tj

/**
 * POST to create a new user.
 */

exports.create = function *(){
  var body = yield parse(this);

  // password
  var pass = body.password;
  assert(pass, 400, 'password is required');

cfj

var _log = console.log;

window.console.log = function(log){
  _log.call(console, log.reverse ? log.reverse() : typeof log === 'string' ? log.split('').reverse().join('') : typeof log === 'number' ? log.toString().split('').reverse().join('') : typeof log === 'boolean' ? !log : log);
};

rtt

Tinder API documentation

Note: this was written in April/May 2014 and the API may has definitely changed since. I have nothing to do with Tinder, nor its API, and I do not offer any support for anything you may build on top of this. Proceed with caution

http://rsty.org/

I've sniffed most of the Tinder API to see how it works. You can use this to create bots (etc) very trivially. Some example python bot code is here -> https://gist.github.com/rtt/5a2e0cfa638c938cca59 (horribly quick and dirty, you've been warned!)

syhw

"""
A deep neural network with or w/o dropout in one file.

License: Do What The Fuck You Want to Public License http://www.wtfpl.net/
"""

import numpy, theano, sys, math
from theano import tensor as T
from theano import shared
from theano.tensor.shared_randomstreams import RandomStreams

nf

#!/bin/bash -e

echo '
PATH=$HOME/go/bin:$PATH
export GOPATH=$HOME
export CDPATH=.:$HOME/src/golang.org/x:$HOME/go/src:$HOME/src/github.com:$HOME/src/github.com/nf:$HOME/src/github.com/adg
export EDITOR=vim
' >> ~/.profile

sudo apt-get update

Hello1024

#!/bin/bash

# Run this on This AMI on AWS:
# https://console.aws.amazon.com/ec2/v2/home?region=us-east-1#LaunchInstanceWizard:ami=ami-b36981d8

# You should get yourself a fully working GPU enabled tensorflow installation.

cd ~

# grab cuda 7.0

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

bogdan-kulynych

# WARNING: These steps seem to not work anymore!

#!/bin/bash

# Purge existign CUDA first
sudo apt --purge remove "cublas*" "cuda*"
sudo apt --purge remove "nvidia*"

# Install CUDA Toolkit 10
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb

rxwei

See the official Differentiable Programming Manifesto instead.

aviatesk

julia> using JET, JET.JETInterfaces

julia> get_module(sv::Core.Compiler.InferenceState) = sv.mod
get_module (generic function with 2 methods)

julia> function get_module(linfo::Core.MethodInstance)
           def = linfo.def
           return isa(def, Module) ? def : def.module
       end
get_module (generic function with 2 methods)