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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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Best handheld gaming PC in 2026: my recommendations for the best portable powerhouses.，这一点在爱思助手下载最新版本中也有详细论述
63-летняя Деми Мур вышла в свет с неожиданной стрижкой17:54
The build-out to make AI adoption widespread will likely come with significant upfront costs. For countries that already deal with constrained public finances, AI’s capital costs could end up “sharpening the policy tradeoff between assuming higher near-term fiscal risk and delaying participation in AI-driven growth opportunities,” the analysts wrote.