Automated Visual Design Techniques

Most of the AI-meets-design conversation right now is about converting. Designs to code. Code to designs. Back and forth. It's great. But what about creating? I started with 4 things: → A blank Figma canvas → Claude Code → Figma Console MCP → Material 3's component library One prompt: "build a mobile fintech login screen using the existing components and tokens." Claude analyzed the full design system, picked the right components, set the right properties, and composed the layout directly on the canvas. Real components, real variables, fully bound to tokens. But I didn't stop there! THEN, I asked it to invent a Brutalist theme. → It spun up one of our custom UI designer sub-agents → Created a new variable mode from scratch (acid yellow, zero radii, Space Mono) → cloned the original layout, and restyled everything Same components, completely different look/feel. Switch modes and it all holds together. 15 minutes. Start to finish. The magic is how to stack tooling, not a single tool. MCP for the canvas, Claude Code for orchestration, sub-agents for specialized design thinking, and a solid design system underneath it all (very important). This is a creative tool, not just a conversion tool. Style exploration, mood boards, rapid variable mode testing, pushing your token architecture to see what it can handle... I did this in 15 minutes. I want to see what you can do in an hour. Grab the Figma Console MCP, plug in your design system, and show me! If you need help getting set up or want to talk about making your design system AI-ready, reach out. Check out the new easy-to-follow community setup guides - https://lnkd.in/eNmzhh5S

I built a web app in an hour that can batch-generate videos from After Effects templates, no manual editing in video editing software required. A big part of the value is that you don’t need every person involved to even have After Effects. If someone needs a new version of a video, they shouldn’t need access to the tool just to update a title or color. This is following up on my post the other day about automating After Effects video templates. I keep thinking about the business side of this. When teams support product training at scale, video production becomes a volume problem fast. Say you need to support: ➡️ 16 products ➡️ Dozens of updates per quarter ➡️ 20+ versions of the same video template for a single tool ➡️ Small text changes that still require full manual exports That is a lot of time going into work that is mostly repeatable. So I built a small web app that connects directly to Plainly’s API. Instead of opening After Effects files one at a time, the app can: ➡️ Select a template ➡️ Swap in updated text ➡️ Change values like colors or other editable fields ➡️ Trigger a render automatically ➡️ Return a download link back I pulled this together quickly because I had already spent time getting familiar with Plainly’s platform and how their API works and used a vibe coding tool to build it. One thing I’d do next is add a visual preview of the animation itself. Maybe allow bulk animation creation. And if someone is changing text or colors, they should be able to see what those values affect before rendering, so they are making informed choices instead of guessing. The biggest takeaway for me is that the best automation opportunities are often not in the creative work, but in the manual repetition around it. When the same updates have to happen across dozens of files, or only a few people have access to the right tools, that’s where automation can save time and cost, and free people up to focus on the work that requires judgment and creativity. #LearningDesign #Automation #VideoWorkflow #AfterEffects #EdTech #InstructionalDesign #eLearning

Your designer left. The source files vanished. And your animated logo needs to ship tomorrow. Most teams panic and hire a contractor. Or they strip the animation entirely. Ship a static logo instead. Here's what smart marketing teams do: They use Cursor to rebuild animations from static images. No original files needed. No technical animation skills required. The 5-step workflow that saves your deadline: 1. Start with static vector art Upload your static logo to Cursor. That's your only input requirement. No Figma files. No After Effects projects. Just a PNG or SVG. 2. Prompt the animation with plain English Don't write code yourself. Tell Cursor: "Make these bars dance up and down." The AI translates your description into working SVG animation. You describe the motion like you're talking to a designer. 3. Refine with specific measurements The first output works but feels off. Use online tools to measure the original animation. Find the exact duration in milliseconds. Feed this data to Cursor. Now the timing matches perfectly. 4. Iterate like giving feedback to a junior Talk to the AI conversationally. "Move it a few pixels left." "Speed up the middle section." No code knowledge required. Just directional feedback. 5. Deploy the scalable SVG You now have a production-ready animated logo. Lightweight, scalable, performant. From panic to deployed in 30 minutes. What this means for your team: Zero dependency on finding original files. No emergency designer hiring at 3x rates. Animations that match the original perfectly. Where most teams waste money: They think recreation requires the original tools. So they pay $2,000 for rush design work. When AI can reverse-engineer from observation. And build production assets in minutes. You're treating AI like a tool that needs instructions. When it's actually a tool that needs descriptions. Stop writing code. Start describing what you see. Found this helpful? Follow Arturo Ferreira

#AI struggles with reading and applying brand guidelines accurately when generating content at scale. That's a no-go for an iconic brand like The Coca-Cola Company ❤️™️ So how do we turn static visual identity guidelines from PDF into an intelligent system where each object and the #CocaCola Logo know exactly how to behave and interact with each other in any channel and any format. In collaboration with Adobe and their Firefly AI we innovated Project #Fizzion empowering designers to intuitively train an intelligent design system - called “StyleIDs” - in real-time within Adobe Creative Cloud tools like Illustrator and Photoshop. These smart StyleIDs autonomously capture designers’ intent and visually translate brand guidelines accurately, and without manual interpretation. Once trained, AI-enabled StyleIDs act as real-time guides, enabling agency partners to generate hundreds of localized campaign variations with precision and consistency — freeing creatives to focus on storytelling, not formatting and reading and making sense of written brand guidelines and visual identity systems. The result: fewer errors, faster execution, and stronger brand integrity at scale in the tools everyone uses, keeping the experience tight to the existing design behavior and process of each creative. Read the full story here: https://lnkd.in/dy5MAbjx Only possible as an amazing team effort: Rapha Abreu Joshua Schwarber Kate Schindel Chris Livaudais Benny Lee Ash King Erin Koenig Dong Yuan Kelvin, Sherrin, April, Brian, Emily, Avanish so many more. Thank you for pushing the boundaries, pivoting, iterrating and being the nicest and kindes along the way. ❤️🔥 #innovation #aidesign #experiencedesign #branding #contentcreation #branddesign #visualdesign #designsystem #design #businesstransformation #digital

Most studios grow by hiring. But the future of design businesses won’t be built on headcount, it will be built on systems. AI doesn’t replace designers. It replaces the repetitive tasks that stop designers from thinking clearly and moving fast. At PROHODOS, we’ve built a workflow where AI handles the execution layer, and we stay focused on strategy, clarity, and product decisions that matter. Here’s the system we use: 1. Meeting → Insight Pipeline Fireflies records client calls. Claude AI turns the transcript into a structured brief. We add direction and make the key decisions. Result: 45-minute meeting → 5-minute review (90% saved) 2. Wireframe → Website Flow Relume generates wireframes from the sitemap. Figma Make structures layouts. Claude drafts first-pass copy. We refine architecture, hierarchy, and narrative. Result: First draft in 30 minutes vs. 8 hours (16× faster) 3. Copywriting Engine Claude creates multiple headline, value prop, and CTA options. We choose, tighten, and align them with the product’s story. Result: Better options in minutes vs. hours (12× faster) 4. Website Visual Engine Midjourney + Nano Banana create branded imagery and conceptual visuals. We adjust direction and maintain consistency across the site. Result: Website-ready visuals in 15 minutes vs. 3 hours (12× faster) 5. Graphic Design Engine Claude generates visual specs. Figma Make builds diagrams, frameworks, and infographics, including the one in this post. Impact: 5 minutes instead of 3 hours (36× faster) What still requires human expertise →Strategic thinking →Business context →Product clarity →Client relationships That’s the model we’ve built at PROHODOS: Manual craft where it matters. Automation where it doesn’t. #DesignSystems #AIAutomation  #ProductDesign #DesignOps

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

Nanobanana 2 is out. And honestly… this is where AI image generation starts getting seriously useful, not just “cool”. Most image models could generate pretty pictures. But they struggled with: • text inside images • consistent characters • layouts • editing existing images • brand visuals Nanobanana 2 fixes a lot of that. Here’s what stands out 👇 1. Accurate text inside images Finally: logos, labels, posters, product packaging that actually spell things correctly. 2. Character consistency Create the same person or character across multiple images or scenes. 3. Style transfer Take the style of one image and apply it to another without breaking the layout. 4. Spatial reasoning Objects, diagrams, labels and elements appear in the correct place. 5. Real image editing Modify photos while preserving the subject and composition. 6. Multi-frame storytelling Generate visual sequences with the same characters and continuity. 7. Product visualization Create realistic product ads, mockups and marketing visuals. 8. Environment generation Change backgrounds or scenes while keeping the subject intact. 9. Complex scene understanding Better lighting relationships and layered scenes. What this unlocks 👇 • ad creatives in minutes • product mockups without photoshoots • visual storytelling • AI-generated marketing assets • brand visuals at scale • faster design experimentation We’re moving from “AI art” → to real production workflows. Designers won’t disappear. But the ones who learn AI-assisted design will move 10x faster. Have you tested Nanobanana 2 yet? 🔁 Repost if you want more breakdowns like this. ➕ Follow for practical AI insights. ___________________________________________ 👋 I’m Amit Rawal, an AI practitioner and educator. Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered. ♻️ Repost if you believe AI isn’t about replacing us… It’s about retraining us to think better. Opinions expressed are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently Google LLC) or its subsidiaries or affiliates.

WYSIWYG is dead. WYDIWYG is here — What You Describe Is What You Get. For the longest time, creative software was built for precision and control. Take Adobe Suite for example—its UX got more complicated over time as new tools were added. Over the last two decades, these tools have turned into a cockpit. It is a natural evolution of what pro-users want. When a pro designer makes something in Photoshop, the effort shows. You can see it in the details. And more importantly, you value the work more as an average user — because just opening the software feels intimidating. But tomorrow, that entire UX breaks. Anybody can describe what they want—and the system will generate outputs you can refine. Sometimes even before you finish describing. The clearer your intent, the better the output. We're entering the WYDIWYG era — What You Describe Is What You Get. The new creative UX won’t be about visibility or control. It’ll be about Imagination latency— how quickly you can go from a vibe in your head to something on screen you can react to. And because of this the answer may NOT be Prompt-to-Image Like us, if you’re building AI-Native creative tools, here’s what is changing 1. Project setup HAS to be automated You won’t waste time resizing assets, digging for reference files, or setting up timelines. 2. The canvas can't go away, it will evolve You start using a tool on canvas with your mouse/pen and AI will autocomplete the design like Gmail or Copilot. One click to accept. 3. Intent becomes the interface You say: “make it feel like a 90s surf VHS,” and can immediately visualise the results. But most still refine-able in layers 4. Feedback becomes revision No more Looms and comment threads. Your teammates can makes direct changes. In that world, you won’t be valued for mastering the tool. You’ll be valued for articulating what you want—and knowing when it’s good enough to ship.

Imagine turning 4 hours of tedious graphic design work into just 10 minutes of effort. Welcome to RPA + GenAI. Overwhelmed by the pile up of tasks over Thanksgiving, I was daunted by my next task. I had to create dozens of illustrations for our upcoming website release for the Groups feature. The thought of manually designing SVG graphics in Adobe Illustrator had me dreading the work. Instead, I decided to combine Robotic Process Automation (RPA), ChatGPT, and Midjourney. Combining these three, I built a workflow that generates image concepts at scale. Here’s how it works: 1. ChatGPT ideates a list of 50 image prompts. (5 ideas x 10 sections of my website) 2. RPA inputs these prompts into MidJourney with a custom style vector. 3. The system outputs rough visuals automatically while I focus elsewhere. From there, I select my favorites, pass them to an illustrator for polish, and get scalable, professional-quality vectors in no time. What used to take hours of manual effort now happens in the background. It’s not perfect, but it’s efficient. It saved me 4 hours of work in just one day. This kind of automation doesn’t just save time. It also unlocks creativity. Midjourney showed me 200 image variations for my 10 website sections. It was like having a hyper personalized Pinterest for web design inspo. #Founders #HowDoYouAI #BuildInPublic

How can designers use Claude Code? Not as a chatbot. As a production engine! Tommaso Nervegna recently published a practical guide to move from static mockups to working software without becoming traditional developers. At first glance, it sounds like “AI helps you code.” It’s not that simple. This isn’t about asking AI to generate snippets and pasting them somewhere. It’s about using Claude Code as an execution layer, where design intent becomes runnable output. What’s happening in this workflow: 🔸 Designers describe outcomes, not syntax 🔸 Claude generates structured project scaffolding 🔸 Iteration happens conversationally, with persistent context 🔸 Components evolve into functional UI, not just visual artifacts 🔸 The feedback loop lives inside the AI workflow, not in Jira tickets That’s a different paradigm. This isn’t “design handoff improved.” It’s closer to: design-as-executable-logic. When AI understands the structure, constraints, and system intent, documentation becomes dynamic. It becomes operational. Still early? Definitely. Still messy? In parts. But directionally… this is big. Because if designers can reliably move from concept → structured logic → functional interface with AI as a collaborator, the bottleneck shifts. Less translation. More orchestration. More systems thinking. We’re getting closer to a world where: Design is infrastructure. Prompts are architecture. And iteration cycles collapse dramatically. 🔗 Check the Practical Guide: https://lnkd.in/d_C7Nad6 Would you use Claude Code as part of your design workflow, or does that blur a boundary you still want to keep? 👇 #DesignSystems #designsystem #ClaudeCode #GenerativeAI #AIDesign #DesignEngineering #DesignOps #ProductDesign #UXStrategy #VibeCoding