HackerTrans
トップ新着トレンドコメント過去質問紹介求人

t14n

no profile record

投稿

The Rise of Worse Is Better (1991)

dreamsongs.com
265 ポイント·投稿者 t14n·2 年前·334 コメント

Ameca Vision and Voice Cloning

youtube.com
1 ポイント·投稿者 t14n·2 年前·0 コメント

コメント

t14n
·9 か月前·議論
fwiw there's a project doing just that: https://github.com/tursodatabase/turso

they have a blog hinting at some answers as to "why": https://turso.tech/blog/introducing-limbo-a-complete-rewrite...
t14n
·昨年·議論
fwiw this problem already exists with my more junior co-workers. and also my own code that I write when exhausted!

if you have trusted processes for review and aren't always rushing out changes without triple checking your work (plus a review from another set of eyes), then I think you catch a lot of the subtler bugs that are emitted from an LLM.
t14n
·2 年前·議論
+1

there are a million million subcultures with pretty stark differences in taste/aesthetics that you can dig up on the internet. looking at what's grossing in mega-dense populations of millions of people then yeah, perhaps in aggregate at large N, things their individuality -- surprise?

there are parts of every major city that feel the same, but if you're willing to take a train out 45 minutes in any direction without google maps, i'm willing to bet you get into spaces that are incredibly local!
t14n
·2 年前·議論
There are some OS specific files (unix.rx, windows.rs, etc) that you can discount (imo).

If you really wanted to codegolf the repo, I'm sure you can make it literally <1024 lines.
t14n
·2 年前·議論
A new-ish field of "mechanistic interpretability" is trying to poke at weights and activations and find human-interpretable ideas w/in them. Making lots of progress lately, and there are some folks trying to apply ideas from the field to Alphafold 2. There are hopes of learning the ideas about biology/molecular interactions that the model has "discovered".

Perhaps we're in an early stage of Ted Chiang's story "The Evolution of Human Science", where AIs have largely taken over scientific research and a field of "meta-science" developed where humans translate AI research into more human-interpretable artifacts.