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?
小苏的妈妈出来送我,她的手上是粘灯留下的红印和金星,我突然想到:好日子都是从手上开始红火的。劳动者,他们的手上都握着太阳和幸福。
Architectural variations: rank-1/low-rank projections, factorized embeddings, custom positional encodings, alternative norms,这一点在safew官方版本下载中也有详细论述
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