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 an IAM role using an SSO provider.
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. I'm certain they're keenly aware that they build shit products -- but they know that the majority of their customers are willing to pay a premium for a worse product if it means avoiding talking to finance and ensuring compliance for any external vendor.
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 outside of Nix for whatever reason (such as migrating to another reproducible packaging tool), having everything in unopinionated `make` is a whole lot easier. Plus, it leverages the advantages of a tool that is dedicated to building code.
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 tool interacting with 3-4 different services, and I'm much more a fan of having the source code repository act as a single source of truth for reproducibility. Plus, using Brazil meant you had to use their entire tech stack, where GitLab/GitHub beat them by far.
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 source tools with lots of competing solutions.
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.
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 independent of the probability of a "1 in 500 year" flood for another city.
Right what the parent comment is saying is that look at a map of the US, how many major cities are there. The odds of two "1 in 500 year" floods happening consecutively are low for any individual city. But if you have enough cities it will happen somewhere. And then you write the article about it.
It's kind of analogous to hitting a golf ball into an open field and then exclaiming "Of all the blades of grass, the ball landed on this one!" It's only interesting if it goes in the hole with the flag, otherwise you have a sort of selection bias going.
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 any arbitrary 3-year span, the probability of consecutively getting a "1 in 500 year" flood for 3 years is (1/500)^3. If we have N cities, then the probability of none of these cities having 3 consecutive floods is (1-(1/500)^3)^N. For even a vast overestimate such as N=20000, it is still a significantly improbable event. Of course, this doesn't account for "3 consecutive years or more within some year range" and it is a gross simplification, but I think there are probably better explanations than selection bias, such as the inaccuracy of the model or the fact that these events might be temporally dependent on each other.
My understanding is that by definition, a 500 year flood has a probability of 1/500 per year. But I live at the top of a hill in the Midwest, so a 500 year flood in my locale has a much different severity than a 500 year flood in Houston.
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 have a good book at http://aima.cs.berkeley.edu that covers many different topics in AI, although it is definitely not comprehensive. I would say that whatever you learn in an undergraduate CS degree would give you a good starting point for learning any particular AI topics.
