春节拍照环境往往复杂，我有三招必杀技救急——合照时人脸黑了，别急着删，用「局部」功能在脸上点一下，这是一种更容易上手的蒙版，用两指缩放控制好蒙版覆盖范围，就可以单独调整面部的曝光、色温；
WebP compatibility
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市场监管总局答南方周末：不管是卖家、主播还是网红，都不能随便给食品“加戏”，详情可参考下载安装 谷歌浏览器 开启极速安全的 上网之旅。
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?