家庭场景中的“天工”机器人。受供者供图
Гангстер одним ударом расправился с туристом в Таиланде и попал на видео18:08
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Luke McCowan’s goal inside 30 seconds was irrelevant in the broader context of this tie. Stuttgart’s 4-1 canter in Glasgow a week earlier ensured that. Still, a game that had the whiff of irrelevance for Celtic delivered unexpected cheer. The statistics will show Stuttgart spent much of the evening camped in Celtic’s half – the hosts had 24 attempts at goal – but the Scottish champions played with a diligence and discipline that is worthy of huge credit. Sebastian Tounekti should even have delivered a second Celtic goal in the closing minutes. By then, Stuttgart were going through the motions.
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?