fabiochiu/medium-articles huggingface dataset:
Long Term Dependencies in Sequence Learning
By: Nikhil Verma
Tags: Gradient Descent, Optimization, Long Term, Sequence To Sequence, Rnn

Sir Kepler, a renowned astronomer with famous three laws under his authorship took long time to device his principles. A Neural Network(NN) would have predicted Tycho Brahe’s trajectories without requiring Kepler’s flash of insight to try fitting the data to an ellipse. Sir Isaac Newton, however, would have had a much harder time deriving his laws of gravitation from a generic model. But what gives NNs, so much of power that they learn data at unprecedented speed and so generic in nature? It is Optimization.

Most deep learning algorithms involve optimization of some sort. Optimization refers to the task of either minimizing or maximizing some function f(x) by altering x. We usually phrase most optimization problems in terms of minimizing f(x). Maximization may be accomplished via a minimization algorithm by minimizing −f ( x ).

Optimization in general is an extremely difficult task. Traditionally, machine learning has avoided the difficulty of general optimization by carefully designing the objective function and constraints to ensure that the optimization problem is convex. When training neural networks, we must confront the general non-convex case. Even convex optimization is not without its complications.

Gradient based Optimization

From our high school we know how first and second order derivative tests are helpful in determining minima and maxima of a mathematical function. This gradient based optimization is mostly used in neural networks to find solution of non-convex functions.

x∗ = argmin f( x )

How Gradient decent works in NN?

The directional derivative in direction u (a unit vector) is the slope of the

function ‘f’ in direction u. In other words, the directional derivative is the derivative of the function f (x + αu) with respect to α, evaluated at α = 0. Using the chain :-

To minimize f , we would like to find the direction in which f decreases the

fastest. We can do this using the directional derivative:-

where θ is the angle between u and the gradient. Substituting in || u ||= 1 and ignoring factors that do not depend on u, this simplifies to min u cos θ. This is minimized when u points in the opposite direction as the gradient.

In other words, the gradient points directly uphill, and the negative gradient points directly downhill. We can decrease f by moving in the direction of the negative gradient.This is known as the method of steepest descent or gradient descent . Steepest descent proposes a new point:-
Constructing trading strategy ensembles by classifying market states
By: Michał Balcerak
Tags: Trading, Money, Machine Learning, Optimization, Bitcoin

Rather than directly predicting future prices or returns, we follow a more recent trend in asset management and classify the state of a market based on market state labels [Source]

I would like to share a paper which I had the pleasure to co-write. https://arxiv.org/abs/2012.03078

If you don’ know me already: I am a theoretical physics grad student with a background in data science who already worked at a hedge fund and has a trading startup. My co-writer Dr. Thomas Schmelzer is already a senior quant — now working at the Abu Dhabi Investment Authority.

Rather than directly predicting future prices or returns, we follow a more recent trend in asset management and classify the state of a market based on labels. This should be already familiar to some of you since López de Prado’s book is quite popular.

Let me know what you think of our findings. Link to our GitHub:

https://github.com/m1balcerak/labels

FULL ABSTRACT:

Rather than directly predicting future prices or returns, we follow a more recent trend in asset management and classify the state of a market based on labels. We use numerous standard labels and even construct our own ones. The labels rely on future data to be calculated, and can be used as target for training a market state classifier using an appropriate set of market features, e.g. moving averages. The construction of those features relies on their label separation power. Only a set of reasonable distinct features can approximate the labels. For each label we use a specific neural network to classify the state using the market features from our feature space. Each classifier gives a probability to buy or to sell and combining all their recommendations (here only done in a linear way) results in what we call a trading strategy. There are many such strategies and some of them are somewhat dubious and misleading. We construct our own metric based on past returns but penalising for a low number of transactions or small capital involvement. Only top score-performance-wise trading strategies end up in final ensembles. Using the Bitcoin market we show that the strategy ensembles outperform both in returns and risk-adjusted returns in the out-of-sample period. Even more so we demonstrate that there is a clear correlation between the success achieved in the past (if measured in our custom metric) and the future.
How Delivery Time Slots can add Greater Efficiency
Tags: Delivery, Efficiency, Optimization, Business, Logistics

Managing the delivery process can be tricky, with route optimization, driver management, vehicle safety checks, and growing customer demand adding complexity on all levels. On top of these multifaceted challenges for delivery companies, customer availability, and requirements for in-person delivery can further complicate the delivery management process. Delivery management software provides the businesses with tools to optimize route planning and manage fleets, but scheduling and route planning based on physical location alone can result in the need to redeliver if a customer is not available when the route is scheduled.

Multiple customers at one address

At first, delivery to multiple customers at one address can seem like a logistical gift to a company. Sending out a driver with multiple deliveries to a single location, such as a shopping mall or industrial park, is efficient and saves resources. Logistics management software can aggregate these multiple customers into one delivery route for ongoing resource savings. However, not all businesses operate on the same schedule. Not all businesses have the necessary personnel available to receive deliveries at the same time as other customers.

If a customer is not available to accept delivery, the delivery company has to reschedule the driver to make a second trip at a later date. Redeliveries can be unavoidable in some situations, but they can add delays to both the shipping company and the recipient. A customer might need a vital part for their manufacturing, for example, or sales inventory for a busy shopping day.

Time slot order splitting adds an extra layer of efficiency

Companies that deliver to multiple customers at a single location may not be able to coordinate customer schedules to allow for single deliveries each day. With Track-POD, companies can fully leverage technology to make multiple stops to a single address as efficiently as possible. Track-POD delivery management software allows companies to dive deep into their logistics management and create time slots for splitting orders. By establishing set time windows, companies can give customers more options. Customers that might need multiple deliveries in one day can benefit from increased access to inventory, and the specific time window availability can significantly reduce redeliveries, resulting in lower-cost shipping and time savings.

Track-POD allows companies to manage deliveries and routes with ease, cutting transport costs through optimizing scheduling, vehicle capacity, time windows, and mileage. With the ability to split orders by time slots, companies can regain control of a source of inefficiency and pass benefits on to customers. It’s just one reason to move your company’s logistics management to Track-POD. From proof of delivery to real-time delivery progress, Track-POD makes delivery visible and efficient. For more information on how Track-POD can optimize your deliveries, contact us today.
Indexing multiple range conditions in MySQL: a case study
By: Lucas Grossi
Tags: Indexing, Engineering, Optimization, Index, MySQL

Conditions

There are still three conditions without index left in our query. We need to add those columns to our index, however we are not 100% sure which order we should follow. A good start is to look into the type of the conditions. Are they single conditions or range conditions? I’ll explain the difference.

Single Conditions

Let’s first look at the canceled_at condition:

IS NULL might look like it’s a non-singular value at first glance, since NULL stands for an undefined value. However, if we look more closely, NULL is a single and concrete value, thus countable and indexable. In other words, we can list NULL as a specific value: 10, 20, 500, 15, 0, NULL , 1, 10923… Based on that, we can conclude that the piece of query above is a single condition, since it points to a well-defined value.

Range Conditions

Now, let’s take a look at the remaining ones:

Do you notice the difference? booking_confirmed_at and to_be_processed_at do not stand for a unique value, but a range of values. After this, everything that gets indexed will lose effectiveness, since MySQL now needs to look inside this subset of values to proceed with any index search.

Golden rule: always first index the columns where the related conditions are single value only, then focus on range conditions.

As such, we’ve chosen canceled_at as our second index column. What now?

Multiple Range Conditions

Now, there are two remaining conditions we want to add to our index. Both are range conditions with a similar filtering range. Which ones should we use, and in which order? Instinctively, you might feel you can add both in any order. Giving priority to the condition that filters out a little bit more or that makes the same compound index reusable might prove useful in other scenarios. That is a fair assumption, but it’s only half correct.

At this point, most of the decisions we made here will lead to similar results. It is highly advised to choose the third column of this index based on the condition that filters out more rows. Also, if applicable, it is good to build the compound index in a way that allows it to be used in other scenarios. Reducing the amount of indices, and their size, is a good space and performance optimisation.

In this case, we have multiple range conditions. As we explained, a range condition will have its own range of things to go over before using the index. That’s the main concept needed to understand our use case:

Once you reach the first range condition, any subsequent condition in your query will not use any further indices

As such, adding both will not change query performance. It will just take more disk space to store the index. You will have exactly the same query speed with or without the fourth column, which I will demonstrate with an example below. The best choice in our case would be an index with three columns, choosing the most suitable to your scenario between the remaining two columns.
JVM Optimization Chapter I
By: Yiğit At
Tags: Openj9, Jvmoverloads, JVM, Java, Optimization

Profiling

Before upgrading the Java version and JVM options, we thoroughly profiled our service that we could improve where it lacks performance-wise issues. Then, we focused on the notion that updating the Java version may progress our response time as well as other resources such as CPU utilization, memory consumption, etc. OpenJDK11 with Hotspot was initially preferred for our service and it greatly scaled until the issues that I previously mentioned before.

Firstly, we searched for shared benchmark results and trade-offs regarding updating the Java version from Java8 to Java11. After reviewing benchmarks results[2][3], Java11 with OpenJ9 was the best option that would cover our issue. Here are some related benchmark results as follows :
Gradient Descent vs Normal Equation for Regression Problems(machine learning)
By: Danial Farsi
Tags: Linear Regression, Optimization, Machine Learning

If you have just started reading machine learning, you are definitely familiar with the concept of optimization, We need to optimize to achieve the best conditions with the best accuracy, In the most basic situations, we become familiar with the two concepts of Gradient Descent and Normal Equation.

What is Gradient Descent?

In fact, it is a technique that aims to improve the cost function.In this method we deal with the coefficients in linear regression. Our goal is basically to find the coefficients that help to optimize our function. In this way, our shapes become the convergent subject backs. We repeat this until we reach the best possible state. Its formula is :

where j represents the no. of the parameter, α represents the learning rate.

What is the Normal Equation?

This method is also used to find the best parameter to optimize the cost function. In this method, we directly calculate the value of the coefficient and there is no duplication. Its formula is :

As you can see, in this method we have to carry heavy calculations. Inverting a matrix, etc. In this method, it is clear that a problem with high parameters imposes a lot of time costs on us, and this method for problems with features. The above is not suitable.

compare

Normal Equation takes less time to compute the parameters and gives almost similar results in terms of accuracy, also it is quite easy to use. According to my, the Normal Equation is better than Gradient Descent if the dataset size is not too large(~20,000). Due to the good computing capacity of today’s modern systems, the Normal Equation is the first algorithm to consider in cases of regression.
Debouncing and Throttling
By: Shifali Gupta
Tags: JavaScript, Performance, Frontend Development, Optimization, Frontend

Debouncing and Throttling are two ways to optimize performance of web application by limiting rate of the execution of a function call. It is particularly useful when we are attaching function to DOM events, because in these scenarios we might be invoking unnecessary function calls.

Debouncing

The debounce pattern lets us control events that are triggered successively, and if the interval within two events is greater than a certain amount of time, only then the function is called.

Implementation

Debounce function takes two arguments — function and delay.

It does not let function make unnecessary calls again and again and will only make function call if the difference between time Intervals of function calls is greater than the delay.

Throttling

Throttling is a technique in which, no matter how many times the user fires the event, the next function call will be delayed for a certain time Interval.

It restricts the maximum number of times that a function can be called. This method is usually used to control window resizing, scrolling and mouse-related events.

Implementation

Throttling function takes two arguments — function and time Interval.

It will check the difference between last function call and the function call. If the difference between two function calls is a certain time, then only the function is called.
A Comprehensive Guide to Profiling Python Programs
By: Eyal Trabelsi
Tags: Python, Performance, Best Practices, Optimization, Programming

A Comprehensive Guide to Profiling Python Programs

Photo by Jason Chen on Unsplash

Today, users have high expectations for the programs they use. Users expect programs to have amazing features, to be fast, and to consume a reasonable amount of memory.

As developers, we should thrive to give our users the best experience possible. We need to find and eliminate the bottlenecks before they affect our users. Unfortunately, as programs become more and more complex, it becomes harder to find those issues and bottlenecks.

Today, I am going to focus on a technique to find those bottlenecks, and this technique is profiling. My goal today is to show you that profiling is not rocket science. Everyone can find their bottlenecks without breaking a sweat.

The code for this article can be found on GitHub, and a more detailed recorded version can be found on YouTube.
Searching cleverly with Fuzzy Search
By: Enes Demirağ
Tags: Search, Fuzzy, Optimization

Searching is a key component of big and complex systems. There are many different ways to find what we are looking for. For instance, if we want to find the location of a file in a directory, we enter the file name and the search algorithm looks ever directory and returns the file when it finds. But sometimes we do not know the exact name of the file. In this case, traditional searchinf algorithms are not sufficient.

For these type of problems, we use Fuzzy Search Algorithms. This way we can find the file, even if input was not completely correct. This algorithm looks for similarity. This method can be used in many cases.

To find the similarity, we need to give ranks to every file and return the one with best rank. In order to do this, we will use Levenshtein Distance method.

Lets write a function that gets an input, and returns the most similar results in a file. Example movie list is here.

def distance(string, pattern): # Levenshtein Distance

string = " " + string.lower() # Make lowercase

pattern = " " + pattern.lower() # Make lowercase



matrix = {} # Empty matrix



for i in range(len(string)):

matrix[i, 0] = i # Fill first row from 0 to length of the string



for j in range(len(pattern)):

matrix[0, j] = j # Fill first column from 0 to length of the string



for j in range(1, len(pattern)):

for i in range(1, len(string)):

if (string[i] == pattern[j]): # If characters are same

matrix[i, j] = matrix[i - 1, j - 1] # Write top-left value

else:

matrix[i, j] = min(matrix[i - 1, j], matrix[i, j - 1], matrix[i - 1, j - 1]) + 1 # Write 1 + smallest value on top, left, or top-left.



return matrix[len(string) - 1, len(pattern) - 1] # return the value in last element





movies = open("movielist.txt").read().split("

") # Get the movie list

movies_with_ranks = {} # Create empty dictionary



query = str(input("Enter movie : ")) # input



for movie in movies: # For every movie in the list

movies_with_ranks[movie] = distance(movie, query) # Calculate its distance and add to the dictionary



sorted_list = sorted(movies_with_ranks.keys(), key = lambda i: movies_with_ranks[i]) # Get a list from dictionary sorted based on values



print("Results:")



# Display first 3 similar movies

for i in range(len(sorted_list)):

print(sorted_list[i])

if (i > 3):

break

Full code can be accessed from here.

References:

Search Algorithms:

Searching and Sorting Algorithms Book:

Fuzzy Search:

Python Package:

Levenshtein Distance:

Levenshtein Distance Tutorials:
How to use Indexing for SQL Query Optimization
By: Ashish Tomar
Tags: Analytics, Optimization, Data, Data Science, Sql

Let’s create an index on the ‘product’ table and include ‘category’ in the index.

Syntax:

CREATE INDEX [index_name]

ON [table_name] ([column_name]); Query:

CREATE INDEX product_category_index

ON product (category);

When you execute this query, it will take much longer than a normal query. The database scans 12 million rows and builds a ‘category’ index from scratch. Let’s say this takes 4 minutes.

Now, let’s test the performance of the old query with indexing.

SELECT COUNT(*)

FROM product

WHERE category = ‘electronics’;

You’ll see that the query will be much faster this time. It’ll probably only take 400 milliseconds this time.

Even the queries which extend further than using ‘category’ as a condition will benefit from the indexing on ‘category’. Let’s see an example.

SELECT COUNT(*)

FROM product

WHERE category = ‘electronics’

AND product_subcategory = ‘headphone’;

This query will take less time than what it would have normally taken, say 600 milliseconds for this query. The database can quickly find all ‘electronics’ products using the index. And from the smaller set of records, it finds headphones in a normal manner.

Now, let’s change the order of the conditions in the ‘WHERE’ clause.

SELECT COUNT(*)

FROM product

WHERE product_subcategory = ‘headphone’

AND category = ‘electronics’;

Even when the ‘product_category’ is mentioned before ‘category’, the database still picks the column which had an index, i.e. ‘category’, and then scans rows for finding the specified ‘product_subcategory’ from that subset of records.

How does it know that?

Fig.3 Possible query plans for the query optimizer (Image by Author)

The database considers all possible paths to execute the query and then chooses the most optimal path.

It’s time for some database lingo now. Each of the possible paths is called a ‘Query Plan’. It is essentially a sequence of steps used to access data in a SQL relational database management system (RDBMS).

And this feature of RDBMS that determines the most efficient way to execute a given query by considering all possible query plans is called ‘Query Optimizer’.

Multi-column Indexing

Now, let’s explore multi-column indexing.

An index can be made for more than one column.

CREATE INDEX product_category_product_subcategory_index

ON product (category, product_subcategory);

Here, we have an index on both ‘category’ and ‘product_subcategory’. The important thing to note here is that the order matters here. It’s like sorting the data first by ‘category’ and then by ‘product_subcategory’.

And the query gets even more fast by using this multi-column index. Let’s say it comes down to 60 milliseconds.
They didn’t listen.
By: Sushanth Shajil
Tags: Fiction, Short Story, Short Fiction

Nathan slid the balcony door open. The 21st floor views were breathtaking. Possibly, the best he’d ever seen. He could see the city lights, as far as the horizon stretched, glimmering & shining brightly through the night.

He was feeling blissful to be finally getting some ‘alone’ time. Jack was babysitting the kids, while his wife, Rebecca flew across states to be with her parents. It had been long since Nathan did something nice for himself.

“A bubble bath, perhaps?” He thought to himself.

“Nah, too girly.”

He quickly scampered towards the kitchen & began to make himself a warm cosy cup of tea. While he waited for his concoction to boil, he waved his finger in front of the array of books on his shelf.

“Hmm. Do I feel like Sci-fi today?”

“Nope. But that’ll do for now.” he said while making the compromise.

He carried the book, slipped into something more comfortable & headed back to his view. “Nothing can ruin me, my book & my evening.” He told himself.

To be fair, Nathan was an all rounder. A wonderful dad, a great husband, a delightful human being to those he loved. But being a role model did come at a cost.

He was tired of putting in the effort. He was tired of making things right for everybody. He had finally got the break he’d been forever waiting for.

As he took in the gorgeous view, he started to appreciate how blessed he really was. He had a great job, two great kids & everything he could ever ask for.

He blew on his hot cup of tea & raised it against the wind.

‘Cheers!’ he said.

“Ahh. Perfection.” He exclaimed after his first sip. The tea had turned out just like he wanted.

While he took his next sip, he noticed something odd on the horizon. The highway (which was usually brightly lit) had suddenly lost power. Before he could figure out what went wrong, The entire city plunged into darkness. All the street lights, billboards & cars went pitch dark.

Every electronic device in the entire city ceased to function. Nathan stood there, puzzled & lost.

As he began to think it through, he had an epiphany.

He realised what had gone wrong. With his tea still in his hand, he slyly smiled into the distance.

“They didn’t listen. I told them & they didn’t listen.”
Kai dan Bunga Matahari
By: A. Z. Salsabila
Tags: Short Story, Short Fiction, Indonesian, Indonesia, Fiction

living the world made of food, coffee, books, ice cream and cats in a garden full of flowers ✨
An Alternate Ending to ‘The Monkey’s Paw’ by W.W. Jacobs
By: Aysia C.
Tags: Fiction, Retelling, Short Fiction

An Alternate Ending to ‘The Monkey’s Paw’ by W.W. Jacobs

Photo by Syed Ahmad on Unsplash

Mr. White clutched the paw with his trembling hands and wished that he had never been given it. The knocking stopped. He ran rapidly down the staircase and towards the door. A sharp wind blew back his already messy grey hair and nearly knocked him off his feet. It blew in dirt, dust, and rocks from the road, temporarily blinding the old man.

He groped for the door handle, but could not find it. The winds blew on, becoming stronger by the second. Soon, Mr. White found himself enclosed by the winds on all sides. It wrapped around him like how a snake encircles its prey. All of a sudden, a powerful gust threw Mr. White against the staircase causing him to hit his head.

He woke up in a cold sweat with his heart beating rapidly. Chess pieces were scattered on the floor. A white-haired old lady was sitting by the fire heavily involved in her knitting. Mr. White looked around the room frantically in search of the paw that caused him so much trouble. He overturned the table, threw open the cabinets, rummaged through the dishes, and looked through the drawers.

“Where is it? It must be around here somewhere,” he said.

Frightened by his actions, Mrs. White rose from her chair and replied, “Where is what? What’s got you all worked up?”

Mr. White grabbed his wife by the shoulders. “The paw, where is it?”

Suddenly, Mr. White heard someone walk down the staircase. In walked Herbert, Mr. White’s son whom Mr. White felt as if he had not seen in a decade.

“What’s got dad all worked up?” said the cheery young man, beaming at his parents.

Mr. White released his wife and threw his arms around his son passionately.

“Dad, I’ve only just seen you thirty minutes ago when I beat you in chess,” said Herbert with a chuckle.

“I’ve told you to stop drinking so much, Mr. White,” chided Mrs. White. “It gives you bad dreams and makes you sound crazy. Now, what is this paw that you speak of?”

Realizing how ridiculous he sounded, Mr. White decided to spare them the details of his dream. Mr. White slept soundly that night knowing that the paw and the three wishes were just figments of his imagination.
I followed the man in green
By: Clark Adler
Tags: Short Fiction, Short Story, Mystery, Suspense, Fiction

I had barely left my apartment complex and I was already regretting not having taken my coat with me. It was cold outside for the first time this year; this was why he was wearing his khaki green jacket. I was almost 2am yet he too was walking along the busy street. He looked back as the streetcar left it’s station knowing the next one would come in half an hour. He would have to walk, but he didn’t care. His used boots indicated that he was used to walking. The heel of his shoe was quickly deteriorating. He would have to buy new ones next winter if not before then. As he walked, the back of the shoe dragged along the pavement. They were too big for him. Perhaps he did not have the money to purchase new boots after all. Nevertheless, he had a metal briefcase in his hand. This is what piqued my curiosity.

Where did he acquire such a briefcase? Some might have assumed he was homeless if it were not for his clean shaven face. I was intrigued and my mind would not rest if I did not try to understand his predicament. The fast food joint was open all night, I could get my burger later. I decided to follow him as he turned the corner onto a dimly lit street. The lights were old and had a yellowish tint to them which didn’t light up the streets as well as the LED lights on the main road. The houses along the street were modest. Their red brick exterior indicated they had been built in the 40’s. The streetlights reflected off the man’s wedding ring. It’s shine pointed to recent marriage, as he did not seem like the type of man to polish his ring. I looked down to my phone, it was now 2:05.

He was walking at a fast pace. Was he late or in a hurry? He kept walking until turning onto a smaller street where he marched rapidly. He then turned left onto a driveway. He had not looked for an address, this was not his first time there. As I walked past the house without looking, as not to arouse suspicion, I heard another man welcome him in. The loud chatter of three or four other men could be heard from the inside. The man walked in with the briefcase. The host thanked him for bringing the chips. The man was not homeless, but an addict. He had gone to his friend’s home for a poker night after his wife had gone to bed. He had become accustomed to losing and would do so again. The chips in the briefcase were his but free will was not. I looped around the block and went to buy my burger. It was an adequate night.
The Queen’s Gift
By: Alice O Writing
Tags: Short Fiction, Short Story, Fiction Writing, Abstract Art, Queen

Sometimes the Queen comes into my room at night and whispers in my ear the names of stars on the other side of the universe. They aren’t cool and intelligent names like Alpha Centurai. She mutters names like, ‘Fugglesoo, Murdy-Mur, Lick-Stop-N-Pop, Fartbox’ and so on.

The only thing that makes her go away is the sun coming up. Her Majesty yelps like a kicked dog and flies away, back to her bedroom in Buckingham Palace before the maids realise she’s missing.

I like the whole routine. Her posh accented voice, the soft murmuring of hundreds of oddball names, it helps drift me off to sleep with dreams so heartbreakingly beautiful that I regret waking up.

The last thing I ever expected was for a second night visitor. One night the Queen came in when I turned off my lamp as per usual, but this time she came with a little creature in the palm of her hand.

“This is Megmump,” she whispered in cultured tones. “You’ll have to take care of her now. She’s come all the way from Biggletock.”

I held out my hands, blind in the midnight dark. The little creature, obscured in deep shadow, fell into my hands. The Queen did not stay and list the strange unknown stars tonight, but instantly flew out of my window and away back into the black night. I knew deep inside that this was the last time I would ever see her.

Slowly, I lowered my hands. The creature plopped onto my lap. Then I carefully stretched my arm over to my bedside table towards the lamp. I needed to see.

I flicked the switch.

My lap was empty.
While on a long thirsty ride on a hot Konkan (western part of India)summer, Mehu suddenly hears the…
By: Anshul Tiwari
Tags: Short Fiction, Short Story, Photography, Fiction Writing, Storytelling

I am Anshul Tiwari, an IT consultant by profession and a poet, teacher, youtuber, photographer and a blogger by passion. https://www.youtube.com/c/itkfunde
Pillow Talk
By: Rebeca Ansar
Tags: Short Story, Short Fiction, Novel, Storytelling, Fiction

Afra glanced at her phone. It was 11 PM, a time that felt early just a few months ago. She remembered when this was the hour she would leave her apartment for a night filled with dancing and debauchery.

Now, the prospect of being anywhere besides the comfort of her bed was punishment.

Sam lay next to her.

“How’s your hip?” he asked.

“It’s alright. Not too painful. I made it through an hour of dance rehearsal but didn’t stay for the second round,” she responded. “How about you? Has your knee gotten any better?”

He groaned. “Don’t ask.”

This had become their pillow talk. Each took inventory of the other’s ailments. It was hardly the stuff of fairy tales.

She stared at the ceiling. A question nagged at her. Where they romantic enough toward each other?

“Do you remember what it was like when we first began dating?” she asked.

“What what was like?” His response was groggy.

“Pillow talk! Sweet nothings. Affirmations of how happy we were.” she chided him with a whisper.

He laughed then sighed comically. “You didn’t like that kind of stuff.”

“Hey,” she propped her head on her palm. “I was just a kid when I first tried an olive. I spit it out and decided that I hated olives.”

“Mhm. No olives for Afra.”

“But, I still try them from time to time. Just to make sure.”

Samar scooted his body closer to hers. She completed the familiar process of adjusting herself into the crook of his arm.

“You still hate them.”

“This bed is huge,” she exclaimed.

“The only downside is that it’s harder to find you again when you roll away in the middle of the night,” he responded. “Then the first thing I hear in the morning is- how come you didn’t hold me last night?”

She quietly acknowledged the truth in this. Sam knew she would never give up her self-proclaimed right to hold him responsible for her roll-aways.

“I’m anxious about growing older,” she suddenly changed the conversation. “What if we don’t like it.”

“You want to go out.”

“It’s Friday night.”

“I’m ready if you are,” he teased.

Afra knew he had caught her bluff.

“Maybe tomorrow.”

He kissed her forehead. “Okay.”
The Governor’s Island
By: Gabriela Marie Milton
Tags: Short Read, Short Story, Literature, Short Fiction

The Governor’s Island

Marcel Rommens; Shutterstock

It was in the beginning of autumn.

Angelo took me to the island.

The morning we arrived we rushed to the Governor’s mansion. I did not see one single soul along the way.

The mansion was a typical Spanish colonial building with clay roof tiles, stucco walls, and an arched entrance door.

A smell of mold and ocean lingered in the corridors. The walls looked like the faces of old patients locked in an asylum.

Angelo and I stopped in front of a huge door at the end of the main corridor. He opened it without saying a word. What was new? He did not speak the entire day.

In the dim light I distinguished the silhouette of a man. The man stood in front of the sole window, his back turned toward me. He was dressed in a military uniform.

His silhouette… Ha! Was I so much in love with Miguel that I was imagining every man in the world looked like him?

The man turned around.

I stopped breathing. The memory of long nights of love came over me with the force of a hurricane determined to wipe everything from the face of the earth.

Miguel. The man in front of me was Miguel.

I do not know for how long we stared at each other.

He spoke first:

“Surprised, Clara?”

That was the most idiotic question Miguel had ever asked me. My legs trembled. Tears burned my eyes. Rage inundated me. I raised my voice.

“Surprised? Who? Me? If Winston Churchill would walk in this room right now, wearing Josephine Baker’s famous top hat instead of his, and Bottega Veneta stiletto sandals I would not blink an eye. From now on until the end of my days I swear nothing is going to surprise me anymore.”

“I can explain.”

“Don’t indulge me. Explain what? That you are the Governor of an empty island where hardly anybody sets foot? And what is it with that military uniform?”

“You should have read Miriam’s files before you came.”

“No. You should have explained what’s going on long time ago. Whatever the name of this insanity is, now it’s too late.”

“Wait.”

I did not. I left the building. I ran toward the ocean. The wind disheveled my hair with the fury with which dishevels awnings in the winter. It was winter in my soul. A cold winter whose ice the intensity of that summer could not dispel.

I heard Miguel’s voice behind me.

“Clara, stop.”

I reached the rugged coastline. I wanted to stop. My left foot slipped. I fell into the void beneath me.

“Nooo!”

For a second Miguel’s voice cut in two the howling of the waters.

I thought to myself: “I am pregnant.”

I felt the impact with the water, and everything turned black.
Work in progress. – Ehm…excuse me miss, your coffee. It’s…
Tags: Short Read, Short Story, Fiction, Short Fiction

-Ehm…excuse me miss, your coffee. It’s ready.

-Oh, yes, awesome! Thank you.

“‘Awesome’? Did I say ‘awesome’? I hate using this word. Awesome? why is it awesome? Is there love and gold offered in my coffee?”, she wondered. Anna always found interesting how some great energetically words compromise their value in every day banter. “I guess it’s the same in every culture. Same thing, different language.”, she thought. Still though, this energetic flush coming out of her mouth is close to what the actors do, she kept on thinking. They say things with the needed energy even though their true feelings lie on the other end of the spectrum.

There it is again, she went down the spiral of random thoughts. Acting…she has always been attracted to acting. She even took a class once. And remember how she used to set up small plays with the neighborhood kids when kid? Why did she stop? Might have been her lack of courage again.

She took the coffee in the paper cup, reminding herself once again that she needs to buy a bamboo take away cup. All the Instagram bloggers have this . That’s the trend. “Do I care for the environment or is this guilt imposed?”, she raised an eyebrow to herself. She has been questioning a lot of her decisions lately. How true are these to her own real desires and needs? Can she see herself in these?

She shook her head to put her thoughts in the closet. They are out again and it’s too early for her and for anyone for that matter. She has a long day ahead and thoughts cannot run wild. ‘Work in progress’, an Instagram post she saw the other day flashed in her mind. “Work in progress and coffee in the mornings”, she said.
Sometimes, a daisy blooms in the mist of weeds.
By: Toyosi Ajiboye
Tags: Short Fiction, Short Story, Daisy, Fiction

Sometimes, a daisy blooms in the mist of weeds. It sprouts from the ground and towers above the rest of it peers

But, it was also the only daisy. It was different. The weeds, they didn’t like that. They didn’t like how the daisy towered above them like it was superior.

They were jealous for they believed the daisy’s beauty surpassed theirs. And while that was true for most anyways, they didn’t understand that beauty came from within and decided to Orchestrate an act that made them even more ugly.

The weeds began to spread their roots and leach nutrients from the daisy, they began to stretch their leaves in a such a manner that the daisy would get so little sunlight and rain that it was negligible.

The daisy was suffering, dying. But all the weeds could see was the one who thought was better than they were was being taken down a peg or too.

The daisy’s stem began to weaken and eventually bent over as their flowers and leaves fell to the ground.

The weeds giggled to themselves about the downfall of the daisy, not realising or perhaps not really caring about the consequences of their actions.

Eventually, the daisy died. For a daisy cannot bloom in the mist of weeds .
You promised each other forever, and for a while you believed it to be true.
By: Toyosi Ajiboye
Tags: Short Story, Fiction, Short Fiction

You promised each other forever, and for a while you believed it to be true.

You were not lovers, no not that. You were what you believed to be friendship soulmates.

But you should’ve known that everything comes to an end at some point. The both of yours just came a bit earlier.

On some days you sit and think; how? Why? When?

You will never get your answers to these questions and not because everything was over and done but because as you’d later come to realise, it was a combination of things.

After a while, the thought of you both drifted to the back of your mind only popping up when something reminded you of them and at first that was quite often.

And on some days, you wonder, what if? But then you shake your head free of the thought and carry on.

You meet new people, connect, but are more cautious now for you have come to realise that nothing will last forever.
A Fatal Domestic Disturbance
By: Donald Gardner Stacy
Tags: Adultery, Short Fiction, Domestic Disturbances, Short Short Story

A Fatal Domestic Disturbance

One evening, yes, it must have been a Friday night, my next door neighbors fell into a vicious quarrel whose ping-pong match of filthy imprecations flew out their open kitchen window, across the width of two side lawns, and through the open window of my upstairs study. At one point Mr Benson unleashed a host of particularly vitriolic and bald malicious slurs against his wife, Marjorie — whom I had once known on an intimate basis, just so we are clear about things. She used to tell me as we sat in bed together side by side in agreeable relaxation after a vigorous bought of sex that her husband was a cruel deceitful bastard who had cheated on her with almost monthly regularity, as though her own menstrual blood had driven him into some despairing departure from the house for the sake of illicit sex with any willing bimbo he might find in lethargic predatory pause in some dive.

Thrown into a rage by his crass transparent lies, his invective, his sneering aggressive presence looming toward her with an unmistakable look of murderous intent upon his face, she flung open one of the kitchen drawers and withdrew a chef’s meat-carving knife whose placement she knew precisely, withdrew this formidable weapon and with a single horizontal slash, cut the throat of her husband with an incision the pathologist later determined to be three and a half centimeters in depth through that portion of the neck where the aorta is located, just below the Adam’s apple.

After that, she let loose a truly blood-curdling scream, a distinctly wrathful howl reverberant with a note of vengeful victory. A few seconds later, during which time I was frozen in place with stupefaction, I called the emergency number and reported the incident.

Never before has this quiescent neighborhood seen such an armada of police cars. I watched from the window as a pair of cops, both of them women, lead Marjorie out of the house, her hands behind her back cinched up with a pair of cuffs. They took her to one of the cruisers and one of the cops opened the back door while the other cop made sure Marjorie got into the car without collapsing on the edge of the street. She acted dazed, as though she were in shock, and she probably was. She had just killed her husband in a fit of rage.
Flowers of Despair. One morning the sky was bleak, and I…
By: Rëy Fahira
Tags: Sadness, Short Fiction, Fantasy, Romance

One morning the sky was bleak, and I saw the rain coming down from the sky. Maybe I’m the only person who hates rain. The smell of grass seemed to be the backdrop of my memories that are now visualized in the rainfall. I frowned my forehead and put my arms around my knees until the oversize sweater I wore covered my ankles.

“Are you okay, Axelle?” said someone who was sitting on the wooden chair beside me.

The scent of flowers from the body that I love suddenly awakened my thoughts about this rain. “You look like an old lady.” He said it with a smirk that seemed bothersome, yet, somehow I loved it very much. The boy is now sitting next to me.

“Do you know what I hate the most, Darrell?” “You hate rain. I know.” He said with a heavy sigh.

A constant rain poured on the outside with thunderbolts as if I wanted to stop staring out. In this room, there was only me and Darrell who had been huddled around each other’s knees behind this window.

“But I’m happy to be with you this time, Darrell,” I said with a big grin, showed off my white teeth to the white boy beside me. “I thought I would not be able to see you again after you moved house.” “Chill out. I will be here whenever you need a company.” He replied cheerfully. “Besides, what can you do without me?” This time, he said cunningly. Well, maybe it’s true. I don’t have any friends to hang out with since I’m not the kind of friend that many people like; unlike him who has many friends. “By the way, do they know you’re here?” I asked.

“Who?” He answered.

“Your parents,” I said softly. “I don’t know.” He replied, “I may think that maybe they have forgotten me.” He said as he put back the porcelain doll he was holding.

Suddenly, my door was knocked on from the outside. A woman with a dazzling face entered without a signal to my room.

“Hey, do you want some cookies?” She said with a gentle smile. “Sure mom,” I replied with a soft smile, grinning at her. “Don’t forget about Darrell. He wants some, too.” I replied to my mother. The woman smiled delicately and closed the door. “Your mother always looks impeccable, and she’s as lovely as you, Axelle.” He mutters while playing baseball on my bed. “A great mother should be nice to her children,” I said to him. However, I noticed that his thin―cheeked face now seemed collapsed. “My mom doesn’t think much of me now because of what happened the other day.” He said with a mournful face.

I stepped out of the window chamber and dragged my treasured lilac blanket that had covered my body from the cold. Seeing Darrell’s face that looks wistful is so heart-wrenching.
Dirt Wart Soldiers
By: Reagan Kirby
Tags: Short Story, Short Fiction, Poem, Written Tales, Poetry

Photo by DenisProduction.com taken from ShutterStock

This is war down in the south

My mountain of mud is tall as the trees

My brother leads his two man militia

Uphill and on towards their victory

The rubber on our make believe horses

Chews up the pavement catching leaves

Riding far from teenage generals

I slam the breaks and skin my knees

Every scar and scab reminds me

Of the battles we fought in vain

And the memory of our daydream conflict

Rewrites our painful history page by page
My Dearest Augustine
By: Reagan Kirby
Tags: Short Fiction, Written Tales, Fiction, Writing, Poetry

My dearest Augustine, how anxious we were to leave the shore

Setting sail beyond the world in search of life we had never seen before

I was so blind for you but you kept your heart locked behind the cabin doors
A Little Plastic Bottle Filled With Gasoline
By: Reagan Kirby
Tags: Short Story, Short Fiction, Written Tales, Prose, Poetry

Photo by Wendelin Jacober from Pexels

He’s got a little plastic bottle filled with gasoline

On that wooden wobbly bedside table held up with a book of matches from last nights bar tab

It was 73 dollars

She hopped right on and boy I’ll tell ya’ those cognacs get expensive

She apathetically entertained arduous anecdotes of antiquated notions

He’s all seasick from after-hours love drunk potions

Clever as he may be, and believe me he was

She’s not going home with him

But he’s got his little plastic bottle filled one quarter shy of a silver dollar

And it’s always never too far out of his reach