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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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最重要的是，这套打法能迅速占领用户心智，从精神产品到拿到手的商品，全面占领。
这家管理规模超过900亿美元、押中过Airbnb、GitHub的机构，每次开口，都会引发一轮跟风——数百家基金开始盯着同一个方向，数十亿美元开始寻找同一类标的。所以读懂它在说什么，不只是了解硅谷在想什么，更是在提前看一张资金流向图。