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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.
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The gains illustrate how fundamental design choices compound: batching amortizes async overhead, pull semantics eliminate intermediate buffering, and the freedom for implementations to use synchronous fast paths when data is available immediately all contribute.
Because every interaction passes through runEffect, we can easily implement a redaction layer to scrub personally identifiable information, like credit card numbers or emails, before they ever hit the trace log.。业内人士推荐快连下载安装作为进阶阅读