Just updated my annual Config Study Guide with the remainder of the recordings — all 70 talks are now organized and ready to watch. (Only the Learning Lab videos are still unavailable.) This is a practical way to work through the talks at your own pace, mark what you’ve watched, and trade recommendations with friends and colleagues.

New weekly live show by Ridd and Tommy Geoco, focused on design, tools, and the people behind the software.

A large survey of tech workers in 2026 finds the industry splitting into two groups: roughly half feel amplified by AI, while 14% feel destabilized and 12% are simply resentful. Designers and researchers are overrepresented in the fragile half. What’s striking is that the fear isn’t ​“AI will take my job” — only 22% name that. The bigger worry is unsustainable pace and doing more work for the same pay. Career optimism is down, burnout is up, and 53% would actively discourage someone from entering their field.

Dylan Field responds to Gal Shir’s ​“quitting design” post that made a splash in the design community this week and supports findings in Lenny’s survey. He points to the recurring psychological loop that plays out every time a new AI model drops: existential crisis, experimentation, recalibration, repeat. His standing argument is that the attention economy makes design more valuable, not less, because anyone can prompt their way to average, and average doesn’t stand out. ​“This is the moment to be more bold, to take more creative risk, to double down on the power of design.”

Figma’s third annual AI survey, covering 8,403 product builders across 10 markets, lands on a deceptively simple conclusion: AI is most valuable when it’s a team sport. Two years ago, 7% said AI meaningfully changed how their teams collaborate – this year, that number is 41%. The most cited reason is the canvas — where teams can actually riff together rather than trading prompts solo. The cross-functional blurring numbers are striking too: designers participating in development jumped from 21% to 41%, developers doing design work from 44% to 60%. The role boundaries are dissolving faster than most teams have figured out what to do about it.

Contra Labs put four frontier AI models through a rigorous landing page benchmark: nine professional designers, 40 live HTML artifacts, 540 pairwise matchups across typography, layout, palette, and grid. Sol (GPT 5.6) dominated loosely-specified briefs with an 81–83% win rate, praised for capturing tone the brief ​“only gestured at.” Fable (Claude 5) flipped the result on structured briefs, jumping from 31% to 72% client-readiness when given detailed specs. The real finding is that these models have different philosophies: Sol fills ambiguity with its own taste, Fable waits for yours.

Alex Barashkov of Pixel Point explains why his team stopped wrestling with Figma for certain jobs — procedural art, custom animations, branded asset generators — and started shipping dedicated apps instead. Toolcraft sets up a React starter with opinionated canvas behavior, a font picker, Lightroom-style sliders, and built-in AI instructions that prevent the agent from quietly breaking things it wasn’t asked to touch.

Meng To sits down with Ridd at Dive Club for a 35-minute screen-share walkthrough of his current Codex workflow. He’s no longer opening Figma to create anything new — all design exploration, prototyping, and iteration happens in Codex, starting from screenshots and referencing his own past work as context. A few specific tactics stand out: using GPT Image 2 to generate UI concepts that then get turned into HTML, feeding a video of an animation to the AI and asking it to describe the right prompt to recreate it, and using design​.md files as a portable style guide that encodes years of taste into something the model can actually read.

Luis Ouriach lays out a clear mental model for design systems at scale: before adding a component, ask whether you actually need a structural change or just a value override. The piece covers theming hierarchy across modes, Extended Collections, and direct token updates; when platform separation is worth the headcount cost (often it isn’t); and why you shouldn’t restructure your system for AI tooling until AI tooling can handle multi-file setups.

Colm Tuite, co-creator of Radix UI and its widely-used color system, pushes back on one of the most persistent design systems myths: that a ​“good” color scale means uniform chroma and lightness steps across hues. In perceptual color spaces like OKLCH, this is physically impossible — yellow simply cannot reach the chroma depth that blue can. But the deeper point is that even if it were achievable, it wouldn’t serve UI design. A palette that works needs purposeful contrast variation, not mathematical regularity.

A short demo from Miggi on Riffs, now in beta on Figma Community. You can post up to five frames or export a motion frame as video straight from a file, add a description, and publish it to your profile. Figma Community becoming the new Dribbble wasn’t on my bingo card for this year!

Figma Community just got a new content type. Alicia Kranjc, Product Manager at Figma, walks through how to publish a Weave workflow to Community so anyone can find, copy, and run it. The demo workflow takes a single image and produces three video hero variations for different websites. Something that took its creator hours to build takes someone else a couple of minutes to use.

Figma’s AI image editing now runs in parallel. You can kick off multiple edits from the toolbar and keep working while they process, with loading indicators for each. Small change, noticeable difference if you’re doing any volume of AI image work.

A deep dive into the principles that separate intentional motion from decoration: easing and timing, anticipation, overshoot, follow-through, hold, and settle. The piece makes a compelling case for grounding animation in real-world physics and film editing rather than trending motion work, and lands on a point worth keeping: as AI tools make quality motion faster to produce, skill and restraint matter more, not less.

Weave Tools are pre-built AI actions that run directly inside Figma from a new Tools panel, sitting alongside plugins and widgets. Moran, Weave’s designer advocate, built the initial set of 30+ tools, covering things like aspect ratio changes, on-brand icon generation, and logo placement on products. The ​“add logo to product” demo is a good proxy for what makes this interesting: it’s not a one-shot prompt, but a multi-step chained workflow under the hood, which is why the output actually holds up — logo bending into the fabric wrinkles of a hoodie. All that complexity is hidden.

Rachel Platt, Figma Education Designer, walks through four ways to stop wasting prompts on setup: Make kits, guidelines files, templates, and file duplication. The most useful tip is the guide​lines​.md file — a plain markdown file Make reads automatically with every prompt. Write it once, get the context for free every session. You can even ask Make to draft the file for you from a description of your design system.

GPT‑5.6 is now available in Figma Make alongside the existing model options. Select it from the model picker in the chatbox. Available on all plans.

The team behind Bud (formerly Orchids), an AI-powered platform for building web apps and internal tools, is joining Figma. Bud was built around the idea that AI could ​“democratize the ability to build software,” and the acquisition fits neatly into Figma’s push into that same territory with Figma Make. The announcement doesn’t say what the team will work on, but the direction isn’t hard to guess.