i've been building a persistent context layer that captures screen + audio continuously using screenpipe and syncs to a vector db. how are you handling the relevance scoring? time-based decay alone doesn't work because…
working with continuous OCR capture across 3 monitors using screenpipe. at 1.2fps you get usable text extraction but use 600mb-2gb ram. biggest issue is OCR can't distinguish directionality - ie. if someone messages…
does jmux handle the case where two agents need to modify the same file?
curious how you handle the volume - for heavy users with 50k+ emails, are you processing everything client-side or hitting the gmail api for each message?
i've been building a persistent context layer that captures screen + audio continuously using screenpipe and syncs to a vector db. how are you handling the relevance scoring? time-based decay alone doesn't work because…
working with continuous OCR capture across 3 monitors using screenpipe. at 1.2fps you get usable text extraction but use 600mb-2gb ram. biggest issue is OCR can't distinguish directionality - ie. if someone messages…
does jmux handle the case where two agents need to modify the same file?
curious how you handle the volume - for heavy users with 50k+ emails, are you processing everything client-side or hitting the gmail api for each message?