IshanMi

I'm working on a long-term project to better understand Operating Systems, video game development, and Rust by building the simplest possible OS in Rust that boots directly into a game of Doom, which will also be re-written in Rust.

I'm giving myself 18 months- it's been super fun so far!

The author mentions that his father was in touch with a PE-backed chemical company a month before he passed, who re-engaged a month after- sounds like that might have been his succession plan?

You're right about Calhoun- I was actually referring to Dennis Muilenburg, the CEO who was fired in 2019 after the two 737 MAX crashes and the subsequent groundings. My comment wasn't clear enough- I'll edit it for clarity.

Personally, I think the issue isn't one of degrees or qualifications, but rather one of values.

The CEO prior to this one (Muilenburg) also had degrees in Aerospace Engineering, but chose to value profit maximization over things like security and a good engineering culture. Innocent people had to pay the price for it.

I remember someone telling me that they thought of luck as being as one of four kinds:

- blind luck (which is what people usually mean by "luck")

- stirring the pot (if you apply to 100 jobs you might get more "luck" than if you applied for just 10)

- domain expertise (the more knowledge you have, the more opportunities you can spot that others might miss)

- reputation (if you're the world's best painter then when people want a painting they'll immediately think of you)

Thank you for sharing- upon reflection I should have been looking for primary source evidence like this instead of reading too deeply into Wikipedia pages, as someone else pointed out. I will definitely be more careful in the future.

Could I ask how you would normally go about trying to find memos like this for other companies?

I think you're right and I clearly read too much into the Wikipedia pages, instead of looking for more primary source documents like those others have posted. I will be more careful in the future. Thank you for pointing that out!

I still remember discovering & using numba for the first time in university. At the time I didn't know any programming languages except for Python, and wasn't super great at Python either. We had to write and run molecular simulations for our numerical methods and simulations class, and people were writing their code in C/C++ and were blazing through it. I remember finding out about Numba through the High Performance Python O'Reilly book, and by just adding a single @jit decorator, my simulations were dramatically faster.

Amazing stuff, honestly felt like black magic at the time.

This paper analyzed ~14,000 users and I'm wondering if that's a big enough sample size considering there are 350M or so users on Twitter? Is there a statistics test you could use to tell?

Or is the number we should compare 14,000 to not the total number of users, but the subset of people who actually migrated, i.e. left Twitter entirely and joined another social network?

Are there any estimates for how many people in total that might be?