Mimis

This program is a virtual “filesystem” built in a native graph database. Each user has a “context forest” wherein they try to see that all sensical paths for a resource resolve. If you can come up with 𝘢𝘯𝘺 path that sensibly describes the resource you're looking for, it'll be there. No more lost files.

Operations are done on combinations of these forests across users. For example, users can specify a trusted set of forests to search through. Content can be ranked based on how consistent and desirable the information encoded in the paths including the resource.

𝔐𝔦̈𝔪𝔦𝔰 differs from a traditional directory tree in several fundamental ways:

★ 𝗣𝗮𝘁𝗵𝘀𝗲𝘁𝘀: When searching through resources, rather than a single path, the user can specify combinations of paths, and display elements that are common between them.

★ 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗙𝗶𝗹𝘁𝗲𝗿: In 𝔐𝔦̈𝔪𝔦𝔰, each user's additions are saved to their own subforest. These are traversed en masse to find which resources were the most often referenced to gauge popularity for competitive spaces like /news/interesting/ where lots of content wants to have the top spots.

★ 𝗡𝗮𝘁𝗶𝘃𝗲 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻𝘀: Folders may contain multiple resources with the same name.

IPFS filesystems are spidered into Neo4j including saving the structure of symlinks. Each user has an isolated import structure and queries are performed by traversing over combinations of them.

The base application is Next.js / TypeScript. There's a React frontend that sends JSON to API endpoints which converts it to Cypher, passes it through Neo4j, and composes a JSON response.

Authentication is accomplished using Sign-In With Ethereum. ENS names are used as the root identifying token for the user-based import trees.