[🇸🇬] 𝟐𝟎𝟐𝟔 𝐗𝐋𝐎𝐕 𝐀𝐒𝐈𝐀 𝐓𝐎𝐔𝐑 <𝐒𝐄𝐑𝐕𝐈𝐍𝐆✶> 𝐈𝐍 𝐒𝐈𝐍𝐆𝐀𝐏𝐎𝐑𝐄 📅 2026.10.01 (THU) 🕗 8PM 📍 THE STAR THEATRE 💰SGD 142 - SGD 332 (excl. fees) 🎫 General Onsale 📅 2026.07.07 (TUE), 10AM (SGT) #XLOV# #SERVING# #SERVING_X# #XLOVinSG# #XLOVinSINGAPORE#

[🇸🇬] 𝟐𝟎𝟐𝟔 𝐗𝐋𝐎𝐕 𝐀𝐒𝐈𝐀 𝐓𝐎𝐔𝐑 <𝐒𝐄𝐑𝐕𝐈𝐍𝐆✶> 📍 SINGAPORE 📅 2026.10.01 (THU) Stay tuned for more details ! #XLOV# #SERVING# #SERVINGTOUR# #XLOVinSG# #XLOVinSINGAPORE#

Xi Jinping stated that we will support Hong Kong and Macao in better integrating into and serving the country’s overall development. #Celebratethe105thAnniversaryoftheCommunistPartyofChina#

General Secretary Xi Jinping presented the July 1 Medal to seven recipients. They have been working at the grassroots level for decades, wholeheartedly serving the people. They are ordinary Party members who have done extraordinary things for the Chinese people. We salute them all.

Robot Xiaoliu clocked in at #WAIC# 2026, and the job today? Giving massages.🤖😜 This year, embodied AI and real-world agents have taken over the show. Today we're launching our full-stack embodied AI solution, moving from disembodied to embodied, from fragmented modules to one closed loop, across four layers: → Models. The Hy-Embodied matrix: RxBrain-1.0 pairs text reasoning with visual imagination, VLM-1.0 hits flagship perception at 1/10 the compute, VLA-0.5 unifies vision, language, and action. → Agent framework. TairosAgent and Apexio integrate the brain, cerebellum, and body into one system, on the upgraded Tairos platform. → Cloud infrastructure. From GPU/HCC compute and Astral Network to TI-ONE, TokenHub, and TRTC/TRRO, covering training, serving, and real-time interaction. → Industry applications. The industry's first cloud-based EaaS, Embodied-AI-as-a-Service, taking embodied AI from breakthrough to scale.

New course on serving LLMs efficiently -- how do you serve models to many concurrent users at low latency and reasonable cost? This short course is built with @RedHat and taught by @cedricclyburn. Efficient LLM serving requires efficient memory management. A 70B-parameter model takes ~140 GB just to load the weights. On top of that, every active request needs its own chunk of GPU memory, the KV cache, to store the token context it has built up so far. In this course, you'll learn to reduce a model's memory footprint with quantization and serve it using vLLM, which handles many concurrent requests efficiently through smart memory management. Skills you'll gain: - Quantize a model and measure the accuracy tradeoff - Serve a model with vLLM and watch it handle concurrent requests efficiently - Benchmark your deployment and make informed tradeoffs between speed, cost, and accuracy Join and learn to serve LLMs efficiently:

"Objection! — He’s Distracting The Courtroom" 🍑 Episode 13: Andre Stone is serving hard evidence: following your heart is always best. From LA roots to international shoots, he’s unstoppable 🎙️ 🔗 @ray_ray_xxx @RebelRhyderXXX @Andres_Stones