If llms are able to write better code with more declarative and local programming components and tailwind, then I could imagine a future where a new programming language is created to maximize llm success.

This so much.

To me it seems so strange that few good language designers and ml folks didn't group together to work on this.

It's clear that there is a space for some LLM meta language that could be designed to compile to bytecode, binary, JS, etc.

It also doesn't need to be textual like we code, but some form of AST llama can manipulate with ease.

At that point why not just have LLMs generate bytecode in one shot?

Plenty of training data to go on, I'd imagine.

The code would be un reviewable.

It would also be harder for the LLM to work with. Much like with humans, the model's ability to understand and create code is deeply intertwined and inseparable from its general NLP ability.

Why couldn't you use an LLM to generate source code from a prompt, compile it, then train a new LLM on the same prompt using the compiled output?

It seems no different in kind to me than image or audio generation.

...by a human :)

Hence very important in the transitional phase we are currently in where LLMs can’t do everything yet.

Would this be addressed by better documentation of code and APIs as well as examples? All this would go into the training materials and then be the body of knowledge.

readability would probably be the sticking point

> I could imagine a future where a new programming language is created to maximize llm success.

Who will write the useful training data without LLMs? I feel we are getting less and less new things. Changes will be smaller and incremental.

Not a fan of the dominance of shadcn and Tailwind when it comes to generating greenfield code.

shadcn/ui is such a terrible thing for the frontend ecosystem, and it'll get even worse for it as AI gets better.

Instead of learnable, stable, APIs for common components with well established versioning and well defined tokens, we've got people literally copying and pasting components and applying diffs so they can claim they "own them".

Except the vast majority of them don't ever change a line and just end up with a strictly worse version of a normal package (typically out of date or a hodgepodge of "versions" because they don't want to figure out diffs), and the few that do make changes don't have anywhere near the design sense to be using shadcn since there aren't enough tokens to keep the look and feel consistent across components.

The would be 1% who would change it and have their own well thought out design systems don't get a lift from shadcn either vs just starting with Radix directly.

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Amazing spin job though with the "registry" idea too: "it's actually very good for AI that we invented a parallel distribution system for ad-hoc components with no standard except a loose convention around sticking stuff in a folder called ui"

> It'd make sense to rename WebDev Arena to React/Tailwind Arena.

Funnily, training of these models feels getting cut mid of v3/v4 Tailwind release, and Gemini always try to correct my mistakes (… use v3 instead of v4)

Same for some Material UI things in react. This is easily fixed by pasting relevant docs directly into the context, but annoying that you have to do that at all.

I've found them to be pretty good with vanilla html and css.

This model also seems to do a decent job with Angular. When I was using ChatGPT it was mostly stuck in pre-16 land, and struggled with signals etc, but this model seems to correctly suggest use of the latest features by default.

Sadly. Tailwind is so oof in my opinion. Lets import megabytes just so we don't have to write 5 whole CSS classes. I mean just copy paste the code.

Don't get me stared on how ugly the HTML becomes when most tags have 20 f*cking classes which could have been two.

In most reasonably-sized websites, Tailwind will decrease overall bundle size when compared to other ways of writing CSS. Which is less code, 100 instances of "margin-left: 8px" or 100 instances of "ml-2" (and a single definition for ml-2)? Tailwind will dead-code eliminate all rules you're not using.

In typical production environments tailwind is only around 10kb[1].

[1]: https://v3.tailwindcss.com/docs/optimizing-for-production

So. We've moved rom "human compiler" to "human compression encoder"?

You're doing it wrong. Tailwind is endlessly customizable and after compilation is only kilobytes. But yes lets complain because we don't understand the tooling....