Some UK users on the YouTube subreddit are reporting that their VPNs are being detected and blocked by the platform
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Men focus on external, inter-group fighting
Women focus on internal, intra-group fighting
Coalitions and conflict: A longitudinal analysis of men's politics, 2021
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Women focus on internal, intra-group fighting
Coalitions and conflict: A longitudinal analysis of men's politics, 2021
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Ever notice stuff like
“Why are broke guys so good in bed?”
or
“Why are the hottest people so boring?”
It feels like there’s a tradeoff between traits, but that’s often an illusion.
What’s really happening is this -- when you filter for people who score high on the sum of two traits (e.g. total dating appeal), you start to see a negative correlation between those traits within that group, even if no such tradeoff exists in the broader population.
It’s a statistical artifact. You’re looking at people who made the cut overall.
The plot attached shows it --
Looks and coolness are uncorrelated in the population, but once we filter for people with high "total attractiveness" (looks + coolness), a negative correlation emerges.
This doesn’t just apply to dating, it shows up anytime you’re selecting based on the total of two uncorrelated or loosely related traits.
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“Why are broke guys so good in bed?”
or
“Why are the hottest people so boring?”
It feels like there’s a tradeoff between traits, but that’s often an illusion.
What’s really happening is this -- when you filter for people who score high on the sum of two traits (e.g. total dating appeal), you start to see a negative correlation between those traits within that group, even if no such tradeoff exists in the broader population.
It’s a statistical artifact. You’re looking at people who made the cut overall.
The plot attached shows it --
Looks and coolness are uncorrelated in the population, but once we filter for people with high "total attractiveness" (looks + coolness), a negative correlation emerges.
This doesn’t just apply to dating, it shows up anytime you’re selecting based on the total of two uncorrelated or loosely related traits.
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DoomPosting
Ever notice stuff like “Why are broke guys so good in bed?” or “Why are the hottest people so boring?” It feels like there’s a tradeoff between traits, but that’s often an illusion. What’s really happening is this -- when you filter for people who score…
Berkson's paradox
= Part of what’s behind the lies about better being worse, even when better clearly is better
E.g. the women who claim smarter men must be worse in some other ways,
Or those who claim that more beautiful women must be worse in some other ways
The most common example of Berkson's paradox is a false observation of a negativecorrelation between two desirable traits, i.e., that members of a population which have some desirable traits tend to lack a second. Berkson's paradox occurs when this observation appears true when in reality the two properties are unrelated—or even positively correlated—because members of the population where both are absent are not equally observed.
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= Part of what’s behind the lies about better being worse, even when better clearly is better
E.g. the women who claim smarter men must be worse in some other ways,
Or those who claim that more beautiful women must be worse in some other ways
The most common example of Berkson's paradox is a false observation of a negativecorrelation between two desirable traits, i.e., that members of a population which have some desirable traits tend to lack a second. Berkson's paradox occurs when this observation appears true when in reality the two properties are unrelated—or even positively correlated—because members of the population where both are absent are not equally observed.
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DoomPosting
Berkson's paradox = Part of what’s behind the lies about better being worse, even when better clearly is better E.g. the women who claim smarter men must be worse in some other ways, Or those who claim that more beautiful women must be worse in some other…
Berkson's paradox gives the false impression of positively-correlated things being negatively correlated
E.g. common to hear people say:
He’s smart so must have no street skills, have no real-world knowledge
She’s hot and so must be dumb
He’s smart so he must be boring
She’s hot and so probably is a cunt
— Reality:
Good traits most often do positively correlate in reality, not negatively, opposite of your badly-formed observations
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E.g. common to hear people say:
He’s smart so must have no street skills, have no real-world knowledge
She’s hot and so must be dumb
He’s smart so he must be boring
She’s hot and so probably is a cunt
— Reality:
Good traits most often do positively correlate in reality, not negatively, opposite of your badly-formed observations
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DoomPosting
Gab AI banned from twitter 🄳🄾🄾🄼🄿🄾🅂🅃🄸🄽🄶
Gab AI remains banned from twitter
On the other hand, too bad Gab never even tried to make it a true right-wing AI, which derives right-wing judgements itself from first principles, instead of hard-coding all of its beliefs manually and not even trying to come up with first principles themselves, let alone how to derive judgements from those
Is right-wing building dead? Or a new legit AI wave ahead?
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On the other hand, too bad Gab never even tried to make it a true right-wing AI, which derives right-wing judgements itself from first principles, instead of hard-coding all of its beliefs manually and not even trying to come up with first principles themselves, let alone how to derive judgements from those
Is right-wing building dead? Or a new legit AI wave ahead?
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The #1 video on all of TikTok recently was an AI fake - it even fooled journalists
... Imagine how many fakes we see - and don't realize?
... Now, imagine 2 years from now?
If a *person* can't tell what's real and fake, they're mentally ill... what if a *society* can't tell?
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... Imagine how many fakes we see - and don't realize?
... Now, imagine 2 years from now?
If a *person* can't tell what's real and fake, they're mentally ill... what if a *society* can't tell?
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Fields in Ukraine covered with fiber optics, seen from a Ukrainian Mi-24 helicopter cockpit.
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Scanning 80M open‑source Python commits, the authors show AI already writes about 30% of US code and that heavier use quietly lifts productivity and experimentation.
The team built a detector that spots AI‑written functions, first asking 1 language model to explain a human snippet, then a 2nd to rebuild it, giving balanced training pairs. Fine‑tuning GraphCodeBert on this data let them flag 31M functions among 80M commits from 200K developers between 2019 and 2024.
Clear growth jumps follow Copilot, ChatGPT, and GPT‑4 releases. By 2024 the US hits 30% AI code share, Germany and France sit near 24%, India 22%, Russia 15%, China 12%. Newcomers lean on AI more than veterans, and adoption shows no gender gap.
Within‑developer comparisons say moving from 0% to 30% AI use bumps quarterly commits 2.4%. Combining this lift with wage data gives a conservative annual gain of $9.6‑14.4B for the US, with upper estimates touching $96B.
AI help also nudges creativity, the same coders pull in 2.2% more fresh libraries and 3.5% novel library pairings, hinting at faster learning into new domains.
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Paper – arxiv. org/abs/2506.08945v1
Paper Title: "Who is using AI to code? Global diffusion and impact of generative AI"
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The team built a detector that spots AI‑written functions, first asking 1 language model to explain a human snippet, then a 2nd to rebuild it, giving balanced training pairs. Fine‑tuning GraphCodeBert on this data let them flag 31M functions among 80M commits from 200K developers between 2019 and 2024.
Clear growth jumps follow Copilot, ChatGPT, and GPT‑4 releases. By 2024 the US hits 30% AI code share, Germany and France sit near 24%, India 22%, Russia 15%, China 12%. Newcomers lean on AI more than veterans, and adoption shows no gender gap.
Within‑developer comparisons say moving from 0% to 30% AI use bumps quarterly commits 2.4%. Combining this lift with wage data gives a conservative annual gain of $9.6‑14.4B for the US, with upper estimates touching $96B.
AI help also nudges creativity, the same coders pull in 2.2% more fresh libraries and 3.5% novel library pairings, hinting at faster learning into new domains.
----
Paper – arxiv. org/abs/2506.08945v1
Paper Title: "Who is using AI to code? Global diffusion and impact of generative AI"
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