One of the best things you can steal from great people is their ideas, and the fastest way to get smarter is to read smarter people. While studying their daily rituals, I’ve started believing this line more and more: “Alpha doesn’t come from common knowledge.”

I found this on the Daily Rituals website, but sadly, the site has shut down. It was about Sir Isaac Newton’s routine:

“One of Newton’s assistants noted the physicist’s habits in the 1680s. Newton rarely went to bed before 2:00 or 3:00 a.m., and sometimes not until 5:00 or 6:00 a.m. Yet, “in a morning he seem’d to be as much refresh’d with his few hours’ sleep as though he had taken a whole night’s rest.” During the day, Newton was never seen taking a walk or engaging in recreation. His assistant recalled hearing him laugh only once. Food was also a dispensable luxury. Newton dined sparingly and often forgot to eat at all. If reminded that he hadn’t touched the meal set before him, Newton would reply, “Have I?” and absentmindedly take a few bites before plunging back into his work.”

Stories like these inspire me every time I read about Newton, or any other artist.

I also saw a tweet by a person named Ron where he said:

“Thiel did a fireside chat my freshman year (2014) of college where someone asked: ‘Could the next Zuckerberg be in this room?’

Thiel: ‘He would never show up to an event like this.’

Basically, there is endless advice out there on how to live, think, and act. But the people who actually do bold things don’t always have time to sit around consuming information all day. They do things, get stuck, and figure them out.

That’s why I believe in questioning most advice and walking your own path. People give advice based on their own situations, goals, and perspectives. Your situation is probably very different. They’ll try to fit you into their world, but you probably need to build your own.

There’s a good quote by Jeffrey Pfeffer: “You can’t be normal and expect abnormal returns.”

Imagine if someone had suggested Vinci, Newton, or Tesla avoid extreme routines and maintain active social lives. Would they necessarily have become who they became?

I don’t think their unusual habits were necessarily the reason they became extraordinary. More likely, their unusual habits were a consequence of being unusually obsessed with something.

Here’s another example from Daily Rituals:

“(Francis Bacon’s) idea of dieting,” Mason writes, “was to take large quantities of garlic pills and shun egg yolks, desserts, and coffee, while continuing to guzzle a half-dozen bottles of wine and eat two or more large restaurant meals a day.”

From history to today, every artist who creates something meaningful seems to have unique, and often weird, traits that make them who they are. For me, that’s what makes studying great people so fascinating.

Thank you so much for reading!

I believe experience is the highest form of knowledge. But I’m still in my early 20s, so there’s no reason to blindly follow the principles of someone who hasn’t lived his life yet.

I’ve studied people I admire, and some of these ideas I’ve experienced myself. I’m writing them down as a note to myself, to remember what I’ve learned so far.

You’re allowed to reject my advice.

1. Believe in spontaneous curiosity.

• The ability to act in the moment when you feel the urge to do something is hugely underrated. When you’re inspired to do something, do it.
• Most great things happen spontaneously, as side projects or hobbies, without the aim of making something big. So do more side projects.
• Don’t be practical. Be delusional.
• Maximize your serendipity. The more risks you take, the luckier you get.

2. Don’t get attached to the self.

• Don’t fix your identity. Every human contains multitudes and has the capacity to think any thought ever conceived.
• Learn to handle the risk of embarrassment, rejection, and failure, and be shameless.
• The biggest weakness is wanting to be liked.
• The attention you’re seeking is your own.

3. Learn to think.

• All learning is about how to think.
• Math is just a way of thinking, and it teaches you how to think. Physics teaches you what is true. If you want to understand how everything works, study math and physics.
• Build your foundation: study computer science, math, physics, and engineering. Nature has no boundaries.
• In this world, we don’t come with innate knowledge. We read books, observe people, and watch movies. As we do, we collect information about the world. Over time, those ideas collide and fuse together to form something new and original.
• Whatever you’re looking for is already written somewhere. Some people have been in your situation and had thoughts similar to yours, so they’ve written them down. When you find their words, you’ll realize you’re not the only one thinking these things right now.

4. Build something.

• The best way to prove your value is to build something.
• The best way to find like-minded people is to do something interesting. Then you’ll have something to offer, and you’ll attract the right people.
• Pick the immediate direction that will put you in a position to work with the smartest people. That’s probably more important than having a good idea.
• Most of us look up to a few people who have already achieved the dream we’re trying to achieve. But the real question is: why aren’t you doing what they actually did? Is it ego? Ignorance? It’s obvious you should copy what works, but you don’t.

5. Learn from the greats, but become yourself.

• I can’t be the next Newton or Elon Musk. Even if Elon guided me personally, I can’t be him. But certainly, I can be myself. I can learn from great people, study the patterns in their lives, and copy what works. But in the end, I have to experience and figure it all out by myself.
• You don’t lose what you don’t have.
• The top three most important decisions in life are what you do, where you live, and who you’re with.
• Write for yourself. Write to remember.
• Writing is basically talking to yourself.
• Live an unscheduled life.
• Agency > courage > intelligence.

And, above all:

Reject most advice.

I’ve been thinking about something.
Have you ever noticed that there are periods where controlling yourself feels almost effortless? You work, exercise, read, do what you planned, and distractions barely seem interesting.
Then, without making any conscious decision to change, you slowly start doing the opposite. You scroll more, procrastinate, eat worse, avoid work, whatever your version of it is.
What I find strange is that this seems to happen in cycles.
Eventually you reach some point where you get so fed up with your own behavior that something changes. You change your environment, go somewhere else, start a new project, make a new plan, delete some apps, start exercising again, get some external pressure, etc.
And suddenly you’re back in control.
Then after some amount of time, you somehow end up in almost exactly the same state again.
I’m starting to wonder whether the individual decisions we make during these periods are actually much less important than the state we are in when we make them.
Because I’ve noticed that the same person can seem to have completely different levels of self-control depending on their environment, what they’re doing, what they have going on, how much external pressure there is, whether they feel like they’re making progress, etc.
And the strange part is that the pattern itself can become predictable. You can almost recognize that you’re entering the same state again, even though you still don’t seem to be able to stop it very easily.
If you’ve experienced this, what have you noticed about the cycle itself?
What makes you enter the “everything is easy and I’m on track” state, what makes you gradually leave it, and what actually causes you to come back?
I’m much more interested in observations about the pattern than advice about how to fix it.

I’ve been thinking about hesitation as a behavioral system rather than a personality trait.
I’ve been doing cold showers every morning for close to 30 days now. Before this streak, I’d repeatedly fail at it. A while ago I read Antifragile and David Goggins’ Can’t Hurt Me, and those books gave me the foundation for thinking about adaptation, exposure, adherence, identity, and how repeated stress can change the system experiencing it. The conclusions below came from applying and extending those ideas myself, not directly from the books.
The thing that became obvious to me is that adherence has to come before optimization.
If the exposure doesn’t happen consistently, there is no adaptation process to study.
The interesting part is what happens when you create a system where you have to continue through a difficult but survivable stressor. At first the stress is novel and resistance is high. If you keep going, the system gets more familiar with the stimulus, capacity changes, the prediction of the stimulus changes, and eventually the relationship with the stressor changes.
This is where I started thinking about hesitation.
For the last couple of weeks I’ve noticed that I can spend around 15 minutes in the morning sitting in the bathtub before actually taking the cold shower.
The shower itself isn’t the problem.
The problem is the transition from knowing what I have to do to actually doing it.
Wake up → anticipate discomfort → hesitate → self-talk → find reasons → generate motivation → eventually commit → act.
Once I cross the initial barrier, momentum takes over and the thing is easy.
I realized the same pattern exists outside cold showers.
Calling someone important.
Starting a difficult task.
Doing something I’m uncomfortable with.
Making a decision with emotional consequences.
The same basic sequence appears:
stimulus → anticipated discomfort → hesitation → internal negotiation → commitment → execution.
So the thing I’m interested in optimizing is not “discipline” in the abstract.
It’s the latency between recognizing the correct action and executing it.
But I don’t think the objective should literally be zero hesitation.
If I don’t have enough information, hesitation can be useful. If I have a knowledge gap or strategy gap, forcing immediate execution is just stupidity.
The useful distinction is:
If the decision is already made and the remaining obstacle is only anticipated discomfort, further deliberation is unnecessary.
That’s the hesitation I want to eliminate.
This led to a bigger realization about measurement.
You can’t systematically change a behavioral system if you can’t reliably observe the policy it is currently running.
Saying “I value discipline” doesn’t tell me much.
Put me into a repeated situation where two things I want conflict.
Immediate comfort vs long-term growth.
Certainty vs exploration.
Social approval vs saying what I actually think.
Short-term pleasure vs a long-term objective.
Then observe what I repeatedly choose.
That’s much more informative.
This is why the cold shower is useful as a laboratory.
The conditions are repetitive. Same person, same general time, same stimulus, same action, same type of discomfort.
That monotony reduces irrelevant variables and makes the behavioral output visible.
If I repeatedly observe:
discomfort → hesitation → delay → relief,
then I have evidence about my current policy.
If I repeatedly observe:
discomfort → action → completion,
then I have evidence that the policy is changing.
The laboratory isn’t just for measurement. It becomes the environment in which the policy can actually be changed.
So the process becomes:
repeated environment → observation → pattern → policy inference → intervention → repeated exposure → feedback → policy modification.
This also changed how I think about values.
I don’t think values need to be treated as some mysterious internal essence.
Operationally, a value shows up when two desired outcomes can’t both be maximized.
You want A and B, but the situation forces a tradeoff.
Which one do you choose?
Under what conditions?
What do you sacrifice?
What happens when you’re tired, afraid, uncertain, or uncomfortable?
That reveals the actual preference structure of the system.
So values are basically preferences over competing outcomes under constraints.
And a policy is the behavioral mapping that implements those preferences across recurring situations.
The important thing is that what I say my values are and what my policy actually does can be different.
The policy is exposed by behavior.
That’s why I think monotony is useful.
You need enough repeated exposure for the noise to disappear and the regularity to become visible.
One isolated decision doesn’t tell you much.
Thirty similar decisions start telling you something.
Then you can form an abstraction over them.
Event:
“I hesitated for 15 minutes.”
Pattern:
“I repeatedly hesitate before uncomfortable actions.”
Policy:
“When anticipated discomfort rises, I tend to delay execution.”
Value/tradeoff:
“When immediate comfort conflicts with long-term growth, my current system sometimes assigns too much weight to immediate relief.”
Mechanism:
“Delaying produces immediate relief, which reinforces the tendency to delay.”
Now there is something you can actually work with.
The same logic applies to the cold shower, but I think the cold shower is only a particularly clean laboratory.
The broader experiment is human behavior.
The goal isn’t to become someone who never experiences discomfort.
It’s to build a policy where discomfort doesn’t automatically trigger negotiation.
If I already know what to do, the ideal sequence becomes:
trigger → predetermined policy → execution.
Instead of:
trigger → emotional reaction → negotiation → motivation → execution.
And there is another important point here.
One skipped cold shower isn’t going to magically corrupt the rest of my life. I think that part of my original thinking was too deterministic.
What matters is reinforcement history.
If I repeatedly train:
discomfort → avoidance → relief,
avoidance becomes easier to produce.
If I repeatedly train:
discomfort → action → completion,
action becomes easier to produce.
The individual repetition matters because it changes the probability of the future response.
So I’m increasingly thinking about self-development as an experimental science.
Don’t just ask:
“What kind of person am I?”
Create conditions where your system has to make the same class of decisions repeatedly.
Measure what actually happens.
Infer the policy.
Identify the tradeoffs it is actually implementing.
Change the policy.
Then run the experiment again.
The cold shower is just one laboratory where the signal is unusually clean.
The thing I’m actually interested in now is how much unnecessary latency exists between knowing what should happen and actually making it happen, and how systematically that latency can be reduced.

I’ve been thinking about a general law in interpersonal dynamics:
Don’t optimize only for immediate emotional relief. Optimize for the behavioral pattern you are training.
For example:
Someone gets surprised or uncomfortable → they pull away → the other person immediately chases → they reconnect → discomfort disappears.
This works in the short term. But repeated enough, both people can learn the loop.
The person who withdraws learns:
“Withdrawal produces pursuit.”
The person who chases learns:
“When connection is threatened, I must pursue.”
So withdrawal can become reinforced, while pursuit becomes increasingly automatic.
The same mechanism exists everywhere:
Anxiety → avoidance → relief → stronger avoidance.
Boredom → stimulation → relief → lower tolerance for boredom.
Uncertainty → reassurance seeking → relief → greater dependence on reassurance.
The important distinction is between state optimization and policy optimization.
State optimization asks:
“What makes this discomfort disappear right now?”
Policy optimization asks:
“If I repeatedly respond to this state this way, what behavior will the system learn?”
In relationships, this doesn’t mean “never chase.”
It means not chasing purely to eliminate your own discomfort, while also not punishing the other person for withdrawing.
You preserve their agency, give them room to regulate, and leave reconnection available.
Then they have to make the decision themselves.
The deeper principle:
A behavior becomes more likely when it reliably produces an outcome that satisfies the system.
So when responding to any difficult state, the question isn’t only:
“How do I feel better now?”
It is:
“What interaction pattern am I reinforcing?”

The deeper I go into building something extraordinary, the more I realize the game isn’t really about having the best strategy. It’s about building the system that can continuously generate, test and improve strategies.

I started thinking about universality in basketball: the greatest players aren’t just good at a fixed set of moves. They’re so adaptive, unpredictable and multidimensional that they break the opponent’s model of them. The moment the opponent learns the pattern, they mutate again.

Then I realized this is basically the same problem in business.

If the market is completely deterministic and nobody competes with you, you don’t need extraordinary strategy. You just solve the problem and capture the value. The difficulty explodes when other minds enter the system. Now you’re playing against adaptive systems. Your advantage becomes the ability to continuously produce new combinations, new models, new ways of solving the problem before the environment adapts.

And then I realized that building something truly unprecedented has the same property. If you’re trying to do something where there is no existing template, you’re operating in open space. There is no proven architecture for it. You can’t simply copy the path.

So the fundamental capability becomes:
Can I build systems that can continuously build better systems?
And eventually that question turns inward.
Because I am the central system through which every other system gets built.
My company, product, strategy, decisions, relationships, learning, everything is downstream of the machinery producing them.
So the highest-leverage system isn’t another business framework.
It’s me.

But even that wasn’t precise enough.
Because I realized I don’t need to become some perfectly optimized human who already knows everything. The actual objective is to build a system that continuously corrects itself against reality.

That led me to the real growth algorithm:
Predict → Act → Reality → Feedback → Update → Repeat.
Reality is the loss function.
Thinking generates hypotheses. Action exposes them to reality. Reality produces prediction error. Prediction error updates the model. The updated model generates better predictions.
That loop, repeated at enormous frequency, is how intelligence becomes judgment.
Books and mentors are incredibly powerful because they compress the search space. One hour with someone who has already made thousands of mistakes can potentially eliminate years of unnecessary exploration.
But they cannot transfer the thing that reality itself installs.

They can give you the model.
Reality gives you the intuition.
And that’s where I realized my own bottleneck.
I’ve spent a huge amount of time building models, abstractions and frameworks. I can often see the architecture of a problem before I’ve actually accumulated the experience of operating inside it.
So there is a massive dissonance:
the sophistication of my thinking can be far ahead of the sophistication of my behavior.
I can discover an incredible algorithm at night, wake up the next morning feeling completely transformed, and then destroy the entire state by returning to an old behavioral policy.
The insight didn’t fail.
It never became policy.
That became the deepest layer of the whole thing.
My thinking system generates possibilities.
My intuition contains patterns learned from previous reality.
But my policy determines what actually happens.
So there is a hierarchy:
Thinking → Insight → Intuition → Policy → Action → Reality.
If the policy layer doesn’t update, everything above it can become almost irrelevant.
It’s like having every tool required to build an extraordinary machine, having the instructions, having the materials, but not knowing how to read the instructions.
The bottleneck isn’t the database.
It’s the interface between intelligence and execution.
And that means my growth algorithm has to be applied not merely to my work, but to the system that determines whether I execute my own highest-level reasoning.
Every recurring state becomes an opportunity:
I predict what I will do.
I observe what I actually do.
I measure the discrepancy.
I update the policy.
I encounter the state again.
The new policy gets tested.
Eventually, what once required conscious reasoning becomes automatic.
That’s what I think exceptional intuition actually is: not magic, but an enormous library of compressed state → action → outcome mappings that have been trained by reality.
And this changes how I think about extraordinary people.
The question isn’t simply:
“How intelligent is this person?”
It’s:
“How quickly can this person turn reality into improved intuition, and improved intuition into better action?”
Someone with extraordinary intelligence but no reality feedback can remain an extraordinary thinker.
Someone who continuously predicts, acts, gets corrected and updates can become extraordinarily effective.
The second system compounds.
And this is why I think the ultimate objective isn’t to become the person who knows the most.
It is to become the fastest self-correcting system.
A system that is stable enough to execute every day, flexible enough to adapt, and rigorous enough not to rewrite its rules whenever emotions change.
90% stable.
10% adaptive.
Constant measurement.
Constant feedback.
Constant updating.
Not reinventing the system every time I feel different.
The system itself becomes the thing that learns.
And suddenly the trillion-dollar problem becomes much more interesting.
I’m not really trying to figure out:
“How does someone build a trillion-dollar company?”
There is no template for that.
I’m trying to solve the more upstream problem:
“How do I build a human system capable of continuously discovering systems that have never existed before?”
And the answer may be surprisingly simple:
Build the feedback loop.
Make it run every day.
Make reality the teacher.
Make policy the execution layer.
Make intuition the compressed memory of reality.
And continuously increase the rate at which the whole system learns.
The ultimate system isn’t the company I build.
It’s the system inside me that can keep building better systems.