Lelouch.dev | Blog Posts (Full Content)

Stuck in a Rut? Here’s Your Way Out

Fri, 04 Oct 2024 00:00:00 GMT

Four months of intensity, then… nothing. For four months, I was locked in, powering through six focused-hours daily with no slacking—filled with real learning and rapid progress in math, computer science topics, and coding.

My routine began with mathematics, gradually incorporating computer science topics and coding, before ultimately returning to math once again.

Then, month five hit me like a gut punch: I kinda felt sick of it.

Raw-dogging math every day was taking its toll. I noticed it was taking me twice as long to learn the material of the same difficulty, and now family holiday trips were lined up.

Suddenly, I found myself losing months' worth of forward momentum. What the heck??

Final straw? Summer heat and travel. First, I had to leave for a family vacation. Nothing kills your flow like stepping out of your environment. Then I came back, but the blazing summer heat made it feel impossible to kickstart myself again.

To top it off, another trip abroad smashed any semblance of routine I had left—my productivity, diet, sleep schedule, and workouts? Wrecked.

It’s October now, and I’m ready to start fixing it. So here’s my plan (and what you can do if you’re in a similar situation).

Fix Your Sleep. Seriously.

If your sleep is off, everything else will fall apart. You might think your day starts when you wake up, but that’s where you’re wrong.

It starts the evening before.

If you fail to prepare—staying up late, scrolling mindlessly, or stressing—you can kiss your next day goodbye.

Lack of sleep isn’t just about feeling tired.

Think about it—Bryan Johnson talks about how lack of sleep kills more than just your energy; it decimates your motivation. When you’re sleep-deprived, it’s easy to spiral. Your vision narrows, your optimism fades, and suddenly you’re convincing yourself that you’re just not cut out for this shit.

On the flip side, wake up well-rested, and suddenly everything seems possible. You can just do things, right??

(P.S. I’ll write more about sleep optimization in a future post—stay tuned.)

Next—Get Your Environment in Check

You wake up refreshed. What’s next? Jump right into work?

No. Absolutely not. Look around you. Is your room a mess? Is your desk cluttered? Clean that up first.

Your physical environment mirrors your mental state, and right now, you need clarity, not chaos. It’s the simplest fix, but you’ll be surprised how often people skip it.

Clean space? Good. Now, let’s start cooking.

Consistent Physical Exercise

Let’s be real: if you're not training at least three times a week, you're not passing the 80/20 threshold. It’s not about getting jacked or ripped—that's a side effect. This is about something much more vital: mental dominance.

Hitting the gym or doing any sufficiently difficult physical exercise is like plugging yourself into a power source. You walk out stronger, not just in body but in mind. You feel like a badass—by default.

Do You Know What You Want? Clarity Is Non-Negotiable.

Most people fail because they lack clarity. They have vague, soft goals and wonder why they never hit them and are never satisfied.

If you want to win, imagine a future that truly excites you—and be specific about it. Don’t say you want to be rich. That’s some vague bullshit.

Bad example: "I want to be successful."

Good examples: "I want to lead a team of 10 engineers building products that advance SOTA AI."

"I want to earn $500K a year as a product manager for a top-tier tech company."

"I want to build a SaaS business that generates $100K in monthly recurring revenue within three years."

Your goals need precision. DO NOT BE A COWARD. Vague goals are for vague people. Be honest with yourself about what lights a fire inside you.

Reverse Engineer Your Vision

Got that clear picture in your head? Now start thinking in probabilities and life routes.

Want six-pack abs? Cool. What has to be true for that to happen? Maybe you're on a calorie deficit and hitting the gym regularly.

Want to run a marathon? What has to be true? You’re probably training consistently, following a structured plan, and ensuring proper nutrition and recovery.

Want to write a book? What has to be true? You’re dedicating time every day to writing, outlining your chapters, and staying disciplined with deadlines.

Now let’s zoom out to your larger goals—whether it's landing a dream job or attracting a certain type of partner—what must happen before those things are true? Break it down into actionable steps. Let’s say you list 30 things that need to happen. Great. That’s your map.

Now, it’s time to start checking off those boxes.

Your Three Daily Non-Negotiable Tasks

I know you want to crush everything at once. Stop right there. Focus on three key tasks for today. That’s it. These three tasks should be doable, but strategic.

They should make you feel proud when your head hits the pillow.

Why only three? Because most people wildly overestimate what they can achieve in a single day/week but completely underestimate what they can accomplish in a month/year (by stacking successful days).

Three tasks keep you focused on what is important so you don’t spend your days on low-impact tasks that don’t move the needle.

If you finish them early? Sure, go ahead and tackle more. But these three are your non-negotiables.

The real goal? To end your day feeling like a champion—like today wasn’t just productive but decisive.

You did what you set out to do, and that builds self-confidence.

Each day, you’re proving to yourself that you can show up, deliver, and finish.

Control What You Can Control

Most people set themselves up for failure by aiming for outcomes they can’t fully control.

Don’t be that person.

Instead of saying, “I’ll learn X by the end of the day,” set a goal you can control, like, “I’ll study for three hours.” You can’t guarantee learning something in one sitting, but you can guarantee showing up.

Consistency > Perfection. It’s the daily grind that builds the results. Stack up those small wins, and soon enough, you’ll be miles ahead.

Struggling With Focus? Avoid These Common Pitfalls

Focus isn’t just about willpower. It’s about setting the right conditions for your brain to perform at its best. Here are a few low hanging fruits:

1. Dopamine Detox: Fun Is Killing Your Focus

Video games, social media, Netflix—they’re engineered to be more fun than doing hard work like math or coding. That’s the dopamine trap. The more you chase those cheap thrills, the harder it is to get anything meaningful done. Eliminate the distractions, or they’ll eliminate your focus.

2. Delayed Gratification

Start practicing delayed gratification. Skip the easy wins—hold off on the dopamine rush until you’ve earned it. The longer you delay, the stronger your focus becomes.

3. Bad Carbs, Bad Brain

Eat junk, and your brain will work like junk. Keep your food light and clean during the day, and save the heavy carbs for when you’re winding down. Energy crashes aren’t part of the plan.

4. Don’t Overstuff Yourself

Overeating will bury your focus in a fog. Keep your meals tight, fueling you just enough to get through the grind.

5. Stimulants? Not the Shortcut You Think

Adderall, Ritalin, Modafinil, Caffeine (etc, etc.)—yes, they work. But here’s the truth: if you’re running on sleep deprivation, bad diet, and a messy routine, they’re not giving you an edge. They’re just bringing you back to baseline—the state where you should already be if your fundamentals were in place (roughly speaking)

You think you’re getting ahead, but in reality, you’re just catching up to those who have their basics dialed in. Fix your foundation first, and then these extras might actually give you a boost.

“But Lelouch, What If I Fail?” Bruh.

Failing a day doesn’t mean you’ve failed your mission. Perfectionism is the enemy here. You’re going to have bad days. It’s inevitable. The real damage isn’t the day you failed; it’s what you do next. The worst mistake? Beating yourself up and letting it destroy your momentum.

The real mistake is letting one slip-up snowball into something bigger. Don’t do that.

Get up. Recalibrate. Move forward.

Final Thoughts

This isn’t the first time I’ve hit a rut, and it sure as hell won’t be the last.

Ruts happen to everyone, and they’re inevitable.

It’s how you respond that defines the outcome. This isn’t the first time I’ve felt lost, and it won’t be the last.

But every time, I build a system that helps me claw my way back.

That’s the point of all this—having a game plan. This article? It’s just as much for future me as it is for you. Because the next time I find myself stuck, I’ll follow this path and break out, just like I have before.

Pushovers Finish Last — Learn to Say No

Fri, 04 Oct 2024 00:00:00 GMT

Can you hop on a quick Zoom call? Can you hang out tonight even though you’re exhausted and should be sleeping? Can you listen to my rant for an hour even though I never take your advice?

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No.

That’s what it takes to achieve... well whatever you want.

I didn’t always get this.

For a while, I was saying yes to every dinner invite, every request for help, every random hangout. I thought I was being supportive, but days passed, and I looked back only to realize I had worked three days out of the last seven.

Every single day was pulled in a different direction by someone else’s needs—none of which moved me closer to my own goals.

By the time I got home, I was drained. I had no energy left to focus on the projects or learning that actually mattered to me.

And that’s when it hit me: I was sacrificing my ambitions to ensure everyone else was happy. That’s not how greatness happens.

In a world obsessed with productivity hacks, endless morning routines, and overstuffed to-do lists, greatness isn’t about doing more—it’s about doing less, a lot less. It’s about ruthless focus on what moves the needle and cutting out everything else.

You need a prolonged, distraction-free environment, laser-focused on mastering this one thing.

"You’re too dramatic!" Oh, am I? Well, tell me, what great thing have you ever accomplished by saying YES to everyone around you?

(I’m waiting…)

Exactly.

Less is More

We live in an age where productivity has become synonymous with being busy. But real productivity is about clarity and results, not checking off 100 boxes for the sake of feeling accomplished.

Think about programming: instead of adding another layer of complexity to your code, what if you focused on removing unnecessary lines? That’s what clarity looks like—cutting away the excess to reveal what truly matters.

Steve Jobs was relentless about saying no. (Maybe even too much.) He believed that "focus is about saying no to 1,000 things" so you can concentrate on the few that matter. That’s why Apple’s products became synonymous with elegance and simplicity (unlike cough Windows).

Jobs didn’t fill the pipeline with cluttered features because he knew that focusing on the essentials would lead to excellence.

Saying No to Good for the Sake of Great

Most people start their day with a yes mentality—yes to meetings, yes to side projects, yes to distractions.

It’s like taking on technical debt willingly, with the expectation that you can just refactor it later. But that debt builds, and you’ll find yourself swamped with the complexity of managing it instead of advancing your project.

Imagine if you started with no instead. No to unnecessary distractions, no to non-essential requests, no to the noise. You’ll find that saying no opens up the bandwidth for the good stuff, the ideas and tasks that actually move you forward.

Take Warren Buffett, for example. He famously said, "The difference between successful people and really successful people is that really successful people say no to almost everything." What does that give you? It gives you the space to make calculated, deliberate moves that compound over time.

Mediocrity Loves Company: Be Ready to Disappoint Others

You will disappoint people. If you want to keep everyone happy, prepare for mediocrity. Every time you say 'no,' someone will be disappointed, but greatness isn’t a popularity contest.

Think of it like version control. Every time you push a new change, not everyone will agree with your decisions. But if you’re pushing for a stable, optimized product, you’ll need to reject the features that don’t align with the long-term vision.

In life, that means pushing back against distractions and protecting your time.

Look at Michael Jordan—in his prime, he was known for his obsessive focus on training. He missed social events, interviews, and countless leisure opportunities because he knew that to be the greatest, he couldn’t say yes to anything that didn’t serve his ultimate goal.

What did he gain? Six NBA championships, worldwide respect, and a legacy as the greatest of all time.

And here’s a controversial thought: those who demand your yes don’t actually want to see you succeed—they want to keep you at their level. Your success threatens their mediocrity.

Conclusion: Don’t Be a Dick About It

Let’s be clear—saying no doesn’t give you a free pass to be rude. You don’t need to bulldoze through relationships just because you’re focused on your goals. The trick is to be firm but polite. Set boundaries, but don’t burn bridges.

When you say no, explain why. Help people understand that it’s not personal—you’re investing in something bigger.

It’s like coding with a team: if you’re working on the critical path, you don’t just disappear without an explanation. You communicate. You keep everyone on the same page. Saying no is about respecting your time, but also respecting the people around you.

Ultimately, greatness doesn’t have to come at the cost of your relationships. You don’t need to isolate yourself to stay focused. The people who matter will understand if you bring them along for the ride, so long as you’re clear and honest about where you're headed.

So, don’t be a dick—be decisive, be respectful, and make sure everyone knows why you’re saying no.

The Thought Process Behind My Learning Plan

Mon, 26 Aug 2024 00:00:00 GMT

About six months ago, I decided to switch from marketing to coding. To make the switch, I would need to map the territory.

After binge-watching countless YouTube videos and soaking in all the advice I could find, I decided to read a book called Machine Learning with PyTorch and Scikit-Learn.

But soon after, I hit a wall. I realized that to truly grasp the algorithms discussed in the book, I needed a solid foundation in mathematics.

That marked the beginning of my math grind.

First Course Correction

I had a clear direction in mind: Khan Academy would be my gateway to mastering the essentials. But, like a ship navigating uncharted waters, I soon found myself altering course.

Within 30 days, I pivoted to full-fledged university courses, introduced a textbook ten days later, and ultimately signed up for Math Academy, scrapping every resource I previously carefully collected.

This constant change might seem like a lack of strategy.

I strongly disagree.

Your North Star and the 10,000 Hour Rule

In marketing, there’s a discussion about the "North Star Metric" (NSM)—a fixed point that guides you through the fog, much like a ship’s compass in a storm.

It’s your long-term vision, the one metric that matters, steering you toward success.

When building a learning roadmap, the NSM is equally critical. It helps you stay focused on your ultimate goal, even as you make necessary adjustments along the way.

But how do you decide what your North Star is? And what was mine?

For me, the North Star was simple: start a company and build fun, cool-ass products that make money. I wasn't interested in research for the sake of research or advancing humanity's frontier.

Structured in the Macro, Chaotic in the Micro

My approach to building a roadmap is to set a big goal, break it down into milestones, and then attack those milestones with daily intensity.

I believe in being structured in the macro—having a clear overall direction—while embracing chaos in the micro, the day-to-day.

I don't worry if my roadmap isn't perfect.

Instead, I focus on putting in the work, clocking those 10,000 hours (not that it's required to reach mastery).

As Tim Ferriss once scoffed at Malcolm Gladwell's "10,000 Hour Rule," it's not just about the number of hours but how you use them.

Do I have everything planned out at every moment? Absolutely not.

I give myself the freedom to do what feels right day-to-day, but I keep it real with myself—understanding what actually moves the needle versus what is just procrastination.

Why It’s Okay to Make Big Changes to the Plan

This year, I shifted my focus multiple times, and you might think that reflects poor planning. But the truth is, it’s entirely expected that your first plan of attack isn’t the best.

Why? Because you lack information. As you gain more insights, you naturally iterate and improve the plan. But there’s another reason I often make learning detours: to avoid burnout.

Sometimes, I find myself slowing down, losing enthusiasm, and feeling stuck—especially when the work becomes challenging, and progress feels slow, like when math started to get really tough.

A great way to reset and refresh is to shift gears and do something completely different.

For example, after my first two months of intense focus, I decided to take a break and dive into something new: I installed Linux, learned the command line, and got familiar with Git and GitHub.

<blockquote class="twitter-tweet" data-theme="dark"><p lang="en" dir="ltr">ML progress report (days 55 & 56)<br>Current stage: Git & Linux<br>Learned for: 2h yesterday & 4h today<br>Satisfied? Yup<br><br>Got curious about Linux and Git/GitHub, so will be learning it next few days. Traveled the last two days (tomorrow, too), so have limited opportunity to study. <a href="https://t.co/NL97mZ58Fx">pic.twitter.com/NL97mZ58Fx</a></p>— Lelouch 👾 (@lelouchdaily) <a href="https://twitter.com/lelouchdaily/status/1786128734824575081?ref_src=twsrc%5Etfw">May 2, 2024</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

It was a complete change of pace that allowed me to return to my main goals with renewed energy.

I’m currently doing this again by learning web development, which helped me build this website and will eventually help me build bad-ass user experiences. This change isn’t a distraction; it’s an intentional shift that keeps me engaged and prevents burnout.

The name of the game is QUICK ITERATION. It’s not about sticking rigidly to a plan; it’s about adapting as you learn more, updating your approach, and continually refining your strategy.

Need a Checklist?

So, how do you actually go about designing your own learning roadmap? Here's an approach that has worked for me:

Define the End Goal: What do you want to achieve? Whether it's becoming a full-stack developer, mastering machine learning, or something else entirely, you need to have a clear destination in mind.
Identify the Skills Needed: What skills do you need to reach your goal? A good idea is to start mapping the territory, which means consuming as much information as possible across various sources to get a big picture of the state of the industry/field. You will not only identify the key players, companies, and technologies, but you will also learn the vocabulary & terminology used (WHICH IS CRUCIAL FOR FURTHER RESEARCH).
Start Mapping the Field: Open a notepad (or something more sturdy) and start writing the list in bullet points. Make sure you put everything on paper—just brain-dump it. Don't worry about the order; the goal is to get all your thoughts out.
Decide Your Learning Style: Are you a top-to-bottom or bottom-to-top learner? I personally prefer top-to-bottom because it gives me an idea of what I'm learning for. This approach lets you understand the broader picture before diving into the nitty-gritty.
Break It Down into Milestones: Once you've mapped out what you need to learn, break it down into smaller, manageable milestones. These milestones should be challenging yet achievable so you can build momentum and feel a sense of accomplishment as you progress.
Stay Flexible: Your roadmap is a guide, not a rigid set of instructions. Be ready to make changes based on new information and insights.

The key is to keep moving forward, even if that means adjusting your course.

Iterate Quickly: Don’t be afraid to pivot or refine your plan. You’ll naturally discover better ways to reach your goal as you learn more. The faster you iterate, the quicker you’ll find what works best for you.

Final Thoughts

One of my favorite quotes by Abraham Lincoln is:

“Give me six hours to chop down a tree, and I will spend the first four sharpening the axe.”

It emphasizes the importance of having the right plan and using the right tools.

BUUUUUT

Avoid getting stuck in analysis paralysis.

You’re not an expert lumberjack just yet, and that’s okay.

The most common message I receive in my DMs is from people paralyzed by uncertainty, unsure of where to start, and wasting precious hours that could’ve been spent taking action.

What action? ANY action.

When you’re just starting out, you don’t have all the information you need to craft the perfect plan.

And you don’t need to. The key is to run with what you’ve got.

As you encounter your first roadblocks and identify new ways to improve, remember your North Star. Stay focused on your ultimate goal, but don’t be afraid to make adjustments along the way.

Progress is more important than perfection.

So, sharpen your axe as best as you can, but when the time comes, start chopping!

The path will become clearer with each swing.

Math For Machine Learning [Resources]

Mon, 26 Aug 2024 00:00:00 GMT

No time to read the whole post? I get it.

Here’s the quick and dirty resource list to get you straight to the good stuff:

(source: Wale Akinfaderin)

Disclaimer: These aren’t necessarily the best resources on the subject, nor have I mastered all of them. Instead, they’re the ones I’ve personally tried and liked or have been recommended by credible friends on X.

Update: about mid-way through learning Calculus 2, a friend recommended MathAcademy.com, an AI-powered platform for learning math.

I immediately fell in love due to its gamified UI, bite-sized lessons, and tremendous thought that went into picking and implementing the cutting-edge cognitive learning theory.

Frankly, I stopped consuming other math resources ever since then and just started grinding lessons on the platform.

(While I love their team, I’m not affiliated in any way.)

At the time of writing this, they’re my go-to recommendation for learning math.

That being said, they’re a paid platform (and not everyone can afford it), so I’ll do my best to present other, more standard resources below!

I highly recommend checking out this book to get an idea of everything you’ll probably need for ML: Mathematics for Machine Learning.

1. Pre-Algebra, Algebra, and Pre-Calculus: Laying the Foundation

Before you can tackle the more advanced topics, it’s crucial to get a solid grasp on the basics. This includes everything from basic arithmetic and simple equations to linear equations and logarithms. Think of this as the groundwork that will support all your future learning.

Khan Academy

The material here is easy enough, and I like the format. The only advice is to practice exercises beyond what Khan Academy provides. I’ve not read any textbooks here, so I can’t recommend more.

Pro Tip: It’s easy to overlook key areas, especially when learning on your own. Make sure to cover each topic thoroughly to avoid gaps in your knowledge later on.

2. Linear Algebra: The Backbone of Machine Learning

Mastering arithmetic is just the beginning. Now, let’s move into Linear Algebra—the backbone of almost every machine learning algorithm. This section covers vectors, matrices, determinants, eigenvalues, and linear transformations, which are all crucial for understanding how machine learning models work under the hood.

Textbook: Linear Algebra and Its Applications by David Lay (primarily)
Courses:
- YouTube Playlist
Interactive website:
- Book with fully interactive figures

Worthy mention: I’ve seen many people recommend Prof. Gilbert Strang’s lectures, but I personally found them difficult to follow due to the low-quality video and confusing reading material.

Calculus

Next up is Calculus, where you’ll learn about limits, derivatives, integrals, and their applications. This is also where you’ll dive into functions of several variables, covering topics like partial derivatives and optimization—both critical for machine learning.

Textbooks (pick either one):

Calculus by Ron Larson (I used the 12th edition)
Calculus: Early Transcendentals by James Stewart

Courses:

Prof. Leonard’s Lectures

Prof. Leonard’s lectures follow Ron Larson’s book, making them a perfect pair, in my opinion.

Supplemental material:

3Blue1Brown Essence of Calculus playlist
‘All of Calculus in under 3 minutes’ videos (good for a quick overview)
- Video 1
- Video 2
- Video 3
- Video 3b

Why It Matters: It’s tempting to memorize formulas, but true understanding comes from knowing how and why these concepts work.

For example, grasping vector calculus is crucial for understanding gradient descent—a fundamental algorithm in machine learning.

Probability and Statistics

Understanding probability theory, random variables, distributions, and inferential statistics is essential for making informed decisions in machine learning. These concepts will help you understand how to handle data and make predictions.

Khan Academy: Probability and Statistics 6.041 Probabilistic Systems Analysis and Applied Probability - an amazing addition by Siddhant

Try This: Apply what you learn by analyzing a simple dataset using Python, and see how statistical methods influence machine learning outcomes.

If you have awesome resources to recommend here, let me know!

Good Channels When Stuck

Some of My Favorite Posts/Videos on Math & ML

ML Resources by @wateriscoding
From High-school to Cutting-Edge ML/AI by Justin
The ML Resources Stash by Arpit
LLM course
Complete Mathematics of Neural Networks & Deep Learning by Adam Dhalla
The Mathematics of Machine Learning by Wale Akinfaderin
Don't worry, I'll be updating this list over time!
Cambridge Math Notes - sent to by @MathmoThe on X and written by Dexter Chua.

My Math Journey So Far

In March this year, I switched from marketing to code—specifically, Machine Learning and AI.

I picked up a book called Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka and his colleagues.

The book starts with building a perceptron model, a fundamental neural network concept.

Perceptron Model: The perceptron is the simplest type of artificial neural network, with a single layer of weights connected to input features. It makes decisions by calculating a weighted sum of the inputs and passing it through a step function. If the sum is above a certain threshold, it classifies the input as one class; otherwise, it classifies it as another. It’s the basis for understanding more complex models.

(source: Machine Learning with PyTorch and Scikit-Learn)

I remember feeling a sense of accomplishment when I first understood how the perceptron worked. But this was just the beginning.

Quickly after that, I learned about the Adaline model (Adaptive Linear Neuron) and this was where things started to get a bit more complicated.

Adaline vs. Perceptron: The key difference between Adaline (also known as the Widrow-Hoff rule or simply the Delta Learning Rule) and Rosenblatt’s perceptron is how the weights are updated. In Adaline, the weights are adjusted based on a linear activation function rather than a unit step function, which is used in the perceptron. Specifically, Adaline uses a linear activation function, 𝜎(𝑧) = 𝑧, which is simply the identity function of the net input.

This subtle change has a big implication: Adaline uses an algorithm called gradient descent for weight updates. I quickly realized that to fully grasp what was happening, I needed to understand gradient descent—a concept I never heard about before.

(source: Machine Learning with PyTorch and Scikit-Learn)

The Importance of Vector Calculus

No problem, I thought, let’s learn gradient descent. However, I soon found out that to truly understand gradient descent (and not just memorize the steps), I needed to dive into Vector Calculus.

Vector Calculus: Gradient descent involves calculating the gradient of a function, which is a vector that points in the direction of the steepest ascent. To minimize a function, you move in the opposite direction of the gradient. This concept is central to optimizing machine learning algorithms, but it’s rooted deeply in vector calculus.

This was a turning point for me. I started getting hints all over the place that math wasn’t just helpful—it was crucial for an in-depth understanding of machine learning.

But I faced a big question: How the hell do I learn math alone?!

Time For An Actual Learning Plan, I guess

The first thing I did was open Google and start searching for a math teacher. Since money wasn’t an issue, I thought hiring a tutor would be the fastest way to upskill. But things didn’t go as smoothly as I had hoped.

A few lessons in, I realized two major problems:

Lack of Preparation: The tutor would start each lesson with, “What do you want to learn today?”—despite me having sent them the areas I wanted to cover beforehand. This approach felt disorganized and left me feeling more lost than before.
Misaligned Teaching Style: Their explanations and analogies didn’t resonate with me. I quickly found that I was learning WAY faster by prompting ChatGPT than through the tutoring sessions.

Transitioning to Self-Learning

After firing the tutor and continuing to use ChatGPT, I soon realized that while LLMs are great at explaining concepts on the spot, they are TERRIBLE at ensuring I covered every essential topic in a particular math area.

This became painfully clear when I advanced to new lessons and found that areas I should have already mastered were completely foreign to me.

I knew I needed a more structured approach—something that would ensure I wasn’t skipping critical concepts.

Khan Academy Arc: I quickly stumbled upon Khan Academy. It seemed like the perfect solution at the time: it’s free, well-structured, and covers a wide range of topics. However, I soon encountered some limitations.

<blockquote class="twitter-tweet" data-theme="dark"><p lang="en" dir="ltr">ML progress report (day 24)<br>Current stage: noob-level math grind<br>Learned for: ~2h<br>Satisfied? Definitely not<br><br>Started doing pre-calculus at Khan Academy. Will put in more hours tomorrow - Pinky promise! <a href="https://t.co/JSpgJXbz7u">pic.twitter.com/JSpgJXbz7u</a></p>— Lelouch 👾 (@lelouchdaily) <a href="https://twitter.com/lelouchdaily/status/1774210394208309474?ref_src=twsrc%5Etfw">March 30, 2024</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

While the material is well-organized and easy to follow, I found that it lacked the depth and rigor needed to truly master the concepts.

The exercises were limited and not particularly challenging, which gave me a false sense of proficiency. I realized that I needed something more comprehensive to really test my understanding and push me further.

That’s when I decided to pick up a course or textbook to use as a guide.

Prof. Leonard’s Lectures: A Game-Changer

My search led me to Prof. Leonard’s lectures, which turned out to be a game-changer. I particularly liked Prof. Leonard’s teaching style and, of course, his buff physique!

<blockquote class="twitter-tweet" data-theme="dark"><p lang="en" dir="ltr">ML progress report (day 39)<br>Current stage: noob-level math grind<br>Learned for: 6h<br>Satisfied? Yes<br><br>Finally started learning Calc 1 by watching Prof. Leonard's lecture series: length ~35h, so I will either:<br>a) only watch lectures for next 7 days<br>b) do 50/50% with code for 14 days <a href="https://t.co/lJCwAAtF9N">pic.twitter.com/lJCwAAtF9N</a></p>— Lelouch 👾 (@lelouchdaily) <a href="https://twitter.com/lelouchdaily/status/1779631232847905226?ref_src=twsrc%5Etfw">April 14, 2024</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

His explanations were clear, and I felt like I was learning quickly and efficiently. It was smooth sailing for a while, and I was making good progress. But then, I realized another grave mistake I was making: I wasn’t practicing enough exercises at all.

The Importance of Practice

It’s easy to fall into the trap of thinking you understand material just because you can follow along with a lecture or video. But true mastery of mathematics comes from practice.

Without working through problems on your own, it’s impossible to solidify the concepts in your mind or to apply them effectively.

Watching videos or attending lectures gives you a false sense of confidence—it's the practice that builds real skill.

Finding the Right Textbook

Realizing the need for more practice, I asked around and tested a few textbooks. Eventually, I landed on Ron Larson’s Calculus textbook, which I really liked.

The explanations were clear and well-structured, and they synced perfectly with Prof. Leonard’s lessons. This combination became my new roadmap for learning.

Conclusion

I continued with Prof. Leonard’s lectures and Ron Larson’s textbook until I eventually switched to Math Academy.

At the time of writing this, Math Academy is my go-to resource for learning math.

The journey hasn’t been a straight line, but by combining structured resources with plenty of practice, I’m slowly starting to feel confident in my math skills.

Appreciate you reading to the end! :)

You Are NOT Dumb, You Just Lack the Prerequisites

Mon, 19 Aug 2024 00:00:00 GMT

I always thought I was too "dumb" to understand math. During my school years, it was evident to me that for some kids math was easy, and for others like myself: painfully difficult.

This belief shadowed me for years, a constant reminder that while believe I am smart... I'm not THAT smart.

Recently, after 150 days immersed in learning math, I had a stark realization.

The struggle wasn't because I wasn't capable, but rather, I was simply missing a shit-ton of pre-requisite knowledge.

It's like trying to defeat a Elden Ring boss... at level 1.

It's like walking into a movie halfway through—you can't understand the plot because you missed the beginning.

The same goes for learning complex subjects like math, CS, whatever.

The gap in comprehension wasn't due to a lack of ability but rather a gap in prerequisite knowledge.

So here's a gentle reminder: You're not dumb—you just might not have all the prerequisites yet.

Building from the Ground Up

Realizing this, I decided to rebuild my math skills from the ground up. I revisited the basics I had glossed over or misunderstood in the past.

Going further back in my material, picking better learning methods and practicing like hell.

This process was slow, often humbling, but it was necessary. Each foundational concept I mastered laid the groundwork for more complex theories and problems.

Have I turned THAT smart yet?

Hell no. In fact, I'm still pretty dumb. But I'm slowly getting there.

Why I Started Documenting My Grind—and Why You Might Consider It Too

Sun, 18 Aug 2024 00:00:00 GMT

I originally began documenting my learning journey on X as a fun, casual pact with a new internet friend. We wanted to keep each other accountable.

What began as a personal log, getting me zero views and likes, unexpectedly changed after my account gained traction on day 28, thanks to a viral post.

Suddenly, I found myself publically my daily struggles in learning ML.

Strategy

The initial plan was straightforward: post daily without stressing about crazy progress but rather focusing on the number of hours of concentrated effort each day.

Actually inspired by Andrej Karpathy's advice to machine learning beginners:

My posts still include:

Day:
Learning stage:
Satisfaction:
Daily activities
Occasionally, insights on how my day went and areas needing improvement

Unexpected Benefits

Here’s why you might want to start a similar journey:

Consistency: Regular posts helped me build a habit of daily focus and effort.
Openness: Sharing your struggles teaches you not to fear discussing your work openly.
Networking: It opens doors to new connections and opportunities.
Feedback: You receive direct criticism and suggestions that can refine your strategy.

Facing Common Objections

"Will it bore people?" People often mindlessly scroll through social media. The chances of overloading your followers are slim; many may not even recall daily specifics, allowing you to reinforce your message without becoming overbearing.
"My work is too mundane." If you think your daily grind is uninteresting, remember, there's likely someone out there in the same shoes you once were, eager for guidance or insight you can provide.

Adding Variety In Learning Material

While my posts have mainly focused on math and entry machine learning, they have diversified over time to include topics like Linux, OSs, electronics, and, lately, web development.

I've realized that many of these topics are intertwined and require a holistic understanding to maximize the scope of what’s possible.

Why "Follow My Journey" Works Better Than "Here's How to Do X"

Pretending to be an expert from the start can make content creation daunting and inauthentic. Especially if you're learning the material yourself on the go!

By sharing your journey, you drop the baggage of having to appear as if you know everything, making the process more engaging and relatable.

You'll also make more real friends along the way :)

From Marketing to Code: Why I Made the Switch

Sat, 17 Aug 2024 00:00:00 GMT

Ever since I was a kid, I dreamed of running my own company, inspired by industry giants and eager to make a mark as they did.

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I had a stark realization in my teenage years after talking to my then mentors: marketing is the secret missing ingredient.

Many great product-focused startups silently die without anyone ever knowing.

Depressing, yet factual.

Since I wanted to operate a B2B company, I decided to start with SEO and content marketing. It had a solid track record of securing leads and required very little starting capital.

That’s where I spent my first 5 working years: 3 years working as head of content marketing for different agencies and another 2 running my own agency working with funded US tech companies.

Unfortunately, post-COVID, the realm became dominated by a single mantra among clients: ROI, ROI, ROI.

Clients prioritized quick bucks or immediate savings over an established long-term strategy, and slowly my passion suffocated in a sea of soulless meetings and cutthroat competition.

Then it hit me.

I was making good money, but I wasn’t learning anything new.

During a routine client meeting, with yet another discussion centered not on quality or innovation but on potential savings and quick returns.

“Our prospects won’t even notice the quality drop. Can we just use AI and push out MORE content?” they said.

This relentless focus on immediate gains over long-term strategy was disheartening.

It wasn’t just the monotony; it was the realization that my values were increasingly at odds with the industry’s direction.

I was so tired of seeing poorly written, ‘nothing-burger’ articles on Google.

The market had become a bloody battle over who could offer the cheapest content, with little regard for loyalty or quality.

I was slowly looking for a new direction…

Aaaaand I’m Out Moment

When ChatGPT-3.5 came out, I disregarded the impact it would make, correctly assessing that the quality was worse than your average writer for the topics we were covering. But when ChatGPT-4 was released, I knew everyone was in trouble.

I accurately predicted two major areas of impact:

Pricing Pressure: Pricing content at a premium was going to be difficult because rarely was the C-suite informed enough to see the difference between an amazing content piece and a decent one, but they knew the difference in the price they were paying.
Global Competition: Way more competition, especially from countries where lower living expenses could allow them to charge packages at razor-thin margins and STILL cover all expenses.

New Beginning

I thought it was a perfect opportunity to move to programming because I thought I was ready to start building cool products I could have control over.

Feeling like a little kid again, I envisioned a future where all of the boring stuff I and others didn’t want to do, could be automated and significantly improved.

I immediately got excited and imagined a different life where profits weren’t the only thing on my mind ALL the time.

I could actually geek around and poke the world, making it even an incredibly tiny bit more to my liking.

I picked up a book, and realized I knew absolutely nothing about anything, and no skills were transferable.

In addition—my math skills were barely beyond elementary.

If I was to truly understand AI, I needed to grasp the algorithms and principles powering these technologies. That meant confronting my biggest limitation: math.

Back to Basics

Determined, I plunged into a rigorous self-study regimen. In my math roadmap article, I’ll be noting (and regularly updating) my approach with a log of all the changes I made along the way. I also wrote a bit about how I approach self-learning, if you wanna check that out.

I tackled calculus head-on, often finding myself perplexed by concepts that seemed designed to confuse me.

From the perceptron model in machine learning to gradient descent, every new topic was a reminder of how much I still had to learn.

Yet, each new challenge excites me!