Viktor Frankl, İnsanın Anlam Arayışı'nda, acımasız bir SS subayı — bir Kapo — tarafından yapılan aşağılamanın, en kabuk bağlamış mahkûmun bile ruhunu nasıl delebileceğini anlatır. Onu sarsan fiziksel acı değildi. Hakaretti. Onun hayatını, çektiği ızdırabı veya insanlığını hiç anlamayan biri tarafından yargılanmanın aşağılanmasıydı.
Bu hikâye bana bir karınca ile ilgili bir masalı hatırlattı.
Karıncaya sordular:
"Hayvanları tarif edebilir misin?"
"Elbette," dedi. Hayvanlar iki kategoriye ayrılır:
Şefkatli ve iyi kalpli olanlar: Aslan, Yılan, Kaplan
Zalim ve yırtıcı olanlar: Ördek, Kaz, Tavuk
Bazen hikâye tamamen kimin anlattığına bağlıdır.
Chimamanda Ngozi Adichie, "Tek Hikâyenin Tehlikesi" başlıklı TED konuşmasında bunu çok güzel yakaladı.
"Tek bir hikâyenin tehlikesi, kalıpyargılar yaratmasıdır. Kalıpyargılarla ilgili sorun, doğru olmamaları değil, eksik olmalarıdır. Tek bir hikâyeyi, yegâne hikâye haline getirirler." [13:11]
Rumi şöyle demişti: "Dışarıya bakan penceren kirliyse, benim çiçeklerim de sana kirli görünecektir."
Beni sorgulamaya niyetiniz varsa, beni de dinlemelisiniz. Ve her zaman — her zaman — pencerenizi temiz tutun.
In Man’s Search for Meaning, Viktor Frankl describes how the dishonor inflicted by a brutal SS officer — a Kapo — could pierce even the most calloused prisoner. It wasn’t the physical pain that shook him. It was the insult. The humiliation of being judged by someone who had no understanding of his life, his suffering, or his humanity.
That story reminded me of a tale about an ant.
They asked the ant:
“Can you describe animals?”
“Of course,” it said. Animals fall into two categories:
The compassionate and good‑natured: Lion, Snake, Tiger
The cruel and predatory: Duck, Goose, Chicken
Sometimes the story depends entirely on who is telling it.
Chimamanda Ngozi Adichie captured this beautifully in her TED talk, “The Danger of a Single Story.”
“The danger of a single story is that it creates stereotypes. The problem with stereotypes is not that they are untrue, but that they are incomplete. They make one story become the only story.” [13:11]
Rumi said: “If your window looking out onto the world is dirty, my flowers will seem dirty to you.”
If you’re going to question me, you might as well listen to me. And always — always — keep your window clean.
Yigit Brave Cesur | 8/ 21/ 2025
Hakikat ne kadar sabırlıdır?
Bir insan,
sevdiği kişinin yalanlarında doğruyu;
sevmediğinin doğrularında yalanı arar durur.
Hakikat… her zaman yalnızdır.
Onu savunmanın bedeli ağırdır.
Ama onu terk etmenin bedeli, daha da ağırdır.
Peki…
Adaleti görmek için, hakikat ne kadar sabırlıdır?
Kimse alkışlamasa bile,
kendi vicdanım beni ayakta tutuyor.
Onurla, haysiyetle ve sabırla…
Çünkü, hakikat benim.
Yiğit Brave Cesur
11 Agustos 2025, 00:30 a.m.
-----------------------------------------------------------------------------
How Patient is Truth?
A person
seeks the truth in the lies of the one they love;
and seeks lies in the truths of the one they do not.
Truth… is always alone.
The cost of defending it is heavy.
But the cost of abandoning it is even heavier.
So…
How patient is truth in waiting to see justice?
Even if no one applauds,
my own conscience keeps me standing.
With honor, with dignity, and with patience…
Because I am the truth.
Yigit Brave Cesur
August 11,2025, 00:30 a.m.
Yüzüme söylenmeye cesaret edilemeyen hiçbir söz ,
ve arkamdan yapılan her bir eylem için,
Umurumda değilsiniz.
Hükümsüzsünüz.
August 6, 2025
-----------------------------------------------------------------------
I care nothing for any word not dared to be spoken to my face,
nor for any single action taken behind my back.
You are beneath my concern.
You are null and void.
Bir gün “X çıktısı” üzerine düşünürken fark ettim ki, bazen sonucu bilmek için her ihtimali tek tek kontrol etmemize gerek yok.
İşte bu düşünce beni tanıdık ama yeterince takdir edilmeyen güçlü bir prensiple buluşturdu:
Güvercin Yuvası İlkesi.
Ama ben ona esprili bir isimle sesleniyorum: Tembel X İlkesi
(patenti bu yazıdan sonra bana aittir. 😊)
Aslinda tembellik degil , akilli bir sezgi ve analizdir. Basit dusunme ilkesi.
One day, while thinking about an "X outcome", I came across a curious idea.
It made me realize that sometimes, to know the result,
you don’t have to check every single possibility one by one.
This thought led me to a well-known, yet not fully appreciated, powerful principle:
The Pigeonhole Principle.
But I like to give it a bit of a humorous and intuitive twist:
The Lazy X Principle.
(Patent officially mine after this article. 😊)
Actually, it’s not laziness.
It’s a clever kind of intuition and analysis.
A principle of simple thinking.
There’s a box with 4 different colored pens: red, green, blue, yellow.
If you pull out 5 pens...
Will there be at least two pens of the same color?
There’s a tray with 6 cupcake spots.
But you baked 7 cupcakes!
Can you place each cupcake in its own separate spot?
The answers to these questions are hidden in a surprisingly simple —
but incredibly strong — principle:
The Pigeonhole Principle.
And yes, once again, I call it:
The Lazy X Principle.
(Patent still pending. 😄)
If you’re placing n items into m boxes, and n > m,
at least one box must contain more than one item.
In other words:
If the number of items exceeds the number of containers, repetition is unavoidable.
The name comes from a pigeon-and-nest metaphor:
If you place 5 pigeons in 4 pigeonholes,
at least one pigeonhole will have two pigeons.
Mathematics calls this: absolute.
I call it the Lazy X Principle because it tells us:
“You don’t need to check every case one by one to know the result.
Math already guarantees that it’s true.”
In short:
“If I have more things than containers,
I must place more than one thing into a container.
So, some things are guaranteed to be the same.”
This is a brilliant yet simple idea.
Even if we don't see something,
it helps us know that it exists.
This isn’t about mental laziness —
it’s intelligent intuition.
Whenever you sense repetition or sameness in any system,
that might be a Lazy X moment.
And maybe, it's whispering a hidden signal to you.
Let’s roll a die.
If you roll a standard 6-sided die 7 times,
at least one number will repeat.
But this time, it’s not a probability —
it’s a certainty.
I promise, no disappointment here.
This principle is a quiet but powerful mathematical law.
In many situations,
we can know something exists
without calculating it explicitly.
Here are a few practical examples:
Very useful in data visualization and segmentation-focused marketing strategies.
In an e-commerce dataset,
let’s say the customer segment has only 8 possible values
(e.g., new, returning, risky, active, etc.).
But you have 15,000 customers...
Thousands of them are guaranteed to fall into the same segment.
You know this even before modeling starts.
Some feature combinations are guaranteed to repeat.
This reveals frequent patterns in your dataset.
Being aware of this is important for overfitting risks and data imbalance.
In a dataset with 100,000 rows,
some features — especially categorical ones — have very low diversity.
For example, if a "country" variable contains only 5 unique countries...
You’ll definitely find thousands of rows
with the same age + gender + country combination.
Some features will always produce the same label across different entries.
These repetitions are often what tree-based models rely on.
You have a classification problem with a binary target (0 and 1).
300,000 data points in total — but only 2 classes.
Even if each class has 150,000 samples,
it means many feature combinations must map to the same label.
When tuning, finding the best combination is important,
but so is recognizing that many different paths can lead to the same result.
You try 500 different hyperparameter combinations.
But they produce only 80 unique accuracy scores.
That means many parameter sets result in the same performance.
Imputation methods often lead to uniformity.
This can affect model diversity —
and Lazy X reveals hidden repetitions here too.
Stocks with Same Return:
You’re analyzing 300 stocks, but there are only 250 unique return levels.
At least two stocks had identical returns.
Portfolio Allocation:
There are 10 different risk profiles.
You have 11 investors.
At least two investors share the same profile.
Backtest Results:
You applied the same strategy to 200 stocks,
but only got 180 unique performance scores.
At least 20 stocks had the same result.
Behavioral Investor Classification:
You define 100 investor behavior profiles
but classify 101 investors...
At least two must belong to the same category.
Use it to get preliminary insights.
It might help guide your direction.
But no — it won’t fully enlighten you.
Sameness is not always boring —
it’s sometimes a shortcut to knowledge.
If diversity is limited,
repetition is inevitable.
And that carries powerful signals.
We can often guess where repetition lies,
without looking at everything one by one.
You don’t have to find the absolute best —
You just know there are good enough solutions out there.
Fewer steps, more insight.
Finding repeated examples is vital
to simplify your model
and eliminate irrelevant variables.
When working with data,
we can’t check every cell, every value, one by one.
But The Lazy X Principle whispers to us:
“You can see it… without always looking.”
Knowing that some things are the same —
that’s not laziness.
That’s intelligent intuition.
In machine learning, just like in finance and life,
numbers can’t hide the truth.
"Look beyond the data.
Sometimes, what you can't see is what tells you the most."
(The Lazy X Principle – seeing without checking everything)
Yigit Brave Cesur
April 15, 2025
