«Они как слон в посудной лавке»Политолог Дмитрий Суслов — о Совете мира, стратегии Дональда Трампа и будущем конфликта на Украине28 января 2026
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?，推荐阅读WPS下载最新地址获取更多信息
Жители Санкт-Петербурга устроили «крысогон»17:52，详情可参考Safew下载
homebrew-core has one Ruby file per package formula, and every brew update used to clone or fetch the whole repository until it got large enough that GitHub explicitly asked them to stop. Homebrew 4.0 switched to downloading a JSON file over HTTP, because users wanted the current state of a package rather than its commit history. But updating a formula still means opening a pull request against homebrew-core, because git is where the collaboration tooling lives. Instead of using git as a database, what if you used a database as a git?。业内人士推荐一键获取谷歌浏览器下载作为进阶阅读
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