On the advice of a commenter in a different HN thread I recently read Cal Newport’s book, “So Good They Can’t Ignore You.” The main premise is that passion results from valuable skills built via hard work over years of deliberate practice. Only after you have those valuable skills, the book argues, do you have the leverage to dictate your life in a way many people find necessary for satisfaction. Passion follows from there.
The book is a fun read, but that’s basically the entire takeaway. And I find it to be a compelling argument.
Passion does need to be found, but it seems logical that your skills will lead you to the passion, rather than the other way around.
That seems like a load of crap, I have a few passions ever since 13 year old and 30 years later they're still in me even though I barely ever practiced them.
If that's the core idea of the entire book then I'm glad I'll save my money and time not to read it. Generalizations are dumb.
What do you drink in the morning for working years for a skill only to hope passion shows up. Imo, if you actually listen to yourself(if you can hear) its easy to find out if you are passionate about certain things. No need to spend 10 years hating woodworking only to find no passion for it lol.Self help authors these days.
Mathematics. Learning and doing pure (proof based) Mathematics. I was really good at it in high school and college, and it was the only thing I truly enjoyed.
So I got curious, picked up a book from undergrad curriculum and started learning myself. Got more curious, and I enrolled in a Masters program and got a Masters.
I don't have much time these days, but eventually I will get back to it and continue learning more. Perhaps one day, after I have retired or scaled down in my current job, I will pursue a PhD in it.
If you are curious about how I was able to accurately pinpoint "Mathematics" as my passion, then read on...
I was very disappointed by the lack of "scienciness" in software engineering. It wasn't even true engineering in my eyes as in, there were no calculations I needed to do, no statistics to keep in mind. It was just pure coding until something works. That wasn't intelecutally satisfying to me.
So I signed up for Andrew Ng's "Machine Learning" course. I really enjoyed it because he is an excellent teacher. But during the course I noticed something peculiar. I would skim through the reading material about AI/ML but would SLOW DOWN during the Math part of it. I would obsess about the PDEs, think deeply about them, even try to prove/derive them which was totally unnecessary for the purpose of the course and learning AI/ML's applications.
Combine this with my conversations with my colleague about AI/ML. He is really passionate about AI/ML and its applications and how to use it to solve real world problems. As far as I am concerned, I don't care about that at all. I ONLY care about the underlying mathematical objects used in it. He would talk about using an prebuilt library or a model and to just apply it to solve something and it would make him happy. Not me. I want to talk about what degree of the PDEs being used. What theorem is used to prove a certain equation.
This is when I realized that I didn't care about applications all that much. This was further validated when I got curious about the undergrad curriculum and picked up the book "The book of proof", and I thoroughly enjoyed it. I LOVED proving theorem and staring at the mathematical symbols on my notepad/whiteboard/chalkboard (yes, eventually I got a HUGE chalkboard installed in my study).
