with traefik you can use some kind of config discovery backend like consul etc..
ah ok but youād still have to know the name, got it. yeah caddy is cool..
can it discover the docker instances like traefik does?
People asked me about nostr.band API, and about aggregation layer APIs in general.
Here is my attempt at coming up with nostr-native way to do APIs.
There is a post on habla: https://habla.news/a/naddr1qqqqygpn2m0xrvukg7f3e69jzs9jh2ur0cypps8029dmayk7qfyqgzutm5psgqqqw4rs394k44
And a working API: https://data.nostr.band/
Please let me know what you think of this.
nifty
in case anyone is in vienna next month.. https://www.eventbrite.at/e/ai-meetup-dotbite-meets-talentgarden-tickets-557588330707 #chatgpt #vienna #devs #ai #meetup
bring them on. fip is the best. a product of oldschool cultural politics / top-down planning and still punk - i don't know how they do it
cool, give it a try. freebsd supports c++20 afaik
interesting. and edge case of binary compatibility between linux and freebsd?
also, federated training is a possibility but makes everything a bit more complicated. i guess the most important question is: what features turn out to be predictive. maybe the protocol itself can be leveraged more (network/metadata).
#[3] ās approach seems reasonably lightweight and could be added to any relay. classifying using larger models and frequently retraining them would be heavier but surely possible on larger nodes.
whatās your take on content filtering for relays? possible approaches: a) mitigation through POW b) based on network features c) based on content features .. n) any combination of the above