#web scraping libraries

[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

[hugefuckingtitsmassivepair]

I was so fixated on writing smutty fanfic at 12 that my family had to install an early version of net nanny on the computer. It pissed me off so bad that I walked 7 miles in a snow storm to my local library, checked out every book they had on computers, and read them front to back until I learned about the dark web. Once I got over the idea of buying a rocket launcher to punish them with, I spent like 3 weeks uploading fanfic from “the dark web” before deciding it was too slow to work long-term. I somehow made a rudimentary code to scrape for passwords (could not replicate that if my life depended on it) and found the admin password to shut off the parental controls. They sold the entire computer after that stunt but I shoplifted a tablet the next week & started uploading from the local McDonald’s so. Anyways, the moral of the story is that I haven’t grown as a person since 12 years old.

[mostlysignssomeportents]

Humans are not perfectly vigilant

I'm on tour with my new, nationally bestselling novel The Bezzle! Catch me in BOSTON with Randall "XKCD" Munroe (Apr 11), then PROVIDENCE (Apr 12), and beyond!

Here's a fun AI story: a security researcher noticed that large companies' AI-authored source-code repeatedly referenced a nonexistent library (an AI "hallucination"), so he created a (defanged) malicious library with that name and uploaded it, and thousands of developers automatically downloaded and incorporated it as they compiled the code:

https://www.theregister.com/2024/03/28/ai_bots_hallucinate_software_packages/

These "hallucinations" are a stubbornly persistent feature of large language models, because these models only give the illusion of understanding; in reality, they are just sophisticated forms of autocomplete, drawing on huge databases to make shrewd (but reliably fallible) guesses about which word comes next:

https://dl.acm.org/doi/10.1145/3442188.3445922

Guessing the next word without understanding the meaning of the resulting sentence makes unsupervised LLMs unsuitable for high-stakes tasks. The whole AI bubble is based on convincing investors that one or more of the following is true:

There are low-stakes, high-value tasks that will recoup the massive costs of AI training and operation;

There are high-stakes, high-value tasks that can be made cheaper by adding an AI to a human operator;

Adding more training data to an AI will make it stop hallucinating, so that it can take over high-stakes, high-value tasks without a "human in the loop."

These are dubious propositions. There's a universe of low-stakes, low-value tasks – political disinformation, spam, fraud, academic cheating, nonconsensual porn, dialog for video-game NPCs – but none of them seem likely to generate enough revenue for AI companies to justify the billions spent on models, nor the trillions in valuation attributed to AI companies:

https://locusmag.com/2023/12/commentary-cory-doctorow-what-kind-of-bubble-is-ai/

The proposition that increasing training data will decrease hallucinations is hotly contested among AI practitioners. I confess that I don't know enough about AI to evaluate opposing sides' claims, but even if you stipulate that adding lots of human-generated training data will make the software a better guesser, there's a serious problem. All those low-value, low-stakes applications are flooding the internet with botshit. After all, the one thing AI is unarguably very good at is producing bullshit at scale. As the web becomes an anaerobic lagoon for botshit, the quantum of human-generated "content" in any internet core sample is dwindling to homeopathic levels:

https://pluralistic.net/2024/03/14/inhuman-centipede/#enshittibottification

This means that adding another order of magnitude more training data to AI won't just add massive computational expense – the data will be many orders of magnitude more expensive to acquire, even without factoring in the additional liability arising from new legal theories about scraping:

https://pluralistic.net/2023/09/17/how-to-think-about-scraping/

That leaves us with "humans in the loop" – the idea that an AI's business model is selling software to businesses that will pair it with human operators who will closely scrutinize the code's guesses. There's a version of this that sounds plausible – the one in which the human operator is in charge, and the AI acts as an eternally vigilant "sanity check" on the human's activities.

For example, my car has a system that notices when I activate my blinker while there's another car in my blind-spot. I'm pretty consistent about checking my blind spot, but I'm also a fallible human and there've been a couple times where the alert saved me from making a potentially dangerous maneuver. As disciplined as I am, I'm also sometimes forgetful about turning off lights, or waking up in time for work, or remembering someone's phone number (or birthday). I like having an automated system that does the robotically perfect trick of never forgetting something important.

There's a name for this in automation circles: a "centaur." I'm the human head, and I've fused with a powerful robot body that supports me, doing things that humans are innately bad at.

That's the good kind of automation, and we all benefit from it. But it only takes a small twist to turn this good automation into a nightmare. I'm speaking here of the reverse-centaur: automation in which the computer is in charge, bossing a human around so it can get its job done. Think of Amazon warehouse workers, who wear haptic bracelets and are continuously observed by AI cameras as autonomous shelves shuttle in front of them and demand that they pick and pack items at a pace that destroys their bodies and drives them mad:

https://pluralistic.net/2022/04/17/revenge-of-the-chickenized-reverse-centaurs/

Automation centaurs are great: they relieve humans of drudgework and let them focus on the creative and satisfying parts of their jobs. That's how AI-assisted coding is pitched: rather than looking up tricky syntax and other tedious programming tasks, an AI "co-pilot" is billed as freeing up its human "pilot" to focus on the creative puzzle-solving that makes coding so satisfying.

But an hallucinating AI is a terrible co-pilot. It's just good enough to get the job done much of the time, but it also sneakily inserts booby-traps that are statistically guaranteed to look as plausible as the good code (that's what a next-word-guessing program does: guesses the statistically most likely word).

This turns AI-"assisted" coders into reverse centaurs. The AI can churn out code at superhuman speed, and you, the human in the loop, must maintain perfect vigilance and attention as you review that code, spotting the cleverly disguised hooks for malicious code that the AI can't be prevented from inserting into its code. As "Lena" writes, "code review [is] difficult relative to writing new code":

https://twitter.com/qntm/status/1773779967521780169

Why is that? "Passively reading someone else's code just doesn't engage my brain in the same way. It's harder to do properly":

https://twitter.com/qntm/status/1773780355708764665

There's a name for this phenomenon: "automation blindness." Humans are just not equipped for eternal vigilance. We get good at spotting patterns that occur frequently – so good that we miss the anomalies. That's why TSA agents are so good at spotting harmless shampoo bottles on X-rays, even as they miss nearly every gun and bomb that a red team smuggles through their checkpoints:

https://pluralistic.net/2023/08/23/automation-blindness/#humans-in-the-loop

"Lena"'s thread points out that this is as true for AI-assisted driving as it is for AI-assisted coding: "self-driving cars replace the experience of driving with the experience of being a driving instructor":

https://twitter.com/qntm/status/1773841546753831283

In other words, they turn you into a reverse-centaur. Whereas my blind-spot double-checking robot allows me to make maneuvers at human speed and points out the things I've missed, a "supervised" self-driving car makes maneuvers at a computer's frantic pace, and demands that its human supervisor tirelessly and perfectly assesses each of those maneuvers. No wonder Cruise's murderous "self-driving" taxis replaced each low-waged driver with 1.5 high-waged technical robot supervisors:

https://pluralistic.net/2024/01/11/robots-stole-my-jerb/#computer-says-no

AI radiology programs are said to be able to spot cancerous masses that human radiologists miss. A centaur-based AI-assisted radiology program would keep the same number of radiologists in the field, but they would get less done: every time they assessed an X-ray, the AI would give them a second opinion. If the human and the AI disagreed, the human would go back and re-assess the X-ray. We'd get better radiology, at a higher price (the price of the AI software, plus the additional hours the radiologist would work).

But back to making the AI bubble pay off: for AI to pay off, the human in the loop has to reduce the costs of the business buying an AI. No one who invests in an AI company believes that their returns will come from business customers to agree to increase their costs. The AI can't do your job, but the AI salesman can convince your boss to fire you and replace you with an AI anyway – that pitch is the most successful form of AI disinformation in the world.

An AI that "hallucinates" bad advice to fliers can't replace human customer service reps, but airlines are firing reps and replacing them with chatbots:

https://www.bbc.com/travel/article/20240222-air-canada-chatbot-misinformation-what-travellers-should-know

An AI that "hallucinates" bad legal advice to New Yorkers can't replace city services, but Mayor Adams still tells New Yorkers to get their legal advice from his chatbots:

https://arstechnica.com/ai/2024/03/nycs-government-chatbot-is-lying-about-city-laws-and-regulations/

The only reason bosses want to buy robots is to fire humans and lower their costs. That's why "AI art" is such a pisser. There are plenty of harmless ways to automate art production with software – everything from a "healing brush" in Photoshop to deepfake tools that let a video-editor alter the eye-lines of all the extras in a scene to shift the focus. A graphic novelist who models a room in The Sims and then moves the camera around to get traceable geometry for different angles is a centaur – they are genuinely offloading some finicky drudgework onto a robot that is perfectly attentive and vigilant.

But the pitch from "AI art" companies is "fire your graphic artists and replace them with botshit." They're pitching a world where the robots get to do all the creative stuff (badly) and humans have to work at robotic pace, with robotic vigilance, in order to catch the mistakes that the robots make at superhuman speed.

Reverse centaurism is brutal. That's not news: Charlie Chaplin documented the problems of reverse centaurs nearly 100 years ago:

https://en.wikipedia.org/wiki/Modern_Times_(film)

As ever, the problem with a gadget isn't what it does: it's who it does it for and who it does it to. There are plenty of benefits from being a centaur – lots of ways that automation can help workers. But the only path to AI profitability lies in reverse centaurs, automation that turns the human in the loop into the crumple-zone for a robot:

https://estsjournal.org/index.php/ests/article/view/260

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2024/04/01/human-in-the-loop/#monkey-in-the-middle

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

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Jorge Royan (modified) https://commons.wikimedia.org/wiki/File:Munich_-_Two_boys_playing_in_a_park_-_7328.jpg

CC BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0/deed.en

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Noah Wulf (modified) https://commons.m.wikimedia.org/wiki/File:Thunderbirds_at_Attention_Next_to_Thunderbird_1_-_Aviation_Nation_2019.jpg

CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0/deed.en

[archivlibrarianist]

"Bots on the internet are nothing new, but a sea change has occurred over the past year. For the past 25 years, anyone running a web server knew that the bulk of traffic was one sort of bot or another. There was googlebot, which was quite polite, and everyone learned to feed it - otherwise no one would ever find the delicious treats we were trying to give away. There were lots of search engine crawlers working to develop this or that service. You'd get 'script kiddies' trying thousands of prepackaged exploits. A server secured and patched by a reasonably competent technologist would have no difficulty ignoring these.

"...The surge of AI bots has hit Open Access sites particularly hard, as their mission conflicts with the need to block bots. Consider that Internet Archive can no longer save snapshots of one of the best open-access publishers, MIT Press, because of cloudflare blocking. Who know how many books will be lost this way?  Or consider that the bots took down OAPEN, the worlds most important repository of Scholarly OA books, for a day or two. That's 34,000 books that AI 'checked out' for two days. Or recent outages at Project Gutenberg, which serves 2 million dynamic pages and a half million downloads per day. That's hundreds of thousands of downloads blocked! The link checker at doab-check.ebookfoundation.org (a project I worked on for OAPEN) is now showing 1,534 books that are unreachable due to 'too many requests.' That's 1,534 books that AI has stolen from us! And it's getting worse.

"...The thing that gets me REALLY mad is how unnecessary this carnage is. Project Gutenberg makes all its content available with one click on a file in its feeds directory. OAPEN makes all its books available via an API. There's no need to make a million requests to get this stuff!! Who (or what) is programming these idiot scraping bots? Have they never heard of a sitemap??? Are they summer interns using ChatGPT to write all their code? Who gave them infinite memory, CPUs and bandwidth to run these monstrosities? (Don't answer.)

"We are headed for a world in which all good information is locked up behind secure registration barriers and paywalls, and it won't be to make money, it will be for survival. Captchas will only be solvable by advanced AIs and only the wealthy will be able to use internet libraries."

[d2071art]

NO AI

TL;DR: almost all social platforms are stealing your art and use it to train generative AI (or sell your content to AI developers); please beware and do something. Or don’t, if you’re okay with this.

Which platforms are NOT safe to use for sharing you art:

Facebook, Instagram and all Meta products and platforms (although if you live in the EU, you can forbid Meta to use your content for AI training)

Reddit (sold out all its content to OpenAI)

Twitter

Bluesky (it has no protection from AI scraping and you can’t opt out from 3rd party data / content collection yet)

DeviantArt, Flikr and literally every stock image platform (some didn’t bother to protect their content from scraping, some sold it out to AI developers)

Here’s WHAT YOU CAN DO:

1. Just say no:

Block all 3rd party data collection: you can do this here on Tumblr (here’s how); all other platforms are merely taking suggestions, tbh

Use Cara (they can’t stop illegal scraping yet, but they are currently working with Glaze to built in ‘AI poisoning’, so… fingers crossed)

2. Use art style masking tools:

Glaze: you can a) download the app and run it locally or b) use Glaze’s free web service, all you need to do is register. This one is a fav of mine, ‘cause, unlike all the other tools, it doesn’t require any coding skills (also it is 100% non-commercial and was developed by a bunch of enthusiasts at the University of Chicago)

Anti-DreamBooth: free code; it was originally developed to protect personal photos from being used for forging deepfakes, but it works for art to

Mist: free code for Windows; if you use MacOS or don’t have powerful enough GPU, you can run Mist on Google’s Colab Notebook

(art style masking tools change some pixels in digital images so that AI models can’t process them properly; the changes are almost invisible, so it doesn’t affect your audiences perception)

3. Use ‘AI poisoning’ tools

Nightshade: free code for Windows 10/11 and MacOS; you’ll need GPU/CPU and a bunch of machine learning libraries to use it though.

4. Stay safe and fuck all this corporate shit.

[natspookie]

Nat x spidey-powers!reader (romantic or platonic up to you)

Got a couple ideas involving reader who is pretty happy just being a hero around NYC instead of an avenger going on big missions:

* Reader botches a stealthy takedown of some robbers in NYC. Gets a little roughed up (minor bruises and scrapes). Then asks Nat for lessons in "how to stealth"

* 4 times reader fails to sneak up on Nat. And the 1 time they succeed.

* Reader getting in over their head with something big happening in NYC and calls Nat for backup

Love your work! No worries if you don't vibe with any of these ideas. I just saw that you were looking for more. Thank you so much for sharing what you write:)

a/n, whennnn i tell you i grinned ear to ear at having a request heheeheh, thank you anon<333

i hope you don’t mind i mixed these requests a bit and write it in fem!reader ? if you want a gender neutral one lmk!!!

★ summary : 4 times reader fails to sneak up on natasha and the one time she does

#1

you tried to sneak up on natasha by tiptoeing your way through the compound. you smiled seeing natasha’s back faced to you in the middle of the kitchen, making her famous peanut butter and jelly sandwich.

“i heard you tiptoe from the entrance” you could hear her smirking “nattie!” you groaned, making your footsteps known and louder now. “try harder” she handed you a sandwich.

#2

natasha is seated in the middle of your shared apartment with a glass of wine in her hand when she was flipping through the tv channels. you open the balcony door a little, squeezing through, when you hear natasha

“i heard your webs get tangled in the tree, dekta” “nattttt” you whined, sitting on her lap as you tucked your face into her neck. “try quieter” she mumbled with a laugh

#3

“third time’s a charm” you mumbled trying to use your spidey senses to hear natasha walk through the hallway. you heard her pause before entering the library of the compound, where you were. you held your breath but she opened the door, clearly unsurprised you were hear.

“i heard you hold your breath” “natasha! why can’t you stop being a super spy for a second! i have the super senses” you whined “what’s the fun in that, spidey?” she laughed, shaking her head

#4

you had come home too late when you tiptoed into you and natasha’s shared room, taking your mask off, wincing at the nasty scratch on your forehead.

“dekta?” natasha sat up with a frown, turning the lamp on “sorry nat, these guys were kinda tough” you laughed as you peeled the suit off “ow ow ow” you said as natasha tried to help “sorry” she muttered

you watched as natasha got out her first aid kid “take a shower will ya? i’ll clean it up after” you nodded, tiredly.

when you got out of the shower, natasha had laid out her pajamas for you.

you walked out of the bathroom ready to hear a handful from natasha. she patted your side of the bed and so you sat there. natasha disinfected the wounds carefully as you bit your lip. “dekta you need to be more careful, i’m serious. look at this…” she pointed to the open wound on your leg “this could get infected”

“i really tried to he careful nat” you said, teary eyed “you can always call me if you need backup okay?” she cupped your face, carefully leaning against your forehead as you nodded.

#5

natasha sighs from the balcony of your shared apartment, watching the sunset. she wished you came back from all your ‘good neighborhood friend’ work. she mostly wished you didn’t forget it was your 1 year anniversary.

she twirled her hair around, leaning her head at the palm of her hand in hopes of catching a glimpse of you swinging through the city. just when she lost all hope, a loud squeal left her mouth as you swooped her body securely and swung through the city.

“Y/N DON’T DROP ME!” she squeezed her eyes shut as you swung through the city that never sleeps. “shh, you’ll be fine nattie, i’ve got you” you squeezed her thrice before landing on top of a building overlooking the brookyln bridge with spiderwebs saying ‘i love you’.

“i thought you forgot” natasha looked at the bridge in awe before turning around to see all her favorite food on a picnic blanket. “i should be hurt but i’ll let you go this once” you winked

“happy anniversary, my spider” natasha placed herself on your lap “happy anniversary nattie, i finally snuck up on you” “don’t gloat” she nudged your shoulder

[pomegranatecrab]

A little fic about tony with a menace cat that is only nice to him:) with some stony of course

The rest of the Avengers move in soon after Steve does, filling the mansion with a range of personalities. Jarvis is unphased by the variety of character, and soon bans Hawkeye from the stove, which Steve thinks only encourages the build up of empty pizza boxes.

He’s eating his breakfast outside, savouring the taste of eggs, the salty richness of bacon and the odd texture of mushrooms, something he’d never tasted before. The silence is odd. There should be bare feeding slapping down the dilapidated road, children ready for the long walk to the library or the corner store, walks Steve usually couldn’t make.

Tony Stark’s mansion boasted a large garden, impeccably maintained and secluded from the bustle of New York. Cobbled paths coil around the large expanse of grass, weaving through beds of flowers, ending at the gazebo that Steve sits in. It overlooks a small pond, home to some brightly coloured fish that had flocked to the surface the moment he stepped onto the platform.

Steve’s watching the orange one he’d dubbed ‘Monocle’ when he notices them.

A pair of his socks, filled with suspicious holes, floating amongst the reeds.

He sighs, scraping his chair as he stands, and is glad that he’s at least tall enough to scoop them out of the water easily, plucking the drenched fabric between two fingers.

There’s a familiar jingle behind him.

Palug jumps elegantly from the stairs onto the table, nose twitching over the bacon. She snaps it up between her teeth, hops onto the chair and politely chews on her prize.

Steve scowls at the cat.

“You-”

“Steve!”

Steve straightens, pretending like he hadn’t been about to engage in a petty squabble with a spoiled house cat.

“Mr Stark.”

Mr Stark waves a hand, rolling on the balls of his feet as he looks around, darting small glances at his face, before settling on Palug.

“Tony is fine, please.” He holds out the book in his hand, faded and worn. “Iron Man mentioned you were interested in this?”

It was a copy of The Gift of the Magi, a thin book with a painting of a woman with long, gorgeous hair on the cover. Belatedly, Steve realises this is the book Iron Man had recommended.

“You didn’t have to go out of your way for me. Thank you.”

Tony smiles. He steps forward to rub a hand over Palug’s back, inciting a heavy litany of purring.

“I first read that at school. The librarian let me take out double the amount of books usually allowed. I’d take them all down to this big tree right on the edge of the school grounds and read until curfew.”

Steve runs a thumb over the wrinkled lines marring the illustration, yellow cracks that web across the fine paper.

“She must have liked you,” he murmurs.

“She said I was the only boy that didn’t carry on like an imbecile,” Tony grins, “high compliments.”

“Thank you,” Steve says, stepping stiffly around Palug, who still gazed at him with beady eyes, despite the content rumbling bubbling from her chest.

He all but books it back to his bedroom.

—-

Steve reads the Gift of the Magi twice and thinks about his old life each time.

He’s jealous, really, that these characters got to make their sacrifices and come back to each other.

But Iron Man had been right. He did like it, and it’s on the third read that he notices the library loan card at the back.

‘Tony Stark’ is etched in careful handwriting in every single box, the dates all varying.

At the bottom a loopy scrawl had been left in black ink.

Mr Stark, you’re the only boy in school who checks this book out. It’s yours. Enjoy your summer.

Mrs Rembly

Steve’s lips twitch.

It’s a bit backhanded, but thoughtful.

[queerdeans]

"Season's Change" — a #Suptober24 ficlet

Day 1: Autumn

When Dean crawls his way to Lisa’s, his face and body healed from the fight with Lucifer but everything else still shattered, summer is already beginning to take root in Cicero, Indiana. The heat of the months that follow feels only right, as Dean’s mind is halfway in the world he’s found himself in, a suburban life, and halfway in Hell, where Sam is locked in a cage with Lucifer and Michael. The flames of it lick at him; the chains from his own time in Hell burn his skin, he swears he can feel it.

After the initial shock of his arrival wears off, Lisa takes some time to set down ground rules: it starts and ends with don’t do anything crazy, Dean. She doesn’t know all of the details of what he’s been through, just how close the world came to ending for her and Ben and everyone in their little suburb; doesn’t know that Dean’s been scraping by on blind hopes, deals with every kind of devil they make, and prayers to a falling angel for the past two years; doesn’t know that he has no idea how to do this picket-fence, apple-pie life.

But she does know the shapes of these things, the weight of them as he sinks into bed with her. She knows that he struggles to hold them; that at any moment they may topple over, and that they might hit her as they drop, or worse, they might hit Ben.

Dean knows it too. He tries to keep it together, he really does. At least, he does when he’s not sneaking into the garage to grab weapons out of the Impala, or gathering ingredients for the latest spell or ritual he’s found in a dark corner of a library or a page so deep in the internet’s web that he’s not sure he’d ever be able to find it again. You could call it a last ditch effort, but he left last ditch miles back, and now he’s in his own territory of hopelessness.

He’s normal. He is. When he’s not creeping out to a crossroads with the knife in his hand, ready to carve up whatever son of a bitch is brave enough to show its ugly face without giving him what he wants. When he’s not dropping to his knees at the bedside, as Lisa tucks in Ben in the next room, folding his hands in front of him, bowing his head, and trying to find words to say a prayer, one that would make sense, one that would reach Cas’s ears. One that might ask what he wants to ask: did you put me back together wrong, there in the cemetery? When you fixed me — what did you leave behind?

In early June, the morning after Dean’s summoned a demon and offered anything, any damn thing, in exchange for Sam’s escape from the cage, Lisa tells him that Ben’s been asking for burgers. And she’s got work, Ben’s at day camp, but Dean’s got the day free — or so she thinks, though his plan involves a follow-up with the crossroads bitch from the night before — so why doesn’t he go to the grocery, grab some chuck, throw it on the grill?

What ensues is a long, hot day of fighting with the fucking thing. First he goes to the store and argues with himself about the meat, which to choose; same with the buns, same with the fixings. Dean loves a burger, of course he does, but he’s never just made one from scratch. Never wandered into the local Kroger to grab ingredients so he can whip them up — where? Outside of the shittiest motel in every backwater town in America that’s got a monster problem?

When he gets back with the stuff, he opens up the grill to check it out, and sees that it’s in deep need of a clean. Even Dean can tell that. So he spends awhile scraping off the char, wiping down the grates, and while he’s out there in the backyard, he notices it needs a mow.

So he mows it. And then he notices the gutter’s loose on the end, so he gets up on the ladder and fixes that. While he’s up there, he decides to stomp around on the roof a bit, check for any weak spots or leaks like he knows what he’s doing. He tries, really tries, to be domestic. To be settled.

It’s not exactly the summoning ritual he’d intended, but well, he’d gotten nothing from the demon the night before, and if he doesn’t grill up some perfect hamburgers tonight, Lisa might throw him out. As much as he feels like a fly trapped in a box and trying to accept it as home, he knows this stability thing is good for him. It’s giving him a springboard from which to figure out his next move. And it’s not so bad; he likes Lisa, likes Ben, likes who he pretends to be when he’s with them.

When evening comes, Ben arrives in a howl of excitement, Lisa traipsing in the door behind him. Dean goes to fire up the grill, ready to make them both happy, because it’s easy and he can do it — better than stopping the damn apocalypse, yeah? — only there’s no gas. He curses, Lisa reprimands him lightly for doing so in front of Ben, and it’s back to the grocery store.

Despite all Dean’s best efforts, the burgers come out hard that night, and Ben calls them hockey pucks and throws his on the ground. The adjustment to living with someone new in the house has been difficult, and Lisa tries to tell Dean this, but well, he doesn’t have to hear it, does he? He feels for himself how difficult it’s been. He apologizes to Ben, then takes him to the Burger King down the road. When Ben dons the paper crown, Dean smiles and takes a picture with his phone.

That night he takes the Impala for a long drive, but he doesn’t go back to the crossroads. He can’t handle two failures in one day. He just fucking can’t.

The summer swelters on and on. His new life gets in the way of his grasps at the old one; he doesn’t have the time for the research, rituals, and other things he needs to try to help spring Sam out of the box. Ben goes to batting practice at the local Little League diamond on Mondays; he has swim lessons at the community pool on Tuesdays; Lisa teaches a late yoga class on Wednesdays so it becomes ‘Dean Night,’ which is synonymous with chicken nuggets for dinner and a movie Ben probably shouldn’t be allowed to watch but enjoys thoroughly; Thursdays the neighbors come over and they all talk about work, and kids, and things like the economy; Fridays, he learns, are good for date night, and he understands the groove of Lisa’s favorite restaurants within a few months; Saturdays and Sundays are variable, sometimes with birthday parties for Ben’s friends, sometimes with outings to museums or amusement parks, but always something to fill the days.

By July, Lisa’s gotten him a job. By August, he’s spent every spare moment, cashed out every credit card he has, and has nearly gotten himself killed a dozen times over trying to unlock the Lucifer box. By September, his exhaustion is palpable, and the grass is growing long again, and again, and again.

The change to autumn is the first full seasonal change he spends in one place since he was four in Kansas. He’s there to see the daily temperatures steadily, blessedly, drop. He helps pack Ben’s lunch for his first day of school and listens to a blow-by-blow account of the day when the school bus drops him off at home. He’s there when Lisa pulls out the autumnal decorations, the felt pumpkins that she places on the bookshelf, the spider web that she strings across the front porch railing, even the witch’s hat that she sets on the dining room table.

Dean tells her that witches don’t really wear those hats, but that they do love a disgusting little pile of bones. And she shoots back that she’s not going to put a pile of bones on the table.

He’s there, still, waiting for something to happen, waiting for a real shift to come, waiting to feel satisfied with this new life he’d wanted, when the trees in the backyard drop their leaves.

It’s a Saturday. Ben’s with his cousins for the weekend and Lisa has a yoga retreat. She leaves early, kissing him on the cheek as he lays in bed, tells him to have a good day, to do something with himself. And he tries to think of what to do: is there a book he hasn’t gotten his hands on yet, is there lore somewhere that he hasn’t heard of? Could he call up Bobby (again) and take the earful of idjits if only it would get him an answer? But no, there’s nothing left to try. Not even Bobby can figure this one out, and he’s told Dean several times now to stop trying.

So he lumbers out into the mid-morning chill, grabs the rake and doesn’t look at the Impala, which he’s now covered up, trying not to tempt himself. He takes the rake and a bag and heads out into the yard. Last time he talked to Bobby, he’d be told to sit down and be happy with his life, that it’s the best one a hunter can hope to get. But how can that be, when there’s a gaping hole in the middle of it? When everything gets pulled in and disappears? Cas is blowing in the wind, Sam’s in Hell, Bobby’s tired of Dean’s bitching — what can he do?

The yard is covered in leaves. He starts in the northwest corner and decides to work logically. It’s physical work, but not difficult. Dean’s gotten soft in the past couple of months and he knows it. He should get a gym membership, only he can’t really imagine himself in that kind of environment. Used to be, the adrenaline of the hunt would carry his aching, tired body for miles; used to be, he could take any kind of punch and get back up. Now, though, as he rakes the leaves into ever-bigger piles, he wonders if those last punches he took were enough to knock him down — permanently.

As he rakes and fills up bag after bag, stuffing the leaves inside of them until they’re full to bursting, his thoughts are drawn toward Cas. Cas, who disappeared without a trace. Cas, who’s probably busy fixing Heaven, and good for him, only Dean wants him to come back now, wants him to lay his hand on Dean’s shoulder and see if he can take another go at fixing him. Maybe he didn’t get deep enough at the cemetery; maybe he didn’t realize there was more to do, more to sew back up.

He doesn’t pause, just keeps working, even as these thoughts spill through his mind. Cas, who’s made it clear that his real life is in Heaven, that his choice is to live with the angels while Dean mucks it out with the humans down here. Cas, who went through hell with him, who had him gasping for air in his own damn coffin, who could come find Dean at any time he wanted but hasn’t yet. Cas, who’s celestial and big and important and probably, right now, is glad that Dean’s tucked away nice in his little suburban life; safe, quiet. Cas, who doesn’t have to clean up Dean’s messes anymore. Cas, who’s probably relieved.

Dean keeps working until each leaf has been raked into a pile, until each pile has been shoved into a bag and carried out to the front curb. He works until the cold is beginning to get to him, to tingle his fingers and toes. He works until he feels something akin to that moment, at the end of a hunt, when everything seems to be teetering on the top of some divide; when it could fall one way or the other, when everything could change for the better or for the worse.

He’s tired of having one foot in his old life and one in his new; it’s time to pick a damn side, for his own sanity — to put that ahead for once, yeah? Sam’s not breaking out anytime soon, not unless something changes, and when it does, Dean’ll be ready. But for now, the only thing that’s happening is he’s driving himself crazy and wearing out his welcome with Lisa and Ben. His new life.

Dean ties up the final bag, and glances back toward the tree line, just for a moment, not sure why he does it. For a second, there, he’d sworn he saw something. He gets the strange feeling in the back of his mind, the feeling that there’s something important that he’s supposed to do, only he doesn’t know what it is.

He carries the bag to the curb and tosses it next to the rest, then heads inside. He locks the door behind him and heads upstairs, to shed his clothes into the laundry basket, to shower with the full-sized shampoo and body wash that he bought at the store weeks ago, to towel off with his towel, to go downstairs and take out his dishes and make his lunch.

Upstairs, in the bathroom, he pauses to look out the window one last time, to see the yard from above. It’s clear, the grass visible but dying, the leaves gone. It looks good. He smiles a bit, because it’s all he can do, and then turns on the hot water.

[snickerdoodlles]

pulling out a section from this post (a very basic breakdown of generative AI) for easier reading;

AO3 and Generative AI

There are unfortunately some massive misunderstandings in regards to AO3 being included in LLM training datasets. This post was semi-prompted by the ‘Knot in my name’ AO3 tag (for those of you who haven’t heard of it, it’s supposed to be a fandom anti-AI event where AO3 writers help “further pollute” AI with Omegaverse), so let’s take a moment to address AO3 in conjunction with AI. We’ll start with the biggest misconception:

1. AO3 wasn’t used to train generative AI.

Or at least not anymore than any other internet website. AO3 was not deliberately scraped to be used as LLM training data.

The AO3 moderators found traces of the Common Crawl web worm in their servers. The Common Crawl is an open data repository of raw web page data, metadata extracts and text extracts collected from 10+ years of web crawling. Its collective data is measured in petabytes. (As a note, it also only features samples of the available pages on a given domain in its datasets, because its data is freely released under fair use and this is part of how they navigate copyright.) LLM developers use it and similar web crawls like Google’s C4 to bulk up the overall amount of pre-training data.

AO3 is big to an individual user, but it’s actually a small website when it comes to the amount of data used to pre-train LLMs. It’s also just a bad candidate for training data. As a comparison example, Wikipedia is often used as high quality training data because it’s a knowledge corpus and its moderators put a lot of work into maintaining a consistent quality across its web pages. AO3 is just a repository for all fanfic -- it doesn’t have any of that quality maintenance nor any knowledge density. Just in terms of practicality, even if people could get around the copyright issues, the sheer amount of work that would go into curating and labeling AO3’s data (or even a part of it) to make it useful for the fine-tuning stages most likely outstrips any potential usage.

Speaking of copyright, AO3 is a terrible candidate for training data just based on that. Even if people (incorrectly) think fanfic doesn’t hold copyright, there are plenty of books and texts that are public domain that can be found in online libraries that make for much better training data (or rather, there is a higher consistency in quality for them that would make them more appealing than fic for people specifically targeting written story data). And for any scrapers who don’t care about legalities or copyright, they’re going to target published works instead. Meta is in fact currently getting sued for including published books from a shadow library in its training data (note, this case is not in regards to any copyrighted material that might’ve been caught in the Common Crawl data, its regarding a book repository of published books that was scraped specifically to bring in some higher quality data for the first training stage). In a similar case, there’s an anonymous group suing Microsoft, GitHub, and OpenAI for training their LLMs on open source code.

Getting back to my point, AO3 is just not desirable training data. It’s not big enough to be worth scraping for pre-training data, it’s not curated enough to be considered for high quality data, and its data comes with copyright issues to boot. If LLM creators are saying there was no active pursuit in using AO3 to train generative AI, then there was (99% likelihood) no active pursuit in using AO3 to train generative AI.

AO3 has some preventative measures against being included in future Common Crawl datasets, which may or may not work, but there’s no way to remove any previously scraped data from that data corpus. And as a note for anyone locking their AO3 fics: that might potentially help against future AO3 scrapes, but it is rather moot if you post the same fic in full to other platforms like ffn, twitter, tumblr, etc. that have zero preventative measures against data scraping.

2. A/B/O is not polluting generative AI

…I’m going to be real, I have no idea what people expected to prove by asking AI to write Omegaverse fic. At the very least, people know A/B/O fics are not exclusive to AO3, right? The genre isn’t even exclusive to fandom -- it started in fandom, sure, but it expanded to general erotica years ago. It’s all over social media. It has multiple Wikipedia pages.

More to the point though, omegaverse would only be “polluting” AI if LLMs were spewing omegaverse concepts unprompted or like…associated knots with dicks more than rope or something. But people asking AI to write omegaverse and AI then writing omegaverse for them is just AI giving people exactly what they asked for. And…I hate to point this out, but LLMs writing for a niche the LLM trainers didn’t deliberately train the LLMs on is generally considered to be a good thing to the people who develop LLMs. The capability to fill niches developers didn’t even know existed increases LLMs’ marketability. If I were a betting man, what fandom probably saw as a GOTCHA moment, AI people probably saw as a good sign of LLMs’ future potential.

3. Individuals cannot affect LLM training datasets.

So back to the fandom event, with the stated goal of sabotaging AI scrapers via omegaverse fic.

…It’s not going to do anything.

Let’s add some numbers to this to help put things into perspective:

LLaMA’s 65 billion parameter model was trained on 1.4 trillion tokens. Of that 1.4 trillion tokens, about 67% of the training data was from the Common Crawl (roughly ~3 terabytes of data).

3 terabytes is 3,000,000,000 kilobytes.

That’s 3 billion kilobytes.

According to a news article I saw, there has been ~450k words total published for this campaign (*this was while it was going on, that number has probably changed, but you’re about to see why that still doesn’t matter). So, roughly speaking, ~450k of text is ~1012 KB (I’m going off the document size of a plain text doc for a fic whose word count is ~440k).

So 1,012 out of 3,000,000,000.

Aka 0.000034%.

And that 0.000034% of 3 billion kilobytes is only 2/3s of the data for the first stage of training.

And not to beat a dead horse, but 0.000034% is still grossly overestimating the potential impact of posting A/B/O fic. Remember, only parts of AO3 would get scraped for Common Crawl datasets. Which are also huge! The October 2022 Common Crawl dataset is 380 tebibytes. The April 2021 dataset is 320 tebibytes. The 3 terabytes of Common Crawl data used to train LLaMA was randomly selected data that totaled to less than 1% of one full dataset. Not to mention, LLaMA’s training dataset is currently on the (much) larger size as compared to most LLM training datasets.

I also feel the need to point out again that AO3 is trying to prevent any Common Crawl scraping in the future, which would include protection for these new stories (several of which are also locked!).

Omegaverse just isn’t going to do anything to AI. Individual fics are going to do even less. Even if all of AO3 suddenly became omegaverse, it’s just not prominent enough to influence anything in regards to LLMs. You cannot affect training datasets in any meaningful way doing this. And while this might seem really disappointing, this is actually a good thing.

Remember that anything an individual can do to LLMs, the person you hate most can do the same. If it were possible for fandom to corrupt AI with omegaverse, fascists, bigots, and just straight up internet trolls could pollute it with hate speech and worse. AI already carries a lot of biases even while developers are actively trying to flatten that out, it’s good that organized groups can’t corrupt that deliberately.

[roanofarcc]

PROJECT SUNSHINE CHAPTER THIRTY-THREE → ONE WEIRD NIGHT

summary: steve harrington x oc | on ao3

when another product of Hawkins National Laboratory escaped a long-survived nightmare alongside her sister, she crashed into one unsuspecting teenage boy and dragged him deeper into the dark mysteries that made up their hometown.

word count. || masterlist

warnings: cannon typical violence, child abuse, horror, gore, and depictions of mental illness. parts of this story were written pre-season 4 release. cannon divergence.

previous chapter ← → next chapter

In the passenger seat of an old, beat-up car, Calum Miller drummed his fingers against his chin in thought. He was unable to let anything go or be; his mind was a tangled web of suspicion that his hometown wasn’t what everyone believed, and he was almost desperate to prove it. 

“You know, maybe the whole Pennhurst idea wasn’t too far-fetched. It’s possible Danielle and even Will ended up there somehow,” he thought aloud. The whole story surrounding Danielle Torres drove him mad. No printed article or half-assed story the long-lost teen uttered convinced Calum that there wasn’t more to the story. He had done his research and in nearly every kidnapping case, it was unlikely the victim survived a week, let alone ten years. Someone- Danielle, her family, the Hawkins P.D., and probably others- was coving something up. They didn’t want the public to know something and Calum wanted to know what. He also wanted to know- no, he needed to know- if it had anything to do with his dad. 

“Jesus Christ,” Tamera huffed. 

“Are you really still upset about the tutoring thing? I know we blew it, but we can think of something else-” 

A dry, humorless laugh sounded from Tamera, and her eyes remained glued to the road. “No, you idiot. I’m not mad you blew the tutoring idea. I didn’t even want to do that! I’m mad that you basically accosted Danielle. She hasn’t come around the library in days,” she said. “I liked talking to her. She’s nice and sweet, and you scared her off! Now I’m gonna be lucky if she ever talks to me again.” 

Calum frowned. He didn’t understand why she didn’t see how weird things were with Danielle’s story. Sure, she seemed nice and all, he wasn’t doubting that, but something wasn’t right about her or how she found her way back to Hawkins. 

“Come on, Mara-” 

She cut him off with a quick glare. “No. Just let it go, please.”

Once again, the friends found themselves trapped in the same loop of a conversation they’d been having for weeks. Calum had thrown almost all of his focus into scraping together clues that would lead him to his dad, and Tamera had helped him where she could. But with the dead ends they kept meeting and then Calum’s questioning of Danielle, Tamera was getting visibly annoyed with him. 

Maybe he was being annoying about it, but he needed to find his dad and fix things. He needed his mom to stop drinking herself into a coma each night because of her husband’s absence. The only thing he had was that Danielle Tores returned the same week his dad and Will Byers went missing. Will returned but his dad didn’t. 

With a sigh, Calum ran a hand through his blond hair and pressed the issue further. “I told you, Mara, I can’t let this go. My dad is out there somewhere, and I have to find him.” 

She shook her head but didn’t ignore him; she wanted to convince him his efforts were misplaced and useless, but it wouldn’t work. Calum was too stubborn for anyone to convince him he was wrong. “There is nothing but that week connecting Danielle or Will or anyone to your dad. You have nothing besides those insane theories with no proof. You’re trying to turn nothing into something.” She paused and her anger melted into something of guilt before she continued, “Your dad is gone. He left, Cal. That’s all there is to it.” 

Her words stung like a slap to the face, but he tried not to let them get under his skin. Tamera sounded like his mom. The woman had shut down every possibility of her husband being taken or vanishing. She insisted he left her, but her behavior told a different story. There were problems between his parents, mostly because his dad spent a lot of time away at work, but they loved each other. That could explain her unraveling in his absence, but something about the way she acted was wrong. She didn’t seem sad or heartbroken; she was relieved and enraged in the most confusing of ways. There was something he was missing; his parents were telling him something. 

His mom didn’t make a livable wage as a hairdresser in downtown Hawkins. The job was more of a side hobby that she used to make some extra cash. It was Calum’s dad who was the breadwinner of the family. He didn’t know what his dad did, but it was some government number-crunching job that he never talked much about out of fear he’d bored his son to death. All Calum knew was that he brought in more than enough money to support them, but when he disappeared, Calum worried he and his mom would sink without the income. He picked up extra shifts at the arcade and got a second job at the movie theater in hopes of making ends meet. 

Then, one night when he returned home late from work, he saw his mom dressed in the same clothes as the day before, not having been to work. Calum mustered up enough courage to ask her how they were going to support themselves without Dad’s money. She shrugged him off and told him not to worry about it and that it was “all taken care of.” That only made him more confused. How was it taken care of? Who were they getting the money from? 

“My dad wasn’t a bad guy,” Calum said, keeping his tone level even though he was screaming on the inside. “There was no reason for him to leave like that. He didn’t take anything, and he didn’t say goodbye. There was nothing, he just vanished.” 

If his dad was going to leave, he would have said something to Calum, that much he believed. 

There was more that occurred that week that only heightened his suspicion that something odd was going on. “You know, that night he didn’t come home there were power outages all over town. Mr. Robinson said it had something to do with that power company in the woods, the one with the military out front.” 

Tamera rolled her eyes. “Oh wow. The Department of Energy had trouble with the power in November. That obviously means aliens came down and abducted by aliens.” 

“I’m being serious!” Calum snapped. Power outages had occurred more than a week than they had all year, according to the Hawkins Post. He knew it was a stretch, trying to connect a series of weird power outages to his dad’s disappearance, but he felt in his gut that there was something weird going on.  

“Don’t you think it's a little strange that a bunch of vans from the Department of Energy were at Nancy Wheeler’s house?” 

“What?” Tamera asked. 

He explained to her what he saw. The Department of Energy was out and about that whole week; Calum recalled seeing their vans around town, but not once did he see anyone working on the powerlines. He was no electrician, but he knew that the Department of Energy vans never came around the summer prior when a wicked storm knocked the power out for three days in the dead of July. Men were out working on lines from dawn to dusk trying to get the power back. What was different that time? His curiosity and suspicion peaked when he saw a line of those vans outside Nancy Wheeler’s home. 

Calum had cut through the neighborhood on his walk home from work. He needed to clear his head in the wake of his dad disappearing. When he reached the top of a hill that overlooked the other half of the neighborhood, he saw the collection of vans at the Wheeler’s home and the series of men and women, dressed in suits, carrying boxes out of the home. It made no sense to him. Who were those people and why were they at the Wheelers? But then he mulled it over and little connections were made. Nancy Wheeler was best friends with Barbara Holland who had also gone missing and never returned during that week. Her little brother was best friends with Will Byers. And her boyfriend, Steve Harrington was supposedly childhood best friends with Danielle Torres. It seemed to perfect to a coincidence.

Tamera a quiet for a long moment before she said, “That is… weird.” It was more than that, but he was happy with her not telling him to give up his pursuits. 

“That’s not all, either. I skipped school two days ago because I didn’t want to play basketball in gym-”

“Which is ridiculous, by the way,” Tamera said. “But continue.” 

“I ran to the store and when I was talking home, I saw Nancy and Jonathan Byers together. They came out of Radio Shack with a bag full of stuff.” The pair was odd, but Calum had seen them together more since the start of the school year. “I couldn’t get close enough to hear most of their conversation, but I swear I heard them mention Barbra Holland.” 

Tamera looked unconvinced. “Barb and Nancy were friends. I’m sure she talks about Barb.” 

“I guess, but did you know Nancy and Steve eat dinner with the Holland once a month? I bet they also think Barb’s still somewhere out there just like her parents do. The Hollands are selling their house to pay for a private investigator.” 

“How do you know all of this?” 

Calum’s mother was still on the fritz, but she had resumed working at the salon, just not as frequently as she had before his dad vanished. Calum helped around the place when he caught a break from work. If there was one place someone could get any information they wanted, it was the hair salon. The mothers, aunts, daughters, and sisters of Hawkins liked to talk about everyone and everything. 

“I have my sources,” Calum said, smoothly. “And those sources confirmed that they saw the private investigator the Hollands hired talking to Danielle more than once. If all of this shit isn’t connected, how do you explain that?” 

“I don’t know.” She paused before glancing at him through slightly more sympathetic eyes. “Just don’t jump the gun on this, okay? I agree that all of that stuff is weird, but there’s still no proof it’s connected to your dad.” But it had to, Calum thought.

He wanted to make Tamera understand, somehow, but before he could get another word out, a car came barreling down the street toward them. 

“Shit!” Tamera yelled as she jerked the wheel to the side and narrowly avoided the speeding car. Bright headlights flooded Calum’s vision and he let out a matching scream alongside Tamera. Their car veered slightly off the side of the road, the right wheels in the grass, as the other car passed, they continued racing down the road. 

Calum clutched his chest, feeling like he just suffered a mini-heart attack. “Jesus Christ! Who the hell was that?” he asked. 

Looking in the rearview mirror, Tamera narrowed her eyes under her large, wire-framed glasses. “I think that was the new kid’s car.” 

“What an asshole.” 

→←

Steve was dead, he had to be. Dead, but in a lot of pain, which didn’t make a lot of sense but even thinking was too painful. Every one of his muscles ached and his head felt too heavy for his body. His eyes were still closed when he tried to move, but it was as if he was crammed into a small space that wouldn’t allow his limbs to stretch. With a groan, he forced his eyes open despite the pounding in his head. 

The world was a blur in front of him. He tried to rub his eyes and clear his vision, but someone grabbed his wrist. “No, don’t touch it.” Dustin’s voice filled his ears as the kid’s face came more into focus. “Hey buddy, it’s okay. You put up a good fight. He kicked your ass, but you put up a good fight.” 

Oh, God. That was the only thought that flowed through Steve’s mind as the events of the night rushed back to him. The feeling of glass shattering over his head, the screams from the kids, and the taste of blood in his mouth all came back to him. 

From right beside Steve, another familiar voice sounded. “Please slow down,” Sunshine groaned. 

“Don’t throw up in here,” Mike replied, his head popping up on the other side of Dustin. 

“Okay, you’re gonna keep straight for half a mile, then make a left on Mount. Sinai,” Lucas instructed from somewhere in front of Steve. 

Steve had no idea where he was. All of the voices pounded against his skull and there was a weird feeling like he was moving. The last thing he remembered was blacking out in the Byers living room, but he was sure he wasn’t there anymore. Why did he feel like he was moving? 

He tried to sit up, but he was in an uncomfortable and awkward position. There were too many people too close to him. All he could do was look forward and focus on figuring out what was going on. Then, it dawned on him that in front of him was the front seat of a car and he was indeed moving. Not only that but the car was being driven by a redhead who sat way too close to the steering wheel. 

“What’s going on?” Steve started to panic. 

“Relax,” Dustin said, in a lame attempt to ease Steve’s worry. “She’s driven before.”

Mike scoffed. “Yeah, in a parking lot.” 

“That counts,” said Lucas. 

On the furthest side of the backseat, pressed up against the door with his hands shoved in his pockets and his hoodie pulled up over his head, Luke shook his head. “No, it definitely does not.” 

Ignoring them, Dustin looked down at Steve with an expression that looked slightly guilty. “They were going to leave you behind, but I promised that you’d be cool, okay?” 

It certainly was not okay; Steve was in a car being driven by a child. “What is happening?” Steve’s words came out a little slurred. He tried to sit up again as he repeated, “No, no, no.” But a small yelp sounded from his other side as his elbow hit something that was not the door. 

“Ow! Steve, stop moving.” Turning his head, Steve noticed that Sunshine was wedged between the door and him. His elbow was jammed into his ribs and her arm was wrapped around his shoulder, holding an ice pack to his cheek that he hadn’t even noticed until that moment. The side of his face felt numb but the panic inside of his was red hot as he peered past Sunshine and out the window to see Hawkins fly by. 

“No! Stop the car! Slow down!” he yelled. 

“I told you he’d freak out,” Mike huffed. 

From the driver’s seat, Max yelled, “Everybody, shut up! I’m trying to focus!” 

“Oh, wait, that’s Mount. Siani,” Lucas said, looking between the map in his hands and the road. Max shot him a confused look before he frantically pointed to the quickly approaching turn. “Make a left! Make a left, now!” 

Max muttered a string of curse words and yanked the steering wheel as hard as she could to make a sharp left turn. Everyone in the car screamed as a mailbox bounced off the hood of the car and flew over them before landing in the dust the car kicked up from its veer off the road. 

The rest of the trip was a blur. Steve was squished in the backseat and his ears rang from his blow to the head the yelling that filled the car. Somewhere along the way, they nearly collided with another car that was unlucky enough to be on the road at the same time as them and Max nearly drove them all into a ditch. 

Steve squeezed his eyes closed and held onto Sunshine’s arm for dear life as he silently prayed for their trip to be over. It felt like an eternity later, but eventually, Max pulled into an empty field and slammed down on the break. The car lurched forward to a sudden stop, and as everyone fell back against their seat, a collective sigh of relief rang out. 

“Incredible,” Mike said, breaking the silence with a look of bewilderment and awe in his eyes. 

Max pulled the keys from the ignition and tossed a look to the backseat. “I told you. Zoomer.” 

Steve didn’t know what the hell she was talking about, and he didn’t care. He needed out of the car before he hurled. 

Everyone was on the same page and made quick work of getting out. Sunshine pushed open the door and nearly tumbled out of the car before she leaned heavily against the side of it. 

Steve rubbed his throbbing temples, but his effort to collect himself was cut short as the kids all started pulling supplies out of the trunk and placed them near a gaping hole in the ground. 

“Guys,” he said, trying to get their attention. “What do you think you’re doing?” His words were still a little slurred and he had to hold onto the car door to keep himself upright.

“Steve,” Sunshine sighed, but Steve couldn’t stop his anger from bubbling up at the kids as they continued to move and ignore him. 

“What are you, deaf? Hello! We’re not going down there! I made myself clear!” 

A hand grabbed his shoulder and forced his attention. Sunshine peered up at him. He couldn’t see much in the darkness of the field, but it didn’t make much to notice the tiredness that adorned her features. Her brows were furrowed and there was blood smeared against her skin; Steve wondered how they kept finding themselves in those kinds of situations, blooded and bruised and exhausted. 

“I thought we were on the same page?” he said, exasperatedly tossing his hands up in the air, causing her to lose her hold on his coat and step backward. “This is insane and dangerous!” He didn’t intend for his voice to come out as loud as it did, and it was more pointed at the kids than Sunshine, but she still flinched. A wave of guilt instantly ran through him. He closed his eyes once more and willed the world to stop spinning so fast.

“Steve, you’re upset, I get it,” Dustin said, approaching the two teens. Steve rubbed his eyes once more and settled his gaze on the kid who held Steve’s backpack and bat. Dustin wore a pair of swimming goggles and a bandana tied around his neck; he looked ridiculous. “The bottom line is, a party member requires assistance and it’s duty to provide that assistance.” 

Steve hated how loyal the kids were to each other; he’d never seen a group of friends so utterly devoted to one another, and if they weren’t standing at the edge of a hole that led into another nightmare or if Steve hadn’t just gotten the shit beat out of him, he’d probably think it was sweet. 

“Now,” Dustin continued, holding out Steve’s backpack toward him. “I know you guys promised you’d keep us safe. So, keep us safe down there.” 

Steve turned and looked at Sunshine, who was already looking at him. She brushed her frizzy hair behind her ears and wiped the dried blood from under her nose. “They’re going to do this with or without us.” Steve knew she was right, and he knew Hopper and Joyce were going to kill them. 

“Fine,” he said and grabbed his backpack. “Let’s go.” 

Tag list. @sattlersquarry , @echoing-oursong , @leptitlu

[sunshinebingo]

Two Halves Apart

After several years spent hiding in the library beneath the House of Wind, Gwyneth Berdara has finally gathered the courage to come out into the light. With her found family by her side - and a Shadowsinger who makes her heart beat a little faster - Gwyn is learning to embrace life, as well as her new powers. She is still slowly healing from everything that has happened to her at Sangravah, but she is almost certain that she cannot be more blessed by the Mother.

However, a spying mission one day puts her on the path of the one thing that can fill the strange void that she has always felt inside her. A void that has been left behind by an unknown twin sister.

@gwynweekofficial Day 4 - Adventure This chapter follows the journey of Gwyn and Catrin as they grow up. (a little angsty sorry)

Word Count: 3.6k

This one is long so you can Read it on Ao3 - snippet below

10 years old

Catrin looked behind her, at the arching rocks curtained by the willow tree that marked the paths to the rivers. No one seemed to have followed her. She dug her webbed fingers in the soil and pulled herself out the water, far enough that her entire tail would be exposed to the air. She nervously tapped her fingers on the ground while she waited to be dry enough for her legs to appear.

She wasted no time to stand as soon as it happened. And without a glance back at her home, Catrin started walking. She didn’t know exactly where she was going. But she knew who she was looking for. Her sister. Not a chosen one like Callie and Daphne. Her twin.

So, with the only light illuminating the forest being that of the moon, Catrin walked and walked and walked. Hoping to do so until she found a girl who looked like her. Copper hair and teal eyes, Mimi had said she looked like. Two legs and two arms with no webbed hands. Gwyneth.

The thorns and branches in the forest scraped her bare skin, the bugs tried to fly directly into her eyes, her feet hurt from the pebbles and other things she walked on. She grew tired and hungry and dehydrated. But still, Catrin walked and walked. 

[zooplekochi]

Automate Simple Tasks Using Python: A Beginner’s Guide

In today's fast paced digital world, time is money. Whether you're a student, a professional, or a small business owner, repetitive tasks can eat up a large portion of your day. The good news? Many of these routine jobs can be automated, saving you time, effort, and even reducing the chance of human error.

Enter Python a powerful, beginner-friendly programming language that's perfect for task automation. With its clean syntax and massive ecosystem of libraries, Python empowers users to automate just about anything from renaming files and sending emails to scraping websites and organizing data.

If you're new to programming or looking for ways to boost your productivity, this guide will walk you through how to automate simple tasks using Python.

🌟 Why Choose Python for Automation?

Before we dive into practical applications, let’s understand why Python is such a popular choice for automation:

Easy to learn: Python has simple, readable syntax, making it ideal for beginners.

Wide range of libraries: Python has a rich ecosystem of libraries tailored for different tasks like file handling, web scraping, emailing, and more.

Platform-independent: Python works across Windows, Mac, and Linux.

Strong community support: From Stack Overflow to GitHub, you’ll never be short on help.

Now, let’s explore real-world examples of how you can use Python to automate everyday tasks.

🗂 1. Automating File and Folder Management

Organizing files manually can be tiresome, especially when dealing with large amounts of data. Python’s built-in os and shutil modules allow you to automate file operations like:

Renaming files in bulk

Moving files based on type or date

Deleting unwanted files

Example: Rename multiple files in a folder

import os folder_path = 'C:/Users/YourName/Documents/Reports' for count, filename in enumerate(os.listdir(folder_path)): dst = f"report_{str(count)}.pdf" src = os.path.join(folder_path, filename) dst = os.path.join(folder_path, dst) os.rename(src, dst)

This script renames every file in the folder with a sequential number.

📧 2. Sending Emails Automatically

Python can be used to send emails with the smtplib and email libraries. Whether it’s sending reminders, reports, or newsletters, automating this process can save you significant time.

Example: Sending a basic email

import smtplib from email.message import EmailMessage msg = EmailMessage() msg.set_content("Hello, this is an automated email from Python!") msg['Subject'] = 'Automation Test' msg['From'] = 'you@example.com' msg['To'] = 'recipient@example.com' with smtplib.SMTP_SSL('smtp.gmail.com', 465) as smtp: smtp.login('you@example.com', 'yourpassword') smtp.send_message(msg)

⚠️ Note: Always secure your credentials when writing scripts consider using environment variables or secret managers.

🌐 3. Web Scraping for Data Collection

Want to extract information from websites without copying and pasting manually? Python’s requests and BeautifulSoup libraries let you scrape content from web pages with ease.

Example: Scraping news headlines

import requests from bs4 import BeautifulSoup url = 'https://www.bbc.com/news' response = requests.get(url) soup = BeautifulSoup(response.text, 'html.parser') for headline in soup.find_all('h3'): print(headline.text)

This basic script extracts and prints the headlines from BBC News.

📅 4. Automating Excel Tasks

If you work with Excel sheets, you’ll love openpyxl and pandas two powerful libraries that allow you to automate:

Creating spreadsheets

Sorting data

Applying formulas

Generating reports

Example: Reading and filtering Excel data

import pandas as pd df = pd.read_excel('sales_data.xlsx') high_sales = df[df['Revenue'] > 10000] print(high_sales)

This script filters sales records with revenue above 10,000.

💻 5. Scheduling Tasks

You can schedule scripts to run at specific times using Python’s schedule or APScheduler libraries. This is great for automating daily reports, reminders, or file backups.

Example: Run a function every day at 9 AM

import schedule import time def job(): print("Running scheduled task...") schedule.every().day.at("09:00").do(job) while True: schedule.run_pending() time.sleep(1)

This loop checks every second if it’s time to run the task.

🧹 6. Cleaning and Formatting Data

Cleaning data manually in Excel or Google Sheets is time-consuming. Python’s pandas makes it easy to:

Remove duplicates

Fix formatting

Convert data types

Handle missing values

Example: Clean a dataset

df = pd.read_csv('data.csv') df.drop_duplicates(inplace=True) df['Name'] = df['Name'].str.title() df.fillna(0, inplace=True) df.to_csv('cleaned_data.csv', index=False)

💬 7. Automating WhatsApp Messages (for fun or alerts)

Yes, you can even send WhatsApp messages using Python! Libraries like pywhatkit make this possible.

Example: Send a WhatsApp message

import pywhatkit pywhatkit.sendwhatmsg("+911234567890", "Hello from Python!", 15, 0)

This sends a message at 3:00 PM. It’s great for sending alerts or reminders.

🛒 8. Automating E-Commerce Price Tracking

You can use web scraping and conditionals to track price changes of products on sites like Amazon or Flipkart.

Example: Track a product’s price

url = "https://www.amazon.in/dp/B09XYZ123" headers = {"User-Agent": "Mozilla/5.0"} page = requests.get(url, headers=headers) soup = BeautifulSoup(page.content, 'html.parser') price = soup.find('span', {'class': 'a-price-whole'}).text print(f"The current price is ₹{price}")

With a few tweaks, you can send yourself alerts when prices drop.

📚 Final Thoughts

Automation is no longer a luxury it’s a necessity. With Python, you don’t need to be a coding expert to start simplifying your life. From managing files and scraping websites to sending e-mails and scheduling tasks, the possibilities are vast.

As a beginner, start small. Pick one repetitive task and try automating it. With every script you write, your confidence and productivity will grow.

Conclusion

If you're serious about mastering automation with Python, Zoople Technologies offers comprehensive, beginner-friendly Python course in Kerala. Our hands-on training approach ensures you learn by doing with real-world projects that prepare you for today’s tech-driven careers.

[mostlysignssomeportents]

This day in history

On SEPTEMBER 24th, I'll be speaking IN PERSON at the BOSTON PUBLIC LIBRARY!

#20yrsago AnarchistU, Toronto’s wiki-based free school https://web.archive.org/web/20040911010603/http://anarchistu.org/bin/view/Anarchistu

#20yrsago Fair use is a right AND a defense https://memex.craphound.com/2004/09/09/fair-use-is-a-right-and-a-defense/

#20yrsago Bounty for asking “How many times have you been arrested, Mr. President?” https://web.archive.org/web/20040918115027/https://onesimplequestion.blogspot.com/

#20yrsago What yesterday’s terrible music https://www.loweringthebar.net/2009/09/open-mike-likely-to-close-out-legislators-career.htmlsampling ruling means https://web.archive.org/web/20040910095029/http://www.lessig.org/blog/archives/002153.shtml

#15yrsago Conservative California legislator gives pornographic account of his multiple affairs (including a lobbyist) into open mic https://www.loweringthebar.net/2009/09/open-mike-likely-to-close-out-legislators-career.html

#15yrsago Shel Silverstein’s UNCLE SHELBY, not exactly a kids’ book https://memex.craphound.com/2009/09/09/shel-silversteins-uncle-shelby-not-exactly-a-kids-book/

#10yrsago Seemingly intoxicated Rob Ford gives subway press-conference https://www.youtube.com/watch?v=WbcETJRoNCs

#10yrsago Amazon vs Hachette is nothing: just WAIT for the audiobook wars! https://locusmag.com/2014/09/cory-doctorow-audible-comixology-amazon-and-doctorows-first-law/

#10yrsago Dietary supplement company sues website for providing a forum for dissatisfied customers https://www.techdirt.com/2014/09/08/dietary-supplement-company-tries-suing-pissedconsumer-citing-buyers-agreement-to-never-say-anything-negative-about-roca/

#10yrsago New wind-tunnel tests find surprising gains in cycling efficiency from leg-shaving https://www.theglobeandmail.com/life/health-and-fitness/health/the-curious-case-of-the-cyclists-unshaved-legs/article20370814/

#10yrsago Behind the scenes look at Canada’s Harper government gagging scientists https://www.cbc.ca/news/science/federal-scientist-media-request-generates-email-frenzy-but-no-interview-1.2759300

#10yrsago Starred review in Kirkus for INFORMATION DOESN’T WANT TO BE FREE https://www.kirkusreviews.com/book-reviews/cory-doctorow/information-doesnt-want-to-be-free/

#10yrsago Steven Gould’s “Exo,” a Jumper novel by way of Heinlein’s “Have Spacesuit, Will Travel” https://memex.craphound.com/2014/09/09/steven-goulds-exo-a-jumper-novel-by-way-of-heinleins-have-spacesuit-will-travel/

#5yrsago Important legal victory in web-scraping case https://arstechnica.com/tech-policy/2019/09/web-scraping-doesnt-violate-anti-hacking-law-appeals-court-rules/

#5yrsago Whistleblowers out Falwell’s Liberty University as a grifty, multibillion-dollar personality cult https://web.archive.org/web/20190910000528/https://www.politico.com/magazine/amp/story/2019/09/09/jerry-falwell-liberty-university-loans-227914

#5yrsago Pinduoduo: China’s “Groupon on steroids” https://www.wired.com/story/china-ecommerce-giant-never-heard/

#5yrsago Notpetya: the incredible story of an escaped US cyberweapon, Russian state hackers, and Ukraine’s cyberwar https://www.wired.com/story/notpetya-cyberattack-ukraine-russia-code-crashed-the-world/

#5yrsago NYT calls for an end to legacy college admissions https://www.nytimes.com/2019/09/07/opinion/sunday/end-legacy-college-admissions.html

#5yrsago Purdue’s court filings understate its role in the opioid epidemic by 80% https://www.propublica.org/article/data-touted-by-oxycontin-maker-to-fight-lawsuits-doesnt-tell-the-whole-story

#1yrago Saturday linkdump, part the sixth https://pluralistic.net/2023/09/09/nein-nein/#everything-is-miscellaneous

The paperback edition of The Lost Cause, my nationally bestselling, hopeful solarpunk novel is out this month!

[sindhu14]

What is Python, How to Learn Python?

What is Python?

Python is a high-level, interpreted programming language known for its simplicity and readability. It is widely used in various fields like: ✅ Web Development (Django, Flask) ✅ Data Science & Machine Learning (Pandas, NumPy, TensorFlow) ✅ Automation & Scripting (Web scraping, File automation) ✅ Game Development (Pygame) ✅ Cybersecurity & Ethical Hacking ✅ Embedded Systems & IoT (MicroPython)

Python is beginner-friendly because of its easy-to-read syntax, large community, and vast library support.

How Long Does It Take to Learn Python?

The time required to learn Python depends on your goals and background. Here’s a general breakdown:

1. Basics of Python (1-2 months)

If you spend 1-2 hours daily, you can master:

Variables, Data Types, Operators

Loops & Conditionals

Functions & Modules

Lists, Tuples, Dictionaries

File Handling

Basic Object-Oriented Programming (OOP)

2. Intermediate Level (2-4 months)

Once comfortable with basics, focus on:

Advanced OOP concepts

Exception Handling

Working with APIs & Web Scraping

Database handling (SQL, SQLite)

Python Libraries (Requests, Pandas, NumPy)

Small real-world projects

3. Advanced Python & Specialization (6+ months)

If you want to go pro, specialize in:

Data Science & Machine Learning (Matplotlib, Scikit-Learn, TensorFlow)

Web Development (Django, Flask)

Automation & Scripting

Cybersecurity & Ethical Hacking

Learning Plan Based on Your Goal

📌 Casual Learning – 3-6 months (for automation, scripting, or general knowledge) 📌 Professional Development – 6-12 months (for jobs in software, data science, etc.) 📌 Deep Mastery – 1-2 years (for AI, ML, complex projects, research)

Scope @ NareshIT:

At NareshIT’s Python application Development program you will be able to get the extensive hands-on training in front-end, middleware, and back-end technology.

It skilled you along with phase-end and capstone projects based on real business scenarios.

Here you learn the concepts from leading industry experts with content structured to ensure industrial relevance.

An end-to-end application with exciting features

Earn an industry-recognized course completion certificate.

For more details:

[ogma-conceptions]

Why Should You Do Web Scraping for python

Web scraping is a valuable skill for Python developers, offering numerous benefits and applications. Here’s why you should consider learning and using web scraping with Python:

1. Automate Data Collection

Web scraping allows you to automate the tedious task of manually collecting data from websites. This can save significant time and effort when dealing with large amounts of data.

2. Gain Access to Real-World Data

Most real-world data exists on websites, often in formats that are not readily available for analysis (e.g., displayed in tables or charts). Web scraping helps extract this data for use in projects like:

Data analysis

Machine learning models

Business intelligence

3. Competitive Edge in Business

Businesses often need to gather insights about:

Competitor pricing

Market trends

Customer reviews Web scraping can help automate these tasks, providing timely and actionable insights.

4. Versatility and Scalability

Python’s ecosystem offers a range of tools and libraries that make web scraping highly adaptable:

BeautifulSoup: For simple HTML parsing.

Scrapy: For building scalable scraping solutions.

Selenium: For handling dynamic, JavaScript-rendered content. This versatility allows you to scrape a wide variety of websites, from static pages to complex web applications.

5. Academic and Research Applications

Researchers can use web scraping to gather datasets from online sources, such as:

Social media platforms

News websites

Scientific publications

This facilitates research in areas like sentiment analysis, trend tracking, and bibliometric studies.

6. Enhance Your Python Skills

Learning web scraping deepens your understanding of Python and related concepts:

HTML and web structures

Data cleaning and processing

API integration

Error handling and debugging

These skills are transferable to other domains, such as data engineering and backend development.

7. Open Opportunities in Data Science

Many data science and machine learning projects require datasets that are not readily available in public repositories. Web scraping empowers you to create custom datasets tailored to specific problems.

8. Real-World Problem Solving

Web scraping enables you to solve real-world problems, such as:

Aggregating product prices for an e-commerce platform.

Monitoring stock market data in real-time.

Collecting job postings to analyze industry demand.

9. Low Barrier to Entry

Python's libraries make web scraping relatively easy to learn. Even beginners can quickly build effective scrapers, making it an excellent entry point into programming or data science.

10. Cost-Effective Data Gathering

Instead of purchasing expensive data services, web scraping allows you to gather the exact data you need at little to no cost, apart from the time and computational resources.

11. Creative Use Cases

Web scraping supports creative projects like:

Building a news aggregator.

Monitoring trends on social media.

Creating a chatbot with up-to-date information.

Caution

While web scraping offers many benefits, it’s essential to use it ethically and responsibly:

Respect websites' terms of service and robots.txt.

Avoid overloading servers with excessive requests.

Ensure compliance with data privacy laws like GDPR or CCPA.

If you'd like guidance on getting started or exploring specific use cases, let me know!