I really like Go. It scratches every itch that I have. Is it the language for your problems? I don't know, but very possibly that answer is "no".
Go is easy to learn, very simple (this is a strong feature, for me) and if you want something more, you can code that up pretty quickly.
The blog article author lost me completely when they said this:
> Why do I care about memory use? RAM is cheap.
That is something that only the inexperienced say. At scale, nothing is cheap; there is no cheap resource if you are writing software for scale or for customers. Often, single bytes count. RAM usage counts. CPU cycles count. Allocations count. People want to pretend that they don't matter because it makes their job easier, but if you want to write performant software, you better have that those cpu cache lines in mind, and if you have those in mind, you have memory usage of your types in mind.
Well if maximalist performance tuning is your stated goal, to the point that single bytes count, I would imagine Go is a pretty terrible choice? There are definitely languages with a more tunable GC and more cache line friendly tools than Go.
But honestly, your comment reads more like gatekeeping, saying someone is inexperienced because they aren't working with software at the same scale as you. You sound equally inexperienced (and uninterested) with their problem domain.
Considering what data? All queries sent to Gemini? Real users? A select few? Test queries from Google?
Does it include AI summaries of google searches? Because if the data includes stuff as simple as "How tall is Lee Pace," that is obviously going to bring the median query down, even if the top distribution is using many times more energy.
But still, the median is not useful by itself. It tells us 50% of the queries measured were under 0.24Wh. It obviously obscures policy-relevant information to not include the mean, but it also obscures what I can do individually without more details on the data. Where am I on this median?
It makes the most sense to provide the entire distribution and examples of data points.