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?
tee() splits a stream into two branches. It seems straightforward, but the implementation requires buffering: if one branch is read faster than the other, the data must be held somewhere until the slower branch catches up.
，这一点在51吃瓜中也有详细论述
IST — 7 p.m.。服务器推荐对此有专业解读
Цены на нефть взлетели до максимума за полгода17:55
Seccomp-BPF as a filterSeccomp-BPF lets you attach a Berkeley Packet Filter program that decides which syscalls a process is allowed to make. You can deny dangerous syscalls like process tracing, filesystem manipulation, kernel extension loading, and performance monitoring.