Track races use a counter-clockwise oval, as does Olympic speed skating and just about every track sport. Never quite realized it was a default until this article.

Also not sure how to reconcile with the grocery store 'people default to turning right'. So we default to turning right, except when we wander or compete, so we go left? I think this goes to my default answer for everything, "it's complicated".

From the article, the reason is "This is probably because the amount of cold gas available for them to ingest has decreased since cosmic noon."

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.

I use ighome.com daily. Sometimes it's hard to find the actual RSS feed for a site, though, and some feeds are broken. A lamentable loss of a great idea, 'hey, tell me what your content is!'

> And the radio tuned to a noisy radio station so it can not listen to conversations, the only thing your phone or tablet can hear is a noisy radio station

... then the RIAA will sue you for recording & storing a music broadcast without a license.

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.

Because the payment is from the journal, not the authors. Likewise reviewers are anonymous for peer review, specifically to avoid such potential conflicts. So 'pay for positive review' isn't an option. Peer review is actually a well worked out system, go figure.

"Eh, that's okay. It's not like it's your computer anyway." [presumed all companies now]

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!

The main change in AI coding for me, is my next job will probably be repairing/re-doing other people's AI-generated code after it inevitably fails. I've used ChatGPT 4o to make code and agree with the folks saying it's like a quick google to stack overflow, with all the same concerns on whether it picked the right answer (out of many) or not.

AI makes simple yet catastrophic errors, for example it coded an API call as 'meta, data = ...' instead of the actual API of 'data, meta = ...'. You think someone that doesn't know that API will verify that or catch that? It's not the kind of error a coder or a cut-and-paster would make, it's a hard-to-spot bug that simply means headaches (and employment) for coders in the future.

Thanks, AI, for future job security?

The problem with the 'we are in a simulation' is that, of all the eras to mimic, why the 2020s? Is this really the best the VR writers could come up with? There are so many more interesting decades to loop us over.

Bonus points if the simulation was as we imagined things, not as they were. Why not put us into Bridgerton? Or the SCA's version of medieval? If we have to stick to history, why not loop over the dot-com era?

If you go with the matrix idea of utopias made the captives unsatisfied and such, there's still a much wider choice than the current rise of fascism etc. Unless the current negatitivity is the intentional backplot for a really interesting culminating storyline. Still, if this is a simulation, they need to fire the writers and start over.

Clearly they lack a sense of irony. These loan-laden self-deemed leaders "convened a private dinner in Las Vegas ... Over rib-eyes and chicken piccata at Scarpetta ... the group brainstormed strategies to weather the slump"

A rather flawed idea that 15 rivals are really going to tell each other their solutions. I guess 'spend less, do actual work' wasn't on a topic that evening. Call this 'expensive dinners that could have been a Slack'.

A professor told me he would post technically correct but overly compex solutions to his own homework problems on Chegg. If a Physics 100 student solved an F=ma problem using his Lagrange equivalent answer, he knew they'd cut-and-pasted. Hard to do in ML, which is more likely to hallucinate a wrong answer than to provide a ridiculous yet correct one.