
u/Ok_Astronomer_7797

Has anyone here seen a serious version of the flat Earth argument?
I only ever see this topic in memes and jokes coming from outsiders. I have never heard the actual theory directly from someone who believes it.
If you genuinely reject the globe, how do you explain the whole system working, from seasons to gravity? I am looking for a serious, start-to-finish breakdown of your perspective.
Gas suddenly looks a lot less necessary
The battery revolution is genuinely mind-blowing. Out in Queensland, Australia, big battery setups have pretty much pushed gas out of the picture when it comes to the energy grid. What’s wild is that this massive shift happened in just two short years, showing just how fast tech is moving and how ready the grid is for cleaner, smarter energy solutions.
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Banks are already trying to move AI data-centre risk off their books
Big banks are already looking for ways to shed risk tied to the data-centre debt boom. According to the FT, JPMorgan, Morgan Stanley, SMBC and MUFG have been trying to move pieces of these loans to a wider group of investors. One Oracle-linked data-centre project in Texas and Wisconsin reportedly came with $38bn of construction debt, and lenders have spent more than six months trying to distribute it. Some banks even looked at selling parts of the loans to non-bank lenders at a discount.
Banks are not leaving AI infrastructure. They want to keep lending into it. But the deals are getting so large, concentrated and borrower-specific that they start pressing against risk limits. So the engineering begins: private debt sales, risk-transfer structures, and SRT-like deals where investors take on the riskier slices of huge data-centre loans.
The comparison with 2008 is not exact, but the instinct is familiar: a hot asset class, giant debt loads, complicated structures to move risk around, and a shared belief that demand will keep rising. If the AI capex cycle slows, the weak point may not be the models themselves. It may be unfinished data centres, overleveraged operators, and banks trying to explain why so much of the “AI future” was sitting on construction debt in the middle of nowhere.
Natural selection in recent human history may have been far more active than the old picture suggested. A new Nature study from David Reich’s group analyzed a huge ancient-DNA dataset: 15,836 ancient genomes from West Eurasia over the last 18,000 years, including more than 10,000 newly reported samples. The result is a much less static view of recent human evolution. Civilization did not freeze the human genome. Across hundreds of alleles, selection kept pushing populations in different directions.
Earlier ancient-DNA work had identified only about 21 clear cases of directional selection in Europeans over the last 10,000 years. Using new statistical methods, lead author Ali Akbari and the team found 479 alleles showing strong signs of being selected for or against. The study also tested selection coefficients across 9.7 million variants, separating long-term selection signals from noise created by migration and population mixing. So recent evolution no longer looks like a short list of famous adaptations. It looks like a much wider sorting process still shaping human biology.
The Neolithic Revolution seems to have been one of the main accelerators. The shift from hunting and gathering to farming did not slow human evolution down. It created a new environment: grain-heavy diets, denser settlements, new pathogens, more stable communities, different reproductive pressures. The study finds selection signals in variants that today are linked to lower predicted body fat, which fits the idea that farming changed the metabolic rules people lived under. The body had to adapt to a world where food production, diet and disease exposure were no longer the same.
Selection also appears in traits connected today with the brain and mental health. The study reports decreases in genetic predictors of schizophrenia and increases in measures of cognitive performance. That does not mean ancient farmers were simply “selected to be smarter”, or that we can read modern trait labels straight back into the past. But it does suggest that the farming world was not only a change in food. It was a new social and cognitive environment too, and human populations were still being filtered by it.
Sweden is one of the clearest examples of nicotine use moving away from cigarettes rather than disappearing altogether. The chart shows cigarette sales falling for decades while snus and nicotine pouches rise, especially after the launch of portion snus in 1973. Swedes did not simply stop using nicotine. A large part of the market moved from burning tobacco to smokeless delivery.
That matters because the main health damage from smoking comes from combustion, not nicotine itself. Sweden now has one of the lowest smoking rates in Europe, with daily smoking at about 4.9% in the 2024 data cited here. The same presentation also shows much lower tobacco-attributable male death rates than the EU median, including lung cancer deaths. So the Swedish case is not really a story about eliminating nicotine. It is a story about changing the form of nicotine use in a way that appears to sharply reduce the harm.
P.S This is not a recommendation to use snus or nicotine pouches. Snus causes severe nicotine addiction, destroys the oral mucosa and gums, triggers cardiovascular and gastrointestinal diseases, and increases the risk of cancer. This material has been shared solely for educational purposes.
The first child usually meets the outside world directly: daycare, other children, winter viruses.
The second child often meets it through the first one.
That is the mechanism in a NBER working paper using Danish administrative data across 37 birth cohorts. Older siblings pick up respiratory infections outside the home and bring them back to an infant who is still in the first year of life. Before age one, younger siblings are hospitalized for respiratory conditions two to three times more often than older siblings were at the same age. The gap is largest in the first months, in fall and winter, and when the siblings are closer in age.
From there, the paper follows the effect into adulthood. Higher respiratory exposure in infancy is associated with lower earnings later on: moving from the 25th to the 75th percentile of exposure reduces both wage income and total income by about 0.8% at ages 25–32. The effect is stronger when exposure happens in the first six months of life.
The route is fairly simple. Early respiratory illness can affect health and learning, and those differences later show up in education, chronic respiratory problems, mental health-related care, and income. The study does not imply that every virus brought home by an older sibling changes a child’s future. It shows that when this pattern is repeated across enough families, the timing of early infection leaves a small but measurable mark on adult outcomes.
GLP-1 drugs. The issue is not just dosage, adherence, or patience.
A new Stanford Medicine publication suggests that some people may have a genetic form of GLP-1 resistance: this class of drugs may work less effectively from the start because of their genetics. The drug is still acting through the same pathway, but its effect on blood sugar is weaker. The researchers point to variants in the PAM gene, which they estimate may be present in about 10% of the population.
The researchers expected carriers of these variants to have lower GLP-1 levels. They found the opposite: GLP-1 levels can actually be higher, while the biological effect is still weaker. That suggests the problem is not a simple lack of the hormone, but that the body is less effective at turning that signal into a result. In a meta-analysis of three clinical trials, about 25% of people without these variants reached target HbA1c after six months, compared with 11.5% and 18.5% among carriers of two PAM variants. They did not find the same difference for other common diabetes drugs.
So some of the “why did GLP-1 work for them but not for me?” stories may be explained not by patient discipline, but by the biology of response. Stanford also makes it clear that this finding currently applies to blood sugar control in diabetes, not to weight loss: the weight-loss data are still too limited for the same level of confidence. But the shift in perspective already matters. The GLP-1 hype is running into an old medical reality: the same drug is not supposed to work equally well for everyone, and part of a weak response may be built into a person’s genetics from the start.
The most unpleasant part of online dating used to be, at least for me, the awkward beginning. The uncertainty. The first move that can be dumb. The attempt to make someone like you. The attempt to understand whether this person is actually for you or completely wrong. And that is exactly the part AI is starting to move into now.
According to Match, 26% of singles in the U.S. already use AI in their dating life. In 2025, that figure has grown by 333%. Among Gen Z, it is already close to half. What matters here is where exactly AI is being used.
44% want it to help filter matches, 40% want it to help build a profile. Plus openers, compatibility screening, and everything else that used to be considered just part of personal experience, and already at those stages we could screen out unsuitable “candidates.”
It is important to understand this: people still want the feeling itself to be real. But everything that leads to it is increasingly being made less random, less embarrassing, less chaotic. Not having to come up with how to start on your own. Not having to figure out the right approach on your own. Not having to sort through obvious mismatches on your own. The most vulnerable part at the beginning of getting closer is gradually being entrusted to these kinds of tools.
And that changes dating. The intimacy remains human. But the approach to it no longer is to the same extent. It becomes more processed even before anything between two people has really started.