Damus
Andrew M. Bailey profile picture
Andrew M. Bailey
@resistancemoney

I’m here to chew bubblegum and talk about bitcoin and I’m all out of bitcoin

Relays (20)
  • wss://nostr.wine – read & write
  • wss://eden.nostr.land – read & write
  • wss://relay.nostr.com.au – read & write
  • wss://nostr.oxtr.dev – read & write
  • wss://nostr.mutinywallet.com – read & write
  • wss://atlas.nostr.land – read & write
  • wss://Nostr.wine – read & write
  • wss://bitcoinmaximalists.online – read & write
  • wss://nostr.fmt.wiz.biz – read & write
  • wss://nos.lol – read & write
  • wss://relay.nostr.bg – read & write
  • wss://purplepag.es – read & write
  • wss://puravida.nostr.land – read & write
  • wss://nostr.mom – read & write
  • wss://nostr.bitcoiner.social – read & write
  • wss://filter.nostr.wine/npub18ams6ewn5aj2n3wt2qawzglx9mr4nzksxhvrdc4gzrecw7n5tvjqctp424?broadcast=true – read & write
  • wss://relay.snort.social – read & write
  • wss://offchain.pub – read & write
  • wss://lightningrelay.com – read & write
  • wss://relay.primal.net – read & write

Recent Notes

resistancemoney profile picture
censorship is all around us, and pervades all big tech platforms. but because it consists in what is not seen, it is often very hard to see.
resistancemoney profile picture
the first time I heard the term 'thought leader', I thought it was a joke and laughed. an insane thing to call yourself. same for 'changemaker'.

little did I know.
resistancemoney profile picture
Gerrymandering illustrates how automation in policy can be better than discretion. If you give a legislature the power to chop things up as they will, they will do so in ways that benefit the dominant local party. A better alternative is for an algorithm to draw districts instead, regularly revising so as to keep seats across a territory in line with popular vote (so that, e.g., when 40% of Californians vote GOP, GOP gets 40% of the House seats for California).

Bitcoin obeys a similar principle. Rather than delegating monetary policy to trusted authorities, it automates that policy in highly predictable ways, and regularly revises (i.e., the difficulty adjustment) to keep things in line with what's expected. The outcomes needn't be optimal for this system to be superior, note; for there is no guarantee that those trusted parties will enact optimal policies, and they often fail in this task! The best argument for automated policy, then, is not that it is for the best always and everywhere, but that it is typically for the better.