Prototyping Workflow Enhancements

🚀 Level up your prototyping workflow: How to share multiple versions of your vibe-coded prototype Working on a complex prototype and need to show stakeholders different variations? Or running A/B tests with users? Here's a game-changer I just set up for our team: The problem: You're iterating on a prototype but need to keep the "stable" version accessible while testing new ideas. Or you want to run user research comparing two approaches. The solution: Deploy each Git branch to its own unique URL. Now our prototypes live at: main → primary "stable" prototype URL variant-a → /variant-a/ variant-b → /variant-b/ Why this matters for designers: ✅ Stakeholder reviews. Use the Github desktop app to switch between versions — "Here's the current version, and here's what we're exploring" ✅ User research — Run proper A/B tests with different participants seeing different URLs ✅ Iteration without fear — Experiment on a branch without breaking what's already working ✅ Documentation — Each variation has a permanent, shareable link The setup takes minutes using GitHub Actions. Once configured, every time you push changes to a branch, it automatically deploys to its own URL. This setup works particularly well at companies with security restrictions on teams that already use Github. Showing always beats telling. If you're a designer working with code-based prototypes, this workflow is a must-have. Happy to share the technical setup if anyone's interested! Also curious — what tools or workflows have changed how you share work with stakeholders?

Wow. I just built 3 mini-apps for PMs in under 10 minutes: an empathy mapper, a journey analyzer, and a competitive analysis tool with Opal (Google Labs). No PRD. No Figma. No tickets. Just an idea → an experience. Instead of debating documents, I’m now sharing working mini-apps with my team ask them "react to this, let’s refine it” I used Opal to prototype the vibe with an: -Empathy Mapper -User Journey Analyzer -Competitive Landscape Tool Each one took minutes. Each one was immediately shareable. Each one changed the conversation. Use Opal when: -You want to validate an idea before writing a PRD -You need a quick tool for a workshop or meeting -You want to make research or concepts visible -You want to better empathize about your user Think of Opal as your 10-minute lab. If it takes longer than that, move it to a full prototype — that’s where other AI prototyping tools come in. Tips for PMs adopting this workflow -Start tiny. Your first Opal app should take under ten minutes. That constraint keeps you focused on intent, not polish. -Think in verbs, not nouns. Prompts like “summarize feedback” or “visualize trends” produce far better prototypes than static descriptions. -Collaborate live. Invite designers, engineers, and stakeholders into the session. Watching the prototype evolve creates alignment faster than any meeting. -Reflect. After every prototype, note what worked. Each build sharpens your prompting instincts and your product intuition. 🔗 Guides + masterclass in the comments 👇

𝐖𝐞 𝐬𝐭𝐨𝐩𝐩𝐞𝐝 𝐰𝐫𝐢𝐭𝐢𝐧𝐠 20-𝐩𝐚𝐠𝐞 𝐏𝐑𝐃𝐬. Now we build prototypes instead — and it’s completely changed how Databricks PMs align on solutions. A product manager’s job is still the same at its core — identify a problem that, if solved, drives adoption or revenue. But what we’ve learned is this: aligning on the problem isn’t the hardest part. Aligning on the solution is. Traditionally, this meant messy slides, slow UX cycles, and static mockups. PMs would test ideas with customers using decks or clickable Figma files that took days (or weeks) to build. Each round of feedback felt like a mini product cycle. With 𝐯𝐢𝐛𝐞 𝐜𝐨𝐝𝐢𝐧𝐠, we’ve flipped that. We now prototype directly to test and iterate live with customers. When customers can use something, not just look at it, the insights are richer, and we can see where expectations diverge from design. We tweak the prototypes between user interviews, learning faster than ever before. Before GenAI, PRDs were 20+ pages long and few people read them. Now we skip them entirely. PMs replace written specs with working prototypes and run “prototype reviews” instead of doc reviews. We’ve even developed a Plan/Build workflow, inspired by Claude Code: 🧠 𝐏𝐥𝐚𝐧 𝐌𝐨𝐝𝐞: use an AI assistant to reason through the design — feeding it jobs-to-be-done, API specs/information architectures, and refining until the assistant truly “gets it.” ( 💡 Pro tip: many on our team use Wispr Flow for voice-to-text — it makes iterating on ideas faster and more natural than typing) ⚡️ 𝐁𝐮𝐢𝐥𝐝 𝐌𝐨𝐝𝐞: prompt your AI assistant to generate *page-by-page* UI prompts for your vibe code tool of choice, switching between modes until the design feels right. Incremental building by page is key here! Most of our prototypes today are UI-only (no backend), but they’re powerful enough to test flows, get real feedback, and lock in what the MVP should be. ➡️ Our next step: connecting to real data — turning prototypes into Databricks Apps customers can actually use. We joke that “no engineers were harmed in the making of this prototype” — but the impact is real. We’re moving from writing about ideas to feeling them. 👋 Would love to hear how other teams are replacing PRDs with prototypes in the comments.

How a $3 cardboard mockup prevented a 3-month schedule delay. (a fake story with a real lesson) A design team had been designing a new assembly line layout for months. CAD models, workflow simulations, ergonomic studies, virtual reality models - everything looked perfect on the computer The client approved the design. Equipment vendors were ready to build. Then the rookie designer suggested something that seemed ridiculous: "Let's build this out of cardboard first." The sourcing team rolled their eyes. They were going to miss the order window. They would have to re-quote. But the engineer spent a Saturday afternoon with cardboard, tape, and measuring tape. Monday morning, within two hours of walking through the cardboard layout, the 'operators' found five critical problems. The parts bins were too far from the assembly station. Workers would be walking 30 extra steps per cycle. The quality inspection station blocked the main workflow and created a bottleneck. The tool changeover required reaching across the aisle during operation. talk about a safety nightmare.. Their beautiful CAD model assumed perfect spacing, but real humans with safety equipment need more room. The cardboard mockup revealed what months of digital modeling missed - how the layout actually feels to use. They redesigned on the spot, moving cardboard boxes around until the flow felt right. The purchasing team was relieved, as the $200,000 order was de-risked. The final installation worked flawlessly on day one. No expensive equipment relocations. No workflow disasters. No worker complaints. Sometimes a few hours with cardboard teaches you more than months with CAD. Sometimes physical #prototypes have a feel that screens and AR can't capture. How are you testing your ideas? Are you waiting until you have the design 'done' to order the samples? Prototyping is learning. Learn faster by building. #manufacturing #design #prototyping #designthinking and below is a chatgpt representation of this story 🤓

From Figma Make to Figma: The Complete Transfer Guide Someone asked me how to get designs from Figma Make into the Figma editor without starting from scratch. Instead of a quick reply, I recorded a full walkthrough because this workflow gap affects so many designers. Here's the solution that's been a game-changer: The Problem: Figma Make is perfect for rapid iteration and stakeholder buy-in. But once you've got approval on a concept, moving those screens into Figma for detailed design work usually means rebuilding everything manually. The Solution: html.to.design plugin by ‹div›RIOTS + Chrome extension combo. Here's my exact process: 1️⃣ Publish your Figma Make prototype (get that shareable URL) 2️⃣ Use the html.to.design plugin in Figma to import the live prototype 3️⃣ Configure import settings: Enable auto layout and components, skip local styles if you have an established design system 4️⃣ For multiple states: Use the Chrome extension to capture specific screens and send them directly to Figma The magic happens in step 4. Since Figma Make prototypes are single-page applications, the Chrome extension lets you capture each interaction state as a separate, editable Figma frame. Pro tip: The plugin automatically creates components for repeated patterns and hover states, giving you a solid foundation to build on. This workflow has saved me hours of manual recreation and keeps the momentum going from concept approval to detailed design. The best part? Everything comes in fully editable with proper auto layout, so you can immediately start applying your design system styles and components. What's your biggest workflow challenge when moving between design tools? Drop it in the comments – always looking for new problems to solve. #UXDesign #Figma #DesignWorkflow #ProductDesign #DesignSystems

Lately I’ve been focusing on using AI to improve how I prototype, not just to move faster, but to get better stakeholder engagement when sharing work.   One pain point I kept running into: I can build end-to-end, very real prototypes in Cursor using Figma MCP, and it works fast. But sharing those prototypes easily across my team isn’t great.   Figma Make, on the other hand, is much better for sharing. Familiar links, easy access, and stakeholders immediately know how to use it. It just doesn’t move as fast once prototypes get big.   Then it clicked 💡 What if I combine the two?   Now my flow looks like: – Build complex, realistic prototypes in Cursor – Open that same repo in Figma Make – Share a familiar, clickable prototype across the team   It’s early, but it already gets the right picture across.   This is starting to change how I think about prototyping, sharing work, and closing the gap between design and build. Next up: figuring out how design systems plug into this workflow and make it even more powerful.

From idea to prototype in hours, not weeks. That's been my recent experience experimenting with Lovable, and it's completely changed how I approach ideation and product thinking. Turning abstract ideas into clickable, interactive prototypes in no time means less talking about the concept, and more showing. In one recent build, the moment I shared the prototype, the conversation shifted from “What do you mean?” to “Is this how you see it?” That one shift sparked faster clarity, better feedback, and deeper alignment. No more endless meetings trying to describe what’s in everyone’s head. Here’s what I’ve learned along the way: 𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗮 𝗰𝗹𝗲𝗮𝗿 𝗼𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁. Even with powerful tools doing the heavy lifting, I start by organizing my thoughts on paper—with a clear outline, defined scope, and key user flows. The tool amplifies good product thinking, but it can't replace it. 𝟮. 𝗔𝗹𝗶𝗴𝗻 𝘆𝗼𝘂𝗿 𝘁𝗮𝘅𝗼𝗻𝗼𝗺𝘆 𝗮𝗻𝗱 𝗻𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗼𝗻 𝗲𝗮𝗿𝗹𝘆. This becomes incredibly clear when you're building a visual prototype. Getting your information architecture right from the start saves significant rework later. 𝟯. 𝗘𝗺𝗯𝗿𝗮𝗰𝗲 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗱𝗿𝗮𝗳𝘁 𝗳𝗼𝗿 𝗰𝗹𝗮𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸. Don't aim for perfection on the first build. Get something clickable in front of people quickly. The real insights come from watching others interact with your prototype, not from endless polishing. You can always go deeper and refine the prototype based on those initial insights. 𝟰. 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗹𝗼𝗰𝗮𝗹 𝗳𝗶𝗿𝘀𝘁. For initial builds, leverage local browser cache before connecting to databases or other external tools. It speeds things up considerably and keeps you agile. 𝟱. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗯𝗮𝘀𝗶𝗰𝘀 𝘀𝘁𝗶𝗹𝗹 𝗺𝗮𝘁𝘁𝗲𝗿. A crucial reminder: never store your LLM API keys in plain text, especially if your project is public or remixable. Low-code tools like Lovable don’t just speed up the work—they unlock momentum, clarity, and collaboration. These change the way we think, not just what we build. Been experimenting with Lovable, Replit, v0 dev, or similar tools? I’d love to hear your best practices. ------------------------- P.S Curious about prototyping, product thinking, or AI workflows? I host Sunday brainstorming sessions — DM me if you'd like to join the next one!

📕 Claude: Practical Playbook for Product Designer Anthropic Claude is quickly becoming one of the most powerful tools in a product designer’s toolkit: not just for actual coding, but for quick prototyping, design systems tasks, and UI refinement. I’ve put together a practical playbook based on real workflows I use in my daily work. 📱 Claude for Quick Prototyping 3 practical ways to use Claude for creating web and mobile prototypes and design assets (HTML/Tailwind prototypes, React components, CSS variables, Framer Motion snippets) → Helps you move faster from design intent to working, code-backed prototypes https://lnkd.in/dAWahFYw 🧹 Claude for Code Refinement 5 practical tips for improving AI-generated code quality (reasoning-first prompts, zoom-out → code → zoom-in, using constraints, diff-first refactoring, “boring code” principle) → Helps you get more reliable, maintainable, and production-friendly code https://lnkd.in/eQNNkZbf 🪄 Generate Animated Effects for the Web with Claude 6 practical UI animations you can generate with Claude (animated gradients, magnetic buttons, scroll reveals, skeleton loaders, progress indicators, subtle 3D card effects) with ready-to-use prompts → Helps you add polished, production-ready motion to interfaces without manually crafting complex animation code https://lnkd.in/efa4m3Sy 🎨 Figma + Claude for Design System Tasks 4 practical workflows for maintaining scalable design systems (Figma Variables tokens, extracting styles from UI, auditing variables, and generating internal documentation) → Helps you keep tokens clean, consistent, and documented without manual, error-prone design system maintenance https://lnkd.in/eFNK3Syf #UX #UI #design #productdesign #AI #aidesign #uxdesign #uidesign #designsystem #userexperience #claude

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.