Just moved things to a websocket connection for the return. Should be working now - here's the image it generated for "Turns out, you don't need water to live": https://diffui.ai/files/brand-shares/2ff1b00a-d698-43ea-a42e...

To add something else to the discussion however, I'd encourage people to skip out on Claude Design for other reasons, and that is the inherent restrictions of LLMs for visual design. LLMs are blind, and spatial relativity is tremendously hard across layers of nested html / css.

If you're early on, I'd recommend starting with diffusion first. GPT-Image-2 is phenominal at UI design, and especially if you're just starting out will let you align on a direction more rapidly than an LLM can. The difficulty will be converting from image->html, but you'll be able to explore different directions more cheaply/faster than you could with Claude Design.

I will note a bias disclaimer here - I quit Figma to work on my own diffusion-based UI design tool. Not promoting that here, but wanted to at least share my findings in this space.

https://diffui.ai

I quit Figma about 4mo ago to start working on this, and the gpt-image-2 drop really legitized the bet. I recently release Brands for diffui, which let you establish a design system and consistently generate with it. I made a Brand out of the recent UFO files release, which allow for some really fun designs:

https://diffui.ai/brand/2ff1b00a-d698-43ea-a42e-7c4a2e670c04 (no account required to generate with this if you want to try)

I distinctly remember my teachers having a debate around whether or not the functions I had programmed into my calculator were "cheating". On one hand, it was a tool and notes that I had access to my peers did not. On the other hand, I had created those tools myself, and if school was supposed to train me for the real world, wouldn't I be able to use the tools I created in the real world?

If I were an engineer at Uber, why wouldn't I select gpt 5.5 pro @ very high thinking + fast mode for a prompt? There's no incentive not to use the most powerful (and thus most expensive) model for even the smallest of changes.

I tried one of these prompts for some tests I'm doing for image->html conversion, and a single prompt cost me $40. For someone that's paying that themselves, I'd pretty much never use this configuration. For someone at a large company where someone else is footing the bill, I'd spin these up regularly (the output was significantly better, fwiw). For engineers they're being rated on what they deliver, not the expenditure to get there.

There are ways to do this cheaply, but there are no incentives for engineers to do so.

> Regardless of the fact that he probably is a jerk

and

> Does Atlassian's CEO realize that we all now know that he really is a rich jerk?

My comment was just meant to provide an insider perspective as a foil to those who had given theirs.

Worked at Atlassian for 5 years, had plenty of interactions with Mike. I wouldn’t categorize him as a jerk. I have plenty of disagreements about decisions he’s made, and I think he heavily over-hired (and is paying for it now), but a jerk he is not.

The reality is Atlassian has mechanisms, for better or for worse, that reward social discontent - Hello (their internal Confluence instance which has Reddit-like upvoting on blogs) and their karma bot on slack. Both of which tend to result in people gamifying these to boost their social status, which as you’ve seen with Reddit, often results in a subset of people realizing negative comments get more attention than positive ones. This got out of hand and they’ve been trying to dial it back, leading to cuts like these. It’s been a problem at Atlassian for a while.

I suspect they're highly oversubscribed, thus the reason why we're seeing them do other things to cut down on inference cost (ie changing their default thinking length).

> Both are very well-designed from first principles, but do not conform to what other interfaces the user might be familiar with

> The lack of homogeneous interfaces means that I spend most of my digital time not in a state of productive flow

There are generally two types of apps - general apps and professional tools. While I highly agree with the author that general apps should align with trends, from a pure time-spent PoV Figma is a professional tool. The design editor in particular is designed for users who are in it every day for multiple hours a day. In this scenario, small delays in common actions stack up significantly.

I'll use the Variables project in Figma as an example (mainly because that was my baby while I was there). Variables were used on the order of magnitude of billions. An increase in 1s in the time it took to pick a variable was a net loss of around 100 human years in aggregate. We could have used more standardized patterns for picking them (ie illustrator's palette approach), or unified patterns for picking them (making styles and variables the same thing), but in the end we picked slightly different behavior because at the end of the day it was faster.

In the end it's about minimizing friction of an experience. Sometimes minimizing friction for one audience impacts another - in the case of Figma minimizing it for pro users increased the friction for casual users, but that's the nature of pro tools. Blender shouldn't try and adopt idiomatic patterns - it doesn't make sense for it, as it would negatively impact their core audience despite lowering friction for casual users. You have to look at net friction as a whole.

Someone needs to write a heist movie set in Spain where a key part of the plan is they steal something while La Liga is blocking some key security route.

There's still a long ways to go before things "just work". It's about equivalent to windows right now in terms of frustrations, it's just that frustrations are more along the lines of "this is a bit wonky" instead of "this is malicious / was their intended behavior". It's gotten a LOT better, don't get me wrong, but it's still far off from what a typical user would need.

I'd love to see either Valve or Nvidia really put in effort into creating their own hardware/software integration on a level that Apple does. I think it'd go a long way to legitimizing it.

I'll copy the highlights here, but the tweets have imagery as well:

> The obvious hype - It crushes benchmarks across the board, and it does so with fewer tokens per task.

> Despite this, they don’t think it can self-improve on its own. There are still areas your average engineer does better with, and despite it accelerating tasks by 4x, that only translates to <2x increase in overall progress.

> They’re probably right to hold this back - its ability to exploit things is unprecedented. Any site running on an old stack right now or any traditional industry with outdated software should be terrified if this becomes accessible.

> Counterintuitively, while it’s the most dangerous model, it’s also the safest. They’ve also seen significant additional improvements in safety between their early versions of Mythos and the preview version.

> Anthropic does a really good job of documenting some of the rare dangerous behaviors the early models had. > Interestingly, Mythos itself leaked a recent internal “code related artifact” on github.

> Mythos is also RUTHLESS in Vending Bench. Agent-as-a-CEO might be viable?

> The last thing: Mythos has emergent humor. One of the first models I’ve seen that’s witty. The examples are puns it came up with and witty slack responses it had when operating as a bot.

* Not going to debate whether or not AI imagery is CSAM here, but the point being you'll get users trying to generate ai images with subjects < 18yrs old.

It looks like it was a collaborative effort across multiple teams, where each team (research, security, psycology, etc etc etc) were all submitting ~10 pages or so. It doesn't feel like slop.

Think about how much things have changed in our industry since GPT-2 has dropped - it WAS that dangerous, not in itself, but because it was the first that really signaled a change in the field of play. GPT-2 was where the capabilities of these were really proven, up until that point it was a neat research project.

Mythos is similar. It's showing things we haven't seen before. I read the full 250 page whitepaper today (joys of being pseudo-retired, had the hours to do it), and I was blown away. It's capabilities for hacking are unparalleled, but more importantly they've shown that they've made significant improvements in safety for this model just in the last month, and taking more time to make sure it doesn't negatively affect society is a net positive.