“I think, therefore I am.” — René Descartes

Descartes reached for that line in 1637. He wanted one fact he couldn’t doubt away, and he found it in the act of thinking itself, not in any particular thought. Everything else might be illusion: the world, the body, other people. But the doubting itself proved something was there to do the doubting. Thought wasn’t just something a mind does. It was proof that a mind exists at all.

Three and a half centuries later, machines are doing something that looks dangerously like thought. They write articles. They output code, construct arguments, and synthesize research. Watching this happen, it’s easy to feel the ground shifting under our feet, like we are being quietly outpaced by something faster and more tireless than us.

But we aren’t being replaced. We are looking at an optimization problem and mistaking it for a mind.

The Math Disguised as Metaphor

To understand the illusion, you have to look at the 2017 Google research paper that triggered the current AI boom: “Attention Is All You Need.”

Before this paper, models processed text clumsily, one word at a time, losing the thread of long sentences. This architecture gave models “attention”—a mathematical mechanism for weighing an entire passage at once and calculating which words statistically mattered most to which others.

It is a landmark piece of engineering. But the name is a trap. We borrowed the language of human consciousness to label a mathematical operation, and now we are falling for our own vocabulary.

When you or I pay attention to something, the word carries an entire interior life inside it: focus, intent, care, the sense of someone behind the noticing. When a Large Language Model “attends” to a prompt, none of that is happening. It is running a staggering number of matrix multiplications, layering linear algebra to achieve exactly one goal: minimizing statistical error to predict the next token in a sequence.

It is not reading. It is calculating. And once you strip away the anthropomorphic vocabulary, the real question stops being how smart the machine is, and becomes what a mind does that a prediction engine never could.

The Mechanics of the Unprompted

The most defining characteristic of human cognition is that it cannot be turned off. We think because we are biologically forced to.

Strip away the sleek chat interface of an AI, and what remains is a function in a state of absolute null. It requires an input—a prompt. Take that away, and it does not sit in the dark quietly turning a problem over. There is no dark, and there is no sitting. It simply ceases to execute. It has no baseline state of existence.

A human mind cannot manage that kind of silence. Thoughts are generated by the friction of living in the physical world. Frustration arrives uninvited at a red light. A solution to a complex coding problem surfaces while you are trying to sleep. A child asks why the sky is blue without being prompted to wonder.

This spontaneous generation of thought is not a bug; it is the engine of human progress. The most ordinary thing a mind does is synthesize new ideas completely unprompted—something no machine has ever done, not once.

The Stakes of Memory

This reveals the other fundamental gap between an algorithm and a human: the concept of stakes. An algorithm does not experience the data it processes.

For a model, memory is a static matrix of weights. During training, every piece of text it consumes is flattened into numerical values based on frequency and proximity. It has no skin in the game.

Human memory, by contrast, plays favorites. It is aggressively biased by what matters to us. Think about how you process a conversation with a close friend versus a brief exchange with a stranger on the same day. To a machine, those are just two transcripts of equal weight. But your mind assigns them entirely different priority. You will carry your friend’s words with you for days, while the stranger is forgotten before the day is over. That uneven, emotional filtering is not a biological flaw. It is the exact mechanism that turns a series of events into a unique, unrepeatable perspective.

Crucially, that perspective is constantly shifting so If you ask a person the same question ten days apart, you might get two different answers. Not because their memory is faulty, but because in those ten days, they lived. They acquired new context, felt new pressures, and evolved.

If you ask a model the same question ten days apart, nothing has moved. It hasn’t lived through anything in between. It only runs the same numbers through the same frozen weights to arrive, with total mathematical confidence, at the same static answer.

The Origin of Intent

AI will inevitably speed up almost any workflow you can name. But it cannot replace the only part of the work that actually matters: the intent behind it.

Writing a script, drafting a legal brief, painting a canvas, or building a system—all of these tasks have a phase of pure execution. But before the execution happens, there is the noticing. Someone has to decide that a specific problem is worth solving. Someone has to feel that a specific story needs to be told. That desire comes from a specific history, a frustration, or a need that nobody handed to you in a prompt window.

A model can write the function or generate the image. What it cannot do is care whether it exists. It has studied human output to learn the probability of what comes next, which is entirely different from having a reason to speak in the first place.

Thought Is All You Need

None of this means AI is a passing fad. It is an extraordinary tool that will eliminate the friction between having an idea and bringing it into reality. It will handle the heavy lifting, the boilerplate, and the repetitive grinding that used to eat the hours of our days.

But execution is not conception. What a machine cannot do is ask the first question. It cannot look at the world, feel that something is missing, and decide that it matters enough to change.

When Descartes was looking for proof of his own existence, he didn’t find it in his ability to process information or recall facts. He found it in the undeniable, spontaneous act of thinking itself. The machines currently drafting our emails and compiling our code have inherited none of that. They have only ever had to prove they can convincingly predict the end of a sentence.

That has always been enough to be incredibly useful. It has never been enough to be a mind.

“Don’t get drawn into the culture, Morty. Stealing stuff is about the stuff, not the stealing.”

Stories matter.

They tell us who we are, explain where we came from, and show us where we belong. Without stories, nations are just lines drawn on maps and governments collecting taxes.

Javed Akhtar once compared cultural identity to an onion, and it makes a lot of sense. You keep peeling back the layers (language, religion, geography, shared memories, and shared tragedies) expecting to find something solid at the center. But eventually, you hit an uncomfortable realization. There is no hidden core. The layers themselves are the identity.

In many ways, Pakistan spent its early existence peeling that onion. The country was built on the idea that the Muslims of the subcontinent were fundamentally separate from the rest. To create that brand-new identity, the state had to distance itself from older histories and older identities.

The problem is that drawing borders is much easier than creating an identity from scratch.

Geography is stubborn. Harappa did not move. Mohenjo-Daro did not move. The Indus River did not suddenly change its direction in 1947. The history of the land stayed exactly where it always had.

That is why Pakistan’s modern fascination with the Indus Valley Civilization is so interesting. Because this isn’t really about archaeology. Nobody suddenly develops an emotional attachment to ancient drainage systems and baked bricks.

This is about stories.

Ancient civilizations offer something every new nation craves: an anchor. Tying yourself to a 5,000-year-old past changes the narrative. It takes a recently drawn border and makes it feel like an ancient, inevitable destiny.

But history is a package deal. You cannot spend decades actively cutting ties with a shared past to prove you are different, only to realize later that a tree without roots cannot stand. You don’t get to treat a civilization like a museum—curating the exhibits that suit your current politics while keeping the rest hidden in the basement.

That is why the argument over who “owns” Harappa misses the point entirely. It was never about ruins or artifacts.

It is about a country still trying to figure out what story it wants to tell.

Rohit is building a phone app where users can search for a name and get the corresponding phone number. He now faces an important decision: should he use a simple (linear) search or a binary search?

If he implements a simple search, it will be less complicated — easier to write, less error-prone, and quick to implement. On the other hand, a binary search will be much faster but slightly more complex to code. To make the right choice, Rohit decides to investigate how much faster binary search really is.

A Quick Refresher: What Is Binary Search?

Binary search is a searching algorithm used to find an element in a sorted array. Instead of checking every element one by one (like linear search), binary search repeatedly cuts the search space in half. This makes it much faster for large datasets.

Here’s a simple Python implementation:

sorted_arr = [10, 20, 30, 40, 50]
search = 40

low = 0
high = len(sorted_arr) - 1
found = False

while low <= high:
    mid = (low + high) // 2

    if sorted_arr[mid] > search:
        high = mid - 1
    elif sorted_arr[mid] < search:
        low = mid + 1
    else:
        print(mid)
        found = True
        break

if not found:
    print(None)

In this example, binary search will find 40 in just 2 iterations. A simple linear search, however, would need 4 iterations. Clearly, binary search is faster.

But how much faster? And does this difference grow as the data grows? Let’s investigate.

Measuring Performance: A Naive Approach

Rohit decides to measure the time difference between the two approaches. Let’s assume each iteration takes 10 ms.

sorted_arr = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

If we search for 100:

• Linear Search: 10 iterations → 10 × 10 ms = 100 ms

• Binary Search: 4 iterations → 4 × 10 ms = 40 ms

It seems binary search is 2.5 times faster. But is that conclusion reliable?

Scaling Up: Where the Real Difference Appears

Rohit increases the size of the array and records how many iterations each algorithm needs to find the last element:

Now the difference is dramatic.

• For 10,000,000 elements, linear search takes 10,000,000 × 10 ms = 100,000,000 ms (over 27 hours).

• Binary search takes 24 × 10 ms = 240 ms (less than a quarter of a second).

If Rohit had trusted his initial estimate of “2.5× faster,” he would have been completely wrong. The speed difference grows massively with larger input sizes.

Why Time Isn’t Enough: Enter Big O Notation

This brings us to an important lesson:
👉 We cannot measure algorithm speed by actual time alone — it depends on the size of the input.

We need a way to describe how an algorithm’s runtime grows as the input grows. That’s where Big O notation comes in.

Big O expresses the runtime of an algorithm in terms of how it scales with input size (n) — not in milliseconds or seconds, but in how many steps it takes as n increases.

For example:

• Linear Search: O(n) — the number of steps grows directly with n.

• Binary Search: O(log n) — the number of steps grows with the logarithm of n.

If Rohit had known this, he wouldn’t have needed to write a timing program to figure out which algorithm was faster — he could have predicted it just by looking at their time complexities.

A Quick Primer on Logarithms

If you’re unfamiliar with logarithms, here’s a simple explanation:
The logarithm answers the question:

“How many times must I divide a number by a base to get down to 1?”

In computer science, we usually use base 2, because many algorithms (like binary search) reduce the problem size by half each step.

Mathematically:

• log₂(n) = k means 2^k = n.

For example:

• log₂(100) ≈ 6.64 (~7) because 2⁷ = 128.

This explains why binary search takes ~7 steps in the worst case for 100 elements.

So when we say an algorithm is O(log n), it means the number of steps grows much more slowly than the input size itself.

Common Big O Notations (Fastest → Slowest)

Summary

• Measuring runtime in milliseconds is misleading — it depends on hardware, language, and implementation.

• Big O notation gives a language-independent way to talk about performance by showing how runtime grows with input size.

• Linear search is O(n) — slow for large inputs.

• Binary search is O(log n) — much faster because it reduces the problem size exponentially with each step.

• Understanding Big O helps you make better algorithmic choices without writing a single benchmark.

With this knowledge, Rohit can confidently choose binary search for his phone app — and you can make smarter choices in your own code too.

“What unites people? Armies? Gold? Flags?”
“Stories,” he answers. “There’s nothing in the world more powerful than a good story.”

That line stuck with me. Because when you think about it, every nation is built on stories—of struggle, of pride, of faith, of identity. And there’s no better place to hear a country’s story than in its national anthem.

So when I came across a dataset on Kaggle that included the lyrics of national anthems from around the world—all translated into English for ease of understanding—I immediately knew what I wanted to do. A few years ago, I watched a video by India In Pixels where he clustered countries by their anthems using TF-IDF. At the time, I found it fascinating—but I also knew we could do better.

Instead of relying on TF-IDF, which treats text more like math than meaning, I turned to a more powerful model: paraphrase-multilingual-MiniLM-L12-v2. While the dataset was in English, this model is still great at understanding deeper semantic meanings within texts—which is exactly what I wanted when working with something as poetic and nuanced as anthem lyrics.

I also decided to use just three clusters—not because it’s “scientific,” but because it intuitively felt right. One cluster for the Western power center led by the U.S., one for China and its sphere, and a third for emerging nations like India, Brazil, and Vietnam. Again, there’s no perfect number of clusters; I simply picked a number that made sense to me.

With this, I set out on a journey—not just to group anthems by linguistic similarity, but to uncover the shared values, hopes, and identities that unite nations through song. In a way, I was looking for the kinds of stories Tyrion spoke about—the ones powerful enough to define a people.

The Approach

SentenceTransformers model paraphrase-multilingual-MiniLM-L12-v2 to encode the national anthems. Even though the lyrics were all in English, this model is still excellent for capturing semantic meaning at the sentence and paragraph level.

Once I had the embeddings, I applied K-Means clustering with k=3. The algorithm then grouped countries based on the thematic content of their anthem lyrics. What emerged was fascinating—three distinct clusters, each with a unique underlying theme.

Let’s start with the big picture before we delve into the specifics of each group. The map below illustrates how countries are clustered based on their national anthems.

Group 1: Religious Devotion

This group immediately stood out for its strong spiritual and religious undertones. Countries in this cluster include:

• Saudi Arabia

• Pakistan

• Turkey

• Lebanon

• Albania

• United States (surprisingly!)

Common themes:

• References to God, faith, and blessings

• Sacredness of the land

• Divine protection and guidance

Example – Pakistan:

“Blessed be the sacred land, Happy be the bounteous realm, Symbol of high resolve, Land of Pakistan, Blessed be thou citadel of faith”

Example – Turkey:

“Fear not! For the crimson banner that proudly ripples in this glorious twilight, shall never fade…”

Even the U.S. anthem aligns thematically:

“God shed His grace on thee…”

The clustering picked up on vocabulary and sentiment—words like “sacred,” “faith,” “divine,” which resonated across the dataset.

Group 2: Natural Heritage

This cluster was dominated by African nations, island countries, and Southeast Asian states:

• Ghana

• Kenya

• Nigeria

• Fiji

• Indonesia

• Papua New Guinea

Common themes:

• Natural beauty and land

• Ancestral pride

• Unity and tradition

Example – Ghana:

“Hail to thy name, O Ghana, To thee we make our solemn vow…”

Example – Kenya:

“O God of all creation, Bless this our land and nation, Justice be our shield and defender.”

These countries often emphasized their heritage, unity among diverse groups, and connection to land and nature.

Group 3: Democratic Ideals

This was the most ideologically diverse group, yet thematically coherent. It included:

• France

• Germany

• India

• Brazil

• Japan

• Canada

Common themes:

• Freedom and independence

• National unity

• Democratic and humanistic values

Example – France:

“Arise, children of the fatherland, The day of glory has arrived!”

Example – Brazil:

“They heard, on placid shores of the Ipiranga river, the resounding cry of a heroic people”

Example – India:

“Thou art the ruler of the minds of all people, Dispenser of India’s destiny”

These anthems emphasize progress, liberty, and the right to self-determination—reflecting both historical struggles and modern aspirations.

Final Thoughts

This clustering revealed something profound: anthems, at their core, are reflections of national identity. They echo a nation’s story—its values, struggles, and dreams.

• Religious Devotion group nations see themselves through faith and divine purpose.

• Natural Heritage group celebrates land, community, and tradition.

• Democratic Ideals group envisions freedom, unity, and progress.

Some results were surprising—the U.S. clustering with Islamic nations due to its religious tones, or India sharing a cluster with European democracies. But that’s what makes this exploration so compelling. Nations that seem different on the surface often share deep, poetic similarities.

One more interesting thing: While cleaning data, I found that Cyprus and Greece actually share the same national anthem!

In the end, I didn’t just learn about data or clustering. I discovered that when countries sing about themselves, they tell stories—stories that transcend borders, stories that unite people.

And as Tyrion said, there’s nothing more powerful than a good story.

notebookdataset links.

Ask someone about engines and pretty soon it’s all “valve timing” this and “piston stroke” that — and you’re standing there nodding like you get it.

But here’s the truth: a motorcycle engine is basically just a loud, slightly unhinged air pump.

Let’s keep it simple. No textbooks, no lectures.

The Four-Step Dance Every Engine Does

Every single time you twist the throttle, your engine is doing the same four moves, over and over:

1. Suck — it pulls in air and fuel like a kid inhaling a milkshake.

2. Squish — it squeezes the mix nice and tight.

3. Boom — lights it up like a tiny, angry birthday party.

4. Blow — shoves the burnt leftovers out the back.

That’s the four-stroke cycle. If your engine ever forgets a step, congratulations: you’re walking home.

One Cylinder? Four? Fifteen? (Okay, not fifteen.)

• Single-cylinder engines (a.k.a. thumpers) are tough, simple, and hit like a sledgehammer at low speeds.

• Multi-cylinder engines (twins, triples, fours) are smooth, fast, and make noise that can wake the dead (in a good way).

I’m a fan of singles — I like simple things. One cylinder, one exhaust, less stuff to worry about. Just hop on and ride.

Multis are awesome too, especially if you want your bike to scream “I AM SPEED” at 14,000 RPM.

Torque vs. Horsepower: Explained Like You’re Five

• Torque = how hard your bike pushes.

• Horsepower = how fast it keeps pushing.

Torque gets you off the line like a cat shot out of a cannon. Horsepower keeps you flying until your eyeballs vibrate.

Different bikes balance them depending on what they’re built for. Adventure bikes? Big torque. Sportbikes? Big horsepower. Your neighbor’s sketchy 50cc scooter? Neither.

Keeping Engines Cool (Because Fire Is Hot, Duh)

Engines get hotter than a black leather jacket in July. So they have two main ways of chilling out:

• Air-cooled: the engine just sticks its head in the wind and hopes for the best.

• Liquid-cooled: the engine carries around a little water bottle and a radiator to cool down properly.

If you’re stuck in city traffic, liquid-cooled is your best friend. Trust me.

Other Stuff That Makes the Engine Not Explode

• Valves open and close to let air in and exhaust out. Think nightclub bouncers, but for explosions.

• Camshafts tell valves when to open. (They’re basically very bossy gears.)

• Transmissions make sure you’re in the right gear so you don’t accidentally turn your engine into scrap metal.

You don’t have to memorize all this. Just know: there’s a lot of little pieces doing a very fast, very angry dance inside.

The Big Picture

At the end of the day, motorcycle engines are pure, beautiful chaos. They suck in air, set it on fire, and use the explosions to fling you down the road with a giant grin on your face.

They’re tough. They’re messy. They sound like thunder. And they’re one of the most fun ways humans have figured out how to break the laws of common sense — with style.

Credit

FortNine’s The Pros and Cons of Every Motorcycle Engine Type” for inspiring this post.
If you want even more jokes, explosions, and smart stuff about engines, go watch the original video. Seriously — it’s awesome.

“To be a scientist is to be naïve. We are so focused on our search for truth, we fail to consider how few actually want us to find it… The truth doesn’t care about our needs or wants… It will lie in wait for all time… and this at last is the gift of Chernobyl: where I once feared the cost of truth, now I only ask—what is the cost of lies?”
— HBO’s Chernobyl

This haunting quote from the series Chernobyl resonates far beyond the realm of science. It speaks to our collective relationship with truth, and how often we are willing to trade it for comfort, convenience, or illusion. Nowhere is this more evident than in how nations, including our own, grapple with the reality of conflict, diplomacy, and national memory.

For decades, successive governments in India have pursued peace with Pakistan. From Track-II diplomacy to Aman ki Asha, our intentions have often been noble. We’ve extended the olive branch time and again, hoping that good faith would be met with good faith. But the results have been heartbreakingly consistent—betrayals, terror attacks, and continued hostility.

And yet, we persist. We continue to believe, or at least pretend to believe, that peace is just one more handshake away. That if we try just a little harder, or speak a little softer, we’ll get through. But what if that belief is not a sign of wisdom, but of willful delusion?

The truth, however uncomfortable, is that our neighbour does not want peace—not the kind we envision, built on mutual respect and coexistence. And every time we lie to ourselves about this reality, we incur a debt. A debt that is not paid in speeches or resolutions, but in the blood of innocent people.

This is not just about foreign policy—it’s about our collective memory and values. We are a people who too easily forget. We forget our villains and our heroes. We whitewash history and celebrate tyrants like Aurangzeb, forgetting the cost they exacted from our ancestors. In doing so, we blur the line between reconciliation and erasure.

The truth is not comfortable. It is not easy. But it is necessary.

We must stop lying to ourselves. We must confront reality as it is, not as we wish it to be. Because truth does not demand belief—it simply is. And the longer we delay facing it, the higher the price we’ll pay.

Let us honor truth—not just in words, but in policy, in remembrance, and in action. Because only then can we truly understand the cost of lies… and perhaps begin to stop paying it.

Richard Feynman is one of my favourite people from history. His curious mind, playful spirit, and depth of feeling have always inspired me.

One thing about him that’s always stayed with me — not because it’s deeper than his physics, but because it shows a different side of him — is a letter he wrote to his wife, Arline, after she passed away. It’s raw, quiet, and unexpectedly powerful.

There’s a part of it that captures exactly how I understand love — especially this:

When you were sick, you worried because you could not give me something you wanted to and thought I needed. You needn’t have worried. Just as I told you then – there was no real need because I loved you in so many ways so much. And now it is clearly even more true – you can give me nothing now yet I love you so that you stand in my way of loving anyone else – but I want you to stand there.

For me, this is what love is. Although, to be honest, I hate the word “love” — it’s used way too much, and way too lightly. But here, in this letter, it’s real. It’s undeniable. It’s not about grand gestures or poetic clichés. It’s just presence, loyalty, and the simple act of still feeling someone even after they’re gone.

October 17, 1946

D’Arline,

I adore you, sweetheart.

I know how much you like to hear that – but I don’t only write it because you like it – I write it because it makes me warm all over inside to write it to you.

It is such a terribly long time since I last wrote to you – almost two years but I know you’ll excuse me because you understand how I am, stubborn and realistic; and I thought there was no sense to writing.

But now I know, my darling wife, that it is right to do what I have delayed in doing, and that I have done so much in the past. I want to tell you I love you. I want to love you. I always will love you.

I find it hard to understand in my mind what it means to love you after you are dead – but I still want to comfort and take care of you – and I want you to love me and care for me. I want to have problems to discuss with you – I want to do little projects with you.

I never thought until just now that we can do that. What should we do? We started to learn to make clothes together – or learn Chinese – or getting a movie projector.

Can’t I do something now? No. I am alone without you and you were the “idea-woman” and general instigator of all our wild adventures.

When you were sick, you worried because you could not give me something you wanted to and thought I needed. You needn’t have worried. Just as I told you then – there was no real need because I loved you in so many ways so much. And now it is clearly even more true – you can give me nothing now yet I love you so that you stand in my way of loving anyone else – but I want you to stand there.

You, dead, are so much better than anyone else alive.

I know you will assure me that I am foolish and that you want me to have full happiness and don’t want to be in my way. I’ll bet you are surprised that I don’t even have a girlfriend (except you, sweetheart) after two years.

But you can’t help it, darling, nor can I – I don’t understand it, for I have met many girls and very nice ones and I don’t want to remain alone – but in two or three meetings they all seem ashes.

You only are left to me. You are real.

My darling wife, I do adore you.

I love my wife. My wife is dead.

Rich.

It’s not often that a physicist who helped build the atomic bomb gives us such a vulnerable piece of his heart. But Feynman, in all his brilliance, reminds us that even the most rational minds can ache with an irrational, beautiful kind of love.

Introduction

As Python developers, we’re always searching for ways to write more efficient, clean, and maintainable code. Enter decorators — a powerful Python feature that allows you to modify or enhance functions without changing their core implementation. In this article, we’ll dive deep into decorators through a real-world performance monitoring example.

What Are Decorators?

At its core, a decorator is a function that takes another function as an input and extends or modifies its behaviour. Think of it like a wrapper that can add functionality to existing code without modifying the original function.

Building a Performance Timer Decorator

Let’s create a practical decorator that measures the execution time of functions — a critical tool for performance optimisation.

import time
import functools
import random

def timer_decorator(func):
    """
    A decorator that measures and logs the execution time of a function.
    """
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        # Record the start time
        start_time = time.time()

        # Execute the original function
        result = func(*args, **kwargs)

        # Calculate and log execution time
        end_time = time.time()
        print(f"Function '{func.__name__}' took {end_time - start_time:.4f} seconds")

        return result
    return wrapper

# Real-World Application Examples
@timer_decorator
def slow_function():
    """
    Simulate a time-consuming task by using sleep
    """
    time.sleep(2)
    print("Slow function completed")

@timer_decorator
def process_large_dataset(size=100000):
    """
    Simulate processing a large dataset with sorting and filtering
    """
    # Generate a large list of random numbers
    data = [random.randint(1, 1000000) for _ in range(size)]

    # Perform some expensive operations
    sorted_data = sorted(data)
    filtered_data = [x for x in sorted_data if x % 2 == 0]

    return filtered_data

def main():
    # Demonstrate the timer decorator
    print("Timing a slow function:")
    slow_function()

    print("\nTiming a data processing function:")
    result = process_large_dataset()
    print(f"Processed {len(result)} even numbers")

if __name__ == "__main__":
    main()

How the Decorator Works

1. The timer_decorator wraps the original function

2. It records the start time before executing the function

3. Runs the original function

4. Calculates and prints the execution time

5. Returns the original function’s result

Best Practices

1. Always use **@functools.wraps** to preserve function metadata

2. Handle potential exceptions in your decorators

3. Keep decorators focused on a single responsibility

4. Use type hints for better readability and type checking

Conclusion

Decorators are a powerful Python feature that allows you to add functionality to functions seamlessly. By understanding and applying decorators, you can write more modular, maintainable, and insightful code.

Happy Coding! 🐍✨

“The known is finite, the unknown infinite; intellectually we stand on an islet in the midst of an illimitable ocean of inexplicability. Our business in every generation is to reclaim a little more land.” — T. H. Huxley, 1887

“The cosmos is all that is or ever was or ever will be.” These words, spoken by the late astronomer Carl Sagan, remind us of the vastness of the universe and our place in it. The size and age of the cosmos are beyond human understanding, yet we have the courage to explore it. Earth is merely the shore of this cosmic ocean, a tiny speck in the vast expanse of the universe. From our humble position, we have learned a small fraction of what there is to know about the cosmos. In this cosmic ocean, we have barely even wet our ankles.

As we look up at the night sky, we are reminded of the infinite mysteries that lie beyond our planet. The stars twinkle like tiny gems in the darkness, and the moon casts a gentle glow over the world. Yet, even the moon is a mere satellite, orbiting a much larger planet in a vast, unending universe.

The cosmos is incomprehensibly vast, and it is difficult to describe its scale using Earth’s units of measurement. The scale of the universe is so immense that we have to resort to other methods of measurement to comprehend its vastness. One such method is measuring distances using the speed of light.

In one second, a beam of light travels almost 300,000 kilometers, which is an incredibly vast distance. In a year, light can cover a distance of 10 trillion kilometers. This distance, the distance covered by light in one year, is known as a light-year.

This unit of measurement is crucial when we consider the size of the universe. For example, the nearest star to our solar system, Proxima Centauri, is about 4.24 light-years away. This means that the light we see from this star today began its journey towards us over four years ago. It is mind-boggling to think that we are looking back in time when we look up at the stars.

When we gaze up at the cosmos, we realize that the Earth occupies a small place in the universe. However, it’s unique in its own right. It’s the emptiness of the universe that makes planets, stars, and galaxies rare, precious, and lovely. Their beauty is only magnified by their scarcity and the fact that they are not the norm. It’s our home, and it’s where we’ve learned to explore and understand the cosmos.

We have learned that there are millions of star systems, each one like an island in space, quarantined from its neighbours by light-years. There must be living creatures glimmering with knowledge on innumerable worlds, each one assuming that their planet and few suns are all that exist. Only slowly do we gain an understanding of the cosmos and its vastness.

Humanity’s comprehension of the cosmos is a relatively recent achievement. In the not-so-distant past, we held the belief that the Earth was flat and the center of the universe. However, our scientific understanding has since evolved dramatically. We have come to realize that the universe is much older than we once imagined. Thanks to advancements in technology, we can now journey beyond our planet, exploring the depths of space and other celestial bodies. As we depart from Earth, we are granted the unique perspective of observing our home as a spherical entity.

Furthermore, our explorations have led us to discover other galaxies, unveiling the fact that we inhabit a mere speck within the vastness of the cosmos, orbiting an unremarkable star in a distant corner of an obscure galaxy. In acknowledging our insignificance in the grand scheme of the universe, we also recognise our fleeting existence within the expanse of time.

Contemporary knowledge tells us that our universe has existed for approximately fifteen to twenty billion years, originating from an extraordinary event known as the Big Bang. The transition from the tumultuous chaos of the Big Bang to the orderly cosmos we are now beginning to comprehend an awe-inspiring transformation of matter and energy. Until the day we encounter more intelligent life elsewhere in the universe, we stand as the most remarkable result of this transformation — the descendants of the Big Bang, driven by an insatiable curiosity to fathom the depths of the cosmos from which we have emerged.

This article is inspired by the book Cosmos written by Carl Sagan.