Vitaly Friedman's Recent LinkedIn Posts

🧠 UX & Psychology: Guides and Cheatsheets. With useful resources on how people think, decide, remember and focus attention — guides, glossaries, Miro/Figjam boards and cheat sheets ↓ 🤔 We think we know what others are thinking (we don’t!). 🤔 Too often we fill in gaps and detail from stereotypes. 🤔 We project our assumptions onto the past and future. 🤔 We tend to avoid or postpone irreversible decisions. 🤔 We edit and reinforce some memories after the fact. 🤔 We don’t see everything and we overlook details. 🤔 We simplify probability and numbers to make sense. 🤔 We are drawn to details that confirm our own beliefs. 🤔 We tend to believe things we choose to believe. 🤔 We transfer our experiences on other people. As GK VanPatter noted, what is often missing in conversations on cognitive biases are divergent thinking bias and convergent thinking bias, with the latter being by far the most prevalent. With the divergent thinking bias, we often generate too many ideas without properly evaluating their quality of feasibility. As a result, we at times resist making decisions and narrow down solutions, and hence often overlook obvious solutions. I definitely fall into that trap frequently — and then get lost in all the fine details, without a clear path forward. With the convergent thinking bias, we rely too much on logical analysis while totally ignoring emotional factors, and then fall prey to confirmation bias when evaluating solutions. So we rush towards a single right answer, and often skip or ignore the option of experimentation. Both of these biases can be very counterproductive when we try to innovate and find solutions to a given problem. They are often overlooked and forgotten — so watch out for them, and flag them for yourself and for your team when designing the next big thing. --- 💎 Useful resources: Psychology Insights Cookbook, by Jerome Ribot 💎 👍🏽 Docs: https://lnkd.in/epGNUd9j Figma: https://lnkd.in/gHEU4R9Y Laws of UX, by Jon Yablonski 👍🏽 https://lawsofux.com/ Psychology UX Glossary, by Peter Ramsey 👍🏽 https://lnkd.in/ek9Pi-CG Cognitive Bias Cheat Sheet, by Buster Benson https://lnkd.in/eCGDp4mP Psychological Principles And How To Apply Them (Figjam), by Maryna Kucherova 🇺🇦, via Paweł Huryn 🇺🇦 https://lnkd.in/eQE2y5HG The Psychology of Design, by Dan Benoni, Louis-Xavier L. https://lnkd.in/d5Z6TpVt Cognitive Biases Explained, by Krisztina Szerovay https://lnkd.in/eCxbBBS9 Behavioral Science Insights (Miro board), by Elina Halonen https://lnkd.in/eW5XgwDk The Big Behavioral Bias (Miro Board), by Robert Meza https://lnkd.in/ei-Xs-tD Behavioral Evidence Hubs 👍🏽 https://www.bhub.org/ https://www.besci.org/ #ux #design #psychology

🚫 “Sorry, but your name has invalid characters.” 40 False Assumptions About Names In Interfaces (https://lnkd.in/esFQZBch) lists wrong assumptions that usually result in poor error messages, lock-outs and dead ends. By Patrick McKenzie. ✅ People often have multiple full names. ✅ People’s names do change over time. ✅ People don’t always have 1 full name which they go by. ✅ Systems often use different names for the same person. ✅ There are dozens of various naming schemes worldwide. 🚫 Names aren’t always written in a single character set. 🚫 Don’t impose space or character limitations. 🚫 Not everyone has a last name, family name or middle name. 🚫 First names and last names aren’t always different. ✅ Always allow people to type their name as they prefer. ✅ Use "Full name" instead of "First", "Middle", "Last" names. ✅ Names may include numbers and punctuation. ✅ Names also include prefixes, suffixes, everything-in-between. ✅ If you must, ask additionally how a user prefers to be addressed. We shouldn’t make any assumptions about people’s names. There are literally dozens of different naming schemes around the world, and validating any names is usually a dangerous path to take. Usually it’s done due to security, to prevent SQL injections or similar attacks by submitting malicious data. Yet very often validation rules are overly restrictive, blocking any special characters, including apostrophes and diacritics. And unfortunately, those rules are often wrong, breaking UX for many people. Review the rules with your engineers. There is no such thing as invalid characters in a person’s name. People whose names break validation aren’t outliers. They are real people with real names that don’t match our validation restrictions. The way out is easy: accept any name that a user provides, whatever characters they include, and whatever way they choose to type it. #ux #names

🏗 How To Tackle Large, Complex Projects (https://lnkd.in/di7M_W6E). With practical techniques to meet the desired outcome, without being disrupted or derailed along the way ↓ 🤔 99% of large projects don’t finish on budget and on time. 🤔 Projects rarely fail because of poor skills or execution. ✅ They fail because of optimism and insufficient planning. ✅ Also because of poor risk assessment, discovery, politics. 🎯 Best strategy: Think Slow (detailed planning) + Act Fast. ✅ Allocate 20–45% of total project effort for planning. ✅ Riskier and larger projects always require more planning. ✅ Think Right → Left: start from end goal, work backwards. ✅ For each goal, consider immediate previous steps/events. ✅ Set up milestones, prioritize key components for each. ✅ Consider stakeholders, users, risks, constraints, metrics. 🚫 Don’t underestimate unknown domain, blockers, deps. ✅ Compare vs. similar projects (reference class forecasting). ✅ Set up an “execution mode” to defer/minimize disruptions. 🚫 Nothing hurts productivity more than unplanned work. Over the last few years, I've been using the technique called “Event Storming” suggested by Matteo Cavucci to capture user’s experience moments through the lens of business needs. With it, we focus on the desired business outcome, and then use research insights to project events that users will be going through towards that outcome. On that journey, we identify key milestones and break user’s events into 2 main buckets: user’s success moments (which we want to dial up) and user’s pain points or frustrations (which we want to dial down). We then break out into groups of 3–4 people to separately prioritize these events and estimate their impact and effort on Effort vs. Value curves (https://lnkd.in/evrKJUEy). 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. If you’d like to learn more, I can only highly recommend "How Big Things Get Done", a wonderful book by Prof. Bent Flyvbjerg and Dan Gardner who have conducted a vast amount of research on when big projects fail and succeed. A wonderful book worth reading! Happy planning, everyone! 🎉🥳

🏔️ Iceberg Model For System Thinking. How to understand and improve complex systems, products and eventually how people think ↓ 🤔 Every product is like a city → hubs, outskirts, infrastructure. ✅ Parts are deeply interconnected, influencing each other. ✅ People rely on “islands of clarity” → workspace + workflows. ✅ Work is built around relationships, deps, ways of thinking. ✅ Non-linear multi-actor workflows, many entry points, legacy. ✅ System thinking → study relationships to be intentional + impactful 🤔 “Wicked problems” → many features, actors, systems, flows, users 🤔 “Feedback loops” → output returns input, influences next decisions 🤔 Cause and effect often not closely related in time and space 🤔 Breaking big problems in small parts obscures deep connections. The Iceberg model is one of the many system thinking models to understand the causes and drivers of a complex issue at hand — with 4 components, ranging from the surface deeply into the inner workings of an organization: 🔸 1. Events: "What's happening?" For a given problem, we explore most visible and immediate aspects of it (“events” or “symptoms”). It’s specific incidents, decisions, or outcomes that are directly related to the problem at hand. Events don’t live in isolation, so focusing on them leads to short-term solutions. Also, events appearing today are likely to be a result of a mental model established years ago. 🔹 2. Patterns + Trends: "What's been happening over time?" We explore patterns and trends emerging within the problem space. They reveal details about the nature of the problem and the factors that might be driving it. We study historical data, observe behaviors, explore relationships between events and their potential causes. We look at feedback loops and reinforcing mechanisms that contribute to the problem. This is how we understand how parts of the system interact and influence each other. 🟣 3. Underlying structures: "What's influencing that behavior or trend?" Systems often don't change because people and structures are incentivized not to change them. Patterns are the result of established structures that guide and enforce them. They can be ways of working, policies, decision making, org structures etc. Here we study deeper causes of the problem, and what drives the patterns we’ve observed. We learn about constraints, bottlenecks, potential points for intervention — and necessary shifts that we must initiate for a change to happen. 🟢 4. Mental Models: "What beliefs stimulate that behavior?" We move from ways of working to the ways of thinking — the culture or beliefs that fuel the structures above it. We study mental models that influence behaviors, decisions and actions. It’s the beliefs, assumptions and values — but also motivations and intentions. Getting here is hard, but it surfaces potential areas for change or intervention — and ultimately find more impactful and lasting solutions to the problem. ↓

💎 UX Tools For Better Thinking (https://untools.co), a wonderful collection of tools and frameworks to help you solve design problems, make better decisions, resolve conflicts and communicate better — with templates, sheets and useful resources, all neatly put together in one single place by Adam Amran. 👏🏽 Personally, I typically use a handful of thinking tools: ⌾ Six Thinking Hats for brainstorming, ⌾ Impact / Value curves for prioritization, ⌾ Iceberg Model for systems thinking, ⌾ Zenko Mapping for finding roadblocks, ⌾ Event Storming for mapping data/user flows. I can also wholeheartedly recommend a wonderful overview of how to choose the right thinking tool — with useful pointers on how to choose the right tool for the right task at hand: https://lnkd.in/dfz9kxvq Also highly recommended: Playbook For Universal Design (https://lnkd.in/eyXKNJ2D), a fantastic little helper with inclusive design methods for UX workshops, with practical guidelines on how to accommodate participants with diverse abilities — and each method includes step-by-step guidelines, video introductions, facilitating guides, materials and PDF/Powerpoint templates. Quite a goldmine to keep nearby, neatly put together and maintained by Dagny Valgeirsdottir, Astrid Kofod Trudslev and Maria Væver Olsen. 👏🏼👏🏽👏🏾 ✤ Useful resources: Library of Visual Frameworks, by Dave Gray https://lnkd.in/eMAP3BS4 Hyperisland UX Methods Resource Kit https://lnkd.in/eshvKWuK 18F Method Cards https://lnkd.in/dRFsiWgS Useful Resources to Find UX Methods, by Stéphanie Walter https://lnkd.in/e5j6-2yy UX Methods and Projects (Airtable), maintained by Vernon Fowler https://lnkd.in/eAHaiaSm Comprehensive Guide To UX Methods and Deliverables, by Fabricio Teixeira https://lnkd.in/eKsgvCYM Are there any tools or particular types of workshops that you frequently use in your work? Or perhaps you’ve written an article or two on how you use them? Please do share them in the comments below, so others can learn from your experience, too! #ux #design

🧠 The Cognitive Bias Gap (Cards) (https://lnkd.in/d5ACMEEa), a lovely free tool that helps identify biases and raise questions to ask, and patterns to watch out for — to make a sustainable change that makes an impact. A useful reference to keep nearby, neatly put together by Robert Meza. In large organizations well-intended initiatives often produce short-term improvements that eventually get offset by negative consequences of that initiative. What looks like bias is often a rational response to incentives and constraints, and to make changes, we must be aware of these responses. As Robert writes, when you find yourself in a situation where something isn't working, before acting on it, scan the patterns to notice which one might be true, then challenge that instinct. Fortunately, the cards highlight questions to ask and strategy to embark on for more sustainable changes. A neat little helper to manage a difficult situation from the perspective of change management — from designing the change, to facing resistance, to understanding how judgements form and eventually getting adoption. One for the bookmarks! And thanks to Robert Meza for putting it together. 👏🏼👏🏽👏🏾

🌳 Design Patterns For Building User’s Trust (https://lnkd.in/etZ7mm2Y), still a fantastic (!) catalog of design patterns to help teams design trustworthy services that work for people — with advantages, drawbacks, limitations and examples for each, from AI to privacy. A wonderful repository, neatly put together by fine folks at IF. Discovered via Sarah Gold. In the noisy and polluted world today, trust doesn’t come for free. It doesn’t emerge by default. It must be earned and meticulously preserved — by treating customers with respect, securing their data, respecting their privacy and asking only for what’s absolutely necessary. I absolutely love how these patterns amplify the need for far more respectful and trustworthy digital experiences. *HUGE* kudos to the fine folks from Projects by IF for contributing to and maintaining the website for all of us to use and learn from! 🙏🏼 🙏🏾 🙏🏾 Also worth exploring: AI Interaction Design Patterns, by Emily Campbell https://www.shapeof.ai AI Interaction Atlas, by Brandon Harwood https://ai-interaction.com Humane by Design, by Jon Yablonski https://lnkd.in/e22vNJ9R Privacy Design Patterns, by UC Berkeley https://lnkd.in/emCkmNVi Deceptive Patterns Hall Of Shame https://lnkd.in/eZdPKM4A Privacy Design Patterns, by Vitaly Friedman https://lnkd.in/dN8yce2 AI Design Patterns, by Vitaly Friedman https://smashed.by/ai-ux Trust is probably the most challenging and rewarding sentiment you can earn. Especially in times of AI-generated content, hallucinations, deceptive patterns, exaggeration and automation everywhere, we need to build trust by building honest, sincere and kind relationships with our customers. Sometimes it means showing your vulnerabilities. Having typos. Having an opinion. Explaining to customers how things actually are, without pre-written templates and AI-generated drafts. Sending a friendly “thank you”-note that has nothing to do with your marketing or sales efforts. People aren’t perfect. And I’d love to see more products being a bit less pixel-perfect and more humane instead. That’s a first step towards building trust when it’s so eroded, so scarce, so neglected — yet so much needed and valued and appreciated.

📣 Friends, I’ll run a free online workshop on “How To Design For Trust and Confidence In AI Products” on Maven on Apr 2 (https://lnkd.in/d72b66Qa). No strings attached — feel free to join in, and invite friends as well! 📅 Apr 2, 2026 (around 1h 30mins) 🗃️ Includes slides and resources 🎟️ Free for everyone → https://lnkd.in/d72b66Qa 🚀 Next AI training (May 26): https://lnkd.in/di4qURzF --- ✤ In the workshop, we’ll cover: 🌈 How to Calibrate Trust in AI (via Kristine K.) Trust calibration spectrum, how to find the balance between aversion and overreliance, why AI products shouldn't aim for maximum trust. 🗂️ How to Handle AI Hallucinations and Poor Context We can't fully prevent them, but we can set up proper guardrails, human-in-the loop, user's control, verification. 🍱 UX Challenges With Confidence Scores Useful design patterns such sandboxing, intent confirmation, consensus meter, context engineering. 🚦 Verified Context Layers (via Leah Tharin) Context compounding, retrieval correctness ≠ factual correctness, how to treat context and keep it clean. 🎟️ Free for everyone: https://lnkd.in/d72b66Qa --- Looking forward to seeing you live, everyone! Please share with your friends and colleagues — and thank you so much for your kind interest, trust and ongoing support throughout all these years! 🙏🏾

🧭 Design Principles (https://principles.design), after all these years still a wonderful collection with 230 pointers for design principles and methods, searchable and tagged, from hardware and infrastructure to language and organizations. Kindly put together by Ben Brignell. 👏🏼👏🏽👏🏾 We often see design principles as rigid guidelines that dictate design decisions. But actually, they are an incredible tool to rally the team around one purpose — and also document values and beliefs that the organization embodies. They align teams and inform decision making. They also keep us afloat amidst all the hype, big assumptions, desire for faster delivery and AI workslop. Most importantly, they help avoid never-ending discussions that are often simply a matter of preference or taste. But design shouldn’t be a matter of taste. It should be guided by our goals and values. Design principles help us get there — and avoid debates that might not matter that much in the first place. In times when we can generate design and code faster, we need to decide well what’s worth designing and building — and what values we want our products to embody. Useful examples: 10 (Legendary) Principles for Good Design, by Dieter Rams https://lnkd.in/dxGfzNRb Principles of Product Design, by Joshua Porter https://lnkd.in/eQjWf6nT Guiding Principles for Experience Design, by Whitney Hess, PCC https://lnkd.in/edavNGiF Principles of Web Accessibility, by Heydon Pickering https://lnkd.in/d3f-CBvh Humane by Design, by Jon Yablonski https://humanebydesign.com Designing Voice UX Principles, by Brian Colcord https://lnkd.in/e5Gca3Ta Agentic Design Principles, by Linear https://lnkd.in/d-pSTBtz AI Chatbot Design Principles, by Emmet Connolly https://lnkd.in/e4xD4zes Voice UX Principles, by Ben Sauer https://lnkd.in/eZH6QWxb How To Establish Design Principles ↳ https://lnkd.in/eTfDtQQB, by Marcin Treder ↳ https://lnkd.in/dHNTHmtH, by Better --- 🧲 Design Principles in Design Systems: 18F: https://guides.18f.org 👍 Audi: https://lnkd.in/dwGm7-eP 👍 Carbon: https://lnkd.in/eAqGd4yc Firefox: https://lnkd.in/d2YFW_V4 👍 Gov.uk: https://lnkd.in/efd8UZdX Intuit: https://lnkd.in/ezHG5bPp NHS: https://lnkd.in/ev-t_QPB 👍 Nordhealth: https://lnkd.in/e9NdzVsX 👍 Uber: https://lnkd.in/duPKUtVC Do you have design principles established by your design team or your organization? Please leave them in the comments! 🙏🏽 #ux #design

🎢 The Trust Calibration Spectrum In UX. Why confidence scores are challenging, and how to find the balance between aversion and overreliance ↓ We often assume that the safest way to build user’s trust is through transparency. We show to users what happens, we explain the rationale behind decisions, we break down outcomes into step-by-step instructions that make sense — and hence make product feel more reliable and trustworthy. However, the truth is a bit more nuanced than that — especially with AI products. As Kristine K. writes (https://lnkd.in/dC-RCVUF), “trust in AI sits on a spectrum. The goal then shouldn’t be to push users toward maximum trust. It should reach calibrated trust: people relying on AI when it's right and overriding it when it's wrong.” We want to prevent aversion, and we want to prevent overreliance. But in practice, people often refuse to use AI even when it would genuinely help them (aversion). Others follow AI recommendations on auto-pilot, even when these recommendations are wrong (overreliance). Aversion emerges with: ⌾ Opacity and lack of explanation ⌾ Negative past experiences ⌾ No sense of control or override Overreliance emerges with: ⌾ Explanations users can’t verify ⌾ Transparency as persuasive heuristic ⌾ Satisfaction-driven design ⌾ Cognitive offloading The target zone is calibrated trust — that's when users rely on the product when it's right, and override it when it's wrong. In fact, the same transparency can push users in either direction. When users can verify, transparency supports calibration. But when they can’t, it becomes persuasion. For example, the problem with confidence score is that often they are merely a number. However, there is rarely a good use cases for showing insights with confidence level of 37%, and confidence score of 89% doesn’t always explain what is missing for it to be a reliable insights. As a result, either don’t act at all, or they do — without a clear understanding about the consequences of their decisions. And because AI sounds authoritative and logical, people often trust as they can’t verify it. Eventually it leads to big-scale mistakes, and that’s when product is perceived as incredibly unreliable, poorly designed and neither useful nor helpful for tasks at hand. A better way is to calibrate the level of transparency to match user’s expertise, their knowledge and their decision making process. Just showing more “thinking” behind the scenes can be helpful, but can also be harmful. And: it’s also about the right dose of transparency — just enough for people to be able to verify and catch errors when needed. I wholeheartedly recommend to read Kristine K.'s fantastic article which goes in all the fine details and research about trust, confidence and transparency — a topic that’s often overlooked and oversimplified: https://lnkd.in/dC-RCVUF. #ux #design