There's a lot of the rider's bias leaking through here. "Men who seemed to be of higher social class", "Middle Class Men", complete assumptions and biases that aren't used to form any sort of conclusion. Frustrating in an otherwise enjoyable and interesting essay.
I work at AWS in the UK and also do interviews in a Software Engineering role. I have no CS degree and at least where I am we do not look at degrees. We are hiring btw.
The man who got his party to government off the back of promising to abolish tuition fees, then immediately tripled tuition fees landing an entire generation with massive debt has questionable character. Who’d’ve thunk it?
Wow nail on the head. Just did this and out of the first 8 videos, 5 were reaction videos, 4 with the exact same "shocked" faces. Scrolling down the page it is much the same. How can google allow whatever algorithm to present such homogenous content on such a general platform?
Notably though, if I log in and visit my home page I have only one video of this nature on the front page in the first ~50, so it does appear possible to teach the algorithm you will not engage with such content even without conscious effort.
- difficult to gauge how much they know, and how slow/fast I should in explaining stuff
Generally I think this is a difficult problem. Some people will agree and nod as if they understood everything without understanding anything, while others will ask dozens of questions when they already understand 99% of what you're trying to explain but just wanted to clarify details. I think the best way to gauge how this is with different people is to explain a problem or task, see how they react and how many questions they ask, then set them on the task and see how they solve it. That gives you a reference for if they agree to everything and solve the task quickly and easily they understood, or if they struggled it gives you an opportunity to talk about them asking questions and saying something if they weren't clear. You can use this to calibrate from there.
Additionally, I've found that people can be more forthcoming with talking about problems they're facing in a role in a more social setting. Going to lunch if in office or in some unstructured chat if remote.
- distraction (I am excited to mentor, hence my brain keep coming back to that (providing resources, reviewing their work, etc.) while I am supposed to do my work)
I think this is human nature to have your mind on the most interesting problem you're facing, which in this case to you at the current time is your mentee. It's something you just have to overcome to focus on your current task. If you have a team try to spread the reviewing across the team, this is helpful to the person as somebody else on the team may learn the same way they do and be able to explain something in a way that makes more sense to them and that's okay, as a mentor understanding that people need more feedback from different sources and providing a way for them to get that both frees yourself up and helps them.
- how detailed/nitpicking should I be in reviewing, how to explain the right concept at the right time
I generally think that nitpicking should be avoided in all reviews, point out things that are wrong for clear reasons and keep to your same review process you use for the rest of the team. Wrong could be opinionated if it doesn't fit the working style of the team. Explain concepts when they ask how something works, or if they have done something that is wrong.
You will see a lot of anecdotal opinions, obviously, as each persons advice will come from their own history but I'd like to echo the comments that there's no real linear path of what you "should" do next after learning Python. You have endless opportunities.
My own 2 cents would be that I would recommend sticking around before moving on. Learning one language until you're fully proficient as you said you're not yet "professional in python", find problems that you can solve so you get used to solving the actual problems not working on learning the language syntax. Python is incredibly versatile and you have endless projects available to you. Once you have done that, you can start to consider what languages would've made sense to use for the project, what would the advantages/disadvantages be and try to pick the most appropriate language for each project.