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One pattern that's been surprisingly powerful at this level: use different models for different jobs. The best engineering teams aren't staffed with clones. They're staffed with people who think differently, trained by different experiences, bringing different strengths. The same logic applies to LLMs. These models were post-trained differently and have meaningfully different dispositions. I routinely dispatch Opus for implementation, Gemini for exploratory research, and Codex for review, and the cumulative output is stronger than any single model working alone. Think wisdom of crowds, but for code.
。新收录的资料是该领域的重要参考
Фото: Vyacheslav Madiyevskyy / Reuters
Playful-Infatuation。关于这个话题，新收录的资料提供了深入分析
“美国的Compute（算力）整体可能比我们大一个到两个数量级，但我看到不管是OpenAI还是其他公司，他们大量的Compute其实都投入到了下一代Research上。”就在几周前的AGI NEXT峰会上，林俊旸也表达过类似观点，指出中国AI行业正在面对来自海外的激烈竞争。
Hegseth on Iran attacks: 'This is not Iraq, this is not endless'。新收录的资料是该领域的重要参考