Messi or Ronaldo - who is the greatest of all time?
https://www.bbc.com/sport/football/videos/c3v2gg2ldr5o?at_medium=RSS&at_campaign=rss
Messi or Ronaldo - who is the greatest of all time?
https://www.bbc.com/sport/football/videos/c3v2gg2ldr5o?at_medium=RSS&at_campaign=rss
researchers investigating the #sasquatch people of the woods discuss how to best protect & treat them with respect & dignity.
#GifsArtidote: if authorities had to admit their existence, human rights would apply, but they're so illusive & strong with abilities we don't possess, they can't be controlled..

researchers investigating the #sasquatch people of the woods discuss how to best protect & treat them with respect & dignity.
#GifsArtidote: if authorities had to admit their existence, human rights would apply, but they're so illusive & strong with abilities we don't possess, they can't be controlled.
therefore so-called #science lies & dissappears #DNA evidence, bc their parasitic systems will collapse when truth comes out to the world. #disclosure


Transformers have become the standard solution for various AI tasks, with the query, key, and value (QKV) attention formulation playing a central role. However, the individual contribution of these three projections and the impact of omitting some remain poorly understood. We systematically evaluate three projection sharing constraints: a) Q-K=V (shared key-value), b) Q=K-V (shared query-key), and c) Q=K=V (single projection). The last two variants produce symmetric attention maps; to address this, we also explore asymmetric attention via 2D positional encodings. Through experiments spanning synthetic tasks, vision (MNIST, CIFAR, TinyImageNet, anomaly), and language modeling (300M and 1.2B parameter models on 10B tokens), we discovered that our transformers perform on par or occasionally better than the QKV transformer. In language modeling, Q-K=V projection sharing achieves 50% KV cache reduction with only 3.1% perplexity degradation. Crucially, projection sharing is complementary to head sharing (GQA/MQA): combining Q-K=V with GQA-4 yields 87.5% cache reduction, while Q-K=V + MQA achieves 96.9%, enabling practical on-device inference. We show that Q-K=V preserves quality because keys and values can occupy similar representational spaces and attention operates in a low-rank regime, whereas Q=K-V breaks attention directionality. Our results systematically characterize projection sharing as an underexplored instance of weight tying in attention, with direct, quantifiable inference memory benefits, particularly valuable for edge deployment. The code is publicly available at https://github.com/anushamadan02/Do-Transformers-Need-3-Projections
IPv6 zones in URLs are a mistake
https://xeiaso.net/notes/2026/ipv6-zones-go-url/
#HackerNews #IPv6 #URLs #mistakes #internet #debate #tech #news
Conflict over identity politics could lead to civil war in long term, says Badenoch
https://www.bbc.com/news/articles/ce9pll8943no?at_medium=RSS&at_campaign=rss
Conflict over identity politics could lead to civil war in long term, says Badenoch
https://www.bbc.com/news/articles/ce9pll8943no?at_medium=RSS&at_campaign=rss