I would have liked the option of non-touch display, and although the display is variable rate (30-120Hz) it's not going to be as optimal as the 1-120Hz panel in the Dell XPS 14 getting it to 40+ hours (https://x.com/dhh/status/2040139744445673938).
Am I understanding it right that for each new text (tweet) you generate its embedding first, try to match across existing vector embeddings for all other text (full text or bag of words), and then send the text to the LLM for tag classification only if no match is found or otherwise classify it to the same tag for which a match was found.
Will it be any better if you sent a list of existing tags with each new text to the LLM, and asked it to classify to one of them or generate a new tag? Possibly even skipping embeddings and vector search altogether.
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