Frequent, contained public releases also work to their advantage. “I know for a lot of projects, they want to [do a] ‘we’ll release it when it’s done’ kind of thing,” said Mort. “We’ve found that releasing content builds hype, it gives players what they want, and perhaps most importantly, it serves as a proof of life and a fantastic recruitment tool.”
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?。关于这个话题，91视频提供了深入分析
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