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As a data scientist, I’ve been frustrated that there haven’t been any impactful new Python data science tools released in the past few years other than polars. Unsurprisingly, research into AI and LLMs has subsumed traditional DS research, where developments such as text embeddings have had extremely valuable gains for typical data science natural language processing tasks. The traditional machine learning algorithms are still valuable, but no one has invented Gradient Boosted Decision Trees 2: Electric Boogaloo. Additionally, as a data scientist in San Francisco I am legally required to use a MacBook, but there haven’t been data science utilities that actually use the GPU in an Apple Silicon MacBook as they don’t support its Metal API; data science tooling is exclusively in CUDA for NVIDIA GPUs. What if agents could now port these algorithms to a) run on Rust with Python bindings for its speed benefits and b) run on GPUs without complex dependencies?
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记者调查发现，除了“主页圈”外，小天才手表体系还衍生出“运动圈”“破解圈”等多个社交圈。“破解圈”针对手表封闭系统，通过突破家长管控模式刷机，可让手表变身为功能齐全的迷你手机。网络上的破解教程随官方系统更新持续进阶，部分学生还会借助AI制定详细的刷机方案。，更多细节参见heLLoword翻译官方下载
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