🔥 10 GitHub Repositories to Scrape Almost Any Website

1. Firecrawl
Turns entire websites into clean, AI-ready Markdown or structured data with just a few API calls. Perfect for feeding LLMs. 🤖

2. Crawl4AI
An open source python crawler built specifically for AI. Extracts clean, structured content optimized for LLMs. 🐍

3. Browser Use
AI Agent that control browsers like a human. It allows an AI agent to dynamically visually navigate, click elements, bypass popups, and extract data. 🖱️

4. Crawlee
A powerful scraping framework for building fast, reliable crawlers with support for Playwright, Puppeteer, and Cheerio. ⚡

5. Scrapy
One of the most popular Python frameworks for large-scale web scraping and crawling projects. 🕷️

6. MarkItDown
Converts PDFs, Office documents, HTML, and many other file types into clean Markdown for AI workflows. 📄

7. Scrapling
A modern Python scraping library that combines speed, browser automation, and smart parsing with a simple API. 🚀

8. Skyvern
An AI-powered scraping tool that dynamically solve CAPTCHAs, log into complex portals, and extract data without requiring any pre-defined HTML selectors or XPaths. 🔓

9. AutoScraper
Automatically learns how to extract similar data from web pages by showing it just a few examples. 🧠

10. curl-impersonate
Makes cURL mimic real browsers like Chrome and Safari to bypass bot detection and access protected websites more reliably. 🕵️

💡 Save this list for your next web scraping or AI automation project.

#WebScraping #AI #GitHub #Python #Automation #LLM

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Wagtail as Django admin on steroids

A good analysis for Django developers: Wagtail can be used not only as a CMS, but also as a more convenient admin interface for regular Django models.

The idea is simple: Django admin quickly provides a UI around models, but customization often becomes a hassle. Wagtail offers a more modern interface, proper handling of fields, grouping via panels, roles, permissions, rich text, media library, versioning, and editorial workflows.

At the same time, there's no need to rewrite the project to fit CMS logic. Wagtail is installed as a regular Django package, added to INSTALLED_APPS, and integrated into urls.py. The business logic, views, forms, and templates remain regular Django components.

The most practical use case: take an existing admin.py, move the models to Wagtail snippets, and gradually replace the old admin interface where you need an interface that's not embarrassing to show to clients.

For internal tools, CRMs, backoffices, and content sections, this can be much more pleasant than endlessly tweaking the standard Django admin.

https://timonweb.com/wagtail/wagtail-as-django-admin-on-steroids/

**Today we will examine __call__ — a data filter object!** 🧠

It allows a class instance to work as a function, preserving the state and filtering rules. ⚙️

Let's create a filter for numbers that only passes even ones and strictly greater than a specified threshold:

class EvenFilter:
    def __init__(self, threshold):
        self.threshold = threshold

    def __call__(self, numbers):
        return [n for n in numbers if n % 2 == 0 and n > self.threshold]

Let's use the filter in practice:

f = EvenFilter(5)
nums = [1, 4, 6, 7, 10]
print(f(nums))  # [6, 10]

Now each instance can have its own rules:

f2 = EvenFilter(8)
print(f2(nums))  # [10]

🔥 So, __call__ turns an object into a "smart function" with memory and customizable logic. 💡

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collections.Counter — counting elements in a single line. 📊

Counting elements without loops with Counter 🔄

Do you need to count how many times each word appears in a text or how many duplicates there are in a list? Don't reinvent the wheel with for loops and dictionaries. The built-in collections module will do everything for you. 🚀

🛠 Code:

from collections import Counter

words = ["apple", "banana", "apple", "cherry", "banana", "apple"]
word_counts = Counter(words)

print(word_counts)
# Output: Counter({'apple': 3, 'banana': 2, 'cherry': 1})

# Bonus: the top 2 most frequent elements
print(word_counts.most_common(2))
# Output: [('apple', 3), ('banana', 2)]

Ideal for basic data analysis and solving tasks on LeetCode. 💻

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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

#Python #DataScience #Coding #Programming #LearnToCode #TechSkills

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Search for a substring in Python 🐍

In this example, two simple ways of finding a substring in a string are shown, which allow to solve the task without unnecessary code 💻

# Example implementation
def find_substring(text, sub):
    return text.find(sub)

#Python #Substring #Coding #DevCommunity #Programming #LearnToCode

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📌 How to make code cleaner with any() and all() 🐍

Do you often have to check lists for compliance with conditions? Forget about cumbersome loops! 🚫🔄

any() — returns True if at least one element is true. ✅
all() — returns True only if all elements are true. 🔒

# Example: checking if there are negative numbers
numbers = [1, 5, -3, 7]

# Bad: through a loop

has_negative = False
for num in numbers:
    if num < 0:
        has_negative = True

# Beautiful:

has_negative = any(num < 0 for num in numbers) # True ✨

What's the difference between is and == in Python?

The == operator checks whether the values of two objects are equal. In contrast, is determines whether variables refer to same object in memory. That is, == compares the content, while is checks the identity of the objects 🐍🔍

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🚀 Looking for a portfolio-ready NLP project?

I recently published an end-to-end walkthrough on Towards Data Science using Kaggle’s Spooky Author Identification dataset.

You’ll see how far classical NLP can go with:

📝 Bag-of-Words and TF-IDF
🔤 Character n-grams
📊 Model comparison
🧩 Ensemble stacking

It’s a practical project for anyone preparing for an ML/DS role, with no deep learning required. I walk through the entire workflow step by step:

🔗 https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/

💡 Replacing if-else with Match-Case

Starting with Python 3.10, we have a powerful tool: Structural Pattern Matching (match-case). This is not just an analog of switch-case from other languages; it's much more flexible. 🚀

Imagine you're writing a command handler for a bot. 🤖

❌ How NOT to do it:

def handle_command(command):
    if command == "start":
        return "Hello! I'm a bot."
    elif command == "help":
        return "Here's a list of available commands..."
    elif command == "stop":
        return "Goodbye!"
    else:
        return "Unknown command."

⚡ How to do it properly:

def handle_command(command):
    match command:
        case "start":
            return "Hello! I'm a bot."
        case "help":
            return "Here's a list of available commands..."
        case "stop":
            return "Goodbye!"
        case _:  # The underscore symbol catches everything else (default)
            return "Unknown command."

The code looks like a clear table, and your eye doesn't get caught up in a bunch of elif statements. 🧐
You can pass data structures in the case statements and check their structure and content on the fly. 🔍
It's easy to combine cases. 🧩

#Python #Programming #MatchCase #CodingTips #Python310 #Developer

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