Of course it’s always been easier to find talent when working in more popular languages. That’s the big risk you take when you choose the road less traveled.

I recently picked up Hare, which is quite obscure, and Claude was helpful as a better— albeit hallucinogenic— Google. I think LLMs may not lead to as much ossification as I’d originally feared.

>My limited testing seems to show LLM assistants do well the more popular the language is (more data in its training), so is the hurdle for adoption of something new going to get even higher

Not only that they also tend to answer using the the more popular languages or tool event when it is NOT necessary. And when you call it out on it, it will respond with something like:

"you are absolutely right, this is not necessary and potentially confusing. Let me provide you with a cleaner, more appropriate setup...."

Why doesn't it just respond that the first time? And the code it provided works, but very convoluted. if wasn't checked carefully by an experienced dev person to ask the right question one would never get the second answer, and then that vibe code will just end up in git repo and deployed all over the place.

Got the feeling some big corp may just paid some money to have their plugin/code to on the first answer even when it is NOT necessary.

This could be very problematic, I'm sure people in advertising are just all licking their chops on how they can capitalized on that. If one thing currently ad industry is bad, wait until that is infused into all the models.

We really need ways to

1. Train our own models in the open, with weight and the data it is trained on. Kinda like the reproducible built process that Nix is doing for building repos.

2. Ways to debug the model on inference time. The <think> tag is great, and I suspect not everything is transparent in that process.

Is there something equivalent of formal verification for model inference?

I agree, I think it makes most sense to add them up to be the true #2.

If you’re working in a large team, and you’re not locked into the MS stack, and you’re not doing anything that needs to be super performant… Java is, by far, your best option.

The tooling and ecosystem aren’t great compared to some of these languages, but Java itself can be pretty damn good.

I used to be a hater many years ago but I’ve since grown to love it.

Ruby became popular mainly because of Rails, which has gone down somewhat in popularity in recent years. That may be why Ruby is less popular now than it was 10 years ago. Also, they (Ruby and Rails) got popular much before 10 years ago, like around 2006 / 2007, when the Web 2.0 wave was starting. I had worked on a couple of dot-com projects in Ruby on Rails at that time, that is how I know.

It’s grating to see Haskell compared to labview, regardless of context lol

Haskell at least is fun.

My favorite Julia also made the list this year... nonzero users means there is hope for fun languages yet.

With the new Intel+NVIDIA RTX SoC deal, we can expect Python and C++ to dominate that list in the next few years. =3