If you were a bootcamp grad doing full stack dev work but wanted to become a research engineer at a FAANG AI lab or a high-output startup, would it be worthwhile to go to grad school or are there better ways for a self-motivated learner to grok research methodologies and contribute to ongoing research?
Do modern day FAANG/FAANG-like companies reward intellectual humility or are prideful sociopaths more likely to win at the workplace by creating reality distortion fields?
Whether it's their grindset, office politics savviness, or job-hopping skills, some software engineers find themselves climbing the ladder pretty quickly while still in their 20s. Do quick progressions have a drawback? Do these SWEs have any major blind-spots compared to their older counterparts?
The proliferation of data and computational methods have proved to be beneficial to a handful of fields (Computational "insert physical/social science here"). Rather than assuming that marrying tech with existing practices would solve everything, are there more optimal methodologies in identifying fields that would see an exponential boost in utility?
Comfort is key, but I was curious on what extra features or design standard shifts would improve the experience of sitting in front of a screen for prolonged periods. For example, one of my friends was talking about how it'd be cool to have a chair that could electrically stimulate his back muscles to relieve his back pain.
Looking at the DNA analysis services provided by the likes of 23andMe and Ancestry, is it viable for a workout app to gain an edge over the various consumer fitness apps out there by using DNA Testing to come up with a curated workout & diet guide. I'm aware that nutrigenetics is still in its nascent phase and can see how concerns about privacy and security can pop up.
I was wondering about what programmatic & design qualities draw the attention of experienced engineers & developers and signal whether you're a novice or a veteran.
