Alexander Onbysh AlexanderOnbysh

FFmpeg cheat sheet

A list of useful commands for the FFmpeg command line tool.

Download FFmpeg: https://www.ffmpeg.org/download.html

Full documentation: https://www.ffmpeg.org/ffmpeg.html

Basic conversion

Here's a simple implementation of bilinear interpolation on tensors using PyTorch.

I wrote this up since I ended up learning a lot about options for interpolation in both the numpy and PyTorch ecosystems. More generally than just interpolation, too, it's also a nice case study in how PyTorch magically can put very numpy-like code on the GPU (and by the way, do autodiff for you too).

For interpolation in PyTorch, this open issue calls for more interpolation features. There is now a nn.functional.grid_sample() feature but at least at first this didn't look like what I needed (but we'll come back to this later).

In particular I wanted to take an image, W x H x C, and sample it many times at different random locations. Note also that this is different than upsampling which exhaustively samples and also doesn't give us fle

Про токены, JSON Web Tokens (JWT), аутентификацию и авторизацию. Token-Based Authentication

Last major update: 25.08.2020

Что такое авторизация/аутентификация
Где хранить токены
Как ставить куки ?
Процесс логина
Процесс рефреш токенов
Кража токенов/Механизм контроля токенов

Keyboard-only Mac Cheatsheet

Hi, I'm Lorna and I don't use a mouse. I have had RSI issues since a bad workstation setup at work in 2006. I've tried a number of extra hardware modifications but what works best for me is to use the keyboard and only the keyboard, so I'm in a good position and never reaching for anything else (except my coffee cup!). I rather unwisely took a job which required me to use a mac (I've been a linux user until now and also had the ability to choose my tools carefully) so here is my cheatsheet of the apps, tricks and keyboard shortcuts I'm using, mostly for my own reference. Since keyboard-only use is also great for productivity, you may also find some of these ideas useful, in which case at least something good has come of this :)

Apps List

There's more detail on a few of these apps but here is a quick overview of the tools I've installed and found helpful

Tool	Link	Comments

Tabs and Windows

Function	Shortcut
New Tab	`⌘` + `T`
Close Tab or Window	`⌘` + `W` (same as many mac apps)
Go to Tab	`⌘` + `Number Key` (ie: `⌘2` is 2nd tab)
Go to Split Pane by Direction	`⌘` + `Option` + `Arrow Key`
Cycle iTerm Windows	`⌘` + `backtick` (true of all mac apps and works with desktops/mission control)

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

Moved to git repository: https://github.com/denji/nginx-tuning

NGINX Tuning For Best Performance

For this configuration you can use web server you like, i decided, because i work mostly with it to use nginx.

Generally, properly configured nginx can handle up to 400K to 500K requests per second (clustered), most what i saw is 50K to 80K (non-clustered) requests per second and 30% CPU load, course, this was 2 x Intel Xeon with HyperThreading enabled, but it can work without problem on slower machines.

You must understand that this config is used in testing environment and not in production so you will need to find a way to implement most of those features best possible for your servers.

	import torch
	from torch import LongTensor
	from torch.nn import Embedding, LSTM
	from torch.autograd import Variable
	from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

	## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium']
	#
	# Step 1: Construct Vocabulary
	# Step 2: Load indexed data (list of instances, where each instance is list of character indices)