rusbus

(I am OP) Both good call outs. Will update the article to include them

Was this in a specific application? I wouldn't necessarily expect that histogram to be particularly bad for most applications.

Do you have any resources worth referencing on this? I assume this isn't something that works with tokio more of a replace tokio?

Note that most of those end up getting inlined in practice

This is exciting because the effect size is so large. But as the author's acknowledged, selection bias is nearly impossible to control for in this non-randomized study:

> and lacks randomized controls. Self-selection is the central threat: students who complete more quizzes may be more motivated or higher-performing generally

But this is still a strong result. I'm excited to see more in this space.

Two years ago someone told me "I wish I could understand what Tokio is actually doing."

This really stuck with me and earlier this year I had both an idea and the time to try to make it happen. I think with dial9 that is finally possible. dial9 is a system to pull in a huge amount of events, very efficiently and analyze them later. Tokio is just one source of those events, but its a very important one.

Once we had Tokio, I realized we could also pull in lots of other sources as well; Linux kernel events, application events, tracing etc. etc.

Folks have already had good luck using dial9 to find tricky issues in prod.

Does anyone else find it incredibly ironic that this article summarizing the paper was obviously written with AI?

All the headings and bullets and phrases like "The findings are clear:" stick out like a sore thumb.

perhaps you did not read the post—the author attempted layer-by-layer first but discovered it was impossible to get a smooth final result.

This is apparently a well known issue among people who create resin-cast structures.