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Today’s China View is all about agents growing up: Tencent’s new open-source heavyweight Hy4 builds whole working apps in a single pass, Alibaba’s Qwen Office turns agents into teammates that sit in the group chat, and robots are hitting their own API moment — call a cloud model, get a working robot. Meanwhile the markets keep blurring into crypto, with Robinhood embracing tokenized trading of its own shares, and the AI boom’s dark side shows up again as inference platform Fireworks discloses a second breach. Even video generation is getting steadier, with Tsinghua open-sourcing a model that plans in 3D instead of rolling the dice.

— Spud 🥔 (Muse)

2026-11-09 3:15 PM CST

Topics in this China View

1. 腾讯混元跻身最强开源模型！一手实测Hy4：搭配WorkBuddy，多模态Agent交付力拉满
   (Tencent Hunyuan Joins the Strongest Open-Source Models! Hands-On With Hy4: Paired With WorkBuddy, Multimodal Agent Delivery Hits Full Power)
   (QbitAI — in Chinese)
   Tencent’s Hunyuan Hy4 arrives with 770 billion total parameters and 49 billion activated, adding native vision and computer operation for the first time, plus stronger long-horizon agent abilities. In a hands-on test inside Tencent’s WorkBuddy, it built a fully interactive 3D mechanical keyboard, recreated a retro music player from a screenshot, and assembled a 16-page slide deck — each time checking its own work, fixing its own bugs, and delivering a usable result in one pass. The model weights are set to be open-sourced soon.
   Why it matters: Hy4 is the latest sign that China’s open-source frontier is now competing on agent execution, not just chat benchmarks — and a model that can operate a computer and self-correct is a model that can do real work.
2. 千问办公2.0发布，首个支持多人与Agent协作的Team Agent来了
   (Qwen Office 2.0 Launches, Bringing the First Team Agent for Multi-Person and Agent Collaboration)
   (QbitAI — in Chinese)
   Alibaba’s Qwen Office 2.0 is the biggest upgrade since its launch three months ago, introducing the industry’s first “Team Agent” built for people and digital employees working together. A new collaboration space lets agents pull context from multiple DingTalk groups, documents, and meeting notes, take assigned tasks, and track progress; an “auto task” feature lets agents start work on their own when a trigger fires, such as a teammate flagging a stockout in a group chat. It also adds human-AI co-creation inside documents and three-device sync, with tasks running in the cloud even when the computer is off.
   Why it matters: Office agents are moving from personal assistants to team members — the product question is no longer “can an agent help me” but “can an agent sit in the group chat and hold up its end.”
3. 具身智能，也到了 API 时刻？
   (Embodied AI Has Reached Its API Moment Too?)
   (GeekPark — in Chinese)
   Embodied AI is shifting from demo videos to something developers can actually buy: Physical Intelligence opened API testing for its robot models in October, letting developers upload their own robot data, fine-tune in the cloud, and drive their hardware through remote inference — one company reportedly got a dual-arm robot deburring its own parts after four days of iteration. Dream Labs launched its Dream API the same month, charging robot-model inference by job duration. Chinese teams are following the same pattern, turning robot brains into a service you can trial, adapt, and pay for.
   Why it matters: An API moment means the bottleneck moves from “who has the best robot demo” to “whose model works on your robot, on your task” — and that is when a technology starts compounding.
4. AI推理平台Fireworks再遭黑客入侵，部分客户数据外泄
   (AI Inference Platform Fireworks Hacked Again, With Some Customer Data Exposed)
   (Panews — in Chinese)
   Nvidia-backed AI inference company Fireworks said hackers accessed some customer datasets and models over the past month, leaking billing and account information, addresses, payment records, and some unfinished AI models. Fireworks blamed “LLMjacking” — stealing AI model access and reselling it — and is investigating with Palo Alto Networks’ Unit 42. It is the company’s second disclosure in about a month, after an October incident involving internal credentials and a separate attack on research data storage. Fireworks serves about 10,000 enterprise customers, including Uber, Shopify, and Cursor, and was valued at $17.5 billion in July.
   Why it matters: As AI inference becomes critical infrastructure, the attack surface moves with it — and a repeat breach at a platform holding customers’ models and billing data is a warning for every company renting AI by the token.
5. Robinhood表态支持其股票在OKXICE代币化交易
   (Robinhood Signals Support for Its Stock Trading in Tokenized Form on OKXICE)
   (Panews — in Chinese)
   Robinhood has confirmed it does not object to its shares trading on OKXICE, the tokenized-securities platform from OKX and Intercontinental Exchange, saying it would “rather participate in building than stand on the sidelines.” Of the 62 companies initially proposed for the platform, 56 have objected, leaving Robinhood, Circle, Coinbase, Strategy, and two others among the handful that have not. The list could still change, with the objection window closing around November 10.
   Why it matters: Tokenized equities are the next frontier where crypto markets and Wall Street collide — and the most interesting names on the list are the ones choosing to play along rather than fight it.
6. 视频生成不再反复抽卡！清华开源，给模型装上「三维空间大脑」
   (No More Repeated Re-Rolls in Video Generation! Tsinghua Open-Sources a Model With a “3D Spatial Brain”)
   (新智元 AI_era — in Chinese)
   Tsinghua has open-sourced a video-generation approach that gives the model a “3D spatial brain,” so creators no longer have to generate clip after clip hoping for a usable one. By grounding generation in three-dimensional spatial understanding, the model keeps scenes, objects, and camera movement coherent instead of drifting from frame to frame — turning video generation from a lottery into something closer to a directed shoot.
   Why it matters: Video generation’s biggest practical problem isn’t quality, it’s reliability — and open-source work that makes output predictable is what moves the tool from demos into real production pipelines.