Design Workflow Optimization

🪂 How To Make Your Design System AI-Ready (https://lnkd.in/dtnpy7CM), a practical guide on how to reduce drifts, minimize mistakes, maintain context and improve the quality of AI-generated prototypes — with structured spec files, automated auditing and token layers. Put together by Hardik Pandya from Atlassian. --- 🔹 1. Design Decisions Are Infrastructure AI-generated prototypes often don't deliver consistently decent results because of tiny inconsistencies scattered all across a design system. Often it's decisions made but not documented, hard-coded values never cleaned up, or relying too much on AI making sense of mock-ups or design flows on its own. Unsurprisingly, better AI prototypes come from better data — but also from better human guidance. We shouldn’t assume that AI knows how to choose the right component, and how to design with accessibility in mind. It needs priorities, a clear path on how we make decisions, design principles, examples, do's and don'ts. In fact, we should treat design decisions as infrastructure. That means that every time we make a decision — not just a design decision, but even decision on how actually prioritize our work and how we make decisions around here — it must find a path into the spec file that is then consumed by AI. --- 🔶 2. Three Layers: Spec Files + Token Layer + Audit To ensure quality, we establish design principles, guidelines, rules in a form of “spec files”). It's structured Markdown files that include spacing rules, color choices, component usage guidelines, priorities etc. AI is going to read and reuse that spec file every time it's going to generate a prototype. Because the spec files are text files, it's much more cost-effective, but also much more accurate just because we don't rely on AI recognizing or decoding patterns from mock-ups, but gets specific guidelines instead. In fact, extending code is often a more effective way than generating code from mock-ups. Token layer lists and keeps updated all tokens used throughout the design system. AI always chooses from a closed set of named variables instead of inventing plausible values ad-hoc. An audit script catches what AI gets wrong. It scans the prototype and flags every hard-coded value and flags it if necessary. It can be a regular software doing that, with AI waiting for its feedback to come back. Finally, when a design system ships updates, a sync routine flags which spec files need updating. The goal is to make sure that AI always reads up-to-date, current specs, not the ones written against an outdated version. --- 🔺 3. Examples of AI-Ready Design Systems ⌾ Atlassian: https://lnkd.in/dVsGc3Cp ⌾ Carbon: https://lnkd.in/d4zq4WWb ⌾ CMS Design System: https://lnkd.in/dHHzV3en ⌾ Nordhealth: https://lnkd.in/d8C4j2ZA Yet again, AI can’t magically resolve technical debt or design debt — it needs guidance, decisions, priorities and principles.

One of the easiest ways to improve your collaboration with product managers and engineers is to improve your design handoff files. - It doesn’t take a lot of time - It drastically improves how people interpret your designs - It reduces backs and forths between engineers and designers - It gives engineers confidence that they are doing the right thing - It increases the chances that what’s implemented will match the design Great handoff files, for me, are an instant sign of design maturity. It shows me that designers think not only of themselves but also the ecosystem of people around them. Coming up with great handoffs boils down to the following: - Setting context - Adding structure - Adding annotations - Including all states - Visualizing the flow - Including a prototype - Doing a run-through Not everything is required for every design initiative; a small feature update may not need a full handoff, whereas a big, impactful one may require everything from the above. In this cheat sheet, I’ve put together my top tips for delivering the “perfect” handoff file. Bonus: I have also created a Figma Annotation and handoff kit with handy components for the above. Find the link in the comments. 👇 — If you found this useful, consider reposting ♻️ PS: By handoff I'm not referring to the process of handing over a design, which should be a collaborative process from problem to solution, but rather the organized design file that is the single source of truth for what needs to be implemented. #uxdesign #uiux #productdesign

Here's how you can create an entire prompt library for any marketing tactic. It reverse-engineers the minds of the best marketers in any discipline. Add a marketing specialty e.g., SEO, Paid, Marketing Automation  You'll get:   - A list of elite strategies and mental models  - Bite-sized lessons from top operators  - A prompt library they'd use to automate their work  Works best with o3, but any model is good. --- <<Prompt>> Act as: a Prompt Engineer, Expert Marketer, and Strategic Operator.   Task: When I give you a marketing discipline (e.g. SEO, Paid Media, Lifecycle), create a Prompt Library designed by an elite marketer in that field.  Your deliverable (in Markdown):   1. Title – “<Discipline> — AI Prompt Library for Marketers”   2. Summary (≤ 100 words) – How top marketers in this discipline think, what drives results, and how they scale their work.   3. Winning Mental Models –   List 5–10 key strategies or operating principles used by elite marketers in this field.   For each, include:   • A name (e.g. "Content-Audience Fit")   • A 1-line description   • A practical, bite-sized lesson   • A source or citation (Operator + Year, or company if applicable)  4. Prompt Library – exactly 10 actionable prompts. Each must include:   🔧 Prompt <#> – <Goal>   <Prompt text, written in the voice of an advanced operator> Why this fits: (≤ 25 words — explain why this prompt reflects cutting-edge thinking in this area.)   How they'd use it: (How the marketer would deploy it in a real-world workflow. Include tool or channel if relevant.) Style:   • Use clear, modern marketing language.   • Prioritize tactics that *scale*, not fluff.   • Be concise, punchy, operator-grade.  Sources:   Use examples, operators, or results from trusted sources post-2015 (e.g. Growth.Design, Reforge, Demand Curve, HubSpot blog, etc).  When ready, reply: "Awaiting discipline."

I have seen more systems struggling because of wrong code than slower ones. The fact remains, most engineers optimise too early. About 8 years ago, my principal engineer once told me: Performance is almost always the last thing you should be thinking about. As an SDE-2, this did not make sense :) After a few follow-ups, I understood why he meant that. The order that actually matters is this. First, is the code correct? Does it do what it is supposed to do? Second, can someone maintain it six months from now without wanting to quit? Third, is it fast to read and write? Only after all three does performance even enter the conversation. The reason this order exists is simple. A fast, unmaintainable codebase is a liability. A performant-but-wrong system is worse than a slow, correct one. You cannot optimise your way out of a bug. Now, this is not universally true. Databases, high-frequency trading systems, and real-time embedded software are domains where performance is a first-class concern from day one. But those are the exceptions, not the default assumption you should bring to every PR. What is certainly true is that for most codebases, premature optimisation adds complexity, reduces readability, and solves a problem that does not exist yet. So, write correct code first. Then clean it. Then, only if the profiler gives you a reason, make it fast.

Heyy designers 👋🏼 If you keep hitting Claude's usage limits mid-project, it's not random - design workflows have specific patterns that burn through tokens faster than any other use case. A few quick ones: → Every "make the padding bigger" message reprocesses your ENTIRE conversation history. Message #10 costs as much as messages 1-9 combined. → Selecting your whole Figma page instead of one component? That's 20-100x more tokens. → Having 4+ MCP tools connected eats ~16% of your budget before you even type. → No design system skill? You're paying for corrections every single conversation. Create a skill for your brand guidelines, design system, or tone of voice - Claude gets it right the first time instead of round 5. → The cognitive offloading tax: We've all been there - typing "make me a landing page" then wondering why the output is generic. Research shows AI makes us mentally lazy over time. The fix? Spend 60 seconds writing a real brief before hitting send. You still need to think. Claude multiplies your thinking, it should't replace it!! The fix isn't always upgrading your plan - it's changing how you work. 🔗 Full breakdown with 10 specific scenarios and fixes: https://lnkd.in/edQnw4_E Follow for more design AI tips 🤖 #claude #ai #design #ux

PCB layouts by prompt are no longer a gimmick. Last weekend, I explored automated M-CAD model generation. This weekend, I focused on automated PCB layout creation (sparked by a question from Eddy Allefs about whether this would also work for PCB layouts). Same idea. Same approach. Different engineering domain. Just like in mechanical part design, the workflow is structured, agent-driven and code-based. Here’s what I tested: Automating a complete PCB layout draft in KiCad using Claude Code. I don’t come from an electronics background. Still, I was able to generate a first PCB layout draft purely through a structured prompt. The workflow: 1. The engineer provides the technical PCB specification in a prompt 2. Claude Code calls Opus 4.6 3. Opus 4.6 generates Python code for the PCB layout 4. Claude Code executes the Python code 5. Function calls are made via the KiCad Python API 6. The PCB layout is created directly in KiCad and can be reviewed and adapted This is exactly the same pattern I used for mechanical CAD generation: Specification → AI-generated code → API execution → Native tool output. What used to take hours of initial placement, layer setup and structural groundwork now happens in minutes. Here’s the core thesis: Prompts and code are becoming the new interface to engineering tools. Not because GUIs disappear. But because iteration speed becomes the competitive advantage. A few takeaways: – Clear specifications beat complex prompts – Well-defined constraints (components, spacing, layers, rules) are critical – The quality of the first draft determines the speed of every iteration after – The approach works across domains: requirements, architecture, mechanical, electrical and embedded software – Experts gain time for validation, optimization and real engineering decisions DFM, EMC, thermal considerations, tolerances and deep reviews remain human responsibilities. But the starting point shifts dramatically. Claude Code and similar agentic tools will be embedded into product development processes. This is just the beginning. More prompts and code in product development. Less manual tool operation. If the interface to engineering tools changes, how does that reshape the role of the engineer? Vlad Larichev | Nitin Ugale | Atilla Akdere | Rick Bouter | Ruben Hetfleisch

A Senior Data Engineer candidate was asked to design an incremental ingestion pipeline during his interview at Google. Another candidate in a different loop at Facebook got the same prompt. CDC pipelines look simple until you add one layer of reality: – Add late arriving updates? Now you need watermarks, reprocessing windows, and correctness guarantees. – Add duplicates and retries? Now idempotency becomes the whole game. – Add schema changes? Now your pipeline breaks at 2 AM unless you plan compatibility. – Add backfills? Now you are doing surgery on live tables without double counting. – Add merge cost? Now your “incremental” job is slower than a full reload. Here’s my checklist of 15 things you must get right when building incremental ingestion with CDC: 1. Start with the business contract → Define what “correct” means: latest state per entity, full history, or both. This single decision changes your table design, merges, and backfills. 2. Choose the right ingestion model: snapshot + CDC vs pure CDC → Snapshot + CDC is safest for bootstrapping and recovery. Pure CDC is leaner but brittle if you miss events. 3. Pick a stable primary key strategy → If your upstream keys are messy, create a durable surrogate key. Your entire dedupe and merge logic depends on this. 4. Capture an ordering signal you can trust → Use a reliable change version: log sequence number, commit timestamp, or monotonically increasing version. Avoid “updated_at” unless you fully trust the source. 5. Design for idempotency from day one  → Assume every event can arrive twice. Your writes must be safe to re-run without changing results. 6. Handle deletes explicitly → CDC isn’t just inserts and updates. Support tombstones or delete flags and define how downstream tables interpret them. 7. Preserve raw events before you transform → Land the raw change feed in a bronze layer. If downstream logic is wrong, raw becomes your rewind button. 8. Build a dedupe rule that survives retries and replays → Dedupe by (primary_key + change_version) or (primary_key + event_id). If event_id is missing, generate one deterministically from the payload plus version. 9. Use watermarks, but never trust them blindly → Watermark = “I have processed up to here.” Still keep a safety lookback window because late data is guaranteed in production. 10. Implement a reprocessing window for late arrivals  → Recompute the last N hours or days on every run based on observed lateness. This is the simplest way to get correctness without constant firefighting. 11. Plan schema evolution with compatibility rules → Decide: backward compatible only, or allow breaking changes with a controlled rollout. Use versioned schemas and block unsafe changes automatically. (Continued in comments.)

🚗 Imagine this: You launch a new car model after years of effort. Production is smooth, the assembly line is world-class… but six months later, the headlines scream “Massive Recall.” Billions lost. Reputation damaged. All because of a design flaw that was locked in during the product development phase. Takao Sakai once said: 👉 “95% of Toyota’s profits are determined in the product development phase, not production.” And it’s true across industries: In aerospace, material choices made at the design table decide 80% of lifecycle costs. In electronics, overengineering features adds cost but not value. In manufacturing, late design changes cause delays that no production efficiency can recover. ⚡ The real challenge? Most companies pour their energy into fixing problems on the shop floor instead of preventing them during development. 💡 The smarter way: Apply Design for Manufacturability (DFM) & Concurrent Engineering. Run early simulations & prototypes to detect risks. Involve quality, supply chain, and production teams at the concept stage. Use Voice of Customer (VOC) to cut out features no one wants but everyone pays for. The truth is simple: ✅ Every mistake caught in design costs a fraction of fixing it in production. ✅ Every smart decision in development compounds into long-term profit. 🔑 What’s one thing your team does during product development that safeguards future profitability? 👇 Share your experience—it might spark ideas for someone else! #Lean #ProductDevelopment #DesignThinking #Innovation #BusinessExcellence #Quality #TQM

Many people have asked me about the process and tools I used to create my spatial interaction animations, so I thought it would be helpful to share a bit of my workflow. On the left is the viewport in Blender, where I modeled, animated, and rendered the core of the interactions. Some elements missing in the 3D—like the 2D UI in the first scene and the clouds in the second one—I added or tweaked later in After Effects. I also used Figma to create some of the UI elements and then imported them into AE. When communicating a vision of an experience, I like to leverage multiple tools rather than just one for everything. I found that this allows me to be faster and enables my creativity to flow more smoothly. Maybe one day I’ll create a more detailed tutorial and full breakdown. But for now, I hope this is helpful. 🙂 #xr #3D #design #motion

City2graph moves one geospatial graph through three engines depending on the job: explore in NetworkX, run heavy algorithms in rustworkx at city scale, train GNNs in PyTorch Geometric, no rebuilding between them. It reads buildings, streets, transit feeds, and Overture Maps directly. A published Liverpool case study runs the full pipeline: open data into a graph, autoencoder embeddings, clusters that map urban structure. 1.5k stars, BSD-3. #GeoAI #GraphNeuralNetworks #UrbanData #PyTorchGeometric #GIS