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Web publishing in Sites and Make has been an all-or-nothing setting. Now admins can set an org-wide default and carve out exceptions per workspace, so security-sensitive teams stay locked down without blocking everyone else.

Mari Kong rounds up four real workflows Figmates are running with the MCP server now that it spans Slides, FigJam, Make, and the Figma agent. Mallory Dean refreshes her AI product launch deck from Slack and Google Drive with one prompt. Prasant Lokinendi generates FigJam kickoff boards from live Asana, Notion, and Hex data. Iris Lin moves designs bidirectionally between canvas and code, ending in a GitHub PR. Yarden Katz drops a code-only screen into Figma, maps it to real components, fixes it with the Figma agent, then pushes it back. None of these are demo scenarios — they’re the actual recurring work.

Matt, designer at Lovable, writes up how they rebuilt their entire color system on OKLCh, derived from just three inputs: a neutral seed, a contrast value, and an accent. The technical choices are solid throughout — perceptually uniform ramps, WCAG contrast by construction, box-shadow borders that composite without layout shift, the state layer — but the most interesting design decision is the intentional reduction of token surface area specifically to make AI-generated UI more consistent. The fewer valid choices a model has, the less it can quietly go wrong.

Christine Vallaure walks through how A2UI, a Google-initiated open protocol, turns a designer’s component catalog into the sole source of truth for AI-generated interfaces. The AI assembles screens fresh for each user request, but it can only name components that already exist in the catalog — so the quality of every screen traces directly back to design decisions made upstream. The interesting flip: the careful work designers often do invisibly, states, tokens, semantic naming, accessibility, stops being a tax and becomes the engine.

“As code becomes cheaper, an increasingly large portion of it is written for prototyping, not production.” Yitong Zhang argues that prototype coding and production coding are diverging into distinct disciplines with near-opposite requirements: prototyping wants speed, mock data, no types, no tests, and wide-ranging agents; production wants the opposite. Cursor, Stripe, and Notion have already shown this with their internal playground environments — and Figma just needs to follow that path. Parallel design exploration means more inference runs, less complexity per run, and better margins than production – making Figma one of the best-positioned companies outside the labs to run agents at scale.

Nikita Prokopov makes the case that great UI isn’t just about nailing the start and end states of an animation, it’s about every frame in between. His rule of thumb: if you freeze the screen at any moment, you should be able to explain what you’re looking at. The post walks through a set of GIF examples — from Safari’s misaligned cursor-and-placeholder animation to YouTube’s inexplicable rectangle transition — showing how mid-animation frames that don’t hold up on their own erode user trust.

Marcin Wichary, Design Architect at Figma, made this interactive essay about fingers, latency, and why our interfaces still fail the hands using them. Starting with 1890s typists who routinely hit 70 wpm when scientists said 40 was the ceiling, he traces a direct line from terminal echo buffers and UI blocking to Notes on a Mac that can’t keep up with your typing in 2026. The embedded demos — where you feel the difference between a blocked UI and a parallel thread — do more to explain debouncing and latency than any amount of text could. This essay is a fantastic companion to Marcin’s Unsung blog on software craft and quality.

Video upload cap goes from 100 MB to 300 MB across Design, FigJam, Slides, Sites, and Buzz on paid plans.

The refreshed HIG Design Principles, posted alongside the WWDC26 talk. Eight tenets — Purpose, Agency, Responsibility, Familiarity, Flexibility, Simplicity, Craft, Delight — each opening with a plain-language imperative (“Be clear and direct,” ​“Care about every detail,” ​“Make it human”) and expanding into sub-principles.

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.

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.

“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.

Three months into life as a public company, Figma has its first activist. Hedge fund Findell Capital sent a letter to Figma’s CEO and board pushing for three things: streamline the product portfolio down to Design, Dev Mode, FigJam, and Make; cut R&D (projected to exceed 30% of revenue in 2026) and stock-based comp (around 27% of revenue versus Adobe’s 8%); and launch an independent investigation into the Anthropic relationship. Findell flags the sequence in April — Mike Krieger resigned from Figma’s board on the 14th, Anthropic shipped Claude Design on the 17th — and notes two remaining board members are material Anthropic investors. The fund still calls Figma ​“a generational company” with ​“a true moat.”

Five workflows that show what Figma Weave is actually for: chaining AI nodes on a canvas to blend two references into a style guide, fan out variations across aspect ratios, run eight distortion filters in parallel, generate rotatable 3D models through Rodin 3D V2, and composite stills into rendered video.

Alexia Danton, Designer Advocate at Figma, walks through seven tactics for stretching Make credits further. The most useful ones are the least obvious: use the Edit tool and ​“Go to source” for small visual tweaks instead of prompting, codify repeated instructions into a guidelines.md file so Make doesn’t relearn your conventions every turn, and reach for Gemini Flash on routine iteration while saving Claude Opus for ambiguity and high-fidelity work.

Make can now connect to a local repo and edit your real production code, not just a sandboxed project. Designers point at an element, adjust properties or leave an annotation, and the agent finds the relevant code, commits the change, and opens a PR through standard GitHub flow (SSH for other providers). It also handles dependency installs and spins up the dev server for you. Closed beta on the Mac Beta desktop app and beta usage doesn’t burn credits.

Brett McMillin shows a concrete loop: an agent reads a coded export flow, finds every state the developer shipped (success, error, loading, edge cases), and generates fourteen designable frames on the canvas using the design system. From there, the designer riffs on three animation directions, the /sync-figma-token skill flags token drift between code and variables, and a generate_figma_design call produces an annotated side-by-side diff.

Emma Webster’s overview of why MCP exists and what it changes. Without context, AI coding tools work from a screenshot — they see the end result, not the decisions that went into it. The Figma MCP server hands agents structured access to components, tokens, and layout decisions instead. Useful as the conceptual baseline before getting into the applied workflows in the lab.

A conversation between Figma’s Design Director of AI Gui Seiz and engineer Alex Kern on how AI inverts the old economics, code used to be expensive and design cheap, now both are cheap and the bottleneck moves to intent. The companion piece to the two MCP posts and the Code to Canvas tutorial elsewhere in this section.

Round up of four AI workflows Figma sees teams adopting: prototyping in code first and pulling it back to the canvas via Codex to Figma, generating dozens of layout variations on the canvas, building a Figma Make prototype before writing the spec, and using Make kits with MCP to carry design system context into the code. The through-line is that the artifact teams align around is shifting from the mockup to the working prototype.