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wills_forward

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投稿

Transformer by Hand in Excel

twitter.com
2 ポイント·投稿者 wills_forward·2 年前·0 コメント

Tool to transcode videos in nothing but a browser

rgbcu.be
2 ポイント·投稿者 wills_forward·2 年前·0 コメント

Satellite internet has quickly changed life in Alaska

adn.com
5 ポイント·投稿者 wills_forward·2 年前·2 コメント

Three week satellite time-lapse video of earth

youtube.com
3 ポイント·投稿者 wills_forward·2 年前·0 コメント

コメント

wills_forward
·25 日前·議論
neat
wills_forward
·2 か月前·議論
There's a lot of opportunity in being the manager who can still see it
wills_forward
·7 か月前·議論
Why not use both? I just built a pipeline for document data extraction that uses PaddleOCR, then Gemini 3 to check + fix errors. It gets close to 99.9% on extraction from financial statements finally on par with humans.
wills_forward
·10 か月前·議論
https://x.com/whowillrickwill/status/1920723985311903767
wills_forward
·10 か月前·議論
The cheap easy take: it's tragically ironic that the software running the infrastructure in Silicon Valley is such a problem
wills_forward
·昨年·議論
So this could universally decrease the memory requirements by un-quantitized LLMs by 30%? Seems big if true.
wills_forward
·昨年·議論
This paper is basically statistical mechanics with a quantum veneer. Two major issues:

1. Scale: They're simulating just 13 qubits with QuTiP and making grand claims about quantum thermodynamics. The computational complexity they're glossing over here is astronomical. Anyone who's actually worked with quantum systems knows you can't just handwave away the scaling problems.

2. Measurement Problem: Their whole argument about instantaneous vs time-averaged measurements is just repackaging the quantum measurement problem without actually solving anything. They're doing the same philosophical shell game that every "breakthrough" quantum paper does by moving around where they put the observer and pretending they've discovered something profound.
wills_forward
·2 年前·議論
It was funny to hear the same guy warning LMMs were getting too powerful now talking about the limits of available original training data.