didip

worker_processes 2;

error_log /var/log/nginx/error.log;
pid /var/run/nginx.pid;

events {
    worker_connections 1024;
    use epoll;
}

jonathanmoore

Share Counts

I have always struggled with getting all the various share buttons from Facebook, Twitter, Google Plus, Pinterest, etc to align correctly and to not look like a tacky explosion of buttons. Seeing a number of sites rolling their own share buttons with counts, for example The Next Web I decided to look into the various APIs on how to simply return the share count.

If you want to roll up all of these into a single jQuery plugin check out Sharrre

Many of these API calls and methods are undocumented, so anticipate that they will change in the future. Also, if you are planning on rolling these out across a site I would recommend creating a simple endpoint that periodically caches results from all of the APIs so that you are not overloading the services will requests.

Twitter

nhoizey

/*
* Takes provided URL passed as argument and make screenshots of this page with several viewport sizes.
* These viewport sizes are arbitrary, taken from iPhone & iPad specs, modify the array as needed
*
* Usage:
* $ casperjs screenshots.js http://example.com
*/

var casper = require("casper").create();

denji

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.

mmisono

#!/usr/bin/env python

import networkx as nx
import matplotlib.pyplot as plt
import math
from random import random
from numpy import arange

N = 1000
K = 5

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

calstad

Quick List of Resources for Topological Data Analysis with Emphasis on Machine Learning

This is just a quick list of resourses on TDA that I put together for @rickasaurus after he was asking for links to papers, books, etc on Twitter and is by no means an exhaustive list.

Survey Papers

Both Carlsson's and Ghrist's survey papers offer a very good introduction to the subject

• Topology and Data by Gunnar Carlsson
• Barcodes: The Persistent Topology of Data by Robert Ghrist

Other Papers and Web Resources

• Extracting insights from the shape of complex data using topology A good introductory paper in Nature on the Mapper algorithm.

Arinerron

android.permission.ACCESS_ALL_DOWNLOADS
android.permission.ACCESS_BLUETOOTH_SHARE
android.permission.ACCESS_CACHE_FILESYSTEM
android.permission.ACCESS_CHECKIN_PROPERTIES
android.permission.ACCESS_CONTENT_PROVIDERS_EXTERNALLY
android.permission.ACCESS_DOWNLOAD_MANAGER
android.permission.ACCESS_DOWNLOAD_MANAGER_ADVANCED
android.permission.ACCESS_DRM_CERTIFICATES
android.permission.ACCESS_EPHEMERAL_APPS
android.permission.ACCESS_FM_RADIO

yossorion

What I Wish I'd Known About Equity Before Joining A Unicorn

Disclaimer: This piece is written anonymously. The names of a few particular companies are mentioned, but as common examples only.

This is a short write-up on things that I wish I'd known and considered before joining a private company (aka startup, aka unicorn in some cases). I'm not trying to make the case that you should never join a private company, but the power imbalance between founder and employee is extreme, and that potential candidates would

whizzzkid

# Instructions for 4.14 and cuda 9.1
# If upgrading from 4.13 and cuda 9.0
$ sudo apt-get purge --auto-remove libcud*
$ sudo apt-get purge --auto-remove cuda*
$ sudo apt-get purge --auto-remove nvidia*
# also remove the container directory direcotory at /usr/local/cuda-9.0/

# Important libs required with 4.14.x with Cuda 9.X
$ sudo apt install libelf1 libelf-dev