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.