You can talk to a bunch of designers who will say the opposite. Claude Design Studio generated this garbage UI, that I fixed manually, but it created great code j never could have that made it work.
These systems should allow rapid iteration on discovery and thinking. One can now make a prototype a day that would have taken a week. That means that we should be able to converge on a much better design in the same amount of time it would have taken to make a v0 that turns how to have systemic flaws.
AI should scale our understanding of systems, not just shovel out half baked features and apps.
Where I’m at when building personal applications for my home / life is: does the code execute and perform the desired task?
If so, what do I care how shitty it is? I’m not publishing these projects (for the most part… I have one joke application up at songshift.reachnick.co) so efficient, clean, secure code are not really a priority for me.
The issue before is that coding is not only difficult and time-consuming to learn, but also that I think it requires a particular type of person to fully grasp this new, non-human language.
I see these SOTA LLMs as akin to the digital camera revolution. Suddenly the moat that has kept people from participating in this art form (for film it was the high cost of film stock, processing film, editing the film prior to non-linear editing programs, etc) has disappeared.
Are people producing low-quality video content now because of the cheap and ubiquitous access? Of course, but we’re also exposed to brilliant filmmakers / artists who simply never would have had the opportunity to try their hand.
By the same token, sure there’s lots of garbage code out there now. But it’s also unlocking imaginations by granting access to the mysterious inner workings of a computer to the average person, letting them use their computers more thoroughly than ever before.
I find it exciting. Bummer for the highly-paid SWEs, but such is life. You can only protect a niche to demand high wages for so long.
For example, you can make AI music, but who will listen? If you form an AI band around AI music and execute an AI marketing strategy like it was a real band, probably thousands, hopefully millions.
If you make AI art, who’s going to look at it? If you make AI art in a very specific style and you can crank out 8x upscaled high resolution versions of it for print, well, you just have a business!
And if you make film, you already know, green screen and chroma key models are far superior, that AI enhancements can help you in the editing room, and that LTX2.3 can fill in the VFX shots when the budget is exhausted.
That is to say - it's entirely possible to have a design that a layperson looks at and goes "wow that's beautiful", and then A/B test it in the real world and your revenue goes down X% because (for example) certain important sections now require more clicks to access.
Or to use a real-world example - you could redesign a train station and make it more beautiful while also increasing the amount of people who get lost because it's now more difficult for some people with poor eyesight to find the right track.
https://en.wikipedia.org/wiki/Michael_Crichton#:~:text=%5B14...
People are never perfectly even in intelligence across all possible disciplines.
Gell-Mann's observation was a sincere and thoughtful caution about the way we transmit information about complicated ideas. Crichton's "amnesia effect" is an excuse to ignore media you dislike.
You're suggesting that (a) their UI skills are lacking (based on what? isn't UI exactly what they were iterating on and trying to improve?), and (b) that a real UI expert would've somehow felt the UI they were working on was consistently garbage, despite how many times they iterate on it?
Which means you're saying you don't believe anyone can actually produce high quality (to an expert) output with AI on the same target they're working on, and if they think they are, that just means they don't have a good sense of quality?
the llm produced something the operator thought was garbage for the design too, and the operator iterated it from garbage to good.
they could also have the llm iterate the underlying code from garbage to good, if they wanted.
most likely a specialist would say its neither good nor bad, since its not considering the right things, and hasnt collected the right useability feedback, but making straightforward designs isnt that hard, and counting clicks and interactions, and avoiding hidden functionality is all measureable stuff
It's only confusing because you don't know the field. Which is kind of the point.
Tell me about it… I was forced to use a program called Farmer’s Wife for a time. What a fucking nightmare of a UX.
AI pixel art looks particularly bad because most users don’t even go through the effort of downscaling and then upscaling it using something as simple as nearest-neighbor scaling, which by itself will squash out a lot of high-frequency noise that manifests in the form of terrible looking "fringing". Proper grid alignment also makes a big difference. It’s not perfect by a long shot, but it helps.
findfantasyxviii.com
Well when you put it that way ... monetizing the Dunning-Kruger effect does actually sound like a very good business idea.
I think that it is extremely tempting to just let Claude run freely over the codebase and turn it into unmaintainable slop. My hot take is that this is fine.
It doesn't take very long to come up with a list of simple yes-or-no style rules (e.g. modules should be descoped to simple files, each module must have tests at filename_test, a reasonable reader should find comments to be concise and not extraneous, etc). It doesn't take very long to set up a precommit hook that starts a short Claude session to check each rule, block if any are failed, and explain the issues.
After that? I've found that it's pretty easy to get good code written, and even easier to maintain it. Obviously anything with a value judgement is an avenue for issues, but even a non-frontier model can generally do a passable job answering questions. As long as you eventually read the code to decide on big-picture refactorings, you'll be in a great place.
basically the AI-slop version of food, yet still they thrive