alfakini

Keybase proof

I hereby claim:

• I am alfakini on github.
• I am alfakini (https://keybase.io/alfakini) on keybase.
• I have a public key ASDKbWiwaCUJnq3RWVwqz3-aDkpboHOldbjGsrIvB6RqUwo

To claim this, I am signing this object:

alfakini

def on_created(self, event):
    file_size = -1
    while file_size != os.path.getsize(event.src_path):
        file_size = os.path.getsize(event.src_path)
        time.sleep(1)

    self.process(event)

alfakini

import sys
import time

from watchdog.observers import Observer
from .events import ImagesEventHandler

class ImagesWatcher:
    def __init__(self, src_path):
        self.__src_path = src_path
        self.__event_handler = ImagesEventHandler()

alfakini

import os
from PIL import Image
from PIL.ImageOps import grayscale
from watchdog.events import RegexMatchingEventHandler

class ImagesEventHandler(RegexMatchingEventHandler):
    THUMBNAIL_SIZE = (128, 128)
    IMAGES_REGEX = [r".*[^_thumbnail]\.jpg$"]

    def __init__(self):

alfakini

× Done - In progress

#	Cidade	Crawler	Parser
1	São Paulo
2	Rio de Janeiro	-
3	Brasília
4	Salvador

alfakini

#!inbox

I am passionate about Ruby, but its execution time compared to other languages is extremely high, especially when we want to use more complex algorithms. In general, data structures in interpreted languages become incredibly slow compared to compiled languages. Some algorithms such as ´n-body´ and ´fannkuch-redux´ can be up to 30 times slower in Ruby than Go. This is one of the reasons I was interested in embedding Go code in a Ruby environment.

For those who do not know how shared libraries operate, they work in a similar way as DLLs in Windows. However, they have a native code with a direct interface to the C compiler.

Note

alfakini

$ wget ...cuda-repo-ubuntu1404_7.5-18_amd64.deb
$ sudo dpkg -i /tmp/cuda-repo-ubuntu1404_7.5-18_amd64.deb
$ rm -f /tmp/cuda-repo-ubuntu1404_7.5-18_amd64.deb

$ wget ...nvidia-machine-learning-repo_4.0-2_amd64.deb
$ sudo dpkg -i /tmp/nvidia-machine-learning-repo_4.0-2_amd64.deb
$ rm -f /tmp/nvidia-machine-learning-repo_4.0-2_amd64.deb

$ sudo apt-get install digits

alfakini

ROOM_ID="" # Can also be the name of the room
AUTH_TOKEN="" # Go here to get it https://chute.hipchat.com/account/api

function getURL {
  url=`osascript -e 'tell application "Spotify" to spotify url of current track'`
  remove='spotify:track:'
  url=${url#$remove}
  url="http://open.spotify.com/track/$url"

  echo $url

alfakini

NUMBER_OF_CORES=4

sudo apt-get install -y libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev
sudo apt-get install -y --no-install-recommends libboost-all-dev
sudo apt-get install -y libatlas-base-dev 
sudo apt-get install -y python-dev 
sudo apt-get install -y python-pip git
sudo apt-get install -y libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler 

alfakini

class AccountLogoUploader < CarrierWave::Uploader::Base
  include CarrierWave::MiniMagick

  max_file_size = 5.megabytes

  version :original do
    process
  end

  private