Interactive Prototyping Methods

Built 8 interactive UI prototypes with Claude Code in about a week to experiment with different ideas for small HTML apps as problem solving tools. Each one is just a single HTML file. No build tools, no frameworks, no npm install. The lineup: 1. Cable Configurator (39KB) — A* pathfinding algorithm for routing cables through a visual editor. You draw obstacles, set start/end points, and it finds the optimal cable path. Real pathfinding, not fake lines. 2. 3D Configurator (30KB) — general-purpose product configurator with parameter controls and live preview. 3. Side Table Designer (17KB) — furniture design tool where you tweak dimensions, materials, and proportions interactively. 4. Draw-Refine — multi-file system where you sketch rough ideas and an AI refines them into cleaner versions. 5. Inline-Draw-Chat — chat interface that lets you draw diagrams mid-conversation. 6. Thinkboard — collaborative thinking tool, basically a freeform canvas for organizing ideas spatially. 7. Tldraw-Chat — chat interface integrated with the tldraw drawing library. 8. Side Table Grid (7.5KB) — grid-based variant of the furniture designer. The pattern across all of them: single HTML file, vanilla JS, canvas-based rendering, no dependencies. The cable configurator implements real A* pathfinding in 39KB of self-contained code. The furniture designer does real-time 3D-ish projection in 17KB. I think there's something underappreciated about single-file prototypes (Simon WIllison was one of the first I saw point this out in his amazing blog). No build step means you can iterate in seconds. No dependencies means it works everywhere forever. The constraint of one file forces you to keep things simple — and simple often means better UX. The cable configurator is probably the most technically interesting one for me. Implementing A* in a visual editor where users can paint obstacles in real-time was a fun evening project. → Single-file prototypes: no build, no deps, no excuses. Cheers! B)

As a product leader, I’ve spent years refining product development cycles — from ideation to launch. But AI is forcing all of us to rethink the how. Recently, I’ve been diving into how AI can enhance prototyping, and tools like blot.new or V0.dev have genuinely impressed me. What have I learned? 🔹 Instead of static designs in Figma → we’re using blot.new to turn those into working UIs It accepts plain-text prompts and instantly scaffolds React components styled with Tailwind CSS. The UI output is clean, componentized, and ready to plug into a real product. 🔹 Product managers can write functional prompts directly No need to wait for handoffs. A PM can now write something like: “A form with email/password input and a login button, responsive for mobile” …and blot.new returns the actual code and live UI preview within seconds. 🔹 A/B tests without code deployments We can test variations of user flows or UI layouts directly in blot.new, collect early feedback, and refine before it ever hits the dev backlog. What this changes: ✅ PMs and designers are now more hands-on with execution ✅ Engineers spend less time on throwaway prototypes ✅ Idea-to-feedback loops are dramatically shorter This shift has been energizing. And we’re just scratching the surface. Curious if others are doing the same. How are you integrating AI into your product workflow? #ProductLeadership #AIinProduct #PromptDrivenDevelopment #PrototypingWithAI #blotnew #TailwindCSS #React #RapidIteration #LeanProduct

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.

Customer discovery via functional prototypes + PostHog is night & day better than the old school way of asking for feedback on Figma mockups. Here's why: I get to observe actual user behavior instead of asking the user to guess how they might use my product. My favorite example of why this matters comes from a Sony Walkman user study. They asked a bunch of people what they thought about a yellow walkman and they said "so sporty! not boring like the black one!". And yet, when they were given the opportunity to take a walkman home after the study, everyone picked the black one. We learned a lot more from user behavior than we did expressed preferences. Here's my setup for now observing user behavior from prototypes: 1. Create a functional prototype in your favorite prototyping tool (Bolt, Lovable, Reforge Build, Magic Patterns, Claude Code) 2. Ask the prototyping tool to integrate PostHog analytics 3. Ask the prototyping tool to instrument key user actions in PostHog Then you get all of these ways of observing actual behavior: - DAUs \ WAUs \ retention curves - I can actually see if people come back and use my prototype instead of taking their word for it - Action metrics dashboards - I can see what actions people are taking vs not - Post-usage survey - I can add a built-in pop-up survey to ask the user a question about the experience after they have engaged with the prototype - Session replays - I can see exactly where people are clicking and how they are using the product to identify usability issues - Heatmaps - I can see what part of my design is working across all sessions I'd never go back to testing with just a mockup after this.

Happy Friday, this week in #learnwithmz, let's explore how AI is revolutionizing product prototyping, from idea to interactive mockup faster than ever. I’m delivering an internal talk on this topic for my team, and thought it would be valuable to share some highlights here as well. 𝐓𝐨𝐩 𝐀𝐈 𝐏𝐫𝐨𝐭𝐨𝐭𝐲𝐩𝐢𝐧𝐠 𝐓𝐨𝐨𝐥𝐬 𝐟𝐨𝐫 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐫𝐬 -Visily Transform text prompts, sketches, or screenshots into editable UI designs. 🔗 https://lnkd.in/gcerJweq - Uizard Generate wireframes and mockups instantly from text descriptions. 🔗 https://lnkd.in/grdSadcb - Microsoft 365 Copilot Prototype ideas directly within your workflow using Word, Excel, PowerPoint, and Teams. Great for early PRDs, visualizations, and cross-team brainstorming. 🔗 https://lnkd.in/gB2PNq9k - V0 by Vercel Create full-stack web apps from prompts, integrating frontend and backend. 🔗 https://v0.dev/ - Bolt Rapidly build and iterate on AI-driven product ideas. 🔗 https://boltai.co - Lovable Design and deploy AI-powered products with minimal coding. 🔗 https://lovable.so 𝐎𝐩𝐞𝐧-𝐒𝐨𝐮𝐫𝐜𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 - NodeTool: Build and automate AI workflows without code. 🔗 https://lnkd.in/gnnB_7UU - ReacType: Visualize and export React applications with drag-and-drop. 🔗 https://lnkd.in/geQbxbEC 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠𝐬 - Speed vs. Precision: AI tools are great accelerators, but manual polish is still needed for complex workflows and specific needs. - Experiment often: The space is evolving fast; test, learn, and share back. - Check before you use: Always check your company’s policies on tool usage, especially when working with sensitive product data or proprietary designs. 𝐅𝐮𝐫𝐭𝐡𝐞𝐫 𝐑𝐞𝐚𝐝𝐢𝐧𝐠 A Guide to AI Prototyping for Product Managers by Lenny Rachitsky and Colin Matthews 🔗 https://lnkd.in/ge6nbzcr Which AI prototyping tools are in your workflow or on your radar? Drop your experiences or recommendations below 👇 #AI #ProductManagement #Prototyping #AItools #learnwithmz

I recorded a quick walkthrough on how to turn Figma design system components into interactive code using Cursor or Lovable.dev. 🔥 No fluff. Real components. In less than 20 minutes, I built interactive prototypes ready for user testing for: ✅ Tag component (with all states) + specifications with all the details ✅ Dropdown with five variations (different designs) What you'll learn: → How to structure Figma components for MCP → The Cursor workflow (Claude 4 Sonnet vs Gemini 2.5 pro) → What breaks (and how to fix it) → When Lovable.dev helps If you're building or scaling a design system, this streamlines your Figma-to-code workflow. 🧪 Tools mentioned: → Cursor → Figma MCP → Lovable.dev Youtube https://lnkd.in/eZ9wfyz8 Who's testing this workflow? 🙌 #designsystem #cursor #ai #productdesign #figmamcp

Want to turn a fuzzy idea into a prototype you can present, test, and even pitch to investors? There’s no one-size-fits-all tool for prototyping—especially for startup founders and designers trying to move fast. But choosing the right tools early can make all the difference. Here are some advanced prototyping tools that can help you go from idea to functional MVP: ✅ Play — Best for iOS app design and prototyping. Play lets you design and ship apps, and even build AI-enabled experiences and prototype with real data. ✅ ProtoPie — Create high-fidelity, interactive prototypes for mobile, web, wearables, and even automotive UIs. Great for user testing complex flows. ✅ Lovable & Bolt — These AI-powered tools help you build functional MVPs without writing much code. With a bit of prompt engineering, sound understanding of code and product thinking, you can quickly generate usable app prototypes. ✅ Bravo Studio — You can make fully functional native apps for iOS & Android ✅ Framer — Perfect for building beautiful, functional websites with speed. Their recent AI update makes launching a site for your product faster than ever. Relume is also worth checking out. ✅ Figma— Figma’s native prototyping continues to improve, and the upcoming features like Figma make look promising. It’s still one of the fastest ways to mock up and share a product idea. At the early stages, you don’t need a fully built product. Prototyping tools let you test assumptions, gather user feedback, and pitch to investors—without burning six figures on development. Got a favorite prototyping tool I didn’t mention? Drop it in the comments 👇

We changed how we test features. Is something worth building - or not? This shift has saved us weeks and a ton of dev resources 👇 The old way: 1. Design in Figma 2. Build it 3. Launch it 4. Then analyze, and gather feedback on whether it makes sense or not ❌ This often leads to building stuff no one ends up using. Now we do this instead: 👉 D3: Design → Demo → Decide 1. Design in Figma 2. Turn the mockup into an interactive demo with our Storylane Figma plugin - Add voiceovers, guides, CTAs - Share with customers & prospects - Track clicks, drop-offs, reactions If it hits → we build it. If not → we move on. We've: ✅ Killed weak ideas early ✅ Pre-sold features before code ✅ Built what people care about You can check the Storylane Figma plugin (it's free) - link in comments 👇 #figmaplugin #interactivedemos #prototype #PLG

🚀 AI Tools For Product Prototyping I'm incredibly excited about how AI tools transform product managers' ability to articulate ideas visually. These tools are supercharging the product discovery phase - I can validate ideas faster with users, get meaningful feedback earlier, and align stakeholders more effectively. Here's my current favorite stack for rapid prototyping: 🎯 Daily Drivers: • Claude/OpenAI - Perfect for quick UI mockups when you need a simple interface • Bolt - Game-changer for substantial prototypes; can critique or draft PRDs (with proper context and review, of course) • Replit - Great for complete product concepts with functionality and database schemas (Bolt+Supabase can do this too, but I find Replit easier to follow) 🌟 Worth Exploring: • Lovable & V0/Vercel - Making product visualization accessible to PMs • Cursor - Powerful for coding, though it can get complex for non-technical folks What tools are you using to accelerate your product discovery process? Share below! 👇 #ProductManagement #AI #Prototyping #Innovation

🎥 From Static to Interactive - Real AI Prototyping with Bolt.new (Ep. 6/9) The reality of AI prototyping! Watch as I transform our UX Pilot designs into a clickable prototype using Bolt.new - including the technical hiccups and problem-solving that come with it. What you'll see in this episode: ✅ Uploading designs to Bolt.new and generating interactive prototype ✅ Real-time troubleshooting when technical issues arise ✅ Testing complete user flows with mixed results ✅ Discovering functionality gaps that need refinement ✅ Mobile preview testing and honest assessment Key takeaways: 📍AI prototyping tools have incredible potential for rapid validation 📍Not everything works perfectly on the first try (and that's okay!) 📍Some features work great, others need iteration 📍Perfect for testing core user flows before development investment 📍Shows the real, messy learning process with AI tools Why this matters: This is authentic learning - showing both successes and challenges. The prototype gives us something testable even with limitations, which is exactly what we need for user validation. Next week: Using Google Gemini to help iterate and improve our prototype! #ProductManagement #AI #Prototyping #LearningInPublic #Latinasintech #womeninstem