Micoloth
Joined 11 years ago
Joined 11 years ago
Maybe i’m just missing something. But it looks like nobody is really using it except for some very specific math research which has grown from within that ecosystem from the beginning.
I think one of the basic problems is that the core language is just not very performant on modern cpus, so not the best tool for real-world applications.
Again- maybe i’m missing something?
Debatable at best..
> Why apples fall, why planets don’t wander off, and why we aren’t all quietly drifting into space every time we sneeze.
Newton didnt really explain the why.. Einstein added something much later, but it might be that really we still don’t have a clue.
All we can do is measure how fast it happens, very precisely
How would you say your project compares to Arroyo?
I was agreeing very much with both parent comment and yours, until your edit.
I loved Euphoria.
> graphicness - Was it graphic at all? > how big everybody's feelings are - Were their feeling that big? > It seemed so obviously.. - Maybe obvious to you? This might say more about you..
I found it brilliant and at times ironic and self aware and very explicit about what its target is (I think it's very much for teenagers)
So i don't know if it is a good example of this trend at all.
Just to say how nuanced these things can be, i guess...
It hits so many right sposts. Thanks for sharing it
You know the ones. They founded the trillion-dollar companies you hear about and became billionaires themselves
Some stylistic choices look very much taken from there, even if some other details (eg font) look much more primitive
- If it’s not based on that, why?
Blander has an Excellent Python-based cross-platform ui which is completely open source. I’ve always thought it’s a crying shame that’s not available as a package to build desktop apps. And I think it would be very good to take things from there, like their very mature graph editor
It's widely used, you can look it up.
A more challenging idea is whether it is possible to reuse the pretrained weights when training a network with a different architecture (maybe a bigger transformer with more heads, or something).
AFAIK this is not common practice, if you change the architecture you have to retrain from scratch. But given the cost of these trainings, I wouldn't be surprised if OpenAI&co had developed some technique to do this, eg across GPT versions..