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Historically, LLMs have been poor at generating Rust code due to its nicheness relative to Python and JavaScript. Over the years, one of my test cases for evaluating new LLMs was to ask it to write a relatively simple application such as Create a Rust app that can create "word cloud" data visualizations given a long input text. but even without expert Rust knowledge I could tell the outputs were too simple and half-implemented to ever be functional even with additional prompting.
依托 PhanthyCloud 的资源池化架构，PhanClaw 显著降低了算力与运维成本，并通过持久化数据机制为 Agent 提供长期记忆能力，避免每次启动都需重新调教。