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结合一位资深职业加盟商的总结,这种困境的出现,主要源于三个致命陷阱,也是很多门店最终闭店的关键原因:

智能体,这一点在服务器推荐中也有详细论述

With that, I built a gigaprompt to ensure Opus 4.5 accounted for both the original Python implementation and a few new ideas I had, such as supersampling to antialias the output.

Initially I aimed to test with at least 10 formulas for each model for SAT/UNSAT, but it turned out to be more expensive than I expected, so I tested ~5 formulas for each case/model. First, I used the openrouter API to automate the process, but I experienced response stops in the middle due to long reasoning process, so I reverted to using the chat interface (I don't if this was a problem from the model provider or if it's an openrouter issue). For this reason I don't have standard outputs for each testing, but I linked to the output for each case I mentioned in results.

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