Tootfinder

For those out of the Loop:
#Google, #Meta, Microsoft, any of the tech companies who provide "portals" to news and content, scrape this content and shove it in a feed that they have control over (obstensibly based on your "habits" and "preferences"). The portal keeps the $ad.revenue$ th…

You cannot be a company arguing about values (right to repair, autonomy) and then trample other people's rights. Shame on @….
https://bsd.network/@dch/1153425494265

dch :flantifa: :flan_hacker: (@dch@bsd.network)
Hey @frameworkcomputer@fosstodon.org I just cancelled my order because you fund #HateSpeech via hyprland & omarchy. I got a minisforum workstation instead of funding your sponsorship. #TransRightsAreHumanRights

Repairing Regex Vulnerabilities via Localization-Guided Instructions
Sicheol Sung, Joonghyuk Hahn, Yo-Sub Han
https://arxiv.org/abs/2510.09037 https://arxi…

Repairing Regex Vulnerabilities via Localization-Guided Instructions
Regular expressions (regexes) are foundational to modern computing for critical tasks like input validation and data parsing, yet their ubiquity exposes systems to regular expression denial of service (ReDoS), a vulnerability requiring automated repair methods. Current approaches, however, are hampered by a trade-off. Symbolic, rule-based system are precise but fails to repair unseen or complex vulnerability patterns. Conversely, large language models (LLMs) possess the necessary generalizabili…

It's a good question about the French public AC charging infrastructure: why? Caught between unreliability and high time-based pricing, who does it serve? Not those who need it, I would say.
https://www.numerama.com/vroom/2134433-bor

Coûteuses et capricieuses, le grand mensonge des bornes de recharge publiques à charge lente
On installe des bornes lentes, sauf qu’entre les pannes, les tarifs absurdes et le matériel douteux, la recharge publique ressemble encore trop à un parcours du combattant. Voilà de quoi nourrir l'édito Watt Else du jeudi 4 décembre. Les bornes AC (ou courant alternatif) représentent 80 % des 180 000 points de

REPAIR: Robust Editing via Progressive Adaptive Intervention and Reintegration
Yisu Wang, Ming Wang, Haoyuan Song, Wenjie Huang, Chaozheng Wang, Yi Xie, Xuming Ran
https://arxiv.org/abs/2510.01879

REPAIR: Robust Editing via Progressive Adaptive Intervention and Reintegration
Post-training for large language models (LLMs) is constrained by the high cost of acquiring new knowledge or correcting errors and by the unintended side effects that frequently arise from retraining. To address these issues, we introduce REPAIR (Robust Editing via Progressive Adaptive Intervention and Reintegration), a lifelong editing framework designed to support precise and low-cost model updates while preserving non-target knowledge. REPAIR mitigates the instability and conflicts of large-…