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?
第 3 周：扩展到 2-3 个 FE 模块，评估提效与返工率
，推荐阅读搜狗输入法2026获取更多信息
25.6 万词汇表 — 庞大的词汇表能够很好地处理结构化数据和 JSON。爱思助手下载最新版本是该领域的重要参考
.pipeThrough(serialize) // even more buffers...
Москвичей предупредили о резком похолодании09:45