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?
First, test your own AI visibility today. Open ChatGPT, Claude, or Perplexity and ask questions where your content should logically appear as a relevant source. Be honest in your queries—use the actual questions your audience would ask rather than phrasing things to favor your content. See whether AI models cite you, and if so, how prominently. This reality check shows you where you stand currently.，详情可参考爱思助手下载最新版本
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