Advanced Prototyping with AI Tools

I redesigned my entire UX/UI process with AI. It’s not about “use ChatGPT to brainstorm.” I mean, I rebuilt the whole pipeline. From product idea to prototype. What used to take months? Now gets done in days. Here’s what it looks like step-by-step: 1. Instant User Flows I drop rough product ideas into ChatGPT. (It's not the public one; it's a custom GPT trained on how I think.) It gives me: - Sitemap - User journey - Logic flows All in less time than it takes to make coffee. 2. Wireframes Without Drawing I stopped sketching. I describe the layout in plain English, and Magician does the rest. "Hero. CTA. Testimonials." Boom. Wireframe. No more dragging boxes like it’s 2015. 3. AI-Built Design System Spacing? Typography? Button styles? I just describe the vibe. Tools like Relume and Uizard take that and build me a full design system. This used to take WEEKS. Now it’s done before lunch. 4. Smarter Figma Time Now everything moves to Figma. But I don’t waste time pixel-pushing. AI plugins handle: - spacing - responsiveness - and accessibility. I just make the ideas click. 5. Prototyping = Auto-On Final step? Auto-connect flows with Figma’s AI tools. Clickable. Shareable. Client-ready. Dev-approved. No extra buttons. No guesswork. Here’s the real punchline: AI didn’t replace my work. It replaced the boring parts, so I can focus on design thinking. It’s not about working faster. It’s about designing smarter. We’re not in 2015 anymore. Let’s build like it’s 2030. What part of your UX workflow do you still do manually? Curious to hear.

Top 6 AI tools for design & workflow in 2026 👇 Yes, not all of them are “design tools.” Yes, that’s exactly the point. I spent time exploring tools beyond just UI screens… Because real product work is not just design anymore. It’s workflows. Automation. AI orchestration. Here are 6 that actually matter right now: 1. Paperclip AI https://lnkd.in/dXkCrnbe Local-first AI for organizing research, notes, and work items. But it goes deeper. It acts like an orchestration layer for AI agents. Goals. Budgets. Audit logs. Agent “heartbeats.” If you deal with messy research or multi-step thinking, this is insanely powerful. 2. Flowstep https://flowstep.ai Prompt → UI designs. It generates wireframes and full interfaces on an infinite canvas. You can iterate fast. Refine layouts. Explore ideas visually. Feels like Figma + AI had a smarter child. 3. Moonchild AI https://moonchild.ai Turn PRDs into actual UI screens. It helps with: User flows UX problem solving Moodboards Design systems This is not just generation. It’s structured product thinking. 4. Dify https://dify.ai Visual builder for AI apps. Drag. Drop. Deploy. You can create: Chat apps Text-generation tools Custom AI workflows If you ever wanted to ship your own AI product without heavy coding, start here. 5. Flowise https://www.flowise.io Low-code builder for LLM workflows. Think: Connecting multiple models Creating agent flows Shipping APIs fast Great for prototyping AI features inside real products. 6. n8n https://n8n.io Automation on steroids. Connect apps. Trigger workflows. Automate repetitive ops. Designers ignore this. Smart designers don’t. Because real impact = design + systems. Here is the shift most designers are still missing. The future is not just UI design. It’s: Design + AI Design + automation Design + systems thinking Tools like Flowstep and Moonchild help you design faster. Tools like Dify, Flowise, and n8n help you build smarter. And tools like Paperclip help you think better. AI will not replace designers. But designers who understand workflows will replace designers who only push pixels. Use these tools for: Speed Exploration Systems thinking Execution Not just aesthetics. Because in 2026… The best designers are not just designing screens. They are designing how things work. If you had to pick ONE tool to explore this week, Which one are you trying first?

In two days, we built a mini-Revit, but one made to talk with Artificial Intelligence. Sometimes, for an AI to create freely, existing tools are just too rigid. We needed a simple, fast environment to test concepts, so we built this MVP. It's a basic BIM modeler with a 3D viewer, but its core is radically different. Instead of buttons and menus, the main "interface" is a conversation with the AI via JSON code. Why JSON? Because AI doesn't think in clicks; it thinks in structured data, and it understands JSON perfectly. Geometry becomes a database, a language that the AI can read and write fluently. You ask it to "design a curved, twisted building," and the AI doesn't draw; it writes the JSON code that describes it. The result appears instantly in the viewer, along with its sections, plans, and data tables. This is what we call "AI-First" software: tools designed from the ground up for the AI era. It's not about adapting AI to our software, but about creating software that adapts to the AI. We know this is just an MVP. Real BIM is a thousand times more complex. But the ability to put complex concepts on the table in minutes is revolutionary. It opens the debate about whether we should keep building on old platforms. Perhaps we need to create new foundations, designed for a true collaboration with artificial intelligence. What do you think? See more of our experiments at ixaia.com #AIinArchitecture #AIFirst #BIM #AEC #Revit #JSON #MVP #RapidPrototyping #Innovation #DeepTech #Contech #Proptech #DigitalTransformation #SoftwareDevelopment #FutureOfDesign #R_D #ComputationalDesign #ArchitectureTechnology #StartupLife #IXAIA

Talk less. Prototype faster. The best teams don’t discuss ideas endlessly; they just build them. But how do you get the right prototype fast enough? Most new product initiatives are not about creating a new product. They're about improving existing ones. In other words, they already have a product, customers, and a design language. The machine is slow, perhaps rusty, but it has worked for ages now. Any attempts to improve the process usually failed or gave barely any noticeable improvement. However, this is where the AI comes in and why I’m genuinely impressed with Reforge Build, which has now been launched in beta! It’s an AI prototyping tool made for product teams, not solo builders. It starts where your product already is and accelerates what comes next. Don't take my word for it, try it yourself: Check out Reforge Build and explore what’s possible with AI that actually understands your product: https://lnkd.in/duh4YC_H But why did it impress me? 1) Looks like your product Upload a screenshot or connect to Figma. Reforge Build instantly matches your real design system: colors, fonts, spacing, everything. No endless cleanup. No imagination is needed when painting a vision of a future successful product to the stakeholders. 2) Understanding the context Add your product data, strategy docs, and customer insights. Build the prototypes using your actual tiers, features, and messaging. This won't be just a rough draft, but something your actual design team could have presented to you after weeks of work. 3) Plans before it generates Instead of vague prompts, you define user needs, metrics, and layout priorities. AI creates a plan before generating, so the first version is already close to your vision. After all, you need a workable prototype, not an AI slop wannabe! 4) Explores options, not just outputs This REALLY left me with my jaw on the floor: Reforge Build generates multiple design directions, compares them side by side, and mixes the best ideas. I can only imagine this is the experience of a Product Manager with multiple design teams ready to work on a single project... 5) Works like a team tool, not a solo hack Comment, remix, reuse templates, so your second iteration takes minutes, not hours. Nobody's perfect, not even your AI teammate, but every teammate gets better with proper feedback! Impressive, isn't it? Would such an AI prototype tool speed up your new feature's go-to-market time? Let me know in the comments! #productmanagement #ai #ux

I tried 10+ AI prototyping apps. Only one stood out. Here's why: Don't sleep on this tool. I tried the usual suspects (Lovable, Stitch, Make, Bolt, v0, etc.) But when I found Magic Patterns, I stopped looking. It had everything I needed for collaborative, AI-powered prototyping, especially in the early stages of the design process. Everyone’s debating which AI prototyping tool generates the best UI designs or code. Or they're showing off a random vibe coded app. But I think the real opportunity for product teams is being overlooked. Early-stage collaborative AI prototyping is where the magic happens. Fast exploration, shared context, real momentum. 3 Reasons why Magic Patterns excels at this: 1. Live AI prototyping with others = game changer Magic Patterns lets you invite people to a shared canvas. Review and interact with multiple prototypes in one view. Fork, remix, and build on ideas instantly. It’s multiplayer AI prototyping done right, perfect for my AI design sprint workshops. And perfect for product teams to rally around a problem and explore ideas. 2. Front-end focus, no backend noise You can explore flows and concepts fast, without getting distracted by databases or logic. Many of the hyped AI tools are focused on vibe coding complete apps. But for early-stage work you just need to quickly explore multiple ideas, iterate, get alignment, and test for feedback. For this purpose, Magic Patterns is exactly what I needed. 3. Thoughtful features that speed up your flow Magic Patterns is perfect for first-time AI prototypers. The beginner friendly interface and useful features like "Presets," "Inspiration," and "Polish", make it easy for anyone to experiment with purposeful ideas. Bonus Reason: Don't mistake Magic Patterns for a basic AI UI tool. There are advanced features and smart workflows I’ll show you that make this the most valuable tool I’ve added to my design process in years. I’m hosting a FREE live walkthrough next week where I’ll demo exactly how I use Magic Patterns inside my AI Design Sprint workshops, including best practices and the frameworks I’ve used in real sessions. This is a glimpse into how design, product, and engineering will work together in the AI era. Once you see it in action, you’ll want to run your next workshop this way. Come hang out. It’s going to be fun, useful, and maybe even a little magical. 🪄 Spots are limited. Drop “magic” in the comments or DM me to reserve your spot.

One of the areas that excites me the most about AI is prototyping. I'm constantly trying out new tools so that I can share my experience. And I think what Figma has achieved with Figma Make is very impressive. But to achieve great results, you need to know when and how to use it. Figma Make excels at the following: - Prototyping complex interactions. - High accuracy when translating a design to code. - Coming up with ideas based on an existing design. I’ve used other vibe coding tools to go from idea to product as quickly as possible, without a starting design. But when it comes to high accuracy in design and prototyping complex interactions that would have taken ages with traditional prototyping, Figma Make can be incredible. Here are a few examples of where I use Figma Make instead of traditional prototyping: - Creating interactive components. - Complex interactions for web apps. - Advanced logic or data-heavy products. - Trying out different responsive approaches. - Anything that requires external libraries, such as data visualization. Nowadays, when I want to communicate an interaction idea to an engineer, I first try and do it in Figma Make. After testing it a few times, it becomes second nature. 1. Think of an interaction you want to prototype. 2. Send your design to Figma Make. 3. Describe and build. 4. Duplicate and try alternatives. In this carousel, I'll be taking you through my workflow and examples in detail. (Swipe to get started 👉) -- If you found this useful, consider reposting ♻️ Are you using AI prototyping in your workflow? And when? Let me know in the comments 👇
 #productdesign #uxdesign #ai #figmapartner

🧠 Double Diamond in the AI Era AI has a huge impact on how we build things. And it changes the very foundation of any design process—double diamond. But despite popular beliefs, AI isn't here to eliminate the double-diamond; it's here to stretch it, compress it, and sometimes even loop it in surprising ways. The fundamentals of good design haven't changed: We still explore broadly, narrow down, experiment, and ship. But how we do it is evolving quickly. Think of it like this: Before AI, the double-diamond felt like a marathon-long research cycle, slow iteration, heavy execution work. Today, it's more like a high-speed circuit: fast insights, and strong focus on implementation (rapid prototyping) and validation which leads to constant learning, and tighter human judgment loops. Here is a quick overview of the new double diamond with helpful AI tools: 🔍 1. Discover (AI-Accelerated Research) Before AI: in-depth interviews, manual note-taking, and long synthesis cycles. With AI: ✓ AI-assisted desk research & competitive scans ✓ Auto-summarized interviews (using tools like Condens, Dovetail, Notion AI) ✓ Sentiment & theme extraction ✓ Rapid user persona hypotheses ✓ Problem-space simulation (prompting ChatGPT or Claude, "act like a surgeon, what would frustrate you here?") Outcome changes: You get to insights faster, but you still need to do validation, interpretation, and framing. AI = speed + pattern surfacing, not necessarily user understanding. 🎯 2. Define (AI-Enhanced Framing & Strategy) Before AI: Manual synthesis, slow reframing. With AI: ✓ AI helps cluster themes (tools Condens, Dovetail) ✓ Drafts JTBD, opportunity map, problem statements ✓ Runs "counterfactual thinking" prompts (e.g., prompting ChatGPT "what if the constraint disappeared?") But it won't tell you which problem you should focus on first and foremost; humans decide which problem matters. ✨ 3. Develop (AI Co-Creation) Before AI: Sketch → wireframe → prototype → code With AI: ✓ AI generates first drafts of flows, UI states, microcopy (tools like Figma First Draft or Framer Wireframer) ✓ AI transforms sketches → wireframes → polished UI ✓ Design tokens, DS components surfaced instantly ✓ Interactive prototypes auto-built (using tools like Figma Make) AI will help you move faster, but it's up to you to strategically choose solution direction, consider UX nuance, constraints, quality bar, and manage innovation guardrails. ✅ 4. Deliver (AI-Integrated Execution) Before AI: final polish, dev handoff, QA. With AI: ✓ Design → code translation (tools like Cursor or Vercel v0) ✓ GPT agents catch accessibility issues/errors ✓ AI QA: heuristic review, friction detection ✓ Real-time versioning & code-sync design systems The designer becomes more editor/conductor than pixel-pusher. 👉 Join my free 30-min workshop, “Vibe design with AI” on January 15: https://lnkd.in/ebMepq69 #AI #design #UX #UI

I strongly believe we are well into the age of generalists and it is time for everyone to step up and become a “builder”. Here is how I go from a clear problem statement and the right ideas to solve it, to a working prototype in under a day. It starts with conversations, not a document. Capture the discussion, the “what ifs”, and the edge cases as transcripts in Granola, then let a tight granola recipie (a well iterated prompt) turn that chaos into a sharp problem statement and PRD-quality clarity. No staring at a blank page, no wrestling with structure—just guiding the AI with tight prompts and editing the output until it reflects the real intent. From the same source, other granola recipes can spin out Figma-ready design specs, technical design outlines, and even a sensible code organization, all aligned to the same core idea. Instead of stopping at artifacts, the flow continues straight into a working product. Feed that context into Lovable and, within minutes a functional prototype appears: screens wired together, mock data in place, core flows working end-to-end. Now the iteration loop lives inside the tool—tweaking UX, refining logic, generating comprehensive unit tests, and tightening details with each pass. Every step is automated by clear prompts, but always with a human in the loop to review, correct, and push for higher quality. The result is a very different way of building vs simple vibe coding. No long documents, no endless decks, no weeks of surface-level alignment. Instead, show the working prototype on day one, invite in-depth conversations around something real, and let ideas fail fast or evolve quickly. In this world, the most powerful “generalists” are not the ones who do everything manually, but the ones who know how to orchestrate AI across every step of the journey—from thought, to spec, to design, to code, to wow. #AgeOfGeneralists #FullStackBuilder #AIForProductManagers #FailFastLearnFast #FutureOfBuilding

🚀 #AI is officially eating software design. I’ve built 10+ zero-to-one products over the years, but my latest one was different. It became the fastest, most detailed, and most affordable product design process I’ve ever experienced — all in ~24 hours. Here’s how the idea turned into a prototype, step by step: 1️⃣ #Google Doc – I started the old-school way: dumping messy thoughts, strategy notes, and half-baked ideas. Just me and a blank page. 2️⃣ #NotebookLM – Turned that chaos into a clean, structured pitch deck. It felt like having a co-founder who instantly “gets” the vision. 3️⃣ #ChatGPT – Took the deck + notes and spun them into a complete PRD. Suddenly, the idea had shape, scope, and logic. 4️⃣ #Claude – Added structure and depth. This is where the PRD began reading like something a full product team had been polishing for weeks. 5️⃣ #Lovable – Fed the refined PRD into Lovable, and the product started coming to life visually. Screens, flows, layouts — all within minutes. 6️⃣ Iteration loop – I bounced insights between Claude, ChatGPT, and Lovable. Every tweak made the designs smarter, tighter, and more aligned with the vision. End result: 🧩 A living, breathing design prototype ⏱️ Created in a day 💸 At a fraction of traditional cost Next: Build it out with Codex, Supabase, and Vercel. AI isn’t just speeding up product design — it’s reshaping how software gets built from day zero.

I just built this AI Toolbox prototype in under 2 hours using Google’s Antigravity. But it still needs months of real engineering work. The app takes user inputs and generates strategic outputs, including downloadable PowerPoint slides. Tools like Antigravity, Cursor, and GitHub Copilot have dramatically reduced the time to test ideas. 𝗪𝗵𝗮𝘁 𝘂𝘀𝗲𝗱 𝘁𝗼 𝘁𝗮𝗸𝗲 𝗱𝗮𝘆𝘀 𝗼𝗳 𝘀𝗲𝘁𝘂𝗽 𝗻𝗼𝘄 𝘁𝗮𝗸𝗲𝘀 𝗵𝗼𝘂𝗿𝘀. For validation, that’s genuinely useful. 𝗜’𝗺 𝘁𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲𝗺 𝗮𝘀 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗼𝗿𝘀, 𝗡𝗢𝗧 𝗮𝘂𝘁𝗼𝗽𝗶𝗹𝗼𝘁: they handle scaffolding and boilerplate, but I still want to understand the important decisions in the code and UX. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝗮 𝗽𝗿𝗼𝗼𝗳 𝗼𝗳 𝗰𝗼𝗻𝗰𝗲𝗽𝘁, 𝗡𝗢𝗧 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲. No auth, rate limiting, persistence, observability, or security hardening. Scaling it properly could take roughly 10–20x the prototype time. The barrier has moved from “can we quickly test if this idea works?” to “is this worth investing in properly?” 𝗘𝗮𝘀𝗶𝗲𝗿 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗶𝗻𝗴 𝗰𝗿𝗲𝗮𝘁𝗲𝘀 𝗠𝗢𝗥𝗘 𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀, 𝗻𝗼𝘁 𝗹𝗲𝘀𝘀. 𝗔𝗜 𝗿𝗲𝗱𝘂𝗰𝗲𝘀 𝘁𝗵𝗲 𝘁𝘆𝗽𝗶𝗻𝗴 𝗰𝗼𝘀𝘁, 𝗻𝗼𝘁 𝘁𝗵𝗲 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗰𝗼𝘀𝘁. Domain experts can validate concepts faster, but engineers should still own everything from prototype to production-ready system. 𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲: 𝗱𝗼𝗻’𝘁 “𝗙𝗿𝗮𝗻𝗸𝗲𝗻𝘀𝘁𝗲𝗶𝗻” 𝗮 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲 𝗶𝗻𝘁𝗼 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻. Treat prototypes as disposable, but salvage ideas and any parts that can meet production quality with refactoring and tests. 𝗕𝗶𝗴 𝗼𝗿𝗴 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: explaining 𝗪𝗛𝗬 a working demo still needs months of work. Prototypes prove WHAT to build, NOT that it’s built. AI tools are great at scaffolding and boilerplate, and equally good at generating code that looks right but hides subtle bugs or security issues. Key insights from builds like this: • Great for early validation = 5–10% of the work • AI tools excel at scaffolding and boilerplate • Fast prototyping ≠ fast production development I am currently testing on my site with a small group. Happy to share learnings if anyone's interested in similar rapid prototyping approaches.