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?
What about other solutions? In the era of Docker we are primed to think about portability. Surely we could find a solution to directly leverage our existing C# codebase. What about running the services locally on specific ports? That won’t work on consoles. What about C# to C++ solutions like Unity’s IL2CPP? Proprietary and closed source. None of the immediately obvious solutions were viable here.，这一点在heLLoword翻译官方下载中也有详细论述
(e.g. custom) product. IBM probably regarded it as a prototype or pilot with，详情可参考同城约会
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