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?
В Москве прошла самая снежная зима14:52
，推荐阅读搜狗输入法下载获取更多信息
The ANE is not a GPU. It’s not a CPU. It’s a graph execution engine — a fixed-function accelerator that takes a compiled neural network graph and executes the entire thing as one atomic operation. You don’t issue individual multiply-accumulate instructions. You submit a compiled program describing an entire computation graph, and the hardware executes it end-to-end.
Николай Коляда. Фото: Владимир Астапкович / РИА Новости
Then, if we had a call like: