📈 Order value
✅ Task success rate
❌ User errors
♿ Accessibility score

---

🔶 Level 2: Organizational Influence — Is research driving change?

Often research gets stuck due to poor interest or low influence. We study if research findings are picked up and acted upon. If they shape strategy and inform roadmaps, and how engaged are stakeholders when it comes to research.

Example metrics:
🧭 Product changes driven by research
📑 References to research in meetings/docs
📩 Number of research requests
👀 Number of observers in research sessions

---

♦️ Level 3: Research Practice — What are we doing and how?

Here the question is how deeply research is embedded in day-to-day operations and product development cycle. We study how much research is happening, and when, and if it's a driver or an afterthought.

Example metrics:
📊 Ratio discovery research / testing
📆 Studies per quarter/year
📁 Use of research templates

We don’t have to measure everything at once. But we can start by measuring something impactful. We always try to locate bottlenecks with high visibility and high priority first, and focus on them. Even if they are internal and not customer-facing.

It won’t always work, but it might help put UX research into a bright spotlight — and that could be a very good start. And a huge thanks to Karin den Bouwmeester for putting it all together! 🙏🏾 

#ux #research

📛 Hidden vs. Disabled Features In UX. Practical guidelines on when to hide, when to disable and when to keep a default state ↓

🤔 Hiding important features hurts their discoverability.
🤔 Disabling features is frustrating without an explanation.
🤔 But some options might be irrelevant/unavailable to users.
🚫 Users might expect a feature to exist, but won’t find it.
✅ We need to show what’s needed, and de-clutter the rest. 

🤔 ”Will a given user ever be able to interact with this element?”
✅ YES → Disable it (as disabled buttons or read-only state).
↳ For temporary restrictions or filter incompatibility.
↳ When a value or status is relevant, but not editable. 
↳ When an action isn’t available yet (e.g. “Export in progress...”).

✅ NO → Hide it (remove from a toolbar, collapse in accordion).
↳ E.g. due to permissions, access controls, safety, security.
↳ For inaccessible features: e.g. admin buttons, overrides. 
↳ Hide such controls by default, reveal once a condition is met.

🚫 Avoid disruptive layout shifts as you show and hide features.
🚫 Don’t remove unavailable options or buttons automatically.
✅ Instead, disable them and allow to “Hide all unavailable options”.
✅ Allow users to hide sections with a lot of disabled functionality. 
✅ Explain why a feature is disabled, and how to re-enable it.

Both hiding and disabling can be utterly confusing and frustrating to users. For both, we need very, very good reasons. And for both, we need very thorough considerations of available alternatives: e.g. enabled buttons, read-only state, better empty states, hide/reveal accordions, error messages, customization.

Disable If you want the user to know it exists but is unavailable. Hide if the value shown is currently irrelevant and can’t be used. But: never hide buttons or filters by default as users expect them to persist. Unlike hidden features, disabled features help users learn the UI and understand benefits of an upgrade.

Whenever possible, I try to keep buttons and features in their default state — enabled, accessible and legible. When a user interacts with that feature, we can explain why they can’t use it and how to enable it. Possible exceptions are confirmation codes and loading or processing states.

---

💎 Hidden vs. Disabled In Design Systems

– Carbon (disabled): https://lnkd.in/e8wTgG57
– Carbon (read-only): https://lnkd.in/eN-5FHMK
– Cloudscape: https://lnkd.in/eb8pTuC8
– Unity: https://lnkd.in/eJYWpcWt
– Vaadin: https://lnkd.in/eXA34Jk4
– Vega: https://lnkd.in/eSP2UgN5
– SAP: https://lnkd.in/eGJEGNqv
– Emplifi: https://lnkd.in/euE44NXX

---

🗃️ Useful Resources:

Disabled Buttons And What To Do Instead, by Adam Silver
https://lnkd.in/ehkvND3Y

Hidden vs Disabled, by Maria Panagiotidi
https://lnkd.in/eW8qg_Qx

Hidden vs. Disabled vs. Read-Only
https://lnkd.in/eg6M98Tg

#ux #design

🥊 Top vs Side Navigation In Complex Products (https://lnkd.in/e54HWryC), with interesting thoughts on which 3-level menu navigation structure works better for a complex product, and how to decide which to use. By Taras Bakusevych.

🤔 On desktop, people often use relatively wide displays.
↳ Limited vertical space, abundance of horizontal space.
✅ Side navigation shows more relevant options at once. 
✅ Top navigation hides some options behind tap/hover.
✅ Filters could work well both in the sidebar and on top.

✅ Side navigation → vertical scanning is easier, faster.
🤔 But: expanded side navigation takes a lot of space.
✅ Collapsible side drawers help make space if needed.
✅ Top navigation eventually runs out of space (“More”).
✅ Side nav works better for many top-level nav items. 

✅ Side nav works better for a tree/folder structures.
✅ If possible, always open/close menus on tap/click.
✅ Hover: use Amazon’s triangle pattern for accuracy. 
🚫 Don’t rely on icon alone: add text labels for icons.
🚫 Don’t rely on hover alone: support tap/click menus.

So how do we decide which option works best for a given project? Typically websites rely on top navigation, while applications often use collapsible layers of side navigation. But personally, I would ask 3 simple questions: 

1. How much space do we need to display content well?
2. How many navigation options do we have on top level?
3. What tasks do users perform in the product most?

If we will be displaying a dashboard with customizable widgets, or a map, or a complex data grid with dozens of columns, then we need to either go for a collapsible side drawer, or top navigation. If it's mostly text content, top navigation will work, unless we want to show the siblings of a page in a sidebar. 

But then if users perform only a small number of tasks and rarely navigate between sections, top navigation might work perfectly well. Navigation shouldn’t get in the way of people completing tasks or accessing content.

But if we expect users to explore vastly different views and sections on any given day, we can achieve faster navigation with a side navigation, and allow users to collapse it to make space if necessary.

In a way, every product is like a city. There are “hubs”, flows, features and actions that are very frequently used by different users. These frequent areas of use must be accessible at all times, fast — hiding them behind a “Menu” button doesn’t help anyone.

Ultimately, in my experience, both options could work fairly well. It’s really a matter of good IA work and showing what matters when users actually need it. If we fail at both, we might have a fast and seemingly “obvious” navigation that only helps users fail faster.

(Useful resources in comments ↓)

Principal UX Designer | AI-Powered Product Design & Strategy | DesignOps & Team Leadership

🔺 Designing Effective Dashboards UX With Action Dots. How to design more useful and actionable dashboards, kindly shared by Nick Desbarats ↓

---

🔹 1. Why dashboards fail

Many dashboards aren’t actionable enough to be useful. They are designed to display all important metrics in one single place, but then it’s rarely clear which of these metrics require immediate attention — and if so, which actions would be needed to actually address these issues.

Frequent reasons why dashboards are abandoned:

• One dashboard trying to do too much
• No action or sentiment indicators
• Data requires too much time to scan
• Every day looks very similar at a glance
• Metrics aren’t always directly comparable
• Metrics requiring action aren’t on a dashboard
• “Buried problems” (hidden behind filters)
• Tactical and strategic dashboards merged

---

🔹 2. Poor types of action indicators

Often action indicators are misleading, flag false positive or bury existing problems. They don’t show the full picture, but instead focus on what has changed. However, trend upwards doesn’t always mean success, and trend downwards doesn’t mean crisis.

Common types of indicators that don’t work well:

• % change vs. previous period (7.3%🔺)
• % deviation from target (10% ❌)
• % deviation from trailing average (8% 🔺)
• Single-threshold (99.9% uptime 🟩)
• Good/Satisfactory/Poor ranges (2% 🟢  / 8% 🟡 / 15% 🔴)

The issue is that they don’t clearly communicate what is a crisis, and what is a regular day. It’s never obvious how bad a change actually is — it’s worse than yesterday, or a week ago, but is it seasonal, or temporary, and has the issue been addressed already?

---

🔶 3. Alternative: Action dot dashboards

For each KPI, we define key threshold values — and then add an action dot depending on where the value for a metric currently falls:

🔴 Crisis (e.g. -15% ↔ -10%): improving it would become user’s top priority
🟡 Actionably Bad (-10% ↔ -5%): bad enough for user to do something
⚪ Neutral (-5% ↔ +5%) : doesn’t require any attention, “everything is OK”
🔵 Actionably Good (+5% ↔ +8%): good enough for user to do something
🟣 Best Case (+8% ↔ +15%): the best a user thinks it could realistically get

Action dots essentially are alert icons. On dashboards with action dots, problematic metrics “pop out” because metrics that don’t require attention don’t have any indicators at all. They make crises very noticeable, easy to learn, easy to spot and easy to understand. 

---

What I love most about action dots dashboards is that they immediately communicate what requires attention, based on user preferences that a user has expressed and the range they prioritized. It doesn’t highlight what doesn’t need highlighting, and shows what requires attention. 

Huge thanks to Nick Desbarats for bringing it in the spotlight, and putting it all together — and I can only wholeheartedly recommend to check Nick's upcoming book and work on dashboards (links are in the comments)! 🙏🏾

Instructor and best-selling author, data visualization and dashboard design | Taught in 15+ countries | Lecturer @ Yale, Columbia | LinkedIn Top Data Visualization Voice

📣 Designing Voice and Tone In UX Writing (+ PDF Worksheets). How to communicate effectively when users fail and succeed ↓ 

✅ Voice is your personality, and tone is your attitude.
✅ Voice never changes, but tone adapts to the situation.
✅ It changes depending on user’s emotional state.

✅ Unpack your brand voice and the tones it can take.
✅ E.g. passionate → enthusiastic, confident, playful.
✅ Arrange your user’s emotional states on a tone scale.
✅ E.g. pleased, satisfied, neutral, confused, angry.
✅ Set writing guidelines for each part of the scale.

✅ Then, find key points and spikes in your user journeys.
✅ You’ll need to find the right tone of voice for each.
✅ Choose dimensions that represent your tone map.
✅ E.g. motivational <-> supportive, concise <-> detailed.
✅ Plot key events of the user journey onto the map.

When users feel neutral, be warm, relatable and get to the point. When users are satisfied, sound confident, enthusiastic, perhaps even playful. But when they are confused, stay helpful, concise, honest and direct.

Always test your copy. Ask users to underline what’s been helpful for them and circle what hasn’t. Word length matters: the shortest version will usually work best. And never (ever!) include exclamation marks in your error messages.

As Michael Metts noted, it’s not a question of whether your product has a voice or changes its tone. It’s a question of how intentional you are about it. And being intentional can make a world of a difference when things turn in unexpected ways.

✤ Voice and Tone in Design Systems

Atlassian: https://lnkd.in/eYfXSAGW
Amplitude: https://lnkd.in/euW96FxP
Canada Post: https://lnkd.in/e24ngYdq
DHL: https://lnkd.in/eUVTtp-G
Duolingo: https://lnkd.in/eMvJCuS6
Girlguiding: https://lnkd.in/eEBiXX6d 👍
Goldman Sachs: https://lnkd.in/e5x7BuPt
JSTOR: https://lnkd.in/eAnyrtcu 👍
MetLife: https://lnkd.in/evVE8sqf
Monzo: https://lnkd.in/edVV8QWz
NASA: https://lnkd.in/eM637kfD
NHS: https://lnkd.in/eYK3QMa2 👍
Progressive’s: https://lnkd.in/evx_8bzY 👍
Schibsted: https://lnkd.in/et_BXg6R
Skrill: https://lnkd.in/e2HGTq4q 👍
Slack: https://lnkd.in/ejZ2QtJa
Zendesk: https://lnkd.in/ekAp2RCn 👍
Wise: https://lnkd.in/eWk-Mvf9

---

✤ Useful resources

Tone and Voice Workshop (Miro, PDF), by Antonia Horvath
https://lnkd.in/epNJmp5B

UX Writer’s Guide To Voice & Tone, by Slater Katz
https://lnkd.in/e2wx8BVt

Voice Principles And Tone Map, by Paavan Buddhdev
https://lnkd.in/eQbDH5qG

Four Dimensions of Tone of Voice, by Kate Moran, NN/g
https://lnkd.in/epMMXFTb

Deliveroo Voice and Tone Case Study, by Rhiannon Jones
https://lnkd.in/ejShWbmd

Brand Voice vs. Tone Worksheet (PDF), by Oskar Duberg
https://lnkd.in/eWpeEHSV

---

✤ Books

– Content Design by Sarah Winters
– Nicely Said, by Nicole Fenton, Kate Kiefer Lee
– Everybody Writes, by Ann Handley
– Conversational Design by Erika Hall
– Writing Is Designing, by Michael Metts, Andy Welfle ✏️

[continues in comments ↓]

🧭 How To Measure The Impact Of Features (https://lnkd.in/eCkjrJR6), a practical reminder by Adrian Raudaschl on how to evaluate features and answer the crucial question, “What does a good outcome for my feature look like?”.

Adrian suggests to measure 4 key metrics to measure which features are performing well, and which aren’t:

👩🏻‍🦱 Feature target audience (%)
We quantify the target audience by exploring what percentage of all product’s users have this specific problem that a feature aims to solve. We can approximate the target audience by studying existing or similar features that try to solve similar problems.

🗂 Feature adoption (%)
We track how many users actually engage successfully with a feature over a specific time period. High feature adoption suggests that the problem was severe and impactful. Low adoption suggests the problem has simple workarounds.

🗃️ Feature retention (%)
We study how many users who engaged with the feature actually returned to it and keep re-using it in their sessions. Usually it’s a strong signal for meaningful impact. However, high retention alone doesn’t say how successful people are in task completion.

If a feature has >50% retention across users, we can be quite confident you have a feature of high strategic importance. A 25–35% retention rate is likely of medium strategic significance, and retention of 10 -20% is of low strategic importance.

❤️ Satisfaction rate (CES)
We run research to understand how easy it was for users to solve their problem after they used our feature. We could use a Customer-Effort-Score survey, and ask only retained users to understand if there are any hidden problems with retention.

🎯 Bonus: Task Completion (%)
Personally, I would always additionally measure task completion rates for most critical tasks and flows that users have to perform. We would identify 10–12 tasks, and track how users actually complete them — along with accuracy and completion times.

One thing that’s worth emphasizing — as Adrian points out — is that low adoption doesn’t necessarily mean failure. It might be that a feature wasn’t surfaced well enough in user journeys or that we over-scoped the target population.

And once we start measuring, we can track and map overperforming features, project feature, core features and liability features across 4 quadrants in a 2×2 matrix. And that could be a great conversation starter to discuss where the product UX should move next.

A great framework to keep in mind to prioritize and track performance of features, and evolve product over time. 

Thanks for putting it together, Adrian Raudaschl (and if you are interested in metrics, I highly recommend you to follow Adrian for practical and useful guides all around just that)! 👏🏼👏🏽👏🏾

#ux #design

🧔🏽‍♂️ Design Patterns For AI Chat Interfaces. With practical guidelines on how to designing more useful, and less annoying AI chat ↓

🚫 Nothing erodes trust more than disguised AI.
🤔 Often users dismiss AI chats almost instinctively. 
✅ Users expect an option to “speak to human”.
✅ Be transparent about who users speak to.
✅ Wait for users to end a chat on their terms. 

✅ Use separate avatars for AI bot and humans.
✅ Context changes over time: collapse older chats.
✅ Support pinning chats + highlight useful bits.
✅ Let users adjust granularity of reasoning traces.
✅ Allow users to restore iterations of canvases.

✅ Allow users to collapse chat without ending it.
✅ For long, complex tasks → full page screen.
✅ For multi-tasking, co-creation → side panel.
✅ For short, momentary tasks → chat widget.
✅ On mobile, full page AI chat works best. 

As many product teams race to not fall behind AI features in their products, we see AI chat interfaces becoming almost second nature every time AI initiative is launched. However, people have difficulty articulating intent in a chat, and good old UI controls — buttons, presets, radio buttons could help there.

Nothing erodes trust more than an AI that desperately pretend to be a human. We might not be able to distinguish AI-generated content from human-crafted content, but human conversations differ significantly from AI chats — and there people spot the difference almost immediately:

– People talk in quick bursts of text → AI is verbose (by default).
– People can respond with 1–2 words → AI respond with sentences.
– People never receive unfinished text → AI “streams” output live.
– Messages can arrive unprompted → AI responds to prompts.
– People have strong opinions → AI is apologetic, overcorrects itself

Knowing that AI is on the other side isn’t really a problem — it’s what is required to build trust. And when people realize they talk to a chatbot, they are more direct, use “keyword” language, and avoid politeness markers. But when service does provide access to humans, it shows that company cares about customers.

Human answer might not be accurate or swift, but it builds a much stronger relationship with the brand, especially when things go unexpected ways.

💎 Useful resources:

Visa Design System: Chat UI Patterns
https://lnkd.in/ewVjfr86

The Quest For Usable AI, by Michael Gower
https://lnkd.in/eeq83btK

Usable Chat Interfaces to AI Models, by Luke Wroblewski
https://lnkd.in/d-Ssb5G7

UX Guidelines For Chat UX, by Raluca Budiu
https://lnkd.in/e7-RErGE

#ux #design

Senior Consultant in Accessibility at IBM

Product & Design

Senior Director of Data Strategy at Nielsen Norman Group

⛺🪵🔥 Effective Story Structures For Storytelling, an incredible (!) overview of story structures for different purposes — from winning hearts and minds to planting a seed in people’s minds, in digital design or in business presentations. Neatly put together all in one single place by Jeremy Connell-Waite. 👏🏼👏🏽👏🏾

🗺️ 50" x 50" Hi-Res Wallchart (PDF)
https://lnkd.in/eUZmRfYm

📖 15-pages document (PDF with each structure on a seperate page)
https://lnkd.in/dzdMsmvp

Nine Principles of Better Stories (PDF)
https://lnkd.in/eFQKm-8q

The Art Of The One Pager (132 pages, 415MB)
https://lnkd.in/euaj9Zc7

Free Storytelling Masterclass (+ PDFs)
https://lnkd.in/ehG-8376

Over the years, I've been falling into the trap of relying on the same structures over and over again, just because I felt very comfortable with it. But over time, it comes across as being a bit stale — and if not tailored to an audience, it becomes less effective and perhaps even slightly confusing to the audience.

There are many ways to tell a story. But depending on the amount of time people have, or the expectations they have, or the purpose of the meeting at hand, different structures could apply better or worse. A fantastic reminder and reference to keep nearby.

Thanks Jeremy Connell-Waite (and highly recommend to follow for anything all around storytelling as well!).

✤ Useful resources

Extensive Storytelling Framework: Pillars of Story Structure (free PDF), by Peter von Stackelberg
https://lnkd.in/eh8yVsTm

Strategic Storytelling for Designers, by Saielle DaSilva
https://lnkd.in/e3Bb9X7C

How To Make Stakeholders Happy With Everyday Storytelling, by Ryan Bigge
https://lnkd.in/eRj2TidZ

Periodic Table of Storytelling, by James Harris
https://lnkd.in/d9Sz92Sq

Storytelling PDF Worksheets, by Steve Rawling
https://lnkd.in/eSN25dhv

A Guide to Becoming a More Effective Storyteller, by IDEO
https://lnkd.in/eCERYDRC

Pixar’s Rules of Storytelling In UX
https://lnkd.in/evGc4jF5

Pixar The Art Of Storytelling (Free Course), by Khan Academy
https://lnkd.in/eKU_TvdA

Free UX Storytelling Guide, by Jeff White
https://lnkd.in/eN6M44xB

Better UX Storytelling, by Mayya Azarova, Ph.D.
https://lnkd.in/efNm-7gV

How To Use Storytelling In UX, by Marli Mesibov
https://lnkd.in/eqmMgnwY

Five Steps To Design With Powerful Storytelling, by Chiara Aliotta
https://lnkd.in/erHB9WDH

Inclusive Customer Experiences With Storytelling (Miro), by Sandra Heinzen
https://lnkd.in/e8UPkerT

UX Storyboarding: Guides and Templates, by yours truly
https://lnkd.in/eSNfHeTJ

Story Arcs For Effective Storytelling (PDF)

🌧️ How To Consolidate Design Systems (https://lnkd.in/ehZd-UGQ), a practical guide with strategies, potential benefits and quick wins for merging existing design systems. Shared by Evgeny Khoroshilov, heavily inspired by Nathan Curtis's thorough article on Consolidating Design System (https://lnkd.in/dCZHZikx).

🚫 In many companies, not every design system is the same. 
✅ Design teams have different needs, budgets, size, skills.
✅ They often see “design system” projects very differently.
✅ It could be a style guide, UI kit, product, process or service.
✅ Audit what kinds of “design systems” your company has.

✅ A: Keep all systems as is, but add a shared practices layer.
🤔 Each brand maintains its main focus, but duplication is likely.
↳ Set up community of practice without changing structure.
↳ Useful when teams have very different needs and priorities.

✅ B: Identify shared practices and move them into subsystems.
🤔 Priorities might be misaligned, risk of poor collaboration.
↳ Involve all teams to shape subsystem for efficiency gains.
↳ Helpful when shared practices are duplicated or differ a bit.

✅ C: Slowly retire old systems and launch a shiny new one.
🤔 Very expensive, with a risk of disruption and poor adoption.
↳ Run a pilot to test and refine, involving key stakeholders.
↳ Good choice with a legacy system that’s falling apart.

✅ D: One system stays and others gradually retire.
🤔 Streamlines operations, but requires re-training + retooling.
↳ Respect needs and work by other teams, and migrate them. 
↳ Helpful if one system can be extended to meet all needs.

Often the main reason for consolidation of design systems is the need of consistency. It is of course important, but sometimes the cost of enforcing consistency might be very high, disrupting everything from workflows and tooling to culture and team spirit.

Consistency for the sake of consistency is a dangerous path to take. There are exceptions, "snowflake" scenarios and specific use cases that often require a particular level of complexity that can’t be avoided or reduced — it must match the complexity of decisions in real life.

I always try to keep in mind that in the end a design system is just a product that we use to build the actual product across teams and brands. It doesn't have to be perfect or comprehensive. It must be useful and easy to use.

So consolidate design systems if you must, but always involve and support teams behind them as much as you can — just to make sure no core functionality is left behind, and that the team has a clear path and goals to move forward.

[Useful resources in the comments due to LinkedIn linking rules ↓]

#ux #DesignSystems

Design System Tech Lead | Web Accessibility, Design Systems

Design system architect

🚀 Finally! After so many years, I’m very happy to update “How To Measure UX and Design Impact” (https://measure-ux.com), my practical guide for designers and managers on how to set up and track design success in your company — with UX scorecards, UX metrics, the entire workflow and Design KPI trees. Now with 25 chapters (8h video), templates, and updated annually.

🍰 25 chapters, with new videos added yearly
🍎 Free preview, examples, templates, workflows
🍧 Add-on: live UX training, running 2× a year
🍿 No subscription: get once, access forever
🎟️ Use the code LINKEDIN to get 15% off today
🚀 Jump to details → https://measure-ux.com

In many companies, designers are perceived as disruptors, rather than enablers. Designers challenge established ways of working. They ask a lot of questions — much needed ones but also uncomfortable ones. They focus “too much” on user needs, pushing revenue projections back, often with long-winded commitment to testing and research and planning and scoping.

Almost every department in almost every company has their own clearly defined objectives, metrics and KPIs. In fact, most departments — from finance to marketing to HR to sales — are remarkably good at visualizing their impact and making it visible throughout the entire organization.

But as designers, we rarely have a set of established Design KPIs that we report to senior management. We don’t have a clear definition of design success. And we rarely measure the impact of our work once it’s launched. So it’s not surprising that most parts of the business barely know what we actually do all day long.

Business wants results. It also wants to do more of what has worked in the past. But it doesn’t want to be disrupted — it wants to disrupt. It wants to reduce Time To Market and minimize expenses; increase revenue and existing business, find new markets. This requires fast delivery and good execution.

And that’s what we are often supposed to be — good “executors”. Or to put differently, “pixel pushers”.

Over years, I’ve been searching for a way to change that. This brought me to Design KPIs and UX scorecards, and a workflow to translate business goals into actionable and measurable design initiatives. I had to find a way to explain, visualize and track that incredible impact that designers have on all parts of business — from revenue to loyalty to support to delivery.

The results of that journey are now public in the video course: “How To Measure UX and Design Impact” (https://measure-ux.com) — a practical guide for designers, researchers and UX leads to measure and visualize UX impact on business. I hope you’ll find it useful.

I’ve tried my best to keep the pricing affordable. But if it’s still a bit too expensive, send me a message and I’ll do my best to make it work. 👏🏽

♻️ Please share with your friends and colleagues if possible. And thank you so much for your incredibly kind and warm support, everyone! 🙏 🙏🏼 🙏🏿

#ux #design

measure-ux.com

UX Metrics and Design KPIs (8h video + live UX training) – A Practical Guide To UX Metrics & Design KPIs

🖌️ Icon Design Handbook (https://lnkd.in/ePDVd_7c), a wonderful growing library of guides, articles and templates on planning, reviewing and designing icons, combining styles, creating variations, principles of icon design — and how to customize and resize icons for better compatibility. Kindly put together by fine folks at Streamline for everyone to learn from.

Useful resources:

Optical Effects In User Interfaces, by Slava Shestopalov
https://lnkd.in/eUW6DRZG

A Practical Guide To Designing Icons (+ Figma Templates)
https://lnkd.in/d8VRdYrU

Designing Well-Balanced UI Icons, by Slava Shestopalov
https://lnkd.in/ed3fg_8d

A Complete Guide To Iconography, by Bonnie Kate Wolf
https://lnkd.in/eT9j6zBa

Free Video Course on Icon Design, by Matt D. Smith
https://introtoicons.com/

Gestalt Principles In Interface Design, by Eleana Gkogka
https://lnkd.in/etRg2Y-w

How To Choose The Right Icons, by Stéphanie Walter
https://lnkd.in/eB36Gzcg

I'd like to also highly recommend to follow Filip Greš for wonderful techniques, tips and techniques all around icon design!

Happy designing, everyone — and happy Friday! 🎉🥳

#ux #design

🥞 The UI Stack For Product Designers. Every screen you interact with has multiple personalities: blank state, loading state, partial state, error state and ideal state. How To Fix a Bad User Interface (https://lnkd.in/ecj6PGPU) highlights strategies to avoid awkward and confusing UIs with the UI Stack, carefully designing personalities for every screen. By Scott Hurff.

We start with the ideal state. It’s the zenith of your product's potential. We then explore the error state, when things go wrong, with unexpected problems, error messages and input validation UX. From there on, we move to the partial state — when the page is sparsely populated. Our job here is to prevent people from getting discourage and giving up on our product.

In the loading state, when users are waiting for an update to come through, we must avoid frustrating rage taps/clicks and allow users to set filters and sorting and search by being mindful on how we represent situations when we're fetching data. We need to avoid an entire page takeover, but rather lazy load content panes or use inline loading.

Finally, we can improve the blank states by using skeleton screens where we show the structure and layout of the page early, or optimistic actions where we assume access by default and hence reduce the perceived speed of uploading data. If it goes unexpectedly wrong, we flag an error and ask users for their input.

Personally, I often start exploring errors and recovery flows early because they often leave users frustrated and disappointed. Looking into "broken" state helps you also figure out how you communicate to your users, your tone and voice, and hence the messaging.

A great reminder that UX is not just what happens when everything goes as planned. It’s also what customers experience when things break unexpectedly.

Useful resources:

The Nine States of Design, by Vince Speelman
https://lnkd.in/eYnwxrUk

Designing For Different States in The UI, by Shane Doyle
https://lnkd.in/eG7ytNW6

All The User-Facing States, by Eric Bailey
https://lnkd.in/eTVY6rVQ

#ux #design

Churnkey Cofounder 🔑 Previously: Founding Team at Casa, Creator of Super Like at Tinder

Engineering Manager @ PopStroke

Principal Product Designer

Accessibility advocate

🎯 Right-to-Left Thinking in UX. Why starting from the future goal instead of current state often brings better outcomes and higher success rates ↓

Design never happens in isolation. It’s a journey to move from the current state to a better future state. And typically it requires planning — mapping the messy, complex reality into the problem space, then explore the solution space, and then orchestrating delivery with all its fine nuances and details.

Yet instead of finding real problems to solve, many companies “validate” already signed-off solutions by finding problems that justify them. As a result, many of us often see only what is in front of us — small inconsistencies, big ideas, huge business initiatives and weak assumptions.

It takes a lot of grit to change that way of working in organizations. But you can frame your work around the desired outcome to increase a chance of success. It also produces more meaningful and noticeable impact — for both users and the business.

One way to do that is by applying right-to-left (R-L) thinking (also called “backcasting”) to your work:

🚩 Right-to-left → Start from the goal, then move backwards to start.
🎯 Expose complexity → Map a path that maximizes chance of success.
🧱 Map what happens → Known success moments, frequent blockers.
🪜 One step at a time → Always focus on immediate previous step.
❓ Mark unknowns → Flag strong assumptions for research to-do.
🚀 Prioritize work → Choose key blockers/successes to work on next.

In practice, Right-to-Left thinking often performs better:

✅ The decisions are always purpose-driven and focused
✅ The end goal is always fixed and non-negotiable
✅ We prioritize purpose, rather than the process
✅ We expose gaps, risks, limitations, constraints early

✅ We move from tactical work to strategic work
✅ We map what actually MUST happen for success 
✅ We maximize the chances of successful outcomes
✅ We avoid tasks that are urgent but not strategically important

Journey maps often represent ideal user journeys that people should take, but rarely do in practice. There is a lot of complexity and chaos that they hide — from back-and-forth to external factors that influence their decisions. 

Often many things must happen at the same time, coming all at once, and working together towards the shared goal. The beginning must be something that we can control. And the goal should be ambitious but feasible. Along with it, we often also map risks, blockers, opportunities and unknowns.

In other words, R-L thinking makes us stop and think very carefully about the right path we must take towards a shared goal. We don’t brainstorm ideas — we think critically. And we don’t focus on a thousand of details that might matter short-term, but won’t make a difference at scale. And that’s a big win for everyone.

#ux #design

💳 Visa Product Design System (+ Figma Kit) (https://design.visa.com), a public design system for a digital payment company focused on accessibility, inclusive design, data visualization and forms — along with plenty of examples and usage guidelines. Wonderful work by the Visa design team, incl. Lilach M. Laurio, Frank Elavsky and many others!

Data Visualization Docs: https://lnkd.in/eYb-SMeb
Figma toolkit: https://lnkd.in/eRZ25U6U

What I absolutely love about the design system is an incredible amount of attention dedicated to accessibility guidelines, best practices and keyboard controls — for every single chart component, including examples for each. Also, each component comes with a dedicated accessibility section.

Helpful guidelines:

Chat: https://lnkd.in/ewVjfr86
Charts: https://lnkd.in/eVde8gJN
Selecting a chart: https://lnkd.in/epK4XQD6
File upload: https://lnkd.in/et-TKUwf
Inclusive language: https://lnkd.in/e37G7X-3
Information architecture: https://lnkd.in/eF-abpSQ
Search: https://lnkd.in/exgzbG8q
Wizard: https://lnkd.in/eD8TPYDy

Huge kudos to the wonderful Visa design team — and for sharing their work publicly for everyone to use, learn from and build upon! 👏🏼👏🏽👏🏾

#ux #designsystems

Leading Data Experience Design Engineering at Visa | Data visualization, human-information interaction design, and accessibility

On the job market! Visualization and accessibility, systems and tools

Behind The Prompt: The Map of Generative AI (+ PDF) (https://lnkd.in/eedq9XC7), a wonderful reference that shows how intense AI actually is, with actors and resources involved to make it work — but also consequences, tensions and impact of it at scale. Wonderful work by the Barcelonese artist collective Estampa, available in multiple languages to review. Discovered via Patricia Ferreiro. 👏🏼👏🏽👏🏾

PDF: https://lnkd.in/ezsi_g8e
Languages: https://lnkd.in/eedq9XC7

AI doesn’t come for free. It often feels like the future is happening in front of our eyes but also silently in the background, and it's not always obvious what exactly must happen for that magical text box to respond back to us. But in the background, there is everything from data cleansing to extreme computing to mining industry to energy consumption.

A useful reference to go through and explore not only how much impact AI has, but also the heavy costs that are associated with it. Too often, these costs are forgotten or overlooked in the world of prevailing AI slop and AI-generated content that has little practical value.

AI can help in automation and finding patterns in areas which are difficult for humans to spot and understand. And often it's a good investment of time and effort and resources — if it's meaningful work that otherwise would take humans ages to get through. And often it’s just barely useful.

I just wish we made the actual costs and impact of AI more obvious and more tangible for people using it.

#ux #ai

Human-Centered AI @ Luar Labs | MSc Data Science & Philosophy | Prev. Data & AI @ Microsoft, Databricks, IBM

🔮 Confirm vs. Undo UX. When to use confirmation dialogs, how to avoid frustration and prevent severe mistakes down the line ↓

🤔 Users need guardrails to prevent critical mistakes.
✅ Confirm verifies user’s intent to perform an action.
✅ Undo trusts users to do the right thing and reverse later.
✅ Choice depends on frequency of use + level of severity.
✅ Low impact + frequent → Undo. Severe + rare → Confirm.

🤔 Users often instinctively Confirm, or overlook Undo.
🚫 Avoid ambiguous terms, e.g. “Yes”, “Confirm”, “Cancel”.
🚫 Avoid double negatives, e.g. Cancelling a “Cancel” action.
✅ Better: “Stay”, “Leave”, “Quit”, “Delete”, “Unsubscribe”.
✅ Add “Forever”, “Permanently” for irreversible actions.
✅ Ask users to type text to avoid instinctive clicks or taps.

🤔 Confirm dialogs break user‘s flows and slow them down.
✅ If you interrupt a user (“Confirm”), make sure it’s worth it.
✅ Only few actions are irreversible, so use Undo by default.
✅ Delay irreversible actions (15 mins) if you can’t undo them.
🚫 Avoid “Undo” as fading toasts; “Undo” button works better.

Decision Tree (high-res): https://lnkd.in/eBBbqJez
PDF: https://lnkd.in/eH3Zjwcp

Often actions have unintended consequences. But they don’t have the same impact. For low severity actions (e.g. moving folders), one-click action might be right. For medium severity (e.g. re-arranging files), prefer Undo. For high severity (e.g. delete entire project), Confirm typing + Undo might be needed.

Only few actions are irreversible. But when they are, Confirm might be a good choice. Especially when actions are global or contradicting, such as deleting of linked items, removing data from other accounts or disconnecting devices. And: it’s a good way to warn users when exiting or closing without saving.

And: always test your wording on “Undo” and “Confirm” buttons. That’s where users often have a lot to lose, and where a product has a lot to gain. That’s where we can make the highest impact of UX, by building trust — and save hours of work or preventing disastrous mistakes.

---

✤ Confirm vs. Undo in Design Systems

Intuit: https://lnkd.in/e96tcg2F 👍
Gov.uk: https://lnkd.in/eRzDuzyV 👍
Line: https://lnkd.in/eNEczxXD
VA.gov: https://lnkd.in/eVRym4am 👍
Cloudscape: https://lnkd.in/eMJSfzM6 👍
Canvas: https://lnkd.in/eqy7AQw5 👍
REI: https://lnkd.in/eNYSZeep
HellyHansen: https://lnkd.in/eAk3ePDr
IBM: https://lnkd.in/eqRspZF4

#ux #design

💝 Design Patterns For Mental Health, with key takeaways, practical guides and resources to help people better — and raise awareness for the World Mental Health Day ↓

✅ Asking for help makes us feel scared, vulnerable — but brave.
✅ Support means more than giving answers — it’s giving hope.
✅ People should feel safe in engaging/expressing themselves.
✅ Suspend assumptions on what users want or need.
✅ Product must scale up support intensity when necessary.

Design patterns:

✅ We need to set up routes to anonymity and ways out.
✅ Only ask people to explain and tell their story once.
✅ Always provide a range of channels to communicate.
✅ Set up a structure around timelines, process, next steps.
✅ Enable access to help from a real person if possible.

Asking for help is difficult. Talking about mental health can feel uncomfortable. Showing vulnerabilities can feel embarrassing. But all of it are also signs of strength and brave. The world is challenging and difficult and unfair and not fully knowable. And getting through it alone can be painful and overwhelming. 

We need to talk about mental health more openly, encourage self-care and design safe, anonymous pathways for people seeking for help. And there, we must show solidarity, respect, understanding and unconditional support. 

No one can do everything. Everybody struggles. Everybody feels like an impostor. But there are always wonderful people around willing to help day and night. And sometimes there is nothing more powerful than a sincere, warm-hearted hug, even if it’s digital, and even if it’s coming from a person miles away.

And: if you are designing a product to help someone get help, hopefully the resources below will give you some good starting pointers. Good UX there can make a whole difference in the challenging and unfair world to people who need our help the most.

⁂ Useful resources:

Design Patterns For Mental Health
https://lnkd.in/eb6gyBM7

Trauma-Informed Principles and Patterns, by Melissa Eggleston
https://lnkd.in/ePCq35uG

Mental Health Digital Playbook, by NHS (backup)
https://lnkd.in/e5DXb28E

A Guide To Trauma-Informed Content Design, by Megan Legawiec
https://lnkd.in/eZfPRkSW

Trauma-Informed Principles, by Kate Every
https://lnkd.in/evWPipRH

Trauma-Informed Design, by Hera Hussain
https://lnkd.in/eBnUEUtS: https://lnkd.in/ezUYTiVF

The Call For Trauma-Informed Design Research and Practice, by Rachael Dietkus, LCSW
https://lnkd.in/e9ZAY8NH

You Are Not A Machine. You Are Not Alone. by Paul Boag
https://lnkd.in/eBRXYhQm

The Self-Care Playbook for UX Researchers (and Designers), by Lizzy Burnam 🐞
https://lnkd.in/ez3fvExz

#ux #design

Award-Winning Designer | LinkedIn Learning Trauma-Informed Design Instructor | Public Speaker | Teacher @UNC-Chapel Hill I ✨ Increasing care in tech and beyond ✨

Senior Content Designer | Bridging People & Products Through Accessible UX Writing.

UCD Leader | Design Ethics | Trauma-informed practice

Founder & CEO, Chayn I Ashoka Fellow | Forbes Under 30 | MIT Innovators Under 35

Founder of Social Workers Who Design | Trauma-Informed Strategist & SME | Ethics Advocate | Design Activist | Social Work Futurist

UX Strategist: 30+ years experience in UX, conversion optimization and design leadership. Consultant and Fractional lead.

Product Marketing Manager @ User Interviews

🍱 You Might Not Need a Dashboard (https://lnkd.in/eBSEkCyb), a well-researched piece on why dashboards have low user engagement, low efficiency, poor comprehension and poor ROI — and why displaying insights directly might be a good idea. By Irina Wagner, PhD, M. Zide and Sarah Zimmers.

When it comes to dashboard design, I often find myself referring to Benjamin Bach’s notion of design tradeoffs in dashboard design (https://lnkd.in/e58-huW6). According to Benjamin, the main challenge with dashboards comes from figuring out just the right tradeoff between 4 dashboard attributes:

📱 Screenspace ← How much data you can display,
🖼️ Abstraction ← Level of detail for data and visual encodings,
🗂️ Pages ← How scattered data is across multiple pages,
🕹️ Interaction ← How much effort is required to see details. 

As it turns out, the more you minimize each of your parameters, the more stress you induce upon the other parameters. The more you maximize some of these parameters, the less stress you induce on them.

In other words, if you want to decrease screenspace, you must either scatter content across many pages, or increase interactivity (with mechanisms for filtering and detail-on-demand), or increase the level of abstraction (by aggregating your data or removing details).

And sometimes users don’t need dashboards — they need actionable insights and key takeaways. Not data, insights. We don’t need to reveal all complexity at once, to everyone, and at the same scale and pace. But we need to support pathways for people to face complexity when they must, and discover only a set of actionable insights when they need.

--

✤ Useful resources:

Dashboard Design Patterns, by Benjamin Bach
↳ Patterns: https://lnkd.in/eSCasdKG
↳ Cheatsheet: https://lnkd.in/enkwg6Pc

Practical Guide For Dashboard UX, by Taras Bakusevych
https://lnkd.in/e5gMMgXv

Data Dashboard UX Benchmarking, by Creative Navy UX Agency
https://lnkd.in/eticxU-N

Design Effective Dashboards, by Kamila Giedrojc
https://lnkd.in/e9SPQMwK

Free eBook: How To Design A Dashboard, by David Matthew
https://lnkd.in/enU-CxwU

How To Design Effective Dashboards For Expert Users
https://lnkd.in/eUfGnxFp

Dashboard UX Patterns
https://lnkd.in/ew9Z5fEN

---

✤ Useful books:

– Practical Dashboards, by Nick Desbarats
– Design for Information, by Isabel Meirelles
– The Big Book of Dashboards, by Steve Wexler
– Dashboard Design, Dr. Michael Burch, Marco Schmid
– Books by Stephen Few and Edward Tufte

#ux #design

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