afp14

it works on WSJ, I use it for that site. for some bypasses, it doesn't "just load the page" but rather pops up a link to archive.ph or similar to pull the article, still convenient

I also point out there is "bypass paywalls" and "bypass paywalls clean", kind of similar to the uBlock family with "uBlock origin" being the "good guy" edition

in cali the power company says "you can use this cost structure or that cost structure, your choice!" - so it's this vile little game where if you choose incorrectly they scalp you, and it takes a bit of arithmetic to compute expected values, power usage at various times of day, etc - something the average joe may not be able to estimate easily. in an ideal world, they would automatically pick the lower of the two.

to a degree I have seen AWS plans work this way, where you choose from options A/B/C and buyer beware if you end up getting scalped.

It's a matter of scale as far as expense. If my company produces 100,000 cars, and the stalk assembly/parts cost $50, now I've saved 50*100000=5 million dollars a year. To me it's clever in the same way that all-wheel drive users existing brakes to manipulate wheel-power delivery versus a complicated (and expensive) 4WD transmission. Clever, yes, but impossible to use in a tactile manner and thus dangerous because it forces eyes-off-road ( a simple steering wheel button might save money and still afford tactile aspects)

I think that's the problem, there aren't hard and fast definitions. I think "economic depression" is an unsettling word, so it keeps switching to "recession" or "slowdown" in the same way that what was once a "moron" or "mongoloid" has become "intellectual disability". I think the powers that be worry that if they announce "we're in a depression right now" it could be a self-fulfilling prophecy to a degree. And eventually, one day if not already, they'll be equally hesitant to use the word "recession" at all.

As far as a bubble, yes, it's the evaporation of a huge amount of money. 1929 stock market crash -> all that market cap was lost. In the 2008 crash, home values crashed thus losing a vast amount of hypothetical worth. We were/are/will see a similar real estate crash in the corporate/office sector since the value of these rental units has plummeted post-covid-work-from-home.

I like presearch.com wholly because they are too young/small to apply these sorts of filters, the results feel "raw" in a good way.

I have no affiliation with them and dislike their cryptocurrency based snake oil. But as far as a search engine that hasn't been tinkered with and is free, not bad, and I recommend them on that basis alone.

So here's my hypothesis as to how it works now - and yes I believe this "problematic bias" is baked into the core model - I think they have a "dumb" simple "is this a person" detector stitched onto the end of the pipeline. If it detects a person, drop the generated images.

This suggests a jailbreak, if you can fool the "dumb" person detector (likely shallow vs deep learning) with your deep learning generator, the images pass through. I had some success last week with "upside down" or "standing on their head" though it may have been patched. You could consider occlusion "wearing a mask" etc. Basically find phrasing that would fool a naive person/face detector in the corresponding image.

As for the model, I truly believe it to be paradoxically pro-white racist. Imagine any stereotype for any race. You say "generate <stereotype> of a <historically white character/role>" and the model swaps out the white race for the stereotyped race, seemingly embracing racist stereotypes. On the other hand, for white stereotypes, they are much harder to produce since the model is hesitant to render white folks.

As I understand it, in fact 960MW is quite close to 1 jigawatt, which is the typical energy required to power a Delorean-class time machine.

It's seemingly simple "oh the technician keeps messing up"

Did the technician mess up (sometimes true), or were they doing their job in good faith - was it the system/protocol/organization that made the task mistake prone? Did someone else actually mess up but the situation made it look like it's the technician's fault? Does this technician do a task/service that is failure prone? Are there other technicians on other tasks that are far less failure prone? Here the former technician would seem poor, the latter, excellent, but it's a function of the task/role and not the person.

I've been "the technician" - I catch a lot of blame because people know I'm anti-blame culture, so I'd rather take the blame on myself that point my finger to the next guy in line. I'm also willing to take on high risk tasks for the greater good even if they suck and are blame prone / risky. I believe in team culture in this way. If the organization doesn't respect that belief and throws me under the bus, I leave - which is quite punishing for them since they remain completely unaware of a major internal problem. If an organization "sees me" and my philosophy, then together we get very very good at optimizing the system to minimize the likelihood of failure / mistakes.