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?
The Algorithm: Ford-Fulkerson to Find the Bottlenecks。关于这个话题，Line官方版本下载提供了深入分析
Aldi said the wage increase will benefit 28,000 staff members, and says it makes its entry-level workers the highest paid of all supermarket staff, including its fellow budget chain Lidl.。旺商聊官方下载对此有专业解读
In December I wrote about package managers using git as a database, and how Cargo’s index, Homebrew’s taps, Go’s module proxy, and CocoaPods’ Specs repo all hit the same wall once their access patterns outgrew what a git repo is designed for.