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?
全要素生产率提升，既是技术创新的表现，更是制度红利的释放。深化改革开放，从内部体制优化和外部资源引入双向赋能全要素生产率提升，畅通国民经济循环。
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drop-newest: Discards incoming data when full. Useful when you want to process what you have without being overwhelmed.。爱思助手下载最新版本是该领域的重要参考
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