Meta-curriculum

A curriculum built from pointers to other people’s material rather than original teaching: a sequence, a scope decision and a set of links. Two of this spoke’s three founding sources are one (coding-interview-university, awesome-system-design-resources), and between them they hold roughly 390k GitHub stars, so the form is not marginal.

What the author actually contributes

Not explanation — selection and order. Which topics are in, which are out, what to read first, what counts as enough. coding-interview-university contributes a multi-month sequence and a decision to leave out frontend, full-stack and SQL; awesome-system-design-resources contributes a topic hierarchy and a difficulty-graded problem set.

That is real work and it is why these repositories are useful. It is also almost entirely invisible.

The assessment problem

A textbook can be reviewed: the argument is on the page, and a reviewer can say it is wrong. A meta-curriculum’s central judgement — what was rejected — leaves no trace at all. You see 400 links and cannot see the 4,000 that were not chosen, so the thing most determining the curriculum’s quality is the thing you cannot inspect.

What remains as a signal is popularity, and popularity is at its least informative exactly here. Stars measure intent to learn — a bookmark costs nothing and implies no reading. A curriculum with 352k stars and a curriculum with 33k stars are not thereby ranked for quality, and this spoke’s corpus contains no completion or outcome data whatsoever (synthesis).

The alternative form

ai-engineering-from-scratch is the counterexample: original lessons, build-from-scratch implementation, capstone artifacts. It costs vastly more to produce and it can be judged, because there is something there to judge.

Related

interview-preparation · coding-interview-university · awesome-system-design-resources · ai-engineering-from-scratch