Design Workflow Optimization

AI field note: my word of the year is 𝔼𝕍𝔸𝕃: celebrating the art and science of rigorous measurement of AI performance, progress and purpose. (1 of 3) This year delivered a wealth of new AI models, architectures, and use cases - all united by one thread: evaluation. Model benchmarking, evaluation, or just "eval" has evolved from a simple, singular measure to a more complex blend of stats, metrics, and measurement techniques. Today's evals help discerning practitioners make pragmatic, informed technology decisions and measures improvements as AI systems are tuned. With AI innovation accelerating, staying up to date on evals ensures informed trade-offs when building intelligent systems, agents, and applications. Let's start by looking at measuring "performance"; the best way we know how to compare model behaviors, and find the right fit-for-purpose. Defining 'good performance' now involves a sophisticated suite of metrics across diverse dimensions. ⚙️ Task eval - beyond raw performance numbers. Today's evals measure how models perform across diverse scenarios - from basic comprehension to complex reasoning, reliability, consistency, and nuanced evaluation of reasoning paths, output quality, and edge case handling. 👛 Token economics - balancing cost, efficiency, and operation. Understanding token costs - both input and output - was essential last year, but evals have evolved beyond raw price per token, to understanding efficiency patterns, batching strategies, and the total cost of operation. ⏲️ Time-to-first-token. Speed is a feature, as they say, and while streaming responses have improved user experiences, this metric has become particularly crucial as models are deployed in production environments where user experience directly impacts adoption. 🔥 Inference compute: The amount of compute used for prediction shapes what problems a model can solve. More compute enables greater complexity but increases costs and latency - making it a pivotal benchmark for 2024. For some light holiday reading to explore this further: Service cards (OpenAI, Amazon), Meta's Llama 3 paper, and Anthropic's evaluation sampling research (links below).

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?

AI performance is won between the specs. A GPU may advertise enormous compute, but real-world AI speed depends on how effectively the entire system moves data, uses memory, serves tokens, and distributes work across hardware. The hardware layer begins with CUDA cores for parallel computation, Tensor Cores for matrix operations, and HBM for feeding data to the GPU. In many inference workloads, memory bandwidth—not peak compute—becomes the actual constraint. As systems scale, communication matters just as much. ↳ NVLink connects GPUs directly ↳ InfiniBand links GPU servers into high-speed clusters ↳ Data movement determines whether expensive compute stays productive Measurement reveals what specification sheets cannot. FLOPS describe theoretical performance. MFU shows how much of that compute a training run actually uses. Compute-bound versus memory-bound analysis identifies whether the workload is waiting for calculations or data. For inference, users experience two metrics immediately: ↳ Time to First Token ↳ Tokens per Second Precision techniques such as mixed precision, quantization, FP8, and FP4 help models use less memory and achieve higher throughput. Then come the techniques that make inference more efficient: ↳ KV Cache avoids recomputing previous tokens ↳ Continuous batching keeps GPUs productive ↳ Flash Attention reduces slow memory operations ↳ PagedAttention improves KV-cache allocation ↳ Speculative decoding accelerates generation ↳ Disaggregated serving scales prefill and decode separately At larger scale, data, tensor, pipeline, and expert parallelism distribute models and workloads across multiple GPUs. The real lesson is simple: AI compute is not one GPU, one benchmark, or one performance number. It is a complete system of compute, memory, networking, precision, inference, and parallelism. Understanding these 25 terms helps teams make smarter decisions about speed, scalability, infrastructure, and cost.

The AI Tools That Keep My Sanity (and Content) Intact – PART 1 Picture this: You’re juggling Instagram, new Facebook Ads Updates, Google Looker Studio Scripts, a client deadline, 3 team meetings, a podcast idea, and that one post you swore you’d write on Sunday—but now it’s Thursday and nothing’s posted. Welcome to the life of an Agency Founder. Here’s how I stopped chasing the clock and started creating with clarity—by building my AI-powered creative team. 1. ChatGPT – My Creative Co-writer When my brain is fried but the deadline’s hot, I’ll literally type: “Give me 10 carousel hooks on burnout that sound like a punchy podcast intro.” In less than a minute, I’ve got ideas that feel like me—just… sharper. 2. Prompt Genie – My Prompt Whisperer Ever stare at ChatGPT like, “What do I even say to you?” Prompt Genie steps in with high-converting prompts for content strategy, Reels scripts, marketing copy—even campaign names. It’s the “ask better questions, get better results” cheat code. 3. AIPRM – The Strategy Vault This Chrome extension is a playbook on steroids. From SEO blog outlines to B2B email funnels, AIPRM lets me tap in into crowdsourced, tried-and-tested AI prompts. One click, and I’ve got a LinkedIn thought-leadership post that sounds like I spent hours on it. 4. Jasper – The Brand Voice Specialist When I’m ghostwriting or working with a client’s brand, I use Jasper to lock in their tone. Whether it’s cheeky, corporate, or emotionally deep—Jasper mimics their voice like it’s been reading their newsletters since 2018. 5. Canva Magic – My Design Studio in a Browser No Photoshop. No overthinking. Drop in your product, let the AI suggest visuals, captions, and even animations. Last week, I turned a plain toothpaste photo into a “wellness ritual” visual series—under 10 minutes. Why does all this matter? Because we’re not just content creators—we’re editors, marketers, strategists, storytellers. And AI? It’s not replacing us. It’s amplifying us. Let the tools do the busywork. You stay brilliant. You stay human. #AIforCreators #WorkSmartCreateSmarter #DigitalStorytelling #CreatorEconomy #ContentMarketingTools #MadeWithAI

🧪 Atomic Design: Building UI Systems That Scale Designing great interfaces isn’t just about making screens look good — it’s about building a system that stays consistent, scalable, and easy to maintain as your product grows. That’s where Atomic Design by Brad Frost comes in — a brilliant methodology that helps UX/UI specialists create robust, modular design systems — not just isolated pages. Here’s how it breaks down: 🔹 Atoms – The smallest building blocks of UI: buttons, inputs, labels. 🔹 Molecules – Groups of atoms forming small functional components (e.g., a search bar with label + input + button). 🔹 Organisms – Larger interface sections made of molecules & atoms, like headers or cards. 🔹 Templates – Page-level layouts that arrange organisms & define content hierarchy. 🔹 Pages – Fully realized screens with real content where the user experience is validated. ✨ Why it matters: Atomic Design gives teams a shared design language, ensures consistency across screens, and allows for scalable growth — so you spend less time fixing inconsistencies and more time improving the user experience. 💬 Whether you're designing a startup MVP or a global product, thinking in systems (not screens) is the fastest way to build cohesive, future-proof designs. ❤️ Save this post for your next design sprint. 🔁 Share with your design team and start speaking the same visual language today. #UXDesign #UIDesign #AtomicDesign #DesignSystems #ComponentDesign #ScalableUI #ProductDesign #UXStrategy #AtomicDesignMethodology #DesignThinking

"Graphic design is dead" they said. AI just killed another industry. But after 18 months creating with AI tools daily? The opposite is true. Design isn't dying. It's evolving at warp speed. Yesterday's workflow: ☒ 3 hours sketching concepts ☒ 2 hours in Photoshop ☒ 1 hour tweaking colors ☒ Endless client revisions Today's AI-powered reality: ☑︎ 20 concepts in 20 seconds ☑︎ Instant color palettes ☑︎ One-click variations ☑︎ Real-time collaboration Here's what most people miss about AI design: AI handles output. You handle outcomes. Tools like Ideogram can generate 100 logos. ↳ But which one tells your brand story? Adobe Firefly creates perfect palettes. ↳ But which one triggers the right emotion? Figma AI builds responsive layouts. ↳ But which one guides user behavior? The gap between AI output and human insight? ↳ That's where designers thrive in 2025. My AI + Design workflow: 1 → Start with strategy What problem are we solving? AI can't answer this. You can. 2 → Generate variations fast Prompt: "Modern tech logo, blue accent, minimal" Get 20 options in seconds. 3 → Curate with taste Pick 3-5 that align with brand values. Your eye matters more than ever. 4 → Refine with precision Take AI drafts into your core tools. Add the human touches AI misses. 5 → Test with real users AI can't predict emotional response. Only humans understand humans. The tools crushing it right now: ✦ Ideogram – Logo concepts at light speed ✦ Midjourney – Brand visuals that pop ✦ Adobe Firefly – Integrated AI magic ✦ Canva Magic – Templates on steroids ✦ ChatGPT – Concept art instantly Lazy designers? Yes, they're toast. Strategic designers? They're 10x more valuable. Clients don't pay for pixels. They pay for: • Visual strategy • Brand coherence • Cultural context • Emotional impact AI can't hop on a discovery call. AI can't understand business goals. AI can't feel what resonates. The new designer toolkit isn't just Adobe anymore. Now it's: → Prompt engineering → AI tool mastery → Strategic thinking → Rapid iteration → Human insight The best designers won't fight AI. They'll ride it like a rocket. More output. Better strategy. Happier clients. The creative process just got an upgrade. And designers who embrace it will thrive. Graphic design isn't dead. It just learned to fly. Follow Charlie and Sana for more AI insights. ♻️ Repost if AI is changing how you create.

I stopped using Claude as a chatbot. I started using it as my Product Design team. 🚀 Most designers use AI for generating copy, rewriting text, or creating random UI ideas. That's only scratching the surface. While working on an AI Interview Engine project, I realized Claude can contribute to almost every stage of product design from problem discovery to developer handoff. Today, my workflow looks very different. How I use Claude to ship AI-powered products 🌱1. Discover & Research - User pain points - Competitor analysis - Market research - Interview questions - Research synthesis Instead of spending hours organizing notes, Claude helps me identify patterns and opportunities faster. 💡 2. Product Thinking & Strategy - PRDs - Feature prioritization - User journeys - Edge cases - Success metrics This is where Claude becomes powerful. Not because it gives answers. Because it helps me ask better questions. 🎯 3. UX Flows & Information Architecture - User flows - Task flows - Journey maps - States and scenarios - Error handling Many UX problems appear before a single screen is designed. 🎨 4. Claude Design + UI Creation - Screen concepts - UX critiques - Design system recommendations - Interaction ideas - Accessibility checks Claude helps me explore more possibilities before committing to a direction. ⚡ 5. Claude Code: This changed my workflow completely. I use it for: - Frontend prototypes - Design system implementation - UX validation - Product simulations - Documentation generation Seeing ideas come alive in code helps uncover issues much earlier. 🌻6. Validation & Iteration - Heuristic reviews - UX audits - Edge case testing - Scenario generation - Accessibility review The goal is not to validate designs. The goal is to validate decisions. 🚀 7. Handoff & Delivery - Functional requirements - Developer documentation - Acceptance criteria - Component behavior - Interaction specifications Developers get more clarity and fewer assumptions. The biggest lesson? AI didn't replace my design process. It amplified it. The more product thinking, judgment, and decision-making I bring, the better the output becomes. That's why I believe the future belongs to designers who can combine: 🧠 Product Thinking 🤖 AI Leverage 🎨 Design Craft 📈 Business Understanding Not just screen design. What part of your design process are you currently using Claude for? 👇 I'd love to learn from your workflow too. #uxdesign #productdesign #uidesign #claudeai #claudecode #artificialintelligence #designsystems #uxresearch #figma #designleadership #aidesign #productdesigner #userexperience #designthinking #aitools #aiindesign #ai #aidesigntools #designercommunity # #juniordesigners #learning #linkedin #creator #uiux

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 👇

🛠️ AI coding skills might become one of the biggest opportunities for Design Systems. Jakub Krehel shared his open-source /better collection with four new Claude skills focused on improving interfaces. At first glance, they're built for developers. But after looking at them, I couldn't stop thinking about how valuable this approach could be for design systems. Also inside Figma as a component. The new skills review accessibility, layout, interface quality, and product writing. Instead of generating something from scratch, they analyze an existing interface, identify issues, and suggest improvements. That shift is what makes them interesting. Some of the new additions include: 🔹 /better-accessibility to identify accessibility issues and recommend fixes. 🔹 /better-layout to improve spacing, hierarchy, responsiveness, and visual structure. 🔹 /better-writing to make product copy clearer and more consistent. 🔹 /better-interface to combine every skill into a complete interface review. What caught my attention isn't the implementation. It's the mindset. I've seen countless AI tools focused on creating UI faster. I rarely see AI focused on improving existing interfaces. That's a much harder problem, and arguably a more valuable one for mature products. Now imagine bringing this concept into Figma. Instead of asking AI to generate a screen, designers could run specialized review skills directly on the canvas to detect inconsistent spacing, missing accessibility requirements, weak visual hierarchy, design system violations, or opportunities to simplify components before handing designs to engineering. To me, that's where AI becomes truly collaborative. Not replacing design decisions, but acting like an experienced design reviewer that's available whenever you need a second opinion. I believe this "review-first" approach will become one of the next big directions for AI in design tools. Would you rather use AI to generate interfaces from scratch, or to review and improve the ones you've already designed? Drop your comment below! 👇 🔗 Jakub Krehel's GitHub: https://lnkd.in/dDw3Ca5Q #AI #Figma #DesignSystems #ProductDesign #UX #Accessibility #DesignOps #InterfaceDesign #ArtificialIntelligence #OpenSource

Workflow design is the next language of creativity. At Adobe MAX, Adobe introduced two new workflow design tools: Firefly Creative Production for Enterprise and an upcoming system called Project Graph. Both use node-based interfaces that visualize and connect Adobe’s growing suite of Creative Cloud APIs. Project Graph takes it a step further. It allows workflows to be packaged, shared, and customized - essentially turning them into reusable, photoshop plugins. Node-based design isn’t new. Tools like ComfyUI, n8n, and Langflow have been building this space for a while, helping consolidate the rapidly expanding universe of AI capabilities. But this is the first time a major creative software company has fully embraced this approach and targeted it at the creative community. The baseline skill set for digital creatives is about to change. Those who can design automated, modular workflows will soon outpace those who can’t. Can’t wait to get hands-on with these and see what’s possible. #AdobeMAX #AI #CreativeTechnology #WorkflowDesign #Automation