“Behind products that just ​‘feel good’, there is an insane amount of design and engineering that you never get to see.” Enri Tarta got rare access to Figma’s product team for a day, and the result is the kind of process tour we almost never get to see.

A design vocabulary guide by Emil Kowalski and Glenn Carstens-Peters. 188 terms across 12 categories — typography, color, layout, IA, accessibility, measurement — defined with the precision designers reach for when they actually know what they’re looking at. It’s a preview of a larger ​“design education for an era of change” arriving fall 2026.

The Community profile redesign adds role, experience, tech stack, pinned work with images and links, and social channels. Combined with the FigPal customization, it reads less like a profile page and more like a creator landing page. Figma clearly wants Community to function as a discovery layer for designers, not just for resources.

Soleio’s guest is Rasmus Andersson: founding designer of Spotify, one of Figma’s first designers, co-creator of GraphQL, and creator of Inter. The part that stayed with me is his description of how Figma operated: one person on one problem for a year, sometimes binned at the end, while shipping something visible every month. He also makes a case that the production side of design — the part AI is eating fastest — was never actually the hard part. What’s left is the intention. The other link in this section is Soleio’s talk on the geometry of luck, making this pair worth watching together.

“Luck isn’t something that you have. It’s something that you leave behind.” Soleio — designer of the Like button and angel investor in Figma, Cursor, and Framer — delivered this as the closing line of his Tokyo Design Forum talk, and it’s the kind of sentence that reorganizes how you think about a career. The talk, now archived by The Forum Library, reframes luck as a generative force you shape through how you live and work, not a resource you accumulate.

Matt Colyer, Figma’s director of product management for developers, makes the case on Dan Shipper’s AI & I podcast that the SaaS apocalypse narrative has it exactly backwards. He’s been running his own agents for two years and is buying more software subscriptions than ever, because shipping and maintaining a personal agent teaches you fast why people pay for someone else to run it. The more interesting half is about design specifically: chat is linear, which makes it good at converging on a single direction but terrible at generating lots of options. Figma’s on-canvas agent is a first attempt at the divergent side — letting you branch frames in different directions, then bring in a convergent agent to cluster them. He also walks through how the MCP server closes the code-to-design loop, and why ​“review” has quietly become the biggest bottleneck in AI-assisted product work.

Designer Fund surveyed 900+ designers across 60+ countries and conducted 20+ interviews with leaders from Anthropic, Framer, Linear, Notion, Shopify, Sierra, and Stripe. The headline number: half of respondents have shipped AI-generated code to production, and designers are now using double the AI tools they were a year ago. Designers are quietly absorbing PM and engineering work, but hiring loops, performance reviews, and team shapes haven’t caught up.

Tom Scott breaks down how the Staff role differs from Senior in kind, not degree. Senior designers execute well-defined features. Staff designers question whether the work should happen at all, spot where teams are solving the same problem in three different ways, and turn that into reusable patterns. Useful if you’re trying to figure out which of your current habits actually point toward Staff, and which are just more Senior.

Daily curated design news and resources for product designers, design engineers, and PMs — read all week, then get the best links in your inbox every Tuesday.

Config 2026 lands June 24–25 at Moscone. The agenda confirms the obvious: this year is about AI workflows and design systems built for them. Build your schedule now — last year the best talks filled up fast.

The May edition is mostly a demo of Figma’s MCP server, with Amy Lima and Anthony DiSpezio walking through three workflows: code-to-canvas import of a vibe-coded prototype, round-tripping a dark mode so variables stay in sync between code and Figma, and using the pre-installed figma-use-scale skill to generate first-pass directions on a production dashboard. The closing update reel includes vector editing up to 10x faster, memory warnings down 92%, macOS eyedropper now samples anywhere on screen, FigJam MCP can read and write boards (ERDs, architecture diagrams), Make Kit packages your design system’s code for Figma Make, and Weave gets a timeline for video.

Luis Ouriach makes the case against single-number design system adoption metrics. His argument: one number collapses three things that should stay separate, across artifacts (a brand token and a complex data table component need different definitions of ​“used well”), surfaces (a logged-in dashboard component has no business on a sign-up screen), and people (a marketer, a senior product designer, and a front-end engineer all want different things from the same system). The throughline is that compliance with a benchmark is not the same as value, and most design system dashboards are quietly measuring the wrong one.

“Claude Design can read a design system carefully when the prompt is about the system. When the prompt is about a composition that uses the system, it stops respecting the components and just generates lookalikes.” TJ Pitre spent a few hours testing Claude Design against two real design systems and concludes that the tool references your system, but doesn’t consume it. Claude would happily inline HTML tags with style props instead of importing them from your component library.

“The pitch for Claude Design’s workflow is roughly: I have a design system, I want to generate new product surfaces from it, and I want AI to do most of the lift. That workflow exists today. You can pair Figma with an MCP server like our Figma Console MCP, or with Figma’s own MCP server, or with Code Connect, and then point an AI app generator at it. Lovable, v0, Replit, Figma Make, Claude Code working inside your repo. Your Figma file stays the canonical source. Your codebase stays the production surface. AI does the generation in between.That flow is more linear, more honest about where source of truth lives, and it produces output that actually uses your component library, because the AI is operating inside the repo where the components live.”

“Design is a process, not an artifact. Design is the act of deciding what should exist, why it should exist, and what ​‘right’ looks like before you’ve committed to building it.” A new piece from Jake Albaugh, dev advocate at Figma, articulating what design actually means in 2026. The four-point framing — design is process not artifact, AI accelerates execution not clarity, 100×0 = 0, collaboration amplifies intent.

Especially the second point: ​“The most expensive thing you can do isn’t build something badly. It’s to execute well on the wrong idea.” If you’ve been struggling to explain why polished AI demos can be worse than no demo at all, this is the piece to send.

Grace Li lists 7 common design smells in GPT 5.5: huge typefaces with tight tracking, lack of textures, bento boxes with unrelated icons, pill-with-dot in hero sections, gradient keywords, grid background, and rounded cards with three nested cards. ​“TLDR: if you’re vibe coding with GPT 5.5, the easiest wins are: loosen the tracking on your headlines, delete the status pill, and pick one accent color instead of a gradient. That’ll get you out of the uncanny valley before the next model release closes the gap on its own.”

GPT 5.5 is strong at programming, but not great at visuals: ​“Across 5,000+ preference pairs, GPT‑5.5 ranks #13 in Website Arena, #16 in UI Component Arena, and #19 in 3D Design Arena. It loses to Claude Opus 4.7 and Sonnet 4.6, to Gemini 3.1, to GLM 5.1, and to Kimi K2.6.”

Kris Puckett, Design Manager at Stripe, spent months building Epilogue, a real iOS app with 14,000 lines of Swift, entirely through conversation with Claude. This essay is a specific and honest account of what the designer-building-with-AI experience actually looks like: what broke, what he learned about asking precise questions, what ​“vague frustration keeps you stuck, specific confusion gets you answers” actually looks like in practice. ​“I realized the bottleneck was never coding ability. It was articulation. The ability to describe what I wanted clearly enough that something else could build it.”

Karri Saarinen, CEO of Linear, writes one of the more grounded takes on AI’s current state. Linear’s cloud agent now fixes more than 1,000 issues per month, but Karri is clear that hard problems remain hard and design tools are still challenging to use. On having a design tool operate directly on the production codebase: ​“A lot of the design work I do is not production design. I am not trying to implement the final version or test every edge case. Most design work is about making decisions, understanding the problem, and finding the fit. That process generates many variations and messy ideas.”

The expertise paradox section is the most useful: ​“AI often feels most impressive in domains where you know the least.” Expertise makes AI harder to use but also more valuable, because experts know how to steer, constrain, and evaluate the output.

“Designers have always (and will always) answer the question ​‘What’s worth making?’ ” Joel Lewenstein, Head of Design at Anthropic, argues that as the cost of software drops, the most important decisions shift from ​“can we make this?” to ​“should we?” Design, in his framing, is what narrows the possibility space fast enough to keep up with the speed of delivery. He describes Claude Design as a tool for getting ideas ​“good enough to move discussion forward,” cutting idea-to-internal-feedback time from days to hours.

Stefan went through every Dive Club episode and pulled out everything about portfolios, hiring, and what makes candidates stand out, then distilled it into a skill that audits any product design portfolio.

Yep! Type Foundry introduces Unifora, a uniwidth variable sans-serif superfamily. If you aren’t familiar with uniwidth typefaces, it’s worth checking out details of this project. Two years dedicated to a single constraint: every weight, every width, every slant — same character widths. This solves a real problem for interface designers: text that doesn’t reflow when you switch from regular to bold.

Ridd interviews Kris Puckett, design manager at Stripe and formerly design lead at Mercury and Dropbox, on becoming an AI-native designer by using tools like Claude Code, OpenClaw, and personal AI to ship his own apps, supercharge his design practice, and build highly personal systems for life and work. I’m deeply curious about the last part and can’t wait to dig into his course Neuma on building a personal AI system.

A first look at Config 2026 speakers — AI artist Holly Herndon, creator of the world’s first 3D-printed fashion collection Danit Pele, designer and author Vicki Tan, designer and founder Matthew Ström-Aw, and mathematician and educator Grant Sanderson from 3Blue1Brown. Config returns to San Francisco on June 23–25. Virtual registration is free.

The new Workflow Lab format, showing an end-to-end process, is a smart way to frame the new AI image tools in context. The three new tools (erase object, isolate object, expand image) are genuinely useful for anyone who’s had to leave Figma to do basic cleanup in Photoshop, and Vectorize finally removes a step that’s been a quiet annoyance for years.

Patrick Morgan makes a clean distinction that vibe-coding discourse keeps blurring: prototype code is for exploration, production code is for endurance. He is building a protected prototyping environment using Claude Code, a place where his team can move fast and then deliberately port the right assets across the boundary into production.

There is a clear parallel with how the design team at Notion works. In the recent episode of How I AI, Brian Lovin showed their collaborative ​“prototype playground,” where the entire team can create, share, and iterate on functional prototypes.

That also reminded me of how my team worked a decade ago, back when front-end development was a tad simpler. We had a separate ​“mockups” directory inside the Rails monorepo, where designers prepared static HTML mockups with production-ready CSS and JS. By the time designs were handed off to engineers in a feature branch, all polish and design details were already baked in. The design team must be fairly technical, but there is no going back to handing off Figma files after working this way.