ericpts

I would be interested to learn more about the 'value proposition' way of looking at things, as spoken of in the article.

Can anyone recommend any good starting points?

Usually the hardest part of a learning pipeline is data gather and cleaning; once it is in a suitable format (such that it is easy to create a structured CSV file), the training part is probably the easiest part: just a few lines of Python code.

I commonly read that Apple products "just work", but it seems like a lot of effort is required in order to get up to date software on MacOS, which in linux distributions is completely effortless as it comes by default (see also the Bash situation).

What books did your group find well written? It would be very helpful for outsiders to know what people who know their stuff consider good learning material.

What would be a good reason for distributing students based on luck (which is what I gathered from the article), versus having everyone take a standardized test and then assigning pupils to schools based on their performance in their test?

The test approach seems the fairest to me, as a grade A student should have a higher priority of getting into a top school than a lower grade student.

Contrast this with having a high achieving student not getting into the school of his choice due to sheer bad luck, and I do not see much benefit from the lottery system.

I am particularly curious why they chose to start from scratch instead of cooperating with Dropbox on mypy.

But you lose some of the performance and safety benefits of templates.

I would argue that the template system within C++ itself is a partial implementation of lisp, with notably bad error messages.

I find that most of the arguments given against C++ (such as memory leaks or dangling pointers) can be avoided by using Modern C++ features, like never allocating memory with new but using std::make_unique or avoiding null pointers with not_null from GSL or std::optional.