Usability & Flow Design
Usability principles for clear, low-friction flows, navigation, settings, dialogs, onboarding, feedback, and error recovery.
40%
Best tweets about UI/UX Design
50 Best Tweets About UI/UX Design (2026)
Browse the best tweets about UI and UX design, featuring research, interaction patterns, usability, accessibility, visual systems, and product outcomes.
Useful UI and UX research, interaction design, usability, accessibility, visual systems, critiques, experiments, and measurable product effects.
- Creators
- 42
- Updated
Original Xholic analysis
What 50 top UI/UX Design posts reveal
The dataset centers on usability and flow design (40% of posts), visual UI systems (34%), and AI-assisted design workflows and evaluation (32%). Across the evidence tweets, authors emphasize reducing friction, defining success criteria for AI-assisted work, reviewing states and edge cases, and connecting design discussions to user and product outcomes. Accessibility appears less frequently (6%) but is represented through specific guidance on contrast, keyboard support, screen readers, captions, responsiveness, and touch targets.
- Dominant tone
- Positive
- Median score
- 8.05
- Leading format
- Opinion
- Recent posts
- 32%
64% of posts
All-time engagement
38% of posts
Published in 90 days
Conversation map
The themes creators return to
Visual UI Systems
Visual hierarchy, typography, color, spacing, responsive layouts, design systems, components, and UI craft.
34%
AI-Assisted Design Workflows & Evaluation
Using AI coding and design agents with references, design briefs, evaluation loops, structured design knowledge, and human review.
32%
Interaction Design
Interaction patterns, microinteractions, motion, drag-and-drop, haptics, drawers, and prototyping.
28%
User Research & Testing
User research, usability testing, behavioral feedback, personas, and translating insight into product decisions.
24%
AI-Native Interaction Patterns
AI-native interfaces, intent-based interaction, approval loops, generative UI, and human oversight of AI experiences.
12%
Design Business Impact
Design decisions evaluated through conversion, retention, task success, drop-off, support costs, and business value.
12%
Accessibility & Inclusive Design
Inclusive, accessible interfaces covering contrast, keyboard and screen-reader support, captions, touch targets, and responsive design.
6%
Tone and stance
Sentiment Positive leads
Author posture Supportive leads
Performance benchmark
- Median likes
- 33
- Median reposts
- 2
- Median replies
- 5
- Median views
- 2.3K
Posts with media make up 64% of this collection. Their median all-time score is 9.67, compared with 6.70 for text-only posts.
Format mix
- Opinion 38% · score 5.21
- Case Study 24% · score 7.21
- Tutorial 16% · score 16.0
- Announcement 10% · score 14.0
Consensus and debate
Where creators agree, and where they do not
Shared view
Clarity and flow over surface polish
Posts repeatedly prioritize reducing friction: short navigation paths, usable settings, completing a decision without leaving the page, and watching people use core tasks rather than relying on feature breadth alone.
Shared view
Usability includes inclusive execution
Posts identify concrete inclusive-design considerations: contrast, keyboard navigation and focus order, screen-reader compatibility, captions or alternative media, responsive behavior, and sufficiently large touch targets.
Shared view
AI needs a design loop
AI-design workflow posts emphasize visual references, explicit success criteria, iterative review, and human testing or approval rather than relying on a single prompt.
Shared view
Design is tied to product outcomes
Several posts frame design discussions around conversion, retention, task success, drop-off, clarity, and support costs rather than visual appeal alone.
Open debate
AI assistance versus AI autonomy
Some posts describe AI as useful when guided by references, iteration, and human review, while others report errors in flows and substantial time spent correcting AI output. The disagreement concerns how much oversight current tools require.
Open debate
Minimalism is not universal
One post recommends conventions associated with a minimal UI, including no more than three colors and navigation under three steps. Another argues that dense interfaces can communicate capability in a different market context.
Open debate
New interfaces, enduring craft
Posts discuss intent-led and AI-native interaction while also arguing that designers should develop interaction-design and adjacent craft skills beyond screen styling.
What performs
Patterns behind standout posts
Practical lists and heuristics appear among score outliers
The analytics identifies these five posts as all-time-score outliers: 1700.92, 697.51, 584.69, 321.57, and 180.96 respectively. Their formats and subjects include UI heuristics, design-resource lists, a mobile-settings tutorial, and a cross-disciplinary design argument.
Resource lists stand out
These two UI/UX Savior resource-list posts are outliers. At the aggregate level, lists have the highest median all-time score in the supplied format analysis, at 641.1; this is based on four list posts.
Media posts have the higher aggregate median
Across the dataset, posts with media have a median all-time score of 9.67, compared with 6.7 for text posts. The cited posts are also among the five identified score outliers, though the supplied data does not establish that media caused their performance.
Statistical standouts
- View standout post 1 Score 1700.9 · 211.29× median
- View standout post 2 Score 697.5 · 86.65× median
- View standout post 3 Score 584.7 · 72.63× median
- View standout post 4 Score 321.6 · 39.95× median
- View standout post 5 Score 181.0 · 22.48× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 20% of the selected posts.
-
1. 0xDesigner
@0xDesigner
2 posts
-
2. IxDF - Interaction Design Foundation
@ixdf_org
2 posts
-
3. Raphael Schaad
@raphaelschaad
2 posts
-
4. 0xMarioNawfal
@RoundtableSpace
2 posts
-
5. Yousr
@rsuyoy
2 posts
-
6. Sunal Sood
@sunalsood
2 posts
0xDesigner focuses on AI design evaluation
0xDesigner’s posts propose explicit success criteria, reference-led prompting, state and edge-case review, and iterative testing in AI-assisted design work.
UI/UX Savior curates design references
UI/UX Savior’s two posts are resource lists covering UI learning sources and inspiration for areas including web design, landing pages, animation, mobile apps, icons, and design systems.
Raphael Schaad spotlights interaction details
Raphael Schaad’s two posts address interaction details: dialog-design issues in a wallet app and an effect for revealing additional information.
How this analysis was made
Themes, sentiment, stance, and post format are classified per tweet. All counts, shares, medians, creator concentration, freshness, and performance comparisons are then calculated directly from the published snapshot.
Xholic's all-time score compares engagement while accounting for reach, post age, and creator consistency. It is used for relative comparisons within this collection.
This report analyzes the exact 50-post snapshot shown below. AI identifies editorial categories and drafts explanations; all statistics are calculated from the snapshot, and every narrative claim is checked against cited posts before publication.
Best UI/UX Design tweets
Top UI/UX Design tweets from 42 creators
Ranked 01–50
- 01
@ybhrdwj ·
signs of taste in web ui: > every interaction happens in 100ms > no product tours > url /slugs are short and simple, no UIDs > persistent resumeable state > not more than 3 colors > no visible scrollbars > all navigation is under 3 steps > copyable svg logo + brandkit > skeleton loading states > copy paste from clipboard > larger hit targets for buttons/inputs > honest one click cancel > cmd + k > very minimal tooltips > copy is active voice, max 7 words per sentence > optical alignment vs geometric > optimized for L to R reading > reassurance about loss
- 02
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@UiSavior ·
Sites on the internet for design inspiration. Web Design → https://t.co/IDTg0fRObK Landing Pages → https://t.co/ulUYJp3EDf Saas Websites → https://t.co/EacR7Liy9H Navbar → https://t.co/1YHU4yfOxW CTA Sections → https://t.co/bFOrpIgWIW Animation → https://t.co/VfRAI7e5Ch Mobile Apps → https://t.co/gxq43FqLG3 Brands → https://t.co/Q38cExyN5c Icons → https://t.co/RyMj2EzdSM Design Systems → https://t.co/s4MyCFjQSD
- 04
- 05
@UfotUbon ·
Brand designers learn illustration — because identities need original visual language, not just layouts. Video editors learn motion design — because cutting footage is not the same as designing movement. Illustrators learn brand storytelling — because images now live inside systems, not just frames. Product designers learn UX writing — because interfaces are shaped as much by words as visuals. UI designers learn interaction design — because screens are judged by how they behave, not just how they look. Motion designers learn storytelling and pacing — because movement without intent is just animation. Specialists are still valuable. But the industry now rewards designers who can extend their craft beyond a single label.
- 06
@felixleezd ·
If you open a Chinese app for the first time, you’ll probably think it’s badly designed. Too many icons and features. Everything crammed onto one screen. If you grew up on Western apps, your instinct is immediate: this is cluttered. But it works. In the U.S., we’ve been trained to associate good UX with minimalism. In China, density often signals value. Open WeChat or Alipay, and it feels overwhelming at first. Information-heavy, feature-packed. But to local users, that density means capability. It says: everything you might need is already here in front of you. If you enter a new market assuming your design taste equals good UX, you’ll misread the signal. Good design is contextual.
- 07
@_vmlops ·
EVER WONDERED HOW GOOGLE, SHOPIFY, IBM, OR GITHUB BUILT THEIR UI? THIS REPO HAS ALL OF THEM awesome-design-systems is a curated collection of design systems from the world's best product companies google material, shopify polaris, github primer, ibm carbon, atlassian, salesforce lightning, stripe, airbnb all in one place each entry shows what it includes components, voice & tone guidelines, designer kits, and source code whether you're building your own design system or just want to see how the pros do it, this is the reference you need 18.9k stars and your design system excuses just ran out https://t.co/sbq3M8PvYc
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@clairevo ·
How I AI: use @claudeai design to create a "Design System" using my current marketing + app github repos iterate on redesign in CD, prototype new app style guide and prototype import .zip of design into @cursor_ai + opus 4.7 - tell it to go page-by-page or component-by-component ~48 hours of babysitting redesign through full app +19,461-13,063, 230 file PR 1 round of bugbot review (1 dark mode finding) human testing on preview + approval shipped live -> significant improvement in polish across the entire app i love her
- 11
@heysatya_ ·
Many designers believe their job is to make apps look pretty. But founders don’t pay for pretty. they care about conversion, retention, and clarity. When designing apps, that’s exactly what we focused on: - Clean onboarding - Modern UI system - Simple and clear user flows - Fast navigation between features - Reducing friction at every step - Clear hierarchy and readability Remember: If users can’t instantly understand your app and want to come back, the design failed - no matter how pretty it is.
- 12
@0xDesigner ·
how to use loops for frontend and UI: loops won't automate design, but it will help get the request right on the first implementation attempt. agents still suck at interpreting design requests and, worse, one-shotting spacing/sizing, behaviors, animations, transitions and states. the core of every good loop is clearly defining success. so for every design loop, you need a visual reference. if you're designing something new, you can start with a screen recording of an app you like. record as many interactions as possible. every click, hover state, error state, etc. or if you're working within an existing design system, you can explore figma mockups with agent. the most important thing is to prompt with a pixel perfect reference: a recording, screenshot or figma mockup(s). you can share another app and ask it to adapt the visual design or interactive behavior to your existing app. you should explicitly ask it to translate the reference and adhere to your design system (formal or informal). the second most important thing is in your prompt, before the loop begins, you ask the agent to interview you to fill in the gaps to fully understand your intent. a tool like claude code or codex will ask questions like "what should happen when a user does X" to cover edge cases. a few back and forths will build the context to help cover the important details you forgot or didn't think to mention. lastly, before starting the loop, it's absolutely critical you describe success. "verify with computer use everything looks and behaves as intended" or something along those lines. you are essentially offloading the tedious review, and it will check its own work on a loop until it matches your design brief.
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@nurijanian ·
trained Claude Code on Refactoring UI principles, maybe it'll be useful for someone It can now review your UI for: ▎ — visual hierarchy (what draws the eye first) ▎ — typography that creates structure, not noise ▎ — color palettes that don't fight themselves ▎ — spacing that looks intentional ▎ — buttons that clearly rank by importance ▎ — empty states that aren't lazy https://t.co/RUdURi7WFZ
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@felixhhaas ·
The Approval Interface 🔥 There's a new interaction pattern emerging in software, and I don't think we've named it yet. It started with Pinterest. When guided search launched, it felt like a small UX trick. You'd type one word, e.g. "living room", and instead of a results page, you'd see suggested refinements. "Cozy." "Minimalist." "Scandinavian." You'd click one, new suggestions appeared, you'd click again. Within a few steps you'd arrived somewhere specific and beautiful without ever typing a second word. One input and everything else was just clicking "next." I'm watching the same pattern evolve into something much more powerful at the moment. At Lovable, I see it happen every day. A user types a single prompt, something gets built, and then a suggestion appears for what to do next. Most users don't ignore it. They click, the AI proposes a next step, the user approves, and this loops. The whole product gets built almost entirely through a sequence of approvals. It's not unique to Lovable either. Look at Cursor, ChatGPT, Notion AI. The interaction loop keeps compressing. The user's job is less about constructing inputs and more about evaluating outputs. The interface runs slightly ahead of you, and you follow or redirect. Instead of initiating, we're moving towards approving and I find this super fascinating for a few reasons: 1/ It radically lowers the activation energy to build. Recognition has always been easier than recall. You approve instead of innovate. This is why non-technical people are building complex products these days. Not because AI can code, but because the interface no longer requires them to know what to ask for next. The system does that. They just say yes, and that kinda changes everything. 2/ It makes judgment the core skill. The valuable thing you bring is discernment. Knowing which suggestion is right. Feeling when the AI is heading somewhere subtly wrong. We call it "taste". This is exactly the kind of human capacity that gets more valuable as the interface gets smarter. 3/ We're in the early days of designing for this pattern. Most interfaces still treat suggestions as a secondary feature. But if approving is becoming the primary interaction, the suggestion layer deserves to be the primary design challenge. 4/ Pinterest built a narrowing mechanism. What AI interfaces are building now is a continuation mechanism. The interface doesn't just help you find what you want but helps you build it, one approval at a time. Looking back, we've spent decades optimizing interfaces for execution. The next decade will be spent optimizing them for anticipation. And the humans on the other side won't be operators anymore. I am pretty sure we'll call them editors. Did a larger deep dive on this on my substack - designplusai(dot)com Also thanks to Andreas Pihlström! Our coffee chat a few weeks ago inspired me to write about this.
- 17
@0xDesigner ·
working theory for a prompt to define design success and create a long running design loop (copy and paste)👇 spawn 12 subagents to each act as their own user persona, with human cognitive limits, and with their own psychographics, motivations, and frictions. allow each each computer use to complete [insert job to be done]. measure and report the following: clarity in 3 seconds: in a 5-second test, ≥80% of subagents can answer "what is this screen for?" correctly. one obvious next action: ≥70% first clicks go to the intended primary action. — before spawning for the user tests, spawn a separate subagent to explicitly audit for: clean hierarchy: primary element gets highest visual saliency score on the screen (no competing CTA). consistent type scale + spacing system across all core screens (no ad-hoc styles). intentional states: empty/loading/error/ success all exist, and each includes a next step. recoverable errors: every error message includes cause + action ("what happened" + "what to do"). mobile friendly: 100% tap targets ≥44px, no horizontal scroll, body text ≥16px. — if after completion of any design task any of these criteria is not met, continue iterating, running synthetic user testing, and auditing for the criteria mentioned above. discern with cynicism and avoid any bias when interpreting any of these criteria.
- 18
@raphaelschaad ·
What do you think about this effect to reveal additional info? Found it interesting. Sharing some micro UX thoughts in-thread …
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- 19
@rsuyoy ·
My design principles for human-machine interactions: - The human needs to execute an action on the machine. - The human has an intent. (you can reverse the order of these first two propositions.) - Crudely, the intent is just what the human wants the machine to do. e.g: “I want this link to be pasted in this specific note.” So, the end goal. - Traditionally, tho, we think of the intent in the separate steps it takes to actually fulfill it; I need to open the notes app, find the right note, open it and paste my link. This is a flaw of modern UX, because we increase the friction between the intent and its expression. (the different steps = friction.) - My principle is: the fewer the steps between the intent and its expression, the better the UX. (usually, the number of screen taps is a good proxy for how frictionless the expression is.) UX has mostly operated under the assumption that though we cannot directly inject intent into a user’s mind, we can shape what they think is possible. It’s been about nudging user intent towards what is doable within the scope of our interface, and subsequently make it obvious how to do it. Of course, this is a sound approach because it tackles the fundamental issue of this traditional framework: when the user has an intent that the application does not accommodate (i.e, the user wants to pin all the tasks that are scheduled for the weekend), a design failure occurs. (they will have to locate all these tasks and pin them one by one...) I, nonetheless, wish to offer a model that mitigates this structural shortcoming of traditional UIs. In the age of LLMs, I think it's big time for it. The new paradigm demoed below, which I call “Language Augmented GUIs,” offers, in my view, a wonderful frictionless ‘intent to action’ model. (action = intent expression.) It broadens the scope of possible actions, renders the case of “intent that isn’t catered by the app” extremely rare, and widely eliminates friction. (number of taps excluding typing on the keyboard = very low.) This will ship in the next update of my app. Other examples in the replies below.
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- 20
@sukh_saroy ·
🚨Drop one markdown file into your project. tell your AI agent "build me a page that looks like this." get consistent UI that actually matches the design system. it's called awesome-design-md -- a collection of DESIGN.md files extracted from 31 real websites. DESIGN.md is a new concept from Google Stitch. same idea as AGENTS.md or CLAUDE.md, but for design: a plain-text file that tells coding agents how your UI should look and feel. no figma exports, no json schemas, no special tooling. just markdown. here's what each file captures: → visual theme and atmosphere -- mood, density, design philosophy → full color palette with semantic names, hex values, and functional roles → typography rules -- font families, size scale, hierarchy table → component stylings -- buttons, cards, inputs, navigation with all states → spacing scale, grid, whitespace philosophy → shadow system and surface hierarchy → dos and don'ts -- design guardrails and anti-patterns → responsive behavior -- breakpoints, touch targets, collapsing strategy → agent prompt guide -- ready-to-use prompts referencing the design tokens 31 sites already in the collection: Stripe (signature purple gradients, weight-300 elegance) · Vercel (black and white precision, Geist font) · Linear (ultra-minimal, purple accent) · Supabase (dark emerald, code-first) · Apple (SF Pro, premium white space) · Notion (warm minimalism, serif headings) · Figma (vibrant multi-color) · Cursor · Sentry · IBM Carbon · NVIDIA · Uber · Claude · Mistral · Ollama · RunwayML · and more. each entry includes the DESIGN.md, a light mode preview, and a dark mode preview. 100% open source. MIT license. (link in the comments)
- 21
@sunalsood ·
AI slop outputs didn’t feel good and the real blocker wasn’t models, it was older datasets There’s no high-quality, open source dataset for UI design decisions. No structure, no intent, no layers of why things work. That gap changes the strategy: Instead of tweaking models, we’re building a foundational design dataset from scratch; open, structured, and purpose-built. So currently working with - 2M UI screens - 25M UI elements - iOS, Android, Web What started as experimentation has turned into a deeper bet! Whoever owns the best design data will define the quality ceiling of design AI
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- 22
@averycode ·
UX matters just as much as features I signed up for a saas that I needed But didn’t end up subscribing bc the interactions were confusing. As a user I didn’t care that it had 10+ features, I needed the core feature to be super easy to learn I don’t have any UX experience so my best reference is getting on a screenshare to watch users use my app It’s rare, but when they agree to this it’s so valuable
- 23
@HiTw93 ·
I’m sharing a few posts on how some of the more interesting skills in Waza are built. This one is about the thinking behind /design. The starting point was simple: I really dislike the kind of AI-generated websites that all look the same, usually with emojis, blue-purple gradients, and a generic polished look that is technically usable but visually forgettable. So I took the UI work I’ve made recently and had Claude Code study the way I prompt, refine, and correct design output. That became a base layer of design best practices and anti-patterns. On top of that, I pulled in the useful parts of Claude’s frontend design skill, which gave the whole thing a stronger foundation. For more specific rules, I learned a lot from pbakaus/impeccable. It contributed many of the concrete constraints: banned font lists, color system guidance, theme direction, CSS anti-patterns, animation rules, and other details that help the model build a more reliable sense of visual taste. I also borrowed part of the structure from getdesign, especially its simplified adaptation of Google Stitch’s nine-part scaffold. That gave /design a clearer knowledge framework instead of just a loose collection of tips. The last piece is context. Before using this skill, I ask a few questions first: who the page is for, what aesthetic direction you want, what you want users to remember, what you definitely do not want, and what kind of micro-interactions should define the experience. Once Claude Code has that context along with /design, the results are usually much better, with far less iteration. If you have strong design ideas, better rules, or useful references, feel free to contribute to Waza. PRs are welcome. Let’s build the most useful skill library for engineers together. https://t.co/auohUmNTXO
- 24
@rsuyoy ·
Little UX experiment I made for Cift. The rationale is: when you enter a dragging state, only so many interactions are available to you, so I might as well morph existing UI into drop targets, allowing for more interactions through drag and drop.
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- 25
@DivyanshT91162 ·
I think DESIGN.md is the most underrated file in AI right now. Not prompts. Not workflows. Not model selection. DESIGN.md. Because the difference between an agent that ships generic slop and one that produces polished, coherent interfaces is usually written down long before the first prompt runs. A strong DESIGN.md gives the agent enough context to reconstruct the product from first principles: • Design philosophy • Visual hierarchy • Component system • Spacing rules • Typography standards • Interaction patterns • UX constraints Without it, every generation becomes a guess. With it, the agent starts making design decisions that actually feel intentional. The AGNT DESIGN.md is a great example of this 👇
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- 26
@sunalsood ·
UI isn't just evolving. it's dissolving. And the designers who only know how to design screens are going to wake up one day wondering where the screen went. Keyboard → Touch → Voice → Context Every few years the way we talk to technology gets simpler. we went from typing commands to tapping screens to just talking. Now tools like Wisprflow, Spokenly and Neosapien ( Wearable tech) are taking it even further you just speak your thought out loud and the system figures out the rest. no typing. no tapping. Just Intent. But the bigger shift isn't about input at all. it's about output. systems are getting smart enough to understand what you need before you even ask. Think about it your phone already knows your morning routine, your calendar already suggests your next meeting, your email already drafts replies. we're slowly moving from "I tell the computer what to do" to "the computer already knows what I need." So what does this mean if you design apps for a living? it means the buttons, forms, and screens we spend all day perfecting might matter less and less, because if the system already knows what you want, why would you need to tap anything at all? The job of a designer is shifting; less "make this screen pretty " more " understand what the person actually needs and deliver it before they go looking for it. Context + understanding intention
- 27
@michalmalewicz ·
I tested Opus 4.8 on professional UX design workflows. (not UI) for a real client project. In parallel, I did all the required work manually, then compared. Negatives: • It got about 20% of the general structure right. • Every single flow node had errors or logical gaps (mostly the latter) • It created at least 30% more screens than were necessary, split simple functionalities into multi-step patterns • The generated low-fidelity prototype was missing crucial parts (huge logic gaps) Positives: • It created around 10 edge-case nodes (and lo-fi renders) for which a normal design process usually doesn't have the time/budget. They required manual fixes. • It did everything within 2 hours instead of 2-3 days. The models are "smart" and capable. So why was the result not that good? A couple of reasons: 1. The project was non-typical and complex 2. Most publicly available case studies of actual UX flows are not detailed enough. AI learns on Dribbble, Case studies from juniors and extrapolates an average Was this exercise useful? Kind of. I do see AI in flow generation but nowhere near automatic. It does make sense in case of flow diagrams and lo-fi prototypes to create your own database of JSON files and always have a built-in tool to manually fix every step of the way. If you get complacent or lazy during this process, you end up with a bad design EVERY SINGLE TIME. I plan to write a more detailed breakdown of this and how I'm trying to bend it to work in a truly beneficial way, beyond some extra edge case flows.
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@designcoursecom ·
It's harder to become a great UI designer than a UX designer. UX design is predominantly: Is this obvious, easy, and logical? UI design is understanding colors, how they work woth each other, alignment, white space, viz. hierarchy, what is achievable and when, typography, and tooling.
- 30
@serhandesign ·
Most people design onboarding screens. Sometimes the thing that actually works is not the screen. It’s the person on it. Especially for habit and emotional products, real humans can build trust faster than polished UI.
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- 31
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@lottsnomad ·
a design principle you should follow: one screen = one action users won’t always tell you what’s wrong their behavior will where they tap where they drop what they ignore behavior is the real user interview
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- 33
@Hormowunmi22 ·
Have you ever finished a design... looked at it proudly... and then heard, "This looks great... but how does it help the business?" That question catches many designers off guard. Not because they're bad designers. Because no one taught them to think beyond the screen. Here's the misconception: Many designers believe their job ends when the interface looks good. Product Designers know that's where the bigger conversation begins. Every feature you design costs time, money, and engineering effort. If it doesn't create value for users and the business, it's difficult to justify. That's why understanding a few business metrics can completely change how people see you. Here are 5 business metrics every designer should understand 👇 1. Conversion Rate This answers one simple question: Are users completing the action we want? For example: If 1,000 people visit a sign-up page and only 80 create an account, your design may have friction. A better onboarding flow could improve that number. Good design should help people move forward. 2. User Retention Getting users is one thing. Getting them to come back is another. Ask yourself: "Would someone want to use this product again tomorrow?" Retention is often a stronger sign of good UX than downloads. 3. Task Success Rate Can users actually complete what they came to do? Can they book a ride? Transfer money? Find a product? A beautiful interface means very little if users can't finish their task. 4. Customer Satisfaction Do users leave feeling confident... or frustrated? Whether it's surveys, reviews, or usability tests, understanding how people feel helps you improve future decisions. Happy users are more likely to recommend your product. 5. Drop-Off Rate Where are people giving up? If users abandon checkout, onboarding, or payment halfway through, that's valuable information. Instead of redesigning everything, investigate where people leave and why. That's where your biggest design opportunities often hide. Here's what changed when I started thinking this way. I stopped asking, "Does this screen look clean?" I started asking, "What business result should this screen improve?" That one question changed how I approached every project. Because companies don't invest in design to make products prettier. They invest in design to help users succeed and help the business grow. When you understand both sides, you stop looking like someone who only designs interfaces. You become someone who helps products succeed. 💬 Let me ask you: Which business metric do you think more designers should pay attention to... and why? Happy Weekend 💜
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@TheRobertAvram ·
I designed 20+ product screens in 10 minutes with Claude using raw business research. Here's what I did: I fed Claude our entire research for a project. Market data. Audience profiles. User types. Core actions. Business goals. Then I told it what information we should have on each screen. Then I asked it to generate an HTML wireframe. What came back was genuinely useful. And I am not talking about design. That is bad even though it had previous designs reference, full brand and access to the Figma file. But the information architecture? How the product could be structured. Screens. Flows. Hierarchy. Relationships between sections. That was really good. And it made it in minutes. Here's what most people get wrong about using AI in design: They try to use it to REPLACE the "$10k designer". We use it to ACCELERATE the thinking. The research still has to be done by humans. The strategy still needs real conversations. The decisions still need judgment. The end design still needs to be done by a senior designer. But the translation from research → structure? AI is exceptional at that. It sees patterns in the data. It organizes information logically. It gives you something to react to instead of a blank canvas. We don't start with this output. We start the conversation with it. "This is what the data suggests. Now let's challenge it." That's the difference between using AI as a shortcut and using it as a thinking partner.
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- 36
@HotAisle ·
I spent Saturday doing a major UX redesign of https://t.co/SoBzRgGVAI. The goal was to make it as minimalist and brutalist as possible: light and dark modes, fewer separators, less visual weight, a cleaner presentation of dense data, and device responsive. Colors are standards based. AI was a huge help, but you still need to direct it and know what you want. Its real advantage is making refactoring and exploring new ideas nearly painless, it's an endlessly patient design partner. RideControl is quickly becoming the bike-trainer workout app I've always wanted. It has sped up my own recovery, and if you want to build fitness quickly, this is it. It's amazing that we can now build things almost entirely with words and screenshots. Free and open source. Runs entirely in Chrome browser. No tracking, account, or login. https://t.co/2I34eAbtag
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@osi_fav ·
User research does not replace taste. It sharpens it. Research tells you what users struggle with. Taste decides how to solve it. You can collect interviews, surveys, and usability tests. But turning those insights into a simple, elegant product still requires judgment. Because users will show you the pain. They will not design the solution. Great product teams use research as a compass, not a crutch. Research reveals patterns. Taste turns those patterns into clear decisions. Without research, you are guessing. Without taste, you are just documenting problems. The real advantage is knowing when to listen to the data and when to shape it into something better. For designers and founders here: What insight from user research completely changed how you designed a feature?
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@RoundtableSpace ·
OpenUI just hit #5 on GitHub trending A full-stack Generative UI framework that lets AI models build interfaces as they stream. This is a different approach to how AI renders output: • OpenUI Lang - a compact language designed specifically for model-generated UI, not human-written code • Built-in component libraries charts, forms, tables, layouts, all ready to extend • Prompt generation from your component library - the model learns what it's allowed to build • Streaming renderer - UI renders progressively as tokens arrive, not after • Works across assistants, copilots, and full product surfaces 67% more token-efficient than JSON. React runtime built in. The gap between "AI generates text" and "AI builds interfaces" is closing fast. Most frameworks treat model output as text to be parsed. OpenUI treats it as a UI to be rendered in real time. Is this what every AI product interface looks like in two years?
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@timothylindblom ·
How to ship a badass UI in 4 steps: 1. Design system. - Pick your colors, spacing, and type before touching screen. 2. Use real data. - Build core screen with real content. - Lorem Ipsum is a lazy way to run your testing. 3. Build intentional flows. - Create detailed views that expand naturally. - Each tap serves a purpose. 4. Add micro animations. - Layer in micro interactions. - The small details create the best apps. In 2026, you need to be shipping FAST without quality sacrifice.
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@RoundtableSpace ·
SOMEONE TURNED THE IPHONE DYNAMIC ISLAND INTO A WORKING POLAROID CAMERA AND THE WHOLE THING LIVES INSIDE THE ISLAND ITSELF It's called Pico Cam and the interaction design is pure skeuomorphism done right. → Drag the island down to open the camera → Tap to snap, then the photo morphs and ejects out of the slot → The picture slowly develops like a real polaroid, and shaking your phone speeds it up → Every sound and interaction is mapped to haptics so you feel each step → Fully native Swift, kept under 5mb after a month of work Built entirely with Codex and it shows what's actually possible with AI coding tools right now.
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@ixdf_org ·
Do you watch videos with closed captions in loud places? 🔊 Or enjoy using your favorite app seamlessly on both your computer and your phone? 💻📱 That’s accessibility design. Accessibility features don’t only support people with disabilities. They make products and services easier for everyone to use. Swipe through to learn the 5 key elements of accessibility in UX design: ✔ Color and contrast ✔ Keyboard navigation and focus order ✔ Screen reader compatibility ✔ Alternative media and captions ✔ Responsive and scalable design Accessibility is about creating products that are easier to use, more inclusive, and more human-centered. 👉 Explore our open-access resource to learn more about accessibility audits and inclusive UX design: 🔗 https://t.co/LS2tvG8bAc #GAAD #InclusiveDesign #UXDesign #DesignForAll
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@thelifeofrishi ·
as a designer building a design tool 🎨 i've spent a lot of time using little interactions to improve UX while keeping whole experience look smooth and beautiful here are some tiny details I've designed(with love) in Orshot Studio 🙂 1) the save interaction, clearly notifying autosaves
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@bmykhaylivvv ·
it is sad I was playing out with 3 design tools on weekends to sketch the design of some of the new concepts for aisdr Magic Patterns, banani(.)co, Stitch by Google just look at these 3 example pages generated by each tool they are super similar it makes me super sad this design is very generic, each single prompt vibe-coded tool has this design another more important thing is that it is "prompt to image (web components mostly)", but these tools do not think on "what would be the best ux solution here" I believe that such tools must have much more reasoning on top of the components generation it is so easy for not to tell "generate react component from %USER PROMPT% and display it to user" -- almost any llm can do it but true product design approach will be super useful here -- make llm care about user, about user flow and ux
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@ixdf_org ·
Great user experiences combine usability and desirability. 🪙 Products become truly desirable when they're designed around a deep understanding of the people who use them. 🔍 That's why user research is so important. It helps you uncover your users' goals, motivations, behaviors, and the contexts in which they use your product. 👤 Personas help you turn those insights into informed design decisions, making it easier to empathize with your users and create products they'll value. When you understand the people you're designing for, you can create products that are not only easy to use but also meaningful, accessible, and enjoyable. As Frank Spillers reminds us, usability and desirability are two sides of the same coin. Learn how to create products users love in our Mobile UX Strategy: How to Build Successful Products course. https://t.co/TQcuRuCqxs #UserResearch #UXDesign #MobileUX #ProductDesign #UserExperience
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