ninjha
Joined 6 years ago
Joined 6 years ago
from your link
not original author but batching is one very important trick to make inference efficient, you can reasonably do tens to low hundreds in parallel (depending on model size and gpu size) with very little performance overhead
now i know the bar is 1000 feet below the earth with teams but matrix is still only maybe a foot or two above the surface
i really want to like it but every few months i try it and it’s clearly just not ready :(
logos on website if you want to see some of our customers lol
True! Devin Review doesn’t make the kind of judgements you mention, it just does its best to find bugs and help you understand the code faster. I managed to review a PR on an airplane (without starlink) with it earlier this week lol
Obviously the working tree should be a commit like any other! It just makes sense!
(I work at Windsurf but not really intended to be an ad I’m just yapping)
E: I found the paper: https://arxiv.org/pdf/2010.11929
> We use standard learnable 1D position embeddings, since we have not observed significant performance gains from using more advanced 2D-aware position embeddings (Appendix D.4).
Although it looks like that was just ImageNet so maybe this isn't that surprising.
Weirdly enough this also means that if you’re running with TCP you can have retransmits at the NFS/SunRPC level and at the TCP level depending on your configuration.