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Starlink Promises Not Delivered: The Myth of Fast Internet

 First thoughts on it the product Plug looked like   electric battery charged guy Musk copied their idea never seen any internet provide sell you a service which such a ugly plug, The box was easy to open. Trying out the product When I downloaded the app it was the slowest  seen on playstore to date bit odd from a so called fast internet provider.made me wonder why for a firm always talking about fast speeds plus most keeps getting tax money all the time for existing. Getting the cable connected did not since they sent it jammed in place giving no way losen it that was a very big flaw expecting any cable to last forever pretty stupid they break over time going by how bad Telsa cars are known to be using  common sense was missed out here. I bought the product expecting it maybe get great internet I never liked Musk for always being a liar  some good examples include pretending he invented  https://www.theengineer.info/2025/07/internet-use-on-pi.html Tel...

Create Python code to make bulk generate AI images

 To generate AI images in bulk, we can use a deep learning library like TensorFlow and a pre-trained image generation model like DALL-E or GPT-3's image generation API. Here's a general outline of the code:

  1. Install necessary libraries: TensorFlow, Pillow (Python Imaging Library)

  2. Load the pre-trained image generation model. For example, if using DALL-E, we can use the OpenAI API to generate images.

  3. Define a function that takes in a list of text prompts and generates an image for each prompt.

  4. The function should call the image generation model for each prompt and save the resulting image to a specified folder.

Here's the Python code that implements this:

python
import requests import json from PIL import Image # API key for OpenAI API_KEY = 'YOUR_API_KEY_HERE' # Function to generate image from text prompt def generate_image(prompt, model): url = 'https://api.openai.com/v1/images/generations' headers = { 'Content-Type': 'application/json', 'Authorization': f'Bearer {API_KEY}' } data = { 'model': model, 'prompt': prompt, 'num_images': 1, 'size': '512x512' } response = requests.post(url, headers=headers, data=json.dumps(data)) if response.status_code != 200: print('Error:', response.json()['error']) return None else: image_url = response.json()['data'][0]['url'] image_response = requests.get(image_url) image = Image.open(BytesIO(image_response.content)) return image # List of text prompts to generate images for prompts = ['cat', 'dog', 'tree'] # Model to use for image generation model = 'image-alpha-001' # Folder to save generated images folder_path = 'generated_images/' # Generate image for each prompt and save to folder for prompt in prompts: image = generate_image(prompt, model) if image is not None: file_name = prompt + '.jpg' image.save(folder_path + file_name)

https://www.theengineer.info/This code uses the OpenAI API to generate images based on text prompts, and saves the resulting images to a specified folder. You can modify the prompts, model, and folder_path variables to suit your needs. Note that you'll need an API key from OpenAI to use their image generation API.



Saysimport requests import json from PIL import Image # API key for OpenAI API_KEY = 'YOUR_API_KEY_HERE' # Function to generate image from text prompt def generate_image(prompt, model): url = 'https://api.openai.com/v1/images/generations' headers = { 'Content-Type': 'application/json', 'Authorization': f'Bearer {API_KEY}' } data = { 'model': model, 'prompt': prompt, 'num_images': 1, 'size': '512x512' } response = requests.post(url, headers=headers, data=json.dumps(data)) if response.status_code != 200: print('Error:', response.json()['error']) return None else: image_url = response.json()['data'][0]['url'] image_response = requests.get(image_url) image = Image.open(BytesIO(image_response.content)) return image # List of text prompts to generate images for prompts = ['cat', 'dog', 'tree'] # Model to use for image generation model = 'image-alpha-001' # Folder to save generated images folder_path = 'generated_images/' # Generate image for each prompt and save to folder for prompt in prompts: image = generate_image(prompt, model) if image is not None: file_name = prompt + '.jpg' image.save(folder_path + file_name)




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