#Technically ten pages if you count the title page and the references. But you don’t

[petrichorvoices]

University will have you do shit like spend an entire day writing a seven-page essay on the Duggars and feminism.

[h3art-n-s0ul]

Apologies

Akaashi x Reader

(Please feel free to reblog and comment)

Word Count: 2k

Genre: Fluff

Summary: Keiji is planning on proposing to you, but nothing seems to go to plan.

Content Warnings: Language, minor comedic sexual references

___________________________

Akaashi had been planning to propose to you for two months. Two very long months. Well he knew he was going to marry you about five minutes after meeting you. But technically, he bought the ring two months ago. He had it all perfectly planned out. In two days, he would propose in the place you two first met. He knew it was cliche but, the place you two met was slightly unconventional.

It was the parking lot in front of the local university library. He had actually gone in to talk to the administration and see if he could rent out the parking lot from them. The request was so odd, it took them a bit of time to respond. It actually worked out perfectly since they were closed this Saturday, so they agreed to let him rent it out for his intended purpose. The staff were quite confused as to why he would want that old parking lot until Akaashi explained. They hadn’t gotten enough funding to redo it in years so there were tens of potholes, cracks, and broken pieces of blacktop. The colored lines were fading so most people just guessed where to park. But that very parking lot was responsible for your meeting.

It was early spring, and there was still a chill in the air. Akaashi was running late to one of his classes and was weaving between vehicles in the parking lot to get to his car. You were busy walking towards the library with a book up to your nose. Multitasking you know? Akaashi didn't see you around one of the cars and obviously neither did you, too invested in your book. Like fate brought you together, you crashed into one another. You completely stumbled backwards, less than gracefully, sending your book flying. And fly it did, right into a muddy puddle.

“I am so sorry!” Akaashi bent down to help you up before retrieving your ruined book. He brushed off the cover and noticed the title.

“Shit, sorry I wasn’t looking where I was going.” You apologized and he handed you the novel.

“No, no, it was totally my fault. I was in a rush. I’m so sorry about your book. I have to say you have great taste in fiction though.” Akaashi laughed lightly.

“Oh um thank you. Again, I am so sorry for running into you, well, I mean I'm not, but, uh-that was totally my bad. I’m sure you need to get going…” You turned to leave before he grabbed your wrist while he reached into his bag to pull out his wallet.

“Please allow me to pay for it.”

“No, really it’s fine. You don’t have to do that. Totally my fault. I'm sorry.”

Akaashi opened his wallet and groaned, “Ugh god I am so sorry I only have my card. Here.” He scribbled on the back of a receipt and handed it to you. “This is my phone number, text me your venmo and I’ll pay you back. I’m really in a rush right now, sorry.”

“No it’s fine really! Um what’s your name if you don’t mind me asking?”

“Akaashi.”

“Great. I’m Y/n. Wish we could have met under better circumstances, but I’m glad we’ve met. I’d like to chat about your taste in books sometime.”

“Me too.”

Eventually, you texted him, but it had nothing to do with venmo. Instead you invited him out to coffee and you two just kept meeting. Akaashi felt bad each time that he hadn’t paid you back, but you reassured him it was fine and he could bring money next time. In actuality you were using it as an excuse for you two to keep meeting up. Until finally, neither of you needed an excuse to see each other. One thing led to another and two years passed. Now you and Akaashi were living together in perfect harmony.

Akaashi had contacted Bokuto before he bought the ring. Who better to consult about this than his best friend?

“OH MY GOD YOU’RE GOING TO PROPOSE?!” Bokuto yelled into his receiver. “About fucking time. Okay hear me out, spell the question out in fireworks. She can’t say no!”

Akaashi chuckled. “Well I was thinking of going for something a little more private and personal. And I don’t even know if she’ll say yes yet.”

“Keiji are you kidding? There’s no way she can say no. You two are so perfect for each other!” Bokuto was so excited for his best friend.

“I don’t know about that first part. I just know she’s it for me. I’m just glad I know her pinterest username. I think I’m going to start there.”

“I admire your resourcefulness. Honestly, I bet you could pop the question in a garbage yard and she’d still say yes.”

Then it hit Akaashi and he knew exactly where he wanted to propose to you.

“Hey thanks for the ideas Ko. I have to go right now.”

“But you just called?”

“I have to run to the bookstore right away.”

It was perfect. He would set the open ring box on top of the book when he got down on one knee, finally paying off his debt to you.

All he had to do now was lie in wait. Just two days. He could do it. It took everything in him not to tell you already. He tucked the book into the back corner of his t-shirt drawer along with the ring. It forced a smile on his face every time he got ready in the morning.

“Hey Darling, I’m going to head out real quick to pick up the new air conditioner, okay?” Keiji yelled from the back bedroom.

“Yeah okay sounds good. Oh wait- can you get take-out? I’m kinda too lazy to make dinner.” You laughed and he walked into the living room where you were sitting. A book sat in your lap. Some things just never change.

“Yeah of course. Panda express?” Keiji smiled at you.

“Oh god I’m so in love with you.” You replied. Akaashi scoffed and leaned down to press a quick kiss to your lips.

“Okay I’ll be right back!” You heard the clatter of his keys and the shut of the door. Your eyes cast downward back to your page. About five minutes later you wiped a bead of sweat from your forehead. ‘God it is way too hot.’ You stood up and walked to your shared bedroom to change into something lighter. Unfortunately, your favorite t-shirt resided in Akaashi’s drawer. You pulled the drawer open in search of the thin, white shirt. You fingered through the various fabrics until they touched something hard and smooth.

“What- is..” You pulled the novel out and saw the title. ‘Why would ‘Kashi hide this…Fuck what if this was like a gift for me or something?’ You thought. You already felt bad before your eyes scanned back inside the drawer. A little black velvet box sat in the back right corner. Holy shit. Your hands were shaking as you slowly grabbed the object and opened it. A bright diamond ring stared back at you. Holy shit.

“H-he was going to propose?” Shock filled your body and you backpedaled to sit on the king bed. You couldn’t think.

‘Maybe the ring wasn’t his? What? No that’s stupid. Well maybe it’s not an engagement ring?’ Your eyes glanced back down at the ring.

‘Nope. Definitely an engagement ring. When was he planning on proposing? Sure you guys had talked about getting married before but- he was planning it this whole time? How long?’ And then the worst thought filled your mind. ‘Holy shit. What if he’s angry at me? I totally ruined the surprise! Maybe I can put it back and pretend I didn’t find it? No, I don’t wanna lie to him! Oh my god what if I start off our marriage with lies! He’d never forgive me! And then we’d have to get divorced in our 40’s! Oh god!’ Before you could pull yourself out of your thoughts, the front door opened.

“Hey love, I forgot my phone!” Panic settled in your body and your hands scrambled to shove the ring underneath the blankets.

“Darling?” Akaashi walked into the bedroom and saw you awkwardly sitting on the bed. He chuckled a bit, “Love? What’s going on? Why are you sitting like that?”

“Oh- me? What do you mean? I was just relaxing.” You tried to block his view from the book by sitting upright.

“Did you finish your book? Why are you all sweaty?” Keiji noticed the anxious aura around you.

“Um well you see-” Come on Y/n. Think of an excuse! Come on!

Keiji raised his eyebrow suspiciously and started to lean over to see behind you.

“MASTURBATING!” You squeaked out.

“What?” Keiji started laughing.

“ I was- um masturbating. That’s why I’m all sweaty. Sorry. God this is so embarrassing you should just leave!” You nervously winced. ‘I’m so fucking stupid.’ You internally facepalmed.

“Um okay. I’m sorry I uh I’ll just get going. Sorry babe.” He flushed red and awkwardly started to shuffle out of the room before seeing the open top drawer. Oh fuck. He immediately turned around to you and sighed. He hung his head low and asked, “You found it didn’t you?”

“KEIJI I SWEAR TO GOD I AM SO SORRY I DIDN’T MEAN TO IT WAS JUST SO HOT AND YOU KNOW HOW I LIKE WEARING YOUR T-SHIRTS-” He collapsed into a heap on the floor and put his face in his hands.

“PLEASE ‘KASHI NO I AM SO SO SORRY PLEASE DON’T BE UPSET WITH ME YOUR SHIRTS JUST SMELL SO GOOD AND YOU KNOW WE DON’T HAVE AC! UM WE CAN PRETEND IT NEVER HAPPENED I MEAN I DIDN’T REALLY EVEN SEE MUCH-” You continued rambling before he got up and grabbed your hands. When you looked at his face he had tears in his eyes.

“OH GOD KEI I AM SO-”

“Why would I be upset with you, love?” Keiji smiled bitterly.

“Y-you’re not mad?”

“No, of course not. I’m mad at myself. I should’ve remembered you liked wearing my t-shirts.” He tucked a piece of hair behind your ear. “I guess this just isn’t really how I pictured this going. I’m so sorry.”

“No, Keiji, I'm so sorry. This is all my fault.” You profusely apologized.

“Wow this really brings me back.” He smirked thinking of your first meeting. “So, I take it as a no?”

“What? WHAT? NO NO NO!” You frantically waved your hands in front of yourself. “IT’S A YES! YES! Keiji, I am so in love with you baby!” You grasped his cheeks in your hands and sniffled.

“Really?” His eyes widened.

“‘Kashi are you kidding? Of course I want to spend the rest of my life with you! There’s no one else I want.” You reassured. Slowly he propped his right leg up and looked up at you while holding your hands.

“Darling...I’m so glad to hear that because I will never love anyone more than you. You’re all I want. Forever. Will you marry me?”

“Yes. Yes. Of course!” You buried your face into the crook of his neck and started bawling.

“And just so you know, I wasn’t planning on proposing in our bedroom. I was actually planning on proposing to you in a shitty parking lot.” Both of you laughed.

“Where we met?”

“Of course.”

“No, no, this was perfect too.” You grinned into his neck.

“I rented out the parking lot too.”

“You didn’t!” You shoved his shoulder in disbelief.

“I did. I was going to finally give you your book.”

“You’re such a romantic, Keiji.”

“And now your fiance.” Both of you couldn’t keep the smiles off your faces.

‘I can’t wait to spend forever with you.”

BONUS:

“CONGRATS YOU GUYS!!!” Bokuto hugged both of you. “Akaashi I thought you rented the parking lot for Saturday though?”

“Yeahhhh...about that.” “She found the ring early.”

“Oh shit. Sorry man.” Bokuto rubbed the back of his neck awkwardly.

“No Ko, it was actually perfect. I don’t really care anyways. As long as we’re together.” You leaned into your boyfriend, fiance, future hubby.

“What did I tell you, Keiji?” Bokuto cawed.

“Yeah, yeah.”

“So what are you doing with the parking lot then?” Ko asked.

“We’re having a panda express picnic date on Saturday.”

(A/n literally could not sleep. Just this on my brain at 2:30 am)

[dokidokey]

who would have thought that passing a 20-page paper 5 days late would lead you to dabi?

word count: 3,765

tags & warnings: bad boy x straight a student au, college au, fluff, a pinch of endeavor slander, brief mention of throwing up, brief mention of abuse (nothing explicit, the word just gets mentioned once!)

notes: hi zeze (@reddriot), i’m your secret santa! sorry this is so late, we’re on our way home at this ungodly hour and i finally have some stable connection now lmao. i’m so so sorry but also, happy holidays! i hope you enjoy what my pretty much dry and blank mind managed to cook up lol i’m so thankful and i met you and got to know you. <3 thank u for everything. and the title lol omg i’m so bad at titles but i kind realized it rhymes with ornaments, so i left it at that.

The day you met Todoroki Touya was not a good day. You would even consider it a bad week, actually. Apparently, you were a week late on a 20-page paper for History and you didn’t even know. It irked you because you have no other excuse except that you didn’t know. There was a totally different due date in your head, one that wasn’t five days ago. So when your teacher shook her head disapprovingly while tucking your 20-page paper against the smooth surface of her desk, you had no other choice but to leave the room.

You wouldn’t want a teacher to see you cry over a late submission. You certainly wouldn’t want other students roaming the hallway to see you cry either, so you had to fight off the warmth pooling at the corners of your eyes. The last corner leading to the campus library was where Todoroki Touya presented himself.

The impact of your bodies bumping against each other came first, then the stinging pinch of something hot against your skin next. A sharp gasp escaped your lips as you pulled your arm away, eyes widening at the sight of a small, circular burn mark on your forearm.

“You burned － Why are you smoking here?” The accusing tone in your voice immediately disappeared and replaced by panic as you watched a quite familiar face bring a cigarette to his lips, perfectly poised between his long fingers. “You’re not allowed to smoke on campus grounds!”

A puff of smoke swirled through the air as he huffed, the corner of his lips twitching as he eyed you up and down. The intense, blue eyes taking over your body sent shivers down your spine, arms protectively crossing over your front to try and shield yourself from his gaze.

“Not if I don’t get caught,” he smirked, bringing the cigarette back up to his lips. The man was familiar; face and most of his skin that’s exposed under the leather jacket were covered in scars, a dark contrast against his fair complexion. You’re sure you will never forget him if you knew him, but the familiarity of his face doesn’t ring any names in your head.

He puffed out the smoke in a harsh breath, the delicate sound seeming so loud in the quiet and isolated hallway. For a moment you forgot about your late History paper and the chances of you getting anything lower than an A.

Both of your palms met the fabric of your denim-clad thigh in a light slap, arms sagging and voice raising. “If you and I get caught－!”

“Then leave.”

The deadpan and harsh delivery of his words left you open-mouthed, the disapproving look of your teacher once again flashing in your mind. The corners of your eyes warmed again, stinging more than the way it did earlier.

You’re croaking out an unwanted explanation before you realized it. “I － I might get detention and－,” you sniffled, trying to prevent the tears from flowing because you know how embarrassing that would be, so much so to this mysterious person who you found familiar but not really. “And my parents－.”

A scoff cuts you off. You watched as he killed the ember of the cigarette using his bare fingers, pinching the lit end between his thumb and forefinger before tucking it in one of his front pockets. If it weren’t for the strong stench of the cigarette, no one would suspect that he was smoking here, in front of you, inside campus grounds.

“Of course. Precious little [Name] can’t have bad grades and a bad record.”

He said it as if it was so bad. You wouldn’t normally find offense on jabs like those, but today wasn’t just your day. Your retort died down quickly in your throat though when you realized he said your name. He knew you.

With furrowed brows and quivering lips, you asked, “how do you know me?”

The dark-haired man leaned on the concrete wall, shoving a hand down the pocket of his pants. “Who wouldn’t know the teachers’ favorite student? Straight A, little miss [Name].”

It was your turn to scoff. “Favorite,” you mocked, eyes rolling, “I didn’t know being the favorite meant not considering the fact that I didn’t know the deadline was 5 days ago without anyone else informing me.”

A smirk blossomed on his stupidly handsome face. “For once you didn’t get away with something, huh?”

“Didn’t get away? I didn’t know! I had no idea! It’s not my fault.”

“Hmm.”

“It’s true.”

“If you say so,” he chuckled, pushing himself off the wall and taking two steps back, eyes still on you. He winked, then turned around. The silence in the hallway felt deafening as you stood there, but the quick footsteps of his figure walking back towards you eats up the quiet. “Or on second thought,” he says, tapping a foot on the floor, “I can excuse you to the teacher about your late paper.”

It seemed like the tears of frustration pooling at the sides of your eyes retreated back to your tear glands, ears more than ready to hear out whatever his proposition was.

“If you act as my fake girlfriend for a Christmas dinner with my family, I’ll tell the teacher that I tricked you about the deadline.”

You looked at him with a raised eyebrow. “And that’s going to work?”

“Have you seen me, doll? I’m that boy your teacher refers to as a bad influence.”

“You should not be hanging out with people like him, [Name.]”

Mrs. Nakamura’s disappointed tone does nothing to stop the smile spreading on your face, though you tried to suppress it to not come off as suspicious. You’re nodding your head like you’re agreeing with her, knowing that that will not happen any time soon because you have a Christmas dinner with your boyfriend’s family in less than three weeks.

“Go on then,” your teacher waved her hand, “you aren’t marked as late but remember what I told you. If you keep that boy around you, trouble’s sure to follow.”

The hallway didn’t feel as dark and lonesome as it did earlier. It’s surely not because of the other person walking along with you. You’d like to think that, but a part of you knew you might just be lying. And it was stupid, really. Were you really harboring a crush over him? You. . . don’t even know his name.

“What’s your name?”

A choked laugh was the reply you got. “What? You don’t know me?”

“You’re familiar. I just can’t put a name on you,” you shrugged.

“Touya. Todoroki Touya,” he answered, grimacing. “But call me Dabi. That’s what my friends call me.”

“Are you saying we’re friends?” You grinned, looking up at him. He was tall, okay. So much more taller than you. You barely reached his shoulders.

“Technically, you’re my girlfriend, so no. We are not friends.”

You decided technicalities weren’t so bad when Dabi almost never left your side. The sudden (and quite cliché yet comic) pair you two made didn’t go unnoticed by the teachers. Mrs. Nakamura reminded you every single day about Dabi and his troubles. You aren’t aware what kind of troubles Dabi is associated in yet, but you’d like to think you’ll get there.

When you agreed to pretend to be his girlfriend, you didn’t think it would be this kind of long-term thing. You thought that maybe he’ll leave you alone after that day and just hit you up again on the day of the dinner, but you were so wrong.

You’ve never liked being wrong as much as you did about him.

“Stop fussing, my mom’s going to love you.”

He’s said that for the fourth time now. You’re making him more antsy than you are with your bouncing leg and deep sighs every ten seconds.

“And your dad?” You glare at him, wiping your clammy hands on your jeans and bouncing your leg again. He rolls his eyes as an answer.

In the short, three weeks you’ve gotten to know Dabi, you learned a lot about him. One, he hates his father passionately. Two, the teachers don’t really like him (but that sounds so mean when worded like that so you like to think he just isn’t the favorite student.) Three, he’s allergic to fish. Four, he pays attention to every single thing you say. Five, he’s actually the eldest out of the four Todoroki children and lastly, (this is more about you than him) maybe you let your little crush fester more than you planned.

You’ve had to berate yourself multiple times that he is not your boyfriend. You and Dabi are not in a real relationship. This is all a product of your grades being saved and an arrangement to fill up an empty seat at his family’s dinner for Christmas.

“What if your sister doesn’t like me,” you say meekly, “or your brother. And your other brother.”

Dabi shifts on the bench you both are sitting on to face you properly, placing a warm hand over your sweaty ones. “Stop it. They’re going to love you.”

It’s your turn to roll your eyes but really, you’re just having a hard time making your brain function properly to process a reply when his hands are there, on top of yours, warm and soothing. It makes your heart do a little happy dance inside your chest that you know it should not be doing, but you can’t help it.

You’re way too deep into this hell, and you don’t know how you’ll take it when he cuts you off after you both benefit from this arrangement you have.

When Dabi pulls you up to stand up before he walks you home, you try to remember how his hands felt against yours.

“You look nice.”

Nice. You had to rummage through your closet for this halter dress, the most decent thing you can find that can fit for a Christmas dinner. It’s 6 PM on the 24th of December and even though this isn’t how you expected to spend the night before Christmas, here you are anyway.

“You look nice too,” you compliment, taking in how Dabi is wearing an actual pair of decent slacks and a button up. A nervous laugh bubbles out of your throat. “You said it was a simple dinner date so I was kind of expecting you to just show up in one of your old, ragged jeans, you know?!”

He quirks his head to the left, the sides of his lips turned up. Dabi offers you his hand as you descend the few steps from your apartment door. “It was,” he says, “but my mom made me wear this when I said I’ll bring a girl over.”

“Haven’t you brought a girl over before?”

A mischievous smile spreads over Dabi’s face, a thumb pressing a gentle pressure on the back of your hand. “No. You’re special because you’re the first one.”

Great. It’s not like you’re not nervous enough about meeting the Todorokis. He just has to tell you you’re the first girl his family will meet. What makes it worse is that you aren’t really Dabi’s girlfriend. It seems a little selfish on both of your parts to let the rest of his family get to know you and then you’ll never see them again because, well, this arrangement can’t last forever, can it?

“And you have a car?” You gasp, eye zeroing in on the sleek, black vehicle parked across the street where you both are heading. “You have a car?”

He chuckles, shaking his head side to side. “This is my dad’s, actually.” He says it again with an eye roll, opening up the passenger door for you. “He only made me use it to impress you.”

“Like I’m not impressed enough?” You huff out a laugh, palms gliding over the dashboard.

“Impressed by what?”

You, you’d like to answer, but for the sake of your sad excuse of a relationship, you keep your mouth shut.

“Things.”

The ride to their place was filled with back and forth banter from you and Dabi. He’s tried to calm you down when a new wave of nervousness surged within you but as you stand in front of their door with hands sweating an entire Pacific ocean, it’s obvious his attempt didn’t work.

“Calm down,” Dabi says, forehead scrunched as he watches you fiddle with the skirt of your dress. You’ve been standing there for about two minutes now and if your goal is to make your nervousness rub off on him, then you’re doing a pretty good job.

“Is my hair okay?” You fuss over some more, smoothing out the unruly strands that weren’t even there. “Is my face－?”

Dabi grabs your hands in his, calloused fingers wrapping around yours. The words die in your throat as you look up at him with wide eyes, mind blanking out at the warmth on your palm.

“You look beautiful, okay? If you touch your hair or smooth your skirt one more ti－.”

“I knew I heard you guys!”

An enthusiastic voice of a girl almost the same height as you rings through your ears and you look over to see his sister, Fuyumi, white and red hair parted in the middle and over her shoulders. You’ve seen her in some pictures in Dabi’s phone because you’re in that stage where you can just casually unlock and go through Dabi’s phone. (You haven’t seen anything unusual yet, just some candid pictures of you that you have no idea how he took. Bless your poor heart after you discovered that album dedicated just for you.)

Fuyumi places her hands on her hips, smiling brightly at you. “I thought Touya was just lying about you to escape the marriage arranged for him but turns out he isn’t.” She opens the door wider for you and Dabi. “Come in. Mom’s been waiting for you.”

The Todoroki household is neat. Minimalist. You aren’t sure if it’s spacious or it’s an illusion due to the lack of decorations inside. Fuyumi immediately hugs you after you and Dabi are completely inside, and she leads you away to meet Natsuo and Shouto. The sight of Natsuo startles you at first. He looks exactly like how you envisioned Dabi to be if he didn’t have scars. And seriously, what’s up with this family having scars? You noticed a dark crimson circling Shouto’s right eye.

Mrs. Todoroki is the most welcoming of them all, if not as much as Fuyumi. Her hand immediately went to your hair, patting softly and smiling delicately at you.

“I never imagined the day would come when Touya finally brings home a girl,” she whispers. The sight of her eyes getting glassy is enough to make your own gloss over, though it’s for an entirely different reason. How cruel can you and Dabi be to pretend and lie like this in front of his mom?

“Oh, please don’t cry! Did I make you cry?” She laughs tearfully, squeezing your shoulder. You choke out a laugh at her reaction, shaking your head no.

“I leave her alone for five minutes and you already made her cry?” Comes Dabi’s voice at the entryway of the kitchen, his tall frame blocking the path. He walks over to where you and Rei are standing, placing a warm hand on the small of your back. “What did mom say to make you cry?”

Rei sniffles and you dab a finger under your eyes, trying to keep your tears at bay. “Nothing,” you reply, unconsciously leaning back on his chest as you keep your emotions in check. In front of you, Rei has a fond look in her eyes as she watches Dabi tuck a strand of hair behind your ears and your wobbly smile directed at her son.

Your little moment is ruined when the front door shuts close with a loud rattle. Dabi tenses behind and you crane your neck enough to see across the living room a tall and broad man with bright red hair.

“That’s your father,” Mrs. Todoroki sighs.

The food is good but the dinner is awkward. Todoroki Enji made sure that either you nor Dabi will be able to sit through tonight peacefully.

“I’m surprised you managed to stick around my son this long,” Enji rumbles, looking at you briefly before going back to his meal. Four months. That’s what you and Dabi came up with for your pretend relationship. You’ve been dating for four months and you both knew each other after getting paired up for a History project. It’s not much of a lie since you did meet because of History.

“I’m surprised Dabi managed to stick around me this long,” you reply nervously, trying to make light of the situation. It seems you only made it worse when Enji’s sharp eyes bote onto yours.

“Dabi?” He inquires, head tilting to the side. The rest of the Todorokis are quiet except him. “You call him that?”

You nod, stomach churning. Any time now and you might just throw up. “You call him by that name, huh?” He chuckles hollowly, shaking his head. “Imagine my surprise when I saw you here, much less as Touya’s infamous girlfriend. If I didn’t know better, he just hired you as a fake girlfriend to run away from tradition.”

Tradition. Right. Dabi has mentioned to you once that his parents were arranged. He’s told you how he knows his father doesn’t really love his mom. You know about the abuse and the way he treats his family.

“Well, that’s where you’re wrong because what Dabi－,” you pause, turning briefly to look at him, “Touya. What Touya and I have is pretty much real.”

Enji scoffs, a large, heavy palm slapping on the smooth surface of their mahogany table. “Tell me that again when you’re still here a year from now.”

“Sure,” you smile, cheeks aching with how forced it is. It baffles you how Dabi’s father has all the authority in this household －how no one dares to object or talk back.

Todoroki Enji decides to surprise when deep chuckles start escaping his lips. “You,” he points a finger at you, “I like you. You’re brave. Not a single person in this household can face me like that. You’re too good for that boy,” he nods over Dabi’s direction. From your peripheral, you can see just how tight Dabi’s hands are clenched, and you reach over to place one over his.

“Actually, he’s too good for me,” you quip back. You have no idea where this sudden surge of confidence is coming from, but that doesn’t matter. You need to say what you have to say. You wouldn’t be seeing this family ever again after this anyways. “Touya is actually a good man. He’s more than what meets the eye. Maybe you’ll know that if you paid enough attention to him － and all your children, honestly.”

There’s no taking back what you just spewed out. Too stunned, you aren’t aware of the smug smirk and raised eyebrows Dabi is sporting. You don’t see the way Natsuo is trying to fight off his smile. Mrs. Todoroki and Fuyumi have a hand in their faces and Shouto, for the first time since you arrived, looks at you wholly and quite in awe. With your blood rushing in your ears and heart beating erratically, you open your mouth to excuse yourself, but Dabi beats you to it.

“Now if you would just excuse us.” And he’s tugging on your hand. You whisper out a quiet “I’m sorry,” when you pass by Rei, and you’re out of the front door.

“So,” you grin, hugging the mug of hot chocolate to your chest with your feet tucked beneath you. “On a scale of one to ten, how good was I at ruining your family’s dinner?”

After that whole dinner fiasco, you both just decided to go home to your apartment. Dabi is currently sprawled over the other end of your couch, his feet perched on the coffee table (you told him three times already to put it down) and three of his shirt buttons are undone. He’s got his own cup of hot chocolate on his hand, the other playing with the frills of your throw pillow.

“An eleven,” he grins back at you. He leans over and places his mug on the table. “That took guts.”

You nod. “It did. It just didn’t sit right with me how he talked about you like that, like － I remember you telling me how he used to be all over you as a child, but after Shouto was born, he neglected all of you. He isn’t － That’s not － What kind of father does that?” You sigh, groaning when you remember Rei and the rest of his siblings had to witness that.

“That is so embarrassing. I’m pretty sure your mom hates me now.”

“Trust me,” Dabi chuckles, sitting upright and moving closer to you, “she does not. You should have seen Natsu. He was about to lose it.”

“Still,” you press, throwing him a dirty look. “Who talks like that to their boyfriend’s dad on the first meeting?”

Dabi stares at you, turquoise eyes brighter than ever. “So I’m your boyfriend now?”

You’re pretty sure your heart just skipped a beat at that. “I mean, t-technically. Right? That was － That was what we － That was what we were pretending to be.”

Reality dawns on you again. This is all pretend. No matter how warm Rei and his sibling welcomed you, no matter how much Rei adored you, you’ll never see them again. This is a one time thing － something beneficial for the two of you. And as much as it breaks your heart that you got attached to Dabi that fast, you try to hide your sadness by saying, “at least I won’t see them again, so technically, talking back to your dad is fine.”

“Do you want to though?”

“I － What?”

Dabi leans closer. “Do you want to stop pretending?”

You don’t answer. You can’t answer. “Is this a trick question?”

He goes closer. The tips of his hair are grazing your forehead. Even this close, Dabi seems to be looming over you. “I wouldn’t mind making it real.”

“I really don’t want to see your father again,” you whisper. Dabi barely closes the gap between the two of you, nose touching yours.

“We can arrange that.”

more notes: tbh this kind of strayed, uh, kinda far from the bad boy x straight a student au but that’s just because most of what i plan ends up straying kind of far from the original idea. but never mind that, i’m happy with how this turned out. EXCUSE ME THAT LAST LINE? WITH THE ARRANGE THING? HELLO? AM I GIVING MYSELF TOO MUCH CREDITS? I MIGHT BE, BUT I DON’T CARE. also ze (´ ▽｀) if you ever get tagged by me on another dabi fic, it’s just me making up for this late post i am sorry.

[something-very-special]

they ran over the seals

More Replicant playthrough observations and general nonsense under the cut. For reference, up to the keystone quest; completed the Forest of Myth and Junk Heap.

This fucking game I swear to god.

A vaguely coherent ramble about sidequests An observation about sidequests in general in this game -- and I don't recall if I ever voiced this somewhere public or it was just a personal observation from my time with the original -- is that the quests in the first half of the game are all relatively easy to complete. There's that one asshat who wants 10 goat hides, but other than him, most of the sidequests are either very much based on finding characters, or gathering a sensible number of items that are either relatively common, purchasable, or given a guaranteed spawn for the duration of that quest.

The sidequests everybody remembers having to do are in the second half, where everybody is demanding and awful and I'm sorry ten MACHINE OILS do you know how goddamn rare those are? They're goddamn rare.

(We'll not discuss Life in the Sands.)

This is generally agreed to, in the technical vernacular, 'suck'. And it's always funny that the most interesting sidequests are the ones with very minimal requirements (Yonah's cooking, getting Popola drunk, the Lighthouse Ladoh my god everything's gone blurry I'm not crying you're crying who am I kidding we're both crying). That particular aspect of the design also feels intentional, not really gating your ability to progress the really meaningful or funny sidequests behind an unreasonable number of rare items. The other aspect of the design is that these quests are not meant to be completed in a single playthrough; most of them are single-stage and just absolutely unreasonable, but if you're going through the game four times you have a... reasonable chance of getting everything you need more or less naturally.

Nobody does that but I think that was the intended design. I think it's a good idea, although the execution of expectation is flawed so I don't really blame people for saying those sidequests suck. (Although I will in turn blame people for saying the sidequests suck as a blanket statement. Yeah getting that guy who burned his kitchen down a billion Broken Motors is aggravating but did you not find that old man's dog? Speak to Ursula on her death bed? Solve a murder? Then again I think tracking down that rotten son who's trying to get away from The Family Business only to learn his father is a con-artist and get literally no reward is the height of comedy so maybe I'm not the greatest point of reference.)

But that asshole in Facade can get bent. I can't exploit my garden properly, jackass! I am no longer a god of time. (I kid, of course.) (This guys sucks even when you can fix your clock.)

Forest of Myth It didn't even occur to me to wonder how they would incorporate the comprehensive voice acting into the Forest of Myth. I like how it plays out, although I wish the voices maybe had a fade as you went deeper into the dream instead of just cutting out at some point, especially for the lines where the characters are being ascribed actions by the narrator that they themselves aren't doing near the start of the Deathdream. But it's just delightful to go back to it. The second half of the game really sticks in your mind both for emotional reasons and because you play it at least three times per full playthrough of the game, but the first half is just so much fun.

Protip: Talk to everybody after you've finished the dream sidequest. Weiss tries to dissuade you. Don't let him dissuade you. I'm still delighted by the Mayor; "We're building a statue of you, made of solid gold. I know you don't own a horse, but we're going to put you on a horse."

I forgot about Yonah being a disaster chef Papa Nier's reaction to the stew is better. Brother is still funny but Papa Nier just expecting to die is comedy gold.

For anybody curious, the joke about the cakes is that Yonah made 'fruit cake' using some of the worst possible fruits for cake-making. If only she'd thrown a tomato into the mix, too.

Lighthouse Lady Every time. what the fuck is a canal I'm aware of the addition of the new-old content but it didn't occur to me until Popola suddenly starts nattering on about fixing the canal when I'm expecting Yonah to talk about a penpal that oh, yeah, I guess Seafront would have had something going on the first half that would play into the second half? (I assume it does. Be weird to introduce these characters just to have groundwork for an added sidequest. ...but it was a cute sidequest.) But look Popola my boy is supposed to be in the next area I visit could we-- I mean he's on the way could we just-- no-- fiiiiiiiiiine. (It was short and sweet, though, and I appreciate that the couple's love is exemplified by them both calling Weiss a floating magazine in tandem.) On a related note but was I the only person suddenly concerned when the sidequest completion maxed out at 50% and not 51%? I had to double-check with a guide just to make sure, since I've spent the last decade telling people to make sure you hit 51% before going on to Part II.

MY BOY I love that nowadays, Emil is everybody's son. But I really wish I could go find somebody only familiar with Automata and just watch their reaction. (I'm guessing there are streams out there that fulfill this but man I'd love to get it in-person.) If you're only familiar with him from Automata this has to be a mindfuck.

Personal anecdote, but I've had the privilege of playing NIER with somebody else almost every time I've gone through it. I had a wonderful experience of doing a replay some years back with somebody who had experienced it with me before but didn't have the most solid memory of the beginning (and had actually missed the entire weapon's lab the first time through). I get to the boy at the piano introducing himself and the 'Wait, what?' was a thing of beauty.

MY ANDROID This was a welcome mindfuck for me; finding Sebastian and having him 'reactivate' in such an unnatural, mechanical way. I don't recall if it was ever officially confirmed that Sebastian is an android (I know that it's just understood that this is the case but I'm not I can't recall a specific one) but the little flair they added to his animation caught me completely off guard. I liked it!

Destroying the food source A lot of people will cite a major inciting incident for the game as being when the protagonist heading back into the village and killing the child Shades just outside the entrance. This moment is such a great bit of subtle foreshadowing that's so easy to miss... but kind of joining that, just before the Knave of Hearts attacks, I realized that the Shades out on the Northern Plains are clearly ramping up for an assault of their own by murdering the sheep. The sheep population at this point is decimated (which is great when you realize you haven't gotten the Sheepslayer trophy and you're about to enter Part II and you don't know if the boar drifting minigame got carried forward with the inclusion of 15 Nightmares). You go out onto the Plains and you will find not only small clusters of sheep left behind instead of the vast, terrifying herds from the start of the game, but until you get their attention the Shades are prioritizing killing the sheep. (Also annoying because that doesn't count toward my sheep murder number.) The Shades will be out there also killing sheep earlier on, but since the whole map is in Overcast mode after talking to Yonah it's especially prevalent to go out to the Northern Plains and seeing the slaughter. And I realized-- they're cutting the Village off from a primary food source. Shades don't eat and they don't have any beef with the local ungulates (at least, no more so than anybody else does), so why are they hunting down the sheep? To deprive their enemies of resources. Sheep are extinct by the timeskip. It's actually really clever of them, and a really clever indication of their sentience and intelligence before it's fully verified.

"Let's get these shit-hogs!" Everything about the way Kaine and Emil interact across the entire game is perfect I will brook no argument this is objective fact.

Emotive Rectangles I wrote an essay about this before but it really bears repeating that the job the original animators did with this scene is just phenomenal. The way Weiss drifts, flits, flips, fans his pages, drunkenly swerves, shoots around the room in defiance... He's a goddamn rectangle, but there is so much emotion and personality in this scene just based on the movements conveyed through a what is effectively just a box. Ten years later and triple-A titles with full facial capture don't have this much seething personality. I really have to give props to the cavia animators, wherever they wound up. That studio could really put some subtle love and care into their titles, utterly unnecessary and easy to miss but you can tell that whoever was working on it was giving it their all. The books are probably the exemplification of this, but every time I go into Seafront and visit the seals I can tell that the guy on seal duty was having just the best day. They made Emil so pretty There's an FMV cutscene right smack in the middle of the original game after the battle against Noir. I understand why it was a necessity on a technical level, but it always looked pretty out of place and a little uncanny valley compared to the rest of the graphical fidelity. That's no longer a necessity so this cutscene is rendered in-engine. I admit I was actually curious to see it redone this way and it looks fantastic. I single out Emil since he is the focal point of cutscene and because his particular high-poly model had some pretty weird difference from his in-engine model, but he and Kaine both look great. But, like, it's almost mean how pretty he is.

They made Brother Nier so pretty Yeah okay you got me he's kind of hot. Kaine's expression when she wakes up and looks him over is... significantly easier to read now. Good voice, too. (Ancient rumors tell that one of the issues with international releases of RepliCant was that they couldn't find an English VA with a voice that 'fit' Brother Nier. He sounded good out the gate but hearing him growl "Let's go TAKE CARE of those KIDS" during the thief sidequest-- I got chills. It sounds so silly but there's a kind of percolating fury to that delivery. Papa Nier was like frustrated but mostly disappointed dad; I felt like Brother was going to take care of those kids, and nobody was going to find the bodies. Younger Brother Nier just never stops looking goofy to me but Older Brother just looks great in motion, between the alterations they made to the movement and just the entire weaponry system. The distinction between the two halves of the game was always a little odd in the Gestalt version-- not odd enough to really raise eyebrows if you didn't know about RepliCant, but of course you can tell that this age gape between the optimistic doe-eyed dogooder and a man largely ruled by his fury and calloused by tragedy is what the timeskip was going for. Swab me down and call me Ishmael, it works. Younger Brother wasn't quite clicking with me-- not because of any writing or voicework issues, but I've got Papa Nier on the back of my mind and it's impossible not to compare and contrast the delivery and dialogue between the two. I know that this is intentional, too; Younger Brother is supposed to be that happy-go-lucky video game protagonist, always doing the right thing and helping people, in order to contrast against the man he becomes. Even just edging into Part II the effect is dramatic and it recontextualizes Younger Brother into a much more effective overall character. And let me reiterate, I enjoyed my time with Younger Brother just fine, I have no issues with him. But he's up against Well Meaning Big Dummy Part I Papa Nier. No contest. And I'm excited to see where Older Brother goes from here.

Speaking of voices I mentioned this before but the delivery on the character's lines is different. The entire game was re-recorded and quite a few lines are still pretty similar to the original, but there are some that are... definitely different. Part of this is a difference in the relationship between characters based on their life experience and ages-- Weiss is much more of an ass to Younger Brother but has a much more even respect for Older Brother (neither of which are like the rapport he established with Father). Some of Kaine's lines feel more aloof, dismissive, and almost tired in the front half of the game. I haven't really gotten to a point to dig into Emil's rapport with the other characters, but the delivery feels more hesitant and uncertain (which I think is more in line with his Japanese VO, but I'm prefacing that on an untrained ear and a presumption rather than recent memory). It's been interesting to see not just where hey adjusted dialogue (and how-- there are some lines that didn't need to be rewritten), but also how they adjust tone and delivery. Dealing with Younger Brother is one thing, but as I said, I'm very excited to see what's different in the second half, especially being much more familiar with that part of the game. Speaking of Voices! Halua got dialogue! I... preferred when it was inferred (and the implications of "I'll always be watching over you" are borderline malicious given the results of their fusion dance, yeah THANK YOU HALUA this is GREAT). Halua's delivery also felt a little too innocent and upbeat both for the situation and when compared to her narrative voice in The Stone Flower, where she comes across as much more cynical and cold. But given what she's been through and the nightmare she's finally escaping I guess she's allowed express happiness. She's certainly earned the right to having a spoken line. No matter what. Every fuckin' time.

"Here we go." This was always a great line to kind of ease in to the officially-official start of Part II-- every time you start up a New Game+ you're greeted with Emil musing about his conflation of Halua to Kaine, and then the phrase "Here we go". There's a lot in that one line. On a personal level he's grounding his thoughts in the moment and steeling himself for what comes next and pushing through his pain and sadness and fear. Whatever Nier told him in the facility he's still terrified, desperately terrified, that Kaine -- who was the one who told him his life had meaning -- is going to reject him. And why wouldn't she? Ultimately they don't know each other, not really. He understands at that moment that his relationship with Kaine is based on confused memories of his sister, that maybe the bond he thought they established isn't actually real. As soon as he frees Kaine he's going to have to confront her, like this, and how could she ever-- she won't-- but he can't just leave her. Whatever happens next. Doesn't matter. Doesn't matter. (God it matters.) "Here we go." On a meta level, that's our introduction into the second half of the game. The first half is all prologue. This is where we'll be spending the rest of our time, even to the point that 'New Game+' skips straight ahead to this moment. Now that we've finished the establishment, this is where it all builds and where it all matters. Here we go, audience. The ride starts now. You get up to this point now in Replicant. You get the same lead-in. My dumb ass even whispered "Here we go", because I can't help myself. And he says, of course he says--! "Anyway." ... ...a-anyway? What the hell kind of line is that? "Here's some deeply personal musings that are also an indication of my own discomfort as I babble to myself just to fill the void so I can stave off thinking for just a few more seconds. ANYWAY." What a... bizarre decision. Just bizarre.

Upgraded melee combat The introduction to the armored Shades always feel kind of rough-- the defenses on those Shades are significantly higher than anything you've faced and the new weapons you're given to combat them just aren't that good. (If you got lucky you could have a fully-upgraded Faith by now, which is nearly three times as powerful as the 'heavy' two-handed sword you're given; if you downloaded the 4 YoRHa pack for Replicant you've probably been able to upgrade one of those weapons once, which are also a really nice strength boost that leaves the freebie heavy swords and spears in the dust). As an introduction to the new weapon types it always feels like rough going. But then you get a chance to get decent weapons and the combat system truly opens up, and compared to the first game you really feel it. At this juncture I would always just bustle off to Facade and grab the Phoenix Spear and never look back-- the raw power compared to the rest of your arsenal coupled with the triangle dash is basically the bread and butter of the rest of the game. It's not exciting, but it's effective. No more triangle dashing, which was deeply disappointing... but both weapons definitely feel good. I am also somewhat ashamed to admit that it wasn't until now that I realized attacks weren't just about rhythmic input-- you can hold the attacks down to do different charged hits and combos depending on when you execute them in your combo, similar to Automata. I, uh... I felt a bit dumb. But hey, wow, it's a welcome adjustment and it makes all of the weapon types feel equally valuable for different purposes. I never liked using the heavy blades in the original release because they just felt too slow for the damage output they did, even if their 'point' was mostly to sheer off armor (and they definitely felt too slow for use in crowd control). Now they're still heavy and slower, but not to the point that you're basically leaving yourself open just trying to attack. Spears now do crazy sweeping combos and multi-hits. Both of these properties were borrowed from Automata and I find myself prioritizing melee combat and almost forgetting I have magic because honestly it just feels intuitive and fun. I feel like Kaine and Emil might have gotten a power boost as well? Not that I can really confirm this but going into some of the Junk Heap rooms I'd focus on killing a few robots in the corner and then turn around and just see a field of item drops and no more robots. Don't take my word on that, of course, but they felt a little more effective, and a placebo effect is still an effect. "You're staging a protest? That's fun!" Emil. Rebel without a cause. Will not hesitate to kill you if you trespass on his property. (Might explain the statues in the courtyard, actually.) I'll have to double-check this dialogue because I definitely remember more of a melancholia before we get to roasting marshmallows. I think Papa Nier actually offers to talk to/implicitly threaten the villagers to let them in the Village whereas Brother offers to sleep outside with them... which is actually kind of funny. In the former it comes off as Emil and Kaine maybe kinda-sorta not wanting to be allowed in the Village for their own reasons (they're not happy reasons but they're reasons nonetheless) and reassuring Father that no, it's okay, it's fun! The latter is almost telling Brother to stay inside because he'll ruin their sleepover.

(They're absolutely having giggly girl talk about him outside the gates, 100%.) they ran over the seals All I want in Seafront is to enjoy the music and run out to the big beach and hang out with the last living seals and they put a fucking pirate ship on top of them. Oh, wow. Gideon. Wow. OG Nier featured a Gideon that tried to keep himself together and then had fits of mania. You'd be concerned about him during some of the dialogue but generally speaking he came across as... functional. The delivery on all of his lines is now so insanely murder bonkers, like every line he's addressing you like you're already chained to the wall of his serial killer dungeon and it's glorious. I don't know if the distinction between the games is deliberate (in that Gideon in Gestalt was just more even-keeled between his 'rip 'em apart' snarlings and was always just totally nutso in RepliCant) but I do appreciate it. It's a good mirror to Brother Nier's own anger, which only ever seems to be mollified when he's talking to his friends (even kindly accepting sidequests there's a pretty consistent -- not universal, but consistent -- air of barely-bridled frustration). The other characters that Brother encounters are various reflections of himself if things had just been a little different-- Gideon was a representation of the kind of obsessive madness that would have eaten Brother alive if he hadn't had his network of support. Gideon's constant fury and bloodlust even bleeds into him just saying "What can I do for you?" He has no anchor to keep himself sane, nobody to stay human for; he's all mania, all anger, and he only takes any real interest in Brother on his return because he sees an opportunity to act out his vengeance. After defeating Beepy and Kalil he even goes so far as to not only blame Beepy for killing Jakob, but for also killing their mother, which is patently insane but really speaks to how far his justifications and fury have taken him. Papa Nier responds to his anger toward Beepy by basically backing away slowly and saying "Oookay then". Brother, however, actually commiserates; "That's enough. [...] We get it. We really do." This is definitely one of those moments where Brother's context works better than Father's; he absolutely sees himself in Gideon. He completely understands him and sympathizes. He recognizes the madness of his own quest, he sees where it could take him, and there's a resignation when he speaks to Weiss: "Revenge is a fool's errand." "...yeah." Papa Nier has a similar delivery and similarly implies that he understands how terrible his quest is, but there's something decidedly haunting in Brother's sympathy. Also just verifying something on the wiki and this bit of 'Trivia' really jumped at me:

Gideon is the only character to only cause the deaths of other characters. In his case, he caused a platform to crush Jakob and ordered the deaths of P-33 and Kalil, with P-33 surviving.

Metal AF.

[olliedollie1204]

for future reference

Virgil works at the reference desk. Logan is looking for a very specific book.

Pairings: Platonic Virgil and Logan

Word Count: 3,613

Tags: Librarian Virgil, Kid Logan, (very loosely) implied but not shown romantic Moceit

based on that one tumblr post that is maybe the cutest thing i’ve ever read? also, Logan mispronounces some words because he’s Babey, so I included a guide at the end to clarify what he was trying to say.

also i meant to make this short and simple but i tripped and came up with an entire new AU, so hopefully if y’all slam that mf like button I will find the energy to write the sequel

(Read it on AO3!)

Working at the reference desk was cool. When you walked through the main door of the library, you’d never suspect that nestled beyond the rows and rows of adult nonfiction, far away from the busyness of the community room or the chaos of the children’s section, was a neat and well-tended desk, behind which sat just one man.

That one man was currently alternating between scanning the sea of tables and chairs in front of him, and reading a cheesy romance paperback under his desk. Listen, he had an image to maintain, okay?

Virgil had always liked the solitude of a good library, almost as much as he’d liked the books themselves. Despite spending many long hours hidden away among dusty shelves when he was younger, he'd never thought about actually working in a library. He wasn’t a people person, and libraries, unfortunately, tended to attract people; so when he found out there was a position where he could get away with isolating himself behind a computer monitor all day long, where his main form of social interaction was helping patrons fix the printer approximately nine hundred times a day, where he could read or play Temple Run or just sit still and daydream for hours on end? He was sold.

He supposed he had to thank the library’s set up for his lack of work; truly, most people never made their way this far into the building, and those who did were usually just looking for a place to sleep for a few hours, so it wasn’t uncommon for him to go an entire shift without speaking to a single person.

It had looked like today was going to be the same, with Virgil halfway through his shift and having only spoken to one patron who was looking for the bathroom. He had just gotten to the part in his book where the farmhand and the farmer’s son were trapped together in the barn during an unexpected thunderstorm, shirts dripping wet and faces flushed from humidity and passion (and maybe Virgil had read this one once or twice already, don’t worry about it).

It was a perfectly normal day. Until the kid showed up.

“Excuse me, sir?”

Virgil certainly did not jump about a foot into the air at the kid’s sudden appearance, but it was a close thing. The librarian quickly sat up in his rolly chair, dog earring the already well-worn novel and shoving it back under the desk.

“Uh, hi,” he replied, gazing down at the child in front of him. He was small and scrawny, with wildly scruffy hair and a large pair of glasses on his face. As Virgil sat up taller, he was able to see that the kid was actually tiny, his chin barely reaching past the edge of the desk. Despite his small stature, he had an oddly serious look on his face.

“How can I, uh, help you?” Virgil asked haltingly.

“I need to find a book about baby names,” the child informed him plainly. His quiet, high-pitched voice felt completely at odds with the grave importance he seemed to place on his request.

“Oh?” Virgil said for lack of a better response. He quickly scanned behind the kid, looking for an adult that might’ve misplaced their incredibly somber toddler, but he quickly brought his attention back to the child in front of him as he nodded.

“My dads told me that I’m going to be a big brother soon and I need to find the names for my baby twin brothers who we are taking from a woman in the city because she is a sugar-ette and she is giving us her babies to keep,” the child replied in one long breath. Virgil blinked at the sudden influx of information.

“Ah,” he replied, absolutely nailing this conversation with this random, unaccompanied baby. “Let me… look that up for you.”

He paused for just a second before jerkily turning on his monitor, opening to the library catalogue’s search engine. Instinctively he opened the filter and clicked ‘search for keywords’ and typed ‘baby names’, until he looked down at the… really small child in front of him, like damn, were all kids that small?

“Um. How…”

How old are you? How many letters of the alphabet do you know? How stupid am I gonna look if I send you to the checkout desk with an armful of dense, high-level books about etymology?

“How high is your reading level?” he settled on. To his surprise, the child puffed out his chest in pride.

“I am five and three quarters years old and I will be going into kindergarten in Set-member and Dr. Picani says that I am reading like a kindergartener and I even can read first grade books, too.”

Okay. Virgil didn’t know who Dr. Picani was, but that wasn’t important. Kindergarten to first grade reading level. He switched the filter to adjust for that new information, but he was quickly met with the realization that the kid was looking at him for… some sort of response, because that’s how conversations work, Virgil, come on.

“That’s cool,” he replied lightly. Lucky for him, the kid didn’t seem to mind his lack of social graces. He just nodded, rocking back and forth on his heels as he watched Virgil type.

“And my Daddy gave me a bunch of chapter books for my birthday and I already read them all because that was last year and he and Papa said that for my next birthday I can get some more chapter books but I hope they are mit-sery books because I like the mit-sery books most of all. Dr. Picani told me that’s because I like to collect and organize information. I like it when Papa reads the mit-sery books to me, even though I can read all by myself, because he is always bad at solving the mit-sery and I have to explain it to him every time.”

At first, Virgil had merely been listening with a polite interest, nodding a little as his eyes scanned the page for what books they had checked in, but as the kid continued to talk (and Virgil was seriously starting to wonder if he ever ran out of breath), he realized he was now listening with a genuine interest. This kid seemed pretty smart for his age, even with his tendency to mispronounce words in his rush to get them out of his mouth, and it was honestly kinda endearing. This coming from Virgil, who was running out of excuses as to why he couldn’t help out with any of the children’s programs that the library hosted in the community room twice a month.

He pulled his eyes back to his computer. “Okay, so, um, it looks like we’ve got a couple books that you might want.” They had more than a couple books about baby names, of course, but Virgil really didn’t wanna hurt the kid’s feelings by giving him a book that was too difficult for him.

“I’m gonna write the titles down on this piece of paper,” Virgil continued, pulling out an index card and one of the weird tiny golf pencils that were at every desk in the library for some reason. “Here’s what the book is called, here’s the last name of the person who wrote it, and here is the number of the shelf where you can find the book, okay?”

He finished writing and slid the paper across the desk to the kid, who hesitated for a moment before taking it.

“... Thank you,” he said stiffly, turning on his heel and marching away. Virgil wasn’t gonna look away until the kid was out of his sight, but to his surprise he stopped just about ten feet away from the desk, looking between the paper in his tiny hands and the tall rows of shelves.

Virgil stood up suddenly, feeling like an idiot. He’d just told an infant to go look for one specific shelf in a giant room of identical shelves. Alone. Fuck.

“Hey, kid,” he called softly, moving around his desk and hurrying to the child. The little boy turned to him, eyes wide behind his glasses lens.

“How about I help you find those books, okay?” Virgil asked, trying not to tower over the tiny child. The kid looked around for a second before nodding quickly.

“Okay, I think that is a good idea, because I know where the books are in the playzone but I think this li-berry is really big and— and maybe I’d get too lost and my dads are scared of me being lost and so I don’t wanna make them scared,” he finished, looking down and scuffing the toe of his shoe against the carpet.

Virgil raised an eyebrow at the end of the kid’s sentence. “Do you know where your dads are?”

The kid nodded quickly. “They’re having storytime in the group room!”

Virgil nodded. He knew there was an adult book club happening in the community room that day, so that definitely made sense. But still, he leaned down, catching the boy’s eye with what he hoped was an appropriately stern face for the circumstances.

“Do your dads know where you are?” he asked. As he expected, the kid began to look slightly guilty, scrunching the hem of his navy polo in his hands.

“Um…” he started. It was the first time Virgil had heard him pause between his words. “Well, technically, they told me to stay with the li-berrian, and they thought I was gonna stay in the playzone with Ms. Dot, but technically, if I can stay with you then I am with a li-berrian and so I’m not in trouble.”

There was a note of self-satisfaction in the kid’s voice, like he’d just solved a riddle as opposed to trying to explain why he disobeyed his parents. Virgil got the feeling that this was a kid who knew how to use his words to his advantage.

“Okay,” Virgil replied, gently pulling the paper out of the kid’s hand and scanning what he’d written. “We’re gonna go look for some books, but then I’m taking you back to the children’s section— uh, I mean the playzone— and Ms. Dot is gonna watch you until your dads are done, deal?”

The child nodded, watching Virgil with intensity, and the librarian gently ushered him to the side and led the two of them down a row of books.

“What’s your name?”

“Logan,” the little boy replied, running ahead a little and turning to wait for Virgil to catch up. “What’s your name?”

Virgil reached Logan at the end of the row just as he answered, “Virgil.”

Without warning, Logan darted ahead again, reaching the end of the next row before turning around to face him. “Daddy says I should call the li-berrians Mr., Ms., or Mx. What are you?”

“Mr. is okay,” Virgil replied, a little bemused by his childish bluntness. “And be careful, okay? I don’t want you to trip and hurt yourself.”

Logan trotted back to Virgil, walking backwards for a minute so he could look at Virgil while he talked. “I’m sorry for running, but I really want to find a book about baby names because my dads are busy making the babies’ bedroom and buying all of the baby clothes and toys and ex-cetera and I want to be a good big brother and I want my baby brothers to have names that are good but my dads are really busy and they don’t even know what they want to name the babies yet!”

Virgil smiled at the indignation in Logan’s little voice. Of course, he knew there were far more important preparations to make when expecting a new child (let alone two new children at the same time), but to a child as young as Logan, the name was probably the most important decision to be made.

“Well, they should be on the next shelf over, so let’s—”

Logan took off before Virgil could finish his sentence, running halfway down the row and looking at Virgil expectantly.

Virgil scoffed, an amused smile on his face. “Yeah, yeah, I’m coming.”

As he entered the row, he began scanning the numbers on instinct; he knew these stacks pretty well, but he didn’t have them memorized.

“Okay, 929.4,” he muttered to himself, bypassing books about genealogies before coming to the section for baby name books. “Here they are.”

Logan came towards him, standing on his tiptoes as he reached his arms up high.

“Mr. Virgil, may I please have the biggest book, please?”

Virgil looked back at the shelf, immediately seeing which book Logan was talking about. He pulled it out, holding it in both hands as he scanned the cover.

“‘Ten Thousand and One Baby Names For You’,” he recited, passing it down to Logan. “Is that enough names to choose from?”

Logan’s eyes were wide, struggling to open the heavy book while still keeping it in his arms. “I never even knew there were ten thousand and one names!”

“Same,” Virgil replied, helping Logan open the book without damaging it. “I think this book has lots of names from all over the world, plus some super old names from the last century.”

“Like the 1990s,” Logan said, nodding seriously, and Virgil had to pretend to cough to avoid laughing outright at the kid’s earnestness. He turned back to the shelf, pulling out a thinner yet still dense book.

“And this one is called ‘The Story Behind the Name’,” Virgil explained, holding it down to show Logan. “It tells you more about what the names mean, where all of the names came from… stuff like that.”

He held the book out for Logan to take, but to his shock the child was looking at him with something akin to distress.

“Do names mean things?”

Virgil blinked. “Oh! Uh, sometimes? Not really. But some names have things that they used to mean, a long time ago, but a lot of people don’t know what they meant. Like—”

He hastily flipped the book open to the ‘L’ section, skimming the page before he found what he was looking for.

“Like, ‘Logan’, for example, is an Scottish name,” he explained slowly, “and it apparently means… uh, ‘from the hollow’? Which, I don’t even really know what that means, so. It’s not that important nowadays.”

He looked back at Logan, who was looking into the distance with a pensive look on his face.

“But what if I give them a name that means something bad,” he pondered slowly, and Virgil’s stomach swooped at the idea that he’d just given this kid something to worry over.

“Well, here,” he said hurriedly, holding the second book out to Logan. “If you take this one, you can check that the names you pick mean good things. Some people like to choose names that remind them of something good, like nature or history or— or their favorite book characters.”

That perked Logan up, causing him to eye the book with a new interest. “Really?”

His gaze flicked between the second book, and the much larger book that he still held in his arms.

“I think I should take both,” he said after a long moment to think. “Just in case.”

He smiled up at Virgil, who literally couldn’t stop himself from smiling back if you’d paid him. Logan was just too darn cute.

“Well,” he said, “how about I carry your books and take you back to the playzone, and you can get started reading these before you check them out?”

Logan nodded, somewhat reluctantly handing Virgil his large book as the two made their way out of the nonfiction section. “That is a good idea, because I am already checking out a lot of chapter books and my book basket is full and so I think my dads will help me carry these books to the checkout counter because they’re really big books.”

“They sure are,” Virgil said conversationally, holding a hand out to stop Logan as another librarian walked by with a cart. Before he could take another step, however, he felt something small and soft wrap around his free hand. Virgil looked down to see Logan holding his hand in his own tiny grasp.

“Papa says I shouldn’t hold hands with strangers,” Logan informed him, idly swinging their hands together, “but I don’t think we’re strangers because I know your name and you know my name and you’re helping me carry my books because you are a nice li-berrian.”

Virgil felt an inexplicable surge of protectiveness over this child he’d met only fifteen minutes ago.

“Sure,” he replied softly, letting Logan continue to talk as the two walked hand in hand back to the populated side of the library.

He almost didn’t want to interrupt Logan when they did finally arrive at the playzone, but he wanted to make sure this kid got back to where he was supposed to be before his dads found out he’d left. Dot looked at him from behind Logan, her eyebrows raising at the sight of Virgil a) not behind his reference desk, and b) attached to the world’s chattiest five year old.

“Hey, Lo,” he gently interjected when Logan took a breath, kneeling down to be on the young boy’s level. “I’m gonna set your books down with your book basket, okay? Where is that?”

Logan paused, eyes flitting around the colorful rug. “Um… it’s… oh! It’s right there!”

Virgil’s eyes followed where Logan was pointing. There, on the ground next to one of the large plush sofas in the reading circle, was one of the library’s book baskets. From here, Virgil could see at least a dozen junior chapter books poking out of the basket.

“Oh!” Logan exclaimed, darting forward and grabbing the handle of the basket in both hands and tugging it back over to Virgil. “Mr. Virgil, look, I raised my hand and asked Ms. Dot if I could please have the storytime book to check out for a little bit because I liked it a lot, even though it’s not a mit-sery book, but it is about cephalopods and those are octopusses and squids and ex-cetera, and she told me to turn around and the shelf behind me had tons and tons of books about cephalopods, and I picked out this book because it has pit-chers but it’s not a pit-cher book, it has chapters, too—”

Logan flopped onto his butt in the middle of the carpet, pulling out each book one by one and explaining to Virgil exactly what it was about and how many chapters it had and how he couldn’t wait for bedtime so he and his dads could read them all together. He chattered on and on and on, and Virgil didn’t even realize when he joined Logan in sitting cross legged on the floor. He didn’t have to talk much, but every now and then Logan would actually pause to breathe, and Virgil would ask another question that set the young boy off onto an entirely different spiel that lasted another ten minutes.

It was so different from working at the reference desk, quiet and hidden and isolated. Different, but not bad.

“Mr. Virgil?”

Logan’s voice was suddenly quieter, and it snapped Virgil back to reality. He looked at the kid, who was looking at his own tiny hands folded neatly in his lap.

“Yeah, Logan?” Virgil asked. “Are you okay?”

Logan nodded. “Yes, thank you, I’m okay. I think you are maybe the nicest li-berrian ever.”

The sincerity in his little voice nearly made Virgil reel back in shock.

“Really?” he asked, and normally he might be embarrassed about how insecure his voice sounded after receiving a compliment from a five year old, but Logan nodded immediately.

“Yeah,” he replied. “Ms. Dot and all of the other li-berrians are nice but I think you are the nicest because I broke the rules and you didn’t tell my dads and you gave me the name books for my twin baby brothers and you let me hold your hand and I like talking about my books and you liked hearing me talk about them. So I think you are— I think you are the best li-berrian I ever met.”

Logan fell silent, looking down at his lap and fidgeting with his shirt hem, and Virgil was honestly a little speechless.

“Oh,” he said slowly. “Um, thank you, Logan. I think you are… the best reader I’ve ever met.”

No sooner were the words out of Virgil’s mouth that Logan looked up at him with wide-eyed shock.

“Really?” he squeaked. Virgil was literally going to get a cavity from all of this sugar.

“Yep,” he replied. “You’re smart and kind and you care a lot about your baby brothers. Your dads must be very proud of you.”

Each word of praise was brightening Logan up bit by bit, and he switched to sitting on his knees and bouncing up and down.

“Will you play checkers with me?” he asked, hands flapping in excitement. “I always want to play checkers but Ms. Dot says I’m not old enough, but you’re definitely old enough, right?”

Virgil laughed outright at that. He thought about his reference desk, sitting unoccupied on the other side of the library. He looked at Logan.

“Sure, kid,” he replied, standing up with Logan’s book basket. Logan grabbed his free hand, and Virgil let him lead them both to the game table, Logan already explaining the rules in anticipation.

Yeah. Different, but not bad. Not bad at all.

~

Post notes: As promised, here's the guide to Logan's incorrect words!

Sugar-ette: Surrogate Set-member: September Mit-sery: Mystery Li-berry: Library Li-berrian: Librarian Ex-cetera: Et cetera Pit-chers: Pictures

[pollylynn]

Clepsydra—A Season 3/4 Caskett One-Shot

Title: Clepsydra WC: 2400

A/N: Post-Knockout (or technically, post–Rise conversation). There are very glancing references to Naked Heat and Heat Rises here. 

How much time? 

He knows better than to ask questions he does not want to know the answer to. Or once he knew better. He once was a man who knew better than to ask, to act, to want. He once was a man. 

He doesn’t know what he is now. A being—a not quite person—caught between was and aching to be. Caught between now and I’ll call. 

When? 

He knew better than to ask that, at least. The man he once was knew better. 

When? 

There’s no profit in wondering. He wonders anyway, just beneath the surface, but on the surface, he works the case alongside the boys. He is at the precinct with the sun each morning—all three of them are. He takes the case home with him each night when even the long summer sun is a distant memory to the sky. He takes it all home. 

He stares at the digital storyboard. He burns through legal pads without number, trying to piece together theories that can give them any kind of lead, any course of action at all. 

He feels hamstrung in all of it. Ryan and Esposito are diligent. They are every bit as determined and fired up as he is. But the ideas that should flow fast and furious from his mind will barely come at all. He feels as if he’s standing on one leg with his right hand tied behind his back, half blindfolded. Without her, he feels like he’s missing half himself. 

How much time?

They are turning in circles before long. They are doubling back, checking and rechecking. They are coming up on nowhere quickly—the point at which it’s all ritual. They are abruptly rear-ended straight into it by the arrival of Captain Gates, whose second official act is to kick him to the curb. Her first is to shut down the investigation entirely. A stalled investigation, a waste of resources, inappropriate to begin with. 

It doesn’t stop them, of course—not the three of them. He works the digital board—the only board, now—all day at home. He rends lined yellow pages in frustration, then dives for the shredded remains in the wire basket under his desk when he’s suddenly convinced he was on to something this time. 

The boys come straight to the loft after work. They come with sleeves rolled up, bearing pizza and beer. They stay until the wee hours, then creep home for barely detectable amounts of sleep. They work—the three of them—but there’s nothing new. There’s been nothing new for . . .

How much time? 

He won’t let himself count the days, the weeks. He wills his mind away from the reality that they have moved into months—plural—long since. He wills his mind away from the merciless, ultimate truth. But it’s there, just beneath the surface.  

On the surface, he tears the book apart. He reduces it to its component phonemes, and Gina is irate. He assumes Gina is irate from the triple-digit number of voicemails that have piled up. He doesn’t speak to her, of course. He doesn’t speak to anyone, really. His mother and Alexis are away. 

He’d sent them away at the very outset—We don’t know, we don’t know. I need to know you are safe. Please. He’d sent them away, and at this point, they are staying away. He knows, distantly, that they are staying, because the silence stretches out when he calls, when they call and he notices that it’s safe to pick up. He doesn’t speak to anyone, really. 

The book is easy. It’s surprisingly easy once he starts knitting it together again. There’s Montrose to create. He’s come up before, in passing, but Nikki’s Captain needs to come to life in this one, and he does—his features, his mannerisms, his voice. They find their way on to the page like the lemon juice secret messages he used to leave for himself as a kid. 

He’d write them out and tuck them in winter coat pockets in summer, hoping to find them at some much later date, hoping he’d forget and rediscover with the heat of a lightbulb or a match from the kitchen drawer. He’d tuck them away, hoping for some pleasant summer surprise in the grey of December. 

It never happened. He was too impatient, his memory too perfect or his technique too sloppy. But that’s what happens now. Writing Charles Montrose—remembering his friend and mentor—is a like discovering a treasure trove of lemon juice secret messages. 

There’s his care for Nikki. There is his mentorship and his love for her. And there are his failings. There are the terrible ghosts that haunt the man, but even writing that is easy, because there is conflict. There is a struggle, and there are warning signs. There is a a story—a tragedy, yes, and his jaw, his spine, his whole body aches when he writes the man’s death—but there on the page is a fucking story that makes sense. It’s easy, compared to the real world, and one night—one moment on a well-honed knife blade between night and morning—he looks up, and he is finished. 

The book, unwritten and written again, is finished. 

He closes the last chapter file just as Nikki opens a book and settles in at Rook’s bedside. He checks the manuscript folder and sees the chapters neatly, chronologically, arranged. 

He’s written from beginning to end—something he never does. He’s done a handful of factual sanity checks, but he has not looked back in any meaningful way. Each chapter’s Last Opened date matches its Date Modified exactly, and each of those maps on to the date he has sent each one off for editing—for proof of life—Chapter X, Draft. And now he is simply done. He .zips the folder and sends it to Black Pawn as an attachment, all at once—no revisions, no worrying each sentence in each chapter to death. No revisions, and no looking back.  

He dials Gina’s number, heedless of the time. 

“It’s done,” he says flatly. He hangs up before she’s finished with her sleep-heavy Hello. 

He sleeps, then. It’s not the first time since Roy Montgomery’s funeral—not the first time since the shooting. The demands of his body aren’t kind enough to have propped him up all that time. He has slept in ten thousand brief snatches and awoken with a start every time. He has awoken with the sharp, aching certainty that they days, the weeks, the months have all been an awful nightmare. 

How much time?

But now, he sleeps straight through most of the day. His phone wakes him. His mother, Alexis, he registers as he fumbles the thing on. His daughter is clipped, cool, distant. His mother oscillates between high sarcasm and cautious hope that sleep—the real sleep she hears in his unguarded voice—will have done him some good at last. 

The doorbell buzzes. He stumbles through the office. Alexis comes back to the phone, softened by two degrees, no more. She says she loves him. She just worries about him. He says the same and promises he won’t forget to call tomorrow. 

He tugs open the door on the third or fourth try. He’s expecting Ryan and Esposito. Except he’s not expecting Ryan and Esposito. He remembers this as he blearily takes in the bike messenger holding a box of manuscript paper like a pizza. He remembers that Ryan and Esposito aren’t coming quite every day any more, because there’s no real need. Because they’re nowhere. Some of the good the sleep has done him ebbs away at the thought. 

He signs for the box and tips the messenger. He slices through the tape holding the cardboard cover on and sees the angry post-it first. Gina’s handwriting, her rage rising up from every stroke of the pen. Edits. Acknowledgments. Not done. 

He tosses the post-it aside, and wants to weep. He sits down hard on the stairs with the manuscript in its box between his feet, and he realizes that he hasn’t.

He recalls, for reasons a dime store shrink could fathom, her dry eyes and the absolute clarity of her words after the hangar—No one outside this immediate family. He recalls the tears on Ryan’s cheeks, glinting in even the dim light. But he has no memory of his own state of being. He can see himself there among them. He can describe his position in the room, where his hands came to rest, the angle of his head. He can say for certain that he did not weep for Roy Montgomery. He has not wept for him. 

He has not wept for her. Not really, though the last tears he can remember shedding were those that fell on to her body as her shockingly warm blood pumped out of her chest and spilled over the ornate brass buttons of her dress uniform. 

He has not wept for the terrible, inevitable conclusion he has put off for days, weeks, months, —plural. He has staved it off with the case, with the book, with this facsimile of a life he has been living, but now it seems he has reached the end, and he wants to weep.

He reaches between his feet instead. He grabs the stack of pages that make up the first chapter by expert feel. He wanders, back to the office and retrieves his dark blue editing pencil. 

He works quickly, slapping one chapter face down and retrieving the next. Once again, it’s easy. He’s critical of the fact that Montrose feels somewhat abruptly introduced—his life requires more exposition than a third book should have—but there’s no remedy for that, other than what he’s managed to do in rendering things as impressionistically as possible.

He paces, pages and pencil in hand. He hunches over the desk. He slouches in the leather chair. He moves through the manuscript with focus that cannot be healthy, but what about this is? What about the man he once was is anything like healthy. 

It’s an odd hour again when he finishes—when he decides he’s finished. He sets his worn-down blue pencil aside five or six pages before the end of the last chapter. That’s as it should be, as it needs to be, as it will stay. Nikki opens the book at Rook’s hospital bedside. 

It’s morning, he thinks, though the hour on his watch dial is ambiguous and there’s a thick cloud cover over the city. The street below his glass office wall probably says morning. He feels heavy in the world. Tired, yes, but also heavy, as though he might go to the floor in an all at once heap any second. 

He should go to bed. He should try for sleep, or rest, or . . . physical stillness, at least but the final pages draw him back. He sinks into his desk chair. He frames the pages with his hands and he reads. The whole of it is clear to him as the words reach inside him. He turns the final page and he sees the book for what it is—a love letter to her. 

That’s what Paula called Heat Wave. It wasn’t. Heat Wave was . . . attempted seduction mashed together with a note passed all the way around a sixth grade classroom. It was the work of a boy pulling the pigtails of the girl he liked, as Beckett herself had so aptly put it. As Kate had so aptly put it. 

This—these pages stacked high beside him, ending on a wounded, aching note—is a love letter. It is an elegy for a man they both loved, and it is the hell that they have rained down on one another, all this last year. It is the secrets she has shared with him and him alone, and it is his heart laid bare to her. 

It is the offering she does not want. 

How much time? The rest of forever. That is in the inescapable truth he has staved off all these days, weeks, months, and he has come to the end of it. Almost. 

There is a page after the last—happy to read to him endlessly and then another page. It’s blank save for a single word, once again, in Gina’s furious handwriting. ACKNOWLEDGMENTS in all caps this time. His head drops to his hands. He presses his palms against his eyes and feels the weight of what he dashed off last year. The few grudging words of thanks to Beckett herself, and the sly jab of the knife—his thanks to Gina for staying on top of me. He is, amidst the wash of everything else, ashamed of that. He is sorry for it and baffled by the instinct that led him into such a cheap, pointless shot.

He sits with everything that has transpired over the last year. He knows there is anger awaiting him in the middle distance. He knows he will live in the days, the weeks, the months to come with the kind of fury born of absolute despair. And still, in this moment, with his head bowed over the thing he has unmade and made new, he is baffled by the instinct to cause her pain. 

So, he decides, he won’t. He takes up his worn-down blue pencil. He scrawls in the space below Gina’s single, angry word, just her name at first, Detective Kate Beckett. Grief travels strangely down his arms at the sight of the letters there. It settles between the bones of his wrists, sending out aching pulses of longing. 

He knows, in the part of himself that his not yet utterly destroyed, that he has to go on. He knows that it’s important he sketches the broad outline of what he means to say, right here and right now, but it seems impossible with tendrils of sorrow winding through his hands. 

The answer, when it comes a long moment later, is one she has given him—an unhesitating, apt assertion of something true. He’s meant to steal it from her all along. He steals it now and gives it back. Detective Kate Beckett, he writes again, and just below it, how to make sense of songs.  A/N: Not really sure where this came from. Someone on AO3 left a really nice comment on “Kindness Yet,”  and for some reason that just put me in a Season 3/4 state of mind. And I’ve always been fascinated by the meta of the books. Also, I’m just not going to bed at all these days, because my head just won't fucking shut up. 

[wiredandrewired]

Was trying to actually work on something but my brain is stuck on loop.  So instead I’m gonna make a post of the Voltron stuff sitting unposted in my writing WIP folder to help me organize my thoughts.

I guess since I’m posting this, if you have anything you wanna say/ask about any of these feel free.  I respond well to outside interest.

1. Project ReVolt is without a doubt the project I’ve posted about the most here.  And talked about in random tags.  And tangents.  Originally it was just the name the project had in my internal brain filing cabinet but it’s kind of spread and stuck to where my wife and I just refer to it as that when we talk about it.

ReVolt is basically going to be a VLD series rewrite more along the lines of how my wife and I would have done it or at least liked to see it done.  In some places it will probably stick pretty damn close to the events of the series canon, but in others go completely off the deep end.  We’re each going to be doing one, so a lot of the headcanon and worldbuilding and such that we’ve worked out together in various other stories and RPs will be consistent between the two stories, but it will also give us a place to veer out and do things without the others’ input (as we’re not gonna let each other see our fics until they post, tee hee).  I’ve done a SHITPOT of rules and infrastructure work using actual alchemy tracts to try and make sense of the series’ largely Powers As The Plot Demands system,  and am pretty convinced I’m going to A)fall hard into my very common Esoterica Ranting Mode pitfall and B)enrage literally everyone who reads it with my character and plot choices.  Most conservative estimate says this will be six ‘books’ long as again, we’re doing literally the entire series.  Current status: at the ‘ridiculously large amount of notes and setting up actual arcs and outlines’ stage, and waiting for the wife to finish ‘Happier HOPEless’.

2. There Are No Monsters Here is a fic I really want to do but cannot seem to get off the ground, set to take place entirely in the ‘last universe’ from season 8--the one native-Honerva died in and crazed-death-god-Honerva picked out as her ideal and tried to wedge herself into.  I guess the basic idea was that, like the ‘main’ universe, it got rebuilt pretty much as it was prior to Nightmare Mom Ruining Everything, and I have it with no one fully remembering the events of season 8 that took place there, but characters really closely tied to those events having some itching feeling that something happened, and all the Altean alchemists agreeing that some kind of massive quantum Event certainly occurred even if they don’t know what.  

Mostly the story exists as  a place for me to have a canon-compliant AU that still lets me explore stuff like Altean history, the racial and cultural tensions of the Coalition, dink around with Oldadins that DON’T die in one fell swoop, a living Daibazaal and Altea, Lotor growing up with a decent-but-not-without-strains relationship with his dad, teen Allura and tiny Lotor being absolute shits to each other while also coming to terms as they grow up with who and what they MUST be both on a political and quantum scale, and generally prove that even a perfect universe isn’t, all in one place.  The title is entirely facetious, and anyone who’s read any of my alien culture headcanons for this series knows that.  Lol.  Current status: lots of bits and pieces, but no good beginning or connective tissue.   I have a lot of notes, some arc outlines, and a few scattered scenes and bits of dialogue from later in the story, but my god, I CANNOT get it off the ground.

3. Someone Must Get Hurt (But It Won’t Be Me) is supposed to be a pretty wholly Honerva-centric fic that starts...sometime in her youth?...and carries forward to an as-yet-undetermined point.  Probably her death.  I mean the first one.  I’m not sure.  Another chance to dig my fingers into Altean culture and Alchemy, this time leading up to All The Bad Shit That Happened, with the added bonus of being done from a focal point of a character I have a lot of really strong feelings about both positive and negative that’s resulted in me somehow being EVEN MORE wrapped up in her than I was before I added abject knee-jerk trauma hatred to the mix.  In no way meant to make Honerva more sympathetic, I think I just want to write her even more like my mother so I’ll feel EVEN BETTER about killing her?  Idk man my feelings about her are so complicated.  Also an excuse to write a shitpot of her and Zarkon because listen, I’m really glad they’re married because I ship them so fuckin hard.   Current Status: SO many notes.  SO much infrastructure.  Like three pages of an opening I’m almost definitely throwing away because I can’t decide where, when, or how to open but feel like this isn’t it.  One short but very telling scene of Honey and Zarkon from late in the story.  I’m obsessed with it but I can’t get anywhere. 

4. Currently Untitled Demon Hunter AU started because my wife talks to me about Happier HOPEless a LOT and I just got an itch in my bones to work on one myself.  In spite of the entire Demon Hunter AU thing getting started by a prompt on a Shance blog, neither Shiro nor Lance are set to appear for at least a chapter?  And I am not confident in my ability to not veer off into utter non-shipping anyway because man, am I bad at it.  Or like...just an entirely different ship for either or both of them.  Current Status: A lot of vague notes, a POWERFUL urge to structure the chapters and overall arc after Ripley’s Gates even though that limits my chapter count and means I will DEFINITELY have 20k+ word chapters, and about seven pages of the first chapter so I guess I’m committed now?

5. Currently Untitled Post Series Fic basically exists for me to vent my frustrations about two main things: The Universe is Fucking Huge And There Are Dangers Other Than Galra, and The Galra Empire Was Huge and Is Not Going To All Fall In Line Behind Voltron Coalition and Especially Behind Keith Who Just Arbitrarily Fucking Decided To Tell Them They Couldn't Pick A New Leader According To Their Own Traditions And Need To Do What They’re Told Now What The Fuck.  Also there was a lot of stuff in the series that got left hanging, and while ReVolt is an IN-series fix-it fic, I wanted something that patched up loose ends in a way that was satisfactory to me but also kind of canon-compliant.  Current Status: A lot of notes and screaming.  No one has seen my progress on this and they might never.

6. Dog Runs And Death Dreams is a warmup file turned deeply self-indulgent series of scenes in which I choose to assume that Shiro’s rare neuromuscular disorder was left so ambiguous so I could plug the symptoms of mine into it.  It’s genuinely not any deeper than that.  The whole thing is set pre-Kerberos, and includes copious Shiro x Adam content because of it, but also not the kind that makes me feel good about writing because that means it includes the ‘slow fizzle’ that leads up to their breakup before the mission.  Ugh.  Working on it does make me feel better when I've been having symptoms, though, and I’ve been letting myself write it, unchastised, in a really loose rambly way that I usually deride myself for.  It’s just cathartic.  Current Status: no notes, no plan, just strain-writing between seizures, but somehow it feels like it has some kind of structure and just keeps growing?  Possibly too close to the bone for me to ever post.

7. Birth and Rebirth was born out of two things: the fact that Zarkon is shown to have two ENTIRELY DIFFERENT reactions to first being presented with his baby son in different flashbacks and different seasons, and the fact that in spite of the flashbacks we get at the end of the series, earlier on, the impression I got of Lotor and Zarkon’s relationship wasn’t of a young man who had never had affection from his father, but who had instead lost it.  Well, three things: I have a lot of underlying issues at work, at play, and at large when it comes to the Galra Imperial Family.  Also, anyone notice the monitor blips in the first baby Lotor flashbacks indicate a heart murmur?  Anyway, it was supposed to be a thoroughly self-indulgent and thoroughly self-hurtful examination of Lotor’s early life and the death by degrees of what was left of his father in the husk Rift Adventures left behind, but I got stuck on it a little way in.   Current Progress: ten pages, a lot of notes, and some wistfulness.  I keep hoping I’ll get inspired to pick it back up again.  Contemplating rewriting some of the beginning, maybe it’ll help?

Bonus entry that is not actually in any form of progress soever:

50/50 Voltron Trashfire Edition is spawned from the ‘50/50′ challenge on an old TF board I used to haunt.  It’s a fifty-prompt smut challenge using the list of ‘50 reasons to have sex’ from some tv show, and the idea is to write a different ship for every prompt (hence the name).  My wife is blazing through it and has several (like twelve?) up on her AO3, but I’ll be utterly blunt: I haven’t written fifty porn fics in my LIFE.  Over ALL my fandoms.  Current Status: Literally all I have done is assign a ship to each prompt, and I might actually have some prompts with just question marks beside them still.  I have one aborted start to one entry.  That’s it.  It’s not happening.  But the empty file is technically in the folder, SO.

[justcreateapp]

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[ava-rosier]

Ta-dah, have a preview of my Theonsa tumblr fic. More like ‘I wanna prove to @theonbaejoys and @soapieturner that I haven’t forgotten about it’. It’s untitled because I haven’t thought up the perfectest wittiest title yet. 

Rated Mild-for-Theon, with a little kick.

“I can't believe I've never done this before.”

“What- slept with me?” Theon wriggles his eyebrows suggestively.

By this point in her life, having known him for fourteen years, Sansa's eye roll is reflexive. “Me on your couch while you were in your bed doesn't count as sleeping together. Or is the number of women you've claimed to have had sex with inflated?” she retorts, making Theon hold his hands out in show a of halfhearted self-defense. She knows he isn't offended, he's teased her worse than this and she's snapped back at him harder than this.

“Hey now, don't go casting aspersions on my ability to count. I'll have you know every single woman left me knowing I'd fucked them so good their mothers probably felt it before they were even born.”

Sansa groans at the tasteless modifier; he snickers. This is classic Theon; he's always been a horndog chasing after girls and, to hear Robb tell it, he's equal opportunity about college women and horny divorcées nowadays.

She holds up her wine glass, waving it in front of his face. “I meant this. I've never drank alcohol before nine in the morning. Actually I don't think I've ever drank alcohol before noon. And drinking at a club at two a.m. doesn't count!” She rushes to add the last before he could catch her on a technicality.

“Math and alcohol at 8:38 on a Sunday morning. Well, you can always count on me to provide you with new experiences here, Princess.” The old nickname rolls off his tongue easily, but it settles on her differently.  Here, thousands of leagues south of where they'd grown up, Theon's pet name for her feels like a special, secret language shared by only them, that no one else knows.

Blinking away this strange, intrusive thought, Sansa glances around the empty pub backyard instead. It's a chilly Sunday morning in Blackcrown and the skies are just gray, gray, and more gray. Even with the building in the way of the wind, it blows strongly enough that droplets of rain periodically hit her in the face. The Salty Hoar has all the markings of a dive bar: sticky, banged up wooden tables that rock unsteadily when you place any weight on them, chairs that don't match, and a very basic bar selection that reluctantly includes some greasy fare. She can see how Theon loves it.

Despite the cloudy day, they both wear sunglasses, and neither can claim to be hungover. The shades do make them look like they have no fucks to give, which perfectly accessorizes the Naval-brand sweats she is borrowing from Theon to wear on the train back to Oldtown in lieu of the rather fussy and dramatic black dress she'd worn the night before, which is now folded up in a plastic shopping bag.

This is the strangest walk of shame she's ever taken.

She takes advantage of the relative anonymity provided by the sunglasses to study the man sitting next to her. Theon slouches in his chair, looking out at the homes and businesses situated on the hill below them, leading to the shoreline. He's always been boisterous and cocksure, perpetually in motion and in your face. But now there's a genuine self-assurance about him, a contrast from his teenage years. Clearly the military has been good for him.

His head nods to the rhythm of a song only he can hear, fingers lightly drumming on the side of his glass. Sansa isn't sure why it feels so alien to be sitting here with Theon Greyjoy, someone she's known over half her life- laughing and talking with him until her cheeks hurt.

Robb. That has to be it. The answer comes to her with the sort of quiet clarity that makes her feel external to the moment. She had only ever interacted with Theon as Robb's best friend. That has to be why it feels so...taboo...to be here without Robb in between them.

“Thanks again for rescuing me last night and letting me crash at your place.” Harry had never treated her particularly nicely, she could see that now. But what she'd thought was a sweet summer flirtation that could grow more serious had only become tense and distant once the fall term had begun and everyone was back in Oldtown.

He'd been too cowardly to tell her outright he didn't want to be in a relationship anymore and instead had resorted to treating her like shit until she got fed up and called him out on it. Of course, she had decided the last straw was when they were out of town and he kept passively-aggressively complaining about every activity Sansa wanted to do. Hence how she had ended up abandoned in an unfamiliar place at a very late hour of the night.

Theon shakes his head, making a little moue with his mouth. “Nah, don't worry about it. And I meant what I said before. I know some guys. Just say the fucking word and we'll castrate this douchenozzle.” He sounds gleeful at the thought.

A wave of fondness sweeps through her.  She's glad, rather than irritated, with her older brother now for having the foresight and determination to put Theon's number in her phone before she moved so far away from her family. “There's no need. I am in a sorority, after all, and all I have to do is tell a few of my sisters what Harry did, and they'll spread the word. He'll be symbolically castrated at the Cit.” She smirks as she fishes through her purse for her phone, having now remembered Margaery's demands of an update this morning. Her friend might actually be awake by now.

“A patented Sansa Stark revenge, nice!” Theon whistles before taking another slow pull of his beer. “I always knew you were gonna be a sorority chick by the way.”

Sansa arches an eyebrow. “Did you now?”  He looks altogether too smug, chest all puffed out.

“Yup.  In high school, you and your girlfriends were into the whole 'wear tiny pajama shorts and have pillow fights to tease the boys' thing. That cute friend of yours, Beth? It always was obvious she wanted a bounce on Robb's cock. Sororities are basically the same thing, just times ten.”

Sansa is torn between gagging at that mental picture, and smacking him over the head because of the warped stereotype about sorority girls. “One: I really don't need to hear about my brother's penis, thank you. And two: we don't have pillow fights, we support each other and organize charities,” she argues. He is unrepentant, however.

“No, but you get all dolled up and go clubbing, don't you? I betcha have lots of guys panting after those legs of yours in a tight skirt.” If it weren't for the sunglasses, Sansa suspects she would see Theon's eyes roving over her body. Did you pant after my legs in those tiny pajama shorts? She wonders with a small frisson of excitement.

“Maybe so.” To distract herself from the way her body is flushing, she scrolls through her notifications before unlocking her phone. Bran had texted her something with the latest meme sweeping through the internet, one or two of her friends had asked what her plans tonight were, and there are a few Tumblr notifications.

“Anything from Dick Move?”

“Nope. I kind of want to block his number outright,” she admits, “but I'm also hoping he'll try to get a booty call out of me someday just so I can completely ignore his text.”

Theon slides his palm through the air in front of him. “Read 10:23 pm.”  He chortles at the thought.

“Exactly.”

She goes to her Tumblr app, promising herself she won't eat through most of her monthly data in one go.  When the page loads, there's a gifset from her favorite historical fantasy show, she makes sure to like it on the spot. Before she can stop him or tilt her phone away, Theon's bending his head close to see what she's got on the screen.

"You’ve got a Tumblr too? No fucking way!” he exclaims.

Why does he have to be so loud? At least she doesn't live in this town and there's barely anyone within earshot. Sansa hisses as she hits the home button on her phone, glaring reproachfully at him. “What's so 'no fucking way' about me having a Tumblr?”

He shrugs, shaking his head in the way men do when they know better than to tell the truth. “Nothing, I'm just surprised. Dunno why.” There's something different about his attention now, even though she can't see his eyes.

“Too. You said 'too'. That means you've got one. If anything I should be surprised you're on that site,” she says accusingly.

“What can I say, I'm a man of surprising depths.” She snorts at that, which he accepts with good humor. He snaps his fingers and points at her, grinning. “You know what you should do? You should give me your url.”

She gives him a look like that's the stupidest thing in the world. “I'm not sure I want you to know the depths of some of my fandom obsessions.”

“Come on,” he cajoles. “It's Uncle Theon-”

“Ok, that is so creepy. Never do that again.”

“Fine. But my point still stands. It's me. D'you think there's much that's going to shock me?”

She bites her lip and contemplates it. Maybe it's the weirdness and giddiness of having wine in the early morning, but Sansa finds herself grabbing a pen and scribbling her url down on a napkin and handing it to Theon, who slides his sunglasses to the top of his head to read it.

“'lemoncakess'. Cute. I bet it's very aesthetic.” As he chuckles at the url that had taken forever for her to settle upon (all the good ones she wanted were being hoarded), Sansa finds herself mentally scrolling through her tumblog's archive, trying to remember if there were any incriminating text posts or embarrassing reblogs.

“Don't diss the aesth. I bet yours is full of shitposting.”

“And then some.” He winks, folding the napkin and shoving it into his jacket. “Expect a visit from T-money in the next few days.”

“Is that your url?” It's horrid enough that she's cringing with secondhand embarrassment.

Her assumption only gets her an eye roll. “Nah. I just figured that if 'Uncle Theon' didn't fly, then neither would referring to myself as 'Big Daddy'.”

“But T-money seemed more acceptable?”

“It's what my buddies call me. It all started this one time I was dared to act like a stripper at a bar. I had tons of chicks- and some dudes- sticking their easily-earned stags in my smalls.”

“That I can believe.” She deadpans.

Theon has a wicked grin on his face even as he drains the last of his beer before grabbing his keys and standing up. “C'mon, sweet cheeks, let's get you to the station. I have the depths of a blog to plunder.”

[zorilleerrant]

How to Write a Basic Essay

Figure out your topic. This may already have been given to you, in which case you should just have the instructions in front of you to refer to. Otherwise, come up with something that fits the assignment (and you can always change it later, this is just a basic topic).

Use the five paragraph essay format if you know it. It’s not the best, but it’s a good start. If you don’t know it:

Write a thesis statement. This is a one sentence summary of the topic of your essay.

Your first paragraph is your introduction. Use it to explain what topic you’re going to write about, including any background information your intended audience needs to know. (If it’s not specified, your intended audience is you before you started taking this class.) End it with your thesis statement.

Your second, third, and fourth paragraphs are your body paragraphs. In each paragraph, you should use an example that supports your thesis statement. Come up with three examples. Your strongest example should be your fourth paragraph, and your weakest example should be your third paragraph. If your remaining example is also weak, you may need a new example or a new thesis statement.

A body paragraph should start with a miniature thesis statement, summarizing the example, along with any introduction to that statement that you need. The introduction should come first, and be no more than one sentence for a true five paragraph essay, although it can be longer if your essay is supposed to be more than 2-3 pages. Support this statement with quotes or paraphrases from the text/sources. Quotes are preferred, but if the quote would be very long (generally, more than three sentences), use a paraphrase. Remember to cite sources for either quotes or paraphrases. End each body paragraph with a short sentence that summarizes and reinforces what you’ve said.

The last paragraph is your conclusion. In it, you should summarize what you’ve said so far, and why it supports your thesis statement. You can also add any additional examples you thought of but didn’t want to fully flesh out. This is a good place to suggest further exploration, such as additional questions your thesis statement brings up, nuances you didn’t have space to address in your essay, or potential counterarguments.

Once you have your five paragraph essay, look for ways to flesh it out. A lot of this will just be writing more in-depth about your examples, adding additional quotes, and discussing more facets of it. You may also want to add in counterarguments you find likely (including, depending on the essay, common misconceptions), and refute them. These paragraphs will become quite long, and it’s perfectly fine to break them up wherever feels natural. If you don’t do that here, you can do it while editing.

Add more examples, as necessary. That fourth or fifth thing you thought of that would really add to your argument, but you didn’t have room for? Add it if your essay should be longer. You may also want to break up your current examples into more than one example, if the nuances are making it seem like several related things instead of one thing, or if the quotes you have seem to group together naturally into more than one group.

Editing begins. This is a good place to take a break, because some time away from your work will make editing easier. The first thing to do is to break your work into smaller sections. If you need to, you can mark them Section I: [topic], Section II: [topic], but most essays won’t require anything like that. Mostly, you’re looking to break up your paragraphs into bite-sized chunks, to make them easier to digest. Paragraphs should in general be at least two sentences (a single sentence that’s very long or impactful may stand on its own), and not more than ten. You should aim for the lower end. You can usually count quotes as a single sentence, even if you quote multiple sentences, but be aware of how this impacts length. If the paragraph looks too long, break it up.

Now that you’ve broken your essay into smaller pieces, look for those paragraphs that seem like they don’t add anything, or like you’re repeating yourself. Delete them. If something in the paragraph is important, but it seems too long, condense it into a single sentence, or leave only the important sentence(s). You can delete extraneous words or sentences in paragraphs with mostly good content, too, but that may be more helpful later in the process.

Edit for flow. This means making sure that each paragraph sounds like it’s on a single topic, and each paragraph sounds like it makes sense following the previous paragraph. You may have to move large sections around so the essay feels like it’s going in order. You’ll probably have to move some paragraphs earlier or later in the essay. If there are some you think add to the essay, but don’t seem to fit anywhere, save them at the end of the document or in a separate file; you may be able to find a place for them later, or you may not need them. You’ll probably also find yourself moving sentences from one paragraph to another, consolidating multiple paragraphs, or splitting a single paragraph into more than one (possibly requiring you to add content; you can mark [add content] to remind yourself to do it later). This is a good time to delete extraneous words or sentences, and work orphaned sentences back into other paragraphs.

Edit for content again. Are your examples fleshed out? Do any of them need more introduction, more explanation, or more quotes? Are any of them too confusing, or do they detract from the main topic of the essay? This may be a good time to replace unhelpful examples with any you put to the side. Fill out all the parts you marked for later. You should also double-check any dates, calculations, names, etc.

Edit for grammar and word choice. Unless it’s been specified otherwise, you shouldn’t use slang or dialectal speech. You should also ask whether or not to use contractions; some teachers strongly prefer that you use them, while others prefer that you not use them. If you don’t know, don’t use them; this is considered more correct in higher level academics. Some word choices or sentence constructions may read as casual; change them to read more formally. You should also check whether you’ve used long or uncommon words; it’s more communicative to use words most people know, and you should try to where possible. Try not to use words that you don’t normally use unless they’re technical terms. You may want to consult with ‘common grammatical errors’ charts. Remember to use the spelling, grammatical, and technical conventions that the assignment specified, or those common to your school or area.

Don’t forget citations! In your bibliography, you should include all the books, papers, and other sources you researched from, even if you didn’t quote them. Quotes should be in-line cited, footnoted, or endnoted. Paraphrases and references should be footnoted or endnoted.

Create a title page or header (as specified in the assignment; if not specified, use a title page) that includes the title of the paper, your name (including student ID, if applicable), the class name (including the course number), and the date (the due date of the assignment).

Format it according to the assignment guidelines and check the length. If it’s more than one third of the page, it counts towards the page count, unless the page count is under 5 pages. If you’re given a page count range, you must write more than the lower number, but you may write up to one page more than the upper number unless otherwise specified. For longer essays (20+ pages), you may be able to write one page less than the specified range, or up to three pages more. If you’re given a word count range, it isn’t meant to be exact, and you should attempt to get close to the number rather than hit it exactly (this will vary by word count), but it’s a stricter guideline than page count.

[theresawelchy]

Machine Learning for Everyone - In simple words

This article in other languages: Russian (original) Special thanks for help: @sudodoki and my wife <3

Machine Learning is like sex in high school. Everyone is talking about it, a few know what to do, and only your teacher is doing it. If you ever tried to read articles about machine learning on the Internet, most likely you stumbled upon two types of them: thick academic trilogies filled with theorems (I couldn’t even get through half of one) or fishy fairytales about artificial intelligence, data-science magic, and jobs of the future.

I decided to write a post I’ve been missing all that time. There's a simple introduction for those who always wanted to understand machine learning. Only real-world problems, practical solutions, simple language, and no high-level theorems. One and for everyone.

Let's roll.

Why do we want machines to learn?

This is Billy. Billy wants to buy a car. He tries to calculate how much he needs to save monthly for that. He went over dozens of ads on the internet and learned that new cars are around $20,000, used year-old ones are $19,000, 2-year old are $18,000 and so on.

Billy, our brilliant analytic, starts seeing a pattern: so, the car price depends on its age and drops $1,000 every year, but won't get lower than $10,000.

In machine learning terms, Billy invented regression – he predicted a value (price) based on known historical data. People do it all the time, when trying to estimate a reasonable cost for a used iPhone on eBay or figure out how many ribs to buy for a BBQ party. 200 grams per person? 500?

Yeah, it would be nice to have a simple formula for every problem in the world. Especially, for a BBQ party. Unfortunately, it's impossible.

Let's back to cars. The problem is, they all have different manufacturing date, dozens of options, technical condition, seasonal demand spikes, and god only knows how many more hidden factors. An average Billy can't keep all that data in his head while calculating the price. Me too.

People are dumb and lazy – we need robots to do the maths for them. So, let's go it computational way here. Let's provide the machine a data and ask it to find all hidden patterns related to price.

Aaaand it worked. The most exciting thing is that the machine copes with this task much better than a real person does when carefully analyzing all the dependencies in mind.

That was the birth of machine learning.

Three components of machine learning

The only goal of machine learning is to predict results based on incoming data. That's it. All ML tasks can be represented this way, or it's not an ML from the beginning.

The greater variety in the samples you have, the easier to find relevant patterns and predict the result. Therefore, we need three components to teach the machine:

Data Want to detect spam? Get samples of spam messages. Want to forecast stocks? Find the price history. Want to find out user preferences? Parse their activities on Facebook (no, Mark, stop it, enough!). The more and diverse the data, the better the result. Tens of thousands of rows is the bare minimum for the desperate ones.

There are two main ways of collecting data — manual and automatic. Manually collected data contains far fewer errors but takes more time to collect — that makes it more expensive in general.

Automatic approach is cheaper — you only need to gather everything you can find on the Internet and hope for the best.

Some smart asses like Google use their own customers to label data for them for free. Remember ReCaptcha which forces you to "Select all street signs"? That's exactly what they're doing. Free labor! Nice. In their place, I'd start to show captcha more and more. Oh, wait...

It's extremely tough to collect a good collection of data (aka dataset). They are so important that companies may even reveal their algorithms, but rarely datasets.

Features Also known as parameters or variables. Those could be car mileage, user's gender, stock price, word frequency in the text. In other words, these are the factors for a machine to look at.

When data stored in tables it's simple — features are column names. But what are they if you have 100 Gb of cat pics? We cannot consider each pixel as a feature. That's why selecting the right features usually takes way longer than all the other ML parts. That's also the main source of errors. Meatbags are always subjective. They choose only features they like or find "more important". Please, avoid being human.

Algorithms Most obvious part. Any problem can be solved differently. The method you choose affects the precision, performance, and size of the final model. There is one important nuance though: if the data is crappy, even the best algorithm won't help. Sometimes it's referred as "garbage in – garbage out". So don't pay too much attention to the percentage of accuracy, try to acquire more data first.

Learning vs Intelligence

Once I saw an article titled "Will neural networks replace machine learning?" on some hipster media website. These media guys always call any shitty linear regression at least artificial intelligence, almost SkyNet. Here is a simple picture to deal with it once and for all.

Artificial intelligence is the name of a whole knowledge field, such are biology or chemistry.

Machine Learning is a part of artificial intelligence. Important, but not the only one.

Neural Networks is one of machine learning types. A popular one, but there are other good guys in the class.

Deep Learning is a modern method of building, training, and using neural networks. Basically, it's a new architecture. Nowadays in practice, no one separates deep learning from the "ordinary networks". We even use the same libraries for them. To not look like a dumbass, it's better just name the type of network and avoid buzzwords.

The general rule is to compare things on the same level. That's why the phrase "will neural nets replace machine learning" sounds like "will the wheels replace cars". Dear media, it's compromising your reputation a lot.

Machine can Machine cannot Forecast Create smth new Memorize Get smart really fast Reproduce Go beyond their task Choose best item Kill all humans

The map of machine learning world

If you are too lazy for long reads, take a look at the picture below to get some understanding.

It's important to understand — there is never a sole way to solve a problem in the machine learning world. There are always several algorithms that fit, and you have to choose which one fits better. Everything can be solved with a neural network, of course, but who will pay for all these GeForces?

Let's start with a basic overview. Nowadays there are four main directions in machine learning.

Part 1. Classical Machine Learning

The first methods came from pure statistics in the '50s. They solved formal math tasks, looking for patterns in numbers, evaluating the proximity of data points, and calculating vectors' directions.

Nowadays, half of the Internet is working using these algorithms. When you see a list of articles to "read next" or your bank blocks your card at random gas station in the middle of nowhere, most likely it's the work of one of those little guys.

Big tech companies are huge fans of neural networks. Obviously. For them, 2% accuracy is an additional 2 billion in revenue. But when you are small, it doesn't make sense. I heard stories of the teams spending a year on a new recommendation algorithm for their e-commerce website, before discovering that 99% of traffic came from search engines. Their algorithms were useless. Most users didn't even open the main page.

Despite the popularity, classical approaches are so natural, that you can easily explain them to a toddler. They are like a basic arithmetics — we use it every day, without even thinking.

1.1 Supervised Learning

Classical machine learning is often divided into two categories – Supervised and Unsupervised Learning.

In the first case, the machine has a "supervisor" or a "teacher" who gives machine all the answers, telling is it a cat at the picture or a dog. The teacher is already divided (labeled) the data into cats and dogs, and the machine is using these examples to learn. One by one. Dog by cat.

Unsupervised learning means the machine is left on its own with a pile of animal photos and a task to find out who's who. Data is not labeled, there's no teacher, the machine is trying to find any patterns on its own. We'll talk about these methods below.

Clearly, the machine will learn faster with a teacher, so it's more commonly used in real-life tasks. There are two types of such tasks: classification – an object's category prediction and regression – prediction of a specific point on numeric axis.

Classification

"Splits objects based at one of the attributes known beforehand. Separate socks by based on color, documents based on language, music by genre"

Today used for: – Spam filtering – Language detection – A search of similar documents – Sentiment analysis – Recognition of handwritten characters and numbers – Fraud detection

Popular algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbours, Support Vector Machine

Here and onward you can comment with additional information to these sections. Feel free to write your examples of tasks. Everything is written here based on my own subjective experience.

Machine learning is about classifying things, mostly. The machine here is like a baby learning to sort toys: here's a robot, here's a car, here's a robo-car... Oh, wait. Error! Error!

In classification, you always need a teacher. The data should be labeled with features so the machine could assign the classes based on them. Everything could be classified — users based on interests (as algorithmic feeds do), articles based on language and topic (that's important for search engines), music based on genre (Spotify playlists), and even your emails.

In spam filtering was widely used Naive Bayes algorithm. Machine counted the number of "viagra" mentions in spam and normal mail. Then it multiplied both probabilities using Bayes equation, summed the results and yay, we got Machine Learning.

Later, spammers learned how to deal with Bayesian filter by adding lots of "good" words at the end of the email. Ironically, the method was called Bayesian poisoning. It stayed at history as most elegant and first practically useful one, though, other algorithms now used for spam filtering.

Here's another practical example of classification. Let's say, you need some credit money. How bank will know will you pay it back or not? There's no way to know it for sure. Though, the bank has lots of profiles of people who took the money before. Bank has data about age, education, occupation and salary and – most importantly – the fact of paying the money back. Or not.

With that data, we can teach the machine, find the patterns and get the answer. There's not an issue. The issue is that bank can't blindly trust the machine answer. What if there's a system failure, hacker attack or a quick fix from a drunk senior.

To deal with it, we have Decision Trees. All the data automatically divided to yes/no questions. They could sound a bit weird from a human perspective, e.g., whether the creditor earns more than $128.12? Though, the machine comes up with such question to split the data best at each step.

That's how a tree made. The higher the branch — the broader the question. Any analyst can take it and explain afterward. He may not understand it, but explain easily! (typical analyst)

The trees widely used in high responsibility spheres: diagnostics, medicine, and finances.

The two most popular algorithms for forming the trees are CART and C4.5.

Pure decision trees are rarely used now. However, they often set the basis for large systems, and their ensembles even work better than neural networks. We'll talk about that later.

When you google something, there are precisely the bunch of dumb trees which are looking for range the answers for you. Search engines love them because they're fast.

Support Vector Machines (SVM) is rightfully the most popular method of classical classification. It was used to classify everything in existence: plants by types faces at the photos, documents by categories, etc.

The idea behind SVM is simple – it's trying to draw two lines between categories with the largest margin between them. It's more evident in the picture:

There's one very useful side of the classification — anomaly detection. When a feature does not fit any of the classes, we highlight it. Now it used at the medicine — on MRI, computer highlights all the suspicious areas or deviations of the test. Stock markets use it to detect abnormal behavior of traders, to find the insiders. When teaching the computer the right things, we automatically teach it what things are wrong.

Today, for classification more frequently used neural networks. Well, that's what they were created for.

The rule of thumb is the more complex the data, the more complex the algorithm. For text, numbers, and tables, I'd choose the classical approach. The models are smaller there, they learn faster and work more clear. For pictures, video and all other complicated big data things, I'd definitely look at neural networks.

You may find face classifier built on SVM only 5 years ago you. Now, you can choose from hundreds of pre-trained networks. Nothing changed for spam filters, though. They are still written with SVM. And there's no good reason to switch from it anywhere.

Regression

"Draw a line through these dots. Yep, that's the machine learning"

Today this is used for:

Stock price forecast

Demand and sales volume analysis

Medical diagnosis

Any number-time correlations

Popular algorithms are Linear and Polynomial regressions.

Regression is basically classification where we forecast a number instead of category. Such are car price by its mileage, traffic by time of the day, demand volume by growth of the company etc. Regression is perfect when something depends on time.

Everyone who works with finance and analysis loves regression. It's even built-in to Excel. And it's super smooth inside — machine simply tries to draw a line that indicates average correlation. Though, unlike a person with a pen and a whiteboard, machine does at mathematically accurate, calculating the average interval to every dot.

When the line is straight — it's a linear regression, when it's curved – polynomial. These are two major types of regression. The other ones are more exotic. Logistic regression is a black sheep in the flock. Don't let it trick you, as it's a classification method, not regression.

It's okay to mess with regression and classification, though. Many classifiers turn into regression after some tuning. We can not only define the class of the object but memorize, how close it is. Here comes a regression.

1.2 Unsupervised learning

Unsupervised was invented a bit later, in the '90s. It is used less often, but sometimes we simply have no choice.

Labeled data is luxury. But what if I want to create, let's say, a bus classifier? Should I manually take photos of million fucking buses on the streets and label each of them? No way, that will take a lifetime, and I still have so many games not played on my Steam account.

There's a little hope for capitalism in this case. Thanks to the social stratification, we have millions of cheap workers and services like Mechanical Turk who are ready to complete your task for 0.05$. And that's how things usually get done here.

Or you can try to use unsupervised learning. But I can't remember any good practical appliance of it, though. It's usually useful for exploratory data analysis but not as the main algorithm. Specially trained meatbag with Oxford degree feeds the machine with a ton of garbage and watch it. Are there any clusters? No. Any visible relations? No. Well, continue then. You wanted to work in data science, right?

Clustering

"Divides objects based on unknown feature. Machine chooses the best way"

Nowadays used:

For market segmentation (types of customers, loyalty)

To merge close points on the map

For image compression

To analyze and label new data

To detect abnormal behavior

Popular algorithms: K-means_clustering, Mean-Shift, DBSCAN

Clustering is a classification with no predefined classes. It’s like dividing socks by color when you don't remember all the colors you have. Clustering algorithm trying to find similar (by some features) objects and merge them in a cluster. Those who have lots of similar features are joined in one class. With some algorithms, you even can specify the exact number of clusters you want.

An excellent example of clustering — markers on web maps. When you're looking for all vegan restaurants around, the clustering engine groups them to blobs with a number. Otherwise, your browser would freeze, trying to draw all three million vegan restaurants in that hipster downtown.

Apple Photos and Google Photos use more complex clustering. They're looking for faces at photos to create albums of your friends. The app doesn't know how many friends you have and how they look, but it's trying to find the common facial features. Typical clustering.

Another popular issue is image compression. When saving the image to PNG you can set the palette, let's say, to 32 colors. It means clustering will find all the "reddish" pixels, calculate the "average red" and set it for all the red pixels. Fewer colors — less the file size — profit!

However, you may have problems with colors like Cyan◼︎-like colors. Is it green or blue? Here comes the K-Means algorithm.

It randomly set 32 color dots in the palette. Now, those are centroids. The remaining points are marked as assigned to the nearest centroid. Thus, we get kind of galaxies around these 32 colors. Then we're moving the centroid to the center of its galaxy and repeat that until centroids won't stop moving.

All done. Clusters defined, stable, and there are exactly 32 of them. Here is a more real-world explanation:

Searching for the centroids is convenient. Though, in real life clusters not always circles. Let's imagine, you're a geologist. And you need to find some similar minerals at the map. In that case, the clusters can be weirdly shaped and even nested. Also, you don't even know how many of them to expect. 10? 100?

K-means does not fit here, but DBSCAN can be helpful. Let's say, our dots are people at the town square. Find any three people standing close to each other and ask them to hold hands. Then, tell them to start grabbing hands of those neighbors they can reach out. And so on, and so on until no one else can take anyone hand. That's our first cluster. Repeat the process until everyone clustered. Done.

A nice bonus: a person who have no one to hold hands — is an anomaly.

It all looks cool in motion:

Just like classification, clustering could be used to detect anomalies. User behaves abnormally after signing up? Let machine ban him temporarily and create a ticket for the support to check it. Maybe it's a bot. We don't even need to know what is "normal behavior", we just upload all user actions to our model and let the machine decide is it a "typical" user or not.

This approach works not that well compared to the classification one, but it never hurts to try.

Dimensionality Reduction (Generalization)

"Assembles specific features into more high-level ones"

Nowadays is used for:

Recommender systems (★)

Beautiful visualizations

Topic modeling and similar document search

Fake image analysis

Risk management

Popular algorithms: Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Latent Dirichlet allocation (LDA), Latent Semantic Analysis (LSA, pLSA, GLSA), t-SNE (for visualization)

Previously these methods were used by hardcore data scientists, who had to find "something interesting" at the huge piles of numbers. When Excel charts didn't help, they forced machines to do find the patterns. That's how they got Dimension Reduction or Feature Learning methods.

Projecting 2D-data to a line (PCA)

It is always convenient for people to use abstraction, not a bunch of fragmented features. For example, we can merge all dogs with triangle ears, long noses, and big tails to a nice abstraction — "shepherd". Yes, we're losing some information about the specific shepherds, but the new abstraction is much more useful for naming and explaining purposes. As a bonus, such "abstracted" model learn faster, overfit less and use fewer number of features.

These algorithms became an amazing tool for Topic Modeling. We can abstract from specific words to their meanings. This is that Latent semantic analysis (LSA) do. It is based on how frequent you see the word on the exact topic. Like, there are more tech terms in tech articles, for sure. The names of politicians are mostly found in political news, etc.

Yes, we can just make clusters from all the words at the articles, but we will lose all the important connections (for example the same meaning of battery and accumulator in different documents). LSA will handle it properly, that's why its called "latent semantic".

So we need to connect the words and documents into one feature to keep these latent connections. Referring to the name of the method. Turned out that Singular decomposition (SVD) nails this task, revealing the useful topic clusters from seen-together words.

Recommender Systems and Collaborative Filtering is another super-popular use of dimensionality reduction method. Seems like if you use it to abstract user ratings, you get a great system to recommend movies, music, games and whatever you want.

It's barely possible to fully understand this machine abstraction, but it's possible to see some correlations on closer look. Some of them correlate with user's age — kids play Minecraft and watch cartoons more; others correlate with movie genre or user hobbies.

Machine get these high-level concepts even without understanding them, based only on knowledge of user ratings. Nicely done, Mr.Computer. Now we can write a thesis why bearded lumberjacks love My Little Pony.

Association rule learning

"Look for patterns in the orders' stream"

Nowadays is used:

To forecast sales and discounts

To analyze goods bought together

To place the products on the shelves

TO analyze web surfing patterns

Popular algorithms: Apriori, Euclat, FP-growth

This includes all the methods to analyze shopping carts, automate marketing strategy, and other event-related tasks. When you have a sequence of something and want to find patterns in it — try these thingys.

Say, a customer takes a six-pack of beers and goes to the checkout. Should we place peanuts on the way? How often people buy it together? Yes, it probably works for beer and peanuts, but what other sequences can we predict? Can a small change in the arrangement of goods lead to a significant increase in profits?

Same goes for e-commerce. The task is even more interesting there — what customer is going to buy next time?

No idea, why the rule learning seems to be the least elaborated category of machine learning. Classical methods are based on a head-on looking through all the bought goods using trees or sets. Algorithms can only search for patterns, but cannot generalize or reproduce those on the new examples.

In the real world, every big retailer builds their own proprietary solution, so nooo revolutions here for you. The highest level of tech here — recommender systems. Though, I may be not aware of a breakthrough in the area. Let me know in comments if you have something to share.

Part 2. Reinforcement Learning

"Throw a robot into a maze and let it find an exit"

Nowadays used for:

Self-driving cars

Robot vacuums

Games

Automating trading

Enterprise resource management

Popular algorithms: Q-Learning, SARSA, DQN, A3C, Genetic algorithm

Finally, we got to something looks like real artificial intelligence. In lots of articles reinforcement learning is placed somewhere in between of supervised and unsupervised learning. They have nothing in common! Is this because of the name?

Reinforcement learning is used in cases when your problem is not related to data at all, but you have an environment to live. Like a video game world or a city for self-driving car.

Neural network plays Mario

Knowledge of all the road rules in the world will not teach the autopilot how to drive on the roads. Regardless of how much data we collect, we still can't foresee all the possible situations. This is why its goal is to minimize error, not to predict all the moves.

Surviving in an environment is a core idea of reinforcement learning. Throw poor little robot into real live, punish it for errors and reward for right deeds. Same way we teach our kids, right?

More effective way here — to build a virtual city and let self-driving car to learn all its tricks there first. That's exactly how we train auto-pilots right now. Create a virtual city based on a real map, populate with pedestrians and let the car learn to kill as few people as possible. When the robot is reasonably confident in this artificial GTA, it's freed to test in the real streets. Fun!

There may be two different approaches — Model-Based and Model-Free.

Model-Based means that car needs to memorize a map or its parts. That's a pretty outdated approach since it's impossible for the poor self-driving car to memorize the whole planet.

In Model-Free learning, the car doesn't memorize every movement but tries to generalize situations and act rationally while obtaining a maximum reward.

Remember the news about AI beats a top player at the game of Go? Although, shortly before this, it was proved that the number of combinations in this game is greater than the number of atoms in the universe.

This means, the machine could not remember all the combinations and thereby win Go (as it did chess). At each turn, it simply chose the best move for each situation, and it did well enough to outplay a human meatbag.

This approach is a core concept behind Q-learning and its derivatives (SARSA & DQN). 'Q' in the name stands for "Quality" as a robot learns to perform the most "qualitative" action in each situation and all the situations are memorized as a simple markovian process.

Such a machine can test billions of situations in a virtual environment, remembering which solutions led to greater reward. But how it can distinguish previously seen situation from a completely new one? If a self-driving car is at a road crossing and traffic light turns green — does it mean it can go now? What if there's an ambulance rushing through a street nearby?

The answer is today is "no one knows". There's no easy answer. Researches are constantly searching for it but meanwhile only finding workarounds. Some would hardcode all the situations manually that lets them solve exceptional cases like trolley problem. Others would go deep and let neural networks do the job of figuring it out. This led us to the evolution of Q-learning called Deep Q-Network (DQN). But they are not a silver bullet either.

Reinforcement Learning for an average person would look like a real artificial intelligence. Because it makes you think wow, this machine is making decisions in real life situations! This topic is hyped right now, it's advancing with incredible pace and intersecting with a neural network to clean your floor more accurate. Amazing world of technologies!

Off-topic. When I was a student, genetic algorithms (links has cool visualization) were really popular. This is about throwing a bunch of robots into a single environment and make them try reaching the goal until they die. Then we pick the best ones, cross them, mutate some genes and rerun the simulation. After a few milliard years, we will get an intelligent creature. Probably. Evolution at its finest.

Genetic algorithms are considered as part of reinforcement learning and they have the most important feature proved by the decade-long practice: no one gives a shit about them.

Humanity still couldn't come up with a task where those would be more effective than other methods. But they are great for students experiments and let people get their university supervisors excited about "artificial intelligence" without too much labor. And youtube would love it as well.

Part 3. Ensemble Methods

"Bunch of stupid trees learning to correct errors of each other"

Nowadays is used for:

Everything that fits classical algorithms approaches (but works better)

Search systems (★)

Computer vision

Object detection

Popular algorithms: Random Forest, Gradient Boosting

It's time for modern, grown-up methods. Ensembles and neural networks are two main fighters paving our path to a singularity. Today they are producing the most accurate results and are widely used in production.

However, the neural networks got all the hype today, while the words like "boosting" or "bagging" are scare hipsters on TechCrunch.

Despite all the effectiveness idea behind those is overly simple. If you take a bunch of inefficient algorithms and force them to correct each other's mistakes, the overall quality of a system will be higher than even the best individual algorithms.

You'll get even better results if you take the most unstable algorithms that are predicting completely different results on small noise in input data. Like Regression and Decision Trees. These algorithms are sensitive to even a single outlier in input data to have model go mad.

In fact, this is what we need.

We can use any algorithm we know to create an ensemble. Just throw a bunch of classifiers, spice up with regression and don't forget to measure accuracy. From my experience: don't even try a Bayes or kNN here. Although being "dumb" they are really stable. That's boring and predictable. Like your ex.

Although, there are three battle-tested methods to create ensembles.

Stacking Output of several parallel models is passed as input to last one which makes final decision. Like that girl, who asks her girlfriends whether to meet with you in order to make the final decision herself.

Emphasize here the word "different". Mixing the same algorithm on the same data would make no sense. Choice of algorithms is completely up to you. However, for final decision-making model, regression is usually a good choice.

Based on my experience stacking is less popular in practice, because two other methods are giving better accuracy.

Bagging aka Bootstrap AGGregatING. Use the same algorithm but train it on different subsets of original data. In the end — just average answers.

Data in random subsets may repeat. For example, from a set like "1-2-3" we can get subsets like "2-2-3", "1-2-2", "3-1-2" and so on. We use these new datasets to teach the same algorithm several times and then predict the final answer via simple majority voting.

The most famous example of bagging is the Random Forest algorithm, which is simply bagging on the decision trees (that was illustrated above). When you open your phone's camera app and see it drawing boxes around people faces — it probably results of Random Forest work. Neural network would be too slow to run real-time yet bagging is ideal given it can calculate trees on all the shaders of a video card or on these new fancy ML processors.

In some tasks, the ability of the Random Forest to run in parallel, even more, important than a small loss in accuracy to the boosting, for example. Especially in real-time processing. There is always a trade-off.

Boosting Algorithms are trained one by one sequentially. Every next one paying most attention to data points that were mispredicted by the previous one. Repeat until you are happy.

Same as in bagging, we use subsets of our data but this time they are not randomly generated. Now, in each subsample we take a part from the data previous algorithm failed to process. Thus, we make a new algorithm learn to fix errors of the previous one.

The main advantage here — very high, even illegal in some countries precision of classification that all cool kids can envy. Cons were already called out — it doesn't parallelize. But it's still faster than neural networks. It's like a race between dumper truck and racing car. Truck can do more, but if you want to go fast — take a car.

If you want a real example of boosting — open Facebook or Google and start typing in a search query. Can you hear an army of trees roaring and smashing together to sort results by relevancy? This is it, they are using boosting.

Part 4. Neural Networks and Deep Leaning

"We have a thousand-layer network, dozens of video cards, but still no idea where to use it. Let's generate cat pics!"

Used today for:

Replacement of all algorithms above

Object identification on photos and videos

Speech recognition and synthesis

Image processing, style transfer

Machine translation

Popular architectures: Perceptron, Convolutional Network (CNN), Recurrent Networks (RNN), Autoencoders

If no one ever tried to explain you neural networks using the "human brain" analogies, you're a happy guy. Tell me your secret. But first, I'll explain it as I like.

Any neural network is basically a collection of neurons and connections between them. Neuron is a function with a bunch of inputs and one output. His task is to take all numbers from its input, perform a function on them and send the result to the output.

Here is an example of simple but useful in real life neuron: sum up all numbers on inputs and if that sum is bigger than N — give 1 as a result. Otherwise — zero.

Connections are like channels between neurons. They connect outputs of one neuron with the inputs of another so they can send digits to each other. Each connection has its only parameter — weight. It's like a connection strength for a signal. When the number 10 passes through a connection with a weight 0.5 it turns into 5.

These weights tell the neuron to respond more to one input and less to another. Weights are adjusted when training — that's how the network learns. Basically, that's all.

To prevent the network from falling into anarchy, the neurons are linked by layers, not randomly. Inside one layer neurons are not connected, but connected to neurons of the next and previous layer. Data in the network goes strictly in one direction — from the inputs of the first layer to the outputs of the last.

If you throw in a sufficient number of layers and put the weights correctly, you will get the following - by applying to the input, say, the image of handwritten digit 4, black pixels activate the associated neurons, they activate the next layers, and so on and on, until it lights up the very exit in charge of the four. The result is achieved.

When doing real-life programming nobody is writing neurons and connections. Instead, everything is represented as matrices and calculated based on matrix multiplication for better performance. In two favorite videos of mine, all the process is described in an easily digestible way on the example of recognizing hand-written digits. Watch those if you want to figure this out.

A network that has multiple layers that have connections between every neuron is called perceptron (MLP) and considers the simplest architecture for a novice. I didn't see it used for solving tasks in production.

After we constructed a network, our task is to assign proper ways so neurons would react to proper incoming signals. Now is the time to remember that we have data that is samples of 'inputs' and proper 'outputs'. We will be showing our network a drawing of same digit 4 and tell it 'adapt your weights so whenever you see this input your output would emit 4'.

To start with all weights are assigned randomly afterward we show it a digit, it emits a random answer (the weights are not proper yet) and we compare how much this result differs from the right one. Afterward, we start traversing network backward from outputs to inputs and tell every neuron 'hey, you did activate here but you did a terrible job and everything went south from here downwards, let's keep less attention to this connection and more of that one, mkay?'.

After a hundred thousands of such cycles 'infer-check-punish', there is a hope that weights are corrected and act as intended. Science name for this approach is called Backpropagation or 'method of backpropagating an error'. Funny thing it took twenty years to come up with this method. Before this neural networks, we taught, however.

My second favorite vid is describing this process in depth but still very accessible.

A well trained neural network can fake work of any of the algorithms described in this chapter (and frequently work more precisely). This universality is what made them widely popular. Finally we have an architecture of human brain said they we just need to assemble lots of layers and teach them on any possible data they hoped. Then first AI winter) started, then thaw and then another wave of disappointment.

It turned out networks with a large number of layers required computation power unimaginable at that time. Nowadays any gamer PC with geforces outperforms datacenter of that time. So people didn't have any hope at that time to acquire computation power like that and neural networks were a huge bummer.

And then ten years ago deep learning rose.

In 2012 convolutional neural network acquired overwhelming victory in ImageNet competition that world suddenly remembered about methods of deep learning described in ancient 90s. Now we have video cards!

Differences of deep learning from classical neural networks was in new methods of training that could handle bigger networks. Nowadays only theoretics would try to divide which learning to consider deep and not so deep. And we, as practitioners are using popular 'deep' libraries like Keras, TensorFlow & PyTorch even when we build a mini-network with five layers. Just because it's better suited than all the tools coming before. And we just call them neural networks.

I'll tell about two main kinds nowadays.

Convolutional Neural Networks (CNN)

Convolutional neural networks are all the rage right now. They are used to search for the object on photos and in the videos, face recognition, style transfer, generating and enhancing images, creating effects like slow-mo and improving image quality. Nowadays CNN's are used in all the cases that involve pictures and videos. Even in you iPhone several of these networks are going through your nudes to detect objects in those. If there is something to detect, heh.

Image above is a result produced by Detectron that was recently open-sourced by Facebook

A problem with images was always the difficulty of extracting features out of them. You can split text by sentences, lookup words' attributes in specialized vocabularies. But images had to be labeled manually to teach machine where cat ears or tail were in this specific image. This approach got the name 'handcrafting features' and used to be used almost by everyone.

There are lots of issues with the handcrafting.

First of all, if a cat had its ears down or turned away from the camera you are in trouble, the neural network won't see a thing.

Secondly, try naming at the spot 10 different features that distinguish cats from other animals. I for once couldn't do it. Although when I see black blob rushing past me at night, even I see it in the corner of my eye I would definitely tell a cat from a rat. Because people don't look only at ear form or leg count and account lots of different features they don't even think about. And thus cannot explain it to the machine.

So it means machine need to learn such features on its own building on top of basic lines. We'll do the following: first, we divide the whole image into 8x8 pixels block and assign to each type of dominant line – either horizontal [-], vertical [|] or one of the diagonals [/]. It can be that several would be highly visible this happens too and we are not always absolutely confident.

Output would be several tables of sticks that are in fact are simplest features representing objects' edges on the image. They are images on their own but build out of sticks. So we can once again take a block of 8x8 and see how they match together. And again and again…

This operation is called convolution which gave the name for the method. Convolution can be represented as a layer of a neural network as neuron can act as any function.

When we feed our neural network with lots of photos of cats it automatically assigns bigger weights to those combinations of sticks it saw the most frequently. It doesn't care whether it was a straight line of a cat's back or a geometrically complicated object like a cat's face, something will be highly activating.

As the output, we would put a simple perceptron which will look at the most activated combinations and based on that differentiate cats from dogs.

The beauty of this idea is that we have a neural net that searches for most distinctive features of the objects on its own. We don't need to pick them manually. We can feed it any amount of images of any object just by googling billion of images with it and our net will create feature maps from sticks and learn to differentiate any object on its own.

For this I even have a handy unfunny joke:

Give your neural net a fish and it will be able to detect fish for the rest of its life. Give your neural net a fishing rod and it will be able to detect fishing rods for the rest of its life…

Recurrent Neural Networks (RNN)

The second most popular architecture today. Recurrent networks gave us useful things like neural machine translation (here is my post about it), speech recognition and voice synthesis in smart assistants. RNNs are the best for sequential data like voice, text or music.

Remember Microsoft Sam, the old-school speech synthesizer from Windows XP? That funny guy builds words letter by letter, trying to glue them up together. Now, look at Amazon Alexa or Assistant from Google. They don't only say the words clearly, they even place the right accents!

Neural Net is trying to speak

All because modern voice assistants are learned to speak not letter by letter, but whole phrases at once. We can take a bunch of voiced texts and train a neural network to generate an audio-sequence closest to the original speech.

In other words, we use text as input and its audio as the desired output. We ask a neural network to generate some audio for the given text, then compare it with the original, correct errors and try to get as close as possible to ideal.

Sounds like a classical leaning process. Even a perceptron is suitable for this. But how should we define it outputs? Fire one particular output for each possible phrase is not an option — obviously.

Here we'll be helped by the fact that text, speech or music are sequences. They consist of consecutive units like syllables. They all sound unique but depend on previous ones. Lose this connection and you get dubstep.

We can train the perceptron to generate these unique sounds, but how will he remember previous answers? So the idea was to add memory to each neuron and use it as an additional input on the next run. A neuron could make a note for itself - hey, man, we had a vowel here, the next sound should sound higher (it's a very simplified example).

That's how recurrent networks appeared.

This approach had one huge problem - when all neurons remembered their past results, the number of connections in the network became so huge that it was technically impossible to adjust all weights.

When a neural network can't forget it can't learn new things (people have the same flaw).

The first decision was simple - let's limit the neuron memory. Let's say, to memorize no more than 5 recent results. But it broke the whole idea.

The much better approach came up later — to use special cells, similar to computer memory. Each cell can record a number, read it or reset it. They were called a long and short-term memory (LSTM) cell.

Now, when neuron needs to set a reminder, it puts the flag in that cell. Like "it was a consonant in a word, next time use different pronunciation rules". When the flag is no longer needed - the cells are reset, leaving only the “long-term” connections of the classical perceptron. In other words, the network is trained not only to learn weights but also to set these reminders.

Simple, but it works!

CNN + RNN = Fake Obama

You can take speech samples from anywhere. BuzzFeed, for example, took the Obama's speeches and trained the neural network to imitate his voice. As you see, audio synthesis is already a simple task. Video still has issues, but it's a question of time.

There are many more network architectures in the wild. I recommend you a good article called Neural Network Zoo, where almost all types of neural networks are collected and briefly explained.

The End: when the war with the machines?

The main problem here is that the question "when will the machines become smarter than us and enslave everyone?" is initially wrong. There are too many hidden conditions in it.

We say "become smarter than us" like we mean that there is a certain unified scale of intelligence. The top of which is a man, dogs are a bit lower, and stupid pigeons are hanging around at the very bottom.

That's wrong.

In this case, every man must beat animals in everything but it's not true. The average squirrel can remember a thousand hidden places with nuts — I can't even remember where are my keys.

So the intelligence is a set of different skills, not a single measurable value? Or remembering nuts stashes' location is not included in the intelligence?

However, an even more interesting question for me - why do we believe that the human brain possibilities are limited? There are many popular graphs on the Internet, where the technological progress is drawn as an exponent and the human possibilities are constant. But is it?

Ok, multiply 1680 by 950 right now in your mind. I know you won't even try, lazy bastards. But give you a calculator — you'll do it in two seconds. Does this mean that the calculator just expanded the capabilities of your brain?

If yes, can I continue to expand them with other machines? Like, use notes in my phone to not to remember a shitload of data? Oh, seems like I'm doing it right now. I'm expanding capabilities of my brain with the machines.

Think about it. Thanks for reading.

DataTau published first on DataTau

[whitelabelseoreseller]

How to Screen and Recruit the Best SEO Content Writers

Posted by Victor_Ijidola

It’s easy to find writers; they’re everywhere — from a one-second Google search to asking on LinkedIn.

But hiring the best ones? That's the daunting task marketers and business owners face. And you do not just need writers, you need exceptional SEO content writers.

Mainly because that’s what Google (aka the largest traffic driver of most sites) has clearly been clamoring for since their Panda update in 2011, RankBrain in 2015, and their “Fred” update (and by the way, Gary Illyes from Google coined “Fred’ for every unnamed Google update) in March, 2017.

It’s obvious how each of these major updates communicates Google's preference for excellent SEO writers:

If you're a frequent Moz reader, you probably know how they work — but if not: Panda penalizes every webpage with content that adds little to no value to people online, giving more visibility to content pieces that do. On its own, the RankBrain update has made Google almost as smart as humans — when choosing the most relevant and high-quality content to rank on page #1 of search engine result pages (SERPs).

The “Fred” update further tackled sites with low-quality content that aren't doing anything beyond providing information that’s already available on the internet. It also penalized sites that prioritized revenue above user experience.

After this update, 100+ sites saw their traffic drop by 50 percent to 90 percent.

It is evident that Google has, through these core updates, been requiring brands, publishers, and marketers to work with SEO content writers who know their onions; the ones who know how to write with on-page SEO mastery.

But how do you find these exceptional wordsmiths? Without a plan, you will have to screen tens (or even hundreds) of them to find those who are a good fit.

But let’s make it easier for you. Essentially, your ideal SEO writers should have two key traits:

Good on-page SEO expertise

A great eye for user experience (i.e. adding relevant images, formatting, etc.)

A writer with these two skills is a great SEO writer. But let’s dig a bit deeper into what that means.

(Note: this post is about hiring exceptional SEO content writers — i.e., wordsmiths who don't need you monitoring them to do great work. So, things can get a bit techie as you read on. I’ll be assuming your ideal writer understands or is responsible for things like formatting, on-page SEO, and correctly uploading content into your CMS.)

1. On-page SEO knowledge

By now, you know what on-page SEO is. But if not, it’s simply the elements you put on a site or web page to let search engines understand that you have content on specific topics people are searching for.

So, how do you know if a writer has good on-page SEO knowledge?

Frankly, “Can you send me your previous writing samples?” is the ideal question to ask any writer you’re considering hiring. Once they show their samples, have them walk you through each one, and ask yourself the following questions:

Question A: Do they have ‘focus keywords’ in their previous samples?

Several factors come into play when trying to rank any page, but your ideal writer must know how to hold things down on the keyword side of things.

Look through their samples; see if they have optimized any content piece for a specific keyword in the past so you can know if they’ll be able to do the same for your content.

Question B: How do they use title tags?

Search engines use title tags to detect the headings in your content.

You know how it works: put “SEO strategy” — for example — in a few, relevant headings on a page and search engines will understand the page is teaching SEO strategy.

Essentially, your ideal SEO writer should understand how to use them to improve your rankings and attract clicks from your potential customers in search results.

Are title tags really that important? They are. Ahrefs, for instance, made their title tag on a page more descriptive and this alone upped their traffic by 37.58%.

So, look through the titles in your candidate’s samples, especially the h1 title. Here’s what you should look for when examining how a candidate uses HTML tags:

i. Header tags should, ideally, not be more than 60 characters. This is to avoid results that look like this in SERPs:

(three dots in front of your titles constitutes bad UX — which Google frowns at)

ii. The subheadings should be h2 (not necessarily, but it’s a plus)

iii. Headings under subtopics should be h3 (also not necessary, but it’s a plus)

Look for these qualities in your candidate’s work and you’ll be able to confirm that they properly implement title tags in their content, and can do the same for you.

But some writers may not have control over the title tags in their published works — that is, the sites they wrote for probably didn’t give them such access. In this case, request samples they published on their own site, where they actually have control over these tags.

Question C: What do they know about internal linking?

Orbit Media once shared how they used internal linking to shoot a blog post from position #29 up to #4.

So, it’s important that your writers know how to contextually link to your older content pieces while writing new content. And it works for good reason; internal linking helps you:

Communicate the relevance and value of your pages to Google (the more links a page gets, the more authority it has in Google’s eyes)

Demonstrate to Google that your site contains in-depth content about any specific topic

Tell Google your site has easy navigation — which means it has good UX and is well-structured.

Internal linking is a major key to search ranking, so you need writers who have internal linking in their pocketful of tools. But also ensure they do it using proper anchor texts; in a recent LinkedIn post, expert editor Rennie Sanusi hinted at two key anchor text elements to look for in your candidate’s samples:

[Anchor texts] should clearly explain where they'll take your reader to

[Anchor texts] shouldn't be too long

Question D: Do they write long-form content?

The average word count of a Google first page result is 1,800+ words long — according to research from Backlinko.

Google has been all about in-depth content since its inception; you’re probably familiar with their mission statement:

Every algorithm change they make is geared toward achieving this mission statement, and ranking long-form content helps them in the process as well.

Because, to them, writing longer content means you’re putting more information that searchers are looking for into your content.

So you need writers who can produce long-form content. Check their samples and confirm they know how to write long-form content on a regular basis.

Question E: Have they ranked for any important keywords?

Ultimately, you need to see examples of important keywords your ideal content writer has ranked for in the past. This is the utmost test of their ability to actually drive search traffic your way.

That's it for finding writers who know on-page SEO. But as you know, that's only one part of the skills that makes a great SEO content writer.

The other important bit is their ability to write content that engages humans. In other words, they need to know how to keep people reading a page for several minutes (or even hours), leading them to take actions that are important to your business.

2. A great eye for user experience

Keeping readers on a page for long durations also improves your ranking.

In the aforementioned Backlinko study, researchers analyzed 100,000 sites and found that “websites with low average bounce rates are strongly correlated with higher rankings.”

And you know what that means; your ideal SEO writer should not only write to rank on search engines, they must also write to attract and keep the attention of your target audience.

So, look for the following in their samples:

Headlines and introductions that hook readers

You need writers who are expert enough to know the types of headlines and opening paragraphs that work.

It’s not a hard skill to spot; look through their samples. If their titles and introductions don’t hook you, they probably won’t hook your audience. It’s really that simple.

Explainer images and visuals

The report also revealed that: “Content with at least one image significantly outperformed content without any images.”

But of course, they have to be relevant images (or other visual types). And many times (if not most of the time), that means explainer images — so look out for those in their samples. And there are two examples of explainer images:

Example #1: Explainer images with text and pointers

This one has elements (an arrow and a text) on it, explaining how the image is relevant to the topic the content is about.

Example #2: Explainer images without text and pointers

Why does this image not have any text or arrows on it? It’s a self-explanatory screenshot, that's why.

As long as it’s used appropriately — where the “online sales of Nike products” is mentioned in the content — it gets its message across.

In general, your ideal SEO writers need to know how to use tools like Skitch and Canva to create these images. Remember, you're on a hunt for the exceptional ones.

References and citing resources

Your ideal writer should link to stats or studies that make their points stronger. This one's pretty self-explanatory. Check the links in their samples and make sure they cite genuine resources.

Examples

Illustrations make understanding easier. Especially if you’re in a technical industry (and most industries have their geeky side), your ideal writer should know how to explain their points with examples.

Simply search their samples — using Command + F (or Ctrl F if you’re using Windows) — for “example," "instance," or "illustration." This works, because writers usually mention things like “for example,” or “for instance” when providing illustrations.

Excellent SEO content writers = Higher search rankings

Getting SEO content writers who have all the skills I’ve mentioned in this article are possible to find. And hiring them means higher search rankings for your content. These writers are, again, everywhere. But here’s the thing — and you’ve probably heard it before: You get what you pay for.

Exceptional SEO content writers are your best bet, but they’re not cheap. They can send your search traffic through the roof, but, like you: They want to work for people who can afford the quality they provide. So, if you’re going on a hunt for them, ready your wallet.

But ensure you get their samples and ask the questions in this guide as you deem fit. If you’re paying for content that’ll help you rank higher on Google, then you really should get what you pay for.

Did you find any of my tips helpful? Let me know in the comments below!

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

from The Moz Blog http://tracking.feedpress.it/link/9375/12724329

[theinjectlikes2]

How to Screen and Recruit the Best SEO Content Writers

Posted by Victor_Ijidola

It’s easy to find writers; they’re everywhere — from a one-second Google search to asking on LinkedIn.

But hiring the best ones? That's the daunting task marketers and business owners face. And you do not just need writers, you need exceptional SEO content writers.

Mainly because that’s what Google (aka the largest traffic driver of most sites) has clearly been clamoring for since their Panda update in 2011, RankBrain in 2015, and their “Fred” update (and by the way, Gary Illyes from Google coined “Fred’ for every unnamed Google update) in March, 2017.

It’s obvious how each of these major updates communicates Google's preference for excellent SEO writers:

If you're a frequent Moz reader, you probably know how they work — but if not: Panda penalizes every webpage with content that adds little to no value to people online, giving more visibility to content pieces that do. On its own, the RankBrain update has made Google almost as smart as humans — when choosing the most relevant and high-quality content to rank on page #1 of search engine result pages (SERPs).

The “Fred” update further tackled sites with low-quality content that aren't doing anything beyond providing information that’s already available on the internet. It also penalized sites that prioritized revenue above user experience.

After this update, 100+ sites saw their traffic drop by 50 percent to 90 percent.

It is evident that Google has, through these core updates, been requiring brands, publishers, and marketers to work with SEO content writers who know their onions; the ones who know how to write with on-page SEO mastery.

But how do you find these exceptional wordsmiths? Without a plan, you will have to screen tens (or even hundreds) of them to find those who are a good fit.

But let’s make it easier for you. Essentially, your ideal SEO writers should have two key traits:

Good on-page SEO expertise

A great eye for user experience (i.e. adding relevant images, formatting, etc.)

A writer with these two skills is a great SEO writer. But let’s dig a bit deeper into what that means.

(Note: this post is about hiring exceptional SEO content writers — i.e., wordsmiths who don't need you monitoring them to do great work. So, things can get a bit techie as you read on. I’ll be assuming your ideal writer understands or is responsible for things like formatting, on-page SEO, and correctly uploading content into your CMS.)

1. On-page SEO knowledge

By now, you know what on-page SEO is. But if not, it’s simply the elements you put on a site or web page to let search engines understand that you have content on specific topics people are searching for.

So, how do you know if a writer has good on-page SEO knowledge?

Frankly, “Can you send me your previous writing samples?” is the ideal question to ask any writer you’re considering hiring. Once they show their samples, have them walk you through each one, and ask yourself the following questions:

Question A: Do they have ‘focus keywords’ in their previous samples?

Several factors come into play when trying to rank any page, but your ideal writer must know how to hold things down on the keyword side of things.

Look through their samples; see if they have optimized any content piece for a specific keyword in the past so you can know if they’ll be able to do the same for your content.

Question B: How do they use title tags?

Search engines use title tags to detect the headings in your content.

You know how it works: put “SEO strategy” — for example — in a few, relevant headings on a page and search engines will understand the page is teaching SEO strategy.

Essentially, your ideal SEO writer should understand how to use them to improve your rankings and attract clicks from your potential customers in search results.

Are title tags really that important? They are. Ahrefs, for instance, made their title tag on a page more descriptive and this alone upped their traffic by 37.58%.

So, look through the titles in your candidate’s samples, especially the h1 title. Here’s what you should look for when examining how a candidate uses HTML tags:

i. Header tags should, ideally, not be more than 60 characters. This is to avoid results that look like this in SERPs:

(three dots in front of your titles constitutes bad UX — which Google frowns at)

ii. The subheadings should be h2 (not necessarily, but it’s a plus)

iii. Headings under subtopics should be h3 (also not necessary, but it’s a plus)

Look for these qualities in your candidate’s work and you’ll be able to confirm that they properly implement title tags in their content, and can do the same for you.

But some writers may not have control over the title tags in their published works — that is, the sites they wrote for probably didn’t give them such access. In this case, request samples they published on their own site, where they actually have control over these tags.

Question C: What do they know about internal linking?

Orbit Media once shared how they used internal linking to shoot a blog post from position #29 up to #4.

So, it’s important that your writers know how to contextually link to your older content pieces while writing new content. And it works for good reason; internal linking helps you:

Communicate the relevance and value of your pages to Google (the more links a page gets, the more authority it has in Google’s eyes)

Demonstrate to Google that your site contains in-depth content about any specific topic

Tell Google your site has easy navigation — which means it has good UX and is well-structured.

Internal linking is a major key to search ranking, so you need writers who have internal linking in their pocketful of tools. But also ensure they do it using proper anchor texts; in a recent LinkedIn post, expert editor Rennie Sanusi hinted at two key anchor text elements to look for in your candidate’s samples:

[Anchor texts] should clearly explain where they'll take your reader to

[Anchor texts] shouldn't be too long

Question D: Do they write long-form content?

The average word count of a Google first page result is 1,800+ words long — according to research from Backlinko.

Google has been all about in-depth content since its inception; you’re probably familiar with their mission statement:

Every algorithm change they make is geared toward achieving this mission statement, and ranking long-form content helps them in the process as well.

Because, to them, writing longer content means you’re putting more information that searchers are looking for into your content.

So you need writers who can produce long-form content. Check their samples and confirm they know how to write long-form content on a regular basis.

Question E: Have they ranked for any important keywords?

Ultimately, you need to see examples of important keywords your ideal content writer has ranked for in the past. This is the utmost test of their ability to actually drive search traffic your way.

That's it for finding writers who know on-page SEO. But as you know, that's only one part of the skills that makes a great SEO content writer.

The other important bit is their ability to write content that engages humans. In other words, they need to know how to keep people reading a page for several minutes (or even hours), leading them to take actions that are important to your business.

2. A great eye for user experience

Keeping readers on a page for long durations also improves your ranking.

In the aforementioned Backlinko study, researchers analyzed 100,000 sites and found that “websites with low average bounce rates are strongly correlated with higher rankings.”

And you know what that means; your ideal SEO writer should not only write to rank on search engines, they must also write to attract and keep the attention of your target audience.

So, look for the following in their samples:

Headlines and introductions that hook readers

You need writers who are expert enough to know the types of headlines and opening paragraphs that work.

It’s not a hard skill to spot; look through their samples. If their titles and introductions don’t hook you, they probably won’t hook your audience. It’s really that simple.

Explainer images and visuals

The report also revealed that: “Content with at least one image significantly outperformed content without any images.”

But of course, they have to be relevant images (or other visual types). And many times (if not most of the time), that means explainer images — so look out for those in their samples. And there are two examples of explainer images:

Example #1: Explainer images with text and pointers

This one has elements (an arrow and a text) on it, explaining how the image is relevant to the topic the content is about.

Example #2: Explainer images without text and pointers

Why does this image not have any text or arrows on it? It’s a self-explanatory screenshot, that's why.

As long as it’s used appropriately — where the “online sales of Nike products” is mentioned in the content — it gets its message across.

In general, your ideal SEO writers need to know how to use tools like Skitch and Canva to create these images. Remember, you're on a hunt for the exceptional ones.

References and citing resources

Your ideal writer should link to stats or studies that make their points stronger. This one's pretty self-explanatory. Check the links in their samples and make sure they cite genuine resources.

Examples

Illustrations make understanding easier. Especially if you’re in a technical industry (and most industries have their geeky side), your ideal writer should know how to explain their points with examples.

Simply search their samples — using Command + F (or Ctrl F if you’re using Windows) — for “example," "instance," or "illustration." This works, because writers usually mention things like “for example,” or “for instance” when providing illustrations.

Excellent SEO content writers = Higher search rankings

Getting SEO content writers who have all the skills I’ve mentioned in this article are possible to find. And hiring them means higher search rankings for your content. These writers are, again, everywhere. But here’s the thing — and you’ve probably heard it before: You get what you pay for.

Exceptional SEO content writers are your best bet, but they’re not cheap. They can send your search traffic through the roof, but, like you: They want to work for people who can afford the quality they provide. So, if you’re going on a hunt for them, ready your wallet.

But ensure you get their samples and ask the questions in this guide as you deem fit. If you’re paying for content that’ll help you rank higher on Google, then you really should get what you pay for.

Did you find any of my tips helpful? Let me know in the comments below!

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

from The Moz Blog https://ift.tt/2YsSgh7 via IFTTT

[daynamartinez22]

How to Screen and Recruit the Best SEO Content Writers

Posted by Victor_Ijidola

It’s easy to find writers; they’re everywhere — from a one-second Google search to asking on LinkedIn.

But hiring the best ones? That's the daunting task marketers and business owners face. And you do not just need writers, you need exceptional SEO content writers.

Mainly because that’s what Google (aka the largest traffic driver of most sites) has clearly been clamoring for since their Panda update in 2011, RankBrain in 2015, and their “Fred” update (and by the way, Gary Illyes from Google coined “Fred’ for every unnamed Google update) in March, 2017.

It’s obvious how each of these major updates communicates Google's preference for excellent SEO writers:

If you're a frequent Moz reader, you probably know how they work — but if not: Panda penalizes every webpage with content that adds little to no value to people online, giving more visibility to content pieces that do. On its own, the RankBrain update has made Google almost as smart as humans — when choosing the most relevant and high-quality content to rank on page #1 of search engine result pages (SERPs).

The “Fred” update further tackled sites with low-quality content that aren't doing anything beyond providing information that’s already available on the internet. It also penalized sites that prioritized revenue above user experience.

After this update, 100+ sites saw their traffic drop by 50 percent to 90 percent.

It is evident that Google has, through these core updates, been requiring brands, publishers, and marketers to work with SEO content writers who know their onions; the ones who know how to write with on-page SEO mastery.

But how do you find these exceptional wordsmiths? Without a plan, you will have to screen tens (or even hundreds) of them to find those who are a good fit.

But let’s make it easier for you. Essentially, your ideal SEO writers should have two key traits:

Good on-page SEO expertise

A great eye for user experience (i.e. adding relevant images, formatting, etc.)

A writer with these two skills is a great SEO writer. But let’s dig a bit deeper into what that means.

(Note: this post is about hiring exceptional SEO content writers — i.e., wordsmiths who don't need you monitoring them to do great work. So, things can get a bit techie as you read on. I’ll be assuming your ideal writer understands or is responsible for things like formatting, on-page SEO, and correctly uploading content into your CMS.)

1. On-page SEO knowledge

By now, you know what on-page SEO is. But if not, it’s simply the elements you put on a site or web page to let search engines understand that you have content on specific topics people are searching for.

So, how do you know if a writer has good on-page SEO knowledge?

Frankly, “Can you send me your previous writing samples?” is the ideal question to ask any writer you’re considering hiring. Once they show their samples, have them walk you through each one, and ask yourself the following questions:

Question A: Do they have ‘focus keywords’ in their previous samples?

Several factors come into play when trying to rank any page, but your ideal writer must know how to hold things down on the keyword side of things.

Look through their samples; see if they have optimized any content piece for a specific keyword in the past so you can know if they’ll be able to do the same for your content.

Question B: How do they use title tags?

Search engines use title tags to detect the headings in your content.

You know how it works: put “SEO strategy” — for example — in a few, relevant headings on a page and search engines will understand the page is teaching SEO strategy.

Essentially, your ideal SEO writer should understand how to use them to improve your rankings and attract clicks from your potential customers in search results.

Are title tags really that important? They are. Ahrefs, for instance, made their title tag on a page more descriptive and this alone upped their traffic by 37.58%.

So, look through the titles in your candidate’s samples, especially the h1 title. Here’s what you should look for when examining how a candidate uses HTML tags:

i. Header tags should, ideally, not be more than 60 characters. This is to avoid results that look like this in SERPs:

(three dots in front of your titles constitutes bad UX — which Google frowns at)

ii. The subheadings should be h2 (not necessarily, but it’s a plus)

iii. Headings under subtopics should be h3 (also not necessary, but it’s a plus)

Look for these qualities in your candidate’s work and you’ll be able to confirm that they properly implement title tags in their content, and can do the same for you.

But some writers may not have control over the title tags in their published works — that is, the sites they wrote for probably didn’t give them such access. In this case, request samples they published on their own site, where they actually have control over these tags.

Question C: What do they know about internal linking?

Orbit Media once shared how they used internal linking to shoot a blog post from position #29 up to #4.

So, it’s important that your writers know how to contextually link to your older content pieces while writing new content. And it works for good reason; internal linking helps you:

Communicate the relevance and value of your pages to Google (the more links a page gets, the more authority it has in Google’s eyes)

Demonstrate to Google that your site contains in-depth content about any specific topic

Tell Google your site has easy navigation — which means it has good UX and is well-structured.

Internal linking is a major key to search ranking, so you need writers who have internal linking in their pocketful of tools. But also ensure they do it using proper anchor texts; in a recent LinkedIn post, expert editor Rennie Sanusi hinted at two key anchor text elements to look for in your candidate’s samples:

[Anchor texts] should clearly explain where they'll take your reader to

[Anchor texts] shouldn't be too long

Question D: Do they write long-form content?

The average word count of a Google first page result is 1,800+ words long — according to research from Backlinko.

Google has been all about in-depth content since its inception; you’re probably familiar with their mission statement:

Every algorithm change they make is geared toward achieving this mission statement, and ranking long-form content helps them in the process as well.

Because, to them, writing longer content means you’re putting more information that searchers are looking for into your content.

So you need writers who can produce long-form content. Check their samples and confirm they know how to write long-form content on a regular basis.

Question E: Have they ranked for any important keywords?

Ultimately, you need to see examples of important keywords your ideal content writer has ranked for in the past. This is the utmost test of their ability to actually drive search traffic your way.

That's it for finding writers who know on-page SEO. But as you know, that's only one part of the skills that makes a great SEO content writer.

The other important bit is their ability to write content that engages humans. In other words, they need to know how to keep people reading a page for several minutes (or even hours), leading them to take actions that are important to your business.

2. A great eye for user experience

Keeping readers on a page for long durations also improves your ranking.

In the aforementioned Backlinko study, researchers analyzed 100,000 sites and found that “websites with low average bounce rates are strongly correlated with higher rankings.”

And you know what that means; your ideal SEO writer should not only write to rank on search engines, they must also write to attract and keep the attention of your target audience.

So, look for the following in their samples:

Headlines and introductions that hook readers

You need writers who are expert enough to know the types of headlines and opening paragraphs that work.

It’s not a hard skill to spot; look through their samples. If their titles and introductions don’t hook you, they probably won’t hook your audience. It’s really that simple.

Explainer images and visuals

The report also revealed that: “Content with at least one image significantly outperformed content without any images.”

But of course, they have to be relevant images (or other visual types). And many times (if not most of the time), that means explainer images — so look out for those in their samples. And there are two examples of explainer images:

Example #1: Explainer images with text and pointers

This one has elements (an arrow and a text) on it, explaining how the image is relevant to the topic the content is about.

Example #2: Explainer images without text and pointers

Why does this image not have any text or arrows on it? It’s a self-explanatory screenshot, that's why.

As long as it’s used appropriately — where the “online sales of Nike products” is mentioned in the content — it gets its message across.

In general, your ideal SEO writers need to know how to use tools like Skitch and Canva to create these images. Remember, you're on a hunt for the exceptional ones.

References and citing resources

Your ideal writer should link to stats or studies that make their points stronger. This one's pretty self-explanatory. Check the links in their samples and make sure they cite genuine resources.

Examples

Illustrations make understanding easier. Especially if you’re in a technical industry (and most industries have their geeky side), your ideal writer should know how to explain their points with examples.

Simply search their samples — using Command + F (or Ctrl F if you’re using Windows) — for “example," "instance," or "illustration." This works, because writers usually mention things like “for example,” or “for instance” when providing illustrations.

Excellent SEO content writers = Higher search rankings

Getting SEO content writers who have all the skills I’ve mentioned in this article are possible to find. And hiring them means higher search rankings for your content. These writers are, again, everywhere. But here’s the thing — and you’ve probably heard it before: You get what you pay for.

Exceptional SEO content writers are your best bet, but they’re not cheap. They can send your search traffic through the roof, but, like you: They want to work for people who can afford the quality they provide. So, if you’re going on a hunt for them, ready your wallet.

But ensure you get their samples and ask the questions in this guide as you deem fit. If you’re paying for content that’ll help you rank higher on Google, then you really should get what you pay for.

Did you find any of my tips helpful? Let me know in the comments below!

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

[lakelandseo]

How to Screen and Recruit the Best SEO Content Writers

Posted by Victor_Ijidola

It’s easy to find writers; they’re everywhere — from a one-second Google search to asking on LinkedIn.

But hiring the best ones? That's the daunting task marketers and business owners face. And you do not just need writers, you need exceptional SEO content writers.

Mainly because that’s what Google (aka the largest traffic driver of most sites) has clearly been clamoring for since their Panda update in 2011, RankBrain in 2015, and their “Fred” update (and by the way, Gary Illyes from Google coined “Fred’ for every unnamed Google update) in March, 2017.

It’s obvious how each of these major updates communicates Google's preference for excellent SEO writers:

If you're a frequent Moz reader, you probably know how they work — but if not: Panda penalizes every webpage with content that adds little to no value to people online, giving more visibility to content pieces that do. On its own, the RankBrain update has made Google almost as smart as humans — when choosing the most relevant and high-quality content to rank on page #1 of search engine result pages (SERPs).

The “Fred” update further tackled sites with low-quality content that aren't doing anything beyond providing information that’s already available on the internet. It also penalized sites that prioritized revenue above user experience.

After this update, 100+ sites saw their traffic drop by 50 percent to 90 percent.

It is evident that Google has, through these core updates, been requiring brands, publishers, and marketers to work with SEO content writers who know their onions; the ones who know how to write with on-page SEO mastery.

But how do you find these exceptional wordsmiths? Without a plan, you will have to screen tens (or even hundreds) of them to find those who are a good fit.

But let’s make it easier for you. Essentially, your ideal SEO writers should have two key traits:

Good on-page SEO expertise

A great eye for user experience (i.e. adding relevant images, formatting, etc.)

A writer with these two skills is a great SEO writer. But let’s dig a bit deeper into what that means.

(Note: this post is about hiring exceptional SEO content writers — i.e., wordsmiths who don't need you monitoring them to do great work. So, things can get a bit techie as you read on. I’ll be assuming your ideal writer understands or is responsible for things like formatting, on-page SEO, and correctly uploading content into your CMS.)

1. On-page SEO knowledge

By now, you know what on-page SEO is. But if not, it’s simply the elements you put on a site or web page to let search engines understand that you have content on specific topics people are searching for.

So, how do you know if a writer has good on-page SEO knowledge?

Frankly, “Can you send me your previous writing samples?” is the ideal question to ask any writer you’re considering hiring. Once they show their samples, have them walk you through each one, and ask yourself the following questions:

Question A: Do they have ‘focus keywords’ in their previous samples?

Several factors come into play when trying to rank any page, but your ideal writer must know how to hold things down on the keyword side of things.

Look through their samples; see if they have optimized any content piece for a specific keyword in the past so you can know if they’ll be able to do the same for your content.

Question B: How do they use title tags?

Search engines use title tags to detect the headings in your content.

You know how it works: put “SEO strategy” — for example — in a few, relevant headings on a page and search engines will understand the page is teaching SEO strategy.

Essentially, your ideal SEO writer should understand how to use them to improve your rankings and attract clicks from your potential customers in search results.

Are title tags really that important? They are. Ahrefs, for instance, made their title tag on a page more descriptive and this alone upped their traffic by 37.58%.

So, look through the titles in your candidate’s samples, especially the h1 title. Here’s what you should look for when examining how a candidate uses HTML tags:

i. Header tags should, ideally, not be more than 60 characters. This is to avoid results that look like this in SERPs:

(three dots in front of your titles constitutes bad UX — which Google frowns at)

ii. The subheadings should be h2 (not necessarily, but it’s a plus)

iii. Headings under subtopics should be h3 (also not necessary, but it’s a plus)

Look for these qualities in your candidate’s work and you’ll be able to confirm that they properly implement title tags in their content, and can do the same for you.

But some writers may not have control over the title tags in their published works — that is, the sites they wrote for probably didn’t give them such access. In this case, request samples they published on their own site, where they actually have control over these tags.

Question C: What do they know about internal linking?

Orbit Media once shared how they used internal linking to shoot a blog post from position #29 up to #4.

So, it’s important that your writers know how to contextually link to your older content pieces while writing new content. And it works for good reason; internal linking helps you:

Communicate the relevance and value of your pages to Google (the more links a page gets, the more authority it has in Google’s eyes)

Demonstrate to Google that your site contains in-depth content about any specific topic

Tell Google your site has easy navigation — which means it has good UX and is well-structured.

Internal linking is a major key to search ranking, so you need writers who have internal linking in their pocketful of tools. But also ensure they do it using proper anchor texts; in a recent LinkedIn post, expert editor Rennie Sanusi hinted at two key anchor text elements to look for in your candidate’s samples:

[Anchor texts] should clearly explain where they'll take your reader to

[Anchor texts] shouldn't be too long

Question D: Do they write long-form content?

The average word count of a Google first page result is 1,800+ words long — according to research from Backlinko.

Google has been all about in-depth content since its inception; you’re probably familiar with their mission statement:

Every algorithm change they make is geared toward achieving this mission statement, and ranking long-form content helps them in the process as well.

Because, to them, writing longer content means you’re putting more information that searchers are looking for into your content.

So you need writers who can produce long-form content. Check their samples and confirm they know how to write long-form content on a regular basis.

Question E: Have they ranked for any important keywords?

Ultimately, you need to see examples of important keywords your ideal content writer has ranked for in the past. This is the utmost test of their ability to actually drive search traffic your way.

That's it for finding writers who know on-page SEO. But as you know, that's only one part of the skills that makes a great SEO content writer.

The other important bit is their ability to write content that engages humans. In other words, they need to know how to keep people reading a page for several minutes (or even hours), leading them to take actions that are important to your business.

2. A great eye for user experience

Keeping readers on a page for long durations also improves your ranking.

In the aforementioned Backlinko study, researchers analyzed 100,000 sites and found that “websites with low average bounce rates are strongly correlated with higher rankings.”

And you know what that means; your ideal SEO writer should not only write to rank on search engines, they must also write to attract and keep the attention of your target audience.

So, look for the following in their samples:

Headlines and introductions that hook readers

You need writers who are expert enough to know the types of headlines and opening paragraphs that work.

It’s not a hard skill to spot; look through their samples. If their titles and introductions don’t hook you, they probably won’t hook your audience. It’s really that simple.

Explainer images and visuals

The report also revealed that: “Content with at least one image significantly outperformed content without any images.”

But of course, they have to be relevant images (or other visual types). And many times (if not most of the time), that means explainer images — so look out for those in their samples. And there are two examples of explainer images:

Example #1: Explainer images with text and pointers

This one has elements (an arrow and a text) on it, explaining how the image is relevant to the topic the content is about.

Example #2: Explainer images without text and pointers

Why does this image not have any text or arrows on it? It’s a self-explanatory screenshot, that's why.

As long as it’s used appropriately — where the “online sales of Nike products” is mentioned in the content — it gets its message across.

In general, your ideal SEO writers need to know how to use tools like Skitch and Canva to create these images. Remember, you're on a hunt for the exceptional ones.

References and citing resources

Your ideal writer should link to stats or studies that make their points stronger. This one's pretty self-explanatory. Check the links in their samples and make sure they cite genuine resources.

Examples

Illustrations make understanding easier. Especially if you’re in a technical industry (and most industries have their geeky side), your ideal writer should know how to explain their points with examples.

Simply search their samples — using Command + F (or Ctrl F if you’re using Windows) — for “example," "instance," or "illustration." This works, because writers usually mention things like “for example,” or “for instance” when providing illustrations.

Excellent SEO content writers = Higher search rankings

Getting SEO content writers who have all the skills I’ve mentioned in this article are possible to find. And hiring them means higher search rankings for your content. These writers are, again, everywhere. But here’s the thing — and you’ve probably heard it before: You get what you pay for.

Exceptional SEO content writers are your best bet, but they’re not cheap. They can send your search traffic through the roof, but, like you: They want to work for people who can afford the quality they provide. So, if you’re going on a hunt for them, ready your wallet.

But ensure you get their samples and ask the questions in this guide as you deem fit. If you’re paying for content that’ll help you rank higher on Google, then you really should get what you pay for.

Did you find any of my tips helpful? Let me know in the comments below!

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

[paulineberry]

How to Screen and Recruit the Best SEO Content Writers

Posted by Victor_Ijidola

It’s easy to find writers; they’re everywhere — from a one-second Google search to asking on LinkedIn.

But hiring the best ones? That's the daunting task marketers and business owners face. And you do not just need writers, you need exceptional SEO content writers.

Mainly because that’s what Google (aka the largest traffic driver of most sites) has clearly been clamoring for since their Panda update in 2011, RankBrain in 2015, and their “Fred” update (and by the way, Gary Illyes from Google coined “Fred’ for every unnamed Google update) in March, 2017.

It’s obvious how each of these major updates communicates Google's preference for excellent SEO writers:

If you're a frequent Moz reader, you probably know how they work — but if not: Panda penalizes every webpage with content that adds little to no value to people online, giving more visibility to content pieces that do. On its own, the RankBrain update has made Google almost as smart as humans — when choosing the most relevant and high-quality content to rank on page #1 of search engine result pages (SERPs).

The “Fred” update further tackled sites with low-quality content that aren't doing anything beyond providing information that’s already available on the internet. It also penalized sites that prioritized revenue above user experience.

After this update, 100+ sites saw their traffic drop by 50 percent to 90 percent.

It is evident that Google has, through these core updates, been requiring brands, publishers, and marketers to work with SEO content writers who know their onions; the ones who know how to write with on-page SEO mastery.

But how do you find these exceptional wordsmiths? Without a plan, you will have to screen tens (or even hundreds) of them to find those who are a good fit.

But let’s make it easier for you. Essentially, your ideal SEO writers should have two key traits:

Good on-page SEO expertise

A great eye for user experience (i.e. adding relevant images, formatting, etc.)

A writer with these two skills is a great SEO writer. But let’s dig a bit deeper into what that means.

(Note: this post is about hiring exceptional SEO content writers — i.e., wordsmiths who don't need you monitoring them to do great work. So, things can get a bit techie as you read on. I’ll be assuming your ideal writer understands or is responsible for things like formatting, on-page SEO, and correctly uploading content into your CMS.)

1. On-page SEO knowledge

By now, you know what on-page SEO is. But if not, it’s simply the elements you put on a site or web page to let search engines understand that you have content on specific topics people are searching for.

So, how do you know if a writer has good on-page SEO knowledge?

Frankly, “Can you send me your previous writing samples?” is the ideal question to ask any writer you’re considering hiring. Once they show their samples, have them walk you through each one, and ask yourself the following questions:

Question A: Do they have ‘focus keywords’ in their previous samples?

Several factors come into play when trying to rank any page, but your ideal writer must know how to hold things down on the keyword side of things.

Look through their samples; see if they have optimized any content piece for a specific keyword in the past so you can know if they’ll be able to do the same for your content.

Question B: How do they use title tags?

Search engines use title tags to detect the headings in your content.

You know how it works: put “SEO strategy” — for example — in a few, relevant headings on a page and search engines will understand the page is teaching SEO strategy.

Essentially, your ideal SEO writer should understand how to use them to improve your rankings and attract clicks from your potential customers in search results.

Are title tags really that important? They are. Ahrefs, for instance, made their title tag on a page more descriptive and this alone upped their traffic by 37.58%.

So, look through the titles in your candidate’s samples, especially the h1 title. Here’s what you should look for when examining how a candidate uses HTML tags:

i. Header tags should, ideally, not be more than 60 characters. This is to avoid results that look like this in SERPs:

(three dots in front of your titles constitutes bad UX — which Google frowns at)

ii. The subheadings should be h2 (not necessarily, but it’s a plus)

iii. Headings under subtopics should be h3 (also not necessary, but it’s a plus)

Look for these qualities in your candidate’s work and you’ll be able to confirm that they properly implement title tags in their content, and can do the same for you.

But some writers may not have control over the title tags in their published works — that is, the sites they wrote for probably didn’t give them such access. In this case, request samples they published on their own site, where they actually have control over these tags.

Question C: What do they know about internal linking?

Orbit Media once shared how they used internal linking to shoot a blog post from position #29 up to #4.

So, it’s important that your writers know how to contextually link to your older content pieces while writing new content. And it works for good reason; internal linking helps you:

Communicate the relevance and value of your pages to Google (the more links a page gets, the more authority it has in Google’s eyes)

Demonstrate to Google that your site contains in-depth content about any specific topic

Tell Google your site has easy navigation — which means it has good UX and is well-structured.

Internal linking is a major key to search ranking, so you need writers who have internal linking in their pocketful of tools. But also ensure they do it using proper anchor texts; in a recent LinkedIn post, expert editor Rennie Sanusi hinted at two key anchor text elements to look for in your candidate’s samples:

[Anchor texts] should clearly explain where they'll take your reader to

[Anchor texts] shouldn't be too long

Question D: Do they write long-form content?

The average word count of a Google first page result is 1,800+ words long — according to research from Backlinko.

Google has been all about in-depth content since its inception; you’re probably familiar with their mission statement:

Every algorithm change they make is geared toward achieving this mission statement, and ranking long-form content helps them in the process as well.

Because, to them, writing longer content means you’re putting more information that searchers are looking for into your content.

So you need writers who can produce long-form content. Check their samples and confirm they know how to write long-form content on a regular basis.

Question E: Have they ranked for any important keywords?

Ultimately, you need to see examples of important keywords your ideal content writer has ranked for in the past. This is the utmost test of their ability to actually drive search traffic your way.

That's it for finding writers who know on-page SEO. But as you know, that's only one part of the skills that makes a great SEO content writer.

The other important bit is their ability to write content that engages humans. In other words, they need to know how to keep people reading a page for several minutes (or even hours), leading them to take actions that are important to your business.

2. A great eye for user experience

Keeping readers on a page for long durations also improves your ranking.

In the aforementioned Backlinko study, researchers analyzed 100,000 sites and found that “websites with low average bounce rates are strongly correlated with higher rankings.”

And you know what that means; your ideal SEO writer should not only write to rank on search engines, they must also write to attract and keep the attention of your target audience.

So, look for the following in their samples:

Headlines and introductions that hook readers

You need writers who are expert enough to know the types of headlines and opening paragraphs that work.

It’s not a hard skill to spot; look through their samples. If their titles and introductions don’t hook you, they probably won’t hook your audience. It’s really that simple.

Explainer images and visuals

The report also revealed that: “Content with at least one image significantly outperformed content without any images.”

But of course, they have to be relevant images (or other visual types). And many times (if not most of the time), that means explainer images — so look out for those in their samples. And there are two examples of explainer images:

Example #1: Explainer images with text and pointers

This one has elements (an arrow and a text) on it, explaining how the image is relevant to the topic the content is about.

Example #2: Explainer images without text and pointers

Why does this image not have any text or arrows on it? It’s a self-explanatory screenshot, that's why.

As long as it’s used appropriately — where the “online sales of Nike products” is mentioned in the content — it gets its message across.

In general, your ideal SEO writers need to know how to use tools like Skitch and Canva to create these images. Remember, you're on a hunt for the exceptional ones.

References and citing resources

Your ideal writer should link to stats or studies that make their points stronger. This one's pretty self-explanatory. Check the links in their samples and make sure they cite genuine resources.

Examples

Illustrations make understanding easier. Especially if you’re in a technical industry (and most industries have their geeky side), your ideal writer should know how to explain their points with examples.

Simply search their samples — using Command + F (or Ctrl F if you’re using Windows) — for “example," "instance," or "illustration." This works, because writers usually mention things like “for example,” or “for instance” when providing illustrations.

Excellent SEO content writers = Higher search rankings

Getting SEO content writers who have all the skills I’ve mentioned in this article are possible to find. And hiring them means higher search rankings for your content. These writers are, again, everywhere. But here’s the thing — and you’ve probably heard it before: You get what you pay for.

Exceptional SEO content writers are your best bet, but they’re not cheap. They can send your search traffic through the roof, but, like you: They want to work for people who can afford the quality they provide. So, if you’re going on a hunt for them, ready your wallet.

But ensure you get their samples and ask the questions in this guide as you deem fit. If you’re paying for content that’ll help you rank higher on Google, then you really should get what you pay for.

Did you find any of my tips helpful? Let me know in the comments below!

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!