live-production

Keyword

live-production

Run-of-show thinking, switching logic, broadcast cues, and the strange choreography of making live media behave.

Entries: 3
Latest: May 17, 2026
Type: Keyword

The Ghost Switcher

media-and-productionapplied-aiProject: autonomous-producerlive-productionsystems-designlocal-firstagentic-systemshuman-in-the-loopstart-here 10 min read

The Ghost Switcher

Part three closes the series by moving from prototype to stakes: a real venue-network failure in London where the remote producer was disconnected, the stream dropped, and the online audience was left staring into the void. From that failure, the article argues that Autonomous Producer is not just about smarter switching, but about removing fragile remote-control dependencies from live production. The answer is a local “ghost switcher” that observes the run of show, source states, transcript cues, and device health; recommends actions in shadow mode; logs every decision; moves through human-confirmed switching; and only earns narrow autopilot after repeated evaluation. Its intelligence is not one giant model, but a resilient local control loop built from constrained commands, structured observations, production rules, and the discipline to hold when the evidence is bad.

The Show Has a Spine

media-and-productionapplied-aiProject: autonomous-producerlive-productionconference-techlocal-firstagentic-systemshuman-in-the-loopstart-here 9 min read

The Show Has a Spine

Part two follows the project from portable livestream workflow into live system architecture, opening with a makeshift hotel-room lab in Killarney where OBS, ATEM control, testing sources, and early automation experiments began turning into something more coherent. The central idea is that an autonomous producer should not be one giant AI model “watching” a show, but a local orchestration system that understands the show through a run of show, device state, sampled video, rolling transcripts, source-state signals, and a director policy. This part argues that scientific livestreams are not just sequences of cuts, but sequences of expectations, cues, timing, identities, and drift, and that the real technical challenge is giving the machine a structured point of view about what is happening before asking it to recommend or execute production decisions.

Notes on the Yellow Pelican

media-and-productionapplied-aiProject: autonomous-producerlive-productionconference-techlocal-firstagentic-systemshuman-in-the-loop 7 min read

Notes on the Yellow Pelican

Part one of this three-part series traces Autonomous Producer back to its practical origin: portable livestream kits built as conferences returned in person while online access remained essential. What began as yellow cases, hand-cut foam, simple signal paths, and laminated diagrams evolved into a more complex workflow involving cameras, OBS, ATEM controls, remote producers, and the latency of operating live shows over a network. The article argues that scientific livestreams often follow a repeatable grammar, and that Autonomous Producer is not a general AI director, but a local system that can observe the show, follow the run of show, execute constrained commands, log decisions, and gradually earn narrow autonomy.