18451
Strength, Power, Reactivity, & Speed Discussion / Re: Advanced Theory & Programming of The Human Organism
« on: November 07, 2010, 06:49:26 pm »
so, this programming actually seems pretty simple, it's all just a loop with filters/assessment/analysis functions, then your training function.. I think the next trick is learning how to optimize the actual trainingFunc() code, without changing anything structural.
For example, something like smolov can improve squat form simply by improving motor patterns involved with squatting, that's code optimization.. I don't think that's the key though, I think the key in code optimization comes from accurately predicting supercompensation. Our program must very accurately be able to:
1. create dips in performance which result in supercompensation
2. accurately predict exactly when peak supercompensation results
3. create nutritional strategies that coincide with the above 2 points, that result in the utmost strength/alertness.. integrate those strategies with supercompensation so to enhance the effect.
Any injury that is sustained in our training would then be considered a bug, so to would sub optimal training systems based on the analysis functions.
A bug causes some kind of crash, an injury is a crash.. The hardest types of bugs to find are those which cause data inconsistency way down the road, such as memory allocation bugs / data corruption bugs and crap like that.. Data corruption would involve ineffective programming of the various analysis/evaluation bugs, because it would prime our trainingFunc() with the incorrect/suboptimal road to success.
Too much overcomplication of the trainingFunc() would easily lead to bugs down the road, because it would create too many code paths, bulky code in general, & too much interference. The analysis functions could be very bulky, as their job is to organize all of the information (past & present) and make decisions based on that data, to supply to our streamlined trainingFunc().
llozlzolzozl
For example, something like smolov can improve squat form simply by improving motor patterns involved with squatting, that's code optimization.. I don't think that's the key though, I think the key in code optimization comes from accurately predicting supercompensation. Our program must very accurately be able to:
1. create dips in performance which result in supercompensation
2. accurately predict exactly when peak supercompensation results
3. create nutritional strategies that coincide with the above 2 points, that result in the utmost strength/alertness.. integrate those strategies with supercompensation so to enhance the effect.
Any injury that is sustained in our training would then be considered a bug, so to would sub optimal training systems based on the analysis functions.
A bug causes some kind of crash, an injury is a crash.. The hardest types of bugs to find are those which cause data inconsistency way down the road, such as memory allocation bugs / data corruption bugs and crap like that.. Data corruption would involve ineffective programming of the various analysis/evaluation bugs, because it would prime our trainingFunc() with the incorrect/suboptimal road to success.
Too much overcomplication of the trainingFunc() would easily lead to bugs down the road, because it would create too many code paths, bulky code in general, & too much interference. The analysis functions could be very bulky, as their job is to organize all of the information (past & present) and make decisions based on that data, to supply to our streamlined trainingFunc().
llozlzolzozl



