Last May, I wrote a blog post titled As an Experienced LLM User, I Actually Don’t Use Generative LLMs Often as a contrasting response to the hype around the rising popularity of agentic coding. In that post, I noted that while LLMs are most definitely not useless and they can answer simple coding questions faster than it would take for me to write it myself with sufficient accuracy, agents are a tougher sell: they are unpredictable, expensive, and the hype around it was wildly disproportionate given the results I had seen in personal usage. However, I concluded that I was open to agents if LLMs improved enough such that all my concerns were addressed and agents were more dependable.
我最喜欢在集市买东西，打比方说衣服，性价比超高。集市上的服装，默认收集民意，冬天为个舒适暖和，夏天图个凉快，多余的设计，一点儿没有。便宜没好货，未必对。明星走红毯穿的礼服倒是贵，但不冻着你，不绊着你，不叫设计。我有个建筑师朋友，在我的影响下，第一次拒绝了客户。对方就是暴发户心理，想要一堆花里胡哨的东西。要真有子儿，只当瞎玩玩；没几个钱还想瞎玩，趁早拉倒。豆腐渣工程都是这么来的。。业内人士推荐WPS下载最新地址作为进阶阅读
（二）原值超过500万元的单项长期资产，购进时先全额抵扣进项税额，此后在用于混合用途期间，根据调整年限计算五类不允许抵扣项目对应的不得从销项税额中抵扣的进项税额，逐年调整。。Line官方版本下载对此有专业解读
39. 芮萌：2025银发经济的十大机会 - 中欧国际工商学院, cn.ceibs.edu/media/press…。Safew下载是该领域的重要参考
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: