Tommy MacWilliam

I asked Katie Dill, Stripe’s Head of Design, what’s changed in the last year with AI and design. Her answer: AI is showing up early in the rough stages where you don’t expect doneness or excellence. Designers are using AI to jam, sketch & try things on for size - moving faster through the process. And there’s less pressure for polish at that stage, making it easier to take risks with new tools. Hear it from Katie 📹

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Today, we released Part II of our Design Leadership series on Substack! In this episode, we dive into the day-to-day responsibilities of a design manager. Writing things out often brings clarity, helping to highlight the tasks and challenges that are typically normalized or left unspoken. So it was another fun discussion with Anukriti V., exploring the nuances of this. Here are three highlights from our conversation: - The importance of meetings for alignment and decision-making - Keeping teams aligned and focused requires intentional effort - Embracing ownership, initiative, and a high standard of craft Link in the comments.

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Anthropic's Claude Sonnet 4.5 is coming to Glean this week. In addition to being the leading model for coding, our evals show that the model delivers significant improvements over its predecessor by efficiently solving 10% of previously unanswerable enterprise queries, thanks to optimized parallel tool calling and fewer planning iterations. See more details on our blog: https://lnkd.in/gXUEvGKW

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Figma’s stock surged ~250% today, closing at $115/share after pricing its IPO at just $33. That’s not a victory lap. That’s a signal the underwriters got it wrong. When a stock jumps that much out of the gate, it means the company may have left billions on the table. That money went to early traders and institutions instead of the business itself. This raises real questions: - Why are we still using outdated pricing models for tech IPOs? - Would a direct listing or auction-style model have captured more value? - Are traditional underwriters misaligned with founders and long-term investors? Figma built a generational product. But Wall Street's playbook might need a rewrite.

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I was just having a conversation with a product designer where I hammered home a point I’ve come to believe: you can’t A/B test your way to greatness. Reading Karri Saarinen's (founder of Linear) rules for crafting standout products only reinforced that conviction. Here’s how Linear builds great products—worth reading for anyone serious about product quality: - The more people you have, the more opinions and deliberation you introduce, which dilutes the quality of the execution. By contrast, small, high quality teams produce good work, quickly. So how do you build these small but mighty teams? Hire craft-oriented people who already produce great work. It’s not about measuring output; it’s about setting a standard. - At Linear, we have connected teams in which everyone is responsible for the quality. There’s no “handoff to dev.” You’re never off the hook. We’ve found that when everyone understands how designs are implemented and shares responsibility for the result, we’re far more likely to achieve high quality. - Today’s startup market, full of high-quality competitors, requires excellence to stand out. A great product requires someone—or many people—to put more care into it than necessary. You could always build something to spec. A door is a door, and if it opens, it technically functions. It’s easy to meet the spec. It’s harder to do the craft. For quality, you need a team that views the spec as the baseline, not the finish line. - You can only create a great product if you design for someone in particular. It’s nearly impossible to design a product for everyone. The more specific your product’s purpose, the better it will perform for its intended use. You can build a product that is flexible, open-ended, and kind of good—one that works fine for different use cases. But it won’t be great at anything specific. That’s a weaker position, because products need a clear value proposition to win a market and users’ trust. - At Linear, we don’t make decisions based on data or experiments—including A/B tests. To design with craft, you must develop and trust your intuition. Yes, you might make wrong choices, but those mistakes become thought experiments: Why did you go wrong? Did you talk to users? Did you listen to them? This approach puts more responsibility on the team without offering easy outs. --- Link to the interview in the comments.

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When the model IS the product, all UX is RLHF. I’ve been thinking about how design changes when the model is at the heart of the product. When every click, edit, and correction becomes a training signal, all UX turns into reinforcement learning from human feedback. We’ve spent decades in software design on USABILITY -- making products that people can *use* easily. Now we need to focus on LEARNABILITY -- making products that people can *teach* easily.

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Taste, Privacy-by-Design & AI-Native Product Teams I’ve been noodling on where privacy work actually might land now that Cursor-style IDEs can spin up a PRD, design doc, and draft pull-request before you finish your coffee. Faster loops aren’t the enemy (even though that's all of our first reactions)—thought-free loops are. Here are three places I see privacy pros adding real value without slowing the train: Sit in the design sprint, not the review queue. AI-generated mocks hit Figma in minutes—jump in that same hour. Ask: Does this risk control feel natural or bolted on? Fix the pattern before it hardens into code. Raise the bar for your toolchain. Adopt better privacy toolchains. Push vendors toward policy-as-code, field-level lineage, and region toggles so AI agents can wire 90 % of controls automatically. Your time is reserved for the edge cases that need human judgment. Point AI at your own work—red-team style. Let models stress-test consent flows, purpose limits, retention rules. Security teams already do this for exploits; direct the cannon at data-use risks, too. Caveat: retrofitting these habits onto a legacy “spaghetti” stack is tougher than starting greenfield—but I think the principles will hold. I'm curious what else your teams are doing today to keep privacy in the loop while the build cycle keeps shrinking? 

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Want to see multiple variations of your new feature idea? Stitch can do that! Here's how it works: - Create a design in Stitch - Ask for variants for a specific UI element (e.g. "add reviews"), or an overarching theme (e.g. "make check-out easier") - Check out multiple options for your new idea Curious to hear how Stitch works for your use cases! Link in comments.

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This is wild. We pay people thousands a month to use our products. They pay us nothing. Sounds backwards, right? Hear me out. Our most valuable product feedback has come from paying design partners - not from internal debates, roadmap docs, or following non-ICP user behavior. Before we had PMF, we made the classic startup mistake. We had a big shiny product vision, locked ourselves in a room, spent 6 months building. It was ambitious. It was polished. After all that work, we launched… and barely anyone used it. When we started building Seona, we flipped the model. The first version was just us sending emails manually telling users what SEO work we had done. No dashboard. No interface. Just outcomes. And what taught us the most wasn’t usage. It was feedback. Honest, detailed, specific feedback from users who were actually trying to get value out of what we shipped. Now, any time we build a new product, we bring in 6 to 8 design partners from day one. 1/ They validate our ideas 2/ They suffer through the early versions 3/ They tell us what makes sense and what doesn’t 4/ And they do it with skin in the game For this, we pay them. Some up to thousands per month. It might feel expensive at first, but it saves hundreds of hours of wasted engineering, aligns your roadmap with actual user need, and gets you early fans who are already bought in. Nearly every design partner we’ve worked with has ended up becoming a customer. Because we built something that solved their problem so precisely, it was a no-brainer. Paying for feedback is one of the best investments we’ve ever made.

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Figma files for an IPO, plans to trade on the NYSE under the ticker FIG, and reports Q1 revenue up 46% YoY to $228.2M and net income up 233% YoY to $44.9M 

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Here's how to get your entire team to start prototyping with AI (anyone can do this): 1. Create a template of your core product UX 2. Get teammates to fork it to add their feature 3. Build a shared prototype repository Jeremy (CPO Vanta) used this approach to transform how his team ships features and gets customer feedback. 📌 Watch the full episode here: https://lnkd.in/gJ2G8dfN

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Wrapping up our Launch Week Commit at Flexprice. Building billing infrastructure isn’t glamorous. It’s one of those things teams postpone, simplify, or patch together… Until product complexity catches up. This week, we shipped the pieces that usually slow teams down: - New ways to aggregate billable events: Count Unique, Latest Value, and fully Custom logic - The ability to apply credits directly to invoices, no manual reconciliation - Calendar-aligned billing : choose between anniversary cycles or fixed calendar dates, so finance teams aren’t left guessing Billing infra should just work, so your team can focus on building product and not fighting spreadsheets or chasing API gaps. It took weeks of iteration, feedback from early teams, and our share of dead ends to get this right. If your pricing is evolving faster than your billing stack, talk to us. We’re building Flexprice as the open-source billing infrastructure for AI & Agentic based companies.

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After managing engineers for years, I discovered something ironic: Team leads are crushing it with AI while senior ICs struggle. Why? Managing people taught them exactly what LLM needs: Breaking projects into clear subtasks Explaining context without assumptions Giving specific, actionable feedback Iterating based on results The delegation skills you built for humans? They're your AI superpower. The best engineers might be prompt-engineering wrong because they never learned to delegate.

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We’ve been hearing more tension lately between designers × engineers — around speed, hand‑off, and visual fidelity. As roles collapse and tools converge, design engineering is breaking out as its own craft. Not just implementation, but bridging product thinking + code + interaction polish. Why now? 🟢 7500+ open “Design Engineer” jobs in the U.S. today — the momentum around this role is real. (https://lnkd.in/gNhRBXUM) 🟢 23 % employment growth projected for design‑technologist‑style roles this decade (U.S. BLS) (https://lnkd.in/gQ8X7vQH) 🟢 Yet 52 % of teams still use no design‑to‑code tooling, and 73 % say their hand‑off workflow isn’t “good enough” (Telerik State of Designer‑Developer Collaboration 2024) (https://lnkd.in/gwgZSXqw) 🟢 Top‑end salaries show the premium: Netflix’s remote Senior Design Technologist role (Games Tech Research Group) lists $100 k – $720 k (https://lnkd.in/gdC9xXCg) 🟢 Leading-edge companies like Vercel are championing the rise of this role to increase both shipping velocity and design fidelity. (Their guide is excellent → https://lnkd.in/gu3wYjpy) So we’re thinking about an advanced course for the hybrids. What would you want covered? - Front‑end frameworks & component patterns? - Tokens + theming? - Figma‑to‑code & dev‑mode power‑moves? - Rapid prototyping beyond Figma? Drop your must‑have topics below 👇 

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