Tootfinder

No one, it seems, knows why cyberattackers would target Suisun City,
a sleepy, marsh-front suburb of 29,000 people on the northeast outskirts of the San Francisco Bay Area.
But target it, they did,
with crippling effects.
Whoever they are.
Before sunrise on Aug. 7,
the city’s information technology network was infected by malicious software that hit critical public safety operations,
including police and fire dispatch and critical records, officials …

Small California city baffled by cyberattack that crippled city hall
A severe cyberattack paralyzed Suisun City Hall and emergency dispatch, sparking local suspicion and law enforcement interest in the small California town.

Zurbaršn was definitely a bit bonkers. Genius, sure, but woa, some of the symbolism is out there! The cherub heads with wings are a recurring theme, hovering with holy payloads. Check out the alt text for all the details.
#art #NationalGallery

RE: https://mastodon.social/@CyReVolt/116717021405520634
So there are those mixed reactions.
This is a great overall view, summarizing actual issues instead of being hyperbolic or overly optimistic:

Improved Approximation Guarantees for Groupwise Maximin Share Fairness
Georgios Amanatidis, Anna Korfiati, Evangelos Markakis, Christodoulos Santorinaios
https://arxiv.org/abs/2606.04731 https://arxiv.org/pdf/2606.04731 https://arxiv.org/html/2606.04731
arXiv:2606.04731v1 Announce Type: new
Abstract: We study the problem of fairly allocating a set of indivisible goods to a set of $n$ agents with additive valuation functions. We focus on the very demanding notion of \textit{groupwise maximin share fairness} (GMMS), which requires that each agent $i$ receives value comparable to their maximin share, where the latter is computed \textit{with respect to any subset of agents that contains $i$}. We show that it is possible to compute $(\phi-1)$-approximate GMMS allocations in polynomial time, where $\phi \approx 1.618$ is the golden ratio). This improves on the previously known guarantee of $4/7$ of Chaudhury et al. [SICOMP; 2021] and Amanatidis et al. [TCS; 2020]. We propose a simple algorithm that maintains the same main properties as the Draft-and-Eliminate algorithm of Amanatidis et al. [TCS, 2020] and we improve on the approximation guarantee analysis by carefully bounding the relevant value within any subinstance induced by the restriction of our allocation to a subset of agents. Our analysis is asymptotically tight for algorithms that share these properties and has the additional benefit of giving improved guarantees for restricted settings; in particular, when the agents agree on the top $n$ goods or when the number of agents is small. To illustrate the challenges of going beyond the guarantees of our algorithm, we also present a variant with an improved approximation of $(\sqrt{10}-1)/3 \approx 0.72$ for the case of three agents. To achieve this improvement we partially characterize the maximin share guarantees of short picking sequences for a small number of goods.
toXiv_bot_toot

Five years ago, the West risked a
full-blown diplomatic crisis with NATO ally Turkey
when 10 ambassadors called for the release of a man they saw as a political prisoner,
prompting an angry President Tayyip Erdogan to order their expulsion.
After two frantic days in 2021,
the sides stepped back from the brink with the U.S., ‌French, German, Canadian and other envoys issuing conciliatory statements
and Erdogan saying they would be more careful in the future…

That kinda worked...
https://phoenixtech.com/news/phoenix-technologies-divests-bios-technology-to-lenovo/
> Gerard Moore, CEO said. “This divestiture will allow the company to focus on developing and supporting its new FirmGuard produc…

Phoenix Technologies Divests BIOS Technology to Lenovo - Phoenix Technologies - Leading PC Innovation since 1979
Phoenix divests BIOS assets to Lenovo to focus on FirmGuard, while maintaining support for existing customers and strengthening long-term strategy.

ICE train sticker spotted at DB Lounge Frankfurt main station