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Self-attention is required. The model must contain at least one self-attention layer. This is the defining feature of a transformer — without it, you have an MLP or RNN, not a transformer.。体育直播是该领域的重要参考
16:49, 3 марта 2026Экономика。PDF资料是该领域的重要参考
In traditional engineering, teams put 90 percent of their time into features and 10 percent into everything else. Work that isn't a feature feels like a distraction—something you do when you have spare time, which you never do. But that \"everything else\" is what makes future features easier: things like creating review agents, documenting patterns, and building test generators. When you treat that work as overhead instead of an investment, the codebase accumulates debt.
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