MCN 2026

Most AI-for-archives work in GLAM comes from engineers at vendor companies or well-resourced institutional AI teams. This session is a practitioner case study from a different corner: a public library manager — without an engineering background or an in-house AI team — building local-first AI infrastructure for a rights-sensitive regional broadcast archive.

The Northeast Ohio Broadcast Archives (NEOBA) are roughly 10,000–15,000 hours of regional broadcast footage. A digitized slice of 349 tapes from 1977–1982 served as the starting material. Cloud AI services were off the table — rights posture, institutional trust, and the sensitivity of local-community material all pointed to local-first processing, as did the budget and staffing reality of a public library.

Two artifacts will be demonstrated. The first is a local AI pipeline running on a single M-series Mac: scene detection, segment-level visual description (Gemma 4 E4B), transcription (mlx-whisper), face and entity detection (InsightFace), and Dublin-Core catalog synthesis (Gemma 4 26B A4B). The second is a public-facing Archive Explorer surfacing 14,242 classified items, 3,446 extracted entities, and 292 semantic clusters — with narrative threads and an AI-assisted chat interface for natural-language discovery that let someone unfamiliar with the collection begin exploring from any angle.

Both are examined honestly: what local-first bought in institutional trust, what it cost in throughput and fidelity, where contemporary AI tools extended what a small library team could do — and where they did not. The session also takes up a question the theme invites directly: as AI capability becomes cheaper and more locally runnable, who gets to build it, for whose collections, at whose institutions? A non-engineer practitioner working from a single workstation inside a public library is an unlikely builder profile for this work in 2026 — and that unlikelihood is part of the point.

Attendees leave with a reference implementation for local AI in a rights-sensitive archival setting, a set of trade-off conversations worth having before cloud-defaulting, and a practitioner perspective on AI-augmented work at institutions whose starting posture is not that of a cloud-first AI lab.