I have about 2 YoE, and I'm sure this changes with more experience.
I often hear this idea online that programmers should follow "just-in-time" learning, meaning you should prefer to learn things you don't know while on the job. ( The way some people talk about it, though, it sounds like you shouldn't dare spend a single minute learning anything new outside of your 9-5. )
This seems generally reasonable advice, especially for simpler things that take a few hours like learning a specific language feature, library, or similar. But when I lean too much on this JIT learning, it feels possibly detrimental.
Many times I do something big and new to me, say, deciding how to approach auth, microservice architecture design, automated testing, containerization, etc., I end up making a big decision after a few hours or days of cursory reading on documentation and blogs, only to come to regret it some months later. At that point, maybe I'll slow down, find a book on the subject, read it, and think, "Oh, darn, I wish I knew that N months ago." It certainly feels like spending more time learning upfront could have avoided mistakes due to lack of knowledge. Though there's no way to go back in time and know for sure.
I'm not asking about any area listed in particular. I feel like, for all of those, I've learned more in the time since, and would probably avoid some of my prior mistakes if I did it again. The question is more: How much do you subscribe to this idea of just-in-time learning? And if you do, how do you know when you've learned enough to be confident, or when you need to slow down and learn in more depth?
Don't know if my experience can be related by other people here. I'm a mathematician and I work in insurance writing python/SQL to transform data into knowledge. Everything python/SQL related I'm learning it on paid hours, like testing libraries or others. While outside my job I'm working on an actuarial sciences MBA, where I'm learning theoretical knowledge that couldn't be learned "on the job". When something I learn on the MBA can be used on the job, I rush to learn how to apply it on python (while being paid for it). For example, a couple of weeks ago we learned how to find the probability distribution parameters of a sample using maximum likelihood estimation, and while on the clock I learned how to that using scipy on the claims database.
I'm already thinking on doing a computer science masters after finishing this MBA, because I'm really enjoying the opportunity of studying theoretical stuff on my time while being paid to practice it and learn how to apply it in real life applications.