Designing Interactive Prototypes

A $12 prototype can make $50,000 of engineering analysis look ridiculous A team of engineers was stuck on a bearing failure analysis for six weeks. Vibration data, FFT analysis, metallurgy reports - they had everything except answers. The client kept asking for root cause and the engineers kept finding more variables to analyze. Temperature gradients, load distributions, contamination levels, manufacturing tolerances. Each analysis created more questions. Then the intern did something that made the engineers feel stupid. She 3D printed a transparent housing and filled it with clear oil so the engineers could actually see what was happening inside the bearing assembly. Took her four hours and $12 in materials. They watched the oil flow patterns and immediately saw the lubrication wasn't reaching the critical contact points. All their sophisticated analysis was based on assuming proper lubrication distribution. Wrong assumption. Six weeks of wasted effort. The visual prototype didn't just solve the problem - it changed how the engineers approach these types of investigations. Now they build crude mockups before diving into analysis rabbit holes. Cardboard, tape, clear plastic, whatever works. Physical models force you to confront your assumptions before you spend weeks analyzing the wrong thing. Sometimes the cheapest prototype teaches you more than the most expensive simulation. #engineering #prototyping #problemsolving

💻 Designing for a user who is moving at 30km/h is a reality check for any UX Designer. I’m currently deep in the wireframing stage for our Rider App, and it’s a masterclass in Anticipatory Design. When you design for delivery partners, the "happy path" doesn't happen in a quiet office. It happens in the rain, in heavy traffic, and under tight deadlines. To make the experience feel seamless, I’m focusing on three things: 🎖️Reducing Cognitive Load: A rider shouldn't have to "think" about the next step. The app should anticipate it. If they just arrived at the store, the order ID should already be front and center. 🎖️Glanceable UI: Information architecture is life or death. I’m stripping away every non-essential pixel so they can get the data they need in a 0.5-second glance. 🎖️Contextual Triggers: Using anticipatory logic to surface the "Contact Customer" button only when they are within 100 meters of the drop-off point. 🥅 The goal? To move from "reactive" tools to "proactive" partners. We aren't just building an app; we're building a tool that respects their time and safety. It’s a tough task to build from scratch, but seeing a wireframe solve a real-world frustration is why I love Product Design. 🤘To my fellow designers: Have you ever had to design for a "non-desk" environment? What was your biggest takeaway? 👇 #ProductDesign #UXPsychology #AnticipatoryDesign #Logistics #UserExperience #harishux

You’ve been sitting on that app idea for months. Maybe years. But when it’s finally time to build, you freeze. What tool do I use? What if I mess it up? Where do I even start? You’re staring at a blank screen. But what if you didn’t need to “build an app”? What if you just needed a prototype that works, and tells you if your idea even has legs? That’s what we did last Friday inside Mighty AI Lab. Here’s the 4-step process we used to go from idea to live prototype in 60 minutes: 1. Start with the Problem–Solution–User Triangle Before building anything, clarify three things: 1. The problem you’re solving (e.g. “Salespeople procrastinate on high-value tasks”) 2. The user you’re solving it for (e.g. “B2B sales reps who work remotely and feel isolated”) 3. The outcome that defines success (e.g. “Help them start difficult tasks in under 2 minutes”) Without this triangle, your app will drift. With it, every feature decision becomes obvious. 2. Use the IDEA Template A simple framework for structuring the app concept: - Intent: What is the core transformation this app enables? → “Reduce friction and resistance so users take action faster.” - Data: What info does the app work with or generate? → “User check-ins, emotional states, task history, time of day.” - Experience: How should it feel to use this? → “Supportive, low-pressure, playful. Like having a coach, not a critic.” - Actions: What tasks should the user be able to perform? → “Log resistance, get tailored nudges, track progress over time.” This turns vague ideas into a real architecture, without writing a single line of code. 3. Build in Claude Artifacts Instead of using 5 tools to cobble something together, we use Claude’s Artifact mode to: - Generate a UI (forms, logic, layout) through natural language prompts - Link intent to interaction—e.g., “When user selects ‘resisting outreach’, show mindset nudge.” - Iterate live while thinking out loud, which unlocks creativity and flow. You’re not coding. You’re designing with language. 4. Test. Adjust. Ship. Don’t wait for “done.” Start with usable. - Share the prototype with 2–3 target users - Ask: “Would this actually help you do the thing you’re avoiding?” - Based on real feedback, make small tweaks that move the needle - Only then consider porting it to something like Lovable or Retool This step saves founders weeks of wasted effort and gives clarity faster than any brainstorm ever could. Here's a real example: Holly came to the session with an idea: A tool that helps salespeople overcome procrastination. In less than an hour, she had a working prototype. Complete with resistance check-ins, mindset coaching, and game-like progress tracking. Not just imagined. Built. We build real prototypes live, every week, inside Mighty AI Lab. Interested? Join here: https://lnkd.in/gjah4Yen

Some of you disagreed with my last post. Fair. Let's talk. Let me explain the topic a bit more and give you a deep dive into how I see the new process. The old way: Think → Research → Wireframe → Design → Spec → Hand off → Build → Test → Iterate Weeks. Sometimes months. Before anyone touches real code. The new way: ���� Step 1: Start with a problem, not a doc. I don't need a full PRD. I need one thing. Example: "𝘗𝘦𝘰𝘱𝘭𝘦 𝘴𝘵𝘳𝘶𝘨𝘨𝘭𝘦 𝘵𝘰 𝘨𝘦𝘵 𝘩𝘰𝘯𝘦𝘴𝘵 𝘧𝘦𝘦𝘥𝘣𝘢𝘤𝘬 𝘰𝘯 𝘵𝘩𝘦𝘪𝘳 𝘱𝘰𝘳𝘵𝘧𝘰𝘭𝘪𝘰." That's it. That's the brief. 👉 Step 2: Build the ugliest working version. I open Lovable or Cursor and prompt my way to a prototype. Not a mockup. Not a Figma file. A real, clickable, functional thing. 30 minutes. Maybe an hour. 👉 Step 3: Use it. Don't refine it. Don't show it to anyone yet. Use it yourself like a real user would. Click every button. Try to break it. Feel where it's awkward. 👉 Step 4: Now design. This is where design skill actually matters. You're not guessing what the experience should feel like. You already know because you felt it. Now you fix what's broken, remove what's unnecessary, and polish what works. Maybe pivot or try other solutions. 👉 Step 5: Show it, don't spec it. Instead of a 20-page spec, I send a link. "Here, try this. What's confusing?" Real feedback on a real thing beats hypothetical feedback on a hypothetical thing every single time. 👉 Step 6: Iterate in minutes, not weeks. Here's where this workflow really pulls ahead. Someone says, "This flow is confusing." You don't update a Figma file, write a ticket, and wait for the next sprint. You open Cursor, fix it, and send a new link. Same conversation. Same day. The feedback loop goes from weeks to hours. Sometimes minutes. And each round gets sharper because you're iterating on something real. 3-4 rounds of this, and you have something more validated than most products get after months of traditional process. 👉 Step 7: Document what you built, not what you plan to build. Documentation becomes a record, not a prediction. It's accurate because the thing already exists. You can do it at the end or during the process. Why this works: You make decisions with information instead of assumptions. You eliminate 80% of the back-and-forth. You design from experience, not imagination. And you iterate at the speed of conversation, not the speed of sprints. Why it feels wrong at first: Because we were trained to think before we build. And thinking first felt responsible. But we did that because we couldn't build. Now we can. And I don't think it's about ignoring thinking. (𝘔𝘢𝘯𝘺 𝘰𝘧 𝘺𝘰𝘶 𝘢𝘤𝘤𝘶𝘴𝘦𝘥 𝘮𝘦 𝘰𝘧 𝘵𝘩𝘢𝘵) I believe it's about doing it at every step. Refining it based on real feedback. Insights you can get internally and from user testing. If you're still reading this, let me know what you think about it all. ✌️

Here’s something I’ve learned after founding multiple startups (including HOPPR ) and tackling healthcare’s toughest challenges: you can’t fix what you don’t fully understand. It’s not enough to notice a gap—you need to see the problem beneath the problem. Why Deep Problem Understanding Matters 1. It Uncovers Hidden Needs Most incumbents build for their current market, leaving entire groups—often called “non-consumers”—frustrated or underserved. The real opportunity lies in solving pain points nobody else wants to touch. 2. It Guides Sustainable Innovation Building on shaky assumptions is a recipe for constant pivots. When you truly grasp the root cause, your product roadmap becomes a compass—pointing you straight toward what customers really need. 3. It Future-Proofs Your Business Industries evolve fast. If you’re crystal-clear on the why behind a problem, you can adapt solutions over time—staying ahead of disruptions rather than becoming a victim of them. My Take As a radiologist-turned-entrepreneur, I’ve seen firsthand how “obvious solutions” can miss the mark if they’re not grounded in a deep understanding of users’ realities. At HOPPR, we’ve made it our mission to spend time with the very people who will use our AI models and our AI platform —so we can build solutions that genuinely improve outcomes. How to Deep-Dive into Problems 1. Ask “Why?” … Again Whenever a teammate or customer says, “We need X,” dig deeper: “Why do you need that? And why is that important?” Keep going until you hit the emotional or systemic root cause. 2. Go Beyond Your Usual Suspects Talk to the so-called “wrong” customers—people on the fringes or those turned away by incumbents. That’s often where you’ll find the insights that spark major disruption. 3. Prototype Fast, Iterate Faster Even the best market research won’t give all the answers. Build a testable solution quickly, gather feedback, and refine based on real-world usage. Speed of learning trumps perfection. Pro Tip: If you catch yourself making assumptions without concrete insight, pause. Get on a call with a user or run a mini-experiment to verify you’re fixing the right problem. Your Turn: What’s one problem in your industry that everyone’s overlooking—or simplifying? Drop your thoughts below. You might just uncover the spark for your next big breakthrough. #DeepProblemSolving #InnovatorsDilemma #StartupLessons

I gave up 2 hours of my weekend to test Claude Design. Here’s what I found as a PM. Not to review features. I wanted to answer one question: Can I prototype a real product flow without pulling a designer in? I picked a real problem: an internal moderation dashboard I’d been trying to get on the roadmap for weeks. No Figma file. No design brief. Just a prompt. 15 minutes later, I had a multi-screen flow with our brand tokens, a review queue, and an approval workflow. Not pixel-perfect. But clear enough to put in front of a VP and unstick the conversation. That’s the real signal. Not “AI replaces designers.” It’s “PM unblocks herself.” What actually works ✅ Idea → reviewable prototype in minutes, not days ✅ Connects to codebase/Figma to auto-apply brand tokens → outputs stop looking generic ✅ Live parameter sliders per design → tweak spacing, tone, layout without re-prompting What to watch out for ⚠️ Token economics are real → complex flows burn Pro allowances fast. Batch your inline edits instead of chaining prompts. ⚠️ No backend/state → it’s a high-fidelity wireframe, not a shippable product ⚠️ Vague prompts = generic output. Context is the multiplier. Where I would actually use this as a PM • Unblocking early stakeholder conversations before design bandwidth opens • Concept validation with users before committing to a sprint • Internal tools nobody wants to prioritize → show, don’t tell What Figma should watch - Not pixel-perfect editing. They’ll always win there. - It’s the upstream layer: exploration, synthesis, early alignment. If Claude Design owns that surface, Figma becomes a finishing tool, not a thinking tool. That’s a workflow shift, not a threat. My honest take Claude Design won’t replace your design team. But it will compress the time between “I have an idea” and “Let’s align on it.” That changes how product teams negotiate scope, prioritize, and move forward. Worth your 2 hours. Test it on a real problem, not a toy prompt. What’s the one flow you’d prototype first? #ProductManagement #AI #ProductStrategy #Prototyping #EnterpriseTech #DesignSystems

Jane Chen faced a problem most would consider impossible: premature babies dying because life-saving equipment was out of reach. Chen took a different path that changed everything: She reframed the challenge from an access problem to a design problem. Instead of asking "How do we get hospitals expensive equipment?" she asked "What if we rethink what the equipment needs to be?" That shift changed everything. Her team at Embrace abandoned traditional incubator designs completely. No electricity requirements. No complex machinery. No dependence on hospital infrastructure. They created a portable infant warmer that could function anywhere - in homes, clinics, rural areas without power. The design matched the actual conditions where babies needed help, not the ideal conditions of Western hospitals. Here's what most leaders miss when they face impossible constraints: Adding more resources rarely solves the problem. Getting ruthlessly clear on what actually matters does. Chen succeeded because she identified the real constraint. It wasn't money. It wasn't technology. It was the assumption that solutions had to look like what already existed. When you get clear on the right constraint, every decision becomes easier. Your team stops debating and starts building. Resources align. Progress accelerates. This is how breakthrough solutions happen. Not through more analysis or bigger budgets. Through the discipline of asking the right question. The best leaders don't solve complex problems by adding complexity. They solve them by finding clarity that cuts through the noise. Want to develop the clarity muscle that turns impossible problems into breakthrough solutions? Listen to the Lead In 30 podcast where I break down practical frameworks like this every week: https://lnkd.in/d_-Knwhy

Prototyping is how ideas turn into evidence. It surface hidden assumptions, generate better stakeholder conversations, test specific hypotheses, reveal unforeseen interactions, and give you a concrete artifact to evaluate before code or tooling locks you in. Use low fidelity sketches and storyboards when you need speed and divergent thinking. They help teams externalize ideas, reason about user goals, and map flows before pixels appear. They are deliberately rough to avoid premature polish. Move to click through wireframes in Figma when the question is structure and navigation. Validate information architecture, menu depth, labeling, and path efficiency while changes are still cheap. When the feel of interaction matters, use interactive digital prototypes to evaluate micro interactions, timing, and visual polish. Treat them as validation instruments, not trophies. Plan change criteria up front so attachment to a pretty artifact does not silence real feedback. Some questions require real performance and materials. Coded prototypes and functional hardware mockups tell you about latency, reliability, durability, ergonomics, and safety. In medical devices and other regulated domains, high fidelity functional and contextual testing is expected for Human Factors validation. Not every question lives on screens. Experience prototyping and bodystorming put bodies in space to surface constraints that lab tasks miss. Acting out a shared autonomous ride with props reveals comfort, cue timing, and social norms. Wearing a telehealth mockup for a week exposes stigma, routine friction, and alert patterns that actually fit domestic life. Before building intelligence, simulate it. Wizard of Oz studies let a hidden human drive system responses while participants believe the system is autonomous. You learn vocabulary, trust dynamics, acceptable latency, and recovery strategies without heavy engineering. AI of Oz replaces the human with a large language model so you can study conversational realism early. Manage risks like model bias, hallucinations, and outages with guardrails and logging so findings remain trustworthy. Strategic prototypes also matter. Provotypes and research through design artifacts challenge assumptions, surface values, and force early conversations about privacy, power, and trade offs that slides tend to dodge.

The classroom went silent when Fernanda wheeled to the front. She'd been my co-facilitator for Design the Future, the person who pushed me to completely reimagine how we approach human-centered design. For 15 years, she'd wrestled with the same exhausting problem every single night. Her electric wheelchair needed charging. But the socket placement meant diving underneath it, contorting her body, spending 45 minutes trying to connect a charging cord in the dark. One wrong angle? She'd wake up with a dead battery. Trapped in her room until someone could help. Thousands of engineers had the expertise to solve this. Nobody asked her about it. Then I brought a group of high school students into the room. Not to design FOR people with disabilities. To design WITH them. "Do you really think teenagers can handle something this technical?" people asked me. My answer: "They're the perfect people for this. They haven't been taught yet that some problems are supposed to stay unsolved." Six days into the program, everything shifted. Fernanda demonstrated the charging adapter those students co-created with her. She reached down. Connected the charger. 20 seconds. Perfect fit every time. The device didn't just work, it gave her back something she'd been denied for 15 years: Control over her own independence. Before she passed away, Fernanda transformed how I think about design entirely. She showed me that proximity matters more than credentials. That lived experience is expertise. That the best solutions come from collaboration, not charity. The framework those students proved works: Start with the person, not the problem → Fernanda wasn't a case study → She was in the room for every prototype iteration → Her lived experience guided every decision Co-create, don't prescribe → Students listened more than they talked → They tested assumptions constantly → They built what she said she needed, not what they thought she needed Measure what actually matters → Did her daily life improve? → Not: How innovative was the solution? → Not: How many awards did we win? Those students? Several are now studying engineering and accessibility design. One started her own consulting firm focused on disability innovation. And Fernanda spent her remaining years with more autonomy than she'd had in over a decade. Here's what I know you already understand deep in your gut: The people living the problem know things that no amount of research can teach you. Yet most nonprofits still build programs in conference rooms, then act surprised when the communities they're trying to serve don't engage. What would transform in your organization if the next program you launched began with this conversation: "Tell me what you're actually experiencing." Not: "Here's what our data says you need."

I watched 9 copy options paralyze a product team for 3 weeks. But we settled it in 48 hours. A launch was coming. Nine headline variations sat in a Slack thread. Three weeks of replies. No consensus. I see this every week with enterprise clients. Smart people, deep expertise, stuck in their own opinions. Most teams default to one of two bad outcomes. They argue until the highest-paid person decides. Or they ship the safest version and tell themselves it can be tested later. Both bury the actual question: which version helps the user complete their goal? Real users decide that. Not the team. Our team at The Good | Digital Experience Optimization ran a rapid test instead. A qualified panel of real users. Each one given a task on the page, with the headline as the only variable. Two days later, we had data. Two of the nine options performed clearly better. Two were clearly worse. Five were noise. The product team shipped one of the winners. Rapid testing is not A/B testing. You don't need live traffic. You don't need weeks of data accumulation. It's not survey data either. Users are not telling you what they think. They're trying to complete a task and either succeeding or failing in front of you. It is the fastest way to break a stalemate without abandoning the data. When your team is stuck choosing between options, skip the meeting. Run a 48-hour rapid test and let real users decide.