matthiasplappert

@interface UIView (MPAdditions)
@end

@implementation UIView (MPAdditions)

- (id)debugQuickLookObject {
    if (self.bounds.size.width < 0.0f || self.bounds.size.height < 0.0f) {
        return nil;
    }
    

matthiasplappert

Keybase proof

I hereby claim:

• I am matthiasplappert on github.
• I am mplappert (https://keybase.io/mplappert) on keybase.
• I have a public key whose fingerprint is BC81 5B1C 87B4 4B43 9C1D 3811 20F2 600F 217E C448

To claim this, I am signing this object:

matthiasplappert

This script automatically sets up my vim environment on any machine. For example in a docker container or an EC2 instance.

Dependencies

• wget
• vim
• git

Usage

matthiasplappert

iOS In App Purchases

Apple summarizes the whole flow of server-side In App Purchases in this graph. It’s a very good starting point to get the overall idea what it necessary.

Overview of Required APIs

• GET available product identifiers
• POST the iTunes transaction receipt for validation and retrieve redeem code as response

That’s pretty much it.

matthiasplappert

menge = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
menge.each do |x|
  if x != 0 then
    menge.each do |y|
      if y != 0 then
        if ((x * y) % menge.length == 0) then
          print x.to_s + " * " + y.to_s + " % " + menge.length.to_s + " = 0\n"
        end
      end
    end

matthiasplappert

#!/bin/sh
java -classpath your/path/to/checkstyle/checkstyle-5.5/checkstyle-5.5-all.jar com.puppycrawl.tools.checkstyle.Main -c your/path/to/checkstyle_swt1.xml -r src/

matthiasplappert

import java.io.File;
import java.io.IOException;

public final class Test {
    private Test() {
        
    }
    
    public static void main(String[] args) {
        try {

matthiasplappert

from keras import backend as K

actor = None  # the following code assumes that actor and critic are Graph networks
critic = None
action_input_name = 'input_action'
output_name = 'output'
batch_size = 64

# Temporarily connect to a large, combined model so that we can compute the gradient and monitor
# the performance of the actor as evaluated by the critic.

matthiasplappert

UIKonf 2014 Slides

• Christopher Downer: Designing Custom Interfaces, slides missing
• Katrin Apel: Mobile Backends as a Service - Ready for Production?, slides
• Max Seelemann: TextKit For The Rest Of Us, slides
• David Rönnqvist: OpenGL (ES) demystified, slides missing
• Nick Lockwood: Image Performance, slides
• Eric Allam: Beyond the Basics - Building Better Transitions on iOS 7, slides
• Sally Shepard: accessibility & voice over, slides missing
• [Rachel Andrew](https://twitter.com/rachelandrew

matthiasplappert

import numpy as np
import gym


env = gym.make('FetchReach-v0')

# Simply wrap the goal-based environment using FlattenDictWrapper
# and specify the keys that you would like to use.
env = gym.wrappers.FlattenDictWrapper(
    env, dict_keys=['observation', 'desired_goal'])