imk2o

import Foundation
import RxSwift
import RxCocoa

// NOTE
// 参考実装
// https://www.swiftbysundell.com/articles/property-wrappers-in-swift/
//
// Exclusively Enforcement問題
// (購読の仕方によって wrappedValue.setter 内で getter が呼ばれるケースがある)

zntfdr

// Original article here: https://www.fivestars.blog/code/swiftui-hierarchy-list.html

import SwiftUI

struct FileItem: Identifiable {
  let name: String
  var children: [FileItem]?

  var id: String { name }

catchdave

#!/bin/bash
#
# *** For DSM v7.x ***
#
# How to use this script:
#  1. Get your 3 PEM files ready to copy over from your local machine/update server (privkey.pem, fullchain.pem, cert.pem)
#     and put into a directory (this will be $CERT_DIRECTORY).
#     Personally, I use this script (https://gist.github.com/catchdave/3f6f412bbf0f0cec32469fb0c9747295) to automate steps 1 & 4.
#  2. Ensure you have a user setup on synology that has ssh access (and ssh access is setup).
#     This user will need to be able to sudo as root (i.e. add this line to sudoers, <USER> is the user you create):

danielgarbien

extension KeyedDecodingContainer {
    
    func decode<T: NSCoding>(_ type: T.Type, forKey key: KeyedDecodingContainer<K>.Key) throws -> T? {
        let data = try decode(Data.self, forKey: key)
        return try NSKeyedUnarchiver.unarchiveTopLevelObjectWithData(data) as? T ?? nil
    }
}

extension KeyedEncodingContainer {

zthxxx

Note

For every body who using Win10/11 with new Office that you need to know:

to active Office without crack, just follow https://github.com/WindowsAddict/IDM-Activation-Script,

you wiil only need to run

irm https://massgrave.dev/ias | iex

klzgrad

为什么流量可以进行分类

这里的“流量”一般定义为中间人观测到的一组由（时间，方向，包大小）元数据组成的序列 [Cai2014]。其源头是应用层的读写操作，经过传输层协议的变换（分片、协议状态机、加密等），流量序列产生一定变化。但是这种变化非常有限，因为流量的发生过程本质是确定性的，随机因素较小，因此对于特定环境中的特定应用（浏览器访问 google.com）各种流量特征体现出相当大的一致性和独特性，这就使“从流量特征识别应用”的监督学习问题成为可能。虽然有若干不利因素使得确定性下降，例如多层次上软件多版本的排列组合爆炸、有状态的缓存、流水线和连接复用、用户随机行为，但是因为版本的幂律分布、应用层读写操作间的依赖关系、流量特征和检测算法的改进等原因，分类依然具有相当的可行性。

分类的对象：流量应用分类与网站指纹攻击

根据分类的对象产生了两个相近但是不同的研究领域。从流量特征中分类应用类型的被称为流量分类（traffic classification），从流量特征中分类所访问网站或者网页的被称为网站指纹（website fingerprinting）。以机器学习的方法而论前者是比后者更弱但本质相同的一个问题。

这两类攻击的威胁类型不同。流量分类威胁的是可用性，如果GFW检出流量是隧道应用然后进行封锁，则破坏了可用性。而网站指纹威胁的是匿名性和隐私，如果从隐秘流量中检出是谁在访问哪个网站，则破坏了匿名性，丝绸之路就是这样被FBI破获的。

tcalmant

#!/usr/bin/env python3
"""
Asyncio-based SOCKS5 Proxy

:author: Thomas Calmant
:copyright: Copyright 2017, Thomas Calmant
:license: Apache License 2.0
:version: 0.0.1
"""

phizaz

import functools


def force_async(fn):
    '''
    turns a sync function to async function using threads
    '''
    from concurrent.futures import ThreadPoolExecutor
    import asyncio
    pool = ThreadPoolExecutor()

pofat

//: Playground - noun: a place where people can play

import UIKit

// This is for dmoe, you can use a generice type to limit your observer to an UIViewController for common usage.
protocol Notifiable {
    var name: Notification.Name { get }
    func observe(by observer: Any, withSelector selector: Selector, object: Any?)
    func post(object: Any? ,userInfo: [AnyHashable: Any]?)
    static func remove(observer: Any)

rondy

FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.

---

Effective Engineer - Notes

• By Edmond Lau
• Highly Recommended 👍
• http://www.theeffectiveengineer.com/

What's an Effective Engineer?