Tootfinder

How Vox built a huge YouTube presence, becoming an incubator that invented its own visual language, as ex-staffers like Johnny Harris grow their own channels (Simon Owens/The Long Story with Simon Owens)
https://thelongstory.substack.com/p/why-the-best-jour…

Why the best journalists on YouTube are all former Vox employees
The company became a skunk works incubator that invented its own language of visual storytelling.

Fast and Simplex: 2-Simplicial Attention in Triton
Aurko Roy, Timothy Chou, Sai Surya Duvvuri, Sijia Chen, Jiecao Yu, Xiaodong Wang, Manzil Zaheer, Rohan Anil
https://arxiv.org/abs/2507.02754

Fast and Simplex: 2-Simplicial Attention in Triton
Recent work has shown that training loss scales as a power law with both model size and the number of tokens, and that achieving compute-optimal models requires scaling model size and token count together. However, these scaling laws assume an infinite supply of data and apply primarily in compute-bound settings. As modern large language models increasingly rely on massive internet-scale datasets, the assumption that they are compute-bound is becoming less valid. This shift highlights the need …

A Study in Thermal: Advantage framework for resource engines
Jakub Czartowski, Rafa{\l} Bistro\'n
https://arxiv.org/abs/2507.05699 https://

A Study in Thermal: Advantage framework for resource engines
Thermal engines have been one of the principal topics of thermodynamics ever since its beginning. Today, in the era of the second quantum revolution, although the thermal processes in constant temperatures are relatively well understood in the language of resource theories, a framework to describe thermal engines is still in its infancy. In this work we formalize the resource theory of engines, initially put forward in [Quantum 8, 1222], and define such quantities as engine efficiency. Then, we…

This https://arxiv.org/abs/2412.09453 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCE_…

Finite-PINN: A Physics-Informed Neural Network with Finite Geometric Encoding for Solid Mechanics
PINN models have demonstrated capabilities in addressing fluid PDE problems, and their potential in solid mechanics is beginning to emerge. This study identifies two key challenges when using PINN to solve general solid mechanics problems. These challenges become evident when comparing the limitations of PINN with the well-established numerical methods commonly used in solid mechanics, such as the finite element method (FEM). Specifically: a) PINN models generate solutions over an infinite doma…