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maartenscholl

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Senior AI researcher at https://macrocosm.group/

Visit my personal website https://www.maartenscholl.com/

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What is going on with the second element in the final example?

    std::pair x {1, 2.0};
    auto [v, w] = x;
Why is the second element a float according to the blog post?
    std::tuple_element<0, std::pair<int, float> >::type&
Fluid Glass 10 months ago

The meandering movement of the droplets around the sides remind me of the Gray-Scott reaction–diffusion system

I experienced the same, I had to disable my adblocker to view it, it seems the content is inside a tag `<article class="social-sharing">` but I am unsure whether this triggered my adblocker.

Not really, the lava lamp's fluid dynamics are very sensitive to initial conditions and the fluids behave chaotically, whereas the bouncy balls have highly predictable trajectories.

The author claims to be an expert in C++ but begins the article with a detour that incorrectly states that static and dynamic dispatch are necessarily orthogonal in C++. In reality you can do both within the same hierarchy using CRTP combined with a virtual base method. Compile-time inlining eliminates the overhead associated with the virtual call, so you can match Rusts flexibility with the same performance.

I had a lot of fun writing low latency parsers for various message standards C++. There are a lot of fun things you can do when you can take ownership of the read buffer and you can figure out how to parse in-situ (modifying the data in place as you move along)

Do fighter pilots accumulate all that much exposure to G-forces in terms of duration? 2000 flight hours sounds like a lot, but aren't they flying steady most of the time?

Wouldn't professional bobsleigh pilots, alpine skiers, formula 1 drivers and downhill mountain bikers accumulate many more hours of high G-force exposure over the period of say a year?

Sure Buettner does focus on the older people of the community by interviewing them, but that does not generalise to the claim of the book (or the website to this day) that this community has a high life expectancy, which is shown to be false by the corrected statistics. This is known as a "population fallacy".

By focusing on the older people only in such a small population, he is introducing selection bias and survivorship bias. Moreover, he did not control or compare studies. I believe there are more than one Adventist community in the US, yet those are not Blue Zones somehow?

I don't think that is right. In the Blue Zones marketing material, they characterise Loma Linda's 9000 Adventists, who make up 40% of the population, as living a decade longer on average. That is the claim being investigated. This claim is hard to reconcile with the CDC's official numbers which show a typical life expectancy for the entire area, unless living next to Adventists somehow lowers the life expectancy for the remaining 60% of the population, which would be far more interesting.

I think we get an apparent contradiction because of how we stratify the data. According to the CDC/KFF study, firearms are still the leading cause of death for people age 1-19, and substance use is only 6th in the list for that age group. If you instead interpret "young age" as another age range, then indeed other causes of death may appear more prevalent.

The reference to the Efficient Market Hypothesis (EMH) here is puzzling. Foremost, the strongest form of the EMH implies that consistent outperformance is not possible, contradicting the rest of the writing and what I believe is its main point: that evidence in the real world is often much stronger than in models. But also when switching from the model world of the EMH to real world markets this application is problematic because of the multiple testing problem. For a trader who recently had a short run of excess profits, the likelihood P(excess returns | skilled) would be high. However, because many diverse traders are in the market and some will show strong performance purely by chance, P(excess returns) or the marginal likelihood of observing such a winning streak would also be high. This dilutes the high likelihood term, leading to a less dramatic update in the posterior P(skilled | excess returns) . Then, the prior P(skilled) should be adjusted to be much more conservative.