What's your basis for claiming that Tinygrad can't compute 2nd order partial derivatives (i.e. Hessians) needed for LBFGS? Tinygrad like PyTorch uses automatic differentiation which has no problem supporting nth order derivatives.
Commercial pilot here. Instead of climate change, we should be talking about continuous descent profiles (CDPs) that have become more common in the past years 5-10 years. These profiles with idle engines allow for a smoother, more fuel-efficient descent by reducing the need for level-off segments. However, CDPs can increase the perception of turbulence during descent. This is because aircraft remain at higher altitudes for longer periods, where atmospheric instability and wind shear are more pronounced. This increased turbulence is not due to climate change but rather the result of these optimized descent procedures aimed at reducing fuel consumption and minimizing environmental impact.
In a broader economic context, they take profitable but unpredictable companies and make them boring. Google is the more recent example. Apple (pre-return of Job) is another. Here's a good article: https://www.inc.com/justin-bariso/apple-googlemckinsey-how-a...