#python tools

[habilelabs]

Let's discover Python and simplicity in programming. In this blog, explore the top 5 Python libraries for data science and get a brief overview of each.

[uncodemytraininginstitute]

The programming language Python has grown to be one of the most widely used in the world. Generators, decorators, and context managers are three advanced Python concepts that will be explored in this article. These concepts offer powerful tools for writing effective and elegant code. Learn more. https://www.vingle.net/posts/6430138

[fancykraken]

Tumblr users: you need to back up your tumblr sooner rather than later

Me, a tumblr user since 2010:

[kamrulislamsakib]

The Evolution of Hacking: From Cyberpunk Fantasy to Real-World Reality

Hacking has been a part of internet culture for as long as there’s been an internet to hack. For some, it’s a rebellious act—tearing down the walls built by corporations or governments. For others, it’s an artform, a way of playing with systems, seeing how far you can push boundaries. But what does hacking really mean today? And why does it still capture our imagination?

Historically, hacking has been portrayed as a dark art. Think back to movies like The Matrix or Hackers—hackers as antiheroes, taking down the system from the inside. But while these portrayals are often exaggerated, they did reflect a truth: hacking represents a desire to reclaim power and control. In a world where tech companies and governments collect personal data and monitor our every move, hacking is one way to level the playing field.

In the real world, hacking has taken on many forms. There’s ethical hacking, where people are hired to test systems for vulnerabilities, and there’s black-hat hacking, which involves illegal activities. But even those who engage in illegal hacking often see themselves as part of a larger rebellion against control and surveillance.

It’s easy to romanticize hacking, to see it as this cool, underground world of rebellious acts and revolutionary potential. But the reality is more complex. Hacking today isn’t just about taking down the man; it’s about finding flaws, building something new, or even just learning how things work beneath the surface. It’s about pushing boundaries, challenging authority, and questioning the systems that run our world.

And perhaps that’s the real reason hacking still resonates with so many people. It’s not just a skill or a rebellion—it’s a form of empowerment. It’s the ability to break free from the constraints placed on us, even if only for a moment.

In a world where everything feels so rigid, so controlled, hacking is the ultimate act of freedom. It’s a reminder that the digital world is malleable, that we can create our own paths through it, and that the rules are often made to be broken.

[a-wa-c]

After running Dead Beats a couple of times and dealing with the ever-changing numbers during combat, I wanted to make an Excel sheet with a bit of autofill and reminders for my own benefit.

Now I'm looking at it, and trying to get the stuff to work and I question whether I am better off trying to relearn Python and make a little widget for that instead.

How hard could it be?

[khuantru]

old vector drawing of Jackson Wang from 'GOT7'

song: Python 🔥🎧

[truebusiness]

Mastering OSINT: The Power of Open-Source Intelligence

Open-Source Intelligence (OSINT) is the art of gathering publicly available information from various online sources. Whether for cybersecurity, investigations, or research, OSINT provides valuable insights. In this blog, we’ll explore key OSINT techniques, tools, and best practices. 1. Sock Puppets: Creating an Anonymous Identity A sock puppet is a fake online identity used to gather…

[terviralide]

15 Aplikasi Coding Terbaik yang Wajib Dicoba di 2025

15 Aplikasi Coding Terbaik yang Wajib Dicoba di 2025 #Terviral.id – #15 Aplikasi Coding Terbaik yang Wajib Dicoba di 2025 – Semakin majunya teknologi membuat kebutuhan akan belajar dan bekerja sebagai #programmer semakin meningkat. Salah satu kunci sukses menjadi #developer andal adalah memiliki #tools dan aplikasi #coding yang nyaman dan sesuai kebutuhanmu. Di tahun 2025 ini, banyak sekali…

[bloggersmith]

Master Playwright Test Automation with Live Online Training

Playwright Test Automation in TypeScript/JavaScript. Join expert-led live online training to boost your QA career opportunities.

[learnerworld]

Code Smarter, Not Harder: Top AI Assistants in 2025

If you work in tech, you already know the drill: there’s always something new to catch up on — a library, a tool, a syntax update, or a surprise function you suddenly need to implement. It feels like a never-ending race, isn’t it?

The pressure to “keep up” is real. But here’s the shift: AI assistants are now helping us learn faster, grow more confidently, and adapt without burning out.

Let’s admit it — as humans, we have limits. Memory fades. Context-switching drains us. And no, we don’t have to be a walking storage device who remembers every syntax rule or function signature.

Instead, we need to act smart. Let these polite, efficient AI assistants help us code smarter — and think clearer. Many of these are free to start with, and you can always upgrade later if needed.

In this article, we’ll explore today’s top AI coding assistants, compare their unique strengths, and help you decide which tools are best suited for your workflow.

Why AI Coding Assistants Matter

AI coding assistants can now suggest, refactor, and even debug code in real time — transforming how developers write software

These tools:

·        Boost productivity by reducing repetitive coding tasks.

·        Improve code quality with intelligent suggestions.

·        Enhance collaboration by integrating with development environments.

·        Reduce errors by detecting vulnerabilities and optimizing code.

Top AI Coding Assistants in 2025

1. GitHub Copilot

Best for: General-purpose coding, multi-language support.

Key Features: Code completion, function suggestions, debugging, documentation generation.

USP: Most widely adopted AI coding assistant, integrated into VS Code & JetBrains.

Limitations: May occasionally generate incorrect or outdated code.

Data Privacy: Copilot for Business does not use code for training; personal usage may contribute.

Recently, GitHub Copilot Agent was also released — an upgrade over the original Copilot, offering more autonomous task execution, chat-based interactions, and deeper IDE integration for navigating code, running commands, and making decisions with minimal prompts.

2. Amazon CodeWhisperer

Best for: AWS-based development.

Key Features: Code completion, security vulnerability detection, AWS SDK integration.

USP: An assistant optimized for cloud-native applications.

Limitations: Less effective outside AWS ecosystem.

Data Privacy: Does not use customer code for training.

3. ChatGPT (Not specifically a Coding assistant but quite popular among learners for coding help)

Best for: Learning, debugging, code explanations.

Key Features: Conversational programming, multi-language support, code generation.

USP: Great for prototyping and explaining complex concepts.

Limitations: Not IDE-integrated, lacks real-time coding assistance.

Data Privacy: OpenAI may use interactions for training unless opted out.

Other Tools:

DeepCode (now part of Snyk Code) is widely used for security-focused static analysis. While I haven’t used it hands-on yet, developers praise its ability to catch subtle vulnerabilities early in the dev cycle.

Sourcery is gaining traction among Python developers for its ability to auto-refactor and improve code readability. It integrates with VSCode and PyCharm, and early user reviews highlight its value in maintaining clean codebases.

Cursor AI is positioned as a “Copilot alternative with deep file awareness.” While I’ve only explored it as such, it appears to focus heavily on project-wide understanding and autonomous generation.

Tabnine and Windsurf (formerly CodiumAI) have made strong cases for privacy-conscious and team-based AI development, respectively.

Where caution is still needed:

· Code quality and correctness: Code quality and correctness: AI can be confidently wrong. Sometimes, I get code that looks perfect, but fails because it uses a function that does not even exist. You need to review and test the code (It will help you there too!)

· Security blind spots: Most tools aren’t security-aware by default. They might generate code that works, but doesn’t sanitize inputs or handle edge cases.

· Enterprise concerns: Teams are still wary of using AI-generated code from tools trained on public repositories. Licensing, IP ownership, and data privacy are legitimate considerations.

Final Thoughts

AI coding assistants are revolutionizing software development, making coding faster, smarter, and more efficient. Whether you're a seasoned developer or just starting out, leveraging these tools can enhance your workflow and boost productivity.

The future is not AI versus developers — it’s developers with AI, building better software together.  The question now is how to adopt them responsibly, not whether to use them at all.

Have you explored any AI coding assistants yet? Which one do you use, and what’s your experience with it?

If you haven’t tried one, have you heard about them? What’s holding you back — trust, accuracy, privacy, or just not the right time? Drop your thoughts in the comments!

[infoanalysishub]

How to Analyze Data Effectively – A Complete Step-by-Step Guide

Learn how to analyze data in a structured, insightful way. From data cleaning to visualization, discover tools, techniques, and real-world examples. How to Analyze Data Effectively – A Complete Step-by-Step Guide Data analysis is the cornerstone of decision-making in the modern world. Whether in business, science, healthcare, education, or government, data informs strategies, identifies trends,…

[habilelabs]

Have you worked on Python and Django in the project? Let's discuss few points which will make you a better Python developer and help you to get a better opportunity.

[bigleapblog]

Learn Finance Tools: Power BI, Python, and Excel at Imperial

[codedetect]

Codequiry: The Trusted Python Plagiarism Checker Tool

Codequiry is an advanced Python plagiarism checker trusted by educators, developers, and institutions. It scans Python code for copied or modified patterns using advanced similarity detection, including peer and online comparisons. Built for accuracy and ease of use, Codequiry ensures code originality and helps maintain academic and professional integrity across all Python programming projects. For more details, please visit our website.

[catchexperts]

Web Scraping 101: Everything You Need to Know in 2025

🕸️ What Is Web Scraping? An Introduction

Web scraping—also referred to as web data extraction—is the process of collecting structured information from websites using automated scripts or tools. Initially driven by simple scripts, it has now evolved into a core component of modern data strategies for competitive research, price monitoring, SEO, market intelligence, and more.

If you’re wondering “What is the introduction of web scraping?” — it’s this: the ability to turn unstructured web content into organized datasets businesses can use to make smarter, faster decisions.

💡 What Is Web Scraping Used For?

Businesses and developers alike use web scraping to:

Monitor competitors’ pricing and SEO rankings

Extract leads from directories or online marketplaces

Track product listings, reviews, and inventory

Aggregate news, blogs, and social content for trend analysis

Fuel AI models with large datasets from the open web

Whether it’s web scraping using Python, browser-based tools, or cloud APIs, the use cases are growing fast across marketing, research, and automation.

🔍 Examples of Web Scraping in Action

What is an example of web scraping?

A real estate firm scrapes listing data (price, location, features) from property websites to build a market dashboard.

An eCommerce brand scrapes competitor prices daily to adjust its own pricing in real time.

A SaaS company uses BeautifulSoup in Python to extract product reviews and social proof for sentiment analysis.

For many, web scraping is the first step in automating decision-making and building data pipelines for BI platforms.

⚖️ Is Web Scraping Legal?

Yes—if done ethically and responsibly. While scraping public data is legal in many jurisdictions, scraping private, gated, or copyrighted content can lead to violations.

To stay compliant:

Respect robots.txt rules

Avoid scraping personal or sensitive data

Prefer API access where possible

Follow website terms of service

If you’re wondering “Is web scraping legal?”—the answer lies in how you scrape and what you scrape.

🧠 Web Scraping with Python: Tools & Libraries

What is web scraping in Python? Python is the most popular language for scraping because of its ease of use and strong ecosystem.

Popular Python libraries for web scraping include:

BeautifulSoup – simple and effective for HTML parsing

Requests – handles HTTP requests

Selenium – ideal for dynamic JavaScript-heavy pages

Scrapy – robust framework for large-scale scraping projects

Puppeteer (via Node.js) – for advanced browser emulation

These tools are often used in tutorials like “Web scraping using Python BeautifulSoup” or “Python web scraping library for beginners.”

⚙️ DIY vs. Managed Web Scraping

You can choose between:

DIY scraping: Full control, requires dev resources

Managed scraping: Outsourced to experts, ideal for scale or non-technical teams

Use managed scraping services for large-scale needs, or build Python-based scrapers for targeted projects using frameworks and libraries mentioned above.

🚧 Challenges in Web Scraping (and How to Overcome Them)

Modern websites often include:

JavaScript rendering

CAPTCHA protection

Rate limiting and dynamic loading

To solve this:

Use rotating proxies

Implement headless browsers like Selenium

Leverage AI-powered scraping for content variation and structure detection

Deploy scrapers on cloud platforms using containers (e.g., Docker + AWS)

🔐 Ethical and Legal Best Practices

Scraping must balance business innovation with user privacy and legal integrity. Ethical scraping includes:

Minimal server load

Clear attribution

Honoring opt-out mechanisms

This ensures long-term scalability and compliance for enterprise-grade web scraping systems.

🔮 The Future of Web Scraping

As demand for real-time analytics and AI training data grows, scraping is becoming:

Smarter (AI-enhanced)

Faster (real-time extraction)

Scalable (cloud-native deployments)

From developers using BeautifulSoup or Scrapy, to businesses leveraging API-fed dashboards, web scraping is central to turning online information into strategic insights.

📘 Summary: Web Scraping 101 in 2025

Web scraping in 2025 is the automated collection of website data, widely used for SEO monitoring, price tracking, lead generation, and competitive research. It relies on powerful tools like BeautifulSoup, Selenium, and Scrapy, especially within Python environments. While scraping publicly available data is generally legal, it's crucial to follow website terms of service and ethical guidelines to avoid compliance issues. Despite challenges like dynamic content and anti-scraping defenses, the use of AI and cloud-based infrastructure is making web scraping smarter, faster, and more scalable than ever—transforming it into a cornerstone of modern data strategies.

🔗 Want to Build or Scale Your AI-Powered Scraping Strategy?

Whether you're exploring AI-driven tools, training models on web data, or integrating smart automation into your data workflows—AI is transforming how web scraping works at scale.

👉 Find AI Agencies specialized in intelligent web scraping on Catch Experts,

📲 Stay connected for the latest in AI, data automation, and scraping innovation:

💼 LinkedIn

🐦 Twitter

📸 Instagram

👍 Facebook

▶️ YouTube

[womaneng]

View this post on Instagram

A post shared by Zeynep Küçük Woman Engineer (@woman.engineer)

💁🏻‍♀️If you’re still juggling tools, context windows, and messy pull requests… 

Let me put you on something better 👇

✅ Plug-and-play with your stack Qodo lives inside your IDE + Git. No switching tabs, no workflow chaos. Just clean, fast integration.

🧠 Quality-first AI This isn’t just autocomplete. Qodo understands architecture, catches edge cases, and writes code you’d actually review and approve.

🛠️ Built-in smart code reviews  Find issues before they hit your PR. It’s like having a senior dev on standby.

👥 Team-friendly from day one  Stay consistent across large teams and complex repos — Qodo Merge makes collaboration frictionless.

✨ Been using it for a year. Not looking back. 

🤩If you’re a dev, you owe it to yourself to try Qodo Merge.

✨Qodo’s RAG system brings real-time, scalable context to your engineering workflow. From monorepos to microservices, get the right code insights, right when you need them. Less guesswork. More clarity. Better code. 🚀