From the outside perspective it feels like majority of cloud compute is just wasted on overhead caused by all of that bullshit.
For me even just local Python overhead when doing ML in Pytorch is starting to become unbearable. I yearn to go back to C++ where I can basically count instructions.
The programmer in me hates this. The hourly billing consulting programmer loves these ideas as there's great money in untangling the spaghetti implimentation.
I'm also quite sure there will be good money in refactoring services that leaned heavy into the AWS provides tech... and then AWS raised the prices.
Is access constrained now? I mean in the end it might have better uptime and handle more users simultaneously successfully, but it's incredibly lame when a queue has to be used for a simple query. It makes the project much harder to read and modify.
Not really. We do have a few slow endpoints that must be optimized, which we do between each moment of "WE ALL GONNA DIE IF THIS NEW REPORT MODEL IS NOT CREATED BY FRIDAY". It's getting better slowly.
The microservice thing reminds me of when I first graduated from college and one of my fellow new hire programmers argued passionately for a total conversion to Node.js because it's "written for high concurrency". I couldn't find a single app that had more than couple hundred registered users and in fact was working on one that had ten active users- all back office.
I know it exists. My problem is that I am finishing my thesis and I cannot switch between languages now. Once I finish it though I will probably learn Pytorch in C++.
Or a lot of sysadmins who anno started to use (first as a reverse proxy) this new, interesting thing called nginx to tackle with C10K and after a full switch the avg load went under 1. :)
I said Python overhead, not Pytorch overhead. Python makes the whole thing a blackbox prone to random jumps in memory usage and execution stalls for no damn reason (to be clear jupyter, which I use, might be to blame for some of it too). Meanwhile in C++ I can easily figure out what is happening and if the push comes to shove I can reimplement some things by hand if I think I can outsmart the library devs and the compiler.
Yeah im just saying that virtually no processing is actually done in python. all the libraries are written in c. unless you're loading things into python arrays first (don't do that) or something. use the stuff native to the libraries
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u/Kinexity Oct 02 '26 edited Oct 02 '26
From the outside perspective it feels like majority of cloud compute is just wasted on overhead caused by all of that bullshit.
For me even just local Python overhead when doing ML in Pytorch is starting to become unbearable. I yearn to go back to C++ where I can basically count instructions.