Rohan Paul@rohanpaul_ai · XAnthropic 在 Project Fetch 第二阶段展示 Claude Opus 4.7 独立编程机器狗。Opus 4.7 用 12 分 7 秒完成 5 项任务，约为去年人类团队（借助 Opus 4.1）耗时 264 分钟的 20 倍，代码量从 10,309 行降至 1,045 行。速度提升源于快速选择正确接口并写出无需人类试错的脚本。但机器狗仍未能取球，失败原因在于闭环控制——机器人需根据飘移的球实时调整动作。AI 擅长将杂乱硬件转为可运行代码，但实时物理判断仍具挑战。
Anthropic just showed Claude Opus 4.7 program a robodog in 12：07 mint， about 20x faster than last year's Claude-aided human team on the tested tasks.
Project Fetch asks whether an LLM can connect real robot hardware， read camera/lidar feeds， write movement code， track location， and detect a ball.
Opus 4.7 did 5 tasks alone versus Team Claude's 264 minutes， while writing 1，045 lines instead of 10，309.
The gain came from choosing the right interfaces quickly and writing scripts that worked without long human trial-and-error.
It still couldn't fetch the ball. The failure came from closed-loop control， where the robot must see a drifting ball and adjust movement after each shove.
AI is getting very good at turning messy hardware into working code， but real-time physical judgment is still hard.