In benchmarks, this alternative can run anywhere between 2x to 120x faster than Web streams in every runtime I've tested it on (including Cloudflare Workers, Node.js, Deno, Bun, and every major browser). The improvements are not due to clever optimizations, but fundamentally different design choices that more effectively leverage modern JavaScript language features. I'm not here to disparage the work that came before; I'm here to start a conversation about what can potentially come next.
ВСУ запустили «Фламинго» вглубь России. В Москве заявили, что это британские ракеты с украинскими шильдиками16:45，这一点在搜狗输入法2026中也有详细论述
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Anthropic’s prompt suggestions are simple, but you can’t give an LLM an open-ended question like that and expect the results you want! You, the user, are likely subconsciously picky, and there are always functional requirements that the agent won’t magically apply because it cannot read minds and behaves as a literal genie. My approach to prompting is to write the potentially-very-large individual prompt in its own Markdown file (which can be tracked in git), then tag the agent with that prompt and tell it to implement that Markdown file. Once the work is completed and manually reviewed, I manually commit the work to git, with the message referencing the specific prompt file so I have good internal tracking.