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DMXing

A working lab for live lighting: QLC+ show pages generated from code, built for busking real gigs and for venue staff to run.

PythonDMX ProtocolClaude APIHardware Control
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Why:Just Curious

The Challenge

The Problem

Programming DMX lighting cues is tedious and technical. Wondered if AI could help translate 'make it look like a sunset' into actual fixture commands.

Why I Took This On

Pure curiosity. Seemed impossible, which made it interesting.

Constraints

QLC+ has a specific XML format for scenes and cues. AI output has to be precise or it breaks.

The Process

01

Initial Approach

Fed Claude the QLC+ documentation and tried direct generation of XML files.

02

What Went Wrong

Raw XML generation was too error-prone. One wrong character and the whole file corrupts.

03

Breakthroughs

Built a translation layer that converts AI suggestions into validated QLC+ format. AI handles creative intent, code handles precision.

What I Learned

Skills Gained

QLC+ XML schemaAI-to-structured-data pipelinesDMX protocol deep dive

Unexpected Discoveries

AI is surprisingly good at understanding lighting mood words. Less good at precise technical output without scaffolding.

What I'd Do Differently

Would have built the validation layer first instead of debugging corrupted XML files.

Where It Stands Now

Current State

Working prototype. Can generate basic scenes and chases from descriptions.

What's Next

Want to add beat detection for music-reactive cue generation.

The Bigger Picture

Direct application to venue work. If this matures, it dramatically speeds up show programming.

The Impact

Personal Impact

A potentially game-changing tool for live production work. Also a lot of DMX knowledge I didn't have before.

Still actively using this

Build Log

Oct 7, 2026

Modelled the office lighting rig and a venue stage in QLC+'s 3D view from real measurements, and wrote new fixture definitions from manufacturer DMX tables, so pages can be designed and checked on screen before they reach real lights.

Oct 7, 2026

Set up two AI sessions to share one codebase safely: one edits and tests against the lights, the other commits and keeps the repo in order, with a shared handoff file and a check of every claim against git.

Oct 7, 2026

Ran a multi-agent research sweep on live lighting busking: six parallel tracks plus a sweep for new ideas, each checked by an independent reviewer. It produced a reference of techniques, pitfalls and eight ways to lay out a busking control surface, sourced and labelled down to the claim.

Oct 7, 2026

Turned a folder of lighting experiments into a structured lab: a private repo organised by rig, Python generators that build QLC+ show files from code, and an installer that sets up any machine's fixture library from the repo.

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