- LOC matter?
- Do PRs?
- Do Tokens?
I’ve never looked up a KPI to figure out who the best engineer is. It’s always obvious to everyone who the best engineer is
Natural leaders inspire. They don’t need KPIs
Wonder what Linus Torvalds’ AI exhaust is??
I've come to think that one unequivocal upside of "affordable ASI" would be to bring about a world of discourse where discursers would hesitate to think of (not to mention carry) themselves as experts. What would be the point? Everyone already knows that everyone has direct access to an expert.
[Perhaps it reminds me of Le Guin's thoughts on tech X power, especially as represented in The Dispossessed ]
it would seem to be not just rude, but even snide, to bias your interlocutor towards not carefully verifying the dependencies of your exhaust!
[In your example, Linus, as a sneak preview of that alt-world in ours, is probably totally OK with not being regarded as an expert in making toy software]
Widely dispersed and affordable AI would help because it (certainly, imho) makes the alternative, "trust but verify", so easy
[No need to think about the downsides of checking out and pointing Claude at lines of Linus' toy!]
And it would be strictly easier than using cloud chatGPT to sow misinformation because there are extra steps in that (--- nonsockpuppets would be inclined to verify the gist of everything they are about to say as indeed misleading)
[As a bonus, this seems compatible with TFA, non-ideological anti-anti-intellectualism, and
https://engines.egr.uh.edu/episode/1495
if, in that "non-anarchist" alt-world, we all assume that technical leaders should be different (not more, not less) than expert engineers
Should exceptional technical direction setters strive to stay anonymous..? Or use sockpuppets? Moral as well as practical quandaries..! ]
...a bunch of stuff specific to certain models the author is currently using.
Experimentation is good for technical leaders, but you want to be focusing on approaches and understanding the fundamental constraints, not detail.
Many of the things that have completely changed in then last year will completely change in the next year.
I did refer to some specific models, though a lot of these learnings are from experience over the past 6 months or so, and continue to generalize as frontier models improve.
Curious if there’s a specific point you feel is too detailed?