That is quite fast and I understand that it requires a lot of practice but it impresses me only moderately.
Thus I think on the failed simulation effect matrix it falls into the middle ground (moderately difficult and moderately impressive).
I like puzzles too and there was a time where I’d put enormous amounts of effort into solving them. But nowadays for me the more interesting puzzles is how to attack the problem. The algorithmic solvability. Could I write a program to solve this? Or is the problem hard just by the number of permutations/edge cases involved? In the latter case I quickly loose interest as the difficulty is accidental complexity added to make it look hard.
That’s consistent with my experience. That is, most people aren’t particularly impressed, or don’t want to let on that they are, and I’m only moderately impressed with myself. And I’m fine with that, since these days I make an effort not to indulge the urge to optimize for impressiveness, except evidently in threads like these.
Contrast this with juggling 5 balls, which is for me about the same level of difficulty (both in terms of learning the skill and performing it once learned). People are much more likely to be visibly impressed, though the way they show it isn’t always agreeable or complimentary.
I make an effort not to indulge the urge to optimize for impressiveness,
Interesting point. I don’t have much trouble getting things done and my goals are mostly not of the impressive kind. I only recently learned to optimize impressiveness as a tool for ‘winning at life’. Reconsidering this and rereading Kays post I notice that I do aim for impressiveness (or at least visibility) of my work to some degree.
I wonder whether the impressiveness optimization desire can be hacked to work for actual goals. I’d think that impressiveness measured by actual effort seems to do the trick. The hard part being of course the measuring outcome. If our scientitic system already fails at that it presumably is a hard problem.
Solved a Rubik’s cube in under 15 seconds. Still having trouble getting my averages below 25, though.
That is quite fast and I understand that it requires a lot of practice but it impresses me only moderately.
Thus I think on the failed simulation effect matrix it falls into the middle ground (moderately difficult and moderately impressive).
I like puzzles too and there was a time where I’d put enormous amounts of effort into solving them. But nowadays for me the more interesting puzzles is how to attack the problem. The algorithmic solvability. Could I write a program to solve this? Or is the problem hard just by the number of permutations/edge cases involved? In the latter case I quickly loose interest as the difficulty is accidental complexity added to make it look hard.
That’s consistent with my experience. That is, most people aren’t particularly impressed, or don’t want to let on that they are, and I’m only moderately impressed with myself. And I’m fine with that, since these days I make an effort not to indulge the urge to optimize for impressiveness, except evidently in threads like these.
Contrast this with juggling 5 balls, which is for me about the same level of difficulty (both in terms of learning the skill and performing it once learned). People are much more likely to be visibly impressed, though the way they show it isn’t always agreeable or complimentary.
Clearly you need to learn to do both at once.
Interesting point. I don’t have much trouble getting things done and my goals are mostly not of the impressive kind. I only recently learned to optimize impressiveness as a tool for ‘winning at life’. Reconsidering this and rereading Kays post I notice that I do aim for impressiveness (or at least visibility) of my work to some degree.
I wonder whether the impressiveness optimization desire can be hacked to work for actual goals. I’d think that impressiveness measured by actual effort seems to do the trick. The hard part being of course the measuring outcome. If our scientitic system already fails at that it presumably is a hard problem.