It's interesting to compare the first two points: novel math derivations and remixing old artwork can seem like disparate paths to greater understanding. Yet often, 'novel' math derivations are more like the artistic remixes, or pastiches. Gian-Carlo Rota, MIT math & philosophy prof, referenced two ways to come across as genius: either keep a bag of tricks and apply them to new problems, or keep a bag of problems and apply them to new tricks.
Eliezer's discussion about his work was interesting too, I hadn't seen that before. Rota also spoke of scientifi...
Coding
In a video by web developer Joshua Morony, he explains why he won’t rely on GPT-4 for his code, despite its efficiency. Me paraphrasing: In coding, knowing why and how a system was designed, edge cases accounted for, etc. is valuable because it contextualizes future decision-making. But if you allow AI to design your code, you’ll lose that knowledge. And if you plan on prompting AI to develop further, you’ll lack the judgment to direct it most coherently.
Writing
Many have written about how it’s the writing process itself that gener...
Banishing the epistemic status disclaimer to the comments, since it clashes with the target audience and reading experience.
Epistemic status: briefly consolidated insights on writing to think, for newer audiences. Partly interpolates Paul Graham, Herbert Lui, Larry McEnerney.
I loved this post. Its overall presentation felt like a text version of a Christopher Nolan mind-bender.
When vacuums exist, it isn’t just random things filling up that time or space, but it’s things that are purposely able to fill up that vacuum.
What do you mean by "purposely?" Didn't Part I exemplify how random, not purposeful things can fill an empty room?
If strong ideas that face friction come out stronger, then why would you need to insulate them behind locked doors from external stimuli? Shouldn't they easily vanquish external stimuli and validate themselves? Unless the point is to recognize the strength of timeless ideas. But even if an idea worked well the first 999 times, it doesn't mean it will also do so the 1,000th time—you shouldn't strive to crystallize tried-and-true ideas into static heuristical husks: Heuristics that almost always work can still critically fail, with black swan moments.
'Always... (read more)