This is a good question for any provider like AWS--what kinds of information do I leak with seemlingly mundane choices like bucket names.
The other attack vector is from insiders. Many organizations "shield" identifiable information behind UUIDs or some other scheme. In the event of a breach, the UUID might mean nothing to most (it's not foolproof, though), but opens more doors for an insider.
> Current performance and test counts on a 40 core system are: $ time make -j $(nproc) check SUBDIRS=.
13s
$ # time make -j $(nproc) check RUN_EXPENSIVE_TESTS=yes
1m22.244s for 9 extra expensive tests
That's pretty respectable, given that coreutils include 98 programs (some are simple like yes(1) and true(1), but most of them are used millions of times a day to do real work: ls(1), kill(1), cat(1), wc(1).
In fact, I used wc(1) to count the number of separate programs inside coreutils.
I expected to read a paper about some obscure Excel trick to manipulate stats output. Instead, this is just old-fashioned manipulation by hand or "imputation" as the paper describes it.
> In email correspondence seen by Retraction Watch and a follow-up Zoom call, Heshmati told the student he had used Excel’s autofill function to mend the data. He had marked anywhere from two to four observations before or after the missing values and dragged the selected cells down or up, depending on the case. The program then filled in the blanks. If the new numbers turned negative, Heshmati replaced them with the last positive value Excel had spit out.
European providers benefit from lower cross-connect fees in datacenters and more internet exchanges for easy peering. It's not surprising they offer more bandwidth at the same cost.
Right. Egress is an imperfect, but reasonable metric for overall utilization. If they started charging for CPU hertz above a certain threshold, that'd be a harder sell.
That's true, but there's an interesting parallel with GitHub's corporate parent, Microsoft, and Microsoft's other platform company LinkedIn[1]. LinkedIn sued scrapers for retrieving data from the site.
LinkedIn isn't a content company either, nor do they really own any content posted there (they don't right?), but a large part of their business moat comes from the network of people posting content there. Scrapers and bots undermine this, something the AI boom facilitates.
There are a lot more AI projects hungry for data to train their models on. This puts content companies in an uncomfortable situation: trademark infringement claims, loss of intellectual property, and more.
Even as early as 15 years ago, MIT switched from scheme to python. The reasons are detailed in an interview[1] (see HN discussion[2]): the educators belived the future of programming would be snapping pre-made libraries together instead of engineering from scratch.