You see, performance is rarely a talking point in retrospectives unless its egregious. If you say a piece of compute is struggling the solution is to always buy more compute. It is rare that anyone would think of optimization unless you’re writing libraries.
I think that’s one of the problem with people that are only of a “finish this task cheaply for now” mind.
Having something run in 0.1 second vs 1 second is fine when a user is running it for one thing. So they consider it good or instantaneous. And later they have to do it for 1000 other things, and they don’t think that saving time in that “instantaneous result” there would save so much more time. They just go “it’s 1000 of these so it takes time”. I’ve found functions that open a db file, read one entry and close it. Then that function is called for each entry they have to read later. In a preprocessing step where they know all the entries they need beforehand. And even the data tyoe to store it is nor preallocated, they just append it on every data read; again on a fixed number of entries they need.
I understand performance not being the most important thing, but I’d expect least a basic understanding of design that has unnecessary processing.
Which is exactly why western AI companies are bankrupting themselves building data centers while China is easily keeping pace with a fraction of the resources. They’re not magic. They just do basic things like make things more efficient.
You see, performance is rarely a talking point in retrospectives unless its egregious. If you say a piece of compute is struggling the solution is to always buy more compute. It is rare that anyone would think of optimization unless you’re writing libraries.
Or if its employee facing only let it get slower and slower to the point where it takes a minute to update one record
I think that’s one of the problem with people that are only of a “finish this task cheaply for now” mind.
Having something run in 0.1 second vs 1 second is fine when a user is running it for one thing. So they consider it good or instantaneous. And later they have to do it for 1000 other things, and they don’t think that saving time in that “instantaneous result” there would save so much more time. They just go “it’s 1000 of these so it takes time”. I’ve found functions that open a db file, read one entry and close it. Then that function is called for each entry they have to read later. In a preprocessing step where they know all the entries they need beforehand. And even the data tyoe to store it is nor preallocated, they just append it on every data read; again on a fixed number of entries they need.
I understand performance not being the most important thing, but I’d expect least a basic understanding of design that has unnecessary processing.
Which is exactly why western AI companies are bankrupting themselves building data centers while China is easily keeping pace with a fraction of the resources. They’re not magic. They just do basic things like make things more efficient.