Python SSL Proxy

Scraping without libraries in Python typically involves making HTTP requests, parsing HTML (or other markup languages), and extracting data using basic string manipulation or regular expressions. However, it's important to note that using established libraries like requests for making HTTP requests and BeautifulSoup or lxml for parsing HTML is generally recommended due to their ease of use, reliability, and built-in features.

Here's a simple example of scraping without libraries, where we use Python's built-in urllib for making an HTTP request and then perform basic string manipulation to extract data. In this example, we'll scrape the title of a website:

import urllib.request

def scrape_website(url):
    try:
        # Make an HTTP request
        response = urllib.request.urlopen(url)
        
        # Read the HTML content
        html_content = response.read().decode('utf-8')

        # Extract the title using string manipulation
        title_start = html_content.find('', title_start)
        title = html_content[title_start:title_end].strip()

        return title
    except Exception as e:
        print(f"Error: {e}")
        return None

# Replace 'https://example.com' with the URL you want to scrape
url_to_scrape = 'https://example.com'
scraped_title = scrape_website(url_to_scrape)

if scraped_title:
    print(f"Scraped title: {scraped_title}")
else:
    print("Scraping failed.")

Keep in mind that scraping without libraries can quickly become complex as you need to handle various aspects such as handling redirects, managing cookies, dealing with different encodings, and more. Libraries like requests and BeautifulSoup abstract away many of these complexities and provide a more robust solution.

Using established libraries is generally recommended for web scraping due to the potential pitfalls and challenges involved in handling various edge cases on the web. Always ensure that your scraping activities comply with the website's terms of service and legal requirements.

Updating CoreML models in an iOS app typically involves fetching a new model file, parsing it, and then updating the CoreML model with the new version. JSON parsing can be used to extract necessary information from the fetched JSON file. Below is a step-by-step guide using Swift:

Fetch and Parse JSON

Fetch a JSON file containing information about the updated CoreML model, including its download URL, version, etc.

import Foundation

// Replace with the URL of your JSON file
let jsonURLString = "https://example.com/model_info.json"

if let url = URL(string: jsonURLString),
   let data = try? Data(contentsOf: url),
   let json = try? JSONSerialization.jsonObject(with: data, options: []) as? [String: Any] {

    // Extract information from the JSON
    if let newModelURLString = json["new_model_url"] as? String,
       let newModelVersion = json["new_model_version"] as? String {
        // Continue with the next steps
        updateCoreMLModel(with: newModelURLString, version: newModelVersion)
    }
}

Download and Save New Model:

Download the new CoreML model file from the provided URL and save it locally.

func updateCoreMLModel(with modelURLString: String, version: String) {
    guard let modelURL = URL(string: modelURLString),
          let modelData = try? Data(contentsOf: modelURL) else {
        print("Failed to download the new model.")
        return
    }

    // Save the new model to a local file
    let documentsDirectory = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask).first!
    let newModelURL = documentsDirectory.appendingPathComponent("newModel.mlmodel")

    do {
        try modelData.write(to: newModelURL)
        print("New model downloaded and saved.")
        updateCoreMLModelWithNewVersion(newModelURL, version: version)
    } catch {
        print("Error saving new model: \(error.localizedDescription)")
    }
}

Update CoreML Model:

Load the new CoreML model and update the app's model.

import CoreML

func updateCoreMLModelWithNewVersion(_ modelURL: URL, version: String) {
    do {
        // Load the new CoreML model
        let newModel = try MLModel(contentsOf: modelURL)

        // Replace the existing CoreML model with the new version
        // Assuming your model has a custom CoreMLModelManager class
        CoreMLModelManager.shared.updateModel(newModel, version: version)

        print("CoreML model updated to version \(version).")
    } catch {
        print("Error loading new CoreML model: \(error.localizedDescription)")
    }
}

Handle Model Updates in App:

Depending on your app's architecture, you might want to handle the model update in a dedicated manager or service. Ensure that you handle the update gracefully and consider user experience during the update process.

Make sure to replace placeholder URLs and customize the code according to your actual implementation. Additionally, handle errors appropriately and test thoroughly to ensure a smooth update process.

If you plan to use a proxy every day, it is recommended to pay attention to paid services. There, the connection is as reliable as possible, with no bandwidth limitations. However, the performance of numerous free proxies is not guaranteed.

Open the "Start" menu and type "Browser Properties" in the search box. Then, go to the "Connection" tab, click on "Network settings" and disable the use of the proxy server. Reboot Windows and check if your Internet connection works. If the problem persists, open the "Advanced" tab in the "Browser Properties" window and check the box next to "Delete personal settings", click "Reset" and restart your computer.

Although free proxies are popular, they are far from being flawless in their work. Many of their IP addresses are blacklisted by popular resources, and the data transfer speed and stability are very unreliable. When choosing a proxy, keep in mind that the new version of IPv6 is not supported by most websites. Note also that proxies are divided into private and public, statistical and dynamic, and support different network protocols.