Readit NewsI ran a performance test back in October comparing M4 laptops against high-end Windows desktops, and the results showed the M-series chips coming out on top.
https://www.tyleo.com/blog/compiler-performance-on-2025-devi...
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> Generally, I believe [Rabbit] R1 has the potential to change the world. This is a thought that seldom comes to my mind, as I have seen numerous new technologies and inventions. However, R1 is different; it’s not just another device to please a certain niche. It’s meticulously designed to serve one significant goal for all people: to improve lifestyle in the digital world.
"great proposal„ however, it needs to be more around deception alice -bob. communication. virus hacking, battle between defense and infiltration.. computation is already looked at in various fields. camoflauge , mimickry, signal processing, and its non random nature, misinformation. ( the anti- truth - but right answer for the moment ).. computation does not involve defending against interception, a key area for biological systems, if a predator breaks the code, it usually can accumulate its preys free energy at a discount . self deception, ( necessary to prevent accidental disclosure of inate algorithms. WE need more hackers , also interested in biological hacking , security, etc."
Damn! I once worked with a guy that was exactly like this. Not just writing but his style of speech irl was like that, incoherent loosely bound ideas around one topic. Ironically, the harder he tried to appear smart the more idiotic were the things that spewed out of his mouth.
We were working with GPUs, trying to find ways to optimize GPU code, he called the team for an informal meeting and told us dead serious, "Why can't you just like, ..., remove the GPUs from the server, then crack them open, turn them outside out and put them back in to see if they perform better". :O
I don't know if this has a name, I just thought the guy had schizophrenia. So glad I moved on from that place.
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> these models dominate both exponential attention and linear attention at long-context training
There is no exponential attention; standard attention is quadratic. Strange mistake.
I ask because in practice, for inference, attention is typically computed with low-precision (4-bit, 8-bit, 16-bit) floats.
Numerical error, in fact, may be a key factor as to why quadratic attention, in practice, exhibits context rot as context gets longer, analogous to an RNN:
https://www.anthropic.com/engineering/effective-context-engi...
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Those panther lake comparisons are from the top end PTL to the base M series. If they were compared to their comparative SKUs they’d be even further behind.
This was all mentioned in the article.