ChadyWady

5 ms·

1.
by ChadyWady 2y ago
The "12 hours" comes from IAM roles... some organizations don't allow folks to use IAM users for security reasons; many are now swapping to a setup like IAM Identity Center where they are granted temporary AWS credentials for
2.
by ChadyWady 2y ago
Their business models has always been basically MS's strategy: Build out a massive sales and consulting organization, and then build products so that sales people can generate feature matrices to compare themselves with the competition
3.
by ChadyWady 3y ago
Looks like they've updated it now.
4.
by ChadyWady 3y ago
Definitely agree that Make and Nix is a nice combination, especially for facilitating faster development like the `hugo serve` use case. I'd also argue it makes your code overall less volatile. If you ever wanted to build your code out
5.
by ChadyWady 4y ago
I would check out < https://nixos.org/ > if you're looking for a similar experience to Brazil. My own opinion is that the Brazil had far too many running parts. I didn't like that reproducibility required the
6.
by ChadyWady 4y ago
That's my primary issue with Windows workflows, though. Unless you really convert over to using pretty much only MS tools, it is extremely hard to use them. Meanwhile the "Unix" workflow tends to be a collection of many open
7.
by ChadyWady 5y ago
I'm impressed but Amazon Chime still appears to be working right now. It's sad because this is the one service that could go down and be a net benefit.
8.
by ChadyWady 9y ago
That makes sense, I was confused since the Birthday problem is more about the pigeonhole principle rather than larger samples being more likely to have individually unlikely outcomes. But for this case, it would still be very unlikely. For
9.
by ChadyWady 9y ago
From what I understand of the article, the "1 in 500 years" measure is based on the estimated probability of a flood on a given year in a local area. So the probability of a "1 in 500 year" flood in Houston is independen
10.
by ChadyWady 9y ago
For ML, the other users gave a good coverage of topics. But AI is an incredibly broad field, and each specialty uses different math topics. Learning all of the math would be infeasible. What are your particular interests? Russell and Norvig