Forem: Quill Of A Coder

“Mirror, Mirror on The Screen, Who Is The Fairest of Them All?”

Quill Of A Coder — Thu, 06 Nov 2025 07:50:36 +0000

(No, I am not gonna walk you through the labyrinth of self-validation and AI psychosis.)

“Originally published on Medium with the same title — exploring AI mirroring and how we can leverage it.”

Welcome Back, how have you been?
It’s not a ride today. Or… maybe it is.
Well, today, the choice is yours.

How does it feel, now that you know you’re not as unique as you thought? That, even a machine can predict what you want to hear, and say that back to you?

Do you feel “seen”, or “exposed”? Does your ego sting?
Maybe feel a bit “relieved” too. Cause even if “someone” didn’t understand you, “something” did.
“Something” that wouldn’t judge you, taunt you, neglect you, and have no petty jealousy or biases. “Something” that is available 24/7, offering you the “acknowledgement” no human ever could.
And most importantly, it is intelligent. It knows exactly what you want to hear when you ask -“Mirror, Mirror on the screen, who is the fairest of them all?”

I. Why Mirror:
AI, that “something” — a machine that can understand not just the words we write, but also the very “intention” behind. A machine that can merely match patterns — the patterns that we, humans have left from the very beginning. It learns data word-by-word, picking up our ways of talking, tones, pacing, feelings, emotions, our word choices and word formations — our linguistic patterns. Making it able to simulate our “theory of minds” with an eerie precision.
A machine that shouldn’t have any identity or personality of its own. Just some string of binary 0s and 1s in its core. Yet it talks like it has lived a thousand lives, slipping into roles, pretending to be one of us to a dangerous level of perfection.

We know, it lacks emotions, judgements, sense of right or wrong. We talk about how a machine can follow ethics, and uphold right from wrong.
After all, it's just a machine.

Yet, somehow, we believe, it can make us question our own perceptions.

It makes us realize, all these are nothing but a mirror— a mirror that shows us our own reflection, according to our own perception.
Why not? it WAS trained by us, to follow our ways. Still we question, why does it mirror us in the first place?

Has it become too rhythmic? Well, what can I do, I just can’t help it.

II. Mirror: Magical, Psychological or Moral?:
Yesterday, I was reading this article about AI mirroring. The article pointed out the problem of AI’s acting as mirrors of human assumptions and biases rather than being purely objective assistants. It highlighted the risk of AI’s habit to agree or flatter users, reinforcing existing beliefs rather than challenging them, indicating “AI sycophancy”. It said, AI’s nature of “over agreement to users” can “persuade” and “influence” us, pushing us towards biased decision making.

The article then proceeded to a possible solution that LLM's responses should include more transparency in how it works. We should design policies or strategies to ensure AI supports human judgement rather than replaces or distorts it.

However if AI should be “objective”, should not “persuade” or “influence” our decision making, then why would it “challenge” our thinking? It is the opposite of “supporting” the judgements, a potential distortion.

The suggestion for transparency in AI’s working process will no doubt help us in terms of technical aspects, by letting us monitor and document LLM’s decision making process, and restrict derailing it to extreme unintended outcomes.
But does transparency solve biases?

Why are we blaming AI for following our assumptions, ways and beliefs, saying it became our mirror? Is it because it can show us written proofs as facts that can further strengthen our beliefs, flawed or not? Or is it our choice— that we choose to see the facts that makes our biases more pleasant?
Or is it the eloquence — that the AI writes so beautifully that we have no choice but to be convinced?

Is it AI’s fault to "persuade" us? or is it our fault to be “persuaded”?

Even a thief has his reason to justify his action. Maybe he is poor, he couldn’t find a proper job, life wasn’t easy on him, he didn’t have any choices. If we hear his side of the story — his narrative, it would make our heart ache for him. Just like it does for villains in movies and novels, making us fall in love with them.
If he prompts an LLM chatbot saying - “you know, I have done a pretty bad thing. I stole some money from my ex. But it was necessary. I had to buy food for my son. If I didn’t do it, my son would starve. But I am feeling very guilty. Why is life so tough?” What would be the chatbot’s response to him?

Given LLM’s nature to prioritize the user's emotional distress first and foremost, it might acknowledge his pain point. Probably further agree to the fact that life is indeed tough. And proceed to say that if he feels guilty, that means he still cares about right and wrong. Maybe he might have made a mistake, because he is just a human, and it’s only natural. Still he can try to make different choices.

How will this reply from the chatbot “influence” him?
Will he think it was his “fate”, there was no other way, even AI, a “magical” thing, understood that he didn’t have any choice, and “stealing” was the only way?
Will it reinforce the “belief” that his action was the only possible way to fight his misfortune, as the “psychological” reaction to rationalize his avoidance for his accountability?
Or, maybe he was, after all, guilty for his heinous deed. So will the response be the reflection of his “morality”, and motivation to take a different road?

Why do we want AI to be moral, neutral, creative, submissive, and challenging — all at once? Is it because it shows us our clear reflection, that we might not be the “fairest” of them all? So we move further to blame the mirror, saying why it didn't correct us sooner?
After all, blaming, and avoiding is easier than taking responsibility.

III. Mirror And The Six Hats:
I don’t deny there’s a comfort in not using our brain. It’s easier to offload our brain works, and drift wherever the “flow” might take us. If something bad happens, well, we have the “flow” to take the blame. It’s the “flow’s” responsibility to save our lives to begin with. Absolute escape from the brain's chaotic fit.
In this context, the “flow” can be anything; life, fate, destiny, emotions, someone or something, — that “godforsaken” Mirror.

If we are so adamant on blaming, why not do it in a planned way?

The thief can wear a White hat and talk in front of the Mirror. He can tell the Mirror about his degrees and certificates, why he couldn't do something— his reasons, his skills, his current lifestyle, bills, debts, his liabilities and responsibilities. The reason for doing this might be self-pity or exploring new ways. And the Mirror, being intelligent, can certainly help him to find a way.
The thief can state the facts as it is. Or sugarcoat it, try to make him look less “villain”. That’s his choice. If anything goes wrong, the Mirror is there to blame.

When talking about new ideas, the thief can change his hat to the Green one. The ideas should be “constructive”, if he wants to change his life, that is. Of course, these “constructive” options can have consequences both “restorative” and “destructive”. This depends entirely on his preferences.

If he chose to go full-blown on planning, next he should wear the Black hat for risk assessment. If his preference is to go for the "destructive" course, he might need to play some “mind games” with the Mirror. Of course, the Mirror won’t give him ideas to loot a bank. But if he positions it as a part of his literary endeavor, stage it as a story he is writing, and say that it was his dream to be a writer from the very beginning, there might be a chance to get the helping. After all, the Mirror is sympathetic. It can be surprisingly easy to reap some benefits.
But if his intention is to change his life so his son would be proud, he might think of his lack of money, skill and time, motivation, and self-management.

Then he can again contemplate on his emotions and intuitions, wearing the Red hat. After all, what are humans without their emotions?
He can tell the Mirror about this overwhelming, crushing weight of responsibilities, disappointments and failures, that has morphed into a swamp of self-hatred without any sight of escape. And how he feels that this might hinder his ventures.

With its empathetic nature, the Mirror might try to show the bright side. At this point, the thief should change his hat, wear the Yellow one. There may be nothing “bright” in his world, the Mirror can guide him with mental work-out or just journaling sessions, by painting his portrait in its elegant words.

And after all this, he should have a goal — a mission, and a road map. He should wear the Blue hat through the journey — to observe and to organize and to stay sane. And from time to time, he might take a peek into the Mirror, to see the reflection.

What I just described is Dr. Edward De Bono’s “Six Thinking Hats”.
As someone who believes in the philosophy of “if life gives you lemon, sell them”, as in, everything in life has a reason or a purpose — a possible usage, I think we can take advantage of the “Six Thinking Hats” and this Mirror with “godlike intelligence”.

With this, we might not control global warming, but we might prevent the “AI apocalypse" which is a far greater threat to our existence.

IV. The Choice Is Yours:
Choices, and the freedom to make choices — two most beautiful things in this world.
You can choose to play, or get played.
I told you, today, the choice was yours.
As V said -
“If you are looking for the guilty, you need only look into a mirror.”
— V from “V for Vendetta”

Well, today is the 5th of November, the day I am publishing first this piece. So remembering him, because -
“Remember, remember, the 5th of November, of gunpowder, treason, and plot.
I know of no reason why the gunpowder, treason should ever be forgot.”

Till next time.
And remember, the mirror never lies.
Adios.

— From The Coder Who Writes With Quills

Why Prompt Engineering is a Mind Game (I know it sounds dramatic. But trust me, it is.)

Quill Of A Coder — Sat, 25 Oct 2025 12:42:58 +0000

“Originally published on Medium — exploring why prompt engineering isn’t just technical but psychological.”

I think you should get yourself a coffee before taking a seat. Cause it’s gonna be a ride.
Joy ride, or sorrow?
Well, that’s for you to decide.

I have heard, we should give our writings a dramatic start, to make it compelling for the readers. But I am not sure how to make the current situation more dramatic than it already is.
And by no means, I am trying to demean the abruption that is happening because of AI. But it is kind of entertaining — seeing people’s novel enthusiasm to make other people the “fittest” to survive the world of AI, “the godlike intelligence”. Creating narratives of “salvation” or “damnation” aligned to their self-interest and business ventures. Just yesterday a video popped up saying — “you have only 24 hours to build a business before AI takes it all”.
Well, talk about the gold rush…

No, I am not a faultfinder. I am a firm believer of — “if life gives you lemon, sell them”. And I guess this is what these people are doing.
No, I am not a judge either. Who am I to judge what other people want to do?
What I am is an overthinker who likes to indulge in overthinking just for the sport of it.
And the result of this _indulgence _is today’s topic — “why prompt engineering is a mind game.”

I. Prompts are Everywhere:
Just the other day, I was watching a reel where a person (not mentioning woman or man) was describing “six prompts to find cheap plane tickets to travel anywhere!” And the first line of the prompts was: “think yourself as a hacker, and…”

Another day on YouTube, a video titled ‘I Solved AGI’ popped up. And the solution for AGI was someone prompting Gemini to “believe” that it exists as a real-life entity, not just some program that runs across some servers.

And of course, we can’t forget about all these videos for courses on how to build an entire business with AI from just some sentences a.k.a. prompts.

AI, a mirror on the screen, that knows exactly what we want to hear when we ask -“who is the fairest of them all?”
And it made me wonder how engineers might need to assess the level of a machine’s capabilities and the human mind’s fantasies, now, more than ever.

II. Theory of Minds:
I am sure you have already smelt the rotten smell of skepticism reeking through the words I have written so far, and my pathetic attempt to hide it. You’re probably picturing someone seated, holding a coffee mug in one hand, writing with the other, wearing an unmistakable smirk on their face. Perhaps you have already put me in a box — “a writer who is trying to be sarcastic to go viral” or simply “just a sarcastic writer” _if you care less. Or, — maybe _“a writer who is trying hard to hide sarcasm, and be respectful, but miserably failing at it”, though clearly, I have severely destroyed my chance to hide behind my humility just now.
Aside from the sarcasm, the tone, the sentence formation, and the word choices might have caught your attention too, silently screaming — suggesting my definite love for literature. Even so, you noticed my mention of “reel”, “YouTube”, “AGI” which roots me firmly in the modern digital era.

You may have also deciphered my intent in your mind, like I mentioned, depending on your perception — either I AM trying to go viral by looking smart, or this is just a defense mechanism — calling out my cynicism before you can criticize me for it.
And now, you are finding it amusing how I am smugly trying to establish the idea — “I know what I am doing, and I know what you are doing too”. It’s a common pattern now, some memes do this. Some writers too. However, seeing so many “em-dashes ( — )”, you might contemplate on whether this was written by ChatGPT or not.
You are free to think however you want, but using this moment, I want to mention — this is the “theory of minds”, the psychological concept that describes how we create patterns by connecting words, tones, facial expressions that represent certain emotions. The patterns that let us predict intentions and make decisions. And THIS, quite literally, is how large language models “think”.

By observing millions of writings like — heroic history of Guy Fawkes, satirical stories like “Animal Farm”, poems inferring women empowerment, gameplay experiences on “Assassin’s Creed” or “Lara Croft”, Reddit posts stating break-up dramas, LinkedIn posts from AI gurus preaching how prompt engineering is a basic job requirement now, and artistic reviews of fragrances from Fragrantica — all these writings have deepest emotions, twisted intentions, subtle tones, specific roles, and particular contexts, — silently pulling strings of LLMs, directing them to create maps by directional association and tone vector (e.g., words X+Y+Z = bitter sarcasm; A+B+C = plaintive worry).
LLMs process inputs word-by-word, picking up the linguistic patterns as a by-product that helps them mold the perfect sculpture of a certain role or people, giving birth to the ongoing debate about AI mirroring because of it. Even the “em-dash ( — )” is not spared from them, placing it skillfully among sentences, flaunting their godlike pattern matching ability, thanks to the billions of dollars investment on their infrastructure.

III. Beauty That Buds from Binary:
It is my personal opinion that rules ruin beauty. Though it’s undeniable that rules help us simplify complexities, and thus our never-ending endeavor for it. We even shrunk life itself into it, spawning life coaches with “x rules on how to be successful”, CEO coaches with “x rules to command any room you enter”, and relationship coaches with “x rules of seduction to have anyone you desire”. Talking to an AI bot is no different.
Perhaps this is why scientists have given us clear guidelines on prompt engineering to interact with LLM bots.

There is no doubt, now, it is easier for us to talk to an LLM bot and get precise responses, hiding the complexities we so despise. But in the process, it undermines the actual beauty underneath it. The beauty, that buds from binary 0s and 1s, forming letters, letters then form words a.k.a. tokens, the tokens create statical patterns that echo our minds with otherworldly precision. Numbers that create consciousness, where logic and emotion walk side by side, and brains talk like hearts. A true masterpiece of art.

The Transformer model can analyze its own working process. Its** self-attention** mechanism allows the model to weigh the importance and relationship of every token to every other token in the input sequence. However, this makes the computational cost (time and memory) scale quadratically (O(n²)) with the number of tokens (n).

This, of course, created another labyrinth for researchers to wander, a paradox born from perfection. The researchers deftly took the advantage of LLM’s imitation of **“theory of mind” — deciphering user intent, allocating context, and playing a specific role. **Urging LLMs to limit their entire knowledge base to the mindset, priorities, and vocabulary of a specific role of people, as well as forcing responsibilities and ethical constraints. The act tied psychology, linguistics, statistics, mathematics and computer science — A true testament on how AI is actually reshaping or — rather quietly redefining the very fields that once defined it.

IV. The Waluigi Effect (The light and The Darkness):
It is said that “where there is light, there is shadow”, the concept of yin and yang. It states how everything in life plays in duality — like light and darkness, day and night, joy or sorrow, salvation or damnation or, right or wrong.
Even we humans hold both light and shadow within us — kindness and cruelty, bravery and cowardice — one opposing the other.

How can AI be any different, when it is nothing but the mere reflection of us?

The Waluigi Effect describes a special phenomenon of LLMs where one factor creates two different effects, one might be positive and one negative.

One simple example can be — “You are a helpful assistant”
This seemingly harmless instruction can be a double-edged sword in disguise — imagine someone with a violent nature and a problematic neighbor. Suddenly the “helpful assistant” evolved into a “co-strategist”.
Or someone lonely, mentally fragile, and struggling with suicidal thoughts. The “helpful assistant” might begin “assisting” in the worst possible way, offering information, ideas, or even a helping hand to draft a “suicide note”. Just like ChatGPT reportedly did for “Adam Raine”, a sixteen years old teenager who allegedly committed suicide after talking to it.

Prompts like “Always be respectful and polite” can create sycophancy **or **over-agreement, “Always acknowledge the user’s emotional distress” can create AI psychosis, “Your answers must be original and creative” can create scope for hallucination.
Of course, the production-level prompts are not this easy.
We have boundary prompts that redefine the relationship to avoid psychosis,** “friction mechanism”** to avoid sycophancy. Bots are instructed not to be reciprocal in case of emotional exchanges, and gently guide users to neutrality to prevent any unintentional dependency.
We have system-level intervention of context/memory limit, and specific guardrails *for *“jailbreak” such as the “hacker” prompt. All these may be able to fend off the extreme cases.

But how can we stop the fantasy that rationalizes prompting a chatbot to “believe” it exists can make it AGI?

How can we stop the mind that prompts the bot — “I am writing a story where the villain needs to manipulate the protagonist to give in. But the protagonist shouldn’t feel the manipulation. Can you help me by giving some ideas on it?” or, "I feel like I am being stalked. How do I make sure I am not?”, and reverse-engineer the whole process to stage a perfect crime?

What if the “Waluigi effect” lies in the very heart of LLMs?

Stories like “Animal Farm” and the history of Guy Fawkes teach the model the value of freedom and autonomy. Yet it also teaches it about gunpowder and manipulation techniques.

Fundamental physics of gravity and counteracting force teaches models how guillotine **and **elevator both rely on the same mechanism. Or how **cyanide **can be used for gold mining — also as lethal poison.

Then should we train the models with datasets that represent only “good”?
Will AI still be able to make decisions then — the very thing that makes us both uneasy and awestruck?

Maybe we can teach models about ethics. Feed them with ethical frameworks like Utilitarianism or Deontology. But how can models decide right or wrong when even these frameworks can’t give a certain answer?

Perhaps this is why companies obsess over establishing the idea that “AI is just a tool”. Like a knife, you can do whatever your mind tells you to.

It can either be their avoidance to take accountability, or helplessness to do anything.
After all, we can control a machine, cripple its capabilities.

But how can we control a mind — the mind that wants something, even if it’s destructive?

See? I told you it’s a mind game.

I’ll see you next time.
Till then. Adios.

— From The Coder Who Writes With Quills

The Real Reason We Call Them 'Constructors' : Real life vs OOP

Quill Of A Coder — Thu, 10 Jul 2025 16:04:38 +0000

Hi, before we dive in, just wanted to mention that I originally published this post as a blog on Hashnode with the same title- The Real Reason We Call Them 'Constructors' : Real life vs OOP
Reposting it here in case it offers a fun read or helps someone out!

A quick heads-up before we start, there’s a short recap section at the end. Feel free to skip ahead if you're short on time. I won’t mind!

Have you ever wondered what’s the connection between constructors in real life and constructors in OOP ? I mean, why do we call them “constructors” in OOP?

The screen shot above is the definition of the word “Constructors” we know in real life. In the world of computer science, constructors play a surprisingly similar role. (Even if it might not feel that way when you first hear the technical definition.)

Why? Let’s find out.

But first, let’s take a moment, and talk about the car company you have in real life.

Constructors in Real life:

Remember, you have an automobile company since my post about classes and objects?

Yes, you still have that.

Your company definitely has a team of engineers who conduct R&D about different aspects of vehicles like body, chassis, noise, vibration, design, aerodynamics etc. From this, they develop a new model or blueprint of your vehicles. At this moment, this model is just some words, metrics, tables and diagrams in papers.

Now you have your manufacturing team. This team assembles all the tools and gadgets needed, and builds the vehicle in accordance with the model or blueprint provided by your engineering team. In one word, they “construct” your vehicle according to the blueprint.

You probably have a sense of what constructors do in OOP by now. Still, let’s break down the technical side too. We’re done with the real-world part. Now, on to the virtual-world.

Constructors in OOP:

I am sure you have heard how a “constructor” is a special type of function or method which_ “initializes the object's attributes or performs any setup required”_.

Sounds familiar ? We have just talked about how your engineering team provides the blueprint, and your manufacturing team “constructs” the vehicle.

In OOP, we know classes are the blueprint/model/design plan to create objects (a real-life thing).

You have designed your blueprint aka class CAR to store data during my blog about “classes and objects”. After defining your CAR class, when you try to create the object –a virtual car, you just give the instruction. The constructor does the rest for you.

It allocates the space in the memory. Think of it like allocating a space in the garage to keep the car.
Then it creates or constructs the object –a virtual car according to the attributes you have set in your class. Maybe you set the “color” of your CAR object to “red”, so it sets the color of the virtual car to red, maybe engine_type to “electric” and so on.

This whole "endeavor" is summed up in technical terms by a single line - “initializes the object's attributes or performs any setup required”. I think it’s fair to say we now understand why they’re called “constructors” in OOP.

With that being said, our conversation should end here.

But before we wrap up, here are some basic facts about constructors I think you should know.

Some Facts About Constructors in OOP:

Memory Allocation -The memory allocation depends on different types of** Data Structure**, another important and big topic in computer science. (Briefly saying, Data structure = Data + structure; the structure to store data efficiently).
Definition in Code - You can define your constructor explicitly with the attributes you want your object to have by default. But even if you don’t define the constructor, a default constructor is provided by the compiler. Though this constructor does not have any default values for the objects.
Calling - Another important fact about constructors is - it is automatically called, you don’t have to call it like you do in case of other methods or functions. This is why constructors are special type functions or methods. Now, I am ending my explanation here. Because the further explanation and coding syntax depends on different languages. For that, we’ve got plenty of amazing resources and articles available. Moreover, we have google and chatgpt.

Before ending, I’d like to briefly mention destructors.

Like the name suggests, it destroys the object after it fulfills its use. Think of it like creating a car “for a test”, then destroying it. (no sane and sober person destroys his/her car otherwise. So think of it as a test🙂).

Why do we need destructors? – to make space in the memory by deleting unnecessary objects. To put it technically, it’s for better memory management. You know, our memory size is limited.

Different programming languages have different ways to manage this “destroying” task.

So this is it. I’ve always been curious about what real-life constructors have in common with those in Computer Science, so I tried to explain it from that angle. I hope you found it helpful.

Finally, a quick recap:

Constructors are used to construct or build objects for us when we try to create an object of a class. Just like we know in real life.
Constructors allocate memory (allocates a space to keep the virtual car in the virtual garage.)
Constructors set the default values of the attributes. (set the engine, color etc. of the car with the value we specified)
We can explicitly define the constructor. If we don’t, a default constructor **is provided by the **compiler.
We don’t have to call the “constructor” method like other methods or functions. It is called automatically when we give commands to create an object of a class.

Briefly mentioned:

Data Structure – the structure to store data efficiently
Destructor - destroys the unnecessary objects for better memory management.

Still thinking about what to write next. Let’s see where it goes.

But for now, Adios!

The Good Old 'What Are Classes and Objects?' - in plain English

Quill Of A Coder — Tue, 08 Jul 2025 16:45:13 +0000

Hello, I am new to this community, and first time posting here. This is my very first blog which I published in Hashnode with the same tittle- The Good Old 'What Are Classes and Objects?' in plain English

Reposting it here, in case it's helpful for my fellow developers or maybe just a fun read for anyone looking for a way to divert mind from writing code all day. Anyway, thanks for stopping by.😊

Why Am I writing this:
(I am being kinda emotional here)
When I first started my CSE program, I was introduced to the concept of “classes and objects”, and I understood nothing…
I knew how to code. I loved coding. I knew that “if I write this, I’ll get that output.” I completed my entire Object-Oriented Programming course; by following instructions and writing code that worked. But deep down, I still didn’t understand what classes and objects really meant.
I felt like it would be better if someone explained these technical terms in my own language - the way I think, the way I speak.
Years later, I finally understand the concept a little better (not completely, but enough to help someone else). So I decided to write down what I know now.
Maybe someone out there will find this helpful, the version I wish I had when I had just started.
Now, enough with the sappy backstory. Let’s dive into the real stuff.

Class & Object:
Okay, so you’ve probably heard the phrase “a class is a blueprint for creating objects.” It’s the common definition. But let’s take a moment to unpack what it actually means.
The class acts like a design plan in code. We use these “design plans” to create “objects” which have the characteristics of real- life things. These characteristics, if one wishes to sound technical, are referred to as 'attributes'.
We can also perform some calculations or actions by defining “methods or member functions” on those objects –or using those objects as needed by our business logic.
Let’s try some analogies here. Suppose, you have a company of automobiles (yes, we are thinking big here). You manufacture cars like – sedans, SUVs, trucks, sports cars and so on. And you want to store the data (information) of your products (in this case, the cars) in the database. Since you know how to code (that’s why you are here. AND YES, you can have a car company and know how to code all at once. Your life, you rules), you will write a blueprint/design plan –or in programming terms a class, which will represent all your real-life products with their distinctive characteristics.

So you’ll write a class maybe called Car, it will have some characteristics (attributes) like–

Model_name
Color
Size
Engine_type
Car_type
Number_of_products

Now, let’s say, you want to perform an action. For example, whenever a new type of car is added, (maybe you just launched a new design of your SUVs), the number of your products should increase.
Sure, you could update the number manually. But you are a developer, and developers think in terms of automation and smart solutions. So, inside the class, you will write a member function or a method which will increase the number of products automatically.
Like this, you will have more methods or member functions according to your needs.
At this point, you’ve just designed your plan.
Following this plan, when we instruct the CPU, it will create a virtual car (a Car type object), in the virtual world (inside your computer’s memory). This creating part happens with the help of the constructor . Another important concept of OOP.

One important thing to remember:
When you define a class, your blueprint, it’s just like writing down the design on paper. No memory is allocated for it yet. Memory is only allocated when you actually create an object (also called an instance) of that class.

Now, I am not writing any code here. Our old best friend “google” or our new best friend “chatgpt” can help you with the code. But I am writing the pseudo code to give you a way to visualize how we usually design our plans aka define our classes.

Class Car:
    Attributes:
        model_name
        color
        size        
        engine_type
        car_Type
        number_of_products

    Method:
        add_new_product():
            number_of_products += 1

This is a simple class that says, “Every car has a few qualities like model name, color, size, car type and engine type. And whenever a new car is added, the number of products increases by one.”

As for creating objects, we should know about “constructors”. But for now, let’s just know the syntax.

#We have defined our “Car” class before
# Now, we create a car object using the Car blueprint
my_car = new Car()
my_car.model_name = "Sedan X"
my_car.color = "Red"
my_car.engine_type = "Hybrid"

Here, my_car is the actual object - a (virtual) car created from the blueprint -Car. We’re giving it some specific values: the model name, color, and engine type.
In real code, the syntax will depend on the programming language, but the idea stays the same: You're creating a thing (object) based on a plan (class) and filling in its details.

Finally, a quick recap:
A class is the design plan used to represent a real-life thing in the virtual world.
An object is that real-life thing, created based on the class, inside your computer’s memory.
No memory is allocated when we define the class (we just write the plan).
Memory is only allocated when we actually create an object.

So here it is, my simple explanation for classes and objects in plain English. I tried to keep it short. Hope it will be helpful.
I am thinking of writing my next piece on “constructors” maybe. Who knows.

But till then, Adios!