Absolutely!

The only field I see LLMs enhancing productivity of competent developers is front end stuff where you really have to write a lot of bloat.

In every other scenario software developers who know what they’re doing the simple or repetitive things are mostly solved by writing a fucking function, class or library. In today’s world developers are mostly busy designing and implementing rather complex systems or managing legacy code, where LLMs are completely useless.

We’re developing measurement systems and data analysis tools for the automotive industry and we tried several LLMs extensively in our daily business. Not a single developer was happy with the results.

Means they aren’t competing. They’re working in a completely different field. Nvidia isn’t producing anything sensible.

Competition in what? Producing bullshit?

But it’s 2⁵² addresses for each star in the observable universe. Or in other words, if every star in the observable universe has a planet in the habitable zone, each of them got 2²⁰ more IPs than there are IPv4 addresses.

“How a shitty company with an even shittier codebase full of bad design decisions uses AI” isn’t going to get me hooked on using AI.

Just when you thought Nvidia couldn’t get worse, they praise Trump.

But spending a lot of processing power to gain smaller sizes matters mostly in cases you want to store things long term. You probably wouldn’t want to keep the exact same LLM with the same weightings and stuff around in that case.

Ye but that would limit the use cases to very few. Most of the time you compress data to either transfer it to a different system or to store it for some time, in both cases you wouldn’t want to be limited to the exact same LLM. Which leaves us with almost no use case.

I mean… cool research… kinda… but pretty useless.

Ok so the article is very vague about what’s actually done. But as I understand it the “understood content” is transmitted and the original data reconstructed from that.

If that’s the case I’m highly skeptical about the “losslessness” or that the output is exactly the input.

But there are more things to consider like de-/compression speed and compatibility. I would guess it’s pretty hard to reconstruct data with a different LLM or even a newer version of the same one, so you have to make sure you decompress your data some years later with a compatible LLM.

And when it comes to speed I doubt it’s nearly as fast as using zlib (which is neither the fastest nor the best compressing…).

And all that for a high risk of bricked data.

Why would I need AI for that? We should really stop trying to slap AI on everything. Also no, I’m not that big of a fan of wasting energy on web crawlers.

Yes I agree on that. A lot of people write “C with classes” and then complain…

“americans” is a bad name but it’s more specific than “united statesians”. But I would fully support dissolving that country and founding a new one (or multiple) with a better name.

I’m a full time C++ developer, mostly doing high performance data processing and some visualization and TUI tools, and as someone loving C++, it’s not as simple as you frame it. In sufficiently complex code you still have to deal with these problems. Rust has some good mechanisms in place to avoid these and there are things on the way for c++26 though.

Nintendo fans will vote just like Apple Fans.

Living under a rock eh?