Hi HN,
Today we’re launching Fabric – an internet drive that combines varied data-types (bookmarks, files, notes, images & more) into a single home, with AI-powered semantic search, so a user can find anything again in their own words.
Without Fabric, this data would scattered across different stores and the user is burdened with finding the right item in the right store (often via brute force chronological scrolling in a file browser, when they cannot remember the precise name of the file/document).
Because Fabric turns everything into a universal object, users are able to attach annotations to them, which provide context on why the content was important when the user returns to it. The annotations also form part of a collaboration surface, where conversations can happen on top of objects.
We are also able to represent that data reliably overlaid on top of websites, if that's what the data object references – e.g. an image on a specific webpage will show the Fabric annotations that were attached to it.
We're focused on researchers and users that perform discovery and inspiration gathering tasks, which in Fabric they can do solo or together.
We still have a long way to go and would love to hear your feedback.
If I browse around and save content to your app, I might want to retrieve this exact piece of content and not any other one that may be semantically close to the query. My experience with vector-based systems (and let's be honest, we are really talking about a recommendation system rather than a search algo here) is that they are hard to combine in a way that is conducive to efficient information retrieval.
You search for a "dog" and you get "cats" because they are similar, but maybe I really want "dog" and not "cat". I find that tremendously inconvenient when apps force you into semantic search and don't let you choose. Sure that might increase the recall, but still please let me choose.