Design Workflow Optimization

I have spent hundreds of hours analyzing design systems. One of the things that confused me for many years is how to structure color scales and tokens. I have experimented with multiple structures at different sizes of design systems, and at a high-level recommend the following approach: 1. Primitive Colors Your design system foundations should always start with a full color scale that is based on your brand identity. We call these colors Primitives, and your variable/token collection should look like this: - purple-600 - purple-500 - purple-400 - And so on.. To create a Primitives palette you will want to start from your main brand colors and use a tool like UIColors, Supapalette, Colorbox to expand to the full scale. (links in comments) This is a great foundation to have, as it gives you a set of shades that can be used in different ways, and ensures all of them have consistent hues, saturation and brightness. However, Primitive colors are simply not effective when used directly in your designs: - They create ambiguity - Their names have no contextual meaning - They are often misused due to similarity If you have had the “why are there 20 different shades of gray?” conversation with an engineer, you know what I mean. So let’s see how we can improve that. 2. Semantic Colors This is my default recommendation to all product design teams that don’t have a highly complex design system. What you will want to do here is create a new variable collection named Semantic, which is what’s visible in your design files, and comprises of: - Brand / Action - Text - Link - Border - Icon - Surface / Background - Bias - Data / Charts Each color should point to a primitive value, e.g. - text-primary → gray-800 - text-secondary → gray-600 - text-tertiary → gray-400 This takes a bit of setting up, but creates immense long-term value. A great example of a simple, theme-level Semantic structure is Shopify’s Polaris (link in comments) 3. Component-level Semantic Lastly, if you are working on a design system with a lot of complexity and, ideally, a dedicated design systems team, you might want to add another level of hierarchy and specify colors at a component-level. In this structure, you would want to create color tokens based on how they are used in each component. - input-text-filled → text-primary - input-text-placeholder → text-secondary - input-text-disabled → text-tertiary This eliminates all guesswork, but also increases the complexity exponentially. It does serve a purpose though. As design systems scale, you may find that: - A theme-level semantic structure is too restrictive - There is still some guesswork - Decisions need to be documented. An example of this is Uber’s Base and Adobe’s Spectrum design system, linked in the comments. I’m curious to know, what structure are you using for your design system and what has worked well for you? — If you found this useful, consider reposting ♻️ #uidesign #designsystems #productdesign

This isn’t just a design trend. It’s a data-driven shift in how homes are created. How practical is this design? Here’s what AI is changing in residential design — backed by numbers: • AI-assisted design tools can reduce concept iteration time by 60–80% • Early-stage AI simulations cut construction change orders by up to 30% • Material optimization reduces waste by 10–20%, improving sustainability and cost control • Lighting and spatial simulations increase perceived space efficiency by up to 25% • Personalized design increases homeowner satisfaction and resale appeal — premium homes with unique architectural features often command 5–15% higher value These pebble stone stairs are a great example. AI helped: – Optimize stone size and layout for anti-slip safety – Simulate light reflection across textures at different times of day – Balance luxury aesthetics with long-term durability – Integrate the stairs seamlessly into the overall spatial flow The key insight: AI doesn’t replace architects or designers. It augments creativity with computation. Humans define taste, emotion, and vision. AI accelerates testing, optimization, and decision-making. The result.... • Better design decisions • Fewer costly mistakes • More sustainable builds • Truly personalized luxury AI is no longer just transforming software and semiconductors. It’s transforming how we design, build, and live. #AI #Architecture via @diycraftstvofficial #DesignInnovation #LuxuryDesign #SmartHomes #PropTech #FutureOfLiving #SustainableDesign

RAG isn’t just about connecting a model to a vector database. It’s a complete system — with 9 moving parts that must work together to deliver reliable, context-aware responses. Over the last few months, I’ve refined this architecture while working on production-grade GenAI pipelines. Each layer has its own purpose — from ingesting and preprocessing data to evaluating and improving retrieval and generation. Here’s how it breaks down: ➟ Ingest & Preprocess: Collect, clean, and normalize data from multiple sources. ➟ Split Into Chunks: Use semantic-aware chunking to preserve meaning. ➟ Generate Embeddings: Choose embedding models based on task and domain. ➟ Store in Vector DB: Maintain a scalable vector store and metadata index. ➟ Retrieve: Combine dense, semantic, and sparse retrieval for best recall. ➟ Orchestrate the Pipeline: Use tools like LangChain or Vertex AI to automate flows. ➟ Select LLMs for Generation: Route queries to the best-fit model or gateway. ➟ Add Observability: Track performance, latency, and prompt quality. ➟ Evaluate & Curate: Continuously test retrieval and fine-tune your system. What most people miss is that RAG is iterative — not a one-time setup. Observability, evaluation, and feedback loops are what turn it from a demo into a production-ready system. If you’re building GenAI workflows, this blueprint can serve as your foundation — then adapt, optimize, and evolve it based on your data and use cases.

It's not sexy to say, but most of AI transformation has nothing to do with AI. There are 10 steps in the sequence of making an internal process or external product AI-native. Only 1 step is AI, and ironically, the other 9 steps are the far harder part. Step 1: Identify the problem - Find the manual process worth automating. turn your brain off autopilot & turn on your "suck meter". - Funny enough, your company becomes more efficient just by mapping out your processes even if you don't introduce AI. Step 2: Understand the workflow - Map how people actually work today. grab an 8.5x11 piece of paper or Excalidraw and create a flow chart of the workflow from beginning to end. - Least sexy part, but generally where the people driving transformation (FDE, GTM engineer, etc) should spend the majority of their time. Step 3: Collect the data - Gather sample inputs, documents, edge cases - Example: for my content machine ai workflow, I gathered past slack messages/notion transcripts to test automated ideation Step 4: Build the prototype [The AI Part] - Whether its engineer-led or SME-led the goal is to test your hypothesis that there's a better way of doing things for yourself as customer zero. Don't worry about code cleanliness, don't worry about scalability. Step 5: Test & iterate - Before you take the process from single player (only you using it) to multiplayer (many users), you want to beat it up with as many rounds of work & feedback + edge cases as possible. Turning every process into a self-improving loop before scaling is key. Step 6: Integrate with systems - Point-in-time data is good for testing the workflow, but live data is necessary before going into production. Step 7: Roll out & train - Whether the new process lives on a live link, on GitHub or an internal library, next step is hand-holding your peers/users through the onboarding process of your new workflow/product. Step 8: Drive adoption - Embed the workflow in your culture where adoption is tracked, ideas & feedback are celebrated, and new/creative use cases become social currency in your business. Step 9: Empower contribution - Treat your new process like an opensource project. Allow users to become contributors. Whether they are literally pushing code or are simply empowered to add ideas/feedback to a kanban board that gets serviced by engineers, make everyone feel like a builder. Step 10: Measure & capture value - If you're in the experimental phase of AI adoption in your company, fuck ROI. The goal is to empower people to throw a lot of shit at the wall & see what's worth focusing on. You don't need to be scientific during this process. - If you're in the scale-up phase of AI in your business, and you need to realize hard ROI, you need to reskill employees attached to this process, undershoot your approved hiring roadmap, or measurably increase ACV/conversion rate/sales cycle speed.

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 👇

I wasted months switching between AI tools...😐 Until I realized I didn't need a better AI. I needed a better AI workflow.🙌 Like many designers, I kept asking: "Which AI tool should I use?" ChatGPT ? Claude ? Claude Code ? Figma AI? I was searching for the one tool that could do everything. It doesn't exist. After months of experimenting, failing, and refining my workflow, I realized something that completely changed how I work: Every AI tool has a different superpower.🚀 Once I stopped comparing them and started combining them, my productivity and more importantly, the quality of my thinking improved dramatically. Here's the AI stack I'm currently exploring in my design workflow: 🧠 Claude → UX strategy, PRDs, user flows & design critique 💬 ChatGPT → Brainstorming, UX writing & creative exploration 🎨 Figma AI → Wireframes, UI generation & rapid iterations ⚡ Raycast → Everyday productivity & AI shortcuts 💻 Claude Code → Turning designs into production-ready code with developers 🧩 zeroheight → Design systems & documentation 🚀 Lovable → Build and validate MVPs in hours 🤖 n8n → Automate repetitive design workflows with AI agents The biggest lesson? AI won't make you a better designer by itself. But the right combination of AI tools can help you: - Think deeper. - Design faster. - Collaborate better. - Ship with more confidence. We're entering an era where knowing how to orchestrate AI tools may become as valuable as knowing Figma itself. 👏 I'm still learning, experimenting, and changing my workflow almost every week. This isn't the "perfect stack."🌱 It's simply the one that's helping me become a better product designer today. 👇 Which AI tool has genuinely changed your design workflow? I'd love to discover a few more. #uxdesign #productdesign #figma #claude #chatgpt #artificialintelligence #aidesign #designsystems #designworkflow #productdesigner #uidesign #futureofwork #designcommunity #learninginpublic #generativeai #uiux #aitools #ai #aidesigns #aiera #designercommunity #juniordesigners ##learning #designers #community #linkedin #creators

Claude Fable 5 is built for more than one-shot prompts. Its real value appears when the work is long, complex, and spread across multiple decisions. The better you structure the goal, context, memory, and validation loop, the more useful its autonomy becomes. Here are nine ways to get more from it: 𝗠𝗮𝗶𝗻𝘁𝗮𝗶𝗻 𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗠𝗲𝗺𝗼𝗿𝘆 ↳ Record discoveries, decisions, failed approaches, and evolving strategy across sessions. 𝗔𝘀𝘀𝗶𝗴𝗻 𝗮𝗻 𝗔𝗺𝗯𝗶𝘁𝗶𝗼𝘂𝘀 𝗢𝘂𝘁𝗰𝗼𝗺𝗲 ↳ Define measurable success criteria, boundaries, milestones, and the final deliverable. 𝗟𝗼𝗮𝗱 𝗠𝗮𝘀𝘀𝗶𝘃𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 ↳ Include repositories, datasets, documentation, history, and authoritative sources. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗮𝗻𝗱 𝗥𝗲𝗽𝗮𝗶𝗿 𝗜𝘁𝘀 𝗪𝗼𝗿𝗸 ↳ Test assumptions, review requirements, detect weaknesses, and continue until conditions pass. 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝗗𝗲𝗻𝘀𝗲 𝗘𝘃𝗶𝗱𝗲𝗻𝗰𝗲 ↳ Compare documents, tables, charts, and competing evidence before drawing conclusions. 𝗕𝘂𝗶𝗹𝗱 𝗙𝗿𝗼𝗺 𝗙𝗶𝗿𝘀𝘁 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 ↳ Define the fundamentals, challenge assumptions, model behavior, and test predictions. 𝗢𝗽𝗲𝗿𝗮𝘁𝗲 𝗨𝗻𝗳𝗮𝗺𝗶𝗹𝗶𝗮𝗿 𝗧𝗼𝗼𝗹𝘀 ↳ Inspect controls, infer workflows, plan actions, and recover from failed attempts. 𝗥𝗲𝗯𝘂𝗶𝗹𝗱 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗙𝗿𝗼𝗺 𝗦𝗰𝗿𝗲𝗲𝗻𝘀𝗵𝗼𝘁𝘀 ↳ Analyze layouts, identify reusable components, infer behavior, and generate functional code. 𝗗𝗲𝗹𝗲𝗴𝗮𝘁𝗲 𝗖𝗼𝗱𝗲𝗯𝗮𝘀𝗲-𝗪𝗶𝗱𝗲 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 ↳ Map dependencies, design migrations, modify connected components, and run regression tests. The goal is not to give Claude more work. It is to give it clearer outcomes, richer context, and stronger feedback loops. Which of these approaches would improve your current workflow most? 

I experimented with a workflow that combines Gravity Sketch, mixed reality, and Runway's Gen-3 video-to-video AI and got some impressive results, here is what I did: 🚀 Step 1: Using Gravity Sketch in VR, I designed stasis tubes with humanoid figures inside. I placed these models throughout my hallway, integrating them into the real space, using mixed reality mode on my Meta Quest 3 headset. 🎥 Step 2: I filmed myself walking through this mixed reality set, holding a 3D object, capturing my real environment with the 3D models layered in. This gave a first-person view of the scene, as if I were navigating through an alien ship. 🧪 Step 3: Finally, I ran the footage through Runway’s Gen-3 video-to-video AI, using prompts to transform the scene into a space marine navigating an alien ship, complete with eerie stasis tubes and ambient sound effects to drive the atmosphere home. A fast, intuitive way to pre-visualize complex scenes that would otherwise take much longer to design and film traditionally. What this means for creative workflows: 🔹Advanced Storyboarding: With mixed reality, you can set up rough models and get a realistic sense of scale and positioning. You can actually walk through you scene, interacting with it and capturing raw footage directly. 🔹 Quick Pre-Visualization: Using video-to-video genAI, this rough footage can quickly be transformed into something more. It’s a great way to experiment with looks, check in with your client vision, and even lighting before diving into final production. 🔹 Future-Ready Workflows: As video-to-video AI improves, this workflow won’t just be for pre-viz. We’re looking at a future where you could create final-quality outputs straight from this setup, acting out scenes in a mixed reality environment while the AI enhances and polishes everything in real time. Moving towards final generated outputs vs rendered. This opens up a lot of possibilities. You could set up a mixed reality scene, interact with it, and create an entire short film without needing a massive crew or extensive post-production. For now, it’s a powerful way to prototype, storyboard, and explore creative concepts quickly and intuitively. ❓Curious about how mixed reality and AI could transform your creative process? Let’s connect-I’d love to share more insights and explore how these tools can push your projects to the next level.

Your CFO wants to know the return on your software development budget? Here are 5 metrics that actually matter in the boardroom - and they're not story points. As a CTO, I've found these key metrics create a meaningful fitness function for your development organization: 1. Business Value per Feature: Don't just ship features - measure their impact. That new checkout process? Track how it changes conversion rates and order values. 2. Lead Time from Idea to Impact: Understand your value stream. Sometimes a 30-minute deployment is stuck behind weeks of stakeholder meetings. 3. Throughput and its composition: Monitor the balance between new features, maintenance, and bug fixes. When maintenance exceeds 25%, it's time to invest. 4. Quality Signals: Track customer experience, operational efficiency, and technical health. These are your early warning system. 5. Team Health: Happy teams deliver better results. Regular pulse checks predict delivery performance weeks before metrics show issues. But never compare teams through these metrics. Each team operates in a unique context with different challenges. Instead, help each team understand and improve their own trends. Metrics should drive improvement, not punishment. Use them as a compass, not a hammer. What metrics do you use to measure development success?

𝗗𝗶𝗱 𝘆𝗼𝘂 𝗽𝗹𝗮𝗰𝗲 𝗮 "𝗖𝗮𝗻𝗮𝗿𝘆" 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻? The sort of early warning detection system which monitors your automated processes and sings when irregularities occur? Why a 🐤 𝗰𝗮𝗻𝗮𝗿𝘆 you ask? Around 1911, miners started to take canary birds into the coal mines to detect the accumulation of toxic gases. These birds, would even sense the smallest traces and emissions, starting to erratically chirp and with that giving miners early warnings to immediately evacuate the mine. Just as the canaries once did in the mines, 𝗮 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 "𝗰𝗮𝗻𝗮𝗿𝘆" can play a vital role in monitoring the health of your automated workflows signalling potential issues before they escalate and perhaps, cause scaled harm. But how do you implement a digital canary into your workflows in your process automation? 𝗜𝗻𝗰𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗲 𝗶𝘁 𝗳𝗿𝗼𝗺 𝘀𝘁𝗮𝗿𝘁: into your design by using code, reconciliation reports, and validation rules to establish effective in-process control checks and monitoring mechanisms and visual dashboards to analyse red flags. Find here 5 examples how to get early alerts in your process automation, even if your automation bots don't know how to sing: ▪️𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗖𝗵𝗲𝗰𝗸𝘀: Implement automated checks at various stages of the process to ensure accuracy and completeness and volume variations. ▪️𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗘𝗿𝗿𝗼𝗿 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: Monitor integration and break points like API's for errors or failures to maintain seamless data flow across systems. ▪️𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆 𝗦𝗰𝗮𝗻𝘀: Validate for duplicate records or inconsistencies to maintain data integrity and remove manual overrides or corrections. ▪️𝗨𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸𝘀: Analyse insights from user feedbacks to check on usability issues, frequent issues and detect sentiment drops with NLP / AI. ▪️𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝗖𝗼𝗰𝗸𝗽𝗶𝘁: Create a centralised dashboard to monitor compliance metrics to detect red flags and and detect deviations from policies. By integrating digital canaries into your process automation strategy, you are not only enhance your ability to detect and respond to issues rapidly but also promote a culture of self-monitoring and continuous improvement. So, did you already place a digital "canary" into your process design and automations? If not, maybe it's time to reconsider adding this early warning system to your automation approach ensuring the health and resilience of your tasks, data & process performance. What early warning systems have worked for you best? #processautomation #intelligentautomation #rpa #processexcellence