nthState

struct Product: Decodable {
    let title: String
}

class MyCombine {

	func getWithCombine() -> AnyPublisher<Product, Error> {

        let url = URL(string: "https://dummyjson.com/products/1")!

nthState

import Foundation
import GameController
import CoreHaptics

/**
 Ensure the controller supports Haptics, then play
 */
class PlayStationHaptics {
  
  var engine: CHHapticEngine?

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import CoreHaptics
import HaptrixSync

func playHapticsFile() {

  guard let url = Bundle.main.url(forResource: "myfile", withExtension: "ahap") else { 
    print("could not load file")
    return
  } 
  

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import CoreHaptics

func playHapticsFile() {

  guard let url = Bundle.main.url(forResource: "myfile", withExtension: "ahap") else { 
    print("could not load file")
    return
  } 
  
  do { 

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let vibe: NSArray = [true, 1000, false, 250, true, 1200]
let dict = [
"Intensity": 0.7,
"VibePattern": vibe
] as [String : Any]
AudioServicesPlaySystemSoundWithVibration(kSystemSoundID_Vibrate, nil, dict)

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#import <AudioToolbox/AudioToolbox.h>
void AudioServicesPlaySystemSoundWithVibration(SystemSoundID inSystemSoundID,id arg,NSDictionary* vibratePattern);

nthState

This configuration worked for me, hope it helps

It is based on: https://becominghuman.ai/deep-learning-gaming-build-with-nvidia-titan-xp-and-macbook-pro-with-thunderbolt2-5ceee7167f8b

and on: https://stackoverflow.com/questions/44744737/tensorflow-mac-os-gpu-support

Hardware

nthState

import Foundation
import UIKit

@IBDesignable class VerticalProgressionBar : UIView {
    
    @IBInspectable var sections: Int = 1 {
        didSet {
            build()
        }
    }

nthState

/**
 Drag marching ants on SKShapeNode stroke
 https://stackoverflow.com/questions/16838907/drawing-marching-ants-using-directx
*/
void main() {
    float w = ((int)(v_tex_coord.x + v_tex_coord.y + (u_time*4)) % 8);
    gl_FragColor = (w < 4 ? vec4(1,1,1,0) : vec4(0.48,0.84,0.99,1));
}

nthState

O(1) - dictionary lookup
O(n) - linear, 100 items = 1 second, 200 = 2 seconds
O(n2) - nested
O(2n) - exponential
O(logN) - binary search, binary tree