And while it used to be a pain to transition from Windows to Mac, it’s far easier these days, especially if you mainly rely on web apps. It also wouldn't be tough for Apple to make short tutorials to help Windows users get their bearings with the macOS basics, like installing apps and juggling app windows. Apple could also make a play for iPhone owners using Windows, who may not be aware of the many ways iOS and macOS are integrated. iPhone mirroring may be a huge draw on its own.
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?,详情可参考搜狗输入法2026
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