Lessons learned the hard way in grad school (so far)
andreykurenkov.com
andreykurenkov.com
It is like those old coyote road runner cartoons. The frustrating thing about the coyote was that he never went back and fixed tiny problems with his attempts, instead he dropped every nearly-successful attempt and moved on to something completely different, just to find that it had different bugs in it.
This sounds like it's worthy of a blog post in and of itself.
Also, the road runner/coyote analogy is superb.
I've heard the same thing about GI Joe. COBRA had working weather satellites---why didn't they just try again in next weeks episode?!
Disclaimer up front, as I say in the text these are not the only lessons I have learned, just the most directly linked to experiences I can use as examples. And, these are of course applicable to most creative work, not just grad school.
PS there was a pretty lengthy discussion on the /r/ML subreddit already, which you might find interesting (https://www.reddit.com/r/MachineLearning/comments/fsi7iv/d_l...)
OMG. What a shit storm that caused. My supervisor was thankfully very good about it (I had not cleared my wonderful idea with him). But after a few months of being accused of being a spy and people being cautioned not to talk to me under any circumstances, I decided that industry was the way forward for me. I realised I was not suited for the cut-throat nature of academic life ;-)
I say this knowing that some people will be shocked, but that anybody in academia will be mostly shocked at my naivety. Seriously bad idea, but one that idealistic, younger me thought was natural.