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?
If you enable --privileged just to get CAP_SYS_ADMIN for nested process isolation, you have added one layer (nested process visibility) while removing several others (seccomp, all capability restrictions, device isolation). The net effect is arguably weaker isolation than a standard unprivileged container. This is a real trade-off that shows up in production. The ideal solutions are either to grant only the specific capability needed instead of all of them, or to use a different isolation approach entirely that does not require host-level privileges.
Даниил Иринин (Редактор отдела «Наука и техника»)，推荐阅读夫子获取更多信息
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Филолог заявил о массовой отмене обращения на «вы» с большой буквы09:36