Tootfinder

When the Outlook "Like" button escapes on to the public Internet
https://lists.nanog.org/archives/list/nanog@lists.nanog.org/message/6LTS7KOK5LHQQKP5WGSMEQCFDXLWWCYY/

A change that proposes to drop i686 packages from x86_64 #Fedora 44 was just published:
https://lists.fedoraproject.org/archiv…

Lights Out Lounge F1 Podcast
Great Australian Pods Podcast Directory: #GreatAusPods

Evidence for a sub-Jovian planet in the young TWA 7 #disk : #Planet Imaged by NASA #Webb Is Lightest Ever Seen: https://webbtelescope.org/contents/news-releases/2025/news-2025-126 and https://www.esa.int/Science_Exploration/Space_Science/Webb/Webb_finds_evidence_of_a_lightweight_planet_around_TWA_7

https://bsky.app/profile/sepsndepsn.bsky.social/post/3lsdkn7xndc2e

🇺🇦 Auf radioeins läuft...
Lucy Dacus:
🎵 Kissing Lessons
#NowPlaying #LucyDacus
https://lucydacus.bandcamp.com/track/kissing-lessons
https://open.spotify.com/track/3KRdLDjxlAY7ku93tOG0b1

LightCom: A Generative AI-Augmented Framework for QoE-Oriented Communications
Chunmei Xu, Siqi Zhang, Yi Ma, Rahim Tafazolli
https://arxiv.org/abs/2507.17352 https://

LightCom: A Generative AI-Augmented Framework for QoE-Oriented Communications
Data-intensive and immersive applications, such as virtual reality, impose stringent quality of experience (QoE) requirements that challenge traditional quality of service (QoS)-driven communication systems. This paper presents LightCom, a lightweight encoding and generative AI (GenAI)-augmented decoding framework, designed for QoE-oriented communications under low signal-to-noise ratio (SNR) conditions. LightCom simplifies transmitter design by applying basic low-pass filtering for source codi…

Former FBI Deputy Director:
Is it a coincidence that the testimony she delivered is exactly what the White House was looking for?
Immediately after the interview, her status in the system is reclassified.
Sex offenders are not allowed traditionally to go to minimum security facilities
https://

A Lightweight Group Multiscale Bidirectional Interactive Network for Real-Time Steel Surface Defect Detection
Yong Zhang, Cunjian Chen, Qiang Gao, Yi Wang, Bin Fang
https://arxiv.org/abs/2508.16397

A Lightweight Group Multiscale Bidirectional Interactive Network for Real-Time Steel Surface Defect Detection
Real-time surface defect detection is critical for maintaining product quality and production efficiency in the steel manufacturing industry. Despite promising accuracy, existing deep learning methods often suffer from high computational complexity and slow inference speeds, which limit their deployment in resource-constrained industrial environments. Recent lightweight approaches adopt multibranch architectures based on depthwise separable convolution (DSConv) to capture multiscale contextual …

Another change proposal was submitted to the Fedora Engineering Steering Committee (FESCo) for approval[1]:
Replace the #Xorg #Xserver with the X11Libre Xserver for #Fedora 43-