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This might sound like an easy out but I consider that there is a choice of doing nothing. Sunsetting a problem can be as simple as forgetting it exists in the first place. This is not merely "giving up" but rather deciding the best action take at the moment is do actively do nothing about the problem.

Over the past few years when I have had problems that pop up or what really happens is that I go looking for problems, the most often solution has been to literally do nothing.

And having that as an active option when I first start looking at the problem and listing solutions ends up having far more options to me for the problem than if I was like "I MUST SOLVE THIS".

It could also mean "wait" is the best possible action I can take now. And instead of being perturbed by waiting it is an active decision to wait.



I've observed this as well. It's very satisfying to finally realize you don't need to solve it in the first place, because your assumptions created a problem where there was none.

E.g. mulling for weeks over optimizing some code until you realize to measure it as-is and it isn't even slow!

Or maybe there's room in the underlying design to shift the weight off the problem, thus "solving" it laterally (by solving some other, easier, problem instead).


In such cases I've seen (and used) a saying in french that goes:

> Il est urgent d'attendre

which loosely translates to:

> waiting is of utmost urgency

The french quote can be traced to a translation of Asimov's Foundation, but I can't seem to find the original version :/


> The french quote can be traced to a translation of Asimov's Foundation

It's actually much older than that, I read it already in 19th century books; no idea when it first came out.


That's about what I recall as well (18~19th) but could not find a definitive source older than Asimov with my time-limited search


> It could also mean "wait" is the best possible action I can take now. And instead of being perturbed by waiting it is an active decision to wait.

As an example: I worked on a PhD in applying machine learning to certain tasks in programming and mathematics. I ended up burning-out and had to quit.

When I started in 2014, most cutting-edge ML research was on image processing like convolutional neural networks. That's a very bad fit for the sorts of tree-structures and text sequences I wanted to use. The state of the art for the latter were RNNs which are notoriously slow (hard to parallelise), suffer exploding/vanishing gradients (needing e.g. LSTM), etc.

Transformers and LLMs solve the issues I was facing; so in hindsight it would have been better to wait a few years (I believe the Attention Is All You Need paper came out in 2017?)


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