Introduction#
When, unfortunately, I failed to attend university entrance exams because of my migraines – for the second admission campaign in row, meaning that I’ve spent two years trying to get enrolled, but in the end wasn’t able to even sit the entrance exams twice – I’ve decided that I’ve had enough.
I don’t really regret spending two years like that. I’ve learnt a lot during this period (which was quite emotionally intense) both about myself and the ways to teach myself. However, spending so much time preparing for exams intended for high schoolers isn’t the most exciting nor career-improving way to live my life.
June’25: The Beginning#
Around the same time I’ve also decided to reread “Ultralearning” by Scott Young and, I believe, it was the exact kind of luck that can be called “serendipity”. The thing is: Scott Young, among other things, got famous because he managed to finish the 4-year MIT Computer Science program in just one year.
I’m no Scott Young, so I don’t even bother trying to fit four years of studying into one. However, I realized that this is my chance to prepare for university to achieve two goals at the same time:
- passing my exams (I need at least 60/100 – the more the better, ovviamente)
- studying at least some first-year subjects before actually enrolling to make my university years a bit easier and to get some actually useful non-exam-related knowledge.
Of course, my goals directly affected the “programs” I chose for this experiment:
- Computer Science degree by Open Source Society University
- Biology course by НОО (taught in Russian)
- Math textbooks by OpenStax (recommended in the “Readings” from Open Source Society University’s Pre-college math)
Another important change was that I’ve stopped trying to do everything that I think has to be done. In July-October 2024 I tried to “get it right from the start”: solve all the ~160-200 test questions and write answers for ~10 open questions, watch 2 lectures and 2 practice sessions, take notes, make Anki cards… And that was just biology, leaving me almost no time and energy for other subjects nor fulfilling rest.
I feel like I still haven’t overcome the resulting burnout, therefore for the first time in my learning practice I’ve decided to focus not on grinding study-dedicated hours every single day, but on following the schedule. All I have to do is to watch six lectures per week, no matter how many hours it takes or whether I’m able to do homework. Obviously, it would be better to have more practical self-assignment, ask more questions, read extra sources, etc. etc., but I just can’t anymore.
Therefore the overall approach is more passive, but it’s better than nothing. At the very least I’ll get neural connections; it’s way easier to learn something new from “aha, I’ve already heard about that somewhere” than from the scratch.
Another thing worth mentioning is a study technique called interleaving. Interleaving is about not studying only one subject at a time, but rotating subjects. Given that I happen to dislike daily streaks (these are wayyyy too restrictive), it feels nice to change things that I work on from time to time. In my case, rotation happens on daily basis: math-bio-CS, math-bio-CS, day off.
July’25: The Heatwave#
In summer of 2025 Europe was struck by an intense heatwave which took lives across several countries. Only looking at 35°C (95°F) heat prediction in my weather app I’ve realized that I indeed live not so far from Europe. Well, not this far to be not affected by global climate change anyway.
It certainly didn’t help that my anti-migraine meds make me more vulnerable to high temperatures, forcing me to take a week and a half break. My brain felt like it’s melting, the fatigue and dehydration also felt worse than usual.
And you know what? I needed a break anyway, and the heatwave allowed me to explore my other interests like music and pixel art. Moreover, I believe that in long-term projects like this one being able to return after a break is more important than to work on the same project every single day.
(that’s why I dislike daily streaks – they don’t work for me!)
Another important thing was that I finally kinda got my shit together. My partner suggested to abandon my idea of getting into university and to study by myself and get a job – but this path was unclear to me despite tons of instructions on how to get into data science in 202X.
OSSU self-taught Computer Science degree inspired me to create my own curriculum, and it feels so liberating to realize that I can choose literally anything I want. Even Haskell, even category theory, even psychology of insight – all these shiny distractions have become a part of the curriculum. And it’s totally fine as long as I stick to the plan and take one course at a time instead of ten.
Study Report: July‘2025#
| Course | Intended Result | Actual Result | Things to Improve |
|---|---|---|---|
| Pre-algebra by OpenStax | To cover 8 topics: 1.1 Introduction to Whole Numbers 1.2 Add Whole Numbers 1.3 Subtract Whole Numbers 1.4 Multiply Whole Numbers 1.5 Divide Whole Numbers 2.1 Use the Language of Algebra 2.2 Evaluate, Simplify, and Translate Expressions 2.3 Solving Equations Using the Subtraction and Addition Properties of Equality | Covered 2 chapters (10 topics): 1.1 Introduction to Whole Numbers 1.2 Add Whole Numbers 1.3 Subtract Whole Numbers 1.4 Multiply Whole Numbers 1.5 Divide Whole Numbers 2.1 Use the Language of Algebra 2.2 Evaluate, Simplify, and Translate Expressions 2.3 Solving Equations Using the Subtraction and Addition Properties of Equality 2.4 Find Multiples and Factors 2.5 Prime Factorization and the Least Common Multiple | This textbook is too slow for me, which makes studying somewhat boring. I would like to cover familiar concepts faster, but I don’t know where exactly I start lacking skills. |
| Biology | To cover: - cell structure (eukaryotic & prokaryotic) - cell cycle - karyotype - mitosis - meiosis - gametogenesis - replication types | Covered: - cell structure (eukaryotic & prokaryotic) - cell cycle | Realized that I have a huge burnout due to failing exams over the last few years. Stopped prepping altogether. |
| Introduction to CS and Programming using Python | To cover: Lectures 1-8 | Covered: Lecture 1: Introduction Lecture 2: Strings, Input/Output, Branching Lecture 3: Iteration Lecture 4: Loops over Strings, Guess-and-Check, Binary Lecture 5: Floats and Approximation Methods Lecture 6: Bisection Search Lecture 7: Decomposition, Abstraction, Functions Lecture 8: Functions as Objects Lecture 9: Lambda Functions, Tuples, and Lists | The course has interestingly intertwining basic Python syntax and algorithms explanations which weren’t present in the courses I took previously. However, there aren’t enough exercises, therefore I have to look for them somewhere else. |
August’25: Let It Go#
In short, I’ve realized two things:
- I shouldn’t rush my formal education, since my health issues won’t go anywhere making it way more difficult to achieve the goals because of which I want formal education in the first place…
- It’s difficult to study several subjects at once because I get extremely confused and start switching study resources every few days.
Study Report: August’25#
| Course | Intended Result | Actual Result | Things to Improve |
|---|---|---|---|
| Introduction to CS and Programming using Python | To cover: Lectures 9-16 | Covered: Lecture 9: Lambda Functions, Tuples, and Lists Lecture 10: Lists, Mutability Lecture 11: Aliasing, Cloning Lecture 12: List Comprehension, Functions as Objects, Testing, Debugging Lecture 13: Exceptions, Assertions Lecture 14: Dictionaries + created a to-do list together with my partner using dictionaries: he was coding, I was giving directions, and both of us taught another something in the process | Sometimes I hesitate to start watching a lecture because I expect it to be cognitively expensive – only to realize that it’s not that difficult once I start. |
I also tried switching from pre-algebra to calculus, but couldn’t make myself study past the first subchapter (?) of chapter 1. While studying pre-algebra is mostly about review of, honestly, basic concepts, studying calculus intensifies overall perceived difficulty and increases friction. At a university I wouldn’t have a choice what & when I need to study, but in self-paced environment I constantly have to solve motivation issues while dealing with burnout and chronic exhaustion. Sigh.
September’25: Mostly Chillin’#
In the last days of August I somehow managed to realize that most of my needs are actually already covered. It made me surprisingly relaxed, therefore a significant part of September was dedicated to rest and following “what is interesting” instead of “what my future career might need”.
Study Report#
| Course & Intended Result | Actual Result | What to Improve |
|---|---|---|
| Introduction to CS and Programming using Python Lectures 15-26 | Lecture 15: Recursion Lecture 16: Recursion on Non-Numerics Lecture 17: Python Classes Lecture 18: More Python Class Methods Lecture 19: Inheritance Lecture 20: Fitness Tracker Object-Oriented Programming Example Lecture 21: Timing Programs, Counting Operations | For some reason lessons covering classes were EXTREMELY boring while I haven’t noticed any substantial changes in how the material was presented. |
| Making Math Click: Understand Math Without Fear Module 1? (4 weeks = 8 lessons) | MODULE 1 1-1: Rethinking Math Struggles 1-2: Starting at the Right Level 1-3: Focus and Diffuse: Your Brain’s Learning Rhythm M1L1 Quiz partially read Growth mindset in mathematics: A bibliometric analysis from the list of optional reading 1-4: From Conscious to Automatic: How Your Brain Builds Mathematical Mastery | Couldn’t understand which pace would be right for me, given that the course is divided into very small pieces (videos are 6-11 minutes long, but there are 3-4 videos + one assignment per lesson). More small things to start = more opportunities to stop learning altogether after finishing another piece = flow issues. |
| Statistics: The Art and Science of Learning from Data (Alan Agresti et al.) | CHAPTER 1: 1.1 Using Data to Answer Statistical Questions 1.2 Sample Versus Population | |
| Древнескандинавский язык / Древнеисландский язык / Old Norse - YouTube (taught in Russian using a textbook in English) | ||
| Italian course (taught by a 4th year student from MSU, Moscow) | visited 2nd half of the first lesson (alphabet, pronunciation, 1st declension Presente Indicativo Semplice) started composing “WIKA Italian Alphabet & Pronunciation & Stress & Accents” | I didn’t want to enroll into any synchronous courses this year, but here I am???? Gosh, some things don’t change, do they writing WIKA is difficult. no way I would be able to do this regularly while having 1000s other things to do at university |