And for nomads perhaps taxes; staying 6 months can be dangerous in some cases; even 60 days consecutive for some countries.

It is useful in some contexts, but pointless in this one. Where I think it is useful is where the original person is stringing together a set of statements which (s)he then claims leads to a conclusion (when it doesn't). So you then construct a similar set of statements, string them together, and make the opposite conclusion. Or string together the opposite statements and make the same conclusion. Presumably the original person will not agree with the conclusion, and the hope is that it becomes apparent that as a result, his original set of statements do not lead to his claimed conclusion. It's hard for that person to debunk your statement without pointing out the flaws in his own logic.

In this case, the original commenter wasn't trying to make a point - just expressing his opinion. It makes no sense to use the tactic here.

This particular style of comment is best used when you want to emphasize that what a person just said is only one perspective and that there exists an equally valid opposing perspective. It works even better if the original post was written with a tone of superiority, though it’s not required. It wouldn’t be surprising to find it commonly used on HN.

If it's sarcastic and rude, then yeah, but this wasn't that.

The other side of a coin is always bound to be the same size as the first side. A counter point to a point is necessarily similar, rhetorically, to the original point it's refuting.

Also, if you're wishing Internet conversations were more real, perhaps looking somewhere besides the Internet may benefit you Makerspaces have hackers, as do hackathon meetups, conventions, hell, sometimes libraries have resources for computer scientists or enthusiasts.

I like the hn format because of the density and accessibility. If I want to talk to another hacker in meat space I gotta go out, make an appointment with them, inevitably buy them coffee, survive the subway, it's inevitably late, so now this guy thinks I'm a jackass, and I have a urge in the back of my head to check my phone for hn while we are small talking before we can appropriately arrive at the twenty minute chat about whatever before doing the entire routine in reverse.

Or I can just read hn.

I'm definitely not rich, and there are some months when I'm not sure I'll make it; but somehow I do, and then there are other months where things work out better than I expected.

But when I walk out of my house and go where I want, when I want, while most of the rest of the people are told when and where to be, I feel free. And when I do work for a client from a hotel room in the Caribbean, after my day of scuba diving, I feel pretty great.

If I continued with my old way of "every job must pay more than the last", I would still be in the oil business, in an office every day. Instead, I now see how much I was wasting on a new car, daily driving commute (time and gas), eating out because I'm too exhausted after a day in the office to cook when I get home, etc... turns out I don't need nearly as much money to live on as I did when I was working for $$.

The reason it was hard to slow down and learn was that promotions were so difficult to get. You can only apply every six months, and so many stars have to align in your favor to get promoted and so many things can derail it. Your project can get cancelled. Your project can flop even if you personally did everything right. One of your senior teammates (whose word carries a lot of weight with the promotion committee) can leave the company.

It was so hard to get promoted when I was trying to get promoted that I felt like if I wasn't focused on promotion, I'd just never get a promotion.

I wrote a longer post about this a few months ago:

https://mtlynch.io/why-i-quit-google/

>Additionally, did you feel that you had the freedom to learn and apply ML?

I theoretically had freedom to learn and apply ML, but it would have been at the expense of my career. For example, my last major project was to launch a new ML pipeline in six months (it sounds like a long time, but it's really hard to do this with all of Google's bleeding edge infrastructure and bureaucracy). I was leading a team with two other developers, both of whom had graduate degrees in ML-focused subjects. I could theoretically have assigned myself more of the ML work, but the most likely way for us to meet our deadline was to let my teammates handle the deep ML work while I did more infrastructure work.