Tootfinder

Fun fact: #Azure Pipelines don't support #YAML files with anchors/aliases.
Also fun fact: both #PyYAML and #RuamelYAML *insist* on emitting anchors/aliases, and at least the PyYAML authors seem pretty, errr, opinionated on emitting them.
#Python

More TV news anchors are considering digital options like video podcasts and YouTube shows, as lucrative TV contracts become less valuable and more scarce (Alex Weprin/The Hollywood Reporter)
https://www.hollywoodreporter.com/business/business-news/tv-new…

With TV News In Free Fall, Anchors Try Breaking Away
As cost cuts roil the networks, A-list talent (and their agents) are mulling ways to go indie to make up for slashed salaries — a move that's not only risky but expensive.

Two press freedom groups are warning that Larry Ellison may “implement the CBS playbook” at CNN by getting rid of all the anchors President Donald Trump doesn’t like.
In a letter sent Thursday to Paramount Skydance, Freedom of the Press Foundation and Reporters Without Borders demanded to see internal documents, alleging that there was “credible concern that Paramount leadership has offered, solicited, or effectuated a corrupt exchange: more favorable coverage of the Trump administrati…

Larry Ellison Promised to Fire CNN Anchors If Trump Approved Takeover
Two press freedom groups allege Ellison agreed to a “corrupt exchange” with Donald Trump.

Sources: HSG, formerly Sequoia China, has set up a $3B continuation fund anchored by its ByteDance stake, offering entry into the company valued at $370B (Bloomberg)
https://www.bloomberg.com/news/articles/2026-05-15/bytedance…

What's up with people who disapprove of Trump but won't vote for Democrats? (G. Elliott Morris/Strength In Numbers)
https://www.gelliottmorris.com/p/2026-07-13-lab-gop-anchors-generic-ballot
http://www.memeorandum.com/260713/p38#a260713p38

Packet-Loss Robust 3D Gaussian Compression via Atomic Packaging and GNN-based Error Concealment
Yuxuan Tao, Xuerui Ma, Hao Zhang, Chunhua Peng
https://arxiv.org/abs/2607.17916 https://arxiv.org/pdf/2607.17916 https://arxiv.org/html/2607.17916
arXiv:2607.17916v1 Announce Type: new
Abstract: 3D Gaussian Splatting (3DGS) and recent compression schemes such as HAC enable high-fidelity real-time neural rendering, but their bitstreams are fragile under packet loss during network streaming. Existing compression methods often separate correlated anchor attributes into independent streams, so losing one packet can create attribute-inconsistent broken anchors and severe rendering artifacts. We propose a packet-loss robust 3DGS transmission and error concealment framework. On the encoder side, anchor-level atomic packaging jointly encapsulates all attributes of each anchor, converting corrupted-attribute failures into clean missing-anchor erasures. Stratified random grouping further disperses packet losses across the spatial domain to avoid large contiguous voids. On the decoder side, we formulate recovery as prior-aware attribute inpainting. A Context-Aware Residual Interpolation (CARI) branch uses hash-grid prior predictions and neighboring residuals to build a robust baseline, while a lightweight two-layer graph neural network with cross-attention over hash-grid priors refines high-frequency attribute residuals. Attribute-wise confidence control falls back to interpolation when learned predictions are unreliable. Experiments under 20 percent random packet loss on BungeeNeRF, Mip-NeRF 360, and Tanks and Temples show that the proposed method substantially improves over no-concealment transmission and limits average PSNR degradation to about 3 dB relative to the lossless HAC reference.
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