#so many buzzwords and they're all accurate

[actual-changeling]

one thing i really really like about txf is that they aren't afraid to kill off characters.

in most shows, melissa would make a miraculous recovery, mulder probably wouldn't lose both his parents, and there are all the episode-specific characters that are killed simply because

a) it fits right into the plot

b) they can and

c) it adds a layer of realism to it.

if you never kill any of the characters—even minor side characters/npcs—then at some point, the dangers will feel less dangerous, less real, because you know everyone will be fine anyway. but not here.

the stakes ARE real and we are shown and told so over and over again.

scully loses her dad, melissa gets killed in her apartment, mulder's dad is killed, mulder's mom kills herself, there is no miraculous, perfect return of samantha, scully gets cancer, OTHER (returning) characters get sick and die, and the list goes on.

nowadays, way too many people are incapable of consuming anything that isn't 99% "everything will be fine" because processing complex emotions requires complex thinking, and boy are people refusing to develop that skill.

ironic to say, but txf is refreshing in regards to that AND has better representation that most shows and movies being created in recent years. do you know how fucking rare it is to have disabled characters that simply exist? whose disability is right there, it's real, they're not somehow hiding it or trying to make it less obvious.

they are like any other characters, and unless it is in some way relevant to the plot, it's usually not even brought up or mentioned. no misery or inspiration porn, no weird "you're not disabled, you're [insert term that's fucking horrible]", nothing.

even with episodes like gender bender, there is no transphobia, no caricatures, it's treated like any other case with any other people.

you'd expect a lot of ableism in a show about the paranormal since "crazy mentally ill person is a danger to everyone" is a popular trope (disappointing but not surprising), yet as someone who has highly stigmatized disorders—not just in general, specifically in the medical field too—I don't think I have ever felt uncomfortable with any of the cases.

people look back on older shows and start criticizing the language but not only were the terms and concepts named differently and have evolved, i'd rather have a show use "bad" or incorrect language but have genuine, caring representation than someone using all the buzzwords and thinking that makes whatever they do not offensive.

(side note: language moves fast, especially in psychiatry but also in other scientific circles, and the same applies to what i'll loosely call 'community language'. as long as there's good intent and an open ear, i couldn't give less of a fuck if they say transgender, transsexual, or transvestite)

i'm rewatching 'the field where i died' and this episode has one of the best, most accurate portrayals of DID i've seen in probably. ever. is it played up a bit? yeah sure, but it doesn't feel mocking or otherwise disrespectful and it refuses to play into any existing stereotypes.

this post got away from me, but bottom line is that this show is genuinely good in a way few shows are.

[trainer-blue]

there are three terms i see being thrown around in a ton of posts "supporting" palestinians that don't actually apply. if you're someone who calls israel an apartheid colonial state committing genocide and you either actually believe it or have seen those terms often enough to copy them yourself, i encourage you to think a little more deeply about what these words mean:

apartheid:

this term isn't one that you use for just any form or extent of racial discrimination. i have never seen anyone use this term in reference to the united states, and i think everyone reading this can acknowledge that racism is extremely prevalent and systemic here. in fact, i've only ever seen this term used in regards to south africa and israel. if you use it about israel, think about what policies are in place that make it an apartheid state in your view, and then think about whether any other country in the world has comparable ones. if so, why is israel considered apartheid when others aren't? here is some information about the term and why it does not apply. why israel isn't an apartheid state arab political parties and participation in israeli government

colonial state:

to most people, colonization involves taking land from indigenous peoples so that people who are not indigenous to the area / have no ancestral ties to that area can control it instead. colonial settlers could, in theory, return to a country of ancestral origin in which they would be a cultural majority or be safe and not expect to be subject to hate crimes because they are of majority status. one can acknowledge that palestinians have been displaced without it being colonialism. jewish people are indigenous. yes, even the white ones. no, not all jews are white. if any of these claims seem far-fetched to you, or you don't understand how jewish people can be indigenous to israel, i recommend reading these posts: jewish indigeneity from an archeological perspective history of jewish presence in israel

genocide:

"the deliberate killing of a large number of people from a particular nation or ethnic group with the aim of destroying that nation or group." if israelis-- even the israeli government, which even "zionists" consider right-wing, fucked, and nonrepresentative of their values-- wanted to wipe out palestinians, we would have seen very different actions from them throughout history. one can acknowledge and mourn the loss of innocent palestinian life during wartime without framing it as something it's not. growth of palestinian population rates

"why does it matter what terms we use? isn't it GOOD to exaggerate or use buzzwords to catch people's attention? how else can we make people understand the true plight of palestinians?"

there is no reason to use terms that don't apply, actually. when so many people parrot these terms without understanding whether or not they're accurate:

1. this actual situation gets muddled, leading to people who have done no research of their own jumping to pick sides because they think they’re rallying against "the new nazis." These people may then support Hamas as “freedom fighters,” attack Jewish people around the world, and celebrate the rape, torture, and death of Israeli women and children because they’re “complicit in colonial apartheid genocide” and no longer considered human.

2. you imply that it is impossible to care about or support civilians affected by war if they’re NOT victims of genocide, colonialism, or apartheid states. Why do you need to rely on these terms to feel empathy for palestinians? If you acknowledged that they’ve been displaced by other indigenous people and are being killed in and affected by war, would your fervor for their cause die out? if so, is that a reasonable response to the realization that the real world isn't cut and dry, and not every conflict has a completely evil side and a side that is completely innocent?

3. ACTUAL instances of genocide, apartheid, and colonialism get watered down. I’ve seen people compare this to the Holocaust, calling Jewish people Nazis. Look back at the resource I linked to above. When you compare steady growth of Palestinian populations with the brutal erasure of ⅔ of the jewish population in europe, you are not only overexaggerating current events, but you’re also saying that the holocaust wasn’t all that bad, actually. To weaponize a people’s own genocide against them is. Gross. Especially when recent events have been catalyzed by Hamas beheading and burning babies–rather reminiscent of the Holocaust–and when people continue to deny that the 10/7 attack even took place. Also. rather. Reminiscent of non-jewish refusal to believe accounts of concentration camps.

similarly, when you water down terms like “apartheid” to mean any form of inequality for racial minorities, you deny the realities of apartheid south africa and imply that that’s “pretty much the same” as racism experienced in other countries around the world

hamas calling for jewish (NOT ISRAELI) death

perspective on equating israel to apartheid south africa

thank you for reading. this is not a call to abandon support for palestinians. this is a call to think about the terms you use and the misinformation you've seen.

[some-pers0n]

For the ask game: 1, 8, 10, 14, 23

Oh boy quite a lot. Let's do it.

1 - The Character Everyone Gets Wrong

For Wof no joke it's gotta be Qibli. So many people write him off as a goofy silly guy who thinks a lot and is really smart when honestly he's a massive overthinker with horrible social anxiety and uses a cocky and confident persona to a) cope and b) try to make friends. He's not a joke-spewing machine that can't read the room and stop when somebody is in distress.

For TF2 it's probably Saxton Hale. Yeah he's funny and cool, but he's not really a good boss like. at all. Not even a good person before the comics. He was characterized as a guy who liked hunting endangered animals and selling terrible and cheap weapons to keep the Gravel War going. It's 500% more funny when he's got jokes about how unions and workers rights are a nasty thorn in his side.

8 - Common Fandom Opinion Everyone is Wrong About

[Insert character] being a Mary Sue. Typically it's Glory or Moon. Glory is a victim of poor character writing and favouritism on Tui's part and Moon's entre character is reduced to a plot device. Those aren't Mary Sues but just poorly written characters in ways different than a Sue. Plus I also just Hate the term and would rather people like...elaborate more on what they say as opposed to using vague buzzwords.

For TF2 it's probably that Engie is boring, both in gameplay and lore. For starters, put on the Gunslinger and Rescue Ranger and stop turtling in the same spots every round. Secondly, Engie's personality is one of the most fascinating due to it kinda being a little cover up. Not even to mention his whole family. So cool and interesting....

10 - Worst Part of Fanon

Hmmmm,,,flanderization is always terrible. I hate it especially with Qinter stuff since a good chunk of it horribly misses the mark of who Qibli and Winter (and even by extension Moon) are and what makes them click. Their whole this is that they have a persona and I feel Qinter as a whole should be based on them putting aside their exaggerated personalities and finding solidarity and comfort within being around each other as their pure selves. This and also the infantalization of characters like Whiteout. All my homes hate that.

Same thing can be said for TF2. Me and my homies hate Nazi Medic. Medic likes his coworkers, he just is a bit silly like that.

14 - One Thing you See in Fics all the Time

For WoF, it's gotta be swearing. I literally don't know why it kinda irks me, but it feels weird when you're writing something serious and straight-forward while the dragon characters say the fuck word. Nothing against it, I just...I dunno.

For TF2 it's always the goddamn shoe-horned in racism/queerphobia. Please man I don't need you to get up on your soap box and tell me that the 60s wasn't good for queers and people of colour I'm trying to read Old Man Yaoi. It's especially annoying when certain characters (ex: Soldier) only exist to really fulfill the role of being the person who make it "period accurate" by including 12 extremely harmful and horrible slurs in every sentence they speak.

23 - Ship You've Unwillingly Come Around To

For WoF: Cleril somehow. The "Cleril is good :)" -> "Peril is toxic and this relationship will never work" -> "Cleril is good :)" pipeline is real. Peril HAS been getting help and is vastly improving. She is doing so incredibly well. She uses Clay as a moral compass, but she's aware of that and trying to change. It's not that bad of a ship ngl.

For TF2: Jagerbombs (Demo/Medic). I'm God's strongest aro/ace Medic enjoyer so I like them best as friends, but honestly they're just great together. Literally written up like 19k words of these morons. Never would've thought in a million years I'd do it (especially considering how I'm the No. 4 Science Party person ever) but they're just wonderful. Honestly like them better than HeavyMed– *gets shot*

[datascienceonleitechnologies]

What Skills You Need to Have to Succeed in Data Science

What Skills You Need to Have to Succeed in Data Science

1. Introduction to Data Science

Data Science has emerged as a crucial field in today's data-driven world, where organizations gather vast amounts of information and seek valuable insights to drive decision-making. This article explores the essential skills required to succeed in the field of Data Science. Whether you are an aspiring data scientist or looking to enhance your existing skillset, understanding the key areas of expertise in data science will empower you to harness the power of data effectively. From programming proficiency to statistical analysis, machine learning, and domain knowledge, we will delve into the fundamental skills that can pave the way for a successful career in data science.

What Skills You Need to Have to Succeed in Data Science

1. Introduction to Data Science

1.1 What is Data Science?

Data Science, it sounds mystical and complex, doesn't it? Well, fear not! At its core, Data Science is all about using data to gain insights, make informed decisions, and solve complex problems. It's like being a detective, except instead of solving crimes, you're uncovering hidden patterns and trends in data.

1.2 The Role of Data Scientists

If you're considering a career in Data Science, welcome to the club! A Data Scientist is the Sherlock Holmes of the digital age. They collect and analyze vast amounts of data to extract meaningful information and uncover valuable insights for organizations. You'll be the go-to person for turning data into actionable strategies.

1.3 Importance of Data Science in Various Industries

Data Science isn't just a buzzword; it's a powerful tool that has revolutionized industries like healthcare, finance, marketing, and more. By harnessing the power of data, organizations can make smarter decisions, improve efficiency, and gain a competitive edge. With the demand for data-driven insights skyrocketing, professionals with data science skills are in high demand.

2. Essential Programming Skills for Data Scientists

2.1 Proficiency in Python

Python, the language of choice for many data scientists, is not only powerful but also user-friendly. Its extensive libraries, such as NumPy and Pandas, make data manipulation and analysis a breeze. So, mastering Python is a must for any aspiring data scientist.

2.2 Knowledge of R Programming

While Python may be the popular kid on the block, R is the seasoned veteran when it comes to statistical analysis and visualization. Learning R will allow you to unlock the full potential of statistical modeling and data visualization, making it a valuable addition to your data science toolkit.

2.3 Understanding SQL and Database Management

Ah, databases. They're the backbone of many data-intensive applications. Knowing how to write SQL queries and manage databases will help you access, manipulate, and extract valuable insights from large datasets efficiently. So, don't forget to make friends with SQL.

3. Statistical Analysis and Mathematics for Data Science

3.1 Understanding Descriptive and Inferential Statistics

Statistics, the language of uncertainty! Descriptive statistics allows you to summarize and visualize data, while inferential statistics helps you draw conclusions and make predictions based on sample data.

3.2 Probability Theory and Distributions

Probability theory is the secret sauce that helps you quantify uncertainty. Understanding probability distributions, such as the normal distribution or the binomial distribution, allows you to model and analyze real-world phenomena accurately. Get ready to impress your friends with your ability to predict outcomes!

3.3 Linear Algebra and Calculus

Fear not, math warriors! Linear algebra and calculus are not as intimidating as they sound. Linear algebra helps you understand the foundations of machine learning algorithms, while calculus enables you to optimize and fine-tune those algorithms. So, dust off your old math textbooks and embark on this mathematical adventure!

4. Data Manipulation and Cleaning Techniques

4.1 Preprocessing and Data Cleaning

Let's face it, real-world data is messy. Preprocessing and data cleaning techniques, such as handling missing values, dealing with outliers, and standardizing data, are crucial for ensuring the integrity and reliability of your analyses. So, roll up your sleeves and get ready to clean some data!

4.2 Data Transformation and Feature Engineering

Transforming and engineering features allow you to uncover hidden patterns and create new variables that improve the performance of your models. It's like turning a plain block of marble into a stunning sculpture – data edition. So, be creative and let your data masterpiece shine!

4.3 Dealing with Missing Data and Outliers

Missing data and outliers can throw a wrench in your analysis. But fear not, data magician! Different techniques, such as imputation for missing data and outlier detection and handling, will help you tame these data beasts. With your skills, no missing value or rogue outlier will go unnoticed!

Now that you know the essential skills needed to succeed in data science, it's time to roll up your sleeves and embark on this exciting journey. Remember, data science is a constantly evolving field, so keep learning, stay curious, and let your passion for data guide you to success!

5. Machine Learning and Predictive Modeling

5.1 Overview of Machine Learning Algorithms

Machine learning is the backbone of data science, and having a solid understanding of different machine learning algorithms is crucial. From decision trees to support vector machines, these algorithms are used to analyze and interpret data, make predictions, and uncover patterns. Knowing the strengths and weaknesses of each algorithm will help you choose the most suitable one for a given problem.

5.2 Supervised Learning Techniques

Supervised learning techniques involve training models on labeled data to make predictions or classifications. This includes algorithms like linear regression, logistic regression, and random forests. Understanding these techniques will enable you to build accurate models that can predict outcomes based on input variables.

5.3 Unsupervised Learning Techniques

Unsupervised learning techniques are used when there is no labeled data available. These techniques help you uncover hidden patterns or groupings within the data. Clustering algorithms like k-means and hierarchical clustering fall under this category. By mastering unsupervised learning techniques, you'll be able to extract valuable insights from unstructured data.

6. Data Visualization and Communication

6.1 Importance of Data Visualization in Data Science

Data visualization is a powerful tool for effectively communicating complex data insights. It allows you to present information in a visually appealing and easily understandable manner. Good data visualization skills will help you convey your findings to stakeholders, making it easier for them to understand and act upon the insights you provide.

6.2 Tools and Techniques for Data Visualization

There are various tools and techniques available for data visualization, including Python libraries like Matplotlib and Seaborn, Tableau, and Power BI. Learning how to use these tools effectively will enable you to create compelling visualizations that enhance the understanding of data and make your presentations more impactful.

6.3 Effective Communication of Data Insights

Being able to effectively communicate your data insights is just as important as being able to analyze the data itself. This involves having strong presentation skills, simplifying complex concepts for non-technical stakeholders, and telling a compelling story with your data. By mastering communication, you'll be able to articulate your findings in a way that resonates with your audience.

7. Domain Knowledge and Business Acumen in Data Science

7.1 Understanding the Industry and Domain-Specific Challenges

Domain knowledge refers to having a deep understanding of the industry or field you're working in. This allows you to identify relevant data sources, understand the specific challenges faced by the industry, and develop data-driven solutions that address these challenges. Having domain expertise gives you an edge in interpreting data and generating actionable insights.

7.2 Applying Data Science Techniques to Solve Business Problems

Data science is not just about analyzing data; it's about using data to solve real-world business problems. This requires translating business problems into data-driven questions, selecting appropriate analytical techniques, and developing models that provide actionable recommendations. The ability to apply data science techniques to solve business problems is what sets successful data scientists apart.

7.3 Translating Data Insights into Actionable Recommendations

Data insights are only valuable if they can be translated into actionable recommendations for decision-makers. This involves distilling complex findings into clear and concise recommendations that can drive positive change within an organization. Being able to bridge the gap between data analysis and practical implementation is essential for success in the field of data science.

8. Continuous Learning and Adaptability in the Field of Data Science

8.1 Staying Updated with the Latest Trends and Technologies

The field of data science is constantly evolving, with new technologies, algorithms, and techniques emerging regularly. To stay ahead, it's important to continuously learn and adapt. This involves staying updated with the latest trends, attending conferences, reading research papers, and actively seeking out new learning opportunities.

8.2 Participating in Data Science Communities and Networks

Engaging with the data science community is a great way to learn from others, share knowledge, and collaborate on projects. Joining online forums, attending meetups, and participating in data science competitions can help you expand your network and gain insights from experienced professionals in the field.

8.3 Cultivating a Growth Mindset and Embracing Challenges

Data science can be a challenging field, but having a growth mindset and embracing challenges will help you thrive. Rather than being deterred by setbacks, view them as opportunities for growth and learning. Cultivating a mindset that embraces continuous improvement and welcomes new challenges is key to long-term success in data science.

In conclusion, possessing a solid foundation in programming, statistics, and mathematics, along with expertise in data manipulation, machine learning, and visualization, is essential for success in data science. Additionally, the ability to apply domain knowledge and business acumen to data-driven insights adds tremendous value to organizations. However, it is important to remember that the field of data science is dynamic and continuously evolving. Embracing a mindset of continuous learning and adaptability will ensure that you stay ahead in this fast-paced industry. By honing these skills and staying abreast of the latest trends, you will be well-equipped to navigate the exciting and ever-expanding world of data science.

FAQ

1. Do I need to have a strong background in programming to succeed in data science?

While a strong background in programming, particularly with languages like Python and R, is highly beneficial in data science, it is not necessarily a prerequisite. However, a solid understanding of programming concepts and the ability to write efficient and clean code is essential for data manipulation, analysis, and model implementation.

2. Is it necessary to have a deep understanding of mathematics and statistics?

Yes, a strong foundation in mathematics and statistics is crucial for data science. Knowledge of concepts such as probability theory, calculus, and linear algebra enables data scientists to understand and develop sophisticated algorithms, build statistical models, and interpret results accurately.

3. How important is domain knowledge in data science?

Domain knowledge is highly valuable in data science as it allows data scientists to contextualize and interpret data within specific industries or domains. Understanding the business challenges, goals, and nuances of a particular field enables data scientists to generate more meaningful insights and make actionable recommendations.

4. How can I stay up-to-date with the latest trends and technologies in data science?

To stay current in the field of data science, it is advisable to actively engage with the data science community. This can involve attending conferences, participating in online forums and communities, reading industry publications and research papers, and exploring continuous learning opportunities such as online courses and certifications. 

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[charleskirkland]

Artificial Intelligence in Real Estate: How AI is Revolutionizing Property Management

Introduction

Artificial intelligence (AI) has been a buzzword in the tech world for years, but many people still don’t understand how it works or why it matters. In this article, I'll explain how AI can be used in real estate and property management to improve efficiency and make business easier.

How the AI Revolution Is Changing Real Estate

The AI revolution is changing the real estate market. It's helping realtors do their job better and more efficiently, allowing them to spend less time on administrative tasks and more time interacting with clients.

This is because artificial intelligence (AI) has enabled us to automate some of the most tedious and repetitive tasks that previously required human intervention. For example, an AI-enabled application can be programmed to analyze thousands of listings in seconds, providing you with an accurate picture of each property so that when it comes time for showing appointments or open houses, no matter how many properties there are or where they're located--you'll have all the information at hand at any given moment without having spent hours researching online yourself!

Using AI to Collect Big Data

AI can help you collect and analyze large amounts of data in order to identify patterns, predict future trends, and find anomalies. AI is trained on historical data to learn what the normal behavior looks like so that it can recognize deviations from this pattern. As a result, it's able to flag potential problems before they happen--such as tenant complaints about plumbing issues or maintenance requests related to HVAC systems.

In addition to providing insight about past performance, artificial intelligence enables you as a property manager or real estate professional (whether you're an individual entrepreneur or part of an institutional organization) with new ways of thinking about how your business operates: what works well; which areas could be improved upon; where opportunities lie for growth?

AI and Automation in Property Management

Automation, as it relates to property management, entails the use of technology to streamline processes and make decisions. For example, automation can be used to automate tasks such as daily check-ins or lease renewal notifications. These types of tasks can be automated using software that integrates with your existing CRM system and other third party applications.

Automated decision making is a more advanced application of AI technology in real estate management where algorithms are used for predictive analysis based on historical data points from past tenants or properties in your portfolio. The goal is for the computer program (or "bot") to learn from these patterns so that when new information comes in about a prospective tenant or property listing, it can predict whether they would be likely candidates based on past experiences with similar applicants/properties

Predicting Trends Through Machine Learning

AI can be used to predict trends in real estate. It helps real estate professionals make better decisions and predictions, which can help them make more money and build their businesses.

Artificial intelligence is changing real estate in many ways.

Artificial intelligence is changing many industries, and real estate is no exception. In fact, AI has already begun to revolutionize property management by making it easier for owners and managers alike to manage their properties.

AI can help with everything from optimizing lease terms based on market trends to automating routine tasks like rent collection and payment processing--and there's much more to come!

Conclusion

AI has the potential to revolutionize the real estate industry. The technology can help property owners and managers make better decisions, collect data more efficiently and even automate some tasks. However, there are still some challenges that need to be addressed before AI becomes an integral part of every business.

[ruiningvoldemortday]

Cali Reads DH- Chapter 33- The Prince’s Tale

I haven’t forgotten about this!  That would really suck with like four chapters left.  The holidays just got super busy with family visiting.  And I was hoping to finish by the end of the year but oh well.  Time to get back into it!

Before I start this chapter I would like to reiterate my disclaimer from waaaay back in Sorcerer’s Stone Chapter 8 The Potions Master.  I  do not like Snape.  I mean I really do not like Snape.  And this chapter all about his backstory and what a *~hero~* he is actually has the opposite effect of what JKR intended.  It makes me dislike him more.  So, if you have an issue with me seeing Snape as a selfish dudebro who thought not getting laid justifies becoming an abuser, please don’t open the Read More.  Because frankly, I’m really not in the mood for thirteen-year-old discourse.

- “I do not wish this to happen”  you could just like… not kill them, Voldie. - “Lord Voldemort is merciful”  bullshit and also stop talking in third person you sound crazy - “One hour.”  VOLDEMORT OUT BITCHES. - It’s been like forever since I’ve quoted AVPM sue me - Okay Harry you so did not permit your friends to die for you that’s not even a little bit what happened at all don’t let him get to you - FRED UGHGHGH MY HEART ;-;  JUST SEEING THE WEASLEYS CRY OVER HIS BODY IS NOT OKAY -  AND REMUS AND TONKS DIED OFFSCREEN I’M STILL NOT OVER THE INJUSTICE OF THIS UGH MY POOR OTP WHY HE WAS SO HAPPY LAST TIME WE SAW HIM AND SHE JUST WANTED HIM TO BE SAFE UGHHGHGHGHGHH. - “He wished he could rip out his heart” SAME BUT IT’S FINE BECAUSE JKR ALREADY DID IT. - And now begins the Snape hate.  You have been warned. - There’s “undisguised greed” while he’s watching Lily at the age of ten.  Just the way that’s described is really really not okay. - Lily’s first sign of magic is actual wandless flight which is really really impressive - It’s actually kind of sad that young Petunia wanted to be a witch so badly but she wasn’t born with magic so she ended up getting THAT bitter about it. - Is this the first time he’s actually spoken to Lily?  Because if he’s been sitting in the bushes watching her long enough to blush when he speaks to her that’s… Yikes. - Protip: if you like somebody, A) don’t stalk them and B) don’t insult their siblings in front of them :) - Am I supposed to feel bad that Snape’s bitterly disappointed? - The constant descriptions of Snape watching Lily greedily are making me really uncomfortable - Say it with me kids: being abused does not justify being an abuser and you are responsible for your own shitty actions :) :) :) - AND YEAH YOU SHOULD PROBABLY NOT ASSAULT YOUR CRUSH’S SISTER BY MAKING A BRANCH FALL ON HER???? - And then he lies about it but please tell me again how cute Snily is I care so much. - Damn Petunia already salty that she doesn’t get to go to Hogwarts.  Can’t say I blame her but calling Lily a freak was harsh. - “She’s only a Muggle” is not a comforting thing to say when Lily’s upset about her fight with a sister.  Christ. - I really, really wish we got to see some of the Marauders without it being colored by the lens of being expected to sympathize with Snape. - “where dwell the brave at heart”  I see you, Sorting Hat, repeating lines by the time Harry gets to school. - I like how Lily’s not taking any of the Marauders’ shit even at the age of 11 - I thought Lucius graduated before the Marauders came to school? - Young Snape is already hanging around sadistic creeps and hurting people for fun and completely ignoring Lily’s concerns for him.  And for others. - “Let me?  Let me?”  YOU GO LILY HE DOESN’T OWN YOU AND HE DOESN’T GET TO THINK HE DOES.  YOU MAKE YOUR OWN DECISIONS GIRL <3 - Snape’s jealousy of James over his crush on Lily is really not okay, especially at this point where Lily doesn’t like him back.  It screams that he only sees her as a prize to be won, and James as a rival getting in the way of getting what he wants.  That’s not why Lily never loved Snape back- it’s because Snape was starting to grow up into the magical version of an incel, all sociopathic and skeevy and grossly misogynistic with absolutely no self awareness at all. - Snape doesn’t even care that she’s concerned about the company he’s keeping.  She doesn’t like James so that obviously means she’s his because THAT’S NOT CREEPY AT ALL. - Time to see why Snape is the victim for spewing racial slurs to his crush! - The scene where Lily is finally done with Snape’s shit is probably my favorite in the chapter.  “It’s too late.  I’ve made excuses for you for years” starts an absolutely MAGNIFICENT Reason You Suck Speech that feels like a long time coming even though it’s only been a few pages. - “You call everyone of my birth Mudblood, Severus.  Why should I be any different?”  Oh shit.  You tell him girl <3 - If Snape had an ounce of humanity he’d be at least mildly concerned for the baby Voldemort’s going to kill but no, it’s all about the girl who rejected him - “You disgust me.”  SAME DUMBLEDORE.  I mean, Dumbledore isn’t much better, but I love watching other characters call out Snape for his creepy bullshit. - “They can die, as long as you have what you want?”  That kind of embodies Snape’s entire line of thinking and Snape’s silence after that line is really really telling. - Why would you count on Voldemort to spare anyone? - Using Lily’s eyes to manipulate Snape is pretty gross of Dumbledore, too.  It’s like Dumbledore is trying to stop Snape from moving on with his life after what happened.  And as not okay as Snape’s inability to handle rejection is, Dumbledore encouraging him to never heal from her death is also not okay. - There’s so much ick in this chapter I can’t even. - Snape for god’s sake get over your dislike of James.  It was never a competition for Lily’s favor.  She chose him.  The kid’s not yours.  Fucking deal. - “Sometimes I think we Sort too soon”  no not really being braver than Karkaroff isn’t really a feat.  And anyway, people can totally display more than one of the House traits. - Okay Snape’s not a murderer it was all engineered that makes EVERYTHING FUCKING FINE DOESN’T IT - “dear Bellatrix, who likes to play with her food before she eats it” <3 <3 <3 <3 - Dumbledore is such a manipulative bastard ugh - “I thought… all these years… that we were protecting him for her”  LOL NOPE it’s all for THE PLAN TEN POINTS TO DUMBLEDORE - “After all this time?”  “Always” Basic white girls will have this line tattooed on them for generations to come.  Because we should absolutely romanticize the fact that it’s been like thirty years and he still can’t handle that she chose James and he takes it out on their child.  Yup. - Obsessing over her handwriting and the word “love” is kind of a drop in the bucket after everything else but it’s still weird my dude. - And ripping the husband she chose (have I put enough emphasis on this?) and her child away from her even in photographic form so you can keep her with you is ALSO WEIRD. - Thank God that chapter’s over. - What the fuck kind of redemption was that? - I need a drink.  I don’t even drink.

[medschoolash]

people are quick to criticize d&d while forgetting that books and tv are two completely different mediums and they're trying to adapt 5 bricks so ofc they're going to cut/simplify a lot things + that when it comes to misoginy/racism/ableism etc. the books aren't better in fact i'd say they're worse; that they actually improved some things like aging up the characters, cersei (which in the books comes as across as a misogynistic caricature while in the show she's amazing), downplaying s*nsan etc.

They are trying to adapt a huge series with a massive list of characters and a very convoluted plot THAT’S NOT EVEN COMPLETED! 

I can’t tell you how many times I scratch my head on here when people degrade the writers for their writing choices because “they don’t have the books and it shows” as if that’s their fault. It’s so weird like the only person who can control the books is GRRM. If DnD don’t have the tools you think they need to craft the accurate book ending you think you know despite having never seen the pages then who’s fault is that besides the book writer’s? 

This translation was always gonna be deviate, and it’s BEEN deviating, which is another interesting thing I see spread on here a lot as well. You can’t tell the exact same story in the exact same way on two different mediums. You can’t even tell a story in a movie the same way you can on television but this concept is often lost in the haste to immediately trash the show for having the audacity to actually be a show that has to end without it’s source material because they can’t make someone else write their books faster. Things will be cut, characters will be combined, storylines will be sped up. That doesn’t make one version of the writing inherently inferior to the other and one story inherently inferior and that’s what a lot of people don’t get or refuse to get because then they wouldn’t be able to thump their chests with each other over being the superior book reading consumer. 

I also agree that some romanticizing of the novels happens around here. People flip back and forth about what they truly want to see. One second they don’t want sexism and abuse and all the social justice buzzwords on television but then they also degrade the show for leaving some of it out? Like the show adding rape for sansa is just so so terrible but they are also terrible for omitting Tyrion’s more blatant pedophilia and disrespect of women? So do y’all want more abuse of women or less or does it just depend on what better serves your personal desires? make that make sense to me anon. I mean with the way people swear the show is a disgusting perversion of the novels you would think GRRM didn’t start his story with literal teenagers and children getting raped, crippled, and thrust in to wars that end in their deaths. You would swear that this man didn’t make the conscious choice to create a fictional race of uncivilized people who rape, and brutalize for sport POC. You would think this man didn’t make the conscious choice to have the literal embodiment of white supremacy go on a mission to save all the brown slaves. I have a hard time believing these people only suddenly got uncomfortable with these topics when the show started. What more than likely happened was it was all acceptable for the books because it’s books and you don’t get nearly as many cool points for virtue signaling on the internet over a book. 

I heard about the Cersei upgrade a long time ago and I’m def grateful for that change because imagine a narrative where Cersei Lannister isn’t a complex nuanced boss bitch. I would hate to see it.

Either way, I truly hope that everyone can find some satisfaction in what they are making the conscious choice to consume. Otherwise you just wasted years of your life being miserable when you didn’t have to be. 

[stefen-james-96]

What's So Trendy About Artificial Intelligence And Machine Learning Solutions?

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