Automated Visual Design Techniques

Every AI model learned from the same templates. That's why your AI-generated landing page looks like everyone else's. I've been measuring this. Ran hundreds of generations through GPT-5.4, Claude, GLM 3.6 and other models across 15 niches. Without design guidance: 30% use Inter as the primary font. 81% are card grids. 78% have low-contrast text. Average of 13 detectable design anti-patterns per page. I've been building Impeccable, an open-source toolkit that teaches AI coding tools real design and detects anti-patterns, and I just shipped v2.0 (link in comments). Here's what's new: - Built an eval harness and found the core skill wasn't improving color and typography diversity the way I expected. Rewrote the detection logic and pushed both significantly further. After the changes: 13 anti-patterns per page drops to 2. - Visual mode & detection engine: 24 rules across typography, color, layout, and motion. Run from the CLI (npx impeccable detect), inside /critique, or with the new Chrome extension. - Chrome extension (just went live): Open DevTools on any page, overlays highlight issues automatically. Copy any finding, paste it into your AI, it has all the context to fix it. - New commands: /shape runs a design discovery interview before any code gets written. /impeccable craft chains that into the full build flow. Works with 11 AI tools. Runs locally. Open source, free. If you're designing with AI today - what's your workflow to get to impeccable design?

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

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

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

Luxury marble epoxy staircases are getting a serious upgrade. Would you do it? Not because of new materials… But because of AI. Today, AI can generate hundreds of staircase designs in seconds: – Perfect marble vein patterns – Metallic epoxy flows – Lighting reflections that feel almost unreal The shift is measurable: • Design iteration cycles reduced by up to 70–90% with generative AI tools • 3D visualization that once took 2–3 days now happens in minutes • Firms using AI-assisted design report 30–50% faster client approvals • Up to 40% reduction in material waste through simulation before execution • High-end interior visualization market growing at ~20% CAGR, driven by AI adoption What used to take weeks of design iteration now happens instantly. But here’s the truth: AI doesn’t replace designers. It amplifies them. The real winners are those who combine: Human taste + AI speed + craftsmanship We’re entering an era where design is no longer limited by imagination— only by execution. And that’s where the real competition begins. #AI #Design #LuxuryLiving #Architecture #Innovation

Anyone can become a designer with AI. But how do you avoid designing slop? I got a masterclass from the man behind the newsletter Designing with AI, Xinran Ma. 🎬 Watch Now: https://lnkd.in/gjfmqJn6 Available Everywhere: Spotify: https://lnkd.in/eyt7agKj Apple: https://lnkd.in/eVZf64gB ✍️ Some of my favorite takeaways: 1. AI Design Is More Than Prompts Designing with AI covers five areas: prompting, ideation, design/prototyping, workflows, and staying conscious. Most people stop at prompts. That's just 20% of the skill. The rest is understanding systems, constraints, and behaviors. 2. Match Tools To Use Cases Custom GPT → effective prompts Lovable → high-quality prototypes Magic Patterns → design variations Google AI Studio → free exploration Cursor → full-stack experiences Claude Code → all-purpose 3. Good Design Passes Four Layers Visual representation, problem-solving, design principles, and implementation feasibility. Most people stop at layer one. They see something pretty and think they're done. Great design works at all four layers. 4. Context Matters More Than Prompt Length Include who the users are, what problem you're solving, what constraints matter, and where this fits in the product. More context equals better outputs. Don't just say "design a button." 5 Add Visual References To Prompts Text alone isn't enough. Upload 2-4 screenshots showing the aesthetic you want. These references anchor AI's output. The difference in quality is massive compared to text-only prompts. 6. Iterate Fast To Get Better Results The magic isn't in the first output. It's in the 10th iteration after you've refined and tweaked. Review, identify what's wrong, tell AI how to fix it, repeat. Speed comes from practice. 7. Always Validate With Real Users AI makes it easy to generate designs. Only users tell you if those designs actually help. Talk to users. Watch them use your prototypes. Listen to their frustrations. Don't skip this step. 8. The Workflow Changed From Linear To Parallel Before AI: sketch, wireframe, design, connect screens, prototype. Sequential. Slow. After AI: describe what you want, generate proof of concept, iterate freely. Parallel. Fast. This is how top designers work now. 🏆 Thanks to our sponsors: 1. NayaOne: The fastest way to test AI and fintech solutions - https://nayaone.com/ 2. Pendo: The #1 software experience management platform - http://www.pendo.io/aakash 3. Maven: Get 15% off Xinran’s course with my link - https://bit.ly/3Y2FUZn 4. Bolt: Ship AI-powered products 10x faster - https://lnkd.in/gyy3VB7Z 5. Gamma: Turn customer feedback into product decisions with AI - https://lnkd.in/g7YNKrJY Don't miss the episode for his live workflows.