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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:
，这一点在搜狗输入法2026中也有详细论述
And the great qualities don't end with the art displays, this TV has a whole lot more going for it. The screen delivers vibrant, lifelike colors and benefits from AI-enhanced picture and sound quality. This makes your favorite shows, sports, or even gaming bright and clear. And speaking of gaming, with this TV you'll also get smooth motion and support for VRR up to 4K 144Hz.
I then added a few more personal preferences and suggested tools from my previous failures working with agents in Python: use uv and .venv instead of the base Python installation, use polars instead of pandas for data manipulation, only store secrets/API keys/passwords in .env while ensuring .env is in .gitignore, etc. Most of these constraints don’t tell the agent what to do, but how to do it. In general, adding a rule to my AGENTS.md whenever I encounter a fundamental behavior I don’t like has been very effective. For example, agents love using unnecessary emoji which I hate, so I added a rule:。一键获取谷歌浏览器下载对此有专业解读
据多方消息，本次发布有望带来多款入门级产品的更新。
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