Tootfinder

Google says it hit a milestone of 1.3 quadrillion monthly tokens processed across its services this summer, up from 980T monthly tokens announced in July (Matthias Bastian/The Decoder)
https://the-decoder.com/google-boasts-1-3…

Google boasts 1.3 quadrillion tokens each month, but the figure is mostly window dressing
Google says it now processes more than 1.3 quadrillion tokens every month with its AI models. But this headline number mostly reflects computing effort, not real usage or practical value, and it raises questions about Google's own environmental claims.

Trump 'in Exceptional Health' Says White House Doctor (Brian Schwartz/Wall Street Journal)
https://www.wsj.com/politics/trump-in-exceptional-health-says-white-house-doctor-643e444c
http://www.memeorandum.com/251010/p158#a251010p158

»LXD-Fork erhält mit IncusOS seine eigene Distribution«
Oha… Erst jetzt entdeckt und danke @… für den Artikel. Sichere Server zu betreiben ist eben ein eigenes Thema für sich und mMn kümmern sich die wenigsten (kleinen/mittleren) Unternehmen ernsthaft darum. Sind dann aber überrascht so wie geschockt wenn die "gehackt" werden.
🛡️

LXD-Fork erhält mit IncusOS seine eigene Distribution
IncusOS ist ein modernes, unveränderliches Betriebssystem auf der Basis von Debian 13 »Trixie«, das ausschließlich für den sicheren und zuverlässigen Betrieb…

DYNAMIX: RL-based Adaptive Batch Size Optimization in Distributed Machine Learning Systems
Yuanjun Dai, Keqiang He, An Wang
https://arxiv.org/abs/2510.08522 https://

DYNAMIX: RL-based Adaptive Batch Size Optimization in Distributed Machine Learning Systems
Existing batch size selection approaches in dis- tributed machine learning rely on static allocation or simplistic heuristics that fail to adapt to heterogeneous, dynamic computing environments. We present DYNAMIX, a reinforcement learning framework that formulates batch size optimization as a sequen- tial decision-making problem using Proximal Policy Optimiza- tion (PPO). Our approach employs a multi-dimensional state representation encompassing network-level metrics, system-level resource uti…

Partitioning and Self-organization of Distributed Generation in Large Distribution Networks
Badr Al Faiya, Stephen McArthur, Ivana Kockar
https://arxiv.org/abs/2509.07918 https:…

Partitioning and Self-organization of Distributed Generation in Large Distribution Networks
Distribution networks will experience more installations of distributed generation (DG) that is unpredictable and stochastic in nature. Greater distributed control and intelligence will allow challenges such as voltage control to be handled effectively. The partitioning of power networks into smaller clusters provides a method to split the control problem into manageable sub-problems. This paper presents a community detection-based partitioning technique for distribution networks considering lo…

#GegendasVergessen #Niewiederistjetzt
Eine wahre Geschichte. Dokudrama 'Nürnberg 45 - Im Angesuicht des Bösen'
Wann: So., 09.11.25 um 21:45 Uhr
Wo: #ARD

ARD History: Nürnberg 45 | Dokumentation & Reportage
Als Ernst Michel (22) zum ersten Mal die Pressetribüne im Saal 600 des Nürnberger Justizpalastes betritt, hält er die Nähe zu den Angeklagten kaum aus. Keine zehn Meter sitzt er, der Auschwitz-Überlebende, von Göring und den anderen ...

Approximate Algorithms for Verifying Differential Privacy with Gaussian Distributions
Bishnu Bhusal, Rohit Chadha, A. Prasad Sistla, Mahesh Viswanathan
https://arxiv.org/abs/2509.08804

Approximate Algorithms for Verifying Differential Privacy with Gaussian Distributions
The verification of differential privacy algorithms that employ Gaussian distributions is little understood. This paper tackles the challenge of verifying such programs by introducing a novel approach to approximating probability distributions of loop-free programs that sample from both discrete and continuous distributions with computable probability density functions, including Gaussian and Laplace. We establish that verifying $(ε,δ)$-differential privacy for these programs is \emph{almost …

A Rate-Distortion Bound for ISAC
Mohammadreza Bakhshizadeh Mohajer, Alex Dytso, Daniela Tuninetti, Luca Barletta
https://arxiv.org/abs/2510.08487 https://a…

A Rate-Distortion Bound for ISAC
This paper addresses the fundamental performance limits of Integrated Sensing and Communication (ISAC) systems by introducing a novel converse bound based on rate-distortion theory. This rate-distortion bound (RDB) overcomes the restrictive regularity conditions of classical estimation theory, such as the Bayesian Cramér-Rao Bound (BCRB). The proposed framework is broadly applicable, holding for arbitrary parameter distributions and distortion measures, including mean-squared error and probabi…

The A.I. insurance market is in its infancy, but Mr. Kvist says mainstream insurers are lining up to back him.
One of his clients is a job recruiting company that uses A.I. to sift through candidates.
“Which is great, but you can now discriminate at a scale we’ve never seen before,” Mr. Kvist said.
“It’s a breeding ground for class-action lawsuits.”
Mr. Kvist believes the work he is doing now will lay the foundation for more complex A.I. insurance policies to come.

Opinion | The A.I. Prompt That Could End the World
A destructive A.I., like a nuclear bomb, is now a concrete possibility; the question is whether anyone will be reckless enough to build one.

I wonder whether the developer took this restaurant owner for a ride, or the restaurant owner jilted the developer who got back at them. Either way…