Tootfinder

Der #Schulwettbewerb #Energiesparmeister 2026 ist gestartet:
Schulen in #MecklenburgVorpommern können sich mit ihren

»Der Versorger verlangte demnach, dass Rechenzentrumsbetreiber für mindestens 85 Prozent der von ihnen bestellten Energie aufkommen müssen. Nach der Einführung dieser Regel fielen die Bedarfsangaben in Anträgen auf Netzanschluss von mehr als 30 Gigawatt auf 13 Gigawatt, was zeigt, dass einige Betreiber den Energiebedarf ihrer neuen Anlagen viel zu hoch ansetzen.«
Es geht um einen Energieversorger in Ohio.
Quelle:

Prognosen fragwürdig: Energiebedarf für Rechenzentren soll sich verdreifachen - Golem.de
Während Prognosen von einem immer stärkeren Energiebedarf ausgehen, offenbart die Taktik eines Versorgers deutlich geringere Zahlen.

"Während Trumps Weltanschauung von einem ganzen digitalen Ökosystem mit Influencern und pseudo-journalistischen Formaten sonder Zahl verbreitet wird, kommuniziert die Europäische Union immer noch vor allem durch Statements und Pressemitteilungen. ...Wie also kann sie glaubhaft Stärke zeigen? Sie sollte ihre Werte demons­trieren, statt sie zu erklären," so Felix Karte , #FAZ (€) Wie die EU-…

Wie die EU-Digitalpolitik auf die US-Provokationen antworten sollte
EU-Spitzen reagieren auf die Provokationen der USA immer noch mit Diplomatie. In der Digitalpolitik hätten sie Möglichkeiten, Trumps Gewaltgehabe offensiv zu kontern.

Been starting a habit of writing down story/game ideas as I have them even though most of them will never seriously get started, let alone finished. It's been fun since writing things down gives me a chance to think them through a bit more than just pondering them in my head. Anyways, here's a #GameDesign idea:
"Grand" - is a "reverse metroidvania" in which as a grandparent, you slowly lose movement options as the story progresses, requiring more and more convoluted routes through the map to reach the same areas. You do still explore "new" areas in memory mode (and unlock movement options like a bike in your memories) before traversing the areas again in the diegetic present. The story follows your quest to protect a grandchild from the machinations of a Kafkaesque state, first trying to track them down within the system and then trying to get them released. Each "boss" is "fought" through an abstracted conversation system where memories, keepsakes, and various kinds of emotional/logical appeals wear down your opponent's nihilism and/or fear until they're willing to help you. Normal "enemies" are just people on the street who might bump into you and drain some of your stamina as you pass by if you don't issue a properly-timed "excuse me" or the like.

My personal sense is that voter ID is not a hill to die on.
Even I can feel the draw of requiring voters to produce something that helps to demonstrate the fact that "I am qualified to cast a ballot in this election."
I recognize that voter ID requirements can (and will be) used to exclude valid voters.
But my answer is not to fight the voter id requirement itself.
Rather I'd invest in making sure that all people get proper voter IDs. (Perhaps I might b…

„Halbherzige Maßnahme“ - Mehrwertsteuer sinkt: Handel lobt – und Kickl tobt #News #Nachrichten …

Der #Offshore-Windpark #Gennaker erhält grünes Licht: Mit 63 Anlagen wird er zum größten #Windpark in der deutschen #Ostsee

Multi-agent learning under uncertainty: Recurrence vs. concentration
Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos, Jose Blanchet
https://arxiv.org/abs/2512.08132 https://arxiv.org/pdf/2512.08132 https://arxiv.org/html/2512.08132
arXiv:2512.08132v1 Announce Type: new
Abstract: In this paper, we examine the convergence landscape of multi-agent learning under uncertainty. Specifically, we analyze two stochastic models of regularized learning in continuous games -- one in continuous and one in discrete time with the aim of characterizing the long-run behavior of the induced sequence of play. In stark contrast to deterministic, full-information models of learning (or models with a vanishing learning rate), we show that the resulting dynamics do not converge in general. In lieu of this, we ask instead which actions are played more often in the long run, and by how much. We show that, in strongly monotone games, the dynamics of regularized learning may wander away from equilibrium infinitely often, but they always return to its vicinity in finite time (which we estimate), and their long-run distribution is sharply concentrated around a neighborhood thereof. We quantify the degree of this concentration, and we show that these favorable properties may all break down if the underlying game is not strongly monotone -- underscoring in this way the limits of regularized learning in the presence of persistent randomness and uncertainty.
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Maßnahmen im Detail - Regierung beschließt Hilfspaket für den Tourismus #News #Nachrichten