Tootfinder

WR Keon Coleman suffered sprained foot/toe in Bills' preseason opener vs. Panthers https://www.nfl.com/news/wr-keon-coleman-suffered-sprained-foot-toe-in-bills-preseason-opener-vs-panthers

BallotBox Scotland: "It’s time everyone got serious about the Scottish Greens"
Some reflections from BBS, and a little pointer into what we should expect for the next parliament.
"The belief that right wing voters have to be courted and coddled, and left wing voters uncomfortable with that will just have to suck it up because they don’t have other options. It turns out they do, in fact, have other options"

Summer Special: It’s time everyone got serious about the Scottish Greens
A long read on a bugbear: few folk seem to care about understanding the Greens, and that failure has cost them over and over again. Following a record result, will that change, or will people just…

" Le tout, sans compter les défaillances techniques notoires de Capgemini, prestataire « en roue libre », aux dires de Nadi Bou Hanna, témoin cité par le parquet, ancien directeur interministériel du numérique"
https://www.

Au procès « Scribe », les « graves difficultés et dérives » du logiciel qui empoisonne la police
Six agents publics étaient appelés, devant la Cour des comptes, à répondre de leur responsabilité dans l’échec du projet de logiciel de rédaction de procédure pénale de la police, malgré des dizaines de millions d’euros investis.

Text2Villa: Hierarchical Generation of 3D Indoor Environments with Physics-Aware Analysis-by-Synthesis
Xiang Tang, Ruotong Li, Xiaopeng Fan
https://arxiv.org/abs/2607.17145 https://arxiv.org/pdf/2607.17145 https://arxiv.org/html/2607.17145
arXiv:2607.17145v1 Announce Type: new
Abstract: Generating 3D indoor scenes from natural language holds tremendous potential, yet existing methods predominantly fail to generate multi-room structures with vertical connectivity and arbitrary polygonal boundaries. Furthermore, they lack a deep grounding in continuous 3D physical laws, leading to severe geometric penetrations and floating artifacts. In this work, we propose Text2Villa, a novel hierarchical generative framework. At the macro level, we construct a multi-story dataset to fine-tune an autoregressive layout generator, ensuring the direct parsing of text into 3D building foundations featuring polygonal boundaries and multi-story connectivity. To enforce physical laws during micro-level asset arrangement, we introduce the Affordance-driven Physical-Semantic Scene Graph (A-PSSG) to explicitly abstract physical affordances (such as support surfaces and containment cavities) into node attributes, establishing strict geometric and semantic edge constraints. Guided by the A-PSSG, we formulate scene instantiation as a constrained closed-loop optimization problem following the analysis-by-synthesis paradigm. By integrating an underlying geometric collision detection engine with the high-level semantic reasoning of multimodal large language models (MLLMs), our heuristic solver dynamically executes physics-aware actions under the observation-evaluation-modification mechanism to effectively resolve mesh collisions, floating artifacts, and fine-grained cavity containment failures. Extensive experiments demonstrate that Text2Villa outperforms previous methods across various metrics, robustly generating high-fidelity and physically plausible villa-level 3D environments from text, thereby providing a reliable and interactive 3D content foundation for downstream applications.
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