• If something takes longer than expected to get to an answer, think about how you could have seen the problem to be able to solve it faster to be able to solve it faster in the future^[1].
  • As an extension, optimize everything. If something is slow and you do it often enough, doing it faster is worth it. Relevant XKCD.^[2]
• Flip a coin if you are struggling to decide between option in a situation where there are relatively low stakes. This exposes to you your gut instinct immediately, which is more than good enough most times, and it is far faster than logically finding an answer.

1. ^{^}

   e.g. the whole strategy of "dynamic programming", write a recursive solution, then memoize it, then reduce the amount of memory if you don't use it anymore. This works because you have changed your perspective from what clever thing can I do to how can I solve this with a smaller subproblem.

2. ^{^}

   This also applies to physical actions like walking and opening doors. Most people do these slowly, but since you spend so much time doing them, it is extremely worth it to focus on what you're doing.

Bayes rule

Berkson's paradox

Goodhart's law

Efficient market hypothesis

Consciousness of abstraction.

Consistency check: After coming up with a conclusion, check that it's consistent with other simple facts you know. This lets you catch simple errors very quickly.
Give an example: If you've got an abstract object, think of the simplest possible object which instantiates it, preferably one you've got lots of good intuitions about. This resolves confusion like nothing else I know. 
Proving too much: After you've come up with a clever argument, see if it can be used to prove another claim, ideally the opposite claim. It can massively weaken the strength of arguments at little cost.
Prove it another way: Don't leave things at one proof, find another. It shines light on flaws in your understanding, as well as deeper principles. 

Are any of these satisfactory?

Dimensional analysis is in instance of type signature checking, as fully understood. Unfortunately very few type systems for existing programming languages support the type transformations necessary to implement it directly.