How would that even function, I mean, did they loop through every single permutation and see the result, or what exactly/ how would that work?

(Source: ran a Warrior)

If Internet Archive is a library, ArchiveTeam is people who run around collecting stuff, and gives it to the library for safe keeping. Stuff that are estimated/announced to be disappearing/removed soon tends to be focused too.

This is in contrast to the Wayback Machine's builtin crawler, which is just a broad spectrum internet crawler without any specific rules, prioritizations, or supplementary link lists.

For example, one ArchiveTeam project had the goal to save as many obscure Wikis as possible, using the MediaWiki export feature rather than just grabbing page contents directly. This came in handy for thousands of wikis that were affected by Miraheze's disk failure and happened to have backups created by this project. Thanks to the domain-specific technique, the backups were high-fidelity enough that many users could immediately restart their wiki on another provider as if nothing happened.

They also try to "graze the rate limit" when a website announces a shutdown date and there isn't enough time to capture everything. They actively monitor for error responses and adjust the archiving rate accordingly, to get as much as possible as fast as possible, hopefully without crashing the backend or inadvertently archiving a bunch of useless error messages.

They are the middlemen that collects the data to be archived.

In this example the archivee (goo.gl/Alphabet) is simply shutting the service down and has no interest in archiving it. Archive.org is willing to host the data, but only if somebody brings it to them. Archiveteam writes and organises crawlers to collect the data and send it to Archive.org

They already have plenty of unused compute /older hardware / CDN POPs, performant distributed data store and everything else possibly needed .

It would be cheaper than the free credits they giveaway just one startup to be on GCP.

I don’t think infra costs are a factor in a decision like this .

Unless I'm just super smart (I'm not), it's pretty easy to write a URL shortener as a key-value system, and pure key-value stuff is pretty easy to scale. I cannot imagine that isn't doing something as or more efficient than what I did.

But as I said in sibling comment to yours, I don't see the point of the distinction, why not just continue them all, surely the mostly unused ones are even cheaper to serve.

705 bytes is an extremely long URL. Even if we assume that URLs that get shortened tend to be longer than URLs overall, that’s still an unrealistic average.

It’s smaller than Google’s index and Google does not represent the entirety of the web either.

For LLM training purposes this may or may not matter, since it does have a large amount of the web. It’s hard to prove scientifically whether the additional data would train a better model, because no one (afaik) not Google not common crawl not Facebook not Internet Archive have a copy that holds the entirety of the currently accessible web (let alone dead links). I’m often surprised using GoogleFu at how many pages I know exist even with famous authors that just don’t appear in googles index, common crawl or IA.