Readit NewsIs there a reference for that, because it's curious. (As in I really hope you have a reference to read.)
I'd thought I had seen that repeated scarring (from being not broken down) upregulates cellular replacement rate, or concomitant inflammation were suspect.
[1] https://archive.cdc.gov/www_atsdr_cdc_gov/csem/asbestos/how_...
My understanding is that basalt fibers seem to be glassy, not crystalline, so the breaking does not happen.
Rust is generally pretty good at this, unlike (say) Go: most functionality is implemented as part of the standard library, and if I want to write my own `Vec` then (for the most part) I can. Some standard library code relies on compiler features that haven't been marked stable, which is occasionally frustrating, but the nightly compiler will let me use them if I really want to (most of the time I don't). Whereas in Go, I can't implement an equivalent to a goroutine. And even iterating over a container was "special" until generics came along.
This article was a really interesting look at where all that breaks down. There's obviously a trade-off between having to maintain all the plumbing as user-visible and therefore stable vs purely magic and able to be changed so long as you don't break the side effects. I think Rust manages to drive a fairly good compromise in allowing library implementations of core functionality while not needing to stabilise everything before releasing anything.
I don't think this is possible in a language that needs a runtime, like Go.
It might turn out differently in the US, but it is hardly a new idea.
I was tired of repeat, sponsored, and "safe" music suggestions from Spotify, so I built a discovery engine that puts the control back in the user's hands.
The core idea is simple: You define a "Discovery Model" with explicit constraints (specific genres, release years, track popularity, etc.). The app then uses this blueprint to source tracks.
The results are fresh for two reasons:
- "Known" Track filtering: Excludes all songs saved in your library and recent listening history.
- Active Curation: Uses your custom model, not a vague, opaque algorithm.
It’s built with a local-first mentality and a focus on privacy. No black-box AI "vibe" mixes, just pure, objective discovery based on your criteria.
Hope ya'll find some new gems!
A "discovery algorithm" that I used (works great for jazz) consisted on looking up which musicians played on an album that I liked on discogs and searching for more albums from them.