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?
Yes, since hashes are calculated differently between ostree and rpm-ostree, they don’t match, so I have to retrieve the equivalent hash with ostree
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第五条 电信、金融、互联网等服务提供者应当依照本法及有关法律、行政法规的规定和国家标准的强制性要求，建立落实网络安全、信息安全、数据安全管理制度，采取技术措施和其他必要措施，依法履行与其服务类型、经营规模、能力相符的网络犯罪防治义务。
“startup” phase when the slice is small. During this startup phase we
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