
Accepting My Fate as a Millennial Software Engineer - dvaun
https://joygao.com/accepting-my-fate-as-a-millennial-software-engineer
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landryraccoon
In my humble opinion, younger engineers are not well served by the notion that
compilers, operating systems, network stacks and low level code are “solved”
problems.

I’d argue this from two angles. First, are people still complaining about
problems with operating systems and languages? Second, is innovation still
occurring? I’d argue that the answer to both questions is a resounding yes.

Even in something as simple as network stacks, the relatively recent explosion
of mobile devices and the IOT has driven change. How about low level
languages? Rust has just shown up on the scene showing that low level
programming wasn’t “solved” by C/C++. And I think we can all still think of
big problems with our favorite operating system.

Last, consider a far older technology - the automobile. Are exciting things
still happening with cars? Sure, innovation has greatly slowed down, but the
electric car has the promise of reinventing the entire industry - and that’s
innovating the most fundamental mechanical components like the drive train and
energy source.

I think computing technology, being way younger than the automobile, still has
room for big improvements at all levels of the stack.

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jenkstom
Commercial software will always be a "black box". Many, many companies use
commercial software. This is not really anything new. I will admit that the
complexity has grown so much that it's nearly impossible to understand any
significant software system in its entirety. That's not new, either, just more
prevalent than it used to be.

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TrinaryWorksToo
Quantum Computing is so new, it's in the realm of op-code level abstraction,
maybe even processor architecture.

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ianai
Definitely processor architecture, if we’re lucky. We’re still looking for
quantum systems to feasibly use for qubits at scale.

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grifball
goto grad school and write papers. you can explore topics that interest you
more deeply, and while you may not know everything about a large topic like
ML, you can focus in on a specific sub-topic and become the world's leading
expert.

[https://www.datarobot.com/blog/a-primer-on-deep-
learning/](https://www.datarobot.com/blog/a-primer-on-deep-learning/)

You probably know most of the information in this post about ML, but this
picture at the top shows what researchers are trying to discover about ML.

Basically, researchers are inspecting the middle layers of neural networks to
determine patterns. It's kinda a backwards approach to understanding nn's, as
we already can use them without understanding them, but this should show that
there's opportunity to really dive into these "deep" questions in academia.

I think this is the actual paper, but it seems to have a wider topic:

[http://www.cs.toronto.edu/~rgrosse/icml09-cdbn.pdf](http://www.cs.toronto.edu/~rgrosse/icml09-cdbn.pdf)

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ianai
I strongly encourage you to go off the beaten path with the three ideas you
have there - but spend time looking for places they have been done. It does
feel at times all we’ve done is build all these things up for webapps.

