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在 AI 场景中，Apache Spark 凭借其强大的批处理能力与 Python 生态兼容性，广泛用于大模型训练前的数据清洗、特征工程与推理任务。而 Ray 因其低延迟、高并发特性，被 OpenAI 等头部机构用于分布式训练与强化学习。两者共同构成 Data + AI 的核心计算底座，支持从数据准备到模型推理的全流程高效执行。
Last May, I wrote a blog post titled As an Experienced LLM User, I Actually Don’t Use Generative LLMs Often as a contrasting response to the hype around the rising popularity of agentic coding. In that post, I noted that while LLMs are most definitely not useless and they can answer simple coding questions faster than it would take for me to write it myself with sufficient accuracy, agents are a tougher sell: they are unpredictable, expensive, and the hype around it was wildly disproportionate given the results I had seen in personal usage. However, I concluded that I was open to agents if LLMs improved enough such that all my concerns were addressed and agents were more dependable.
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