E = mc² — Energy = Milk × Coffee²: The Equation of My Life Right Now

Rajnikant Dhar Dwivedi — Sun, 29 Mar 2026 00:00:00 +0000

“The most beautiful thing we can experience is the mysterious. It is the source of all true art and science.” — Albert Einstein </blockquote>
There’s a photo I took recently standing in front of that famous equation, E = mc², spelled out on the wall behind me as “Energy = Milk × Coffee².”
I laughed when I saw it. Then I thought about it for a second longer than I should have.
And honestly? It’s not wrong. Not for me. Not right now.
Because the past month? It has been pure energy expenditure — the kind that drains you, that takes everything you have, that makes you sit at your desk at 2 AM wondering why you chose this life. But then morning comes, you make your coffee (with milk, always), and something inside you clicks back on. Like a server rebooting. Like a model finishing its training run.
Let me tell you everything.

The Month That Swallowed Me Whole</h2>
March. The end of the Indian financial year.
If you’re a builder, a freelancer, a student managing their own finances, or just someone trying to do things the right way you know what March feels like. It doesn’t ask for your time. It takes it.
I disappeared for a while. Not from ambition. Not from my dreams. But from writing, from sharing, from this blog. The work behind the scenes never stopped if anything, it accelerated but the financial year closing demanded everything else I had left.
Reconciliation. Compliance. Numbers upon numbers. A different kind of math than what I’ve been learning, but math nonetheless.
And now it’s done.
Now I breathe. Now I write. Now I come back.
This blog is that breath.

The Photo on the Wall</h2>
Let me talk about that image for a moment.
I’m standing in front of a wall that reads E = mc² Einstein’s mass-energy equivalence but with a twist beneath it: Energy = Milk × Coffee².
It’s a joke. A café joke, probably. But I kept staring at it.
Because E = mc² is one of the most elegant, devastating, and beautiful equations ever written. It says something radical: that mass something solid, something physical, something you can touch is just energy in a very committed relationship with the speed of light, squared.
Energy and matter are not separate things. They are the same thing, expressed differently.
And I think that’s what I’m doing with my life right now. I’m not separating study from work, passion from profession, learning from building. I’m discovering they’re the same thing just expressed at different velocities.
The milk and coffee? That part is just honest. I run on it.

Falling in Love With Quantitative Finance</h2>
I told myself Devote was my first priority.
And it still is I want to be clear about that. But something happened recently that I didn’t fully anticipate. Something crept up on me quietly, the way the best things always do.
I started learning quantitative finance.
At first it was just exploration. Curiosity. The kind of thing you read about at midnight and think, this is interesting. But then the interest became fascination, and the fascination became something that feels very close to love.
Let me tell you what I’ve learned so far about 70% of the foundations and why it has me absolutely hooked.
What Is Quantitative Finance, Really?</h3>
Most people hear “finance” and think spreadsheets. Suits. Bloomberg terminals. Interest rates and newspaper headlines.
Quantitative finance is something else entirely. It is the application of mathematics, statistics, and computational models to financial markets. It is the discipline that sits at the intersection of physics, mathematics, computer science, and economics — and it is brutally, beautifully rigorous.
The people who work in this field — quants, they’re called don’t just analyze markets. They model them. They write equations that describe the probability of a stock price being at a certain level in the future. They build algorithms that make trading decisions in microseconds. They construct portfolios that hedge against risk with mathematical precision.
It is, in a very real sense, applied mathematics at industrial scale.
The Foundations I’ve Been Building</h3>
Here is what 70% of the basics looks like, and why each piece matters:
1. Probability Theory and Statistics
This is the bedrock. Before you can model anything in finance, you need to understand randomness — genuinely understand it, not just be comfortable with it. Random variables. Probability distributions. Expected values. Variance. Covariance. The Central Limit Theorem and why it matters more than most people realize.
In finance, prices don’t move predictably. They are random processes or at least, they behave as if they are. Understanding the shape of that randomness is everything.
2. Stochastic Calculus
This is where it gets beautiful and difficult at the same time.
Standard calculus deals with smooth, deterministic functions. But stock prices aren’t smooth. They jump. They fluctuate. They are, mathematically, Brownian motion the same kind of random walk that particles in liquid take when bumped by surrounding molecules.
Stochastic calculus is the language that describes how these random processes evolve over time. It gives us tools like Itô’s Lemma, which is essentially the chain rule of calculus but for random processes. It is the mathematical engine behind the Black-Scholes formula.
3. The Black-Scholes Model
If quantitative finance has a founding text, this might be it.
Published in 1973 by Fischer Black and Myron Scholes (with critical contributions from Robert Merton, who later won the Nobel Prize for it), the Black-Scholes model gives you a formula for pricing options financial instruments that give the holder the right, but not the obligation, to buy or sell an asset at a specified price.
The formula is elegant. It takes as inputs: the current price of the asset, the strike price of the option, time to expiration, the risk-free interest rate, and the volatility of the asset. It outputs a fair price for the option.
What stunned me when I first derived it was the philosophy inside the math: the idea that you can construct a riskless portfolio by combining an option with the right amount of the underlying asset, and that this riskless portfolio must earn the risk-free rate (otherwise there’s an arbitrage opportunity). From this one elegant principle, the entire formula falls out.
4. Financial Time Series Analysis
Markets generate data. Enormous amounts of it. And that data has structure patterns, seasonality, autocorrelation, volatility clustering.
Time series analysis is the toolkit for understanding this structure. Models like ARIMA for mean prediction. GARCH models for volatility prediction (because volatility itself is volatile periods of calm tend to be followed by more calm, periods of turbulence by more turbulence). Cointegration for finding pairs of assets that move together in the long run.
This is where the data science I already know starts to blend seamlessly with finance, and where things start to feel very natural to me.
5. Portfolio Theory
Harry Markowitz changed the world in 1952 with a simple but profound idea: diversification can be quantified.
It’s not enough to just pick good assets. The question is how you combine them. Because assets that are negatively correlated — that tend to move in opposite directions reduce the overall variance of a portfolio without necessarily reducing its expected return.
This gave rise to the concept of the efficient frontier: a curve in risk-return space that represents the optimal portfolios for every level of risk. You don’t want to be below this curve. And the math tells you exactly where it is.
6. Risk Measures: VaR and CVaR
Value at Risk (VaR) and Conditional Value at Risk (CVaR, also called Expected Shortfall) are how financial institutions measure potential losses.
VaR answers: “What is the maximum loss we can expect, with 95% (or 99%) confidence, over the next day?” CVaR goes further: “Given that we’ve exceeded the VaR threshold — given that we’re in the bad 5% or 1% — how bad does it actually get, on average?”
These are not just academic tools. They are regulatory requirements. Banks and hedge funds live by them. And building software that computes them accurately and efficiently is genuinely difficult and genuinely valuable.
7. Factor Models and Alpha
Not all returns are equal. Factor models starting with the Capital Asset Pricing Model (CAPM) and expanding into the Fama-French three-factor and five-factor models decompose asset returns into components attributable to known risk factors (like market risk, size, value) and an unexplained residual called alpha.
Alpha is what everyone is chasing. It’s the return that can’t be explained by known factors the excess return generated by skill (or luck, or both). The entire hedge fund industry is, in one sense, a global competition to find and exploit alpha before it disappears.
8. Algorithmic Trading Concepts
Execution algorithms. Market microstructure. Order book dynamics. The difference between market orders and limit orders and why it matters when you’re executing large trades. Transaction cost analysis.
And of course: statistical arbitrage finding pairs or baskets of assets that are historically correlated, detecting when they diverge, and betting on reversion to the mean. It’s mean reversion, but industrial. Automated. Fast.

This is 70% of the foundations. And I am not done.
The remaining 30% — machine learning for finance, reinforcement learning in trading, deep learning on financial data, exotic derivatives, fixed income mathematics, credit risk modeling — is coming. I am coming for it.
Because here’s the thing: I’m not learning quantitative finance to become a quant trader. I’m learning it because I need to build tools for the people who are.

The Bigger Picture: Building an Indian Palantir</h2>
Let me tell you about the other thing. The real thing. The thing that wakes me up at 4 AM not with anxiety but with ideas.
I am building what I can only describe as an Indian Palantir.
Let me explain what that means.
What Palantir Actually Is</h3>
Palantir Technologies is one of the most consequential and controversial technology companies in the world. Founded in 2003 by a group of people including Peter Thiel, Alex Karp (the current CEO), and others who came out of the legendary PayPal founding group a cohort of builders and thinkers who have arguably done more to shape modern technology than any single organization outside of Bell Labs.
The company’s name comes from Tolkien: the palantíri were seeing stones, used to observe distant events and communicate across vast distances. The metaphor is apt, and deeply intentional.
Palantir builds data integration and analysis platforms but that description fails to capture what they actually do. More precisely: they build systems that help organizations make sense of enormous, messy, fragmented, often contradictory datasets. They find the signal in the noise. They turn raw data into operational intelligence.
Their two flagship products are:
Gotham the defense and intelligence platform. Used by governments, militaries, and intelligence agencies to integrate and analyze data at massive scale. Counter-terrorism. Battlefield intelligence. Logistics. Resource allocation. It is the kind of software that, when it works, genuinely saves lives and changes the outcome of conflicts.
Foundry the commercial platform. Used by corporations, hospitals, supply chain managers, financial institutions. To optimize operations, identify inefficiencies, model scenarios, and make better decisions with the data they already have but can’t fully use.
Both products share a core philosophical commitment: the primacy of the analyst. Palantir’s software is designed to augment human judgment, not replace it. The tools are powerful, but they are tools for thinking, not substitutes for it.
What I Am Building</h3>
I am building my own version of this. For India. For the Indian context. With Indian needs, Indian scale, and Indian ambitions.
I’m currently deep into building my own Gotham-equivalent a data integration and operational intelligence platform. This is not a toy project. This is not a weekend hack. This is the thing I am committing my professional life to.
And I want to help my country.
I wanted to join the army once. I genuinely considered it. There is something about the idea of service of doing something that matters, of being part of something larger than yourself that calls to me deeply.
But the army wasn’t my path. I knew it, even as I admired it.
And then I realized: service doesn’t require a uniform. Service requires capability deployed in the right direction.
India has extraordinary data needs. The military, the defense sector, law enforcement, intelligence, public health, logistics, infrastructure these are domains where the gap between available data and actionable intelligence is enormous. Where the right tools could make a staggering difference.
I want to build those tools.
I won’t tell you the name of my company yet. There are trademark processes underway, and until everything is properly registered and protected, the name stays internal. But when it’s ready and it will be ready you’ll know.

The Machine in My Room</h2>
To build what I’m building, I need to compute.
And I do.
I have set up my home as a private server. A small data center, essentially, running in a space most people would use for something else entirely.
The centerpiece is a machine you might recognize: the NVIDIA Jetson AGX Orin or more likely what I have is from their supercomputer-class compact line, possibly the NVIDIA DGX Station in its compact form, or the Jetson AGX Orin Developer Kit configured for serious workloads. A roughly $3,000 investment in raw computational power, in a form factor small enough to sit on a desk.
It is an extraordinary piece of hardware. GPU-accelerated computing, capable of running inference on large models locally. Capable of training smaller models from scratch. Capable of handling the kind of data processing pipelines that would have required a small server room a decade ago.
This machine runs constantly. Training runs. Model evaluations. Data pipelines. Experiments. Tests. Iterations.
This is where the quantitative finance models will be built and tested. This is where the Gotham components are being designed and refined. This is where the ideas become code, and the code becomes systems, and the systems become something real.
My home is the lab. The bedroom is the workshop. The desk is the factory floor.

The PayPal Mafia and Why They Matter to Me</h2>
I mentioned Peter Thiel and Alex Karp. Let me go deeper, because this matters to me personally.
Alex Karp, Palantir’s CEO, is one of the most unusual and compelling figures in technology. He has a doctorate in neoclassical social theory from Frankfurt. He practices Nordic martial arts. He speaks with a philosophical seriousness about the role of technology in democracy and national defense that is almost jarring coming from a Silicon Valley executive.
He built Palantir not as a product company in the traditional sense but as what he has called a “mission-driven” company one oriented around a set of values about what technology should do in the world. He is not interested in building technology that is neutral. He believes technology has a side, and he has chosen one.
I admire that. Deeply.
But beyond Karp, there is the larger constellation: the people who built PayPal in the late 1990s and early 2000s, and who have spent the decades since reshaping the world in ways that are staggering in their cumulative effect.
Elon Musk. Peter Thiel. Reid Hoffman. Max Levchin. David Sacks. Roelof Botha. Ken Howery. Chad Hurley (YouTube). YouTube, LinkedIn, Palantir, SpaceX, Tesla the list of companies that trace back in some direct way to that original founding group is almost incomprehensible.
What made them different? What was in the water at PayPal that produced this?
I think about it a lot. And I think the answer is something like: they took the idea of changing the world seriously. Not as a marketing line. Not as an investor pitch. As a commitment. They were building things they genuinely believed mattered, with people they genuinely believed in, under conditions of genuine difficulty and genuine risk.
They were not optimizing for comfort or safety. They were optimizing for impact.
That is the model I want to follow. Not in imitation of their specific paths I am in India, in a different era, with different problems to solve but in that fundamental orientation. Build something real. Build something hard. Build something that matters.

The Quantitative Finance → Defense Tech Pipeline</h2>
Here’s the thing that ties it all together, and why quantitative finance isn’t just a side interest for me.
Palantir’s history is deeply intertwined with hedge funds and quantitative finance. The same data integration and pattern-recognition capabilities that make Gotham powerful for intelligence analysis also make Foundry powerful for financial analysis. The modeling techniques overlap. The data infrastructure overlaps. The computational challenges overlap.
I am learning quantitative finance because:
1. I will build tools for hedge funds. Not just Gotham equivalents for defense. But Foundry equivalents for institutional finance. Portfolio optimization platforms. Risk management systems. Algorithmic trading infrastructure. These are valuable products with real customers who have real needs, and I can build them.
2. The skills transfer directly. Stochastic modeling, time series analysis, large-scale data integration, real-time computation all of this is as relevant to defense intelligence as it is to financial intelligence. A system that detects anomalies in financial data uses fundamentally the same architecture as a system that detects anomalies in behavioral or geospatial data.
3. Revenue enables mission. Palantir understood this early. They built commercial revenue streams not just because profit is good but because commercial revenue funds the mission-driven work that government contracts sometimes can’t support. I intend to do the same. The hedge fund tools generate the revenue. The revenue funds the Gotham work. The Gotham work serves India.
It is one coherent strategy, not two separate interests. Quantitative finance is not a detour. It is the foundation.

What I Believe (And Am Willing to Say Out Loud)</h2>
I am 19 years old. I am sitting in India, running servers in my home, learning stochastic calculus, and building software that I believe will matter.
That sounds either visionary or delusional, depending on your priors. I understand that.
But I want to say something clearly, without hedging:
I am going to do this.
Not because I am special in some innate, unchangeable way. But because I refuse to stop. Because I have made a decision not casually, not in a moment of excitement, but with full understanding of what it requires that this is what I am doing with my life.
The financial year just closed. I lost a month to administrative work that felt meaningless compared to what I’m building. But I did it, I did it correctly, and now I’m back.
There will be more months like that. More interruptions. More moments where the gap between where I am and where I want to be feels enormous. That is the nature of building something real over years.
I’m not scared of the gap. I’m working on the gap.

The E = mc² of My Life</h2>
Let me come back to that equation on the wall.
Einstein showed us that mass and energy are equivalent that the thing you think is solid and fixed is actually just energy in a very stable form. The conversion factor is enormous (c², the speed of light squared), which is why we don’t walk around turning into light. But the equivalence is real. Matter is frozen energy. Energy is liberated matter.
I think about this when I look at where I am and where I’m going.
Right now, a lot of what I’m doing looks like mass. Like accumulation. Learning. Building. Reading. Running experiments. Refining ideas. It is slow. It is dense. It does not yet radiate very much.
But it is not static. It is not stagnant. It is potential energy in the most literal sense a store of capability and understanding that has not yet been fully released.
And when it converts when the models are trained, the platforms are built, the tools are deployed, the company is real and public and doing what I’ve planned — the release of that potential will be something.
The coffee helps. The milk, too.
But the mass the years of work, the late nights, the financial year closings and the server restarts and the stochastic calculus I did not fully understand the first time and had to do again that mass is what makes the energy real.
E = mc².
The energy of what I’ll build equals the mass of everything I’ve put in, times the velocity of commitment, squared.
I’m all in.

What Comes Next</h2>
I won’t give you a complete roadmap here. Some things I am deliberately not sharing yet, for legal and strategic reasons. But here is what I can tell you:

The quantitative finance curriculum continues. The remaining 30% reinforcement learning for trading, deep learning on financial time series, credit risk, fixed income is next. </li>

The Gotham-equivalent platform is in active development, running on my home server, being refined and expanded. </li>

The hedge fund tooling is being designed in parallel a suite of quantitative tools for portfolio management, risk analysis, and algorithmic strategy development. </li>

The company will be named publicly when trademark registration is complete. Not before. When you see the name, you will understand why I waited. </li>

I am building for India first. Not because the rest of the world doesn’t matter, but because I know this country, I love this country, and I believe in this country’s extraordinary capacity to build things that change the world. I want to be part of that. </li> </ul>

To Anyone Reading This</h2>
If you’re a student: you’re not too young. You’re not in the wrong city. You’re not missing the right connections or the right resources. You have what you need to start. The starting is everything.
If you’re a builder: keep going. The months that disappear into administrative work, into learning that feels slow, into experiments that don’t work those months are the mass. They are not lost. They are stored.
If you’re someone who has been following this space: something real is coming. I can’t tell you everything yet. But I can tell you that I am not building in the dark, and I am not building alone in my head. I am building in my home server, in Python and C++ and mathematics, with a clarity of purpose that I have never felt more certain about.
And to everyone who doubted, everyone who suggested smaller goals, everyone who said the army was more realistic, or that this was too ambitious, or that India wasn’t ready for this kind of technology company:
I hear you. I understand where that comes from. I am not angry about it.
I’m just going to show you.

— India. March 2026. Back to building.

E = mc² Energy = mass × (the speed of commitment)² Everything I put in will come out. </blockquote>

Rajnikant Dhar Dwivedi - quantitative finance

E = mc² — Energy = Milk × Coffee²: The Equation of My Life Right Now

The Bigger Picture: Building an Indian Palantir</h2>
Let me tell you about the other thing. The real thing. The thing that wakes me up at 4 AM not with anxiety but with ideas.</em></p>
I am building what I can only describe as an Indian Palantir.</strong></p>
Let me explain what that means.</p>

Rajnikant Dhar Dwivedi - quantitative finance

E = mc² — Energy = Milk × Coffee²: The Equation of My Life Right Now

The Bigger Picture: Building an Indian Palantir</h2> Let me tell you about the other thing. The real thing. The thing that wakes me up at 4 AM not with anxiety but with ideas.</em></p> I am building what I can only describe as an Indian Palantir.</strong></p> Let me explain what that means.</p>

The Bigger Picture: Building an Indian Palantir</h2>
Let me tell you about the other thing. The real thing. The thing that wakes me up at 4 AM not with anxiety but with ideas.</em></p>
I am building what I can only describe as an Indian Palantir.</strong></p>
Let me explain what that means.</p>