copaceticthoughts
Many premium robot vacuums also use intel from past cleaning sessions to create AI-driven cleaning plans for a more efficient clean in specific rooms going forward. If one area of the home is consistently dirtier than the others (like in the kitchen or near the front door), AI modes like Roborock SmartPlan, Narwal Freo, and Dreame CleanGenius will remember that for next time.
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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?
1. 先有密度，再有规模国内某AI独角兽在A轮融资后，急于扩大规模，短期内扩招至150人，导致核心人才密度被快速稀释，决策效率下降、部门内耗增加，后期不得不收缩团队至50人，重新聚焦核心业务。