Wanted queries rate: 2500/s
研究团队设计的准确率要求是90%，在此基础下该Agent竟然成功还原了67%的用户身份，耗时只有短短几分钟。
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The core idea is to pipeline every stream as to maximally reduce latency.
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:
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drop-oldest: Drops the oldest buffered data to make room. Useful for live feeds where stale data loses value.