Amboss on Nostr: Our pathfinding performance research begins today! Using our machine learning ...

Our pathfinding performance research begins today!

Using our machine learning predictions, participants will be able to generate a comparison between their node's default pathfinding and our enhanced pathfinding.

These results will be used to further improve payment reliability on the Lightning Network and deliver more research about decentralized payment channel networks.

If you'd like to participate in our benchmark research and access our advancements in pathfinding and payment reliability, be sure to sign up here:
https://rpo.dev/pathfinding

quoting nevent1q…2vm8

Payment reliability is the most important metric for the Lightning Network as a payment system.

We teamed up with @HelloStillmark to deliver research that leverages machine learning to bring enterprise payment reliability to the Lightning Network.


Our New Research: "Channel Balance Interpolation in the Lightning Network via Machine Learning" decimates the tired and risky approach of probing to find a reliable payment route.

Combining crowdsourced data and machine learning means reliable payments on LN with less spam.

We're applying this methodology to a new pathfinding-as-a-service feature that is showing extremely promising early results.

We're looking for exchanges, rewards programs, or play-to-earn with high outbound payment volume to put our pathfinding service to a real-world test.

To learn more about our research or to join us as part of our payment operations solution, be sure to visit: https://rpo.dev/pathfinding

This research would not be possible without our collaborators:
@HelloStillmark
@vsingh_5
@emaros96

And peer reviewers:
@renepickhardt
Dr. Christian Kummerle
@alexbosworth