Given the main article was paywalled, here's a summary of Go variations. Besides the minor stuff like 'where to put captured stones, cup or side', there's one fundamental difference: Whether to count area (count every stone towards score, aka Chinese counting) or territory (count empty points within your territory, aka Japanese counting).
This article is great, gives an overview plus specifics on 6 different rule sets in use: gomagic.org
Doesn't help to buy name brand on Amazon if a reseller has counterfeit items. Amazon trusts sellers and bins everything together, so the 'Eveready' and the 'we swear it is Eveready' go into the same box.
This bit a lot of people during eclipses because they buy high-reputation eclipse glasses direct from that Name Vendor's Shop via amazon, but amazon ships the no-names, then Name Vendor has to eat the return cost/chargeback for upset customers.
For those that also didn't know what skeumorphism is, via wikipedia:
a derivative object that retains ornamental design cues (attributes) from structures that were necessary in the original.[3] Skeuomorphs are typically used to make something new feel familiar in an effort to speed understanding and acclimation. They employ elements that, while essential to the original object, serve no pragmatic purpose in the new system, except for identification. Examples include pottery embellished with imitation rivets reminiscent of similar pots made of metal and a software calendar that imitates the appearance of binding on a paper desk calendar.
So, you know, familiarity.
To add on in agreement, key to the 'Trump followers pleading with Musk to take a second look' is not that damage is being done to the government, it's (to quote thehill article):
"There are good people, people that voted for Donald Trump, who are losing their job," Levi said."
Unfortunately it's not 'things are off the rails', it's back to 'wait, I didn't think this would also affect our side too'.
I gave a tutorial, an AI notetaking/summarizing tool then produced an "Engagement" metric, with a low 30%. It did not say how this was produced, only hints in their FAQ. Basis included # of cameras turned on, % of eyes watching screen versus off-screen, and # people talking.
There were 50+ people but for the tutorial only 3 had cameras on: me, the moderator (frequently looking off-screen) and a Guy. So if the Guy looked away, that plunged the "Engagement" metric. Plus that it was a Tutorial (95% me speaking) and *poof* low engagement.
My fear is management will latch onto these undocumented AI-generated Metrics and use them in performance evaluations. "Get your scores up, A., 30% is terrible!" But it's not a real measure!
Innovation is hard to schedule. -- Dan Fylstra