Question 1

How do I create a basic plugin for TuneFlow using Python?

Accepted Answer

Install tuneflow-py, define a plugin class that modifies the song data model, and refer to the demos repository for examples. The SDK handles execution, so you focus on logic like changing notes or adding effects.

Question 2

What's the difference between TuneFlow plugins and VST plugins?

Accepted Answer

TuneFlow plugins access the entire project data model and support remote execution, while VST plugins typically process real-time audio/MIDI on a single track within a local DAW environment, with less project-wide control.

Question 3

Can TuneFlow plugins run offline?

Accepted Answer

Yes, plugins can execute locally if deployed on the same machine as the DAW, but remote features require internet. The SDK supports both, though setup for offline use might involve additional configuration.

Question 4

How to integrate a machine learning model into TuneFlow?

Accepted Answer

Embed your model in the plugin code, use the SDK to access project data like audio clips or MIDI, and apply changes based on model outputs—examples include AudioLDM for sound generation or demucs for source separation.

Question 5

Are there performance trade-offs with remote plugin execution?

Accepted Answer

Yes, network latency can slow down plugin responses, so it's best for non-real-time tasks like AI analysis or batch editing. For time-sensitive effects, local execution is recommended but may limit deployment options.

Question 6

Is Python or TypeScript better for TuneFlow plugin development?

Accepted Answer

Python is ideal for data-heavy AI integrations, while TypeScript suits web-based tools; both have SDKs, so choose based on your team's skills. The README encourages contributions for other languages, but support is currently limited to these two.

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