#web Scraping YouTube Data

[scrapingdog]

Learn how to extract property listings, prices, and more from real estate websites using web scraping tools—no coding needed!

[helshollowhalls]

This also applies to the people who think it's a good idea to scrape fanfic off of AO3 and sell the data to train AI, btw.

Some things might be free and publicly available, but that doesn't mean they're yours. I repeat.

Some things might be free and publicly available, but that doesn't mean they're yours.

is da wittle openai upsetti spaghetti uwu

Look, you let your AI scrape art off the web without having the artist's consent. Those artists are well within their right to fight back.

And now you are labeling that 'abuse'? Get for fucking real. Who started this entire shitshow? We didn't painstakingly hand feed our own art to your stupid AI, only to turn around and start smear campaigns on twitter for shits and giggles.

I think some people are forgetting that the internet and everything on it isn't theirs to take for free and without consequences. There's stuff on the world wide web that yes, is available to look at for free but someone still has the rights to it.

Just because something is available publicly online doesn't mean you can take it if you so desire and suffer no consequences. Just because you paid for something online doesn't mean you now own it in it's entirety and can do with it whatever you want regardless of if there were any rules and stipulations attached to the transaction (i.e. sharing content paywalled on patreon or YT memberships on reddit or what have you).

The internet isn't your playground and if you choose to behave like a bratty five year old stealing tools from the other kids playing in the sand box, you have no right to point fingers if they fight back and you end up with buckets full of sand hitting you in the face.

You dug yourself that hole by disrespecting - abusing - an artist's right to their own work. Now deal with the consequences.

[mariacallous]

Are there generative AI tools I can use that are perhaps slightly more ethical than others? —Better Choices

No, I don't think any one generative AI tool from the major players is more ethical than any other. Here’s why.

For me, the ethics of generative AI use can be broken down to issues with how the models are developed—specifically, how the data used to train them was accessed—as well as ongoing concerns about their environmental impact. In order to power a chatbot or image generator, an obscene amount of data is required, and the decisions developers have made in the past—and continue to make—to obtain this repository of data are questionable and shrouded in secrecy. Even what people in Silicon Valley call “open source” models hide the training datasets inside.

Despite complaints from authors, artists, filmmakers, YouTube creators, and even just social media users who don’t want their posts scraped and turned into chatbot sludge, AI companies have typically behaved as if consent from those creators isn’t necessary for their output to be used as training data. One familiar claim from AI proponents is that to obtain this vast amount of data with the consent of the humans who crafted it would be too unwieldy and would impede innovation. Even for companies that have struck licensing deals with major publishers, that “clean” data is an infinitesimal part of the colossal machine.

Although some devs are working on approaches to fairly compensate people when their work is used to train AI models, these projects remain fairly niche alternatives to the mainstream behemoths.

And then there are the ecological consequences. The current environmental impact of generative AI usage is similarly outsized across the major options. While generative AI still represents a small slice of humanity's aggregate stress on the environment, gen-AI software tools require vastly more energy to create and run than their non-generative counterparts. Using a chatbot for research assistance is contributing much more to the climate crisis than just searching the web in Google.

It’s possible the amount of energy required to run the tools could be lowered—new approaches like DeepSeek’s latest model sip precious energy resources rather than chug them—but the big AI companies appear more interested in accelerating development than pausing to consider approaches less harmful to the planet.

How do we make AI wiser and more ethical rather than smarter and more powerful? —Galaxy Brain

Thank you for your wise question, fellow human. This predicament may be more of a common topic of discussion among those building generative AI tools than you might expect. For example, Anthropic’s “constitutional” approach to its Claude chatbot attempts to instill a sense of core values into the machine.

The confusion at the heart of your question traces back to how we talk about the software. Recently, multiple companies have released models focused on ��reasoning” and “chain-of-thought” approaches to perform research. Describing what the AI tools do with humanlike terms and phrases makes the line between human and machine unnecessarily hazy. I mean, if the model can truly reason and have chains of thoughts, why wouldn’t we be able to send the software down some path of self-enlightenment?

Because it doesn’t think. Words like reasoning, deep thought, understanding—those are all just ways to describe how the algorithm processes information. When I take pause at the ethics of how these models are trained and the environmental impact, my stance isn’t based on an amalgamation of predictive patterns or text, but rather the sum of my individual experiences and closely held beliefs.

The ethical aspects of AI outputs will always circle back to our human inputs. What are the intentions of the user’s prompts when interacting with a chatbot? What were the biases in the training data? How did the devs teach the bot to respond to controversial queries? Rather than focusing on making the AI itself wiser, the real task at hand is cultivating more ethical development practices and user interactions.

[jbfly46]

Your All-in-One AI Web Agent: Save $200+ a Month, Unleash Limitless Possibilities!

Imagine having an AI agent that costs you nothing monthly, runs directly on your computer, and is unrestricted in its capabilities. OpenAI Operator charges up to $200/month for limited API calls and restricts access to many tasks like visiting thousands of websites. With DeepSeek-R1 and Browser-Use, you:

• Save money while keeping everything local and private.

• Automate visiting 100,000+ websites, gathering data, filling forms, and navigating like a human.

• Gain total freedom to explore, scrape, and interact with the web like never before.

You may have heard about Operator from Open AI that runs on their computer in some cloud with you passing on private information to their AI to so anything useful. AND you pay for the gift . It is not paranoid to not want you passwords and logins and personal details to be shared. OpenAI of course charges a substantial amount of money for something that will limit exactly what sites you can visit, like YouTube for example. With this method you will start telling an AI exactly what you want it to do, in plain language, and watching it navigate the web, gather information, and make decisions—all without writing a single line of code.

In this guide, we’ll show you how to build an AI agent that performs tasks like scraping news, analyzing social media mentions, and making predictions using DeepSeek-R1 and Browser-Use, but instead of writing a Python script, you’ll interact with the AI directly using prompts.

These instructions are in constant revisions as DeepSeek R1 is days old. Browser Use has been a standard for quite a while. This method can be for people who are new to AI and programming. It may seem technical at first, but by the end of this guide, you’ll feel confident using your AI agent to perform a variety of tasks, all by talking to it. how, if you look at these instructions and it seems to overwhelming, wait, we will have a single download app soon. It is in testing now.

This is version 3.0 of these instructions January 26th, 2025.

This guide will walk you through setting up DeepSeek-R1 8B (4-bit) and Browser-Use Web UI, ensuring even the most novice users succeed.

What You’ll Achieve

By following this guide, you’ll:

1. Set up DeepSeek-R1, a reasoning AI that works privately on your computer.

2. Configure Browser-Use Web UI, a tool to automate web scraping, form-filling, and real-time interaction.

3. Create an AI agent capable of finding stock news, gathering Reddit mentions, and predicting stock trends—all while operating without cloud restrictions.

A Deep Dive At ReadMultiplex.com Soon

We will have a deep dive into how you can use this platform for very advanced AI use cases that few have thought of let alone seen before. Join us at ReadMultiplex.com and become a member that not only sees the future earlier but also with particle and pragmatic ways to profit from the future.

System Requirements

Hardware

• RAM: 8 GB minimum (16 GB recommended).

• Processor: Quad-core (Intel i5/AMD Ryzen 5 or higher).

• Storage: 5 GB free space.

• Graphics: GPU optional for faster processing.

Software

• Operating System: macOS, Windows 10+, or Linux.

• Python: Version 3.8 or higher.

• Git: Installed.

Step 1: Get Your Tools Ready

We’ll need Python, Git, and a terminal/command prompt to proceed. Follow these instructions carefully.

Install Python

1. Check Python Installation:

• Open your terminal/command prompt and type:

python3 --version

• If Python is installed, you’ll see a version like:

Python 3.9.7

2. If Python Is Not Installed:

• Download Python from python.org.

• During installation, ensure you check “Add Python to PATH” on Windows.

3. Verify Installation:

python3 --version

Install Git

1. Check Git Installation:

• Run:

git --version

• If installed, you’ll see:

git version 2.34.1

2. If Git Is Not Installed:

• Windows: Download Git from git-scm.com and follow the instructions.

• Mac/Linux: Install via terminal:

sudo apt install git -y # For Ubuntu/Debian

brew install git # For macOS

Step 2: Download and Build llama.cpp

We’ll use llama.cpp to run the DeepSeek-R1 model locally.

1. Open your terminal/command prompt.

2. Navigate to a clear location for your project files:

mkdir ~/AI_Project

cd ~/AI_Project

3. Clone the llama.cpp repository:

git clone https://github.com/ggerganov/llama.cpp.git

cd llama.cpp

4. Build the project:

• Mac/Linux:

make

• Windows:

• Install a C++ compiler (e.g., MSVC or MinGW).

• Run:

mkdir build

cd build

cmake ..

cmake --build . --config Release

Step 3: Download DeepSeek-R1 8B 4-bit Model

1. Visit the DeepSeek-R1 8B Model Page on Hugging Face.

2. Download the 4-bit quantized model file:

• Example: DeepSeek-R1-Distill-Qwen-8B-Q4_K_M.gguf.

3. Move the model to your llama.cpp folder:

mv ~/Downloads/DeepSeek-R1-Distill-Qwen-8B-Q4_K_M.gguf ~/AI_Project/llama.cpp

Step 4: Start DeepSeek-R1

1. Navigate to your llama.cpp folder:

cd ~/AI_Project/llama.cpp

2. Run the model with a sample prompt:

./main -m DeepSeek-R1-Distill-Qwen-8B-Q4_K_M.gguf -p "What is the capital of France?"

3. Expected Output:

The capital of France is Paris.

Step 5: Set Up Browser-Use Web UI

1. Go back to your project folder:

cd ~/AI_Project

2. Clone the Browser-Use repository:

git clone https://github.com/browser-use/browser-use.git

cd browser-use

3. Create a virtual environment:

python3 -m venv env

4. Activate the virtual environment:

• Mac/Linux:

source env/bin/activate

• Windows:

env\Scripts\activate

5. Install dependencies:

pip install -r requirements.txt

6. Start the Web UI:

python examples/gradio_demo.py

7. Open the local URL in your browser:

http://127.0.0.1:7860

Step 6: Configure the Web UI for DeepSeek-R1

1. Go to the Settings panel in the Web UI.

2. Specify the DeepSeek model path:

~/AI_Project/llama.cpp/DeepSeek-R1-Distill-Qwen-8B-Q4_K_M.gguf

3. Adjust Timeout Settings:

• Increase the timeout to 120 seconds for larger models.

4. Enable Memory-Saving Mode if your system has less than 16 GB of RAM.

Step 7: Run an Example Task

Let’s create an agent that:

1. Searches for Tesla stock news.

2. Gathers Reddit mentions.

3. Predicts the stock trend.

Example Prompt:

Search for "Tesla stock news" on Google News and summarize the top 3 headlines. Then, check Reddit for the latest mentions of "Tesla stock" and predict whether the stock will rise based on the news and discussions.

--

Congratulations! You’ve built a powerful, private AI agent capable of automating the web and reasoning in real time. Unlike costly, restricted tools like OpenAI Operator, you’ve spent nothing beyond your time. Unleash your AI agent on tasks that were once impossible and imagine the possibilities for personal projects, research, and business. You’re not limited anymore. You own the web—your AI agent just unlocked it! 🚀

Stay tuned fora FREE simple to use single app that will do this all and more.

[rikaklassen]

Cakelin Fable over at TikTok scraped the information from Project N95 a few months ago after Project N95 announcing shutting down December 18, 2023 (archived copy of New York Times article) then compiled the data into an Excel spreadsheet [.XLSX, 18.2 MB] with Patrick from PatricktheBioSTEAMist. 

You can access the back up files above.

The webpage is archived to Wayback Machine.

The code for the web-scraping project can be found over at GitHub.

Cakelin's social media details:

Website

Beacons

TikTok

Notion

Medium

Substack

X/Twitter

Bluesky

Instagram

Pinterest

GitHub

Redbubble

Cash App

Patrick's social media details:

Linktree

YouTube

TikTok

Notion

Venmo

[pythontraininginchdsblog]

Learn Python Programming | Start Your Coding Journey

In the modern tech-driven world, learning programming is no longer just an added skill—it’s a necessity. Whether you’re a student, a working professional, or someone looking to make a career shift, mastering a programming language can be your ticket to numerous high-paying opportunities. Among all the programming languages available today, Python stands out as one of the most versatile, beginner-friendly, and powerful options. If you're thinking of diving into the world of coding, there's no better place to start than with Python.

This blog will guide you through the benefits of learning Python, what makes it an ideal first language, and how you can kickstart your journey, especially if you're based in a tech-savvy hub like Chandigarh.

Why Choose Python?

Python is a high-level, interpreted programming language known for its simple syntax and readability. It is widely used in various domains such as web development, data science, artificial intelligence, automation, game development, and more. Here are a few reasons why Python is the perfect starting point:

Easy to Learn and Use: Python’s syntax is clean and closely resembles the English language, making it easier for beginners to understand and write code without getting overwhelmed.

Massive Community Support: With millions of users worldwide, Python has a vibrant community. You’ll find endless tutorials, forums, libraries, and documentation to support your learning.

Versatile and Powerful: Python is used by some of the biggest tech giants like Google, Netflix, Facebook, and Instagram. Its applications range from small scripts to large-scale enterprise solutions.

High Demand in Job Market: Python developers are in high demand across industries, making it a valuable skill to add to your resume.

What You Can Build with Python

The real magic of Python lies in its wide range of applications. Once you grasp the fundamentals, you can dive into a variety of projects and fields, such as:

Web Development: Using frameworks like Django and Flask, you can create powerful web applications.

Data Science and Machine Learning: Python is the go-to language for data scientists. Libraries like Pandas, NumPy, Scikit-learn, and TensorFlow make complex tasks easier.

Automation and Scripting: Automate repetitive tasks, manage files, scrape websites, and more with simple Python scripts.

Game Development: Python frameworks such as Pygame let you design and build basic games.

IoT and Robotics: Python is frequently used in Raspberry Pi projects and robotics, making it ideal for tech enthusiasts and hobbyists.

How to Start Learning Python

Starting your Python journey requires a mix of theoretical knowledge and hands-on practice. Here’s a structured approach for beginners:

Understand the Basics:

Learn variables, data types, operators, and control structures (if-else, loops).

Practice functions, lists, tuples, dictionaries, and sets.

Explore Advanced Topics:

Object-Oriented Programming (OOP)

Exception handling

File handling

Get Hands-On:

Work on mini-projects like a calculator, to-do app, or contact book.

Explore real-life scenarios where you can apply Python.

Use Online Resources and Courses:

Platforms like Coursera, Udemy, and Codecademy offer quality Python courses.

YouTube channels, coding blogs, and interactive platforms like HackerRank and LeetCode are excellent for practice.

Join a Python Course or Training Institute:

To accelerate your learning, consider joining a dedicated training institute that offers structured learning, mentorship, and certification.

Why Join a Python Course in Chandigarh?

Chandigarh has emerged as a major IT and educational hub in North India. For learners in the region, enrolling in a reputed institute for Python training can provide numerous advantages:

Personalized Learning Experience: With expert mentors guiding you, you can avoid common pitfalls and gain clarity on complex topics.

Practical Exposure: Institutes often include live projects, internships, and hands-on training, giving you a taste of real-world applications.

Career Assistance: From resume building to mock interviews, reputed institutes help bridge the gap between learning and landing your first job.

Certification: A recognized certificate in Python programming adds significant value to your portfolio.

In the heart of this growing educational ecosystem lies an excellent opportunity for aspiring programmers. If you're seeking a reliable and practical training option, enrolling in a Python Training in Chandigarh program can give you a structured and career-focused learning path.

Moreover, if you're looking for a course that covers everything from the basics to advanced concepts, a comprehensive python course in chandigarh is exactly what you need to master the language.

What to Look for in a Good Python Course

Choosing the right course can significantly affect how fast and how well you learn Python. Here are a few things to consider:

Curriculum Depth: Ensure the course covers both fundamentals and advanced topics.

Project-Based Learning: Real-world projects help solidify your understanding.

Experienced Trainers: Look for courses led by industry professionals or certified trainers.

Flexible Learning Options: Online and offline classes, weekend batches, and recorded lectures can be useful for working professionals or students.

Support and Community: A good course provides access to forums, one-on-one doubt sessions, and mentorship.

Python Certification and Career Opportunities

After completing your Python course, it’s essential to validate your knowledge through certification. Many online platforms and training institutes offer certifications that are recognized by employers. Some internationally acknowledged certifications include:

PCEP – Certified Entry-Level Python Programmer

PCAP – Certified Associate in Python Programming

Microsoft Python Certification

Having one or more of these certifications can greatly enhance your resume and increase your chances of landing a job in fields such as:

Software Development

Data Science

Artificial Intelligence

Backend Web Development

Automation Engineering

QA Testing

Cybersecurity

Final Thoughts

Python is more than just a programming language—it’s a gateway to some of the most exciting and lucrative careers in today’s digital economy. Its beginner-friendly nature, coupled with its wide range of applications, makes it the ideal first language for anyone looking to enter the world of coding.

If you're serious about upgrading your skills or stepping into the IT industry, start with Python. Learn the basics, build projects, earn a certification, and you'll find doors opening in web development, data science, AI, automation, and beyond.

And if you're located in or around Chandigarh, don’t miss the opportunity to enroll in a Python Training in Chandigarh program that provides hands-on learning, mentorship, and career guidance. Start your coding journey today by choosing the right python course in chandigarh that aligns with your goals.

Stay curious, keep coding, and let Python be the foundation of your digital future!

[jcmarchi]

Denas Grybauskas, Chief Governance and Strategy Officer at Oxylabs – Interview Series

New Post has been published on https://thedigitalinsider.com/denas-grybauskas-chief-governance-and-strategy-officer-at-oxylabs-interview-series/

Denas Grybauskas, Chief Governance and Strategy Officer at Oxylabs – Interview Series

Denas Grybauskas is the Chief Governance and Strategy Officer at Oxylabs, a global leader in web intelligence collection and premium proxy solutions.

Founded in 2015, Oxylabs provides one of the largest ethically sourced proxy networks in the world—spanning over 177 million IPs across 195 countries—along with advanced tools like Web Unblocker, Web Scraper API, and OxyCopilot, an AI-powered scraping assistant that converts natural language into structured data queries.

You’ve had an impressive legal and governance journey across Lithuania’s legal tech space. What personally motivated you to tackle one of AI’s most polarising challenges—ethics and copyright—in your role at Oxylabs?

Oxylabs have always been the flagbearer for responsible innovation in the industry. We were the first to advocate for ethical proxy sourcing and web scraping industry standards. Now, with AI moving so fast, we must make sure that innovation is balanced with responsibility.

We saw this as a huge problem facing the AI industry, and we could also see the solution. By providing these datasets, we’re enabling AI companies and creators to be on the same page regarding fair AI development, which is beneficial for everyone involved. We knew how important it was to keep creators’ rights at the forefront but also provide content for the development of future AI systems, so we created these datasets as something that can meet the demands of today’s market.

The UK is in the midst of a heated copyright battle, with strong voices on both sides. How do you interpret the current state of the debate between AI innovation and creator rights?

While it’s important that the UK government favours productive technological innovation as a priority, it’s vital that creators should feel enhanced and protected by AI, not stolen from. The legal framework currently under debate must find a sweet spot between fostering innovation and, at the same time, protecting the creators, and I hope in the coming weeks we see them find a way to strike a balance.

Oxylabs has just launched the world’s first ethical YouTube datasets, which requires creator consent for AI training. How exactly does this consent process work—and how scalable is it for other industries like music or publishing?

All of the millions of original videos in the datasets have the explicit consent of the creators to be used for AI training, connecting creators and innovators ethically. All datasets offered by Oxylabs include videos, transcripts, and rich metadata. While such data has many potential use cases, Oxylabs refined and prepared it specifically for AI training, which is the use that the content creators have knowingly agreed to.

Many tech leaders argue that requiring explicit opt-in from all creators could “kill” the AI industry. What’s your response to that claim, and how does Oxylabs’ approach prove otherwise?

Requiring that, for every usage of material for AI training, there be a previous explicit opt-in presents significant operational challenges and would come at a significant cost to AI innovation. Instead of protecting creators’ rights, it could unintentionally incentivize companies to shift development activities to jurisdictions with less rigorous enforcement or differing copyright regimes. However, this does not mean that there can be no middle ground where AI development is encouraged while copyright is respected. On the contrary, what we need are workable mechanisms that simplify the relationship between AI companies and creators.

These datasets offer one approach to moving forward. The opt-out model, according to which content can be used unless the copyright owner explicitly opts out, is another. The third way would be facilitating deal-making between publishers, creators, and AI companies through technological solutions, such as online platforms.

Ultimately, any solution must operate within the bounds of applicable copyright and data protection laws. At Oxylabs, we believe AI innovation must be pursued responsibly, and our goal is to contribute to lawful, practical frameworks that respect creators while enabling progress.

What were the biggest hurdles your team had to overcome to make consent-based datasets viable?

The path for us was opened by YouTube, enabling content creators to easily and conveniently license their work for AI training. After that, our work was mostly technical, involving gathering data, cleaning and structuring it to prepare the datasets, and building the entire technical setup for companies to access the data they needed. But this is something that we’ve been doing for years, in one way or another. Of course, each case presents its own set of challenges, especially when you’re dealing with something as huge and complex as multimodal data. But we had both the knowledge and the technical capacity to do this. Given this, once YouTube authors got the chance to give consent, the rest was only a matter of putting our time and resources into it.

Beyond YouTube content, do you envision a future where other major content types—such as music, writing, or digital art—can also be systematically licensed for use as training data?

For a while now, we have been pointing out the need for a systematic approach to consent-giving and content-licensing in order to enable AI innovation while balancing it with creator rights. Only when there is a convenient and cooperative way for both sides to achieve their goals will there be mutual benefit.

This is just the beginning. We believe that providing datasets like ours across a range of industries can provide a solution that finally brings the copyright debate to an amicable close.

Does the importance of offerings like Oxylabs’ ethical datasets vary depending on different AI governance approaches in the EU, the UK, and other jurisdictions?

On the one hand, the availability of explicit-consent-based datasets levels the field for AI companies based in jurisdictions where governments lean toward stricter regulation. The primary concern of these companies is that, rather than supporting creators, strict rules for obtaining consent will only give an unfair advantage to AI developers in other jurisdictions. The problem is not that these companies don’t care about consent but rather that without a convenient way to obtain it, they are doomed to lag behind.

On the other hand, we believe that if granting consent and accessing data licensed for AI training is simplified, there is no reason why this approach should not become the preferred way globally. Our datasets built on licensed YouTube content are a step toward this simplification.

With growing public distrust toward how AI is trained, how do you think transparency and consent can become competitive advantages for tech companies?

Although transparency is often seen as a hindrance to competitive edge, it’s also our greatest weapon to fight mistrust. The more transparency AI companies can provide, the more evidence there is for ethical and beneficial AI training, thereby rebuilding trust in the AI industry. And in turn, creators seeing that they and the society can get value from AI innovation will have more reason to give consent in the future.

Oxylabs is often associated with data scraping and web intelligence. How does this new ethical initiative fit into the broader vision of the company?

The release of ethically sourced YouTube datasets continues our mission at Oxylabs to establish and promote ethical industry practices. As part of this, we co-founded the Ethical Web Data Collection Initiative (EWDCI) and introduced an industry-first transparent tier framework for proxy sourcing. We also launched Project 4β as part of our mission to enable researchers and academics to maximise their research impact and enhance the understanding of critical public web data.

Looking ahead, do you think governments should mandate consent-by-default for training data, or should it remain a voluntary industry-led initiative?

In a free market economy, it is generally best to let the market correct itself. By allowing innovation to develop in response to market needs, we continually reinvent and renew our prosperity. Heavy-handed legislation is never a good first choice and should only be resorted to when all other avenues to ensure justice while allowing innovation have been exhausted.

It doesn’t look like we have already reached that point in AI training. YouTube’s licensing options for creators and our datasets demonstrate that this ecosystem is actively seeking ways to adapt to new realities. Thus, while clear regulation is, of course, needed to ensure that everyone acts within their rights, governments might want to tread lightly. Rather than requiring expressed consent in every case, they might want to examine the ways industries can develop mechanisms for resolving the current tensions and take their cues from that when legislating to encourage innovation rather than hinder it.

What advice would you offer to startups and AI developers who want to prioritise ethical data use without stalling innovation?

One way startups can help facilitate ethical data use is by developing technological solutions that simplify the process of obtaining consent and deriving value for creators. As options to acquire transparently sourced data emerge, AI companies need not compromise on speed; therefore, I advise them to keep their eyes open for such offerings.

 Thank you for the great interview, readers who wish to learn more should visit Oxylabs.

[bigdatascraping]

BIGDATASCRAPING

Powerful web scraping platform for regular and professional use, offering high-performance data extraction from any website. Supports collection and analysis of data from diverse sources with flexible export formats, seamless integrations, and custom solutions. Features specialized scrapers for Google Maps, Instagram, Twitter (X), YouTube, Facebook, LinkedIn, TikTok, Yelp, TripAdvisor, and Google News, designed for enterprise-level needs with prioritized support.

[harshats]

ADVANCE SEO

An Advanced SEO Course is designed for professionals, marketers, and business owners who already have a foundational understanding of SEO and want to take their skills to the next level. These courses cover advanced strategies, technical optimizations, data analysis, and cutting-edge trends to help websites rank higher and drive more organic traffic.

What You’ll Learn in an Advanced SEO Course:

Technical SEO Deep Dive

Site architecture optimization

Advanced schema markup (JSON-LD)

Core Web Vitals & page speed optimizations

JavaScript SEO & rendering issues

Canonicalization & hreflang implementation

Advanced Keyword & Content Strategy

Semantic search & NLP (Natural Language Processing)

Topic clustering & pillar-page strategies

Advanced competitor keyword gap analysis

AI-powered content optimization

Link Building & Off-Page SEO

Advanced link prospecting & outreach strategies

HARO (Help a Reporter Out) & digital PR

Skyscraper technique & broken link building

Spam link detection & disavow best practices

Data-Driven SEO & Automation

Google Search Console & GA4 deep analysis

Python for SEO (automating tasks, scraping data)

Predictive SEO & forecasting traffic

Rank tracking & SERP feature targeting

E-A-T & Algorithm Updates

Google’s E-A-T (Expertise, Authoritativeness, Trustworthiness)

Surviving Google algorithm updates (Helpful Content Update, Core Updates)

Local SEO & Google Business Profile optimization

International & Enterprise SEO

Multi-regional & multilingual SEO strategies

Handling large-scale websites (eCommerce, SaaS)

Managing SEO for CMS platforms (WordPress, Shopify, etc.)

Best Advanced SEO Courses (Paid & Free)

Paid Courses:

Ahrefs Academy (Free & Paid) – Advanced link building & keyword research

Moz SEO Training – Technical SEO & local SEO

SEMrush Academy – Competitive SEO & PPC integration

SEO That Works (Backlinko – Brian Dean) – Advanced SEO strategies

Udemy – Advanced SEO: Take Your Skills to the Next Level

Free Resources:

Google’s SEO Starter Guide (Advanced sections)

Search Engine Journal / Search Engine Land (Advanced guides)

YouTube Channels: Ahrefs, Moz, Neil Patel

Who Should Take an Advanced SEO Course?

SEO specialists looking to upskill

Digital marketers managing large websites

Content marketers & bloggers aiming for top rankings

Web developers handling technical SEO

[actowizsolutions0]

Automated Web Scraping Services for Smarter Insights

Transforming Business Intelligence with Automated Web Scraping Services

In today’s data-driven economy, staying ahead means accessing the right information—fast and at scale. At Actowiz Solutions, we specialize in delivering automated web scraping solutions that help businesses across ecommerce, real estate, social platforms, and B2B directories gain a competitive edge through real-time insights.

Let’s explore how automation, AI, and platform-specific scraping are revolutionizing industries.

Why Automate Web Scraping?

Manually collecting data from websites is time-consuming and inefficient. With our automated web scraping services, powered by Microsoft Power Automate, you can streamline large-scale data collection processes—perfect for businesses needing continuous access to product listings, customer reviews, or market trends.

ChatGPT for Web Scraping: AI Meets Automation

Leveraging the capabilities of AI, our solution for ChatGPT web scraping simplifies complex scraping workflows. From writing extraction scripts to generating data patterns dynamically, ChatGPT helps reduce development time while improving efficiency and accuracy.

eBay Web Scraper for E-commerce Sellers

Whether you're monitoring competitor pricing or extracting product data, our dedicated eBay web scraper provides access to structured data from one of the world’s largest marketplaces. It’s ideal for sellers, analysts, and aggregators who rely on updated eBay information.

Extract Trends and Consumer Preferences with Precision

Tracking what’s hot across categories is critical for strategic planning. Our services allow businesses to extract marketplace trends, helping you make smarter stocking, marketing, and pricing decisions.

Use a Review Scraper to Analyze Customer Sentiment

Understanding customer feedback has never been easier. Our review scraper pulls reviews and ratings from platforms like Google, giving you valuable insight into brand perception and service performance.

Scrape YouTube Comments for Audience Insights

If you're running video marketing campaigns, you need feedback at scale. With our YouTube comments scraper, built using Selenium and Python, you can monitor user engagement, sentiment, and trending topics in real-time.

TikTok Scraping with Python for Viral Content Discovery

TikTok trends move fast—our TikTok scraping in Python service helps brands and analysts extract video metadata, hashtags, and engagement stats to stay ahead of viral trends.

Extract Business Leads with TradeIndia Data

For B2B marketers, sourcing accurate leads is key. Use our TradeIndia data extractor to pull business contact details, categories, and product listings—ideal for targeting suppliers or buyers in India’s top B2B portal.

Zillow Web Scraping for Real Estate Intelligence

Need real estate pricing, listings, or rental trends? Our Zillow web scraping solutions give you access to up-to-date property data, helping you analyze market shifts and investment opportunities.

Final Thoughts

Automated web scraping is no longer a luxury—it’s a necessity. Whether you're in ecommerce, social media, real estate, or B2B, Actowiz Solutions offers the tools and expertise to extract high-quality data that fuels business growth.

Get in touch today to discover how our automation-powered scraping services can transform your decision-making with real-time intelligence.

[engineering-anthology]

30 days of productivity

(because 100 is scary)

03/30

There's a joke somewhere along the lines of being "in the weeds" with my many assignments, but instead of weeds, it's ocean weeds, i.e. algae (given the algae focus of both of my big projects this semester!). However I don't have the time to workshop this joke to completion right now :_(. Maybe later when I have more time to think.

My paper and data analysis are still overdue. Ouch. But I have to submit whatever I have done tonight regardless of whether I'm happy with it so I don't hold up the grading schedule. Feels bad, but it'll probably feel really good to let go of it--it's just supposed to be a draft after all. I intended to turn it in last night, but my body forced me to sleep, I woke up with a nasty headache and promptly gave up on everything, so I missed most of the morning and only got a little bit of work in this evening. I needed the rest though, so I'm going to choose to not feel guilty about it. Or at least I'll do my best to not feel guilty :P

What I did today:

more data wrangling. made progress, spent < 2 hours on it though.

wrote most of an abstract, introduction, and some background

Upcoming tasks:

extract some kind of numbers from my data and calculate their uncertainties. this one is imminent if it doesn't happen in the next hour, it won't happen at all!

write the rest of my background section

fix bibliography, my numbering got a little confused between my previous versions of the intro and background and this version

email electronics professor about rescheduling exam

crank out the implementation of my computational bioconvection project this weekend! I have a draft report due tuesday! eeeek!

Musings: I have a new favorite album to study to! (it changes often though, so get used to this sentence) In the age of AI lofi drowning the bgm scene, having actual human producers whose tunes you vibe with is sooo lovely. City Girl has always been the kind of producer I can turn back to when my brain needs music that's both interesting (as far as lofi beats go) and familiar (but only because I've looped it so many times). Also, isn't the album art gorgeous???? All of her albums are so aesthetically lovely and I want them framed on my walls.

Do you think it would be bad to out what university I attend? I feel like my school and its stupid big ego are important props for explaining my woes. I've been pretty good about online hygiene so far, so I don't have that spooky problem some people have where due to the *magic of web scraping* their entire lives can be pulled up via ChatGPT. I don't want to ruin it, but I want to talk freely about the stupid and hilarious and horrifying things that happen at this big-ego-institution.

As a little aside, one of the reasons I had the confidence to apply to said big-ego-institute is that a blog that I followed in high school was open about the fact that she attended big-ego, answered my questions and encouraged me! She still floats through my mind sometimes and I'm so grateful for her kindness. I kinda want to be that person.

okie dokie, back to the grind! I'm partially saying this because I need to hear it, but don't feel guilty for the time you take to rest this weekend. If you have to crank out assignments that take a lot of brainpower to put together, that resting time is crucial for building thoughtful connections between ideas. (just make sure that the rest is intentional and not scrolling through tumblr or youtube, look at some trees or flowers or rocks or something)

Good luck and have fun! I believe in you <3

[mariacallous]

Over 170 images and personal details of children from Brazil have been scraped by an open-source dataset without their knowledge or consent, and used to train AI, claims a new report from Human Rights Watch released Monday.

The images have been scraped from content posted as recently as 2023 and as far back as the mid-1990s, according to the report, long before any internet user might anticipate that their content might be used to train AI. Human Rights Watch claims that personal details of these children, alongside URL links to their photographs, were included in LAION-5B, a dataset that has been a popular source of training data for AI startups.

“Their privacy is violated in the first instance when their photo is scraped and swept into these datasets. And then these AI tools are trained on this data and therefore can create realistic imagery of children,” says Hye Jung Han, children’s rights and technology researcher at Human Rights Watch and the researcher who found these images. “The technology is developed in such a way that any child who has any photo or video of themselves online is now at risk because any malicious actor could take that photo, and then use these tools to manipulate them however they want.”

LAION-5B is based on Common Crawl—a repository of data that was created by scraping the web and made available to researchers—and has been used to train several AI models, including Stability AI’s Stable Diffusion image generation tool. Created by the German nonprofit organization LAION, the dataset is openly accessible and now includes more than 5.85 billion pairs of images and captions, according to its website.

The images of children that researchers found came from mommy blogs and other personal, maternity, or parenting blogs, as well as stills from YouTube videos with small view counts, seemingly uploaded to be shared with family and friends.

“Just looking at the context of where they were posted, they enjoyed an expectation and a measure of privacy,” Hye says. “Most of these images were not possible to find online through a reverse image search.”

LAION spokesperson Nate Tyler says the organization has already taken action. “LAION-5B were taken down in response to a Stanford report that found links in the dataset pointing to illegal content on the public web,” he says, adding that the organization is currently working with “Internet Watch Foundation, the Canadian Centre for Child Protection, Stanford, and Human Rights Watch to remove all known references to illegal content.”

YouTube’s terms of service do not allow scraping except under certain circumstances; these instances seem to run afoul of those policies. “We've been clear that the unauthorized scraping of YouTube content is a violation of our Terms of Service,” says YouTube spokesperson Jack Maon, “and we continue to take action against this type of abuse.”

In December, researchers at Stanford University found that AI training data collected by LAION-5B contained child sexual abuse material. The problem of explicit deepfakes is on the rise even among students in US schools, where they are being used to bully classmates, especially girls. Hye worries that, beyond using children’s photos to generate CSAM, that the database could reveal potentially sensitive information, such as locations or medical data. In 2022, a US-based artist found her own image in the LAION dataset, and realized it was from her private medical records.

“Children should not have to live in fear that their photos might be stolen and weaponized against them,” says Hye. She worries that what she was able to find is just the beginning. It was a “tiny slice” of the data that her team was looking at, she says—less than .0001 percent of all the data in LAION-5B. She suspects it is likely that similar images may have found their way into the dataset from all over the world.

Last year, a German ad campaign used an AI-generated deepfake to caution parents against posting photos of children online, warning that their children’s images could be used to bully them or create CSAM. But this does not address the issue of images that are already published, or are decades old but still in existence online.

“Removing links from a LAION dataset does not remove this content from the web,” says Tyler. These images can still be found and used, even if it’s not through LAION. “This is a larger and very concerning issue, and as a nonprofit, volunteer organization, we will do our part to help.”

Hye says that the responsibility to protect children and their parents from this type of abuse falls on governments and regulators. The Brazilian legislature is currently considering laws to regulate deepfake creation, and in the US, representative Alexandria Ocasio-Cortez of New York has proposed the DEFIANCE Act, which would allow people to sue if they can prove a deepfake in their likeness had been made nonconsensually.

“I think that children and their parents shouldn't be made to shoulder responsibility for protecting kids against a technology that's fundamentally impossible to protect against,” Hye says. “It's not their fault.”

[yetisidelblog]

A powerful surveillance company called ShadowDragon is helping ICE and other law enforcement agencies track and monitor people—by scraping personal data from across the internet using a tool called SocialNet.

Now, Mozilla is leading a campaign to stop it. According to Mozilla, SocialNet collects data from platforms like Amazon Web Services, X (formerly Twitter), LinkedIn, Facebook, and YouTube, among others—making it easier for law enforcement to secretly surveil and target people, especially immigrants, activists, and communities of color.

Add your name to tell Amazon, Meta, LinkedIn, YouTube, and X: Block ShadowDragon now and stop fueling ICE’s surveillance state.

ShadowDragon’s SocialNet tool links together personal information scraped from dozens of platforms to build detailed profiles and maps of people’s online lives. The company boasts that it can track names, aliases, connections, locations, and more—all without a warrant or meaningful oversight.

ICE is already using this data to fuel its aggressive deportation machine. Mozilla is demanding that tech companies cut off access to their platforms for ShadowDragon and its surveillance tools. It’s time for us to amplify that call.

Tell tech companies: Block ICE’s surveillance contractor. Click here to add your name to the petition.

@upontheshelfreviews

@greenwingspino

@one-time-i-dreamt

@tenaflyviper

@akron-squirrel

@ifihadaworldofmyown

@justice-for-jacob-marley

@voicetalentbrendan

@thebigdeepcheatsy

@what-is-my-aesthetic

@ravenlynclemens

@writerofweird

@anon-lephant

@mentally-quiet-spycrab

@therealjacksepticeye

[athulyavijayan]

Data Science vs. Machine Learning vs. Artificial

Intelligence: What Is the Difference

Technology advancement has popularized many buzzwords, and Data Science,

Machine Learning (ML), and Artificial Intelligence (AI) are the three most

commonly mixed terms. Although all of these domains share a wide range of

overlapping functions, they play distinct roles in the arena of technology and

business. Knowledge about their distinction is essential for experts, businesses,

and students pursuing a career in data-driven decision-making.

What is Data Science?

Definition

Data Science is a multi-disciplinary area that integrates statistics, mathematics,

programming, and domain expertise to extract useful information from data. It is

centered on data gathering, cleaning, analysis, visualization, and interpretation to

support decision-making

Key Components of Data Science

1. Data Collection – Extracting raw data from multiple sources, including

databases, APIs, and web scraping.

2. Data Cleaning – Deleting inconsistencies, missing values, and outliers to

ready data for analysis.

3. Exploratory Data Analysis (EDA) – Grasping data trends, distributions, and

patterns using visualizations.

4. Statistical Analysis – Using probability and statistical techniques to make

conclusions from data.5. Machine Learning Models – Utilizing algorithms to predict and automate

insights.

6. Data Visualization – Making graphs and charts to present findings clearly.

Real-World Applications of Data Science

Business Analytics – Firms employ data science for understanding customer

behavior and optimizing marketing efforts.

Healthcare – Doctors utilize data science for disease prediction and drug

development.

Finance – Banks use data science for fraud detection and risk evaluation.

E-commerce – Businesses understand customer ratings and product interest to

make recommendations better.

What is Machine Learning?

Definition

Machine Learning (ML) is a branch of artificial intelligence that deals with the

creation of algorithms that enable computers to learn from data and get better

over time without being directly programmed. It makes it possible for systems to

make predictions, classifications, and decisions based on patterns in data.

Key Elements of Machine Learning

1. Supervised Learning – The model is trained on labeled data (e.g., spam

filtering, credit rating).2. Unsupervised Learning – The model learns patterns from unlabeled data (e.g.,

customer segmentation, anomaly detection).

3. Reinforcement Learning – The model learns by rewards and penalties to

maximize performance (e.g., self-driving cars, robotics).

4. Feature Engineering – Variable selection and transformation to enhance model

accuracy.

5. Model Evaluation – Model performance evaluation using metrics such as

accuracy, precision, and recall.

Real-World Applications of Machine Learning

Chatbots & Virtual Assistants – Siri, Alexa, and Google Assistant learn user

preferences to enhance responses.

Fraud Detection – Banks utilize ML models to identify suspicious transactions.

Recommendation Systems – Netflix, YouTube, and Spotify utilize ML to

recommend content based on user behavior.

Medical Diagnosis – Machine learning assists in identifying diseases from X-rays

and MRIs.

What is Artificial Intelligence?

Definition

Artificial Intelligence (AI) is a more general term that focuses on building

machines with the ability to emulate human intelligence. It encompasses

rule-based systems, robots, and machine learning algorithms to accomplish

tasks usually requiring human intelligence.

Types of Artificial Intelligence1. Narrow AI (Weak AI) – Built for particular functions (e.g., facial recognition,

search engines).

2. General AI (Strong AI) – Seeks to be able to do any intellectual task that a

human can accomplish (not yet fully achieved).

3. Super AI – A hypothetical phase in which machines are more intelligent than

humans.

Important Elements of AI

Natural Language Processing (NLP) – AI's capacity to process and create human

language (e.g., chatbots, voice assistants).

Computer Vision – The capability of machines to interpret and process images

and videos (e.g., facial recognition).

Expert Systems – AI-powered software that mimics human decision-making.

Neural Networks – A subset of machine learning inspired by the human brain,

used for deep learning.

Real-World Applications of AI

Self-Driving Cars – AI enables autonomous navigation and decision-making.

Smart Assistants – Google Assistant, Alexa, and Siri process voice commands

using AI.

Healthcare Innovations – AI helps in robotic surgeries and personalized

medicine.

Cybersecurity – AI detects cyber threats and prevents cyberattacks.

Data Science vs. Machine Learning vs. AI: Main Differences

How These Disciplines Interact

AI is the most general discipline that machine learning is a part of.

Machine learning is an approach applied in AI to create self-improving models.

Data science uses machine learning for business intelligence and predictive

analytics.

For instance, in a Netflix recommendation engine:

Data Science gathers and examines user action

Machine Learning forecasts what content a user will enjoy given past decisions.

Artificial Intelligence personalizes the recommendations through deep learning

and NLP.

Conclusion

Data Science, Machine Learning, and Artificial Intelligence are related but have

different applications and purposes. Data science is all about data analysis and

visualization to derive insights, machine learning is about constructing models

that can learn from data, and AI is concerned with developing machines that can

emulate human intelligence.

Knowing these distinctions will assist professionals in selecting the ideal career

path and companies in optimizing these technologies to their advantage.

Whether you aspire to have a career in data analytics, construct AI-based

applications, or work on machine learning models, there are promising avenues

in each one of these professions in the modern data-centric age

[chandra2026]

Learning Selenium: A Comprehensive and Quick Journey for Beginners and Enthusiasts

Selenium is a powerful yet beginner-friendly tool that allows you to automate web browsers for testing, data scraping, or streamlining repetitive tasks. If you want to advance your career at the Selenium Course in Pune, you need to take a systematic approach and join up for a course that best suits your interests and will greatly expand your learning path. This blog will guide you through a structured, easy-to-follow journey, perfect for beginners and enthusiasts alike.

What Makes Selenium So Popular?

For those looking to excel in Selenium, Selenium Online Course is highly suggested. Look for classes that align with your preferred programming language and learning approach. Selenium is one of the most widely used tools for web automation, and for good reasons:

Open-Source and Free: No licensing costs.

Multi-Language Support: Works with Python, Java, C#, and more.

Browser Compatibility: Supports all major browsers like Chrome, Firefox, and Edge.

Extensive Community: A wealth of resources and forums to help you learn and troubleshoot.

Whether you're a software tester or someone eager to automate browser tasks, Selenium is versatile and accessible.

How Long Does It Take to Learn Selenium?

The time it takes to learn Selenium depends on your starting point:

1. If You’re a Beginner Without Coding Experience

Time Needed: 3–6 weeks

Why? You’ll need to build foundational knowledge in programming (e.g., Python) and basic web development concepts like HTML and CSS.

2. If You Have Basic Coding Skills

Time Needed: 1–2 weeks

Why? You can skip the programming fundamentals and dive straight into Selenium scripting.

3. For Advanced Skills

Time Needed: 6–8 weeks

Why? Mastering advanced topics like handling dynamic content, integrating Selenium with frameworks, or running parallel tests takes more time and practice.

Your Quick and Comprehensive Learning Plan

Here’s a structured roadmap to learning Selenium efficiently:

Step 1: Learn the Basics of a Programming Language

Recommendation: Start with Python because it’s beginner-friendly and well-supported in Selenium.

Key Concepts to Learn:

Variables, loops, and functions.

Handling libraries and modules.

Step 2: Understand Web Development Basics

Familiarize yourself with:

HTML tags and attributes.

CSS selectors and XPath for locating web elements.

Step 3: Install Selenium and Set Up Your Environment

Install Python and the Selenium library.

Download the WebDriver for your preferred browser (e.g., ChromeDriver).

Write and run a basic script to open a browser and navigate to a webpage.

Step 4: Master Web Element Interaction

Learn to identify and interact with web elements using locators like:

ID

Name

CSS Selector

XPath

Practice clicking buttons, filling out forms, and handling dropdown menus.

Step 5: Dive Into Advanced Features

Handle pop-ups, alerts, and multiple browser tabs.

Work with dynamic content and implicit/explicit waits.

Automate repetitive tasks like form submissions or web scraping.

Step 6: Explore Frameworks and Testing Integration

Learn how to use testing frameworks like TestNG (Java) or Pytest (Python) to structure and scale your tests.

Understand how to generate reports and run parallel tests.

Step 7: Build Real-World Projects

Create test scripts for websites you use daily.

Automate login processes, data entry tasks, or form submissions.

Experiment with end-to-end test cases to mimic user actions.

Tips for a Smooth Learning Journey

Start Small: Focus on simple tasks before diving into advanced topics.

Use Resources Wisely: Leverage free tutorials, forums, and YouTube videos. Platforms like Udemy and Coursera offer structured courses.

Practice Consistently: Regular hands-on practice is key to mastering Selenium.

Join the Community: Participate in forums like Stack Overflow or Reddit for help and inspiration.

Experiment with Real Websites: Automate tasks on real websites to gain practical experience.

What Can You Achieve with Selenium?

By the end of your Selenium learning journey, you’ll be able to:

Write and execute browser automation scripts.

Test web applications efficiently with minimal manual effort.

Integrate Selenium with testing tools to build comprehensive test suites.

Automate repetitive browser tasks to save time and effort.

Learning Selenium is not just achievable—it’s exciting and rewarding. Whether you’re a beginner or an enthusiast, this structured approach will help you grasp the basics quickly and progress to more advanced levels. In a matter of weeks, you’ll be automating browser tasks, testing websites, and building projects that showcase your newfound skills.

So, why wait? Start your Selenium journey today and open the door to endless possibilities in web automation and testing!