Yang Song
1.5K posts

Yang Song
@ysongtwitt
Solution Architect, Alibaba Qwen. Previously Autonomous Driving Engineer, Ph.D. in Robotics.


I asked Codex to set up ROS middleware, configure a CSI camera, benchmark Gemma 4 models on my Jetson Orin Nano, adapt an OpenClaw-style runtime for VLM + reasoning (“Robotclaw” as I call it), and even build an iOS app to stream LiDAR, camera, GPS, and IMU data from an old iPhone to my robot rover. It’s honestly wild how capable these coding agents are now, and how much time they save. I even “write” way more tests now because the marginal cost is so low.

we need more FDEs in robotics. someone who can: - deploy the robot, watch the telemetry, and spot what's wrong - trace a bug through coupled systems where the sensor is blaming the controller and the controller is blaming the sensor - reproduce a problem that only shows up one in ten runs - compare performance across different units running the same software - decide which tests can be skipped and what actually needs attention - close the loop between "that was weird" and "here's the fix" in minutes and give them the tools to do this at scale!







📣Meet Qwen3.7-Max — our latest flagship, made for the Agent Era. A versatile foundation for agents that actually get things done: 🧑💻 Coding agent, end to end. Frontend prototypes, multi-file refactors, real debugging — nails it. 🗂️ A reliable office and productivity assistant. Get your work done through MCP integrations and multi-agent orchestration. ⏱️ Long-horizon autonomy. 35 hours straight on a kernel optimization task — 1,000+ tool calls, zero hand-holding. 🔌 Scaffold-agnostic. Claude Code, OpenClaw, Qwen Code, or your own stack. Consistent reliability everywhere. API's up on Alibaba Model Studio. You can also take it for a spin on Qwen Studio. Go build something wild!🏃🏃♂️ 📖 Blog: qwen.ai/blog?id=qwen3.7 ✅ Qwen Studio: chat.qwen.ai/?models=qwen3.… ⚡️ API:modelstudio.console.alibabacloud.com/ap-southeast-1…


our qwen 3.7 max is released, try our best toward agentic frontier🚀 qwen.ai/blog?id=qwen3.7 #qwen



这可能是自动驾驶研发领域「数据工程」最硬核的一次暴力提效。 看到日本 XR 专家 @tokufxug 推荐的开源库 **123D**,我第一反应是:那些还在为了 KITTI、Waymo 等不同数据集格式反复写解析脚本的工程师,真的该看看什么叫「物理级的格式对齐」。 这个库的工程逻辑极其「残暴」: - **全量数据集协议平移**:它将 heterogeneous(异构)的传感器数据,暴力统一到了 Apache Arrow 格式。这意味着无论原始数据是 Protobuf 还是 ROS bag,在你的代码里都变成了标准化的 Columnar(列式)流。 - **Zero-copy 暴力吞吐**:基于 Arrow 的内存映射机制,数据读取几乎没有序列化开销。这种对 I/O 瓶颈的极致压榨,让训练 Foundation Models 的吞吐量直接原地起飞。 - **存储语义对齐**:它采用引用原始路径的策略,避免了 TB 级传感器数据的物理重复存储。这是在用最聪明的架构管理最暴力的数据。 这就是我一直盯着的:**具身智能的门槛,往往不在算法,而在于你处理「物理现实数据」的基建带宽。** 关注 @VedaAI00,同步全球最炸裂的自动驾驶与工程数据实践。👇

















