If one uses the terminal quite a lot, it is essential that one finds out fast ways of doing routine stuff. Aliases are one of the many tricks in the hat.

Here is how you can set up a bash alias:

alias smallcmd='very long and verbose command'

You can then type

smallcmd

and it will replace it by the longer version. To make this permanent, add the aliases to the end of your .bashrc file.

I have even aliased ‘git pull’, ‘git push’, ‘git commit’, and ‘git status’ to three letter mnemonics, which are really handy since I do a lot of work using git.

Secondly SSH aliases are something I didn’t know, until very recently. Instead of typing long unwieldy username and hostnames, one can simply add an alias for the machine in their SSH config file, usually in (~/.ssh/config). For example:

Host ubuntuvm
  HostName 172.16.29.143
  User reddragon

Now, I can just do

ssh ubuntuvm

to SSH to my Virtual Machine!

I was trying to implement the Miller-Rabin algorithm for one of my home-works, in Python. I had already done this in C++ to solve PON, but I wanted to try out Python, and see how fast I can get it to. I wrote a function to do $a\^b$ mod $n $ which used repeated squaring to get the result. However, the implementation wasn’t fast enough. Dhruv suggested that I profile the code.

Here is how you profile a python script: python -m cProfile script.py

And this is what I came to know

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172 function calls (66 primitive calls) in 31.492 seconds
    
       Ordered by: standard name
    
       ncalls  tottime  percall  cumtime  percall filename:lineno(function)
            1    0.000    0.000    0.000    0.000 __future__.py:48(<module>)
            1    0.000    0.000    0.000    0.000 __future__.py:74(_Feature)
            7    0.000    0.000    0.000    0.000 __future__.py:75(__init__)
            1    0.020    0.020    0.020    0.020 hashlib.py:55(<module>)
            6    0.000    0.000    0.000    0.000 hashlib.py:94(__get_openssl_constructor)
            1    0.063    0.063   31.492   31.492 millerRabin.py:1(<module>)
        108/2   16.644    0.154   16.644    8.322 millerRabin.py:12(fastModularExp)
            2    0.008    0.004   16.672    8.336 millerRabin.py:21(millerRabin)
            1    0.000    0.000    0.000    0.000 os.py:743(urandom)

Note how the fastModularExp() function took a staggering 16 seconds. Now we came to know that the builtin pow function in Python already does fast modular exponentiation. And since it is a part of a C library, it is quite fast. After doing this, I submitted it as a solution for PON and got accepted with 0.35 seconds, which was faster than my 0.90 submission with C++ :-)

Though I am no expert myself, but having gone through a couple of coding interviews, people ask me suggestions on how to prepare for such interviews. Here is a course that was taught at MIT, on how to do well in Google (and, other companies’) coding interviews. This is good at giving you feelers about the level of the questions in such interviews.