As a first step, I wouldn’t put that much stock into Gwern’s guides. I’ve found that Gwern has his own way of doing things but it rarely seems to generalize at least in my experience. Self-experimentation is good but no matter what you can’t get much out of an N=1 sample unless you are that particular person.
I find that going to any sort of persistent store incredibly harmful for my flow state while programming, so I try to get as much as possible into Anki. I think you’ll find that if you sum the time spent attempting recall and the 3-5 seconds per lookup you’ll also get far more than five minutes for any reasonably well-used concept.
I also find that the concepts in my Anki decks tend to be the ones that come up when I’m problem solving in general or trying to be creative. In a psychology (not neuroscience—none of this is neuroscience, much like programming is unrelated to byte patterns except as an implementation detail) sense, Anki is just generally raising the activation level of those concepts, and so when you try to think of things, you will think in terms of those concepts. That’s why the self-programming cards thing works. But also, it means that when you think about anything, you think in terms related to your Anki concepts.
The OP of the second post you linked seems like they didn’t use a lot of Anki functionality. Anki’s most popular plugin (maybe second most since I think kanji is still implemented as a plugin) is image occlusion, which seems like it would perfectly mesh with flash cards. However, I still use spatial memory with Anki just by associating Anki values with directions. It’s not hard to do.
Overall, I think it’s something you should invest in. No matter what you say about its value, it is a reliable way to move things from RAM (let’s say) into L2 cache. This is something you should have familiarity with.
You can also check my comment history for a small OCaml utility, Space, that automates some aspects of making Anki cards.