✨ a-nation 2026 — Tickets On Sale Now!! The legendary music festival returns to Tokyo Odaiba for the first time in 23 years! Two days of non-stop energy and unforgettable performances 🔥💿 📍 Venue: Odaiba a-nation Special Stage, Tokyo 🗓 Dates: October 3 (Sat) & 4 (Sun), 2026 🕒 Time: Doors 12:00 / Show 13:00 (tentative) （UCT+9） 🎤【Lineup】 ■Oct 3: EXILE / Bullet Train / CHANYEOL / Strawberry Prince / WINNER / NCT JNJM / NCT WISH & more… ■Oct 4: Ayumi Hamasaki / J.Y. Park / Da-iCE / TRF / JUN. K / AK-69 / FANTASTICS & more… Click to secure your spots! ✨ a-nation 2026 チケット発売中！！ 23年ぶりに東京・お台場へ戻ってくる、秋の大人気音楽フェス🔥 超豪華アーティストが2日間にわたり、ステージを熱狂の渦に変える💿⚡️ 📍 会場：東京・お台場 a-nation 特設会場 🗓 日程：2026年10月3日（土）・4日（日） 🕒 時間：開場 12:00 / 開演 13:00（予定）（UCT+9） 🎤【出演アーティスト】 ■10/3：EXILE / 超特急 / CHANYEOL / すとぷり / WINNER / NCT JNJM / NCT WISH ほか… ■10/4：浜崎あゆみ / J.Y. Park / Da-iCE / TRF / JUN. K / AK-69 / FANTASTICS ほか… 👉チケットはこちら： #a_nation2026# #お台場# #EXILE# #浜崎あゆみ# #音楽フェス#

Do you think you can handle a girl with psychic powers? 🌀✨ Say "yes" if you're brave enough to see all MOMO AYASE hotness tonight! 🍑 This month P@tr0n/tinyasa will have total 4 characters. Just a few days left to get them all~ 
你觉得你能应付得了有超能力的女孩吗？🌀✨ 如果你有勇气看今晚 MOMO AYASE 的全部火辣内容，就大声说“想看”吧！🍑 本月 P@tr0n/tinyasa 将会集结 4 位角色。距离全部解锁只剩最后几天喽～ 超能力を持つ女の子を扱えると思う？🌀✨ 今夜、MOMO AYASE の最高にホットな姿を見る勇気があるなら、**『はい』**って言って！🍑 今月の P@tr0n/tinyasa は合計 4 キャラクターが登場。全部手に入れられるのはあと数日だけよ～ #dandadan228# #momoayase# #ダンダダン#

The rewards for this month will also be delivered to everyone a bit late. I've just returned home these past few days and have been incredibly busy. However, this time there are 20 photos each for levels 4 and 5 to share with everyone. Lately, I've been contemplating what kind of work to present to everyone. If anyone has any recommended characters, feel free to message me. Love you all. 這個月的獎勵一樣會晚點送到大家手上唷 ~~ 這幾天剛回國 整個超級忙碌的 QQ 不過這次等級4跟5各有20張照片要給大家哦！ 最近一直在思考要呈現什麼樣的作品給大家 如果大家有想推薦的角色 也歡迎私訊推薦給我唷 >""< 愛大家<3 #Cos#

Actor Yang Zi stepped down as Vice Chairman of Juli Group but still holds a 17% stake. The share price of the listed company under his family’s conglomerate has halved in less than two months, after surging more than 180% last year. 演员杨子，卸任巨力集团副董事长，仍持有17%股份！家族旗下上市公司股价不到2个月“腰斩”，去年涨超180%

【Japan Rooftop Passion 🔥】 (20:13 min) Went with Sam to a “bookstore” in Japan 📚… the moment we stepped in we were totally shocked 😳! Turns out it’s a massive 5-floor adult superstore 💦! Sex toys everywhere you look 👀, couples, married people, solo guys all seriously shopping around, and the DVD section is just insane 📀! Couldn’t help but play some risky exposure inside the store 🫣… heart racing like crazy! After that we headed straight to a nearby yakitori izakaya 🍖🍻, vibe was absolutely wild! A little tipsy and I got super bold, flashing right in front of everyone 🤭! We couldn’t hold back anymore, so we sneaked up to the rooftop and started going at it 🌃💦! The first spot was still pretty good, but we were paranoid someone might come up 😨. Moved behind the air-con units instead… right when we were hitting the peak, suddenly the door went “click”… someone actually came up… ‼️😱 ————————- 【日本天台激情🔥】（20:13分钟） 跟Sam去日本逛“书店”📚，一进门直接傻眼😳！原来是五层楼超大规模成人用品店💦！玩具多到看不完👀，情侣、夫妻、单身猛男都在认真挑，DVD区更是夸张到爆炸📀！忍不住在店里玩起刺激露出🫣，心跳超快！ 逛完直冲附近烧烤酒吧🍖🍻，气氛超嗨！微醺的我越来越大胆，当着大家直接脱到露🤭！我们俩彻底憋不住，偷偷溜上天台开干啦🌃💦！第一个角落还能好好享受，但随时怕被人撞见😨！后来换到空调机后面，刚进入最爽时刻，突然「咔」门被推开，有人真的上来了…‼️😱

"I'm Fluoresce. Let's write a new story together." Sharing two lovely pics of my big Fluoresce cosplay~ >///< I must be the happiest Fluoresce in the world! 🥰 Lately, I've been super busy working on my FF cosplay— Costume, props, and wig all handmade by me✨ Totally grinding for it!! I promise this one won’t let you down!! Please look forward to it~ 🌟(⁎⁍̴̛ᴗ⁍̴̛⁎) 「我是芙露德莉斯，讓我們一起書寫新的故事吧。」 來兩張大卡的美照送上～ >///< 我應該是全世界最幸福的大卡了吧！？🥰 最近每天都在忙著趕工 FF 的角色~ 服裝、道具、假髮通通自己來✨超級肝肝der！ 這次也一定不會讓你們失望的！！ 請大家好好期待喔～🌟(⁎⁍̴̛ᴗ⁍̴̛⁎) #鳴潮# #芙露德莉斯# #Cos# #WutheringWaves# #Fluoresce# #cosplay#

#秋天的回响# China produced and sold over 12 million new energy vehicles in the first three quarters. September saw a fresh record for NEV penetration. New energy vehicles made up 61.6% of all new car sales. 今年前三季度，中国新能源汽车产销量双超1200万辆。其中，9月新能源汽车月度新车销量占比再创新高，达到61.6%。 #数据看中国# #China# #Jiangxi# #世界经济看中国#

In January-July 2026, China‘s automotive exports reached 6.14 million units, a significant increase of 66.8% year-on-year. China has jumped to become the world‘s largest automotive exporter, and the strong pull of new energy vehicles is not to be overlooked. From 2020 to 2025, China‘s exports of new energy vehicles increased from 69,000 to 2.615 million units, a growth of more than 36 times over five years. 2026年1—7月，中国汽车出口达614万辆，同比大幅增长66.8%。中国跃居世界第一大汽车出口国，新能源汽车的强势拉动功不可没。 2020年至2025年，中国新能源汽车出口规模从6.9万辆增至261.5万辆，5年间增长超36倍。 #Chinese# Automobiles

🦾 Why Data, Not Models, Is the Real Moat in Embodied AI The timing of this question is hard to miss. This week, the World Humanoid Robot Games released a 2,500+ hour dataset covering 12 scenario categories, 44 operations, and more than 10,000 tasks. Crucially, it also includes failures and edge cases. But raw hours tell only part of the story. Zhihu contributor 于超, an assistant professor at Tsinghua Shenzhen International Graduate School, shares his team’s view on why data has become embodied AI’s hardest-to-replicate advantage. 1️⃣ Robot scaling is fundamentally asymmetric Like language models, robot policies appear to benefit from more data, larger models, and greater compute. But these three inputs do not scale equally. Model architectures can be studied and reproduced quickly. General-purpose compute can, in principle, be purchased. High-quality robot data is different. It must be accumulated through physical interaction, and competitors cannot recreate it overnight. That asymmetry is what turns data into a moat. 2️⃣ Robot data must be manufactured LLMs inherited decades of internet data. Robots did not. Every useful trajectory must be produced through physical interaction. Even a simple cup-grasping task changes with the object, lighting, environment, camera angle, and robot body. So raw hours are not enough. What matters is the diversity of embodiments, tasks, objects, failures, and recoveries. Open X-Embodiment needed more than 20 institutions and 22 robot platforms to collect over one million trajectories. DROID used 50 collectors for a year, producing only 350 hours of data. New methods such as UMI and egocentric recording make collection easier. But every hour still requires real people, equipment, and time. 3️⃣ A successful trajectory can still be bad data Robot data quality is more complicated than whether a task was completed. On the hardware side, camera accuracy, encoder readings, force sensors, calibration, communication latency, and synchronization across modalities can all corrupt a trajectory. The human operator adds another source of noise. Teleoperating a robot is not the same as performing the action directly. Operators hesitate, pause, readjust, and develop habits for compensating for the control system. A task may succeed even when parts of the demonstration should never be imitated. Success is therefore only the coarsest possible label. One trajectory can contain both excellent behavior and inefficient or misleading actions. Training on the entire trajectory without distinction effectively tells the robot to learn both. 4️⃣ The next challenge is information density Collecting more trajectories is only half the problem. Teams must also identify which parts are worth learning from. Yu Chao’s team developed STEAM to detect local progress within a trajectory without frame-by-frame annotation or manually designed rewards. It separates useful progress from hesitation, failure, and recovery. The key question is shifting from “How many trajectories do we have?” to “How much useful information does each trajectory contain?” 5️⃣ Embodied data is physically expensive Text can be copied. Videos can be downloaded. Robot data requires a physical production process. Collecting one hour may involve a robot, sensors, teleoperation equipment, an operator, a suitable environment, task materials, and engineers who maintain and calibrate the system. Real factories, stores, and homes add even more complexity. And pressing the record button is only the beginning. Transmission, cleaning, governance, and storage can cost more than collection itself. The author offers a rough calculation. If a company wants one million hours of real-world data and reduces the combined collection and management cost to RMB 200 per hour, the total still reaches RMB 200 million. 🔑 The real moat compounds over time Quantity, quality, and cost explain why embodied AI data cannot be replicated through a short burst of spending. Large, diverse, high-quality datasets require physical infrastructure, operational discipline, and years of accumulation. As robot policies continue to benefit from scaling, the durable advantage will belong to teams that can repeatedly: 🔹 Collect broader real-world experience 🔹 Identify the most informative behavior 🔹 Preserve failures and recovery signals 🔹 Turn noisy trajectories into useful learning data In embodied AI, having data and knowing how to use it are becoming two very different capabilities. And the second may ultimately matter even more than the first. 🔗 Full analysis: #EmbodiedAI# #Robotics# #PhysicalAI# #RobotLearning# #AIData# #ScalingLaw# #Tsinghua#