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?
这种软硬件结合的思路，与华为纯靠软件识别并停止通知的方案有着本质区别。三星的方案适用场景更广，也更加优雅。这项技术一旦落地落地普及，那些严重影响观感的防窥膜几乎可以被彻底淘汰了。
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那时他在香港做土木工程，吃饭喝酒谈生意，免不了在夜总会挥金如土，客人与小姐之间照样规规矩矩，彼此尊重。