zlacker

>>meetpa+(OP)
My frustration with using these models for programming in the past has largely been around their tendency to hallucinate APIs that simply don't exist. The Gemini 2.5 models, both pro and flash, seem significantly less susceptible to this than any other model I've tried.

There are still significant limitations, no amount of prompting will get current models to approach abstraction and architecture the way a person does. But I'm finding that these Gemini models are finally able to replace searches and stackoverflow for a lot of my day-to-day programming.

>>segpha+J4
> no amount of prompting will get current models to approach abstraction and architecture the way a person does

I find this sentiment increasingly worrisome. It's entirely clear that every last human will be beaten on code design in the upcoming years (I am not going to argue if it's 1 or 5 years away, who cares?)

I wished people would just stop holding on to what amounts to nothing, and think and talk more about what can be done in a new world. We need good ideas and I think this could be a place to advance them.

>>jstumm+jH
I mean, if you draw the scaling curves out and believe them, then sometime in the next 3-10 years, plausibly shorter, AIs will be able to achieve best-case human performance in everything able to be done with a computer and do it at 10-1000x less cost than a human, and shortly thereafter robots will be able to do something similar (though with a smaller delta in cost) for physical labor, and then shortly after that we get atomically precise manufacturing and post-scarcity. So the amount of stuff that amounts to nothing is plausibly every field of endeavor that isn't slightly advancing or delaying AI progress itself.

>>joshjo+L41
If the scaling continues. We just don't know.

It is kinda a meme at this point, that there is no more "publicly available"... cough... training data. And while there have been massive breakthroughs in architecture, a lot of the progress of the last couple years has been ever more training for ever larger models.

So, at this point we either need (a) some previously "hidden" super-massive source of training data, or (b) another architectural breakthrough. Without either, this is a game of optimization, and the scaling curves are going to plateau really fast.