由于表面粗糙、精度不足等问题，金属件打印完成后须经历一系列复杂后处理流程，其中成本主要集中在打磨、抛光等表面精加工流程。更值得关注的是，目前大量后处理环节仍依赖人工操作。这不仅意味着高昂的人力成本，还存在重复性劳动导致的人员疲劳、效率波动以及质量一致性难以保证等问题。
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I completely ignored Anthropic’s advice and wrote a more elaborate test prompt based on a use case I’m familiar with and therefore can audit the agent’s code quality. In 2021, I wrote a script to scrape YouTube video metadata from videos on a given channel using YouTube’s Data API, but the API is poorly and counterintuitively documented and my Python scripts aren’t great. I subscribe to the SiIvagunner YouTube account which, as a part of the channel’s gimmick (musical swaps with different melodies than the ones expected), posts hundreds of videos per month with nondescript thumbnails and titles, making it nonobvious which videos are the best other than the view counts. The video metadata could be used to surface good videos I missed, so I had a fun idea to test Opus 4.5: