const text = await Stream.text(readable);
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可当我伸手拉开后车门时，瞬间愣在了原地：后排已经坐了两个人，再往副驾驶一看，也坐着一位乘客。这意味着，这辆五座车将“满载”运行，而我将成为后排的“夹心”。“别介意啊”，车主仿佛看穿了我的心思，一边招呼我上车一边解释，“咱顺风车不就图个互相方便嘛，后排坐三个人没问题，大家多体谅下”。我看着已经塞好的行李，再想想临时改行程根本来不及，心里虽满是不情愿，却还是硬着头皮上了车，只轻声说了句：“你要是早说要坐五个人，我也好有个准备。”
icon-to-image#As someone who primarily works in Python, what first caught my attention about Rust is the PyO3 crate: a crate that allows accessing Rust code through Python with all the speed and memory benefits that entails while the Python end-user is none-the-wiser. My first exposure to pyo3 was the fast tokenizers in Hugging Face tokenizers, but many popular Python libraries now also use this pattern for speed, including orjson, pydantic, and my favorite polars. If agentic LLMs could now write both performant Rust code and leverage the pyo3 bridge, that would be extremely useful for myself.
