Okay I'll admit it, maybe I knew a tad too much about computer hacking and modding for a 10 year old kid. There's nothing wrong with being a tech nerd. I was just into it for all the wrong reasons. I liked to prank my friends online. And I don't just mean rickrolling them or changing stuff on Wikipedia. I was a bad guy. I would delete valuable items from my friends' accounts on their favorite games. I'd find out what creepypastas scared them the most and then change pictures on their most visited websites to them. I just thought it was funny. I liked watching them use every swear word in the dictionary or fall out of their seats after a jumpscare.
My favorite victim was my sister Ella. Boy did I enjoy screwing around with her stuff. She was obsessed with that game Animal Jam. The funniest thing I ever did to her was getting her first account banned the day after she bought her membership. I downloaded a bot that allows you to mass send the same Jam-a-Gram to everyone on your buddies list. I also figured out how to make someone your buddy automatically without them having to accept your friend request. I logged onto Ella's account and automatically friended everyone in the same land I was in. Then I mass sent them inappropriate Jam-a-Grams until people finally reported me enough to get Ella permabanned. I still laugh thinking about the temper tantrum she had.
I felt a little bad knowing that our mom had spent her own money on the membership card, so I told myself I wouldn't mess around with Ella's membership again after she got a second account. I still played other jokes on her like making her switch animals at random or changing her den to a giant model of a phantom (which she was terrified of). Eventually she got tired of me screwing around and locked her den. Fair enough. But that wasn't gonna stop me. If I could make all minigames unwinnable, I could easily bypass the "only me" den lock setting. So I did.
When I got inside her princess castle den, everything was where she last left it. I opened the decorating menu and noticed that there were animals inside the castle. They weren't moving, so I thought they were just plushies. I moved my tiger character closer to the animals. They were all horses. Not plushies, but the animal character models. None of them had a username tag that I could click on. They were all gray and were all in the sitting pose facing each other. I made my tiger run around their circle a few times, thinking they'd acknowledge me. The horses just stayed still. I typed "Hey guys" in the chat. My message popped up in the speech bubble and stayed there for a few seconds. It disappeared with no response.
That evening I asked Ella to show me her den. She opened the decorating menu and the gray horses were gone. I chalked it up to being something left over from when I messed around with the phantom models.
My friend asked me to raid the den of this user that was cyberbullying him. I was going to put so much furniture that it would overload and make the game severely lag to a point where if it didn't crash automatically, it would take about a minute for your character to move a couple steps. I bypassed the den lock and was about to get to work. Then I saw the two gray lions in front of me.
"I'm stronger." said the first lion. "I'm smarter." replied the second. I couldn't pass up the opportunity to be a little troll. "You are both weak and dumb", I typed in the chat. The lions turned around to face me. "But only one of us can go." one of them said. Forgetting what I came for, I stayed around to hear what they were talking about. There’s nothing like some good Animal Jam drama, right?
“So the question is, which one of us does the new paradise need more?” asked one of the lions. This was strange to me. Nobody talks in complete sentences and proper grammar in this game. And what was the “new paradise”? Surely they must be roleplaying. Why not join the fun since I’m here? I asked what roleplay theme they were doing. They didn’t respond to me and kept speaking to each other. “We need to decide now. The end is approaching fast.” I barricaded the whole den with furniture like I was supposed to, and went on my way.
I went to Jamaa Central to go shopping, and noticed there was a crowd around the Mira statue. Surrounding the statue were three different animals: a bunny, a fox, and a sheep. They were all gray and didn’t wear any clothes. Everyone else sporting their usual colorful fur and accessories looked upon them from a small distance. It was almost like there was some sort of imaginary line separating the two groups.
“Mira’s feathers have begun to fall.” said the sheep. “We have been ignoring nature’s warnings for too long.” added the bunny.
No one else was talking. There wasn’t anyone advertising what they had for trade or inviting others to play a minigame. All of the animal avatars were turned to face the statue.
“The inevitable destruction of Jamaa awaits. Your existence will cease to be.” the fox said. This piqued my interest because I thought it was some weird roleplay. I decided to join in with what little context I had. “Soon, your den will be filled with phantoms!” I said in the chat, playing along.
The gray animals spun around to face me. “The tiger is a false prophet.” announced the fox. I responded with “Rude :P “, using the silly tongue emoticon. If these guys were gonna screw with me, then it’s only fair I mess with them, right? So I clicked on the fox’s player card and sure enough, she had a locked den. I knew exactly what I was going to do: make my little prophecy come true and turn her den into the giant phantom model. I bypassed the den lock and watched the loading bar fill.
When the game finished loading, I was alone in total darkness. The sidebar with your Jam-a-Grams and friend list was gone. There was no globe button to return to the map, but there was a name tag for the location- “The Ark”. I tried to walk around, but my tiger didn’t budge.
Two spaces to the left and right of me lit up like spotlights and revealed a gray monkey and fox. They were displaying strange animations I had never seen before. The monkey had its eyes closed and hands clasped together, as if it was praying. The fox raised its tail and rested its head on the floor. There was no music but the sound of a flowing stream.
Behind them stood a horse bowing its head repeatedly and an eagle with its wings folded in, concealing its head. I wanted to ask what this place was, who these people were and why I was there. I tried to chat, but I could no longer type freely. I was restricted to Bubble Chat, a list of generic Animal Jam-approved phrases to help you navigate the game (such as “Let’s be buddies!” or “How do I earn gems?”). But these phrases were definitely not part of the usual Bubble Chat list. Some of the options I saw were: “Oh Mother Nature, bless me with your kindness.” “Amen.” “The flood is coming.” “You aren’t worthy of salvation.” “Listen to Noah’s warning.”
I felt something that I had never felt when playing Animal Jam before. I felt like I wasn’t supposed to be here.I don’t know what I stumbled upon, but I knew in my heart that I found something bad. Someone really didn’t want me to see this. I’m worried that whoever is trying to keep this secret would do anything to make sure it didn’t get out.
The area continued to illuminate, revealing even more animals in these strange positions. At the end of the room was a large wooden boat with a door that looked like you could walk through. It opened and out came a gray wolf. He had the skull eyes and was the only one wearing any items of clothing; he had what looked like some sort of cane or scepter on his back. Chat bubbles appeared over every animal, which had the same message: “Hail Alpha Noah”. They repeated the message every few seconds.
“Cease exaltation.” said the wolf’s chat bubble, stepping out of the shadows. The other animals stopped talking. He walked into the center of the room. “It appears that we have saved the last voyager, have we not?”
“Yes, Alpha.” said a sheep. “We have saved the tiger.” The sheep turned towards my avatar, and the other animals followed suit. I clicked around desperately, hoping to find a way out but to no avail. I had no idea what was about to happen next and I wasn’t exactly eager to find out.
Noah the wolf drew closer. I took a deep breath and reminded myself that it was only a children’s game. Nothing was going to happen to me in real life. I shouldn’t let that creep me out. But what if I stumbled across something that really was evil? My imagination ran wild. Was this a front for some sort of real criminal activity? Was I being drawn into a plot? Why do it on Animal Jam of all places?
My worries were soon interrupted by a pink and purple panda running out into the middle of the circle. I thought that help had arrived. Finally, someone who looked like a regular player. Maybe she could help me get back to the normal Animal Jam map.
“Hello?” the panda called out with her chat bubble. All of the gray animals sharply turned towards her. “Who are you and why are you here?” asked Noah. The panda told him her username and that she tried to follow her buddy somewhere, got lost, and ended up in this weird place.
“We already have a panda on the Ark.” said Noah. “You don’t need to be here.”
I don’t understand. Why was this random player not needed, but I was? It seemed like we were both just in the wrong place at the wrong time. Wait. That’s not right. The panda said she got lost on the way to somewhere else on the map. I was prying around in other Jammers’ dens. She didn’t mean to come here. I asked for it.
The panda asked Noah how to get back to the world map. “I am deeply sorry, but I cannot grant you salvation. You are simply not fit for the Ark, yet I cannot let you return to Jamaa either. If you go back and tell everyone what you experienced, you will only stir up jealousy among the other animals over who is worthy of salvation. Then the Ark will be overcrowded, it will sink, and no one gets saved.” This speech was divided up into several chat bubbles because of the character limit.
Noah walked back to the giant boat. “Let this be a reminder as to what will happen to the heathens when the Great Flood strikes. Praise Mother Nature and be grateful to me that you were fortunate enough to be the single member of your species that will live on.” All of the other animals turned their backs on the panda. I did too, out of fear.
The middle of the circle opened up into a deep blue water texture, almost like the ocean. The panda’s still model bobbed up and down beneath the waves. Every time she disappeared behind the water and came back, she sank down lower. Then she closed her eyes, transitioned to the sleeping animation, and sank down into the dark water until she couldn’t be seen anymore.
I just saw an animal avatar drown.
After seeing that, I knew what I had to do: fit in with this group or else. Only move when they move. Say what they say at the same time they do. I know that nothing happened in real life to whoever was behind the panda character. I still didn’t want to find out what exactly that was.
“Shed no tears for your heathen friends and family. Turn your backs on them like you did just now.” said Noah. “Oh, I have forgotten a very important matter. The last voyager.” He prowled towards my character from across the circle. All eyes were on me now. I started to fidget in my seat. I don’t know what the “last voyager” is, and it doesn’t sound like a very good thing.
“The tiger. He represents vitality and luck. We are so fortunate to have found the great tiger after so long. You truly possess these traits, constantly finding yourself surrounded by wealth and fortune.” he said. “Do you believe you embody the tiger’s greatest traits?”
I scrolled through the Bubble Chat phrase list and found “Yes.” I didn’t quite get what Noah meant, but if I appeared confident, he probably would leave me alone.
“Then drink from the water of Mother Nature’s sea.” A golden cup appeared next to my avatar. I assumed it worked just like the hot cocoa or smoothies, so I clicked on it to drink. Surely enough, it shrank and disappeared just like I expected it to.
“Rejoice, voyagers, for our toils are finally over. When Mira releases her tears and floods the world, we are prepared. We will be saved. We will be all that remains of Jamaa.”
Everyone entered into the strange praying animation I saw earlier. I clicked the animation menu and there was a button with a boat. I clicked it and the tiger sat, bowing its head.
The room illuminated once more to reveal a giant clock set to 12 o’clock. The sound of a church bell rang out.
The clock started to tick backwards.

Nineteen ninety-five, the Spice Girls did sing, Furbies went fuzzy, a Tamagotchi swung. Dial-up whirred, the internet new, A pixelated world, in shades of blue. Nineteen ninety-six, Pokemon took hold, Charizard battled, a story untold. Britpop's brash anthems, a Union Jack dress, Blur's Parklife chimed, Brittania's excess. Nineteen ninety-seven, Princess Diana's goodbye, A nation in mourning, beneath a tearful sky. Tony Blair's smile, a New Labour dawn, The promise of progress, a future reborn. Nineteen ninety-eight, The Spice Girls went their way, But teen dreams lived on, in the pop music sway. Mobile phones bloomed, a brick in the hand. Texting's first steps, across the land. Nineteen ninety-nine, the Y2K scare, A digital phantom, hanging in the air. The euro arrived, a new currency's gleam, The world spun onward, it seemed like a dream. Two thousand, the dawn of a century new, Blackberry's first click, a message flew. Big Brother's gaze, a reality show's test, Society's reflection, put to the test. Two thousand and one, 9/11's cruel sting, The Twin Towers fell, a terrible thing Fear gripped the world, a new landscape arose, Threats unseen, casting long shadows. Two thousand and two, the Queen's Golden Jubilee, A nation united, for all to see. World Cup fever, Beckham's bending free kick, England's near triumph, a bittersweet trick. Two thousand and three, the Iraq War's start, Protests and anger, tearing worlds apart. Texting went wild, with thumbs a-blur, Emoticons blossomed, a digital stir. Two thousand and four, the Boxing Day tsunami's roar, A wave of destruction, on distant shores. Facebook launched, a social media climb, Connecting the world, one post at a time. Two thousand and five, the London Bombings' pain, Innocence shattered, in the rushing rain. Tube lines silent, a city in shock, But resilience bloomed, from the very rock. Two thousand and six, the World Cup we saw, England's "It's Coming Home" anthem did roar. Wagatha Christie's scandal, tabloid delight, Celebrity culture, bathed in harsh light. Two thousand and seven, the iPhone's bright gleam, A touchscreen revolution, a digital dream. Harry Potter's magic, the final book's call, A generation's childhood, answered once and for all. Two thousand and eight, the financial crash, Banks in turmoil, a future unclear, a dash. For safety and comfort, in a world turned strange, The credit crunch tightened, a worrying range. Two thousand and nine, Obama's first term, Hope and inspiration, a world to reform. Twitter's chirps joined the social media fray, Information cascaded, throughout the day. Two thousand and ten, the Icelandic ash cloud, Flights grounded, a travel nightmare, a shroud Of volcanic dust, disrupting the flow, A reminder of nature's unpredictable show. Two thousand and eleven, Osama Bin Laden's demise, Justice delivered, with tears in some eyes. Royal wedding fever, for William and Kate, A fairytale romance, to celebrate. Two thousand and twelve, the London Olympics' flame, Usain Bolt's lightning, a world-beating game. Mo Farah's double win, a nation's delight, Golden memories, shining ever so bright. Two thousand and thirteen, Boston Marathon's blast, Innocence shattered, far too fast. Ed Sheeran's melodies, a voice that soared, Music's embrace, when the world seemed ignored. Two thousand and fourteen, the Scottish independence vote, A nation divided, by a single note. The ALS Ice Bucket Challenge, a viral trend, Raising awareness, for a cause to transcend. Two thousand and fifteen, same-sex marriage legalised, Love's victory echoed, hearts equalised. Paris attacks struck, a night of despair, The world in mourning, a burden to share. Two thousand and sixteen, Brexit's divisive vote, Leaving the union, setting a course afloat. Trump's shocking win, a political quake, The ground shifted, a future to make. Two thousand seventeen, Grenfell Tower's plight, A fire's fury, through the silent night. Seventy-two souls lost, a tragedy untold, A nation in grief, for the young and the old. Two thousand eighteen, the World Cup win for France, Mbappé's youthful brilliance, a winning chance. MeToo's reckoning, voices arose, A call for equality, to shatter old woes. Two thousand and nineteen, the Notre Dame blaze, A Parisian landmark, lost in a daze. Greta Thunberg's message, a climate change plea, School strike Fridays, for a world to see. Two thousand and twenty, a year unlike any, Covid's cruel hand, touched almost every penny. Lockdowns and isolation, a world on hold, The fight for survival, a story untold. Two thousand and twenty-one, vaccines emerged, A glimmer of hope, a future surged. The Tokyo Olympics, a muted scene, Pandemic's shadow, on a global screen. Two thousand and twenty-two, war in Ukraine's heart, A conflict's eruption, tearing worlds apart. Cost of living crisis, a squeeze on the purse, Uncertain futures, a growing disperse. Two thousand and twenty-three, the Queen's final sigh, A nation in mourning, beneath a tearful sky. Seventy years' reign, a legacy grand, A new chapter dawns, for a changing land. Two thousand and twenty-four, the world keeps its pace, With triumphs and troubles, in this human race. But through it all, a spirit endures, A flicker of hope, for dreams to secure.

Water + Fire = Steam
Steam + Fire = Engine
Engine + Engine = Rocket
Rocket + Rocket = Satellite
Water + Wind = Wave
Earth + Wave = Sand
Sand + Sand = Desert
Wind + Earth = Dust
Dust + Earth = Planet
Planet + Desert = Mars
Water + Water = Lake
Lake + Lake = Ocean
Ocean + Ocean = Sea
Sea + Mars = Venus
Venus + Mars = Love
Earth + Water = Plant
Plant + Plant = Tree
Tree + Tree = Forest
Tree + Forest = Wood
Tree + Wood = Paper
Paper + Paper = Book
Fire + Wind = Smoke
Smoke + Plant = Incense
Incense + Paper = Prayer
Prayer + Paper = Bible
Bible + Fire = Hell
Hell + Tree = Apple
Apple + Apple = Apple Tree
Book + Apple Tree = Newton
Newton + Newton = Einstein
Einstein + Love = Relativity
Relativity + Satellite = GPS
GPS + Satellite = Location
Bible + Bible = Church
Smoke + Steam = Cloud
Cloud + Water = Rain
Rain + Cloud = Rainbow
Rainbow + Church = Gay
Fire + Sand = Glass
Glass + Glass = Window
Window + Glass = House
House + House = Town
Town + Town = City
Earth + Fire = Lava
Lava + Water = Stone
Rain + Forest = Rainforest
Rainforest + Plant = Oxygen
Stone + Earth = Rock
Rock + Oxygen = Metal
Metal + Oxygen = Rust
Rust + Oxygen = Iron
Iron + Stone = Statue
City + City = Metropolis
Statue + Metropolis = Liberty
Statue + Liberty = Statue of Liberty
Statue of Liberty + City = New York
New York + Statue of Liberty = America
Water + Oxygen = Hydrogen
Wind + Plant = Dandelion
Dandelion + Hydrogen = Helium
Forest + Forest = Jungle
Jungle + Metal = Tin
Tin + Oxygen = Titanium
Titanium + Oxygen = Titanium Dioxide
Titanium Dioxide + Stone = White
White + Iron = Silver
Silver + Oxygen = Argentum
Argentum + Tin = Pewter
Pewter + Helium = Periodic Table
Periodic Table + Jungle = Carbon
Carbon + Water = Life
Life + Earth = Human
Human + America = President
President + Human = Obama
Obama + America = Change
Gay + Change = Straight
Straight + Change = Curve
Fire + Planet = Sun
Paper + Location = Map
Dandelion + Incense = Wish
Wish + Water = Genie
Genie + Book = Magic
Rock + Metal = Sword
Magic + Sword = Wizard
Fire + Rocket = Explosion
Book + Explosion = Bomb
Bomb + Periodic Table = Uranium
Earth + Steam = Mud
Mud + Mud = Clay
Clay + Clay = Brick
Brick + Brick = Wall
Gay + Rock = Stonewall
Stonewall + Wall = Great Wall
Great Wall + Stone = China
Metal + Metal = Gold
Water + Plant = Swamp
Swamp + Smoke = Dragon
Dragon + Wave = Tsunami
Tsunami + Earth = Island Island + Island = Continent
Continent + China = Asia
Asia + Continent = Europe
Europe + Earth = France
France + City = Paris
White + Periodic Table = Salt
Salt + Paris = Paris Hilton
Rainbow + City = Oz
Oz + City = Emerald
Emerald + Periodic Table = Chlorine
Titanium + Silver = Platinum
Tin + Iron = Bronze
Bronze + Platinum = Copper
Chlorine + Copper = Green
Green + Salt = Pickle
Pickle + Periodic Table = Pickle Juice
Paris Hilton + Pickle Juice = Skank
Gold + Skank = Prostitute
Prostitute + Prostitute = Brothel
Silver + Forest = Money
Money + Brothel = Politics
Mountain + Mountain = Mountain Range
Statue of Liberty + Mountain Range = Mount Rushmore
Mount Rushmore + Mountain Range = Rocky Mountains
Rocky Mountains + Periodic Table = Rocky Periodic Table
Politics + Rocky Periodic Table = Politic Periodic Table
China + Politic Periodic Table = Communism
Communism + America = Russia
Russia + America = Cold War
Cold War + Uranium = Nuclear War
America + Nuclear War = Apocalypse
New York + Explosion = 9/11
9/11 + Explosion = Terrorist
Terrorist + Life = Suicide
Suicide + Life = Death
Death + Rock = Grave
Grave + Periodic Table = Radium
Radium + Fire = Atom
Radium + Atom = Radiation
Jungle + Radiation = Mutant
Apocalypse + Mutant = X-Men
X-Men + Wizard = Magneto
Platinum + Oxygen = Platinum Oxide
Platinum Oxide + Hydrogen = Fuel Cell
Fuel Cell + Oxygen = Electricity
Magneto + Electricity = Magnet
Map + Magnet = Compass
Compass + Sun = North
North + Suicide = South
Curve + South = Bend
Engine + Fuel Cell = Car
Earth + Location = Land
Car + Land = Road
Road + Bend = U-Turn
Salt + Salt = Salt Lick
Salt + Salt Lick = Cow
Cow + Salt Lick = Milk
Fire + Fuel Cell = Energy
Energy + Periodic Table = Element
Element + Periodic Table = Chemistry
Chemistry + Periodic Table = Science
Science + Science = Technology
Technology + Technology = Computer
Computer + Technology = Hacker
Computer + Hacker = Virus
Virus + Virus = Bacteria
Milk + Bacteria = Yogurt
Yogurt + Yogurt = Cheese
Cheese + Atom = Quark
City + Explosion = War
Death + Human = Ghost
Ghost + War = Warrior Warrior + Warrior = Knight
Rainbow + Gay = Pride
Knight + Pride = Chess
Quark + Chess = Checkmate
Checkmate + Checkmate = Stalemate
Stalemate + Checkmate = Draw
Draw + Draw = Pencil
Carbon + Carbon = Diamond
Diamond + Gold = Ring
House + Sword = Castle
Castle + Castle = Palace
Castle + Palace = Kingdom
Kingdom + Castle = King
King + Ring = Lord of the Rings
Einstein + Einstein = Black Hole
Glass + Black Hole = Lens
Bacteria + Lens = Microscope
Microscope + Lord of the RIngs = Lord of the Flies
Lord of the RIngs + Lord of the Flies = Lord of the Flies and Rings
Lord of the Flies and Rings + Periodic Table = Lord of the Periodic Tables
Green + Plant = Leaf
Green + Emerald = Grass
Grass + Grass = Lawn
Lawn + Engine = Lawnmower
Lawnmower + Leaf = Compost
Compost + Compost = Fertilizer
Fertilizer + Lord of the Periodic Tables = Ammonia
Mount Rushmore + Rocky Periodic Table = Rocky Mountain
Rocky Mountain + Rocky Periodic Table = Rocky Mountain Periodic Table
Rocky Mountain Periodic Table + Ammonia = Cleaning Product
Cleaning Product + Pencil = Eraser
U-Turn + Eraser = Rewind
South + Location = Direction
Direction + Rewind = Backward
Backward + Direction = Reverse
Reverse + Location = Opposite
Plant + Dandelion = Weed
Weed + Water = Bong
Bong + Smoke = High
Tree + Church = Christmas Christmas + Water = Ice
Human + Periodic Table = Chemist
Human + Chemist = Scientist
Scientist + Plant = GMO
GMO + Plant = Corn
Corn + Human = Farmer
Farmer + Plant = Food
Food + Ice = Freeze
Ice + Freeze = Ice Cube
Ice Cube + Ice = Ice Cream
Ice Cream + Iron = Lick
Lick + Lick = Tongue
White + Titanium Dioxide = Paint
Paint + White = Black
Black + Green = Olive
Sword + Liberty = Justice
Justice + Justice = Judge
Judge + Justice = Law
Law + Periodic Table = Law of Periodic Table
Olive + Law of Periodic Table = Olive Oil
Olive Oil + America = Italy
Italy + Law of Periodic Table = Roman Numeral
Roman Numeral + Forest = C
C + Roman Numeral = 100
100 + 100 = 200
200 + 200 = 400
400 + 100 = 500
500 + Roman Numeral = D
C + D = E
D + E = F
E + F = S
F + S = FS
9/11 + Roman Numeral = IX
FS + IX = Fix
Fix + S = Fixes
Fix + Fixes = Repair
Fuel Cell + Energy = Battery
Battery + Rocky Periodic Table = Lithium
Lithium + Roman Numeral = Li
Li + Element = Liquid
Liquid + Metal = Mercury
Roman Numeral + Mercury = 8
Repair + 8 = Repeat
Repeat + Repeat = Loop
Wind + Dandelion = Seed
Seed + Wave = Surf
Surf + Wave = Surfer
America + Surfer = California
Jungle + Rainforest = Amazon
Amazon + Periodic Table = Amazonium
California + Amazonium = Silicon Valley
Silicon Valley + America = Google
Google + Loop = Gmail
Gmail + Paper = Mail
Mail + Paper = Letter
Letter + Letter = Word
Curve + Curve = Circle
Copper + Bronze = Brass
Brass + Paris Hilton = Trumpet
Trumpet + Brass = Music
Music + Wall = Headphones
Music + Headphones = Dance
Music + Wind = Flute
Dance + Flute = Pan
Pan + Rock = Pancake
Pancake + Pancake = Cake
Cake + Cake = Birthday Cake
Birthday Cake + Circle = O
9/11 + 9/11 = 9/22
Castle + Rocky Periodic Table = Castle Rock
Castle Rock + Rocky Periodic Table = Castle Rocky Periodic Table
Castle Rocky Periodic Table + Element = New Element
Fire + Roman Numeral = V
V + Roman Numeral = 5
Cloud + Roman Numeral = X
X + 5 = 10
10 + New Element = 11
10 + 11 = 21
21 + New Element = 22
22 + New Element = 23
23 + 9/22 = 1
1 + Letter = A
Book + Jungle = Tarzan
Tarzan + Jungle = Monkey
Monkey + Book = Curious George
Curious George + Periodic Table = Curium
Curium + Gay = Curious
Curious + Gay = Questioning
Questioning + Questioning = Curiosity
Curiosity + Curiosity = Question
Question + Question = Answer
Helium + Gay = HeHe
HeHe + Gay = Haha
HeHe + Haha = Lol
Answer + Lol = Meme
Meme + Einstein = Theory
Theory + Quark = String
String + String = Rope
Island + Human = Castaway
Castaway + Ocean = Sailor
Rope + Sailor = Knot
Gay + Love = Marriage
Marriage + Love = Divorce
Knot + Divorce = Not
Fire + Tree = Ash
Not + Ash = Is
Human + Is = I
I + O = IO
Book + Book = Bookshelf
Book + Bookshelf = Library
Library + Rainbow = Unicorn
Rust + Paint = Orange
Orange + Orange = Orange Tree
Orange Tree + Orange = Orange Juice
Orange Juice + Orange = Vitamin C
Orange + Vitamin C = Health
Health + Human = Doctor
Doctor + Dance = Dancer
Unicorn + Dancer = Fairy
Fairy + Lord of the Rings = Galadriel
Galadriel + Lord of the Rings = Elf
Gay + Elf = Elton John
Mountain Range + Metropolis = Mt. Fuji
Mt. Fuji + City = Tokyo
Mt. Fuji + Tokyo = Japan
Water + Ocean = Fish
Fish + Glass = Aquarium
California + Aquarium = Los Angeles
Los Angeles + Silicon Valley = Hollywood
Book + Newton = Gravity
Black Hole + Gravity = Universe
Book + Library = Knowledge
Knowledge + Rainbow = Wisdom
Universe + Wisdom = God
God + Ocean = Poseidon
Poseidon + Electricity = Lightning
Lightning + Energy = Laser
Laser + Energy = Light
Light + Lens = Camera
Camera + Paper = Photograph Photograph + Camera = Picture
Picture + Hollywood = Movie
Movie + Japan = Anime
Elton John + Anime = Your Mom
White + Bible = Angel
Angel + Hell = Devil
Devil + Apple = Adam
Adam + Apple = Eve
Human + Human = Family
Human + Family = Baby
Human + Baby = Child
Child + Baby = Teenager
Teenager + Child = Adult
Adult + Adam = Man
Man + Eve = Woman
Woman + Gay = Lesbian
Your Mom + Lesbian = Your Dad
Your Mom + Your Dad = You
IO + You = Iou
E + Iou = Eiou
A + Eiou = Aeiou
Aeiou + Letter = Vowel
Letter + Vowel = Alphabet
Word + Alphabet = Dictionary
Dictionary + Book = Thesaurus
Thesaurus + Tongue = Synonym
Synonym + High = Tall
Tall + Opposite = Short
Brothel + Prostitute = Business
Business + Business = Corporation
America + Continent = North America
Asia + America = USA
North America + USA = Canada
Canada + Ice = Hockey
Hockey + Hockey = Ice Hockey
Ice Hockey + Corporation = NHL
Electricity + Periodic Table = Francium
Francium + Hydrogen = FrH
Iron + Helium = Fe
FrH + Fe = Fresh
Fresh + Gay = Fruit
Metal + Gold = Coin
Grass + Salt = Hay
Coin + Hay = Straw
Straw + Fruit = Strawberry
Black + Strawberry = Blackberry
Bong + Gravity = Moon
Moon + Sun = Eclipse
Life + Eclipse = Vampire
Vampire + Water = Blood
Blood + Paint = Red
Red + White = Pink
Pink + Blackberry = Purple
Red + Pink = Magenta
Purple + Magenta = Violet
Purple + Violet = Indigo
Indigo + Green = Blue
Blue + Music = Blues
Blues + Location = Chicago
Mount Rushmore + Periodic Table = Elementary School
Elementary School + Elementary School = High School
High School + High School = College
College + America = Football
Football + Corporation = NFL
NFL + Chicago = Bears
Bears + NHL = Boston
Wind + Weed = Kite
Kite + Boston = Benjamin Franklin
Benjamin Franklin + Short = Ben
Death + Anime = Death Note
Anime + Death Note = L
Mountain + Roman Numeral = M
L + M = N
N + I = IN
Ben + IN = Benin

This morning, as I have done every day for the past thirty years, I walked the mile from my house to the town square. It’s a pleasant walk, first down a quiet residential street lined by stately Victorians and shaded by arching elms. Then, after about 3/4 of a mile, I turn right onto Waters Street and continue on it until I reach the town square. At its center is a 19th-century Neoclassical courthouse, which, although no case has been heard in it in nearly fifty years, still stands proud.
There are over a dozen antique shops and art galleries on the square, catering to the thousands of tourists who invade the town on summer weekends and at Christmastime. I almost never go inside them; most of what they sell is tacky, overpriced junk. But today, when I saw what was in the window of Miller’s Antiques, my poor heart almost gave out.
Lying between two wicker reindeer, garlands around their necks, was a small hunting knife, its blade free from rust, its ivory handle, fashioned into the form of a mermaid, stained red from blood. Even though it had been nearly 90 years since I had last seen it, I recognized it immediately. It had belonged to a stranger who had come to my family’s farmhouse in the Great Smoky Mountains—hundreds of miles away from this antique shop—when I was a young child. I never learned the man’s name, nor where he had come from, nor why he had decided to pay a visit to our family. I never learned where he went after he mysteriously vanished. All I know was that his knife saved my life.
I rushed into the shop. The proprietor, a balding, middle-aged man, looked up at me briefly before returning to the magazine he was reading.
“The knife in the window, where’d you get it?” I asked.
He groaned, shocked that someone had the audacity to ask him a question. “It was in some old storage unit I bought at auction.”
“Who owned that unit?”
“I don’t know. What’s it to you, anyways?”
“I’ve seen that knife before. Many years ago.”
He looked at me like I was crazy. “Grandpa, I think you should go home and take a nap.”
“There’s nothing you can tell me about it?”
“Not a damn thing. But since you’re so interested in it, why don’t you buy it? You can have it for $500. I’ll even throw in a sheath, what do you say, gramps?”
I didn’t even try to haggle. As I hurried home with the mysterious knife, I thought about that strange winter when I was ten. In the years that followed, I had convinced myself that some—if not all—of the bizarre events were a product of my imagination. But now I have no doubt. I know what happened to me was no dream.
I think it was early December when the stranger came. The maples and birches and shagbark hickories had all shed their leaves, but there was not even a dusting of snow on the ground. I was in bed when I heard the knocking. Three quick knocks, a long pause, and three more quick knocks.
My younger brothers and I rushed to our bedroom window and peered out. A haggard old man was standing outside the front door, his hair and beard long and untamed. Over a pair of overalls, he had on an oversized coat that hung loose over his emaciated frame. Despite the cold, he wore neither shoes nor a wool cap. Nor did he have a burlap sack on his back; it appeared that he had no belongings except for his threadbare clothes.
After looking at him for several seconds, I returned to bed. There was nothing unusual about a beggar. One would come around every month or so. That might not seem like a lot—given that we were in the midst of the Great Depression—but we lived on a remote plot of land in a remote corner of Western North Carolina. The closest farm, that of my Uncle John, was a mile away. Blackberry Hollow, the nearest town, was a five-mile journey. The county seat, the site of the closest train station, was nearly 30 miles away. Still, some intrepid beggars made the journey to our farmhouse, asking for food and work and shelter. My dad never had any work to offer, even during harvest time, but he always gave them a hot meal and let them sleep in the barn.
At five the next morning, when I was getting ready to milk Guinevere, our Jersey cow, my dad handed me a tin plate with hot cornbread and fatty bacon. “That’s for our guest,” he said. “Don’t know what his name is, he doesn’t speak a lick of English, but he seems like a pleasant fella.”
Wondering what someone who didn’t know English was doing in the boondocks, I headed to the barn. The stranger was sitting on a hay bale, whittling down a stick with that knife of his. He looked to be about 70, his hair and beard snow white. When he saw me, he smiled, revealing two decaying teeth.
When I got closer to him, I noticed his eyes were a violet color. Even today, I can still picture them distinctly. In all my years, I have never seen anyone with eyes like his.
As I was setting the plate down, he began to talk rapidly, but I couldn’t understand a word of what he was saying. I’m not sure how to best describe his speech, except that it sounded—for lack of a better word—happy. It put me at ease. You had to be on guard around beggars, for some were unstable, violent individuals, but I had no fear around this man, even though he had a knife. I could tell he was a gentle soul.
I extended my hand to him, but he seemed baffled by the gesture. I then pointed to myself, told him my name, and pointed at him. I was hoping that even if he didn’t know English, I could still find out his name, but he just looked at me in confusion.
I thought that the stranger would leave that morning, like the other beggars who came around did, but he didn’t. Nor did he depart the following morning; he seemed content to stay in a smelly barn with a milk cow and two mules. My dad didn’t try to shoo him off. Through gestures, he was able to get the stranger to understand that, in exchange for room and board, he would milk Guinevere and muck out the barn. He knew what to do; he had clearly worked on farms before.
Whenever the stranger wasn’t doing his chores, he would just sit on a hay bale and whittle down a stick. I never saw him whet his knife, but it was always razor sharp.
Word of him spread around town, and, over the following days, several dozen people came to our barn and attempted to communicate with the violet-eyed man. The first two were my Uncle John and Aunt Marie. Uncle John, like most of the farmers in Western North Carolina, was not an educated man; I’d be surprised if he had more than five years of formal schooling. He certainly didn’t know any language except English. But his wife, my Aunt Marie, was from France. She met my uncle when he was serving in World War I, what was known then as the Great War. It was rumored that she had been the most renowned cabaret singer in all of Paris, that she had sung for visiting dignitaries and royalty. That was likely an exaggeration, but she was certainly a fine singer, her voice was as sweet as that of a meadowlark.
You would have thought that if anyone in our town would be able to identify the stranger’s tongue, it would have been Aunt Marie, but she just shook her head after listening to him speak. “I’ve entertained people from all over Europe, heard them converse in dozens of languages, but I have never heard anything like what’s coming out of that man’s mouth.”
The other visitors didn’t have any more luck. Ms. Campbell, the school teacher, stopped by with an atlas. She showed him a map of Europe, but he just seemed utterly confused. She then proceeded to read a long list of European cities, but he showed no reaction to their names.
Some thought that the man might be filled with the Holy Spirit and be speaking in tongues. The Reverend Arthur Duncan, the pastor of the Primitive Baptist church in Blackberry Hollow, came by one afternoon. I didn’t think he’d be of any use for he was as crazy as a loon. Every Sunday he would drone on and on for hours about how the Devil was at work in the Smokies and list the various reasons why everyone would be damned to Hell for eternity.
The righteous reverend didn’t even step foot in the barn. He just took one look at the stranger, proclaimed that he was possessed by the Evil One, and said that he should be taken to the river and drowned. Thankfully, everyone ignored the nutter.
The preacher wasn’t our strangest visitor. Ma McDermott, a local folk healer and midwife, stopped by early one Sunday morning. There were lots of rumors about that hideous old hag. Some townsfolk claimed that she had been born over 200 years ago in the Scottish Highlands. As a young girl, she had been accused of witchcraft. The villagers dragged her from her home and smeared her with tar. Knowing that she was about to suffer a painful death, she prayed to the Devil for deliverance. Just before she was set alight, the Devil swooped down from the sky and carried her across the Atlantic to the Smokies—a place where she could practice her sorcery in peace, for back then the only whites in the vicinity were the occasional trapper or trader.
The Devil required constant offerings from the woman whose life he saved. Sometimes he’d be satisfied with a rooster or a sheep, but, at other times, his thirst could only be quenched by human blood. My dad always warned me not to go deep in the woods at night, for if I did, Ma McDermott would snatch me.
Back then, I never believed the rumors. I thought it was just prattle from superstitious townsfolk, like their stories of vengeful ghosts and puckish fairies. Still, I was scared of her. When she limped into the barn that morning, her arthritic hands tightly gripping a hickory walking stick, I retreated to a far corner of the barn, wanting to get away from her but still curious whether she could communicate with the stranger.
Like everyone else, she couldn’t. She asked the stranger something in a foreign tongue—presumably Gaelic—but, like before, he showed no sign of comprehension.
On her way out of the barn, the witch’s pale yellow eyes met mine. As she mouthed something inaudible, she raised a long, bony finger and pointed it at me. I started shuddering, even though I told myself that she was just an eccentric old lady, that she had no magical powers.
I’m pretty sure that I didn’t sleep a wink that night, sure that the crone would climb through my window, take me out into the woods, and sacrifice me to the Devil.
One afternoon, about a week after Ma McDermott’s visit, I went out to fish at a place called Copper Falls. Carrying my cane pole over my shoulder, I headed north on a narrow dirt path that ran past our farmhouse. I followed it for about a mile until I reached the base of Johnson’s Hill. There, I turned left and walked about 50 yards into the woods until I reached the best fishing spot in all of the Smokies. The waterfall wasn’t that tall, maybe ten feet or so, but underneath it was a deep pool where bass and trout always congregated.
The falls weren’t just known for its great fishing but for a series of petroglyphs etched into nearby rocky outcroppings. Some were mundane, depictions of elk and bear and mountain lions, while others were truly bizarre. The one I remember most distinctly was easily over 6 feet tall and depicted a cyclops-like creature, antennae protruding from its grotesque head.
When I was 7 or 8, a trapper and his two coonhounds passed by me as I was fishing. In a strange accent, sort of like my Aunt Marie’s, he asked me if I knew who had made the carvings. I told him that I guessed that it was the Cherokees. He started laughing and, without saying another word, walked away.
From my fishing spot, I had a relatively clear view of the dirt path as it meandered its way down the hill. It was rare to see any travelers on it, but on that day, I saw a man in a white suit, riding a white draft horse, crest the hill. He was tremendously fat, nearly as wide as he was tall, so perfectly round that I thought that if he fell off the poor horse which, strong as it was, was struggling to support his weight, he would roll down the hill like a ball.
Nowadays, it’s not unusual to see someone that large, but during the 30s, it could only mean one thing: The circus was in town! That would have been a treat. Blackberry Hollow was so remote that I only remember it coming once during my childhood. The highlight of that show was this strange French magician, think his name was Jean-Marvot. He made my brother disappear. He draped his purple cloak over him, and the next moment he was gone. We found him an hour later in a daze, wandering a barren field two miles from the magician’s tent.
Wondering if Jean-Marvot would be amongst the showmen, I ran to the top of the hill, sure that I was going to hear calliope music. I scanned the horizon, but there was no traveling caravan of performers. I concluded that the fat man was likely just an itinerant preacher and went back to fishing.
When I returned home later that day, the fat man’s horse was tied up by the barn. Inside, he and my dad were sitting at the kitchen table. Not wanting to get drawn into a conversation about religion, I started heading to my room, but then I heard them mention the stranger.
It turned out that the fat man was not an itinerant preacher. He was Doc Wilcox, a physician who lived 30 miles away in the county seat. He had heard talk about a strange man who spoke a language no one could understand and had decided to investigate the rumors himself. He, like everyone else, was unable to decipher the stranger’s talk, but he was friends with a linguist who taught at the University of Tennessee in Knoxville. He was sure that his friend would finally unravel the mystery.
Doc Wilcox wanted my dad to accompany him and the stranger to the county seat and then onto Knoxville by train. I begged my dad to let me join him, for the only town I had been to was Blackberry Hollow, and that was nothing more than a general store, a one-room schoolhouse, two churches, and a dozen or so farmhouses. I told him that my brothers could take care of the farm in my absence. That was true. Since we didn’t raise tobacco, there wasn’t much to do in the winter.
After much pleading, he agreed. The next morning we hitched up the wagon and set off for the county seat. I thought that the stranger might be reluctant to come with us, but he hopped right in.
It was nearly sunset by the time we reached it. It wasn’t a big town, had maybe 2 or 3 thousand inhabitants, but I was still in awe. I had never seen a paved street before, let alone an automobile. It was clearly the stranger’s first time too, he was even more wide-eyed than me.
We spent the night at Doc Wilcox’s house, a three-story Victorian mansion painted yellow. It was just him, his wife, and their two children, but they had seven bedrooms. I remember that figure precisely, I thought it was so absurd that such a small family would have so many bedrooms. In addition, they had electricity, central heating, and indoor plumbing. All firsts for me.
Not wanting to stink on the trip into Knoxville, I took a bath that night. Back home, we bathed about once a week. We’d draw water, heat it, pour it into a galvanized tub, and then each member of the family would bathe, one after the other. Disgusting, I know. But that was rural life back then.
But there was no need to do that at Doc Wilcox’s. I just turned the faucet and hot water filled the porcelain tub. It was a magic sight for a farmboy from the boondocks.
The next morning, the four of us walked to the train station. I had never even seen a train before, but I knew what they were of course. The stranger apparently did not. When the train pulled in, he started trembling, like he thought a giant metal beast was charging at him. He tried to run away, but my dad held him tight, and eventually he calmed down.
The ride to Knoxville seemed to take forever, but I didn’t mind. I stared out the window the whole time, watching the landscape pass by. I had been trekking around the Smokies since I could walk, but until that train ride I never knew how vast—or how truly majestic—those mountains were.
Finally, we pulled into Knoxville’s Southern Railway Station. We boarded a street car and headed south along Gay Street—Knoxville’s main drag—towards the Tennessee River.
Knoxville was a medium-sized city. According to the 1930 Census, its population was just over 100,000, making it the 81st largest city in the country. But I felt like I had been transported to Oz and the Emerald City. Gay Street was lined by skyscrapers, some of them over ten stories in height, housing office buildings and hotels and theaters and department stores. The tallest was the Andrew Johnson Hotel, a 200-foot soaring red-brick edifice. I was sure that if I looked out from its top floor, I would be able to see our little farmhouse, nearly a hundred miles away.
At the river, the streetcar turned right, heading towards the university. At its entrance, we got off and began making our way across campus. Some of the students snickered upon seeing us, wondering what two hillbillies and a hobo were on a college campus. One of them even threw an apple core at the stranger, but he showed no anger.
After walking for about ten minutes, we arrived at a stately brick building. We climbed the marble staircase to the second story and entered a large room with intricately carved oak paneling. Seated at a long mahogany table were six men, all dressed in dark suits. Five of them were much older than my dad—the youngest of that bunch looked to be about 45—while the gentleman on the far right of the table couldn’t have been older than 25.
Introductions were made. I think Doc Wilcox’s friend was called Dr. Jones, but I’m not positive. He was sitting at the center of the table and had long white sideburns, the only member of the panel who wasn’t clean shaven. Can’t remember the names of any of the other men.
The panel tried to get the stranger to tell him his name, but, as was the case with me, they had no luck. They then proceeded to question him in what must have been at least twenty languages. I’m pretty sure one was French—I didn’t know the language, but my Aunt Marie used to sing me lullabies her native tongue—and one sounded like Gaelic—Ma McDermott, along with a handful of other elderly residents of the Smokies, spoke it—but I had no clue what the other languages were. The stranger was equally confused and just sat there in silence.
“Is he usually so reticent?” Dr. Jones asked my dad.
“I’ll let my boy answer,” my dad said. “He spends more time with him.”
“He talks around me,” I replied. I didn’t know what reticent meant and was praying that I had correctly guessed its meaning, so the professors wouldn’t think that I was an idiot.
As soon as I had finished speaking, the stranger began talking. The linguists furiously scrawled with their fountain pens, trying to transcribe his strange speech. After a few minutes, he fell silent. The panelists exchanged puzzled glances.
“Well, it’s not Romani,” Dr. Jones finally said. “When I heard about him, I was sure that he was a gypsy—some of them are fair skinned—but he’s clearly not. I don’t know the language, but it’s related to Hindi, which I’m fluent in.”
“Doesn’t sound like an Indo-European language,” another professor said. “Or any language family that I’m familiar with.”
“Could it be Polari, the showmen’s cant used in Britain?” the youngest panelist asked.
Dr. Jones looked at him like he was an idiot. “Don’t be silly. It’s heavily influenced by Italian. And unless your ears are clogged by wax, you should be able to tell that whatever language the man was speaking has no relation to any Romance language. Not to mention that there are no native Polari speakers. If he knew Polari, he would know English.”
“Basque, perhaps?” another one of the linguists asked. “It’s thought to be a pre-Indo-European language.”
Dr. Jones shrugged. “Guess it’s possible.”
One of the linguists left and returned a few minutes later with a thick, leather-bound book. They repeated a few phrases from it, but the stranger showed no understanding. They got dictionaries for several other languages, I don’t remember most of them, I think Chechen was one, and Finnic another, but I’m not 100% sure.
Next, they gave him pen and paper, but he seemed confused on what to do. I would have been shocked if he knew how to write in any language, most of the older country folks couldn’t even sign their name. Someone placed a globe in front of him. He enjoyed spinning it, but he didn’t give any indication to his place of origin.
“Perhaps we are casting too small of a geographical net,” the youngest panelist said. “I know he’s fair skinned, but I read about a tribe in a remote corner of northwestern India that is descended from Alexander the Great’s troops.”
Dr. Jones laughed. “Perhaps he speaks Martian. Or maybe he’s an Atlantean, that could explain his violet eyes. No, gentlemen, this man is mad. I know it sounds like he’s speaking a natural language—there’s a rhythm to his speech, his voice rises and falls as he speaks. And there are short pauses between what are presumably words and longer pauses between what are presumably sentences. But no, I think this man is spewing gibberish. Please excuse me, I’ll be back.”
A few minutes later, Dr. Jones returned, carrying a large box. “Get ready to record, gentlemen,” he said. “We’re finally going to get to the bottom of this.”
Inside the box were cards with illustrations of various objects. The first one that Dr. Jones held up was of an apple. I thought that the stranger wouldn’t be able to comprehend what they were asking of him, but he seemed able to and the panelists scribbled down his reply. Sadly, I don’t remember what it was. I remember much of what the linguists said, even though they were using technical jargon I was completely unfamiliar with, but I can’t recall a single example of the stranger’s tongue.
Next up was a picture of a cow. Another reply, another scribble of pens. Then there was an illustration of a pineapple. The stranger looked at it for several seconds, before shrugging and saying something, which I would guess translated to “I don’t know.”
This went on and on. Some of the cards just had solid blocks of color. Some had circles in varying quantities. One had stick figures depicting a mom, dad, and child. Dr. Jones would point at one of them, but the stranger appeared confused, repeating his “I don’t know” response. Dr. Jones pointed to my dad and then to me, but the stranger was never able to comprehend what the academician was getting at.
After finishing the stack, Dr. Jones shuffled the cards and showed him about twenty of them for a second time.
“He gave the same responses the second time around, didn’t he?” Dr. Jones asked.
The other linguists nodded.
Dr. Jones examined his colleagues’ notes for several minutes. “Well,” he said, “My initial hypothesis was wrong. I think he is speaking an actual language. His responses to the cards were not some random vocalizations. The number system that he employed was clearly base 10. For example, when I showed him a card with seventeen circles, he responded with ‘ten and seven.’ However, he used distinct words for eleven and twelve though, a trait found in Germanic languages.”
“I also noticed that he gave the same response for blue and green. And for fingers and toes,” the youngest linguist said.
Dr. Jones nodded. “The fingers and toes isn’t surprising. Germanic and Finnic languages commonly make the distinction, but many others don’t. Blue and green is a little more surprising. It’s somewhat common for a language not to make that distinction—Korean and Japanese don’t, for example. But based on the literature I’ve read, as languages develop, they acquire separate terms for blue and green before adding words for purple, pink, orange, gray, and brown. But our subject provided distinct responses when shown those colors. Dr. Wilcox, do you think he is possibly blue-green colorblind?”
“I’d need to consult my books,” Doc Wilcox said. “I’ve never heard of it, the most common form of colorblindness is red-green, but I suppose it’s possible.”
“It’s quite bizarre,” Dr. Jones said. “Although I think this man is speaking a natural language, I don’t believe it’s his native tongue. I think he is demented. He was probably a missionary to some remote tribe. For some unknown reason, in his senescence, he has lost his use of English and can only speak in that other tongue.”
“That sounds plausible,” one of the linguists said. “I’ve got a few observations that will hopefully narrow down what language he’s speaking. He didn’t recognize a pineapple, which is indigenous to South America, and for the citrus fruits — which are native to parts of Asia, Australia, and Melanesia—he provided a single response, distinct from his “I don’t know” response. I think he recognized that they were fruit, but didn’t have distinct terms for the different varieties. In addition, he was confused upon seeing the pictures of the elephant and the kangaroo. If I had to guess, I’d imagine that the language he is speaking is native to North America or Siberia.”
“He had terms for cattle and sheep though,” the youngest linguist said. “Those aren’t native to North America—”
“They were introduced hundreds of years ago by the Spanish,” Dr. Jones said. “Nearly all of the tribes, except for maybe some remote Inuit ones, have words for them. Siberia seems plausible, but most of the missionary work there was done by Russians, and this fellow doesn’t look Russian. I think it’s safe to assume he is speaking one of North America’s indigenous languages.”
The other panelists nodded in agreement.
Dr. Jones turned to Doc Wilcox. “I’ve got a colleague who’s attending a conference in Boston right now. He’ll be back in a few days. His specialty is the autochthonous tongues north of Mexico. He’s not fluent in all of them of course—think there are over 300 if I remember correctly—but he should be able to identify at least the language family he’s speaking. Do you mind lodging him at your place till my colleague returns? I would put him up in a hotel here, but the department has no funding for it.”
Doc Wilcox nodded. “No problem at all, I’ve got plenty of extra space.”
My dad smiled when he heard this. He was a generous man, but he didn’t want to be stuck with the stranger forever.
“Lovely. And if my colleague can’t identify it, we can call the Knoxville News Sentinel and have them take a photo of him. I’m sure someone will recognize him. If he has no family to take him in, he’ll probably have to go to a home for the aged. It’s not safe for him to be wandering about the wilderness, with winter coming. He’ll surely freeze to death.”
We left the university and took the street car back to Gay Street. It was late in the afternoon, and there was no train back to North Carolina until the next morning.
We stopped at the counter of a five and dime to eat. Having not eaten since breakfast that morning, I was famished. It being the first time I was ever in an eatery, I remember exactly what I ordered: malted milk with strawberry syrup, a plate of liver and onions served with string beans and creamed peas and fried potatoes, and, for dessert, three slices of cherry pie with two scoops of vanilla ice cream. It was so delicious, gobbled it all up.
I remember that Doc Wilcox ordered grilled barracuda. I had never heard of barracuda, but he insisted that its meat was sublime. He was gracious enough to let me take a bite. Tasted fine, but didn’t come close to the trout I caught at Copper Falls.
After supper, we went to the Andrew Johnson Hotel, where Doc Wilcox had reserved us rooms. I trembled the entire elevator ride up to the 17th floor, sure that it would plummet and send me to my death.
The next morning we headed to the station and boarded our train. About an hour into the journey, the stranger gestured that he needed to use the bathroom. Doc Wilcox pointed to where it was, about twenty feet in front of us.
Thirty minutes later, he still hadn’t returned.
“I think you should check on him,” Doc Wilcox said to me. “He’s probably sick, last night’s meal was surely too rich for someone who’s not used to it.”
I went to the bathroom and knocked. No response. I tried the door, expecting it to be locked, but it wasn’t.
The bathroom was empty. There was a small window, about four feet off the ground, that was open. It seemed too small for him to have crawled through, but there was no other explanation. One of us would have seen him if he had walked out.
I ran back with the news. Doc Wilcox gasped, but my dad remained calm. “Son,” he said, “people like him don’t stick around in one place very long.”
“But wouldn’t he hurt himself jumping from the train?” I asked. For some reason, even though I couldn’t communicate with him, I had grown fond of the man.
“Just because he doesn’t speak English doesn’t mean he’s an idiot. I’m sure he waited till it slowed down as it approached a station. He’s fine, don’t worry.”
Despite my dad’s assurances, I was worried sick. I said a silent prayer for the violet-eyed stranger.
It was late in the afternoon when we arrived back in North Carolina. Doc Wilcox offered to let us stay the night at his place, but my dad wanted to get back to the farm. I was a little nervous about traveling at night, but my dad assured me everything would be fine. According to him, mules had good night vision. Plus, the moon would be nearly full.
We got our mules from the livery and set off. We probably made only ten miles before nightfall. But my dad and his mules, even though they went to the county seat only once a year or so, seemed to know their way. Relaxed, I curled up and fell asleep.
I was awoken by the howling of wolves. I jolted up and looked around. We were cresting Johnson’s Hill, only a mile from home.
“It’s OK,” my dad said to me. “They’re far off in the distance, a few miles away at least.”
They sounded much closer. In fact, they sounded like they were only a few hundred yards away, down by my fishing spot at Copper Falls.
“Relax,” my dad said, sensing that I was still afraid. “They’re skittish things, been hunted nearly to extinction in these parts. The few that remain in these mountains know how to avoid humans. They’re not going to attack a wagon.”
As we descended the hill, I looked towards the falls, sure that I would see a pack of gigantic wolves ready to lunge at us. There weren’t any, but there was a campfire, and, in the light of the flames, I saw an old woman dancing.
I pointed it out to my dad but he just kept his eyes on the road. “Don’t fret, son,” he said. “Just some hobos. The fire will keep the wolves away. Nothing to be afraid of.”
I wanted to avert my eyes from the flames, but I couldn’t. As we neared the bottom of the hill, I saw clearly that the crone who was dancing was Ma McDermott. Usually, the arthritic cripple could barely move, but that night her feet were moving like the ground was on fire. As she danced, she began to sing in Gaelic, her voice harsh and raspy.
Suddenly, the petroglyphs started glowing red. The hideous carvings—the cyclopses and the ogres and the other monstrous beings—left their rocky confines and joined hands with the witch. They were still 2-dimensional lines, with no substance to their forms, but they were alive.
I tried to call out to my dad, but my throat was swollen shut. As the witch danced in the night, white smoke began rising from the campfire, rising above the bare trees and high into the night sky, coalescing into the form of a gigantic wolf’s head.
From the heavens, it let out a thunderous howl.
My dad seemed unable to see or hear it, but the mules sure could. Even though they were tired from the long journey, they took off galloping.
I closed my eyes, sure that at any moment a band of smoke wolves would attack our wagon, but we made it home safely.
“Wonder what spooked the mules?” my dad asked as we were leading them into the barn.
“You didn’t see the wolf’s head in the sky?” I asked.
He laughed. “Son, you were just dreaming. It’s been a long couple of days. Get some sleep.”
It was then that I saw the stranger’s knife, lying on the hay bale where he always sat, its blade glinting in the moonlight. As my dad was leaving the barn, I pocketed it. We had a shotgun and two hunting rifle at home, but I had a feeling that those weapons would be powerless against Ma McDermott and her smoke wolves. However, I thought the stranger’s knife might prove effective.
Neither Ma McDermott nor her wolves visited our farm that night. Or the night after. Or the one after that. I started to believe that I had dreamt the whole thing. However, I still did not go back to Copper Falls. Instead, I fished the other brooks and creeks near our farmhouse. But the fish weren’t biting.
After about a month of subpar fishing, I gained the courage to go back to the falls. It wasn’t an ideal day for fishing—it was freezing and a strong wind was blowing in from the south—but still I went. I had an itch that could only be scratched by some good fishing.
I took the stranger’s knife with me. Even though I had almost convinced myself that it was a dream, having it gave me comfort.
Shortly after sunset, I saw Rev. Duncan, the crazy Baptist preacher, walk down Johnson’s Hill, carrying a small burlap sack that was tied shut with twine. I hid behind some bushes, knowing that if he saw me, the crazy man would likely accuse me of sorcery.
I thought that he would continue down the path, but instead, upon reaching the bottom of the hill, he headed into the woods. He stopped where I had just been standing—where I had left my cane pole—and looked around. Thankfully, the ground was frozen solid and I hadn’t left any footprints.
He began walking around, peering into the bushes, coming within ten feet of me. I was sure that he was going to hear my rapidly beating heart, but somehow he didn’t. After searching the area, he returned to where I had been fishing, set the burlap sack down, took out a gold pocket watch from his overcoat, and looked up at the hill.
I considered running away, but I knew Rev. Duncan would likely catch me. And who knows what he would do then.
I prayed that he would leave. I stayed where I was behind the bushes, stayed until the only light in the sky was from the moon and stars, but the preacher did not go anywhere. In my right hand, I gripped the stranger’s knife, ready to use in case he found me.
After God knows how long, the person Rev. Duncan was waiting for finally arrived. The full moon illuminated Ma McDermott as she slowly descended the hill, her tall hickory stick in her right hand, a dagger in her left.
I thought that the reverend had lured Ma McDermott to the falls under false pretenses, that he planned to kill her, but when she finally made it to where the preacher was standing, they greeted each other like old friends.
“I think there’s someone nearby,” Rev. Duncan said.
The witch smiled. “Then there will be two sacrifices tonight.”
Unlike the preacher, she had no trouble finding me. Her eyes soon fixed on the bush I was hiding behind. “Come out, little boy,” she said as she cackled.
I lunged at her, knife raised, but she pointed her hickory stick at me and recited an incantation.
I was stopped dead in my tracks. I was a statue, almost completely frozen. I could still see, could still hear, could even blink and move my eyeballs, but, no matter how much I tried, I couldn’t even wiggle my toes.
The witch hobbled over to me and scratched my cheek with one of her overgrown, yellow fingernails. “What such a pleasant surprise,” she said. She turned to the preacher. “We’ll save him for last. Let’s get to work.”
As the witch sang a song in Gaelic, the preacher knelt down by the creek and made diminutive effigies from the river mud, each one about six inches tall, using tiny twigs for their arms and pebbles for their eyes. When he had about ten of these humanlike figurines, he began positioning them in a circle.
As he was arranging them, the thing inside the burlap sack started moving. At first, I thought it was a chicken they were going to sacrifice, but then I heard a cry like that of a baby from inside.
“Hurry,” the witch said to the preacher, who was flaking off pieces of a pine stump to use as fire starter. “Just get some twigs, I can set them alight.”
The preacher gathered some sticks and set them down in the circle. The witch raised her stick and they were instantly set ablaze. As the wood began to burn, the figurines the preacher had made began walking slowly around the circle.
The witch resumed singing, faster than before. The preacher untied the burlap sack and reached into it. I was expecting him to pull out a baby, but he didn’t.
It was a fairy, about a foot in height, dressed in a roughspun moss-colored garment. There were no wings on his back, but it was clear as day that he wasn’t an infant. His ears were sharply pointed, his face was deeply wrinkled, and his beard long and red.
The fairy tried to free himself, but his hands and feet had been bound with coarse rope. He looked at me, as if begging for help. But I was unable to move.
As the preacher held the fairy tight, the witch sauntered over to him. She raised her dagger high in the air. As she was reciting an incantation, the stranger’s knife left my hand.
I hadn’t thrown it, I was still frozen. But the knife was somehow flying through the air, curving towards the neck of Ma McDermott. Neither she nor the preacher had any idea what was coming. She was lowering the blade when the stranger’s knife embedded itself in the witch’s neck. She staggered backwards, dropped the dagger, and collapsed.
As the witch lay dying, blood pouring from her neck, the preacher dropped the fairy and fled up Johnson’s Hill. A few seconds later, Ma McDermott drew her final breath.
Almost immediately, everything returned to normal. The dancing effigies fell to the ground and the white smoke, which had been coalescing into the form of a wolf’s head, was quickly dispersed by the wind. I looked behind me, making sure the monstrous petroglyphs were in their rocky confines. They were.
With the witch’s dagger, I freed the fairy, who was seemingly unharmed. He said something to me before scampering off in the woods. It wasn't in English. Nor was it in the language the stranger spoke. If I had to guess I would say it was Gaelic. It was similar to the language Ma McDermott had been singing in.
I left the witch where she lay, the stranger’s knife still embedded in her neck, and hurried home. My dad asked me where I had been. I told him that I had run into a friend while I was out fishing and that he had invited me over to his farm for dinner. I never told him what happened at Copper Falls. I never told anyone.
That Sunday, the preacher wasn’t at the pulpit. The gossip was that he had run off with some Jezebel. There was also talk about how Ma McDermott had been murdered. Still, despite this, I tried to convince myself that it was all a dream.
And, as time went on, I believed it more and more. Until today. Until the stranger’s knife, for some reason, entered my life again.

you WILL (might) see this as a turkic-polish language even though i just scrambled Khroskyabs phonology and added 97M diacritics with 794 quadrillion letters on almost every word
Goal: we believe basically every word you can think of should have its own root
Phonology:

C	Labial	Alveolar	Retroflex	Palatal	Velar	Uvular	Glottal
Nasal	m̌, m	ň, n		ñ [ɲ]	ŋ̌, ŋ	ŋ̇ [ɴ]
Plosive	ph, p, b	th, t, d			kh, k, g	qh, q	‘
Fricative	f	s, z	š, ž	ś, ż	x, ğ	x̣ [χ], ŕ [ʁ]
Affricate		ch, c [ts]	čh, č [ʈʂ]	ċh, ċ [ʨ]
Approximant	w̃, w	l	ř [ɻ]	j
Trill/Tap		ŗ, r	ṛ [ɽr]

every h is just aspiration
the glottal stop is only for seperating affixes and stuff from the root (sometimes)

V	Front	Mid	Back
Close	i, ü	y [ɨ]	u
Mid	e, ö	ə	o
Open-Mid	æ
Open	a		å [ɑ]

Phonoticatics:

1. (C)(C)V(C)(C)(N)
2. intervocalic glottal stops
3. svo

Pronouns	N O M I N A T I V E
1st person	a
2nd person	ře
3rd person	šå
1st person pl	uṛe
2nd person pl	y
3rd person pl	å

example word: owy (to eat)

Tense	Farpast	Past	Present	Future	Farfuture
Simple	owy'ö	owy'əm	owy	owy'e	owy'uňa
Continuous	owyxiňə	owy'oŗæ	owy'æ	owybåm	owy'öly

​

Number	Cardinal	Ordinal
0	buŕun	-
1	üňəŋqhe	üňəŋqhe'a
2	ċhukhå	ċhukhå'a
3	eřoŋżem	eřoŋŋżema
4	pykæmkhömn	you get it now
5	yŋjåŋ
6	iŋ̇om
7	ŋephenċu
8	qåmånli
9	emŋżeŋ
10	cüjæm
11	cüjæm i üňəŋqhe
12	cüjæm i ċhukhå
13	cüjæm i eřoŋŋżem
20	on
30	öŋrænm
40	čən
50	khuŋomn
60	asökheċæŋ
70	imŋ
80	aŗəğenpån
90	phəmcim
100	əŋn
1K	əganñy
1M	aŋ̌ækhöŋ

many in Ŗosŕaj is ṛüfükhamšænśö even if you already specified how many stuff amount you did
THE ENTIRE DICTIONARY!!!!!!!!!!! (work in progress) (not even 1/12ths done) (without the other words) (please help)
Fruit root list
ğåŋŋ̌üŋåm - abiu
ŋ̇üxogo - açaí
låŋ̌om - acerola
mæm - akebi
ox̣åŋthyn - ackee
üċe - african cherry orange
ṛužå - american mayapple
ŕom̌ykə - apple
aqhåřin - apricot
fagüŋřömċu - aratiles
yxæšəm - araza
čuwönŋ̇aw̃e - avocado
ŋ̇əxux̣üŋ - banana
anm - bilberry
ykhənbe - blackberry
acåmm - blackcurrant
öcinsin - black sapote
očhyŋċa - blueberry
ŋæbe - boysenberry
eŋŋæ - breadfruit
fuw̃yŋ - buddhas hand (also called fingered citron)
tiğože - cactus pear
čhyŋæqånğu - canistel
åŕoŋ - catmon
əŋ̌aňex̣oron - cempedak
yčhonśi - cherimoya
čhyŋx̣iŋ - cherry
odəŋśam - chico fruit
x̣æqhimčåŋn - cloudberry
paňo - coco de mer
dənŋ - coconut
aŋ - crab apple
ñyzanbomthə - cranberry
ox̣åkhåċöm - currant
əŋ - damson
bæsenñonm - date
x̣ekå - dragonfruit (or pitaya)
ün - durian
oğikhužem - elderberry
ünoŋ - feijoa
řæwæŋ - fig
womqu - finger lime (or caviar lime if you like not being disgusted)
üm - gac
jüsimkåŋ - goji berry
bəŋthəm - gooseberry
nåjichəŋ - grape
åqhaŋ - raisin
utemŋ - grapefruit
ňum - grewia asiatica
xaduŋ - guava
ipumšenčyŋ - hala fruit
qikhamja - honeyberry
wümŋ - huckleberry
qo - jabuticaba
oqhim - jackfruit
åğæŋdəŋ - jambul
imthynm - japanese plum
æŋla - jostaberry
ŋyżaża - jujube
licümṛi - juniper berry
ökajån - kaffir lime
čotöm - kiwano
eżəŋ - kiwifruit
əcetå - kumquat
ölæxonjeŋ - lanzones
bəm - lemon
ŋ̇onm - lemonlime* (lemon and lime hybrid)
aċhom - lime
um̌üŋ - longan
kæsünm - loquat
qhumchåm - luio
såṛåm - lychee
thåśəŗi - magellan berry
oloqhån - macopa (wax(not really) apple)
æŕonšæm - mamey apple
æm - mamey sapote
æw̃ynphyn - mango
ozænkhön - mangosteen
ŋ̇iŋsåtaŋ - marionberry
żöm - medlar
aċyŋ - cantaloupe
qöxun - galia melon
temŗeŋcö - momordica fruit
ŋ̇očhænkömn - honeydew
ŋ̇ædæmzəmŋ - mouse melon
azynx̣æmden - muskmelon
åčhöphom - watermelon
üw̃ymrončhån - miracle fruit
iŋmňam - monstera deliciosa
acyrim - mulberry
roŕy - nance
əğyn - nectarine
yzamṛom - orange
x̣eċišimŋ - blood orange
žim - clementine
åŋċy - mandarine
öšymn - tangerine
tox̣unŋ - papaya
śefym - passionfruit
ğuchonŗåŋ - pawpaw
qhyjə - peach
ŋ̇ynoŋčeše - pear
ošonxy - persimmon
ykænŕumdüŋ - plantain
m̌ömŋ̇öri - plum
məchimjiŗo - prune
æsöm - pineapple
ṛi - pineberry
opæŋṛå - plumcot
xenthiżæmdyŋ - pomegranate
eśum - pomelo
öwümřüntu - quince
åñå - raspberry
8500~ words left without a meani- wait no i would loose motivation too quickly
​
example sentence:
We ate four cloudberries.
Uṛe owy'əm pykæmkhömn ṛüfükhamšænśö.
[uɽre owɨʔəm pɨkæmkhømn ɽryfykhamʂænɕø]

How would one go about 'teaching' a local model how to use new emoticons? I have had luck with using a lorebook for new vocabulary terms from urban dictionary, but emoji are trickier since you need to provide context on when and how they should be used. I am using Noromaid-v0.1-mixtral-8x7b-Instruct-v3 with oobabooga/text-generation-webui and my understanding is that you can't use LORAs with llama.cpp; I got an error when I clicked Apply LoRAs.
My use case is I created an otaku character and want them to use kaomoji (Japanese emoticons) in their responses. The model is aware of what they are but when I ask it to use one it either uses an * action * to say that it typed one, or outputs the first 2 or 3 characters of an emoticon before the python process hangs. Is there a specific formatting I should use to get it to work in a lorebook? Do I have to add all of them to a character's example of dialogue?

Whenever I replace a section of a string with a emoticon, I get a random space added a few characters further and if I continue to do those replaces, the character is pushed even further.
The replaces in the dictionary:
"[willpower]" : "🗣️",
"[intellect]" : "📚",
"[combat]" : "👊🏻",
"[agility]" : "🥾",
"[wild]" : "❓",
The replace code:
# Process all set replaces
for key in text_replaces:
message = message.replace(key, text_replaces[key])
The text before replacing:
*Revelation* - Choose two skills ([willpower], [intellect], [combat], or [agility]). Until the end of the investigation phase, each investigator gets +1 to...
The results I get after each replace (observe the space in the words "investigation", "phase" and "each"):
*Revelation* - Choose two skills (🗣️, [intellect], [combat], or [agility]). Until the end of the in vestigation phase, each investigator gets +1 to...
*Revelation* - Choose two skills (🗣️, 📚, [combat], or [agility]). Until the end of the investigati on phase, each investigator gets +1 to...
*Revelation* - Choose two skills (🗣️, 📚, 👊🏻, or [agility]). Until the end of the investigation p hase, each investigator gets +1 to...
*Revelation* - Choose two skills (🗣️, 📚, 👊🏻, or 🥾). Until the end of the investigation phase, e ach investigator gets +1 to...

I have recently bought a Titan Pocket secondhand. I am mostly loving it. It's the perfect size for 1- or 2-handed typing and is, in my opinion, just a very cool-looking device. Reminds me a lot of my dearly departed Blackberry Classic.
But there's an extremely annoying issue with autocorrect turning every typo into all-caps rather than correcting it to the most similar real word. For example, "isnt" is corrected to "ISNT," "Im" to "IM," and any word missing a letter such as "trtle" to "TRTLE" rather than "turtle."
I discovered a workaround for this issue, so I'm sharing in case anyone else needs it.
Go to Settings / system / languages and input / advanced / personal dictionary /add. In the first field type the correct version of the word ("won't"). In the shortcut field type the way you tend to misspell it ("wont"). Then hit the back arrow to return to Personal Dictionary and you should see that entry added to your list. Do this with all the words autocorrect ruins most frequently. Words you add here will override the default autocorrect settings and now be corrected according to whatever you put in.
If you know a better / more comprehensive fix, drop it in the comments!

Anyone else find it really bothersome keeping up with the hidden meanings NTs have when texting especially with emoticons and emojis? I was really behind on the :) -> 🙂 -> 😊 transition and I had a few problematic experiences where people mistook my ":)" for passive aggression and this is just one example. I feel like I have to keep a dictionary for NT emoji meanings and text slang having different meanings than irl slang.

Weird question, I know.

Package ‘readme’ August 8, 2023 Title An Algorithm for Text Quantification Version 2.0 Authors 'Gary King king@harvard.edu [aut], Anton Strezhnev astrezhn@law.upenn.edu [aut, cre], Connor Jerzak cjerzak@g.harvard.edu [aut, cre]' Description An R package for estimating category proportions in an unlabeled set of documents given a labeled set, by implementing the method described in Jerzak, King, and Strezhnev (2023, copy at http://GaryKing.org/words). This method is meant to improve on the ideas in Hopkins and King (2010, AJPS), which introduced a quantification algorithm that harnesses the Law of Total Expectation. We apply this law in a feature space we craft to minimize the error of the resulting estimate. Automatic differentiation, stochastic gradient descent, and batch re-normalization are used to carry out the optimization. Other pre-processing functions are available, as well as an interface to the earlier version of the algorithm for comparison. The package also provides users with the ability to extract the generated features for use in other tasks. Depends R (>= 3.3.3) License Creative Commons Attribution-Noncommercial-No Derivative Works 4.0, for academic use only. Encoding UTF-8 LazyData true Maintainer 'Connor Jerzak' connor.jerzak@gmail.com Imports tensorflow, limSolve, FNN RoxygenNote 7.2.3 R topics documented: readme-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 cleanme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 download_wordvecs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 readme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 undergrad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Index 9 1 2 readme-package readme-package A algorithm for quantification that harnesses the Law of Total Expectations in an optimal feature space Description An R package for estimating category proportions in an unlabeled set of documents given a labeled set, by implementing the method described in Jerzak, King, and Strezhnev (2018, copy at http://GaryKing.org/words). This method is meant to improve on the ideas in Hopkins and King (2010), which introduced a quantification algorithm that harnesses the Law of Total Expectation. We apply this law in a feature space we craft minimizes the error of the resulting estimate. Automatic differentiation, stochastic gradient descent, and batch re-normalization are used to carry out the optimization. Other pre-processing functions are available, as well as an interface to the earlier version of the algorithm for comparison. The package also provides users with the ability to extract the generated features for other tasks. The package provides two main functions: undergrad and readme. • undergrad takes as an input a word vector corpus (or pointer to such a corpus) and a vector housing cleaned text for cross-referencing with the vector corpus. It returns document-level summaries of each of the dimensions of the word vectors (10th, 50th, and 90th quantiles of each dimension within each document are calculated). Options also exist for generating a document-term matrix from the text. Useful for those wanting control over the linkup between documents and word vector corpus. • readme takes as an input raw text (or optionally, the output from undergrad). It also takes as an input an indicator vector denoting which documents are labeled and a vector indicating category membership (NAs for unlabeled documents). The algorithm then generates an optimal projection for harnessing the Law of Total Expectation in calculating the estimated category proportions in the unlabeled set. Usage For advice on usage, see Examples. Many users will just interface with the readme function, as this approach takes care of much of the pre-processing in an automatic fashion. Some users may want more control over the linkup between the word vector corpus and the raw text; in that case, combining undergrad with readme is a good option. For bug reports or support, please contact connor.jerzak@gmail.com. Authors • Connor Jerzak, Anton Strezhnev, and Gary King. • Maintainer: Connor Jerzak cjerzak@gmail.com References • Hopkins, Daniel, and King, Gary (2010), A Method of Automated Nonparametric Content Analysis for Social Science, American Journal of Political Science, Vol. 54, No. 1, January 2010, p. 229-247. https://gking.harvard.edu/files/words.pdf • Jerzak, Connor, King, Gary, and Strezhnev, Anton. Working Paper. An Improved Method of Automated Nonparametric Content Analysis for Social Science. https://GaryKing.org/ words cleanme 3 Examples

set seed

set.seed(1)

Generate synthetic 25-d word vector corpus.

my_wordVecs <- matrix(rnorm(11*25), ncol = 25) row.names(my_wordVecs) <- c("the","true", "thine", "stars", "are" , "fire", ".", "to", "own", "self", "be")

Generate 100 ``documents'' of 5-10 words each.

my_documentText <- replicate(100, paste(sample(row.names(my_wordVecs), sample(5:10, 1), replace = T), collapse = " ") )

Assign labeled/unlabeled sets. The first 50 will be labeled; the rest unlabeled.

my_labeledIndicator <- rep(1, times = 100) my_labeledIndicator[51:100] <- 0

Assign category membership randomly

my_categoryVec <- sample(c("C1", "C2", "C3", "C4"), 100, replace = T) true_unlabeled_pd <- prop.table(table(my_categoryVec[my_labeledIndicator==0])) my_categoryVec[my_labeledIndicator == 0] <- NA

Get word vector summaries

my_dfm <- undergrad(documentText = my_documentText, wordVecs = my_wordVecs)

perform estimation

readme_results <- readme(dfm = my_dfm, labeledIndicator = my_labeledIndicator, categoryVec = my_categoryVec, nBoot = 2) print(readme_results$point_readme) cleanme cleanme Description Standard preprocessing code for ASCII texts. Removes HTML tags, URLs, linebreaks. Converts standard emoticons to tokens. Removes non-informative punctuation. Usage cleanme(my_text, finalEncoding = "ASCII") Arguments my_text Vector of character strings containing the raw document texts. finalEncoding A character string indicating the desired encoding for the text vector (default = "ASCII") 4 readme Value A vector of character strings with the processed texts, each token is separated by a space. download_wordvecs download_wordvecs Description Downloads default word vector dictionary to ’readme’ install directory. Usage download_wordvecs( url = "http://gking-projects.iq.harvard.edu/files/glove.6B.200d.zip", targetDir = NULL ) Arguments url URL of word vector file. Defaults to the pre-trained GloVe Wikipedia 200- dimensional vectors. targetDir Target directory to download files. If NULL uses readme installation directory. Value Path to downloaded word vector dictionary readme readme Description Implements the quantification algorithm described in Jerzak, King, and Strezhnev (2018) which is meant to improve on the ideas in Hopkins and King (2010). Employs the Law of Total Expectation in a feature space that is tailoed to minimize the error of the resulting estimate. Automatic differentiation, stochastic gradient descent, and knn_adaptbatch re-normalization are used to carry out the optimization. Takes an inputs (a.) a vector holding the raw documents (1 entry = 1 document), (b.) a vector indicating category membership (with NAs for the unlabeled documents), and (c.) a vector indicating whether the labeled or unlabeled status of each document. Other options exist for users wanting more control over the pre-processing protocol (see undergrad and the dfm parameter). readme 5 Usage readme( dfm, labeledIndicator, categoryVec, nBoot = 15, sgdIters = 500, numProjections = NULL, batchSizePerCat = 10, kMatch = 3, batchSizePerCat_match = 20, minMatch = 8, nbootMatch = 50, justTransform = F, verbose = F, diagnostics = F, nCores = 1L, sigma_ep = 0, nCores_OnJob = 1L, regraph = F, conda_env = NULL, otherOption = NULL, wt_catDistinctiveness = NULL, wt_featDistinctiveness = NULL, tensorflowSeed = NULL ) Arguments dfm ’document-feature matrix’. A data frame where each row represents a document and each column a unique feature. labeledIndicator An indicator vector where each entry corresponds to a row in dfm. 1 represents document membership in the labeled class. 0 represents document membership in the unlabeled class. categoryVec An factor vector where each entry corresponds to the document category. The entires of this vector should correspond with the rows of dtm. If wordVecs_corpus, wordVecs_corpusPointer, and dfm are all NULL, readme will download and use the GloVe 50-dimensional embeddings trained on Wikipedia. nBoot A scalar indicating the number of times the estimation procedure will be re-run (useful for reducing the variance of the final output). sgdIters How many stochastic gradient descent iterations should be used? Input should be a positive number. numProjections How many projections should be calculated? Input should be a positive number. Minimum number of projections = number of categories + 2. batchSizePerCat What should the batch size per category be in the sgd optimization and knn matching? kMatch What should k be in the k-nearest neighbor matching? Input should be a positive number. 6 readme batchSizePerCat_match What should the batch size per category be in the bagged knn matching? nbootMatch How many bootstrap samples should we aggregiate when doing the knn matching? justTransform A Boolean indicating whether the user wants to extract the quanficiation-optimized features only. verbose Should progress updates be given? Input should be a Boolean. nCores How many CPU cores are available? Default is 1. nCores_OnJob How many CPU cores should we make available to tensorflow? Default is 1. Value A list consiting of • estimated category proportions in the unlabeled set (point_readme); • the transformed dfm optimized for quantification (transformed_dfm); • (optional) a list of diagnostics (diagnostics); References • Hopkins, Daniel, and King, Gary (2010), A Method of Automated Nonparametric Content Analysis for Social Science, American Journal of Political Science, Vol. 54, No. 1, January 2010, p. 229-247. https://gking.harvard.edu/files/words.pdf • Jerzak, Connor, King, Gary, and Strezhnev, Anton. Working Paper. An Improved Method of Automated Nonparametric Content Analysis for Social Science. https://gking.harvard. edu/words Examples

set seed

set.seed(1)

Generate synthetic 25-d word vector corpus.

my_wordVecs <- matrix(rnorm(11*25), ncol = 25) row.names(my_wordVecs) <- c("the","true", "thine", "stars", "are" , "fire", ".", "to", "own", "self", "be")

Generate 100 ``documents'' of 5-10 words each.

my_documentText <- replicate(100, paste(sample(row.names(my_wordVecs), sample(5:10, 1), replace = T), collapse = " ") )

Assign labeled/unlabeled sets. The first 50 will be labeled; the rest unlabeled.

my_labeledIndicator <- rep(1, times = 100) my_labeledIndicator[51:100] <- 0

Assign category membership randomly

my_categoryVec <- sample(c("C1", "C2", "C3", "C4"), 100, replace = T) true_unlabeled_pd <- prop.table(table(my_categoryVec[my_labeledIndicator==0])) my_categoryVec[my_labeledIndicator == 0] <- NA

Get word vector summaries

undergrad 7 my_dfm <- undergrad(documentText = my_documentText, wordVecs = my_wordVecs)

perform estimation

readme_results <- readme(dfm = my_dfm, labeledIndicator = my_labeledIndicator, categoryVec = my_categoryVec, nBoot = 2, sgdIters = 500) print(readme_results$point_readme) undergrad undergrad Description Preprocessing for readme function - creates a document-feature matrix (saved as a data frame in output) to be passed to readme. Users can either input word-specific vectors using the wordVecs_corpus or wordVecs_corpusPointer parameters. Primarily intended for users wanting control over the pre-processing protocol. numericization_method controls whether word vector summaries (the default, numericization_method = "vector_based") or transformer-based document features (if using "transformer_based"). Usage undergrad( documentText, wordVecs = NULL, word_quantiles = c(0.1, 0.5, 0.9), reattempt = T, reattempt_regex = list(c("\#", ""), c("#\S+", ""), c("[[:punct:]]+", ""), c("ing\b", ""), c("s\b", ""), c("ed\b", ""), c("ies\b", "y")), unique_terms = T, verbose = T, numericization_method = "vector_summaries", textEmbed_control = list(tokenizer_parallelism = T, model = "bert-base-multilingual-uncased", layers = -2L) ) Arguments documentText A vector in which each entry corresponds to a “clean” document. Note that the function will take as a “word” all whitespace-separated elements in each vector entry. For example, "star." would have to have an exact analogue in the vector corpus, otherwise it will be dropped in the calculations. wordVecs A matrix where each row denotes a word and each column a word vector. Words should be stored as the rownames of the matrix. word_quantiles A numeric vector denoting the quantiles (0-1) used to summarize each word vector dimension. Defaults to 0.10th, 0.50th and 0.90th quantiles. reattempt If TRUE, attempts to match terms missing from the wordVec corpus with alternate representations. 8 undergrad reattempt_regex A list of character vectors containing regular expression pairs to be used for generating alternate representations of words to attempt to match with the wordVec corpus when terms initially cannot be matched. Order matters. unique_terms If TRUE, removes duplicate terms from each document - each document is represented only by the presence or absence of a term. verbose If TRUE, prints updates as function runs numericization_method Determines whether word vector summaries are used for documents (faster, specified by ‘numericization_method = "vector_summaries"‘), or whether transformerbased document features are used (slower, specified by ‘numericization_method = "transformer_based"‘) textEmbed_control A list with elements denoting the parameters passed to ‘text::textEmbed‘ if ‘numericization_method = "transformer_based"‘. Default is ‘list(tokenizer_parallelism = T, model = "bert-base-multilingual-uncased", layers = -2L)‘) Value A data.frame consisting of the word_quantiles quantiles of the word vectors by document. Each row corresonds to a document, and the columns to a particular summary of a particular word vector dimension. Examples

set seed

set.seed(1)

Generate synthetic word vector corpus.

my_wordVecs <- matrix(rnorm(11*50), ncol = 50) row.names(my_wordVecs) <- c("the","true", "thine", "stars", "are" , "fire", ".", "to", "own", "self", "be")

Setup ``documents''

my_documentText <- c( "the stars are fire .", #document 1 "to thine own self be true ", #document 2 "true stars be true ." #document 3 )

Get document-level word vector summaries.

my_dfm <- undergrad(documentText = my_documentText, wordVecs = my_wordVecs) print( my_dfm ) Index cleanme, 3 download_wordvecs, 4 readme, 4 readme-package, 2 undergrad, 7 9

Hey y'all.
I'm so excited to announce that today we'll be discussing chapters 1 - 7 of Frank Herbert's Dune.
This book is dense and there is a LOT to unpack so don't worry if you missed something or if the book feels convoluted. It's supposed to feel that way and I believe that if you stick to it, it will get easier to follow.
Here are some definitions taken direct from the back of the book (with minor adjustments to avoid spoilers) to get the best out of our discussion.

• Arrakeen: first settlement on Arrakis; long time seat of planetary government.
  
• Arrakis: the planet known as Dune, third planet of Canopus.
  
• Bene Gesserit: the ancient School of mental and physical training established primarily for female students after the Butlerian Jihad destroyed the so-called "thinking machines" and robots.
  
• Caladan: third planet of Delta Pavonis, birth world of Paul.
  
• CHOAN: acronym for Combine Honnete Ober Advancer Mercantiles, the universal development corporation controlled by the Emperor and (this spoiler is implied but I think it's still a spoiler) the Great Houses with the Bene Gesserit as silent partners.
  
• Fremen: the free tribes of iraqis, dwellers in the desert, remnants of the Zuni wanderers. "Sand pirates" according to the imperial dictionary.
  
• Gom Jabbar: the high-handed enemy, the specific poison poison needle tip with meta cyanide used by Bene Gesserit Protectors in the death alternative test of human awareness.
  
• Mahdi: in Freeman messianic legend, "the one who will lead us to paradise."
  
• Mentat: the class of imperial citizens trained for supreme accomplishments of logic. Human computers.
  
• Muad'dib: the adapted kangaroo mouse of Arrakis, a creature associated in the Freman earth spirit mythology with a design visible on the planet's second Moon. The creature is admired by Freman for its ability to survive in the open desert.
  

As a friendly reminder, bookclub does have a strict spoiler policy. If you are not sure what constitutes as as spoiler please visit our spoiler policy link here.
Useful Links:

• Dune Schedule
  
• Pronunciation of names Be wary of spoiler at 4:00 - 4:15 in this video!!!
  
• Pronunciation of Words and Phrases
  
• Marginalia post You can discuss outside of these chapters there but pleas use spoiler tags.
  
• Chapter. Summaries Be wary of spoilers in the analysis section.
  
• Lines to identify chapters.
  

Next Thursday the 14th u/Blackberry_Weary will be leading the second discussion. We will be discussing chapters 8 - 14.

I have been a user of the Duo since it was released, I also picked up a Duo 2 (typing this on my duo 2) and currently have Z-Fold 4. I have been in-patiently awaiting what Microsoft would do next with the device. While I think what Google has accomplished with the Fold is nice, I think they left a lot of room for Microsoft to carve out a place for itself between Ggogle and Samsung. What I think Microsoft should focus on now that the Google Pixel Fold is here (here are a few things) :

1. Source the same displays as Google. This should help with the supply chain issues, and hopefully help drive down the cost.
   
2. Surface Pen support on inner and outer displays. Also, take some cues from Samsung and build some special sauce for the pen. To start, increase triggers for actions from single, double, and hold on eraser to include double tapping screen, and hovering, while holding side button. Better yet, if they could build a way to record custom triggers and actions, it would make to an even more powerful tool. Also, allow typing in any text field. And have built in Dictionary, Thesaurus, translation, ai chat, and bing search features.
   
3. Keep the core experience similar to current Duo/ Duo 2. Here I mean the homescreen/ apps opening in 2-up mode, although it would be nice to add the ability to put a 3rd or even 4th app. Since it's an uninterrupted display I think they can be more versatile here.
   
4. Build a productivity hub similar to old blackberries to aggregate all communications, tasks, and appointments in one place. Should be able to access at all times with a simple gesture.
   

Additionally, I think they should add some special Microsoft sauce. Here I'm thinking they should leverage some of there other Microsoft offerings to further enhance and differentiate the Duo line, here are a few suggestions:

1. Leverage their experience with intune to build a secure workspace on the device, similar to Samsungs Secure folder.
   
2. Build Powerautomate for the Duo which will allow automation of on device features, but also leverage what can be done on the web.
   
3. Bring Microsoft Journal to the Duo. This would be a powerful tool. It's unique ink centric note taking style would be a great way to promote the use of the pen. Also, if the ai could identify intents so if you jot down a name and number when you click on it, it gives you the option to create a contact, call, message, or teams chat.
   

This is in no way an all encompassing list, I have many other thoughts, such as adding fast charge, wireless charging, not rocking when laid flat, pen magnetically attaches w/o case. etc. But I would also love to hear what the community thinks.

But anyways, on Friday the 17th, I removed the ornaments using a large black trash bag Sarah had left. Most of the trash bags I use have drawstrings, but these ones didn’t.
The ornaments were stored on the second shelf from the top, and included three stuffed Santas, three metal snowflakes in red with pinecones attacked, three stuffed snowflakes made out of burlap cloth with green over it and embroidery, and three white glass hearts, hung with red ribbon. There was also one larger ornament, which was made of seven pinecones, and which had sparkles on it, red berries, and plastic pine sprigs. I also kept, next to the ornaments, the turkey statue that said “Thanksgiving greetings,” a statue of a Santa holding a cardinal and a birdhouse, with a pine tree over his head which covered his eyes, and which had ornaments on it, which my parents had given me, and a pine-scented candle in a diamond shape, with gold stars on it; twenty-three, I think; that had two wicks and white wax.
Talley and Sarah had left the candle and the Santa before Christmas. I remember that I lit it for the first time in early December; December 3rd, I think.
The night before, I had stayed up late saving Orthodox memes, and then gotten a Mountain Dew and a package of the ‘extreme reaper’ flavor of Blue Diamond almonds from 7-11.
I guess I also bought something for $35.59 on Amazon, at 2:02 a.m., but I don’t know what it was.
The next morning, I deleted a bunch of the non-Christian books I had saved on my Amazon wishlist, because at that time I thought they were prohibited. I later decided to move all my non-Orthodox books to the shelf under this coffee table, but I didn’t have any at that time.
I think I also listened to an ‘Agnus Dei,’ and a ‘Magnificat’ that morning; I think Bach’s.
I found two extra almonds in the bag from the night before, and ate them.
I guess that morning, I also changed my Amazon picture from an image of Moses holding the two tablets to one of a wedding, and then to one of Jesus and Mary.
I went to Harmons and got a salad; the “berry and spring mix” one, which has walnuts, strawberries, blackberries, raspberries, and a raspberry vinaigrette. I then decided not to eat it, and ate bread with olive oil instead. I took the salad, and dumped it into the dumpster, and threw away this non-descript brown paper bag that had the recycling symbol on it.
That day, I’d also been misinformed that there was a “cookie fair” at Prophet Elias, because I was looking at the calendar for a different Prophet Elias church.
I walked there, I remember behind a guy who seemed autistic and who I had to pass in a way that felt awkward to me, and stopped at the Chevron gas station, the store of which is called ‘Holiday.’
At that time, I thought that being Christian involved following Kosher, and so I looked for a Kosher snack. I wanted to get the Albanese “peach o’s,” but they had gelatin, and so I was not going to get anything.
Then a man in the store said he would buy me something, and I accepted. I picked a box of ‘Mike and Ike: Original Fruits,’ and then ate part of the box as I walked.
When I arrived, nobody was there, of course, because there was no cookie fair, and I was really distressed. I prayed that I would be sent someone who could help me, or at least make me feel less alone.
Then Fr. George pulled up, and said he was on his way to Holy Trinity, and told me to email him in January about catechism.
I walked home, and I remember finding a receipt to a pizza place; Domino’s; for a 14”Hawaiian pizza with no bacon, no red peppers, and jalapeños, and a 12” pizza with pepperoni and mushrooms. The total was $18.39.
Two books had been delivered when I got home: “Dictionary of Demons and Complete Deliverance,” and St. Philaret’s Catechism.
That day Talley and Sarah came to put up the tree, and also left me a massive inventory of items from Trader Joe’s, most of which was candy.

8 Things About Ringtok Messenger Students Must Know

Save time and improve learner autonomy with Ringtok messenger. You must have heard about the multiple apps specified for snapping, entertainment purposes, or used for providing ways to be more social. None of them pop-up as a top-tier choice for students. However, the Ringtok app knocks down these traditional communication patterns and bliss users with the latest features other than voice and video calling**.** Students can use it for translation of text and audio purposes. They can build communities and discuss their interest in groups where they can add innumerable people at once. Everything is easy-peasy, and you can do everything in real-time, from preparing assignments and exams to planning for an event and sharing location. You can find below in the article how its features work for students.
8 Ways in Which Ringtok Messenger Facilitates Students
Re-do the task, encourage your fellows to speak in front of multiple people, and hang out with your buddies using the all-in-one Ringtok application. The point is that you can be public and private in your conversations. Use the one-to-one chat or calling option if you want to avoid anyone coming in between your secrecy. Or you can use the group convos option to address several people simultaneously. Send videos and files of any size and type long text without issues. It beholds far better features for the students. Some of the features and their benefits for students are mentioned below.
● Know More Than Your Regional Language
Ringtok messenger assists students in becoming native speakers of other languages. Instead of collecting newspapers, scripts, and dictionaries to understand the words of your overseas professors and class fellows, use the app translation services. The prime focus of any grad, scholar, and novice nowadays is to "discover something new in their field." Whether they are life sciences or art students, they approach the professors of their targeted institutes and countries for better research opportunities. The only bridge which is hard-bitten to cross is the "lingual difference." Well, being a user of this app, there is no barrier. Be more confident and deal with other lingo speakers. Let this free chat application translate your texts and audio into over 100 languages.
● Practice Sessions on Video Calling
Quizzes, competitions, and presentations are a part of the everyday routine in students' lives. There is a saying, "practice what you preach," and that's why everything delivered in front of folks must be authentic and delivered nicely. This is a platform that makes it easier for students to prepare the material for quizzes and competitions while rehearsing it with their fellows, friends, and professors on audio and video calls. They can converse for as long as they want to without any poor quality and time restrictions. So, whenever there is a competition, or you are feeling low in confidence, call your buddies at any moment and let them boost you virtually.
● Plan Annual Dinners for Seniors
Go in the direction of your dreams with the Ringtok calling app. Seniors are always the stars of any institute that leaves blueprints for their juniors. So, to celebrate their achievements, juniors plan annual dinners where they perform skits and plays. But what if you want to keep it a surprise and have only an off day for the preparation? Do it asap by using the "send an instant message" feature. Create groups, add all members, and send the audio of your plans and any image reference for event decoration. You can even type a long text of key highlights of the event. There would be no storage issues, so prepare to make the day of your seniors the best one. Throw a farewell party and have fun.
● Get Ready for Exams
Don't stress, and let the Ringtok messenger facilitate you to strive for higher. You might expect it from something other than an IM app you can use for study intents. We always assume that social apps serve daily casual chatter. Numerous apps have succeeded in erasing this narrative. Many statistical reports reveal that messaging apps have become imminent for learning and teaching purposes. Approach your teacher near exams to clear out your queries (even if they are in another country), and share the material in individual chatting without worrying about limitations. Being a freebie app, it is also free from locality restrictions. Use it from anywhere.
● Be Livelier in Live Streams
Plenty of students have a career path in fashion, music, or painting. For that purpose, Ringtok calling app allows students to flourish in their career pathways. Those with enchanting skills start expressing their talents on live streams, which indirectly provides them a way to show the world their art. They can get a fan following worldwide. Even if you are active on streams at 10 AM and another person's time zone is 2 PM, they can enjoy your streams without any problem. So, turn on the data network (2G,3G,4G,5G to Wi-Fi) and be open about your talents.
● Stand for Humanity in Communities
Students are mainly part of humanitarian organizations that work for human rights. Even students with influential personalities also perform duties for collecting funds for them. Ringtok messenger has an option for communities that keeps this funding and awareness process actionable within seconds. Students create communities and add people from divergent vicinities. Whenever there is a need for funds, they drop the details of cases and the required amount into the community so everyone can donate accordingly.
● Exchange Words on Voice Notes
Not everyone is always in the mood to type a long drifty text. Similarly, even if you have time to type, the receiver may be in a hustle or not have time to read that text. What to do? Wait! Wait! Ringtok chatting app has an alternative for that as well. If you are stuck on any vital question, record your query in voice notes and send it to your friend so they can listen and respond quickly. The voice note could be of 2 seconds to 2 minutes. Such an app is ideal for discussing your ideas, visions, and queries in groups or in-person chatter. It will be highly secured and remains between the people discussing it.
● Comicality in Chats
During student age, people are primarily in their "humorous era." "He, who laughs, lasts." If you want to be that kind of person, download this messenger app immediately. Developers have also modulated this app with features that allow them to be funny. The GIFs, stickers, and emoticons options are here, which help you express videos. You can share the links of funny reels, YouTube videos, or your own recorded comedy skits in the inbox of your friends or public groups.
Final Thoughts
An instant messaging app, Ringtok messenger, has become one of the most wanted apps since its launch. It is helping students in building a better future in multiple ways. Such as, they can learn new languages, enroll in international courses, communicate without paying tons of money, and enjoy HD calling without having trouble with tunnel voices and poor networks (as it works phenomenally on all networks). Therefore, get this app on your mobile phone and log in or sign up for it from your Android or iOS (it is compatible with both). So, download it now! Is there any reason left that prevents you from using this app in school days?

Without dallying for a moment, I present to you The Testicular Method (soucre):

Testicles was the first philosopher, or even man, to coin the phrase "Go with your gut." He then expanded on the phrasing to include "Use your instincts," "Shoot first, ask questions later," and "Put your balls to the wall." The Testicular Dictionary was formed, containing proper usage of Testicular principles. Busting one's balls originally defined a game in which repeated kicks to the testicles resulted in sterility, thinning the gene pool. Testicles was quoted as saying Nuts following an Athenian loss to the Spartan abdominal team.
The core ideal of the Testicular method is to create a concept that satisfies your given situation, preferably providing an easy and simple solution to nearly all problems. The exact steps are outlined below:

• A problem with an unidentifiable solution presents itself.
• A solution is brought forth based on instincts and testicular fortitude.
• The problem is temporarily alleviated, or not.
• Naysayers begin to question the solution.
• A proof is provided, and explanation for the solution gives it believability.
• If the problem is still not dissipated, outside factors or Democrats may be blamed, but the initial solution remains correct.

The Testicular method played a key element in the fictional plot holes, development of String Theory, the Iraq War and vegans.

It doesn't matter that my source [sic] is moderately neglectful of logic and flagrantly false to facts, to say nothing of balls-out stupid. ITS ALL TRANSCRIBER ERRORS, THE SOLUTION OF WHICH IS TO BE FOUND IN PLATO"S UNWRITTEN TEACHINGS. Needless to say, if it turns that Plato himself was balls-out stupid, this could only be the highest endorsement, since it is THE TESTICULAR WAY, as evidenced in such famed but perverted cases as Trump and Attila the Hun, to say nothing of the superbabundant but blinkered players of Chmess.
Postscript on huge balls
Growing HUGE ASTRAL COJONES is superior to the usual method, mainly because you are not limited to only two balls, let alone such oddities as three or one, let alone the case in which your balls have yet to drop or may never drop, nor even in the case in which one has no balls. Which is to say, you are not limited to those balls your parents, through no fault of their own, gave you or failed to give you, hence not being limited by the urgent problem of still being able to walk when your balls grow so huge as to threaten to drag against the sidewalk, rather than so huge and so numerous as to threaten to drag against the surfaces of neighbouring Kosmoi.
For those whose balls have grown so big they cannot walk, possible solutions include but may not be limited to a mechanized perambulator, or a bed with several bearers who shall bear one unto death, which in this world probably means you are a president or a captain of industry or another for whom vast numbers of persons who one is acquainted with indirectly every day become nothing more than statistics, scarcely the constellations upon constellations which any person must necessarily be. One can only caution those who follow to grow their cojones only, or mainly, on the astral.
Needless to say if the astral is too much the confining space, then I shall certainly defer to your preference for whichever of the toposes of the total mindscape you prefer, if not the total inexhaustible mindscape itself, even if your balls are so weird looking that I have to study math for 20 years just to discern the barest outlines of your balls. Then again, if the total mindscape is your ambition then your balls have already have grown so huge and so numerous that they have come to occupy all the space of all possible worlds, which are as plenty as blackberries, at which point you are ready to make a baby. But this injunction to make a baby is not to be taken literally unless you really insist, mainly because it takes time away from planting seeds in one's own soul, for one's soul could only be be a field so expansive, the soils of which are so superabundant that it exceeds the bounds of even that which THE GOOD LORD, if not THE GOOD KING, could possibly have anticipated. Either way, you shall need a good midwife in due course.