Tootfinder

Zelensky doesn't see corruption as a problem, prosecuted activist says: https://benborges.xyz/2025/07/18/zelensky-doesnt-see-corruption-as.html

Opnieuw duizenden stempassen niet bezorgd: 'Het is een enorme operatie'
#TK2025

Apparently an older boy generated the ads with AI and ran away.
https://www.rockpapershotgun.com/nexon-respond-to-claims-theyre-using-ai-generated-tiktok-streamers-to-advertise-the-first-descendant…

Nexon respond to claims they're using AI-generated Tiktok streamers to advertise The First Descendant
The First Descendant publishers Nexon have addressed AI-looking streamer adverts for the game on TikTok, saying they and the platform are investigating.

Democrat convinces colleagues to stand up to Ron DeSantis' anti-Pride agenda with impassioned speech - LGBTQ Nation
https://www.lgbtqnation.com/2025/08/democrat-convinces-colleagues-to-stand-up-to-ron-desantis-anti-pride-agenda-with-impassioned-speech/

In boon for House GOP, Florida Supreme Court sides with DeSantis, upholds congressional map (Gary Fineout/Politico)
https://www.politico.com/news/2025/07/17/florida-supreme-court-sides-with-desantis-upholds-congressional-map-00459893
http://www.memeorandum.com/250717/p143#a250717p143

Harmonized Gradient Descent for Class Imbalanced Data Stream Online Learning
Han Zhou, Hongpeng Yin, Xuanhong Deng, Yuyu Huang, Hao Ren
https://arxiv.org/abs/2508.11353 https://…

Harmonized Gradient Descent for Class Imbalanced Data Stream Online Learning
Many real-world data are sequentially collected over time and often exhibit skewed class distributions, resulting in imbalanced data streams. While existing approaches have explored several strategies, such as resampling and reweighting, for imbalanced data stream learning, our work distinguishes itself by addressing the imbalance problem through training modification, particularly focusing on gradient descent techniques. We introduce the harmonized gradient descent (HGD) algorithm, which aims …

Apple and NBCUniversal announce an Apple TV and Peacock Premium bundle for $14.99 per month, a 30% discount over the individual subscriptions (Alex Weprin/The Hollywood Reporter)
https://www.hollywoodreporter.com/business/…

Apple TV and Peacock Ink Extensive Streaming Bundle Deal
In addition to getting both services at a discount of more than 30 percent, Apple TV will add select Peacock programming, while Peacock will get selected Apple TV shows.

“What if AI Doesn’t Get Much Better Than This?” – a pretty good summary for those who don't want to read Gary Marcus’s rather… elaborate… treatizes.
> I think it’s safe, at least for now, to turn your attention away from the tech titans’ increasingly hyperbolic claims
🧘‍♂️
https://ca…

What if AI Doesn’t Get Much Better Than This? - Cal Newport
In the years since ChatGPT’s launch in late 2022, it’s been hard not to get swept up in feelings of euphoria or dread about the ... Read more

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…

DFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning
Lianshuai Guo, Zhongzheng Yuan, Xunkai Li, Yinlin Zhu, Meixia Qu, Wenyu Wang
https://arxiv.org/abs/2508.11530

DFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning
Decentralized Federated Learning (DFL) has emerged as a robust distributed paradigm that circumvents the single-point-of-failure and communication bottleneck risks of centralized architectures. However, a significant challenge arises as existing DFL optimization strategies, primarily designed for tasks such as computer vision, fail to address the unique topological information inherent in the local subgraph. Notably, while Federated Graph Learning (FGL) is tailored for graph data, it is predomi…