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I have a strategy. I call it "Engine vs Power-ups". The "engine" is very core, broadly useful things you learn (or in this case do) that will help you not only

I have a strategy. I call it "Engine vs Power-ups". The "engine" is very core, broadly useful things you learn (or in this case do) that will help you not only now but over the long term. In terms of learning this would be things like mathematics, a foreign language, sales, writing, interpersonal skills, CS, etc, which will still be valuable in 50 years. In terms of your tech stack choices, this would mean technologies that will be easier to maintain, require fewer updates in the future and have a sufficient level of performance. In contrast, "power-ups" are extremely useful right now, but not so broadly applicable or evergreen. In terms of learning, this means things like the knowledge of the hottest social media growth channel of the moment, frequently changing JavaScript frameworks, popular 3rd-party tools and platforms (i.e. Firebase APIs, Auth0, various "back-end as a service" offerings, etc). Keep in mind, the "engine" vs "power-up" concept is more of a spectrum than a dichotomy. Learning a programming language, an OS or an open source protocol is somewhere in the middle. The knowledge is probably good for 5 years, but maybe not 30. The strategy I try to use is to spend most of my time working on the engine, but at key times when there's an opportunity, I go all-in on power-ups. https://alchemist.camp/learning-machine/engines-powerups https://alchemist.camp/learning-machine/engines-powerups

natalyarostova 6y ago
That's neat -- since it's pretty much the same way I think of self-learning. Sometimes you need to just go read up on discrete math, or theoretical stats, or algorithms in order to level up your fundamentals. Then you get to go 'spend' that on cool shit. I finally started learning tensorflow the other day, after ~3 years of studying math/stats (while working full time) so that I could actually reason about tensorflow (note: I'm using TF for specific purposes, for something like computer vision the level of stats I studied isn't necessary)

natalyarostova 6y ago

That's neat -- since it's pretty much the same way I think of self-learning. Sometimes you need to just go read up on discrete math, or theoretical stats, or algorithms in order to level up your fundamentals. Then you get to go 'spend' that on cool shit. I finally started learning tensorflow the other day, after ~3 years of studying math/stats (while working full time) so that I could actually reason about tensorflow (note: I'm using TF for specific purposes, for something like computer vision the level of stats I studied isn't necessary)