Stunning Performances🤩 Mone Chiba skated beautifully to capture the gold, while Alexandra Ignatova won the Free Skate to climb into the silver medal position. Meanwhile, Haein Lee claimed her first Grand Prix medal with a bronze! 🏆 🥇 Mone Chiba 🥈 Alexandra Ignatova 🥉 Haein Lee #FigureSkating#

✨ Celebrate 10 Years with Mone Kamishiraishi! Experience her heartfelt vocals, unforgettable performances, and the warmth that has touched fans for a decade. 💐 Join Mone Kamishiraishi 10th Live "bouquet" for ALL and celebrate this special anniversary together! 💙 📍Venue: Yokohama Akarenga Park Outdoor Special Stage ⏰Date&Time: 13:00 (UTC+9) October 3 & 4, 2026 🎫Sale Start: August 14, 12:00 (UTC+9) Click to secure your spots! ✨ 上白石萌音デビュー10周年を一緒にお祝いしよう！ 心に響く歌声と温かなステージで、10年間の感謝をお届けします。💐 Mone Kamishiraishi 10th Live「bouquet」for ALLで、特別なアニバーサリーのひとときをぜひご一緒に！💙 📍会場：横浜赤レンガパーク野外特設ステージ ⏰開演日時：10月3日·4日 13:00 (UTC+9) 🎫販売開始: 8月14日 12:00 (UTC+9) 👉チケットはこちら： #上白石萌音# #横浜# #デビュー10周年# #Mone_Kamishiraishi#

New vLLM Office Hours on advanced model compression, with Kyle Sayers and @_soyr_. They cover: - LLM Compressor's sequential pipeline and data parallel schemes - Settings most users never change - What observers do and how they affect quantization error - Expert pruning with REAP and MoNE

I mean, you can’t really do Clair de Lune much better than that. Kaitlyn Weaver outdid herself with that choreo, it’s a masterclass from Mone Chiba. I’ll be curious to see if my exuberance here is because this program is new or if the next time I see it, I will feel the same

A bunch of Championship changed hands today. 🏆 NEW CHAMPIONS TODAY 🏆 AEW All In: London AEW World Championship: Will Ospreay AEW Women’s World Championship: Mercedes Moné AEW International Championship: Kazuchika Okada AEW TNT Championship: Darby Allin AEW World Trios Championship: The New Level & Swerve Strickland AEW Women's World Tag Team Championship: The Brawling Birds WWE NXT Heatwave: NXT Championship: Grayson Waller NXT Women’s Championship: Kelani Jordan NXT Tag Team Championship: The Vanity Project NXT North America Championship: Jackson Drake

I'm one of 100+ signatories to a public letter calling for five minimum requirements to make embedded evaluations credible, by establishing evaluator independence, protection from retaliation, and real access. ———— We, the undersigned, are encouraged to see frontier AI companies call for embedding third-party organizations to evaluate rapidly escalating AI capabilities and risks. We believe that all frontier AI companies should embed evaluators to independently assess AI risks, including evaluating the systems themselves and any significant incidents of real-world harm, as well as the companies’ training, deployment, oversight, operational, and safeguard practices. To be credible, embedded third-party evaluations must have scientific objectivity, transparency, independence, and robust protections against interference from the evaluated companies, including at least: 1. Frontier AI companies should rely on evaluators that are meaningfully independent, that maintain full editorial control, and that disclose and mitigate potential conflicts of interest. This includes at a minimum that embedded evaluation organizations should not be owned or governed by frontier AI companies, should not have other significant commercial business with them, and should not accept any form of payment or other reward contingent on the evaluator’s findings. 2. Frontier AI companies should incorporate differing viewpoints and areas of expertise, including by embedding multiple evaluation organizations across a range of priority risk areas, each with deep relevant technical expertise, as well as by allowing and encouraging evaluators to share how conclusions differ among evaluators and between evaluators and company employees. 3. Embedded evaluators should be transparent, including transparency about their methods and findings, the nature of their access, and the broader terms of the evaluation. Frontier AI companies should actively facilitate this transparency, including limiting the scope of non-disclosure agreements. They should also allow evaluators prompt and unfiltered communication with the companies’ boards and other privileged oversight bodies, as well as public release of findings and evidence, subject only to a time-limited redaction process restricted to protecting critical interests in intellectual property, customers’ sensitive information, individual privacy, security, and public safety. 4. Embedded evaluators should be shielded from retaliation from the companies they embed with for choosing reasonable evaluation methods, discovering information, or drawing conclusions that are unflattering to those companies. This includes reasonable protections against retaliatory litigation, as well as funding mechanisms that give them confidence they will remain funded even in these cases. 5. Frontier AI companies should grant embedded evaluators access equivalent to that of their own highly privileged employees for the purposes of their evaluations, and with exceptions to protect sensitive data belonging to the company’s customers and other third parties. This includes access to the same relevant systems, data, tools, and physical spaces as those available to senior internal company employees responsible for carrying out comparable risk assessments, as well as candid and direct one-on-one communication with relevant staff. This list is not comprehensive, and conditions like these to ensure credible evaluations should be increasingly standardized, codified, and enforced. One example is the set of terms defined in the AEF-1 standard, which has already seen early adoption, but far more work will be necessary to ensure that embedded evaluators are effective and meaningful. Embedded evaluations cannot address all oversight needs and should be treated as a complement to, rather than a replacement for, broader efforts by frontier AI companies to expand external oversight, including greater public transparency and additional, broader forms of access for independent researchers. ———— Full letter with links and signatures: I'm grateful to the AI Evaluator Forum (@aievalforum) for organizing this.