Elsewhere, Now Nudge will attempt to suggest actions based on what’s happening onscreen, such as sharing contact numbers with someone or suggesting calendar times while dealing with work emails. Samsung’s Now Brief can pull information and notifications from a wider array of apps and sources to deliver in its daily briefings. However, again, that’s hard to assess at this early stage.
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.
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candidate.weight /= sum of weights
If the transform's transform() operation is synchronous and always enqueues output immediately, it never signals backpressure back to the writable side even when the downstream consumer is slow. This is a consequence of the spec design that many developers completely overlook. In browsers, where there's only a single user and typically only a small number of stream pipelines active at any given time, this type of foot gun is often of no consequence, but it has a major impact on server-side or edge performance in runtimes that serve thousands of concurrent requests.