Interactive Design Patterns

Fixing Frustrating UX Patterns for 2026 UX problems aren’t visual They’re behavioral If you ignore how users scan, you create friction Heatmap Insight Users don’t read They scan Heatmaps show: → Uneven focus → Fast drop-off → Missed content Guide attention or lose it F Pattern: For content-heavy pages Scan: → Top → Left → Across Fix: Strong headlines. Clear hierarchy Z Pattern: For landing pages Scan: → Top → diagonal → bottom Fix: Align CTAs with eye flow Layer-Cake Pattern: Users skim headings They skip the rest Fix: → Strong titles → Clear sections → Easy scanning Spotted Pattern: Attention jumps Users look for: → Keywords → Icons → Visual cues Fix: Highlight key elements Marking Pattern: Focus = one area Everything else is ignored Fix: One focal point Less clutter Bypassing Pattern: Users skip weak content Especially generic intros Fix: Start with value Cut fluff Commitment Pattern: Users engage when it matters Relevance drives depth Fix: Build trust fast Be clear Good design looks nice Great design guides behavior If users can’t find value fast, they won’t stay long enough to care

🔮 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

This is the UI question that exposed me in an interview years ago. A senior engineer once asked me: “Can you walk me through how you’d design a drag-and-drop calendar like Google Calendar?” I froze. Not because it was hard, but because I had never practiced UI patterns beyond tutorials. Most frontend engineers fail interviews not because they can’t code, but because they’ve never designed: - a calendar event system - a drag + drop kanban - an infinite scroller - a multi-step wizard - a virtualized list of 100k items - a real-time chat feed - a dynamic form builder These are the patterns that separate coders from engineers. So I decided to fix the gap for today’s developers. I’ve added 20 advanced UI Pattern Interview Questions on iocombats, each one with: 🎥 Interactive demos 💻 React implementation 🧠 Design reasoning 📦 Data models & interaction logic 📌 Real interview-style scenarios ⚡ Optimizations + edge cases Here’s one example: Calendar Event Scheduling (https://lnkd.in/dcdQEiJT) — drag to resize — conflict detection — recurring events — timezone-safe rendering — 3 views (day/week/month) — pointer interactions — event stacking logic It’s basically a mini system design question for frontend engineers. These 20 patterns will level up your thinking instantly. Explore them here 👇 https://lnkd.in/ds8n-Dag Follow Ghazi Khan & iocombats for frontend/full-stack development, UI challenges, and jobs-related content.

After building 6 AI products this year, we distilled the most effective UX patterns for trust and transparency into a practical guide. Inside, we cover: • 10 essential UX patterns for AI • 5 psychological foundations of user trust • Real-world examples you can apply immediately Download it here: https://lnkd.in/dGVeQ7iG ------------------------------------ On a personal note: I’ve written about both great and frustrating AI experiences. It’s a rare moment for designers. We get to invent patterns that have never existed before. But that also means we need to raise the bar together and define what good design truly means for AI products. This guide is our attempt to start doing so.