500+ OSS dependencies in an average app
當被問及此事時，中國外交部發言人毛寧表示，她「不了解具體情況」，並補充說「中國一貫反對一切形式的暴力攻擊」。
。夫子是该领域的重要参考
牛犇反駁稱，習近平過去14年的行為顯示，他是精明的風險管理者，而非魯莽的賭徒。在台灣和南海問題上，他持續試探底線，卻始終避免可能引發戰爭或美中直接對抗的行動。他偏好「灰色地帶」戰術——循序漸進強化中國地位，而非孤注一擲。
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?