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rushteron Ask HN: Could you share your personal blog here?

Most of my posts are about Python's internals and some security stuff

rushteron How to track and display profile views on GitHub

Github does not send any extra information.

    "GET /counter.svg HTTP/1.1" 200 565 "-" "github-camo (62249a1c)"

I've reverted the badge.

rushteron How to track and display profile views on GitHub

You can't view profile statistics right now. Personally, my intent was to demonstrate the concept. I don't have plans using it.

rushteron How to track and display profile views on GitHub

I've removed it because it does not display relevant information anymore (someone fetches ten times a second via wget).

I also don't have plans to use it in the future. I wanted to demonstrate the concept.

rushteron How to track and display profile views on GitHub

You can check it here https://github.com/dwyl/hits

Also https://twitter.com/holman/status/427937383376379904

rushteron How to track and display profile views on GitHub

I agree, but I think such information should be available to all people. Some people will be doing this anyway, and they can hide this by using a transparent pixel.

rushteron How to track and display profile views on GitHub

I did some research before writing an article. GitHub started proxying images in 2014, and there are a lot of repositories that use this technique to keep their stats. I think GitHub is OK with that.

rushteron Memory Management in Python

Yes, it can, by calling the free function.

Which framework do you use for deep learning? It can allocate some object on its own.

Can you give me some stats when using a model and after it's no longer in use and can't be accessible? You can get it by calling the sys._debugmallocstats() function.

rushteron Memory Management in Python

Author of the article here.

I don't think system allocators are clever enough to process and allocate 100-500k of very small objects each minute when Python is performing something very intensive.

It's a pretty standard way to speedup allocation for dynamic languages. Game developers use similar techniques as well.

I have some stats on Python's allocator:

https://rushter.com/blog/python-object-allocation-statistics...

rushteron Optimization tricks in Python: lists and tuples

My article describes the tricks which are used inside the Python interpreter. Every improvement in the interpreter saves an insane amount of computing power considering its popularity.

rushteron Optimization tricks in Python: lists and tuples

Maybe I've missed something, but you can also get the same id (it's basically an address in the memory) because of the how memory allocation works. There is a special allocator in CPython, which preallocates big chunks of memory and constantly reusing it without allocation overhead. I have an article on this too.

rushteron Optimization tricks in Python: lists and tuples

Thanks, it's a typo, I was referring to the mutable object (list).