mmr

Intro

Finding great talent is hard. Tech companies are always hiring. This short video will cover 5 tips to hire.

Main

• 1. Great engineers knows other great engineers > Implement a referral program:
  • Rich source -- 30% from referrals (6 out of 19 in Force)
  • Shorter hiring times -- 36% reduction
• Longest tenyard -- ~47% longer are such a rich source of talent that it -- 6 out of ~20 in Force (~30%! from referrals), risk is considerably lower and tenyard is 40% longer.

mmr

#!/usr/bin/fish

function git_current_branch
    set -l ref (command git symbolic-ref --quiet HEAD 2> /dev/null)
    set -l ret $status
    if test $ret -ne 0
        if test $ret -eq 128
            return
        end
        set ref (command git rev-parse --short HEAD 2> /dev/null); or return

mmr

irssi() {
    local nick=${1:-mmr}
    local server=${2:-irc.freenode.net}
    docker run -t -i \
      -v /etc/localtime:/etc/localtime \
      -v $HOME/.irssi:/home/user/.irssi \
      --name irssi \
      irssi irssi -n $nick -c $server
}

mmr

2015-07-31 19:59:59 UTC:172.31.21.110(36736):user@db:[869]:STATEMENT:  UPDATE "my_table" SET "my_field" = 'value'
2015-07-31 19:59:59 UTC:172.31.17.44(38844):user@db:[2306]:LOG:  EXECUTOR STATISTICS
2015-07-31 19:59:59 UTC:172.31.17.44(38844):user@db:[2306]:DETAIL:  ! system usage stats:
        !       0.000031 elapsed 0.000000 user 0.000000 system sec
        !       [4.164260 user 1.196074 sys total] 
        !       0/0 [0/1408] filesystem blocks in/out 
        !       0/0 [0/22947] page faults/reclaims, 0 [0] swaps
        !       0 [0] signals rcvd, 0/0 [0/0] messages rcvd/sent
        !       0/0 [18272/680] voluntary/involuntary context switches

mmr

In [1]: from timeit import timeit

In [2]: a = lambda: dict()

In [3]: b = lambda: {}

In [4]: n = 1000000

In [5]: [(timeit(a, number=n), timeit(b, number=n)) for x in range(10)]
Out[5]: