0: https://en.wikipedia.org/wiki/Wero_(payment)

Comments URL: https://news.ycombinator.com/item?id=44239019

Points: 4

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=43348692

Points: 3

# Comments: 0

- "Benevolent Dictators" of companies or projects have to obey the law - They can't forbid competition or alternatives - Every participant can leave at any time - If they burn the organization to the ground, the worst case scenario is the organization get replaced and people move on

I think it shows that we're using the word "dictator" way too casually in that case.

However, it doesn't seem trivial to do deduplication in that case without removing relevant/necessary context.

Comments URL: https://news.ycombinator.com/item?id=42047248

Points: 3

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=42022335

Points: 78

# Comments: 33

While high torque motors got way cheaper, especially with MIT Cheetah "clones" getting easily available, they're still at least 200-500 a pop (depending on the torque needed for each articulation) from what I could find.

I might not know where to search for the real gems though. Where do you search for cheap powerful servomotors?

From Nvidia and AMD, I read sparse fp8 at 7 PFLOPs for B100 [0] vs 5.22 PFLOPs for mi325x [1]

Nvidia doesn't give the dense fp8 so that's the easiest comparison I could get.

[0] https://resources.nvidia.com/en-us-blackwell-architecture [1] https://www.amd.com/en/products/accelerators/instinct/mi300/...

We don't have to imagine far, it's slowly happening. Pytorch for ROCm is getting better and better!

Then they will have to fix the split between data-center and consumer GPU for sure. From what I understand, this is on the roadmap with the convergence of both GPU lines on the UDNA architecture.

There is a good reason why the ML community took Python as the favorite language overall.

Comments URL: https://news.ycombinator.com/item?id=41798265

Points: 2

# Comments: 0

There is a recent paper from Meta that propose a way to train a model to backtrack its generation to improve generation alignment [0].

[0] https://arxiv.org/html/2409.14586v1

RNN are constantly updating and overwriting their memory. It means they need to be able to predict what is going to be useful in order to store it for later.

This is a massive advantage for Transformers in interactive use cases like in ChatGPT. You give it context and ask questions in multiple turns. Which part of the context was important for a given question only becomes known later in the token sequence.

To be more precise, I should say it's an advantage of Attention-based models, because there are also hybrid models successfully mixing both approaches, like Jamba.

That would be interesting to know if his solution could match the 4k$ in term of usability or if there is some issue like refreshing rate that make the piezo based system necessary for a good user experience.

[0] https://www.youtube.com/watch?v=BXi1tG78AW4