Falcorian

Yeah, working from home for four years now and I only have one office and desk. It's used for work, of course, but also when I'm writing for my self, or playing video games with my friends.

I don't have any problem disconnecting from work, but it probably helps that I have a different computer for work that goes in a drawer at the end of the day.

I do follow points 2 and 3 though and I think those are essential.

It's definitely a culture thing, probably more at the company level but certainly country level culture matters as well.

At my San Francisco based fintech, the culture that's developed is:

For one-on-ones and small meetings where everybody is expected to participate, cameras are on.

For large one to many broadcasts (like an all hands) generally people turn the camera off.

But I would say probably 95% my meetings have cameras on, so I'm constantly "seeing people".

Yes, the models are not constantly learning. They only update their knowledge when they are retrained, which is pretty infrequently (I think the base GPT models have not been retrained, but the chat laters on top might).

False. Every time you transfer money from an account at one bank to another you're using ACH. Same with moving funds from banks to stock brokers or brokers to banks.

The real reason it doesn't compete is its slow, and many banks have terms of service that say something like "You can only set it up between accounts you own" so sending to other people is often technically a violation of your agreement with a bank.

They prefer to have them at the end because:

1. You don't want these cars to get banged around a lot and maybe have their weight change.

2. So you put them at the very end where they take the least wear and tear.

3. But they're not allowed to be the last car because they often don't have brakes.

So you put them as the second-to-last car.

This answer based on combining two of the captions from the page:

> Always the "Second to Last Car in the Train"

> Andrew B Baird: No brakes so it is not permissible to be the last car.

And

> The scale cars are now more robust? I notice that this car is not on the end of the train.

Because the same two numbers (routing and account) are used to deposit money AND debit money.

So if you post your account numbers I can pull all your money out. Now you'll likely get it back if you file a fraud claim, but that's an extra Hassel, and your out finds until they give you a provisional credit.

It depends on the company and the role. In general, I would be prepared for some white boarding questions, although I've found they're generally "a little easier" than the ones the engineers whine about.

As a Physics PhD, I certainly went into grad school wanting to get a faculty position; is suspect most people did in my cohort.

Of course we're mostly data scientists now, because only 5% win the faculty lottery.

Data science wasn't a path that existed when we started. I wonder if its existence changes what people going into a Physics PhD expect.

I found the OSX specific XDG section helpful, because I've been setting it to ~/.cache for years! Good to know there is a better way that the OS understands (a bit a least).

Here is an overly simple example:

Pre-compute the recommended hotels for my top users every night. Now when that user comes back, they see a slightly stale recommendation, but it's lightning fast.

You can also pre-compute and cache some of the inputs cheer model, like maybe a vector representation of the description of a hotel.

That's my experience as well: apt is only "slow" in that sometimes it has to download and write gigs of stuff to disk.

Now brew and conda, they often take minutes to solve for the environment, which is what I think of when you say "slow". That part is probably mostly algorithmic, and it should be fast, unlike network and disk.

Exactly.

At my company, Type A are called "Data Analysts", but at Google Facebook they're called "Data Scientists". Type B are "Data Scientists" at my company, but "Machine Learning Engineers" (or SWE-ML or some other combination) at Google and Facebook.

As a Type B, at my company, I'm on the same pay scale as the SWEs. The Type As are not.