I mostly just attended ActivityPub-related events at this FOSDEM, and there wasn’t a lot of talk about storage or content-addressing, most of the focus was on interoperability, product/UX thinking, and testing. There was, however, a few points (mostly hallway track, rather than in the public Q&As of talks) where there was talk of multi-instance/cross-instance infrastructure that would help lower per-MAU hosting costs and increase UX for the fediverse as a network. Here are a few such systems/services where I thought perhaps IPFS/DASL/IROH or some other form of content-addressed storage might help:
- reducing network traffic by using “SRI”-like content-addressing of media attached to public posts, e.g. so that relays or CDN-like intermediaries (rather than originating hosts) could be queried (once) for images being replied to, annotated, and retooted. There’s not much specified about
as:Public
-only [Mastodon-API-defined] “Relays”, but hopefully in the coming months this will become a more public part of the conversation, particularly as Mastodon gGmbH productizes/operationalizes some of the previously adhoc shared services powering their platform. - CDN-like distribution networks would also be needed for Fediverse software to achieve comparable UX/latency to media-intensive commercial social software (e.g. peertube and pixelfed catching up to youtube and instagram in load times and UX, to say nothing of hosting costs)
- queryable, nonpublic moderation records in a unified format (of the sort required for scaleable DSA compliance) would also benefit from being syncable and easily aggregate-able, particularly as they could span multiple moderation authorities (i.e. instances). See User Story #2 in this old Fediverse Enhancement Proposal I wrote as part of a grant from the Sovereign Tech Agency.
- Language models useful for “feed generation” or algorithmically weighting individualized feeds could be hosted as instance-local services. The “local LLM” model kind of presumes some kind of “package manager” for updating models periodically (the economics and ergonomics of such “subscriptions” to LLM updates/refinements are a hazy prediction I hear from both commercial and open-source ML researchers).