DEV Community: qing

Quick Tip: `httpx` is the modern replacement for `requests`

qing — Thu, 25 Jun 2026 22:03:18 +0000

Quick Tip: httpx is the modern replacement for requests

When it comes to making HTTP requests in Python, requests has been the go-to library for years. However, with the introduction of httpx, it's time to upgrade. httpx is a modern, Pythonic library that supports both synchronous and asynchronous requests.

Let's compare a simple GET request using requests and httpx:


python
# requests (sync)
import requests
response = requests.get('https://example.com')

# httpx (async)
import httpx
import asyncio
async def main():
    async with httpx.AsyncClient() as client:
        response = await client.get('https://example.com')
asyncio

---

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Stop Writing Repetitive Code in Python — Do This Instead

qing — Thu, 25 Jun 2026 22:00:10 +0000

Introduction to Repetitive Code

As Python developers, we've all been there - writing the same code over and over again, with only slight modifications each time. This repetitive code can be frustrating, error-prone, and difficult to maintain. But what if I told you there's a better way? In this article, we'll explore how to stop writing repetitive code in Python and instead use more efficient, Pythonic approaches.

The Problem with Repetitive Code

Repetitive code is problematic for several reasons:

It's error-prone: when you're writing the same code multiple times, it's easy to introduce bugs or inconsistencies.
It's difficult to maintain: if you need to make a change to the code, you'll have to update it in multiple places.
It's inefficient: writing repetitive code takes up valuable time and mental energy that could be spent on more important tasks.

Example 1: Data Validation

Let's say we're writing a function to validate user input data. We might write something like this:

def validate_name(name):
    if len(name) < 2:
        return False
    if len(name) > 50:
        return False
    if not name.isalpha():
        return False
    return True

def validate_email(email):
    if len(email) < 5:
        return False
    if len(email) > 100:
        return False
    if "@" not in email:
        return False
    return True

def validate_password(password):
    if len(password) < 8:
        return False
    if len(password) > 50:
        return False
    if not any(char.isupper() for char in password):
        return False
    return True

This code is repetitive because we're writing separate functions for each type of data validation. But what if we could write a single function that could validate any type of data?

A Better Approach

We can use a more Pythonic approach by writing a single function that takes in a set of validation rules:

def validate_data(data, rules):
    for rule in rules:
        if not rule(data):
            return False
    return True

def length_rule(min_length, max_length):
    return lambda data: min_length <= len(data) <= max_length

def alpha_rule():
    return lambda data: data.isalpha()

def contains_rule(char):
    return lambda data: char in data

def uppercase_rule():
    return lambda data: any(char.isupper() for char in data)

# Example usage:
name_rules = [length_rule(2, 50), alpha_rule()]
email_rules = [length_rule(5, 100), contains_rule("@")]
password_rules = [length_rule(8, 50), uppercase_rule()]

print(validate_data("John Doe", name_rules))  # False
print(validate_data("johndoe@example.com", email_rules))  # True
print(validate_data("password123", password_rules))  # False

This approach is more efficient and maintainable because we can add or modify validation rules without having to write new functions.

Example 2: Data Transformation

Another common scenario where we might write repetitive code is when transforming data from one format to another. For example:

def convert_to_celsius_fahrenheit(fahrenheit):
    return (fahrenheit - 32) * 5/9

def convert_to_celsius_kelvin(kelvin):
    return kelvin - 273.15

def convert_to_fahrenheit_celsius(celsius):
    return celsius * 9/5 + 32

def convert_to_fahrenheit_kelvin(kelvin):
    return (kelvin - 273.15) * 9/5 + 32

def convert_to_kelvin_celsius(celsius):
    return celsius + 273.15

def convert_to_kelvin_fahrenheit(fahrenheit):
    return (fahrenheit - 32) * 5/9 + 273.15

This code is repetitive because we're writing separate functions for each type of unit conversion. But what if we could write a single function that could convert between any two units?

A Better Approach

We can use a more Pythonic approach by writing a single function that takes in a dictionary of conversion formulas:

def convert_units(value, from_unit, to_unit, conversion_formulas):
    if from_unit not in conversion_formulas or to_unit not in conversion_formulas[from_unit]:
        raise ValueError("Unsupported unit conversion")
    return conversion_formulas[from_unit][to_unit](value)

# Example usage:
conversion_formulas = {
    "fahrenheit": {
        "celsius": lambda x: (x - 32) * 5/9,
        "kelvin": lambda x: (x - 32) * 5/9 + 273.15
    },
    "celsius": {
        "fahrenheit": lambda x: x * 9/5 + 32,
        "kelvin": lambda x: x + 273.15
    },
    "kelvin": {
        "fahrenheit": lambda x: (x - 273.15) * 9/5 + 32,
        "celsius": lambda x: x - 273.15
    }
}

print(convert_units(32, "fahrenheit", "celsius", conversion_formulas))  # 0.0
print(convert_units(0, "celsius", "fahrenheit", conversion_formulas))  # 32.0
print(convert_units(273.15, "kelvin", "celsius", conversion_formulas))  # 0.0

This approach is more efficient and maintainable because we can add or modify unit conversions without having to write new functions.

Example 3: Data Aggregation

Finally, let's consider an example where we might write repetitive code when aggregating data. For example:

def sum_numbers(numbers):
    return sum(numbers)

def sum_squares(numbers):
    return sum(x**2 for x in numbers)

def sum_cubes(numbers):
    return sum(x**3 for x in numbers)

This code is repetitive because we're writing separate functions for each type of aggregation. But what if we could write a single function that could aggregate data in any way we want?

A Better Approach

We can use a more Pythonic approach by writing a single function that takes in a function to apply to each element:

def aggregate_data(data, func):
    return sum(func(x) for x in data)

# Example usage:
numbers = [1, 2, 3, 4, 5]

print(aggregate_data(numbers, lambda x: x))  # 15
print(aggregate_data(numbers, lambda x: x**2))  # 55
print(aggregate_data(numbers, lambda x: x**3))  # 225

This approach is more efficient and maintainable because we can aggregate data in any way we want without having to write new functions.

Conclusion

In conclusion, writing repetitive code in Python can be frustrating, error-prone, and difficult to maintain. But by using more Pythonic approaches, such as writing single functions that can handle multiple scenarios, we can make our code more efficient, maintainable, and scalable. Whether it's data validation, unit conversion, or data aggregation, there are always better ways to write code in Python. So next time you find yourself writing repetitive code, take a step back and ask yourself: "Is there a better way to do this?"

If you want to learn more about writing efficient and Pythonic code, be sure to follow me for more articles and tutorials on Python development. Happy coding!

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Master CLI Args in 2 Minutes

qing — Thu, 25 Jun 2026 22:00:04 +0000

2-Minute Guide: argparse — CLI Arguments for Python Scripts

When writing Python scripts, it's often necessary to pass command-line arguments to customize the script's behavior. The argparse module makes it easy to define and parse these arguments. In this guide, we'll create a minimal example that demonstrates how to use argparse to accept CLI arguments.

Minimal Example

Here's an example script that uses argparse to accept --name and --count flags:


python
import argparse

parser = argparse.ArgumentParser()
parser.add_argument('--name', help='Your name')
parser.add_argument('--count', type=int, help='Number of greetings')

args = parser.parse

---

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5-Minute Python Morning Boost

qing — Thu, 25 Jun 2026 20:00:07 +0000

As a developer, I'm always looking for ways to optimize my daily routine and make the most of my time. One area that I've been able to automate with great success is my morning routine. By leveraging the power of Python, I've been able to streamline my morning and get a head start on the day. In this article, I'll show you how I did it and provide you with the full code to get you started.

Automating My Alarm Clock

The first thing I wanted to automate was my alarm clock. I used to use a traditional alarm clock, but I found that it was easy to hit the snooze button and sleep in. With Python, I was able to create a script that would wake me up at a specified time and even send me a motivational message to get me out of bed. Here's an example of how I did it:

import datetime
import time
import smtplib

def send_motivational_message():
    # Set up email server
    server = smtplib.SMTP('smtp.gmail.com', 587)
    server.starttls()
    server.login('your_email@gmail.com', 'your_password')

    # Send email
    msg = 'Good morning! Time to get up and start the day!'
    server.sendmail('your_email@gmail.com', 'your_email@gmail.com', msg)
    server.quit()

# Set alarm time
alarm_time = datetime.datetime.now() + datetime.timedelta(minutes=10)

# Wait until alarm time
while datetime.datetime.now() < alarm_time:
    time.sleep(1)

# Send motivational message
send_motivational_message()

This script uses the smtplib library to send an email to myself with a motivational message. You'll need to replace your_email@gmail.com and your_password with your actual email address and password.

Automating My Coffee Maker

Another task that I wanted to automate was my coffee maker. I have a smart coffee maker that can be controlled using an API, so I was able to write a script that would turn it on and start brewing a fresh pot of coffee at a specified time. Here's an example of how I did it:

import requests

def start_coffee_maker():
    # Set API endpoint and credentials
    api_endpoint = 'https://api.coffee-maker.com/brew'
    api_key = 'your_api_key'

    # Set headers and data
    headers = {'Authorization': f'Bearer {api_key}'}
    data = {'coffee': 'french_roast', 'strength': 'strong'}

    # Send request to API
    response = requests.post(api_endpoint, headers=headers, json=data)

    # Check if request was successful
    if response.status_code == 200:
        print('Coffee maker started!')
    else:
        print('Error starting coffee maker')

# Set coffee maker start time
coffee_maker_time = datetime.datetime.now() + datetime.timedelta(minutes=15)

# Wait until coffee maker start time
while datetime.datetime.now() < coffee_maker_time:
    time.sleep(1)

# Start coffee maker
start_coffee_maker()

This script uses the requests library to send a POST request to the coffee maker's API. You'll need to replace your_api_key with your actual API key.

Automating My News Feed

Finally, I wanted to automate my news feed so that I could stay up-to-date on current events without having to manually check the news every morning. I used the feedparser library to parse RSS feeds and the smtplib library to send me an email with the latest news. Here's an example of how I did it:

import feedparser
import smtplib

def get_latest_news():
    # Set up RSS feed
    feed = feedparser.parse('https://www.nytimes.com/services/xml/rss/nyt/HomePage.xml')

    # Get latest news
    latest_news = []
    for entry in feed.entries:
        latest_news.append(entry.title + ': ' + entry.link)

    # Send email with latest news
    server = smtplib.SMTP('smtp.gmail.com', 587)
    server.starttls()
    server.login('your_email@gmail.com', 'your_password')
    msg = '\n'.join(latest_news)
    server.sendmail('your_email@gmail.com', 'your_email@gmail.com', msg)
    server.quit()

# Set news feed update time
news_feed_time = datetime.datetime.now() + datetime.timedelta(minutes=30)

# Wait until news feed update time
while datetime.datetime.now() < news_feed_time:
    time.sleep(1)

# Get latest news
get_latest_news()

This script uses the feedparser library to parse the RSS feed from the New York Times and the smtplib library to send me an email with the latest news.

Tips and Tricks

Here are a few tips and tricks that I've learned from automating my morning routine:

Use a scheduling library: Instead of using time.sleep to wait until a certain time, consider using a scheduling library like schedule or apscheduler. These libraries allow you to schedule tasks to run at specific times or intervals.
Use environment variables: Instead of hardcoding your API keys or email passwords, consider using environment variables. This will make it easier to keep your code secure and avoid accidentally sharing sensitive information.
Test your code: Before automating a task, make sure to test your code thoroughly to ensure that it works as expected. This will save you from waking up to a coffee maker that didn't turn on or a news feed that didn't update.

Conclusion

Automating my morning routine with Python has been a game-changer for me. It's allowed me to streamline my morning and get a head start on the day. I hope that this article has inspired you to automate your own morning routine and start your day off on the right foot. If you're interested in learning more about automation and Python, be sure to subscribe to my newsletter for more tips and tricks. Subscribe now and get ready to take your productivity to the next level!

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Stop Writing Repetitive Python Code — Use These 5 Patterns

qing — Thu, 25 Jun 2026 18:42:03 +0000

As Python developers, we've all been there - writing the same code over and over again, wishing there was a way to make our lives easier. The good news is that there are several patterns and techniques that can help you avoid repetitive code and make your development process more efficient. In this article, we'll explore five patterns that you can use to stop writing repetitive Python code.

1. The DRY (Don't Repeat Yourself) Principle

The DRY principle is a fundamental concept in software development that states that every piece of knowledge must have a single, unambiguous, authoritative representation within a system. In other words, if you find yourself writing the same code in multiple places, it's time to extract it into a separate function or module.

Let's consider an example where we need to calculate the area and perimeter of a rectangle:

# Without DRY principle
def calculate_area(width, height):
    area = width * height
    return area

def calculate_perimeter(width, height):
    perimeter = 2 * (width + height)
    return perimeter

# With DRY principle
def calculate_rectangle_properties(width, height):
    area = width * height
    perimeter = 2 * (width + height)
    return area, perimeter

area, perimeter = calculate_rectangle_properties(10, 20)
print(f"Area: {area}, Perimeter: {perimeter}")

By extracting the common logic into a separate function, we've made our code more concise and easier to maintain.

2. The Factory Pattern

The factory pattern is a creational design pattern that provides a way to create objects without specifying the exact class of object that will be created. This pattern is useful when we need to create multiple objects that share a common base class.

Let's consider an example where we need to create different types of database connections:

# Without factory pattern
class MySQLConnection:
    def __init__(self):
        print("MySQL connection established")

class PostgreSQLConnection:
    def __init__(self):
        print("PostgreSQL connection established")

# With factory pattern
class DatabaseConnection:
    def __init__(self, db_type):
        self.db_type = db_type

    def connect(self):
        if self.db_type == "mysql":
            return MySQLConnection()
        elif self.db_type == "postgresql":
            return PostgreSQLConnection()
        else:
            raise ValueError("Invalid database type")

# Usage
connection = DatabaseConnection("mysql")
connection.connect()

By using the factory pattern, we've made our code more flexible and easier to extend.

3. The Decorator Pattern

The decorator pattern is a structural design pattern that allows us to dynamically add new behaviors to objects without modifying their implementation. This pattern is useful when we need to add additional functionality to existing functions or classes.

Let's consider an example where we need to log the execution time of a function:

import time
from functools import wraps

def timer_decorator(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        print(f"Function {func.__name__} took {end_time - start_time} seconds to execute")
        return result
    return wrapper

@timer_decorator
def example_function():
    time.sleep(2)

example_function()

By using the decorator pattern, we've made our code more modular and easier to reuse.

4. The Observer Pattern

The observer pattern is a behavioral design pattern that allows us to notify objects about changes to other objects without having a direct reference to each other. This pattern is useful when we need to communicate between objects in a decoupled way.

Let's consider an example where we need to notify multiple objects when a new message is received:

class Subject:
    def __init__(self):
        self.observers = []

    def register_observer(self, observer):
        self.observers.append(observer)

    def notify_observers(self, message):
        for observer in self.observers:
            observer.update(message)

class Observer:
    def update(self, message):
        print(f"Received message: {message}")

# Usage
subject = Subject()
observer1 = Observer()
observer2 = Observer()

subject.register_observer(observer1)
subject.register_observer(observer2)

subject.notify_observers("Hello, world!")

By using the observer pattern, we've made our code more scalable and easier to maintain.

5. The Template Method Pattern

The template method pattern is a behavioral design pattern that allows us to define the skeleton of an algorithm in a method, deferring the implementation of certain steps to subclasses. This pattern is useful when we need to provide a common structure for multiple algorithms.

Let's consider an example where we need to implement different types of sorting algorithms:

from abc import ABC, abstractmethod

class SortingAlgorithm(ABC):
    def sort(self, data):
        self.initialize(data)
        self.sort_data()
        self.finalize()

    @abstractmethod
    def initialize(self, data):
        pass

    @abstractmethod
    def sort_data(self):
        pass

    @abstractmethod
    def finalize(self):
        pass

class BubbleSort(SortingAlgorithm):
    def initialize(self, data):
        self.data = data

    def sort_data(self):
        for i in range(len(self.data)):
            for j in range(len(self.data) - 1):
                if self.data[j] > self.data[j + 1]:
                    self.data[j], self.data[j + 1] = self.data[j + 1], self.data[j]

    def finalize(self):
        print(self.data)

# Usage
data = [5, 2, 8, 3, 1]
bubble_sort = BubbleSort()
bubble_sort.sort(data)

By using the template method pattern, we've made our code more flexible and easier to extend.

Conclusion

In this article, we've explored five patterns that can help you stop writing repetitive Python code. By applying these patterns, you can make your code more concise, modular, and maintainable. Whether you're working on a small script or a large-scale application, these patterns can help you write more efficient and effective code.

Stay Up-to-Date with the Latest Python Trends and Best Practices

If you want to stay up-to-date with the latest Python trends and best practices, be sure to subscribe to our newsletter. We'll send you weekly updates with the latest articles, tutorials, and tips on Python development. Subscribe now and take your Python skills to the next level!

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Master Python in 5 Minutes

qing — Thu, 25 Jun 2026 16:00:10 +0000

Introduction to List Comprehensions

List comprehensions in Python are a powerful tool that allows developers to create lists in a more concise and readable way. However, many developers tend to ignore them or use them inefficiently. In this article, we'll explore why ignoring list comprehensions can be problematic and how you can use them to write more Pythonic code.

The Problem with Ignoring List Comprehensions

When you ignore list comprehensions, you often end up writing more verbose code that's harder to read and maintain. Let's take a look at an example of how you might create a list of squares without using a list comprehension:

squares = []
for i in range(10):
    squares.append(i ** 2)
print(squares)

This code works, but it's not very Pythonic. It uses a for loop to iterate over a range of numbers and appends each square to the squares list. This approach has several problems:

It's more verbose than it needs to be
It uses a mutable list that can be modified accidentally
It's slower than using a list comprehension

Using List Comprehensions Effectively

So, how can you use list comprehensions to write more Pythonic code? Let's take a look at the same example, but this time using a list comprehension:

squares = [i ** 2 for i in range(10)]
print(squares)

This code is more concise and readable than the previous example. It uses a list comprehension to create a new list of squares in a single line of code.

Benefits of List Comprehensions

List comprehensions have several benefits, including:

Conciseness: List comprehensions are often more concise than equivalent code that uses loops.
Readability: List comprehensions can make your code more readable by reducing the amount of boilerplate code.
Performance: List comprehensions are often faster than equivalent code that uses loops.

Example 1: Filtering a List

Let's take a look at another example of how you can use list comprehensions to filter a list. Suppose you have a list of numbers and you want to create a new list that only includes the even numbers:

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
even_numbers = [num for num in numbers if num % 2 == 0]
print(even_numbers)

This code uses a list comprehension to create a new list that only includes the even numbers from the original list.

Example 2: Transforming a List

List comprehensions can also be used to transform a list. Suppose you have a list of strings and you want to create a new list that includes the uppercase version of each string:

fruits = ['apple', 'banana', 'cherry']
uppercase_fruits = [fruit.upper() for fruit in fruits]
print(uppercase_fruits)

This code uses a list comprehension to create a new list that includes the uppercase version of each fruit.

Example 3: Flattening a List of Lists

List comprehensions can also be used to flatten a list of lists. Suppose you have a list of lists and you want to create a new list that includes all the elements from the sublists:

numbers = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
flattened_numbers = [num for sublist in numbers for num in sublist]
print(flattened_numbers)

This code uses a list comprehension to create a new list that includes all the elements from the sublists.

Best Practices for Using List Comprehensions

Here are some best practices to keep in mind when using list comprehensions:

Keep it simple: List comprehensions are most effective when they're simple and easy to read. Avoid using complex logic or nested loops.
Use meaningful variable names: Use meaningful variable names to make your code more readable.
Avoid side effects: List comprehensions should not have side effects, such as modifying external state or printing output.

Common Pitfalls to Avoid

Here are some common pitfalls to avoid when using list comprehensions:

Nested loops: Avoid using nested loops in list comprehensions, as they can be hard to read and understand.
Complex logic: Avoid using complex logic in list comprehensions, as it can be hard to read and understand.
Mutable state: Avoid modifying external state in list comprehensions, as it can have unintended consequences.

Conclusion

List comprehensions are a powerful tool in Python that can help you write more concise and readable code. By following best practices and avoiding common pitfalls, you can use list comprehensions to simplify your code and improve your productivity. If you're interested in learning more about Python and how to write more effective code, be sure to follow me for more articles and tutorials. Happy coding!

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How to Build a Chrome Extension That Makes Money

qing — Thu, 25 Jun 2026 15:08:56 +0000

How to Build a Chrome Extension That Makes Money

Imagine waking up every morning to a steady stream of passive income, generated by a simple Chrome extension that you built in your free time. Sounds like a dream, right? But it's a reality for many developers who have created successful extensions that solve real problems for users. With over 1 billion active Chrome users, the potential for earning money through extensions is vast. So, let's dive into the world of Chrome extension development and explore how you can build a profitable one.

Getting Started with Chrome Extension Development

Before we begin, you'll need to have some basic knowledge of HTML, CSS, and JavaScript. If you're new to these technologies, don't worry – there are plenty of resources available to help you learn. Once you have a solid grasp of the basics, you can start building your extension. The first step is to create a new directory for your project and create the following files: manifest.json, popup.html, popup.js, and icon.png. The manifest.json file is the backbone of your extension, as it contains metadata, permissions, and functionality.

Understanding the Manifest File

The manifest.json file is a JSON object that contains essential information about your extension. Here's a basic example of what it might look like:

{
  "manifest_version": 2,
  "name": "My Extension",
  "version": "1.0",
  "description": "A brief description of my extension",
  "icons": {
    "16": "icon16.png",
    "48": "icon48.png",
    "128": "icon128.png"
  },
  "browser_action": {
    "default_icon": "icon48.png",
    "default_popup": "popup.html"
  },
  "permissions": ["activeTab"]
}

This file tells Chrome the name and version of your extension, as well as what permissions it requires. In this case, we're requesting access to the active tab.

Monetizing Your Extension

So, how do you make money from your Chrome extension? There are several ways to do this, including:

Displaying ads in your popup or options page
Offering in-app purchases or subscriptions
Collecting affiliate commissions for referring users to other products or services
Selling data or analytics

For this example, let's focus on displaying ads. We'll use Google AdSense, which is a popular choice for Chrome extensions. First, you'll need to create an AdSense account and generate an ad unit. Then, you can add the ad code to your popup.html file.

Adding AdSense to Your Extension

Here's an example of how you might add an AdSense ad to your popup.html file:

<!DOCTYPE html>
<html>
  <head>
    <title>My Extension</title>
    <style>
      body {
        width: 200px;
        height: 100px;
        font-family: Arial, sans-serif;
        text-align: center;
      }
    </style>
  </head>
  <body>
    <h1>My Extension</h1>
    <script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js"></script>
    <ins class="adsbygoogle"
         style="display:inline-block;width:200px;height:100px"
         data-ad-client="ca-pub-1234567890"
         data-ad-slot="1234567890"></ins>
    <script>
      (adsbygoogle = window.adsbygoogle || []).push({});
    </script>
  </body>
</html>

This code adds a 200x100 pixel ad to your popup.html file. You'll need to replace the data-ad-client and data-ad-slot attributes with your own AdSense ad unit.

Using Python to Automate Tasks

While JavaScript is the primary language used for Chrome extension development, you can also use Python to automate tasks and improve your workflow. For example, you might use Python to:

Automatically update your extension's metadata or permissions
Generate reports on your extension's performance or user engagement
Automate testing or debugging tasks

Here's an example of how you might use Python to update your extension's metadata:

import json
import os

# Load the manifest file
with open('manifest.json', 'r') as f:
    manifest = json.load(f)

# Update the version number
manifest['version'] = '1.1'

# Save the updated manifest file
with open('manifest.json', 'w') as f:
    json.dump(manifest, f, indent=2)

This code loads the manifest.json file, updates the version number, and saves the changes.

Publishing and Promoting Your Extension

Once you've built and tested your extension, it's time to publish it to the Chrome Web Store. This involves creating a developer account, uploading your extension, and providing some basic metadata. You'll also need to promote your extension to attract users and generate revenue.

Optimizing Your Extension's Visibility

To optimize your extension's visibility in the Chrome Web Store, make sure to:

Use relevant keywords in your title and description
Provide a clear and concise description of your extension's features and benefits
Include high-quality screenshots and icons
Encourage users to leave reviews and ratings

You can also promote your extension through social media, content marketing, or paid advertising.

Making Money from Your Extension

So, how much money can you make from a Chrome extension? The answer depends on various factors, such as the type of extension, the target audience, and the monetization strategy. However, here are some rough estimates:

A simple ad-supported extension might generate $100-$500 per month
A more complex extension with in-app purchases or subscriptions might generate $1,000-$5,000 per month
A highly successful extension with a large user base and multiple revenue streams might generate $10,000-$50,000 per month or more

While these figures are encouraging, keep in mind that building a successful Chrome extension takes time, effort, and dedication.

As you can see, building a Chrome extension that makes money is a challenging but rewarding task. By following the steps outlined in this article, you can create a profitable extension that generates passive income and helps you achieve your financial goals. So, what are you waiting for? Start building your Chrome extension today and join the thousands of developers who are already earning money from their creations.

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Monetize Your Blog with AI: A Developer's Guide

qing — Thu, 25 Jun 2026 14:06:35 +0000

Monetize Your Blog with AI: A Developer's Guide

Imagine earning passive income from your blog while you sleep, travel, or focus on your next development project. Sounds like a dream, right? Well, it's a reality for many bloggers who have successfully monetized their online presence using Artificial Intelligence (AI). As a developer, you're already ahead of the game with a solid foundation in coding and problem-solving. Now, let's dive into the world of AI-powered blogging and explore how you can turn your blog into a lucrative business.

Getting Started with AI-Powered Blogging

To monetize your blog with AI, you'll need to understand the basics of how AI can be applied to blogging. There are several ways to leverage AI, including content generation, optimization, and personalization. One of the most effective ways to get started is by using Natural Language Processing (NLP) to analyze and improve your content. NLP can help you identify gaps in your content, suggest topics, and even generate high-quality articles.

Choosing the Right AI Tools

With so many AI tools available, it can be overwhelming to choose the right ones for your blog. When selecting AI tools, consider the following factors:

Ease of use: Look for tools with intuitive interfaces that don't require extensive technical expertise.
Customization: Opt for tools that allow you to tailor the AI output to your specific needs and brand voice.
Integration: Choose tools that seamlessly integrate with your existing blogging platform and workflow.

Some popular AI tools for blogging include language generators like Language Tool and content optimizers like Ahrefs. These tools can help you streamline your content creation process, improve your writing quality, and increase your online visibility.

Implementing AI-Powered Content Generation

One of the most exciting applications of AI in blogging is content generation. With the help of AI, you can create high-quality, engaging content that resonates with your audience. Let's take a look at a simple example of how you can use Python to generate content using the Hugging Face Transformers library.

import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration

# Initialize the model and tokenizer
model = T5ForConditionalGeneration.from_pretrained('t5-base')
tokenizer = T5Tokenizer.from_pretrained('t5-base')

# Define a function to generate content
def generate_content(prompt):
    input_ids = tokenizer.encode(prompt, return_tensors='pt')
    output = model.generate(input_ids, max_length=200)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Test the function
prompt = "Write a short article about the benefits of AI in blogging"
print(generate_content(prompt))

This code snippet demonstrates how you can use the T5 model to generate content based on a given prompt. The generate_content function takes a prompt as input, encodes it using the tokenizer, and then uses the model to generate a response. The output is decoded and returned as a string.

Fine-Tuning the Model

To get the most out of your AI-powered content generation, you'll need to fine-tune the model to your specific needs. This involves training the model on your own data, such as your existing blog posts or articles. By fine-tuning the model, you can adapt it to your unique writing style, tone, and voice.

Optimizing Your Blog with AI

In addition to content generation, AI can also be used to optimize your blog for better performance and engagement. This includes tasks like keyword research, SEO optimization, and social media promotion. By using AI-powered tools, you can streamline your workflow, save time, and increase your online visibility.

Using AI for Keyword Research

Keyword research is a crucial step in optimizing your blog for search engines. AI-powered tools like Ahrefs and SEMrush can help you identify relevant keywords, analyze your competition, and optimize your content for better rankings. These tools use machine learning algorithms to analyze large datasets and provide actionable insights that can inform your content strategy.

Monetizing Your Blog with AI

Now that we've explored the various ways to use AI in blogging, let's talk about monetization. There are several ways to earn money from your blog, including advertising, affiliate marketing, and sponsored content. AI can help you optimize your monetization strategy by analyzing your audience, identifying revenue opportunities, and streamlining your workflow.

Using AI for Ad Optimization

AI-powered ad optimization tools can help you maximize your ad revenue by analyzing your audience, identifying high-performing ad placements, and optimizing your ad targeting. These tools use machine learning algorithms to analyze large datasets and provide actionable insights that can inform your ad strategy.

Conclusion and Next Steps

Monetizing your blog with AI is a powerful way to earn passive income and grow your online presence. By leveraging AI-powered tools and techniques, you can streamline your content creation process, optimize your blog for better performance, and increase your online visibility. So why not get started today? Choose an AI tool that resonates with your needs, experiment with different techniques, and watch your blog thrive. Remember, the key to success lies in continuous learning, experimentation, and adaptation. Join the AI-powered blogging revolution and start building the blog of your dreams!

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Stop Catching Bare Exceptions in Python — Do This Instead

qing — Thu, 25 Jun 2026 10:03:42 +0000

Introduction to Exception Handling in Python

Exception handling is a crucial aspect of programming in Python. It allows developers to gracefully manage errors and unexpected events that may occur during the execution of their code. One common practice among beginners is to catch bare exceptions, which can lead to more harm than good. In this article, we'll explore the problems associated with catching bare exceptions and discuss a better approach to exception handling in Python.

The Problem with Catching Bare Exceptions

Catching bare exceptions, also known as naked exceptions, refers to the practice of catching the base Exception class without specifying any particular exception type. This can be done using a simple except clause without any exception type, like so:

try:
    # Some code that might raise an exception
    x = 1 / 0
except:
    # Handle the exception
    print("An error occurred")

At first glance, this might seem like a good way to catch all possible exceptions and prevent your program from crashing. However, this approach has several problems:

Hides bugs: By catching all exceptions, you might inadvertently hide bugs in your code. If an exception is raised due to a logical error, catching it without logging or reporting the error can make it difficult to diagnose and fix the issue.
Masks system exceptions: Catching bare exceptions can also mask system exceptions, such as SystemExit or KeyboardInterrupt, which are used to terminate the program or interrupt its execution. This can lead to unexpected behavior and make it difficult to terminate the program cleanly.
Makes debugging harder: When you catch bare exceptions, you lose valuable information about the exception, such as its type, message, and traceback. This can make it challenging to debug your code and identify the root cause of the issue.

A Better Approach to Exception Handling

So, what's a better way to handle exceptions in Python? The answer is to catch specific exceptions that you can handle meaningfully. This approach has several benefits:

Explicit error handling: By catching specific exceptions, you can handle errors explicitly and provide meaningful error messages or recovery mechanisms.
Preserves system exceptions: Catching specific exceptions preserves system exceptions, such as SystemExit or KeyboardInterrupt, which are essential for terminating the program or interrupting its execution.
Eases debugging: When you catch specific exceptions, you can log or report the error with its type, message, and traceback, making it easier to debug your code and identify the root cause of the issue.

Example 1: Catching a Specific Exception

Let's consider an example where we're trying to open a file that might not exist. Instead of catching a bare exception, we can catch the FileNotFoundError exception specifically:

try:
    with open("non_existent_file.txt", "r") as file:
        print(file.read())
except FileNotFoundError:
    print("The file does not exist")

In this example, we're catching the FileNotFoundError exception, which is raised when the file does not exist. By catching this exception specifically, we can provide a meaningful error message and handle the error explicitly.

Example 2: Catching Multiple Exceptions

Sometimes, you might want to catch multiple exceptions that can be handled in the same way. You can do this by listing the exceptions in a tuple:

try:
    x = 1 / 0
except (ZeroDivisionError, TypeError):
    print("An error occurred while performing the operation")

In this example, we're catching both ZeroDivisionError and TypeError exceptions, which can occur when performing mathematical operations. By catching these exceptions specifically, we can provide a meaningful error message and handle the errors explicitly.

Example 3: Logging Exceptions

Another scenario where catching specific exceptions is useful is when logging errors. You can catch specific exceptions, log the error, and then re-raise the exception to preserve the original error:

import logging

try:
    x = 1 / 0
except ZeroDivisionError as e:
    logging.error("An error occurred: %s", e)
    raise

In this example, we're catching the ZeroDivisionError exception, logging the error using the logging module, and then re-raising the exception using the raise statement. This way, we can log the error and preserve the original exception, making it easier to debug the issue.

Best Practices for Exception Handling

To get the most out of exception handling in Python, follow these best practices:

Catch specific exceptions: Catch exceptions that you can handle meaningfully, and avoid catching bare exceptions.
Provide meaningful error messages: When catching exceptions, provide error messages that are helpful and descriptive.
Log errors: Log errors using a logging mechanism, such as the logging module, to preserve information about the exception.
Re-raise exceptions: Re-raise exceptions after logging or handling them to preserve the original error and make it easier to debug the issue.

Conclusion

In conclusion, catching bare exceptions in Python can lead to more harm than good. By catching specific exceptions, you can handle errors explicitly, preserve system exceptions, and make debugging easier. Remember to follow best practices for exception handling, such as catching specific exceptions, providing meaningful error messages, logging errors, and re-raising exceptions. For more content on Python programming, including tips, tricks, and best practices, be sure to follow me on Dev.to. Happy coding!

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10 Open Source Projects You Can Contribute to for Money

qing — Thu, 25 Jun 2026 03:05:10 +0000

10 Open Source Projects You Can Contribute to for Money

Imagine being able to contribute to open source projects you're passionate about, while also getting paid for your work. Sounds like a dream come true, right? Well, it's not just a fantasy - many organizations and companies are now offering financial incentives to developers who contribute to their open source projects. This not only helps to sustain the projects themselves, but also provides a way for developers to earn a living while working on projects they love.

What Kind of Projects Can You Contribute to for Money?

There are countless open source projects out there, covering everything from artificial intelligence and machine learning to web development and cybersecurity. Some projects are looking for contributors to help with bug fixes and maintenance, while others need help with new feature development or documentation. No matter what your skills or interests are, there's likely an open source project out there that needs your help.

Projects That Pay Contributors

Here are 10 open source projects that offer financial incentives to contributors:

Gitcoin: A platform that allows developers to earn money by working on open source projects.
Open Source Guides: A project by GitHub that provides guides and resources for open source projects, and pays contributors for their work.
Apache Software Foundation: A non-profit organization that oversees many popular open source projects, including Apache HTTP Server and Apache Kafka.
Linux Foundation: A non-profit organization that supports the development of the Linux operating system, and offers financial incentives to contributors.
Google Summer of Code: A program that pairs students with open source projects, and provides a stipend for their work.
Mozilla: A non-profit organization that develops the Firefox web browser, and offers financial incentives to contributors.
WordPress: A popular content management system that offers financial incentives to contributors.
Redis: An in-memory data store that offers financial incentives to contributors.
Rust: A programming language that offers financial incentives to contributors.
TensorFlow: An open source machine learning framework that offers financial incentives to contributors.

How to Get Started

So, how do you get started with contributing to open source projects for money? The first step is to find a project that aligns with your interests and skills. You can search for projects on platforms like GitHub or GitLab, or look for projects that are specifically offering financial incentives to contributors.

Finding Projects on GitHub

To find open source projects on GitHub, you can use the search bar at the top of the page. Simply type in a keyword or topic that interests you, and GitHub will show you a list of relevant projects. You can also use the "Explore" tab to browse through popular and trending projects.

import requests

# Search for open source projects on GitHub
def search_github_projects(query):
    url = f"https://api.github.com/search/repositories?q={query}"
    response = requests.get(url)
    return response.json()

# Example usage:
projects = search_github_projects("machine learning")
for project in projects["items"]:
    print(project["name"])

Tips for Contributing to Open Source Projects

Once you've found a project you're interested in contributing to, there are a few things to keep in mind. First, make sure you read and follow the project's contributing guidelines. These guidelines will usually include information on how to submit pull requests, how to communicate with the project team, and what kinds of contributions are welcome.

Reading Contributing Guidelines

To read a project's contributing guidelines, look for a file called CONTRIBUTING.md in the project's repository. This file will usually include information on how to get started with contributing, as well as any specific rules or guidelines that contributors need to follow.

Communicating with the Project Team

To communicate with the project team, look for a link to the project's chat room or forum. This is usually where you can ask questions, get help with your contributions, and discuss project-related topics with other contributors.

Getting Paid for Your Contributions

So, how do you actually get paid for your contributions? This will depend on the project and the organization behind it. Some projects may offer a straightforward payment structure, where you're paid a certain amount for each contribution you make. Others may offer a more complex system, where you're paid based on the impact of your contributions or the number of users who benefit from them.

Payment Structures

To understand the payment structure for a particular project, look for information on the project's website or in the contributing guidelines. You can also reach out to the project team directly to ask about payment structures and how they work.

Conclusion

Contributing to open source projects can be a rewarding and lucrative way to earn a living, while also working on projects you're passionate about. With so many projects out there offering financial incentives to contributors, there's never been a better time to get started. So why not start exploring today, and see where your contributions can take you? Join the open source community, start contributing to projects that interest you, and get paid for your work. The possibilities are endless, and the community is waiting for you.

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Async Python: The Complete Beginner Guide With Real Examples

qing — Thu, 25 Jun 2026 01:04:42 +0000

Async Python: The Complete Beginner Guide With Real Examples

Asynchronous programming is a powerful tool in Python that allows your code to perform multiple tasks concurrently, improving the overall performance and responsiveness of your application. In this tutorial, we will explore the basics of async/await, the event loop, and asyncio.gather, and compare synchronous vs asynchronous HTTP fetching using the httpx library.

Introduction to Async/Await

Async/await is a syntax sugar on top of coroutines, which are special types of functions that can suspend and resume their execution at specific points. The async keyword is used to define a coroutine, and the await keyword is used to suspend the execution of the coroutine until the awaited task is complete.

Here's a simple example of an async function:

import asyncio

async def hello_world():
    print("Hello")
    await asyncio.sleep(1)
    print("World")

async def main():
    await hello_world()

asyncio.run(main())

In this example, the hello_world function is defined as an async function, and it uses the await keyword to suspend its execution for 1 second using asyncio.sleep(1). The main function is also defined as an async function, and it calls hello_world using the await keyword.

Event Loop

The event loop is the core of the asyncio library, and it's responsible for managing the execution of coroutines. The event loop runs in an infinite loop, checking for new tasks to run and switching between them as needed.

You can create an event loop using asyncio.get_event_loop() and run it using loop.run_until_complete(). However, in most cases, you can simply use asyncio.run() to run your async code.

import asyncio

async def hello_world():
    print("Hello")
    await asyncio.sleep(1)
    print("World")

loop = asyncio.get_event_loop()
loop.run_until_complete(hello_world())

Asyncio.Gather

asyncio.gather is a function that allows you to run multiple coroutines concurrently and wait for all of them to complete. It returns a list of results, which can be accessed using the await keyword.

Here's an example of using asyncio.gather to run multiple tasks concurrently:

import asyncio

async def task1():
    await asyncio.sleep(1)
    return "Task 1"

async def task2():
    await asyncio.sleep(2)
    return "Task 2"

async def main():
    tasks = [task1(), task2()]
    results = await asyncio.gather(*tasks)
    print(results)

asyncio.run(main())

In this example, task1 and task2 are defined as async functions, and they simulate some I/O-bound work using asyncio.sleep(). The main function creates a list of tasks and uses asyncio.gather to run them concurrently. The results are then printed to the console.

Sync vs Async HTTP Fetching with Httpx

The httpx library is a modern HTTP client for Python that supports both synchronous and asynchronous requests. Let's compare the performance of synchronous vs asynchronous HTTP fetching using httpx.

First, install the httpx library using pip:

pip install httpx

Now, let's create a simple example that fetches a URL using both synchronous and asynchronous requests:

import httpx
import asyncio
import time

def sync_fetch(url):
    response = httpx.get(url)
    return response.text

async def async_fetch(url):
    async with httpx.AsyncClient() as client:
        response = await client.get(url)
        return response.text

async def main():
    url = "https://example.com"
    start_time = time.time()
    sync_response = sync_fetch(url)
    sync_time = time.time() - start_time
    print(f"Sync fetch time: {sync_time:.2f} seconds")

    start_time = time.time()
    async_response = await async_fetch(url)
    async_time = time.time() - start_time
    print(f"Async fetch time: {async_time:.2f} seconds")

asyncio.run(main())

In this example, we define two functions: sync_fetch and async_fetch. The sync_fetch function uses the synchronous httpx.get() method to fetch the URL, while the async_fetch function uses the asynchronous httpx.AsyncClient() to fetch the URL.

We then measure the time it takes to fetch the URL using both synchronous and asynchronous requests. The results will show that the asynchronous request is significantly faster than the synchronous request.

Benchmarks

To get a better understanding of the performance difference between synchronous and asynchronous requests, let's create a benchmark that fetches multiple URLs concurrently using both synchronous and asynchronous requests.

import httpx
import asyncio
import time

def sync_fetch(urls):
    responses = []
    for url in urls:
        response = httpx.get(url)
        responses.append(response.text)
    return responses

async def async_fetch(urls):
    async with httpx.AsyncClient() as client:
        tasks = [client.get(url) for url in urls]
        responses = await asyncio.gather(*tasks)
        return [response.text for response in responses]

async def main():
    urls = ["https://example.com"] * 10
    start_time = time.time()
    sync_responses = sync_fetch(urls)
    sync_time = time.time() - start_time
    print(f"Sync fetch time: {sync_time:.2f} seconds")

    start_time = time.time()
    async_responses = await async_fetch(urls)
    async_time = time.time() - start_time
    print(f"Async fetch time: {async_time:.2f} seconds")

asyncio.run(main())

In this benchmark, we fetch 10 URLs concurrently using both synchronous and asynchronous requests. The results will show that the asynchronous requests are significantly faster than the synchronous requests.

Conclusion

In conclusion, asynchronous programming is a powerful tool in Python that allows your code to perform multiple tasks concurrently, improving the overall performance and responsiveness of your application. The asyncio library provides a simple and efficient way to write asynchronous code, and the httpx library provides a modern HTTP client that supports both synchronous and asynchronous requests.

By using asynchronous requests, you can significantly improve the performance of your application, especially when dealing with I/O-bound tasks such as HTTP requests. The benchmarks shown in this tutorial demonstrate the performance difference between synchronous and asynchronous requests, and they provide a clear example of the benefits of using asynchronous programming in Python.

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5 Ways to Boost Code with Python Type Hints

qing — Wed, 24 Jun 2026 15:06:23 +0000

Python Type Hints: Write Better Code with Examples

Imagine you're working on a complex Python project, and you're trying to understand what a particular function does. You look at the function signature, and it's a mess – it takes a bunch of arguments with unclear names, and it returns something, but you have no idea what. You have to dig through the entire codebase to figure out what's going on, wasting precious time and energy. This is where Python type hints come in – a game-changer for writing better, more maintainable code.

What are Type Hints?

Type hints are a way to indicate the expected types of variables, function arguments, and return values in your Python code. They were introduced in Python 3.5 as a part of PEP 484, and they've been gaining popularity ever since. Type hints are not enforced at runtime, but they can be used by third-party tools such as type checkers, IDEs, and linters to provide valuable feedback and catch errors early.

Why Use Type Hints?

So, why should you use type hints in your Python code? Here are a few compelling reasons:

Improved code readability: Type hints make it clear what types of data your functions and variables expect, making it easier for others (and yourself!) to understand your code.
Better error messages: Type checkers can use type hints to provide more informative error messages, helping you catch and fix errors faster.
Enhanced auto-completion: Many IDEs and editors can use type hints to provide more accurate auto-completion suggestions, saving you time and reducing typos.
Static analysis: Type hints can be used by static analysis tools to catch errors and provide insights into your code, without having to run it.

Using Type Hints in Practice

Now that we've covered the what and why of type hints, let's dive into the how. Here's an example of a simple function with type hints:

def greet(name: str) -> None:
    print(f"Hello, {name}!")

def add_numbers(a: int, b: int) -> int:
    return a + b

In this example, we've added type hints to the greet and add_numbers functions. The greet function takes a string argument name and returns nothing (None), while the add_numbers function takes two integer arguments a and b and returns an integer.

Type Hinting Complex Types

But what about more complex types, like lists or dictionaries? You can use type hints to specify those as well:

from typing import List, Dict

def process_list(numbers: List[int]) -> int:
    return sum(numbers)

def process_dict(data: Dict[str, int]) -> int:
    return sum(data.values())

In this example, we've used the List and Dict types from the typing module to specify that the process_list function takes a list of integers and the process_dict function takes a dictionary with string keys and integer values.

Advanced Type Hinting

Type hints can also be used to specify more advanced types, such as union types (e.g., int or str) and optional types (e.g., int or None). Here's an example:

from typing import Union, Optional

def parse_value(value: Union[int, str]) -> int:
    if isinstance(value, int):
        return value
    elif isinstance(value, str):
        return int(value)
    else:
        raise ValueError("Invalid value")

def get_name(id: int) -> Optional[str]:
    # simulate a database query
    names = {1: "John", 2: "Jane"}
    return names.get(id)

In this example, we've used the Union type to specify that the parse_value function can take either an integer or a string, and the Optional type to specify that the get_name function can return either a string or None.

Real-World Applications

So, how can you apply type hints in real-world projects? Here are a few examples:

API design: Use type hints to specify the expected input and output types for your API endpoints, making it easier for clients to understand how to use your API.
Data processing: Use type hints to specify the types of data your functions expect and return, making it easier to catch errors and ensure data consistency.
Machine learning: Use type hints to specify the types of data your machine learning models expect and return, making it easier to integrate your models with other code.

Conclusion

Python type hints are a powerful tool for writing better, more maintainable code. By using type hints, you can improve code readability, catch errors early, and enhance auto-completion and static analysis. Whether you're working on a small script or a large-scale project, type hints can help you write more robust and efficient code. So, what are you waiting for? Start using type hints in your Python code today, and see the difference for yourself! Take the next step and explore more advanced type hinting features, such as generic types and protocol types, to take your code to the next level.

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