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Comment by triyambakam | Hacker News Reader

Has anyone read this text and can comment on how good it is? I've been wanting to learn more about cryptography as a SWE who's mostly only done traditional web apps/microservices.

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Unrelated to the article. If you want to play with various algorithms in your browser you can go here:

https://github.com/gchq/cyberchef

I can recommend the book Serious Cryptography published by nostarch press as a solid source

Serious cryptography is one of the best textbooks I have ever read.

I agree. Jp does a good job in that book. It’s very accessible yet taught me so much.

Covers modern stuff like ECC too, which this book doesn’t seem to.

I would recommend this book that was posted on HN before and is much more applied: https://www.manning.com/books/real-world-cryptography?a_aid=...

This is great! I'm bookmarking it, I'll probably use some stuff in my sequel to the "Practical Cryptography" course I just released: https://qvault.io/practical-cryptography/

I'm wondering this too. If I want to specialize in something, what subfield has the most ROI?

- Distributed systems

- ML/AI

- Crypto

When I first read this, I had the (snarky! unfiltered!) thought, "Well why dontcha go Google it? That's not exactly a philosophical question."

But that got me thinking... what if it was a philosophical question, the kind of question that prompts responses that tell you what it's really like to work in [x].

HN, what's it really like to work in [Distributed systems, ML/AI, Crypto]?

HN will have the inside scoop. For example, the salary increase when moving from generic SWE to ML engineer. Or backend engineer to Netflix scale distributed systems.

As someone with domain expertise in both distributed systems and crypto, I'll say "not crypto"; it's relatively well-understood and most of the people who get value out of it don't need to work on it, they can just use what already exists. By comparison, one needs detailed knowledge of distributed systems to make effective use of them, so expertise has broader value. I suspect ML/AI is similar to distributed systems in impact and value, maybe with broader market applicability, but I don't know as much about it in practice.

There is a different metric too: level of shame vs level of fame when you screw up / achieve something.

- distributed systems? Fame on achievement: not much, shame on failure: not much.

- ML/AI? Fame: crazy amount, shame: not much

- Crypto? Fame: not much, shame: you better change industry :-D

It can change rapidly. I’d suggest follow which interests you most.

Cryptography is super fun =)

The things you can do with it are utterly mind-bending, plus you can prove to yourself that things are correct (unlike ML/AI =P)

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