专业度更高，更会干活的 Expert 智能体社区，涵盖从技术开发、创意写作到音视频图片生成等多模态领域，超过 1.6 万个专家，且还在持续增长。大多数场景下，我们几乎都能直接找到现成可用的专家；即便没有完全匹配的，用几句话还能快速创建一个自己的 Expert。
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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The Penn-Wharton model found in a preliminary analysis that AI could reduce deficits by $400 billion by 2035. But the Congressional Budget Office framed AI and associated investment as wild cards in determining the U.S. fiscal and economic outlook. While the CBO projects AI will enhance total productivity by 1% in the next decade, its most recent budget report conceded that this prediction was “highly uncertain.” If adoption is slow or costs higher than anticipated, it would significantly alter GDP growth and, consequently, government revenue.。一键获取谷歌浏览器下载对此有专业解读
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