Recent submissions

Comment · 6 days ago
The result is still surprising even if the result is Astra can edit and write weights of a publicly known model. It means that models can, at least in principle, be trained on a task that it wasn't considered possible before.
Comment · 1 month ago
Comment · 9 months ago
It looks like the B programming language.
Comment · 10 months ago
> All memory must be statically allocated at startup. No memory may be dynamically allocated (or freed and reallocated) after initialization. This avoids unpredictable behavior that can significantly affect performance, and avoids use-after-free. As a second-order effect, it is our experience that this also makes for more efficient, simpler designs that are more performant and easier to maintain and reason about, compared to designs that do not consider all possible memory usage patterns upfront as part of the design. > TigerStyle

It's baffling that a technique known for 30+ years in the industry have been repackage into "tiger style" or whatever this guru-esque thing this is.

Comment · 1 year ago
> The artificial intelligence chip leader's chief in October said that the U.S. can win the AI battle if the world, including China's massive developer base, runs on Nvidia systems. He, however, lamented that the Chinese government has shut it out of its market.

Laughable

Comment · 1 year ago
People believe that more parameters would lead to overfit instead generalization. The various regularization methods we use today to avoid overfit hadn't been discovered yet. Your statement is mostly likely about this.
Comment · 1 year ago
text-davinci-001 still feels the more human model
Comment · 1 year ago
I hope this time they can come up with a model at least or par with open source offerings.
Comment · 1 year ago
Thanks for the answer!
Comment · 1 year ago
Genuine question: is this a wrapper around Google's gopacket?
Comment · 1 year ago
Comment · 1 year ago
Since you've mentioned, which hash functions are better while being faster than XXH3?
Comment · 1 year ago
One way of doing that is by each time step delta perform two integrations, one which give you x_1, and another x_2, with different accuracies. The error is estimated by the difference |x_1 - x_2| and you make the error match your tolerance by adjusting the time step.

Naturally, this difference becomes big when two objects are close together, since the acceleration will induce a large change of velocity, and a lower time step is needed to keep the error under control.

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