The literal title is "Python Numbers Every Programmer Should Know" which implies the level of detail in the article (down to the values of the numbers) is important. It is not.
It is helpful to know the relative value (costs) of these operations. Everything else can be profiled and optimized for the particular needs of a workflow in a specific architecture.
To use an analogy, turbine designers no longer need to know the values in the "steam tables", but they do need to know efficient geometries and trade-offs among them when designing any Rankine cycle to meet power, torque, and Reynolds regimes.
You absolutely do not need to know those absolute numbers--only the relative costs of various operations.
Additionally, regardless of the code you can profile the system to determine where the "hot spots" are and refactor or call-out to more performant (Rust, Go, C) run-times for those workflows where necessary.
At least with respect to aviation, we don't have any non-combustion power-trains that can remotely come close to the power-to-weight ratios of turbine engines.
The earliest cars were replacing the animal muscle power of carriages--a trivially easy feat given that the most primitive steam and combustion engines easily 10x both the raw power, power-to-weight, and power-density of a team of horses.
Traditional helicopters also have the effective lift-weight ratios to tackle the density*altitude of mountain rescue that these "air-taxis" have _zero_ hope to achieve with the the vastly lower power-weight of electrical drive-trains and their lift-inefficient multi-rotor designs.
True. Generally, the more isolated the exercise and the smaller the muscle the "safer" it is to train-to-failure at a higher duty-cycle.
Put another way, you can do crunches to failure every single day, but you'll want to keep some reps in the tank for squats and you'll want to plan on at least 12-24 hours of recovery between squat sessions.
At very low reps and high weight, particularly for highly coordinated motions (squats, dips, pull-ups, Pulver press back-extensions), there's a much higher chance for injury due to insufficient support at one or more positions within the entire range of concentric and eccentric efforts by all activated muscles. We all have, at the very least, minor intrinsic asymmetries that need explicit addressing.
There's also intra-set recovery. Roughly (very roughly) speaking, your endo-neuro-muscular system "adapts best" where there is a refractory period for a reset-to-quiescence between exertions.
There is real truth to "muscle memory" and the exclusive way to achieve that (and avoid injury) is through a sufficient amount of well-formed repetitions. The only way to achieve those repetitions is by using a resistance that's sufficiently low.
Asymmetry is normal and you cannot address it (outside of repeatability of movement, aiming for no form degradation during high load).
As long as your movement does not degrade horribly, asymmetry is fine.
Even before strength training, your one arm is dominant, more precise. But this has an effect on your leg as well.
Doing unilateral work will never change that asymmetry. As you get stronger, due to drastically different activations of the nervous system between the sides, you will get slightly different adaptations.
Looking at powerlifters, most of them have visibly different sizes of hip, leg musculature between sides. They even have drastic flexibility differences where one hip goes deeper, or the musculature makes the barbell sit skewed on the back.
To be clear, by "addressing" I did mean altering form and training to lessen the risk of injury due to asymmetry. FWIW, I wear a heal-cup in my right shoe and do additional rotator cuff warm-ups to due minor leg asymmetry and an old injury.
I've really enjoyed using my newer instant pot as a sous vide lately. The fact that the lid closes on it is nice for saving energy, though it does take longer to get to temperature than my anova. But the anova is less convenient.
It is helpful to know the relative value (costs) of these operations. Everything else can be profiled and optimized for the particular needs of a workflow in a specific architecture.
To use an analogy, turbine designers no longer need to know the values in the "steam tables", but they do need to know efficient geometries and trade-offs among them when designing any Rankine cycle to meet power, torque, and Reynolds regimes.
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