Here we go. Figma has just dropped their S-1, or their registration for an initial public offering (IPO).

Rollup of stats from Figma’s S-1.

While a lot of the risk factors are boilerplate—legalese to cover their bases—the one about AI is particularly interesting, “Competitive developments in AI and our inability to effectively respond to such developments could adversely affect our business, operating results, and financial condition.”

Developments in AI are already impacting the software industry significantly, and we expect this impact to be even greater in the future. AI has become more prevalent in the markets in which we operate and may result in significant changes in the demand for our platform, including, but not limited to, reducing the difficulty and cost for competitors to build and launch competitive products, altering how consumers and businesses interact with websites and apps and consume content in ways that may result in a reduction in the overall value of interface design, or by otherwise making aspects of our platform obsolete or decreasing the number of designers, developers, and other collaborators that utilize our platform. Any of these changes could, in turn, lead to a loss of revenue and adversely impact our business, operating results, and financial condition.

There’s a lot of uncertainty they’re highlighting:

• Could competitors use AI to build competing products?
• Could AI reduce the need for websites and apps which decreases the need for interfaces?
• Could companies reduce workforces, thus reducing the number of seats they buy?

These are all questions the greater tech industry is asking.

Darragh Burke and Alex Kern, software engineers at Figma, writing on the Figma blog:

Building code layers in Figma required us to reconcile two different models of thinking about software: design and code. Today, Figma’s visual canvas is an open-ended, flexible environment that enables users to rapidly iterate on designs. Code unlocks further capabilities, but it’s more structured—it requires hierarchical organization and precise syntax. To reconcile these two models, we needed to create a hybrid approach that honored the rapid, exploratory nature of design while unlocking the full capabilities of code.

The solution turned out to be code layers, actual canvas primitives that can be manipulated just like a rectangle, and respects auto layout properties, opacity, border radius, etc.

The solution we arrived at was to implement code layers as a new canvas primitive. Code layers behave like any other layer, with complete spatial flexibility (including moving, resizing, and reparenting) and seamless layout integration (like placement in autolayout stacks). Most crucially, they can be duplicated and iterated on easily, mimicking the freeform and experimental nature of the visual canvas. This enables the creation and comparison of different versions of code side by side. Typically, making two copies of code for comparison requires creating separate git branches, but with code layers, it’s as easy as pressing ⌥ and dragging. This automatically creates a fork of the source code for rapid riffing.

In my experience, it works as advertised, though the code layer element will take a second to render when its spatial properties are edited. Makes sense though, since it’s rendering code.

If you want an introduction on how to use Cursor as a designer, here’s a must-watch video. It’s just over half-an-hour long and Elizabeth Lin goes through several demos in Cursor.

Cursor is much more advanced than the AI prompt-to-code tools I’ve covered here before. But with it, you’ll get much more control because you’re building with actual code. (Of course, sigh, you won’t have sliders and inputs for controlling design.)

David Singleton, writing in his blog:

Somewhere in the last few months, something fundamental shifted for me with autonomous AI coding agents. They’ve gone from a “hey this is pretty neat” curiosity to something I genuinely can’t imagine working without. Not in a hand-wavy, hype-cycle way, but in a very concrete “this is changing how I ship software” way.

I have to agree. My recent tinkering projects with Cursor using Claude 4 Sonnet (and set to Cursor’s MAX mode) have been much smoother and much more autonomous.

And Singleton has found that Claude Code and OpenAI Codex are good for different things:

For personal tools, I’ve completely shifted my approach. I don’t even look at the code anymore - I describe what I want to Claude Code, test the result, make some minor tweaks with the AI and if it’s not good enough, I start over with a slightly different initial prompt. The iteration cycle is so fast that it’s often quicker to start over than trying to debug or modify the generated code myself. This has unlocked a level of creative freedom where I can build small utilities and experiments without the usual friction of implementation details.

And the larger point Singleton makes is that if you direct the right context to the reasoning model, it can help you solve your problem more effectively:

This points to something bigger: there’s an emerging art to getting the right state into the context window. It’s sometimes not enough to just dump code at these models and ask “what’s wrong?” (though that works surprisingly often). When stuck, you need to help them build the same mental framework you’d give to a human colleague. The sequence diagram was essentially me teaching Claude how to think about our OAuth flow. In another recent session, I was trying to fix a frontend problem (some content wouldn’t scroll) and couldn’t figure out where I was missing the correct CSS incantation. Cursor’s Agent mode couldn’t spot it either. I used Chrome dev tools to copy the entire rendered HTML DOM out of the browser, put that in the chat with Claude, and it immediately pinpointed exactly where I was missing an overflow: scroll.

For my designer audience out there—likely 99% of you—I think this post is informative as to how to work with reasoning models like Claude 4 or o4. This can totally apply to prompt-to-code tools like Lovable and v0. And these ideas can likely apply to Figma Make and Subframe.

Crazy. Methinks there’s a non-zero chance that someone at v0 read my article and decided to give us designers the control we want.

Can’t wait to try it out.

Peter Yang has been doing some amazing experiments with gen AI tools. There are so many models out there now, so I appreciate him going through and making this post and video.

I made a video testing Claude 4, ChatGPT O3, and Gemini 2.5 head-to-head for coding, writing, deep research, multimodal and more. What I found was that the “best” model depends on what you’re trying to do.

Here’s a handy chart to whet your appetite.

I’ve been focused a lot on AI for product design recently, but I think it’s just as important to talk about AI for web design. Though I spend my days now leading a product design team and thinking a lot about UX for creating enterprise software, web design is still a large part of the design industry, as evidenced by the big interest in Framer in the recent Design Tools Survey.

Eric Karkovack writing for The WP Minute:

Several companies have released AI-based site generators; WordPress.com is among the latest. Our own Matt Medeiros took it for a spin. He “chatted” with a friendly bot that wanted to know more about his website needs. Within minutes, he had a website powered by WordPress.

These tools aren’t producing top agency-level websites just yet. Maybe they’re a novelty for the time being. But they’ll improve. With that comes the worry of their impact on freelancers. Will our potential clients choose a bot over a seasoned expert?

Karkovack is right. Current AI tools aren’t making well-thought custom websites yet. So as an agency owner or a freelance designer, you have to defend your position of expertise and customer service:

Those tools have a place in the market. However, freelancers and agencies must position themselves as the better alternative. We should emphasize our expertise and attention to detail, and communicate that AI is a helpful tool, not a magic wand.

But Karkovack misses an opportunity to offer sage advice, which I will do here. Take advantage of these tools in your workflow so that you can be more efficient in your delivery. If you’re in the WordPress ecosystem, use AI to generate some layout ideas, write custom JavaScript, make custom plugins, or write some copy. These AI tools are game-changing, so don’t rest on your laurels.

Brad Feld is sharing the Cursor prompts his friend Michael Natkin put together. It is more or less the same that I’ve gleaned from the Cursor forums, but it’s nice to have it consolidated here. If you’re curious to tackle any weekend coding project, follow these steps.

Some fun, free tools for designers, curated by Danil Vladimirov.

Tabitha Swanson for It’s Nice That:

A few years ago, I realised that within a week, I was using about 25 different design programs, each with their own nuances, shortcuts, and technological learning curves. (That number has continued to grow.) I also began to notice less time to rest in the state of full technological proficiency in a tool before trends and software change again and it became time to learn a new one. I’ve learned so many skills over the years, both to stay current, but also out of genuine curiosity. But the pressure to adapt to new technologies as well as perform on social media, update every platform, my portfolio, website and LinkedIn and keep relations with clients, is spiritually draining. Working as a creative has never felt more tiring. I posted about this exhaustion on Instagram recently and many people got in touch saying they felt the same – do you feel it too?

I get it. There’s always so many new things to learn and keep up with, especially in the age of AI. That’s why I think the strategic skills are more valuable and therefore more durable in the long run.

I was recently featured on the Design of AI podcast to discuss my article that pit eight AI prompt-to-code tools head to head. We talked through the list but I also offered a point of view on where I see the gap.

Arpy Dragffy and Brittany Hobbs close out the episode this way (emphasis mine):

So it’s great that Roger did that analysis and that evaluation. I honestly am a bit shocked by those results. Again, his ranking was that Subframe was number one, Onlook was two, v0 number three, Tempo number four. But again, if you look at his matrix, only two of the tools scored over 70 out of 100 and only one of the tools he could recommend. And this really shines a dark light on AI products and their maturity right now**.** But I suspect that this comes down to the strategy that was used by some of these products. If you go to them, almost every single one of them is actually a coding tool, except the two that scored the highest.

Onlook, its headline is “The Cursor for Designers.” So of course it’s a no brainer that makes a lot of sense. That’s part of their use cases, but nonetheless it didn’t score that good in his matrix.

The top scoring one from his list Subframe is directly positioned to designers. The title is “Design meet code.” It looks like a UI editor. It looks like the sort of tool that designers wish they had. These tools are making it easier for product managers to run research programs, to turn early prototypes and ideas into code to take code and really quick design changes. When you need to make a change to a website, you can go straight into one of these tools and stand up the code.

Listen on Apple Podcasts and Spotify.

I tried early versions of Stable Diffusion be ended up using exclusively Midjourney because of the quality. I’m excited to check out the full list. (Oh, and of course I’ve used DALL-E as well via ChatGPT. But there’s not a lot of control there.)

As a certified Star Wars geek, I love this TED talk from ILM’s Rob Bedrow. For the uninitiated, Industrial Light & Magic, or ILM, is the company that George Lucas founded to make all the special effects for the original and subsequent Star Wars films. The firm has been an award-winning pioneer in special and visual effects, responsible for the dinosaurs in Jurassic Park, the liquid metal T-1000 in Terminator 2: Judgement Day, and the de-aging of Harrison Ford in Indiana Jones and the Dial of Destiny.

The point Bedrow makes is simple: ILM creates technology in service of the storyteller, or creative.

I believe that we’re designed to be creative beings. It’s one of the most important things about us. That’s one of the reasons we appreciate and we just love it when we see technology and creativity working together. We see this on the motion control on the original “Star Wars” or on “Jurassic Park” with the CG dinosaurs for the first time. I think we just love it when we see creativity in action like this. Tech and creative working together. If we fast forward to 2020, we can see the latest real-time virtual production techniques. This was another creative innovation driven by a filmmaker. In this case, it’s Jon Favreau, and he had a vision for a giant Disney+ “Star Wars” series.

He later goes on to show a short film test made be a lone artist at ILM using an internal AI tool. It’s never-before-seen creatures that could exist in the Star Wars universe. I mean, for now they look like randomized versions of Earth animals and insects, but if you squint, you can see where the technology is headed.

Bedrow goes on…

Now the tech companies on their own, they don’t have the whole picture, right? They’re looking at a lot of different opportunities. We’re thinking about it from a filmmaking perspective. And storytellers, we need better artist-focused tools. Text prompts alone, they’re not great ways to make a movie. And it gets us excited to think about that future where we are going to be able to give artists these kinds of tools.

Again, artists—or designers, or even more broadly, professionals—need fine-grained control to adjust the output of AI.

Watch the whole thing. Instead of a doom and gloom take on AI, it’s an uplifting one that shows us what’s possible.

Karri Saarinen, writing for the Linear blog:

Unbounded AI, much like a river without banks, becomes powerful but directionless. Designers need to build the banks and bring shape to the direction of AI’s potential. But we face a fundamental tension in that AI sort of breaks our usual way of designing things, working back from function, and shaping the form.

I love the metaphor of AI being the a river and we designers are the banks. Feels very much in line with my notion that we need to become even better curators.

Saarinen continues, critiquing the generic chatbox being the primary form of interacting with AI:

One way I visualize this relationship between the form of traditional UI and the function of AI is through the metaphor of a ‘workbench’. Just as a carpenter’s workbench is familiar and purpose-built, providing an organized environment for tools and materials, a well-designed interface can create productive context for AI interactions. Rather than being a singular tool, the workbench serves as an environment that enhances the utility of other tools – including the ‘magic’ AI tools.

Software like Linear serves as this workbench. It provides structure, context, and a specialized environment for specific workflows. AI doesn’t replace the workbench, it’s a powerful new tool to place on top of it.

It’s interesting. I don’t know what Linear is telegraphing here, but if I had to guess, I wonder if it’s closer to being field-specific or workflow-specific, similar to Generative Fill in Photoshop. It’s a text field—not textarea—limited to a single workflow.