Years of Teaching, Scattered Across Random Folders

An experienced instructor accumulates a huge archive: notes, slide decks, handouts, assignment variants from different years and cohorts. Some material is duplicated, some is outdated, some is the best thing they have — but finding it among hundreds of similarly-named files is hard even for the person who wrote it.

Digercules goes through the teaching materials archive and builds a catalog from it: what topic, for which course and year, what duplicates an earlier version, what's worth updating first, and what can be reused as-is for a new cohort.

How it works

Processing runs either entirely on the client's own hardware, or — if that hardware isn't powerful enough — is delegated to a specific external machine over a closed peer-to-peer channel (not a public cloud): the compute is physically located in the Caucasus and Eastern Europe, the client is always told explicitly which machine and which jurisdiction is doing the processing, and only text/structured results come back — never the source files.

Objections

"These materials were built up over years, I already know what's where" — one person's knowledge doesn't scale to a change of instructor, department collaboration, or simply the volume that's grown over decades.

"We have a shared folder on the LMS (learning management system, e.g. Moodle) or the department drive" — a shared folder records what got uploaded to it; Digercules adds what the LMS doesn't have — a content index that lets you search by topic instead of filename or course.

First contact

A pilot on one course's archive across several years — a catalog of materials identifying duplicates and current versions. For a school/department, the decision on a pilot like this sits with a vice principal or department head, not a line instructor; for private practice, the instructor decides themselves.