Kotlin Multiplatform (KMP) 中使用 Protobuf
Anthropic 昨天点名 DeepSeek、月之暗面、MiniMax 三家中国 AI 实验室「蒸馏」Claude 模型，全网炸锅。
。heLLoword翻译官方下载对此有专业解读
俯身下瞧，正在解冻的泥土有些湿润，颜色深了一层，显出大地有了不易察觉的脉动。树根周边，几针鹅黄的纤弱到让人心疼的草尖，正顶开碎土，探出一点小芽。那嫩黄，是生命最初的颜色，亮亮的、怯怯的，纯粹得不染一丝尘埃。它们被微风一逗，便颤巍巍的，像是要笑，又像是害羞，最后终于奋不顾身地破土上冲。
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?