Interactive Design Visualization

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)

Explore the design space of notebook visualization tools with SuperNOVA! Our interactive browser showcases 160+ notebook vis tools surveyed from 8M+ open-source notebooks, highlighting key design implications and trade-offs. 👉 https://lnkd.in/etiMEW2K We present a four-dimensional framework for designing notebook visualization tools. 1️⃣ Notebook-vis data communication (none, one-way, bidirectional) 2️⃣ Data source (runtime, code, external) 3️⃣ Sensemaking context (on-demand, always-on) 4️⃣ Modularity (modular, monolithic) Big thanks to fantastic collaborators Seongmin Lee, David Munechika, and Duen Horng "Polo" Chau! ❤️ Add your notebook visualization tools into the SuperNOVA collection! 📖 Paper: https://lnkd.in/eMPi9-vH 💻 Code: https://lnkd.in/eBWiRwdw 🔍  Survey: https://lnkd.in/etiMEW2K #CHI2024

Visualization has shifted from being strictly a means of communicating design to an integral part of the design process. In many cases, design preceded visualization. Today, the design process is a continuous feedback loop where design is informing visualization, and the visualizations provide immediate and vital feedback to our design. Input is nearly immediately assimilated into the output, and vise versa. Virtual sketching, real-time rendering, shared documents, virtual meetings, etc. have all contributed to our ability to test and retest in a hyper-collaborative environment. This loop informs our internal design team, and also communicates with the external audiences we are presenting to. Three mainstays in my virtual collaborations are Zoom (or any other screen sharing app), Sketchup, and Procreate/Photoshop for live sketching. During a zoom call, we will fly around our model, choose a view, and live sketch to establish the direction. The 3d model is effective in assessing spatial relationships and scale, while the live sketch helps establish composition, character, and narrative. This collaboration makes for a more effective final product while augmenting the design process.

As industrial designers, we constantly strive to find better, faster ways to ideate and iterate. One of the most exciting developments in design workflows recently is leveraging AI tools like MidJourney’s Edit & Retexture functionality to transform basic CAD forms into high-quality visual concepts in minutes. It was a while since I used Midjourney. But thanks to seeing one of the LinkedIn posts by Hector Rodriguez , I was itching to try it. I recently experimented with this approach using a foundational CAD model. I had made this as one of the form explorations through CAD for a coffee machine.I prompted MidJourney to retexture and visualize it in various material and finish combinations. The results? A series of diverse, photorealistic outputs that allows me to explore design possibilities I may not have considered otherwise. This workflow highlights some key strengths: 1. Speeding Up Concept Ideation: AI tools can generate multiple aesthetic directions from a single CAD base almost instantaneously. This means you can explore and test design ideas quickly, without committing hours to detailed rendering or material adjustments in software like Blender or Keyshot. 2. Streamlining CMF Exploration: Traditionally, exploring different colors, materials, and finishes (CMF) can be a long-drawn-out process, requiring meticulous work in rendering software or Photoshop. With AI, you can bypass this step and instantly visualize multiple CMF options. This not only saves significant time but also allows for rapid iteration and refinement. 3. Accelerating Design Evolution: With rapid outputs, you can visualize the potential of your design’s form and materiality in real-world contexts. This allows for informed decision-making early in the process, saving time during later-stage refinements. 4. Enhancing Creative Exploration: By integrating AI tools, we can step beyond our usual design instincts and uncover unexpected design solutions. This not only enriches the process but also pushes boundaries in creativity and innovation. For industrial designers, this hybrid approach—merging CAD fundamentals with AI-enhanced retexturing—opens up new opportunities to iterate faster and more effectively. Once the most promising directions are identified, we can dive into refining the details, ensuring manufacturability, or rendering them perfectly in Blender, Keyshot, or similar tools. This newfound workflow feels like a game-changer to me, especially for balancing creativity with tight deadlines. What do you think about this tool? #industrialdesign #ConceptIdeation #CMF #CMFExploration #productdesign #MidJourney #ai

Last night on BIM After Dark Live, I sat down with Bill Allen from Chaos (formerly EvolveLAB) to walk through the newest Veras updates… and honestly, this might be the biggest shift in AI-assisted visualization we’ve seen in AEC so far. Here’s what blew my mind: • Veras is now integrated directly inside Enscape Real-time to AI-enhanced renders without exporting, snipping, or jumping between tools. • Select-by-Object + Select-by-Material Finally—controlled AI editing. Swap a car, update materials, or refine a portion of a render without rebuilding the whole image. • Image Interrogation + User Presets Veras can now describe your image, reverse-engineer the stylistic prompt, and turn it into a reusable preset. Massive productivity boost for teams. • Brand new Image-to-Video generation Turn any still render into a 5-second animation—seasons changing, lights turning on, people walking. Zero animation skills required. • The big one: a new Nano-Banana render engine This new engine is a game-changer. Cleaner geometry, fewer artifacts, better retention of design intent—and for the first time, 2D inputs can generate 3D-looking outputs. Yes, we turned a 2D AutoCAD plan into a perspective 3D model using only a prompt. Not perfect yet… but the trajectory is undeniable. If you’re curious where AI visualization is truly heading for architects, designers, and VDC teams, this episode is worth the watch. ▶️ Full Episode: https://lnkd.in/ekRGTtPi

Build your own interactive geographic data visual. Here is how. VisQuill Lens lets you place interactive lenses over a map and explore how your data is distributed across geography. Load your own data, assign categories to lenses, drag them across the map and adjust the radius. The visual updates in real time. It runs in the browser and is also available as a Power BI custom visual. Once configured, the visual can be exported as a self-contained web page and hosted on any static server, ready to embed in a dashboard or share as a link. Find it at visquill.com/visuals In this short video I sketch how to build one using OpenStreetMap POI data for London covering groceries, cafés and fast food: starting with a single lens for groceries, then assigning fast food to a second lens, and finally loading a finished example with all three categories in place. Source: OpenStreetMap contributors, via Overpass API. #DataVisualization #Geospatial #London #PowerBI #VisQuill #LocationAnalytics #RetailAnalytics #InteractiveVisualization #OpenStreetMap

This is what a Figma Make prototype looks like with over 1000 prompts. 450: Designing interaction behaviour 350: Fixing bugs/Make errors 100: Making it functional 30: Pull and render data proof of concept I wanted to test pulling in data from Smartsheet's sheets API to see how we can get our teams closer to designing against real data. Through Make I was able to pull a file list, use an OpenAI assistant to interpret the data and generate a dashboard using chart.js rendering based on the sheet contents. Design is what takes this from "a grid with widgets" to a beautifully sophisticated interaction model. Every animation and interaction is designed with intention. This is something you simply can't do with static design screens. Because the design was happening in an interactive surface with real data, I could quickly identify an exhaustive list of interaction behaviours and implement changes within minutes. This is a part of the SDLC that takes weeks or even months. Waiting with anticipation to see how the product design industry evolves to design interaction-first.

How to 10x your design with Figma Make (GUIDE ⭐️). I spent 40+ hours testing Figma Make prompts. Most designers waste time with vague prompts and get garbage outputs. Here are the exact prompts and proven workflow that actually work: 1️⃣. Prompt formula that works: Bad: "Create a dashboard" Good: "Create a SaaS analytics dashboard with: → Left sidebar navigation (240px wide) → Top bar with user profile → 4 metric cards in a grid → Line chart showing revenue trend → Use blue (#2563EB) as primary color" The more you specify = higher quality. 2️⃣ Workflow: Import your Design System first Before your first prompt: → Go to your main Figma file → Export your component library → Import it into Make → Add this to every prompt: "Use components from [Your Library Name]" Now everything matches your brand automatically. 3️⃣. Prompt for Interactive States: "Create a login form with: → Email and password inputs → Show error state when fields are empty → Disabled button state when form is incomplete → Success message after submission → Add smooth transitions between states" Gets you working prototypes, not static screens. 4️⃣. Advanced Prompts: Data States "Create a user list screen with three states: → Loading (skeleton screens) → Success (populated table with 10 users) → Empty (illustration + 'No users yet' message + 'Add User' CTA)" One prompt = complete UX coverage. 5️⃣. The "Design System Drift” Fix: Notice Make using wrong colors? → Try this Prompt: "Analyze my imported library and list all color tokens, then regenerate using only those exact values" It'll self-correct and stick to your system. 6️⃣. Responsive Design Prompt: "Create a pricing page with 3 tiers. Make it responsive: → Desktop: 3 columns side-by-side → Tablet: 2 columns with 3rd below → Mobile: Stacked vertically → Use Auto Layout for fluid scaling" This gets you mobile + desktop in one shot. 7️⃣. Magic Troubleshoot Prompts: Output looks off? → Try: "Redesign this following Material Design principles" → Or: "Make this follow iOS Human Interface Guidelines" → Or: "Apply Gestalt principles for better visual hierarchy" Give it design frameworks to follow. It works magic. Designers who master prompt engineering in 2026 will ship 10x more than everyone else. Bookmark this. 🔖 It will make you a superhuman, go try it (sponsored by Figma): https://lnkd.in/gEvjTSbr *** If you found this useful, consider reposting ♻️ to your network and follow Felix Lee. #FigmaPartner

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

Sketch → xFigura → Nano Banana Pro → Kling → VEED With ZERO 3D Modelling !! Most commercial building visualizations still take 6–12 hours across modeling, rendering, entourage, revisions, and post-production. And the painful part isn’t “design” — it’s the repetitive production work. Sketch → client-ready animation in under 5 minutes. Sounds unrealistic? It’s exactly what most AEC teams need right now. In this video, I’m showing my AI-first visualization pipeline that took a commercial building from: What gets generated in this one flow:  1. Realistic massing + material interpretation  2. Populated scene with trees / context  3. Exploded view for clarity + storytelling  4. Technical front elevation style output  5. A stitched animation sequence ready to share with a client Important detail: ✅ No 3D models were made for this video. No Revit/SketchUp massing, no manual modeling, no heavy rendering setup — just a fast AI pipeline for early-stage visualization. Results I consistently see with this workflow:  • 90% faster turnaround (hours → minutes)  • 5–8 iterations possible in the time it usually takes to render 1  • 60–70% reduction in back-and-forth during early-stage approvals Cleaner communication because the client can “see” the idea immediately This isn’t about replacing architects. It’s about replacing the most time-consuming parts of visualization production. Want the exact workflow + prompts + tool settings? Comment “Workflow” and I’ll share the link for this workflow in our upcoming Visualization Masterclass. #AI #AEC #Architecture #Visualization #Design #GenerativeAI #Workflow #BIM #InteriorDesign