Rapid Prototyping Workflows That Boost Team Collaboration

🚀 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?

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

TL;DR: Slack → GPT → Spec → Prototype 💡 Ever find your best ideas buried in a Slack thread—and the manual process of extracting them slowing you down? 💬 I ran into this often at Bumble Inc., so I built a custom GPT to help. Instead of manually recreating brainstorms or sketching early prototypes, I now take screenshots of the Slack discussion and send them to my GPT. It parses the chat, extracts the key ideas, and formats them into a mini spec—snack-size and ready to drop into Figma for prototyping. ⚡ This includes annotating who talked about which part of the idea, so when we go back to discuss things we can talk to that person and get even more context. It’s essentially “rapid prototyping,” but instead of sketching wireframes, I'm turning entire conversation threads into structured specs. Not only is it faster, but it also helps me spot conversation imbalances—like when we spent too long on one idea and barely touched another. I’m always looking for ways to pull more value out of Slack discussions—what’s working for you? 🤔 #ProductManagementAI

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!

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 👇

Uber published something that matches exactly what we're seeing at Nearmap: AI prototyping is collapsing the gap between "idea" and "shared understanding." Their best line: "Two hours of prototyping unblocked four weeks of discussion." That's not hyperbole. When you put a clickable thing in front of a stakeholder instead of a written description, the conversation changes immediately. You stop debating what something might feel like and start debating whether it's the right thing to build. What resonated most for me: The PRD isn't dead — it's just no longer the starting point. At Uber, they found that prototyping first produces a sharper PRD, not no PRD. Problem framing → early prototype → informed spec → final design. That sequence matters. Exploration got cheap. One of their PMs explored six concepts in 20 minutes. That's the real unlock — not speed to ship, but speed to learn. When trying a direction costs almost nothing, you stop converging prematurely on the first idea that feels safe. They opened it up beyond product and design. Engineers, ops teams, even sales built their own tools. This is where it gets interesting. When the cost of making an idea tangible drops to near zero, the best ideas win regardless of who had them. We're living this at Nearmap. Our product team uses Claude Code and MCP to build working property intelligence demos — not mockups, actual data flowing through actual systems — in hours. It changes how we pitch, how we align internally, and how fast we can test whether a product direction holds up against real customer needs. The companies that figure out how to prototype with real data, not just pixels, are going to move at a fundamentally different speed. https://lnkd.in/gWAZGB3m

How do multiple people collaborate on the same prototype when using tools like Lovable? It seems we building a World of solo workers. AI-native prototyping is changing the rhythm of product creation. I usually prompt ideas between meetings as soon as I have an inspiration :). Instead of long design cycles, we now have: idea → prompt → prototype → iterate → deploy. But when more than one person is shaping the prototype, things get interesting. Some patterns I want to experiment with: A) Separate thinking from building Have one shared doc where the team writes prompts, constraints, and hypotheses before touching Lovable. It reduces prompt chaos. B) Assign a “prototype driver” Too many people prompting the tool at the same time creates noise. One person drives. Others critique. C) Prompt as version control Save the prompts that generated meaningful changes. Treat them like commit messages. D) Snapshot often When the prototype reaches a meaningful state, duplicate it. AI iteration can easily destroy something that was working. E) Define roles early Example: One person focuses on UX flows One on data / logic One on prompting the system behavior Without roles, everyone edits everything. We’re still figuring this out. AI tools made prototyping dramatically faster, but they also changed how teams collaborate. Curious to hear from others experimenting with Lovable, Replit, v0, or Cursor: How are you collaborating on AI-generated prototypes with your team? What works? What breaks? #ArtificialIntelligence #ProductDesign #ProductManagement #AI #leadership

PMs: Stop writing AI prompts. Start building AI prototypes Prototyping used to require engineering resources. Now it requires Tuesday afternoon. AI tools let PMs build working prototypes in hours instead of weeks. This isn't just faster—it fundamentally changes how product development works. Three workflows that used to be sequential are now collapsed: Stakeholder alignment → Show them a working prototype instead of debating slides for three meetings User validation → Test assumptions with real users before writing a single eng ticket Feasibility proof → Demonstrate it actually works before asking engineering to commit sprint capacity This is the unlock. Not "AI makes PMs code" but "PMs can now de-risk decisions that used to require significant engineering investment." The bottleneck shifted from "can we build this?" to "should we build this?" That's a fundamentally better problem to have.

🚀 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

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.