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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Finally, really small actuators made using traditional electric motors tend to get too hot for their own good at such scales – another headache.
栈（stack）是先进后出的数据结构。单调栈在入栈时通过一定规则保持栈内元素有序（单调递增或单调递减），从而把「找左侧/右侧第一个更大/更小元素」从 O(n²) 降到 O(n)。