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156 sats \ 2 replies \ @optimism 31 Jul \ on: AI Use For Optimizing Routing Nodes? lightning
What is missing in cln/lnd that needs fuzzy logic to get better results?
I don't know. I'm just curious. I do know that channel selection for optimizing routing is sometimes viewed as an art more than a science. LNDg makes suggestions, or the autopilot like Rene Pickhardt's, so I was wondering whether any tools utilizing AI might be in the works.
From Amboss:
using AI-driven insights derived from over five years of network data.
I'm not discounting the possibility that this is just marketing drivel.
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this is just marketing drivel.
Even if they have a massive tensor file with five years of captured data, past performance isn't a guarantee for the future, especially not in a network like LN that has been rapidly evolving over these same five years. Are patterns from early LN still valid? I don't think so, but happy to be proven wrong.
tools utilizing AI might be in the works.
I think that there could be useful scenarios, but remember that LN still has known weaknesses in the channel closure process - which is why you run watchtowers - so you need to make a judgement call every time you open a channel. I'd not leave using judgement to something that literally has no capacity for judgement 1
Footnotes
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unless there's nothing at stake for you and losing a couple M sats is a drop in the bucket - a luxury I used to have on testnet3, but not anymore since I gave my testnet sats to a colleague in 2018. ↩
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