新时代以来，幼有所育、学有所教、劳有所得、病有所医、老有所养、住有所居、弱有所扶……在以习近平同志为核心的党中央坚强领导下，我们一件接着一件办，一年接着一年干。我们取得的一切原创性思想、变革性实践、突破性进展、标志性成果，无不闪耀着一个光辉的起点——为了人民。
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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?