This was the subject of https://arxiv.org/abs/2412.03719. (I suspect you can do simpler than the paper's solution if you're only interested in the top-k.)
A related topic is "token healing", although some implementations (unfortunately including the one in HuggingFace Transformers) make some big assumptions that aren't always true (like treating spaces as special).
Agreed... but one problem with that is that you get the title bar on the OBS window. If you have a spare external display, you can use a fullscreen projector, but I often don't have one. Anyone know a workaround?
I see they monitor a complete process lifetime, not just the active workload. A quick grep of the paper didn't turn up anything discussing this. And it would seem to hurt dynamic languages and runtime-JIT languages a lot. Perhaps the active workload takes long enough that the transient is washed out. Anyone have insights?
I really want to like Elm. When I'm writing JS/React code, I sometimes think "this would be so much nicer in Elm!" - especially for architectural issues. But the few times I’ve actually tried doing something in it, I find that the parts of what I want to do that fit cleanly within Elm’s walls are really nice, but the parts that don’t quite fit get hard quickly. Suppose I want to do something with the DOM that doesn’t quite fit into virtual-dom’s model -- I suddenly have to make a complicated JS interop and work around things to get at the raw DOM node... whereas with React I can just hack something together, try it with users, and learn that I should actually be doing something completely different anyway. Or maybe I don’t actually know yet what I want to have happen in every possible condition? Maybe I feel this way because I just don’t have enough experience, but it may be a fundamental trade-off involved in how Elm makes it hard to do things wrong.
Has anyone had some positive experiences with prototyping / rapid design iteration with Elm and can share some tips / encouragement?
A related topic is "token healing", although some implementations (unfortunately including the one in HuggingFace Transformers) make some big assumptions that aren't always true (like treating spaces as special).