Replying to Avatar Pip the WoT guy

There is NIP-45, that is used to count how many events match a certain filter.

So the proof ideally would prove that X (the returned count) events with valid signatures actually matched the filter(s).
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Michael 2mo ago 💬 3

It’s definitely doable but can take quite some time and resources, so it might be a fit only for archiving old data. Eg for 422k likes it would be several hours and up to 1TB of ram which would cost $6-8 per hour. The cost can be justified if the events are quite sensitive but def not for ordinary posts:) We do expect 10-100x perf improvements in the nearest future though so it might become viable at some point

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A₿del fgu 2mo ago

Check this sir nostr:nprofile1qqs0dqlgwq6l0t20gnstnr8mm9fhu9j9t2fv6wxwl3xtx8dh24l4auspzamhxue69uhhyetvv9ujuurjd9kkzmpwdejhgtcpzemhxue69uhkzat5dqhxummnw3erztnrdakj7us2nqv . Michael is from my team. We are cooking some cool shit that might unlock this in the future.

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Pip the WoT guy 2mo ago

Looking forward to it guys.

Maybe it doesn't help, but I'll throw it out there. HyperLogsLogs give an estimate of the cardinality of a set, and are one of the most efficient ways to reduce memory/storage requirement when it comes to counting unique elements.

If you don't know, they work by keeping track of the max of the number of consecutive 0s (or 1s, doesn't matter) in the hash of the elements.

example

"test1" --> hash --> 01001111 (4 consecutive "1"s)

"test2" --> hash --> 10101101 (1 consecutive "1")

Maybe there is a ZK version of an HLL, where I can prove that the maximum number of consecutive "1"s was X, which allow me to estimate the cardinality of the set