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.
To make my experiment more compelling, one should try to implement a Z80 and ZX Spectrum emulator without providing any documentation to the agent, and then compare the result of the implementation. I didn’t find the time to do it, but it could be quite informative.
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智能体发展的另一个关键方向，是成为人类的“个人助手”或“智能代理”。与其他类型的智能体相比，这一方向的门槛更高，因为它需要更强的个性化能力、长期记忆、跨场景泛化能力以及更严格的安全边界。个人助手型智能体有潜力重塑人类与世界的交互方式——无论是购物、社交，还是信息获取——并可能对很多产业的商业逻辑产生颠覆性影响。。关于这个话题，im钱包官方下载提供了深入分析
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这算是相当简单的任务，不少国产 AI 手机助手在一年前都已经攻克了这种场景。