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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?
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长期以来，国资LP的“审计压力”和“个人责任追究”是束缚耐心资本发挥作用的隐形锁链。因为科技创新天然伴随着高失败率，如果要求国资基金像银行信贷一样保本增值，那么“投早投小”只能是一句空话。报告中提到的完善容错机制，正是要打破这一僵局。这意味着未来的考核体系将从“项目审计”转向“组合审计”，从“单一成败”转向“体系效能”。