All of these tests performed far better than what I expected given my prior poor experiences with agents. Did I gaslight myself by being an agent skeptic? How did a LLM sent to die finally solve my agent problems? Despite the holiday, X and Hacker News were abuzz with similar stories about the massive difference between Sonnet 4.5 and Opus 4.5, so something did change.
Web streams has no synchronous path. Even if your source has data ready and your transform is a pure function, you still pay for promise creation and microtask scheduling on every operation. Promises are fantastic for cases in which waiting is actually necessary, but they aren't always necessary. The new API lets you stay in sync-land when that's what you need.
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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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