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The Housekeeper's Claim

The Housekeeper's Claim

For ten years, my professor husband, Daniel Whitmore, and I loved each other deeply, treating each other with respect and care. Our only regret was that he had azoospermia, so we were never able to have a child. Then, on our tenth wedding anniversary, the housekeeper, Megan Wright, suddenly claimed she was pregnant with Daniel's child. I thought she was joking. Two days later, she threw a paternity test result in my face. The test proved that the child she was carrying was Daniel's. Megan sued us. On the day of the hearing, she cried in front of the courthouse, telling the media how Daniel had molested her. She said we were a pair of perverts who treated her like a breeding machine and kept her confined in our home. Daniel and I couldn't defend ourselves at all. The netizens labeled us demon employers and nailed us to a pillar of shame. Daniel lost his job at the university, and his students were ashamed of him. I was fired from my company that same day. In the end, Daniel couldn't withstand the pressure of public opinion and committed suicide in prison. I became a rat on the streets, hunted down and beaten by netizens. Driven insane, I wandered into traffic and died miserably in the middle of the road. Even at death, I couldn't understand how Megan had become pregnant with the child of my husband, who had azoospermia. When I opened my eyes again, I had been reborn to the day before Megan claimed she was pregnant with Daniel's child.
2.6K viewsCompletedAdded to Library 77 Times as how does machine learning works
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A Stripper's Nightmare

A Stripper's Nightmare

"You're scared of me...terrified even, but you still want me so bad." He whispered huskily. Danielle Owens is a 24 years old college student, saddled with the responsibility of raising her younger brother and taking care of her deadbeat father. With bills to settle and debts to pay, Danielle works as a stripper in a nightclub, hoping to make ends meet and still lead a normal life. Her life however, is turned around suddenly, when she is noticed by a cruel, cold hearted and arrogant Mafia Boss, who will stop at nothing to have her in his bed, much to her displeasure. Her long time crush at school, also noticed her for the first time and things get even more crazy for Danielle, as she's thrown into a word of hurt, betrayal, humiliation, love and glamour. What does she do, when she finds out she actually needs the help of her ruthless mafia boss?
1016.0K viewsCompletedAdded to Library 416 Times as how does machine learning works
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You're Losing It All This Time

You're Losing It All This Time

Days before Christmas, my girlfriend, Tessa Coleman, decides to take her assistant to the beach for the holiday. I don't argue or make a scene. I even help her pack her bags. But she sneers at me, mocking me for finally learning how to be sensible now that my legs are useless. As soon as she leaves, I reach out to her sworn enemy. In my last life, when I tried to stop her with my crippled legs, her assistant ended up brutally murdered at the beach. Tessa had acted like nothing had happened, but once I recovered, she hacked off my legs and killed me mercilessly. That was when I finally realized that she had hated me all along. This time around, I'll make sure she loses everything.
5.0K viewsCompletedAdded to Library 146 Times as how does machine learning works
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My Alpha Mate

My Alpha Mate

Blurb: Tasyra is a twenty year old werewolf who does not know her identity, only that she was born and bred in captivity. Following the death of her mother, she becomes a servant at the young age of ten. She undergoes a lot of hardships and is constantly humiliated by the Luna who hates her for reason she did not know. Finding out she is mated to the Alpha of the pack she works in, Tasyra finds herself soaked in more humiliation and suffering as she gets rejected by her mate after sharing a night in his bed and his mother and Luna-to-be makes life extremely hellish for her. Will she be able to overcome these challenges and fight her way out of it? Will she endure to the end and protect her unborn babies? Or will there be a new path for her, a string of hope to hold and pull through and discover who she truly is meant to be? Will be her mate bond with the Alpha bring them both joy or will they remain separated by the evil machinations of those against their happiness?
2.0K viewsOngoingAdded to Library 40 Times as how does machine learning works
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I Want A Divorce, Mr. Billionaire!

I Want A Divorce, Mr. Billionaire!

Hazel's world is far from perfect. But, she’s happily married to the love of her life, Carter, a dashing billionaire and scion of the powerful Whitlock clan. Despite the raised eyebrows and skeptical glances from his elitist relatives, Hazel works tirelessly to be the perfect wife, striving to meet their lofty expectations. Carter's love is equally profound…. Or so she thought. On the day of their third anniversary, Hazel finds out she’s pregnant, and that Carter is having an affair with her best friend. Heartbroken and desperate, Hazel conceals her pregnancy and demands for a divorce, only to be met with Carter's ruthless ultimatum: stay and give him an heir or face financial ruin. With nowhere to turn, Hazel’s only hope is to seek the assistance of the best divorce lawyer in town - Marius Thorne. Little does she know that her relationship with her lawyer will turn out to be intimate and passionate, thus, making her enjoy all the perks of having him by her side.
1044.4K viewsCompletedAdded to Library 1.1K Times as how does machine learning works
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Tamed By My Master's Moon

Tamed By My Master's Moon

Adams Martin does everything humanly possible to avoid people after surviving COVID-19 and suffering from PTSD. Following his therapist’s instructions, he adopts a dog from the shelter to help him navigate being around people. The only problem is, that his dog occasionally shifts from dog to man and back.   Kehan Gammy (Alex) is the embodied soul of the first Inugami, created by the gods. When he was sent to the human world as punishment for letting a powerful, dark soul escape into the human world, he didn’t expect to have to be bound to a human and have to blend in as one, learning everything like a baby and adapting to his new-found body. But Adams was easy to bond with, giving him a human name, Alex, he’d appeared as a common dog. When his exit from the underworld set off strange events happen in New York. He has to devise a plan that doesn’t involve Adams Martin in Will he be able to protect everything he wants to preserve or will he return with yet another failure?
1.0K viewsOngoingAdded to Library 34 Times as how does machine learning works
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The Silver Dragon

The Silver Dragon

***Warning this book does contain sexual assault, suicide attempt/ideation, physical violence, and adult language. Please read at your own discretion.***** Davina lives an ordinary human life with her parents, having no clue werewolves, vampires, elves and witches and warlocks truly exist. Everything changes when her family is attacked, leaving her orphaned and in the care of the man she believes is her uncle. But little does Davina know this isn’t the first time Davina has been left orphaned and the ones that raised her are not her parents. She has no idea just how important she is and how the magical world relies on her for their survival. Will the local Alpha of the Palmetto Pack be able to accept that Davina is his mate, despite her being the very reason his father is dead? Their meeting will spark a series of events that will unfold the mystery of Davina’s past and what she truly is.
1038.9K viewsCompletedAdded to Library 855 Times as how does machine learning works
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Her Tears

Her Tears

Scarlett is a responsible wife who does everything she can to achieve her dream of having a family with her husband, but it all comes crashing down because of one sin. A sin in which she caught her husband and sister having an affair. How far can she go?
1021.0K viewsCompletedAdded to Library 461 Times as how does machine learning works
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Me Against the Comments

Me Against the Comments

At one in the morning, the neighbor upstairs suddenly knocked on my door. He said there was a leak in his apartment and asked if our place had been affected. I was just about to open the door when my vision was flooded with comments. [Open the door, and you're dead! That man outside is not your neighbor!] [Didn't the old man upstairs who lived alone go to Marcasia last week to find his new love interest? There shouldn't be anyone up there at all!] I immediately pulled away from the doorknob. At that moment, an emergency notice popped up in the residential property chat. [Unit 1307 has a burst pipe with severe leakage. Property management will inspect the building's water system.] [Is anyone home in 1207? We need to check whether your ceiling is leaking. Please open the door.] Unit 1207 was my place. The comments flooded my vision again. [What kind of property management does inspections at one in the morning? They're in on it together!] [Bea, stay hidden! Your destined man will descend from the heavens to save you!] I nodded solemnly, as if I was taking them very seriously. I turned around and grabbed my climbing rope. Amid the hysterical screaming of the comments, I leapt straight off the balcony. I'm someone who got a second life. Last time around, my entire life was ruined by listening to these brain-dead comments. This time, I'd rather die from the fall than end up as a breeding machine again.
1.2K viewsCompletedAdded to Library 44 Times as how does machine learning works
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On Thin Ice

On Thin Ice

Jordan and Nora have been best friends since they were seven. Same rink, same team, same terrible vending machine hot chocolate every first practice of the season. It’s always been easy. Then Nora gets a scholarship offer from a hockey academy across the country, and suddenly nothing is easy anymore. They’ve got one last season and somewhere between the playoffs and the goodbye, they’re going to have to figure out what they actually are to each other. Some things you can only say on the ice. Some things you can’t say there either.
371 viewsOngoingAdded to Library 14 Times as how does machine learning works
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