Tootfinder

Hadrian, which is building largely automated factories to produce space and defense parts, raised a $260M Series C, bringing its total funding to ~$500M (Aria Alamalhodaei/TechCrunch)
https://techcrunch.com/2025/07/17/hadria…

Hadrian raises $260M to build out automated factories for space and defense parts | TechCrunch
Hadrian wants to revolutionize American manufacturing through automation.

Trump says he and Putin ‘largely agreed’ on land swaps, security guarantees for Ukraine: https://benborges.xyz/2025/08/16/trump-says-he-and-putin.html

Just had an MRI.
Last time was decades ago.
Last time the noise was largely clicks, bangs, and buzzes.
But now they have an entire techno-space opera of new sounds.

“The Tudor state scrapped Latin worship and the lyrics and music that went with it were largely discarded. Most of this stuff is lost. But..."
https://www.theguardian.com/music/2025/aug/16/monastic-music-t…

Absolutely fucking useless. https://bsky.app/profile/did:plc:4xepeetzgz3xn6ss2ty45ta5/post/3lu6wopovs227

@… Understandable, I just don’t agree :)
Of course I would RATHER the third option you (presumably purposely) didn’t include: Good food without the work.
But if I have to pick, I’m putting in the work (I already cook at home 5-7 nights a week, the kitchen is largely my domain in this house).

At least my daughters’ bedroom which doubles as my home office is now largely in order. The biggest issue is that our tumble dryer smells as though something has crawled inside it and died. I’ve tried cleaning it with a white vinegar solution but I’m not sure that it’s been entirely successful… and until I fix that we can’t begin clearing the huge holiday backlog of laundry 😣

A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning
Juan Diego Toscano, Daniel T. Chen, Vivek Oommen, George Em Karniadakis
https://arxiv.org/abs/2509.14198

A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning
Residual-based adaptive strategies are widely used in scientific machine learning but remain largely heuristic. We introduce a unifying variational framework that formalizes these methods by integrating convex transformations of the residual. Different transformations correspond to distinct objective functionals: exponential weights target the minimization of uniform error, while linear weights recover the minimization of quadratic error. Within this perspective, adaptive weighting is equivalen…

CineTrans: Learning to Generate Videos with Cinematic Transitions via Masked Diffusion Models
Xiaoxue Wu, Bingjie Gao, Yu Qiao, Yaohui Wang, Xinyuan Chen
https://arxiv.org/abs/2508.11484

CineTrans: Learning to Generate Videos with Cinematic Transitions via Masked Diffusion Models
Despite significant advances in video synthesis, research into multi-shot video generation remains in its infancy. Even with scaled-up models and massive datasets, the shot transition capabilities remain rudimentary and unstable, largely confining generated videos to single-shot sequences. In this work, we introduce CineTrans, a novel framework for generating coherent multi-shot videos with cinematic, film-style transitions. To facilitate insights into the film editing style, we construct a mul…

A Universal Banach--Bregman Framework for Stochastic Iterations: Unifying Stochastic Mirror Descent, Learning and LLM Training
Johnny R. Zhang (Independent Researcher), Xiaomei Mi (University of Manchester), Gaoyuan Du (Amazon), Qianyi Sun (Microsoft), Shiqi Wang (Meta), Jiaxuan Li (Amazon), Wenhua Zhou (Independent Researcher)
https://arx…

A Universal Banach--Bregman Framework for Stochastic Iterations: Unifying Stochastic Mirror Descent, Learning and LLM Training
Stochastic optimization powers the scalability of modern artificial intelligence, spanning machine learning, deep learning, reinforcement learning, and large language model training. Yet, existing theory remains largely confined to Hilbert spaces, relying on inner-product frameworks and orthogonality. This paradigm fails to capture non-Euclidean settings, such as mirror descent on simplices, Bregman proximal methods for sparse learning, natural gradient descent in information geometry, or Kullb…