zacmps

What benchmarks are you referring to?

Rust itself doesn't have a scheduler of course, I assume this is comparing against tokio or one of the other async executors?

This is the paradox of tolerance. There are a few suggested "resolutions", the most common being "tolerate everything except intolerance".

I now daily drive firefox, there are unfortunately plently of broken sites. Nebula's video player is broken in widescreen for example.

From memory this is true and nvml (Nvidia management library) is the way to get stats that doesn't cause the GPU to wake.

I agree, but it does have faults. Performance is woeful, and managing an on-prem instance is (literally) a full time job.

Titles and thumbnails have a huge impact on video performance, and when it's your main income it seems reasonable to try to marginalise the impact.

Badly phrased but not wrong, this is the minimum frame rate for humans to perceive motion as supposed to a slide show of images.

The maximum frame rate we can perceive is much higher, for regular video it's probably somewhere around 400-800.

I would love it if you had time to publish it!

I think what you're looking for is a bike.

Could you elaborate what you think the problems are? I guess they should be using some form of multiple comparison correction?

Not really? At the moment it's done by some user-land job scheduler. That could be something container based like k8s, something in-process like ray, or a workload manager like slurm.

Until you need to schedule GPUs or other heterogenous compute...

LLM summaries of papers often make overly broad claims [1].

I don't think this is a good example personally.

[1] https://arxiv.org/abs/2504.00025

But there has been continual improvement over that time, both in the ecosystem, and in the language (like a syntax for generics).

NileRed?

Not currently, but it is being worked on https://github.com/ray-project/ray/issues/53976.

If you use something like cockroachdb you can have a multi-master cluster and use regional-by-row tables to locate data close to users. It'll fail over fine to other regions if needed.

I suspect the number of langauges it can do with reasonable accuracy is actually much smaller, probably <15.

For literature search that might be ok. It doesn't need to replace any other tools, and if 1/10 it surfaces something you wouldn't have found otherwise it could be worth the time on the dud attempts.

This is not the view everyone holds. For an example of work in the opposite direction see https://dioxus.notion.site/Dioxus-Labs-High-level-Rust-5fe1f....

Try everything from voidtools, it's incredibly fast (both search and indexing).

Might be fixable with promoting and/or a much lower temperature?

I've wanted someone to write an extension utilising this idea since GPT-3 came out. Is it available to use anywhere?

Do you know any resources for learning these heuristics?

What does it do?

Correct

People would do the same if curl was publicly funded, that doesn't mean it would be a bad idea.

It's not something I've needed to deal with personally.

We have run into added content filters in Azure OpenAI on a different application, but we just put in a request to tune them down for us.

I've found the highest accuracy solution is to OCR with one of the dedicated models then feed that text and the original image into an LLM with a prompt like:

"Correct errors in this OCR transcription".

In some countries (NZ for example) this does not exempt you from reporting requirements unless your total spend falls under NZD$15,700.