I've been building a labelling tool, a desktop app (PyQt6) for capturing and annotating gesture datasets think of it as a companion tool for gesture recognition pipelines like Gesto, making it easier to collect, label, and export hand landmark data for training models.
I'm at a fork in the road on direction, and I'd love your input:
1. Open source it release it freely (currently GPL-3.0), let the community use it, contribute, and shape where it goes.
Something you thought was a great idea at the time, but looking back you'd do completely differently.
Could be: Launching too early Launching too late Building features nobody asked for Not talking to users enough Focusing on the wrong metrics Having zero launch plan
If you use Aqua Voice, WisprFlow or SuperWhisper, you know the drill: talk to a wall, hit stop, pray the AI didn't hallucinate.
Nobody does live transcription because it completely breaks standard AI models. We spent months figuring out local agreement, audio buffering, and aggressive real-time self-correction just so you can finally edit at the speed of thought.
Live transcription is not final transcription, but faster. It is a trust problem.
When someone is still speaking, the model is decoding partial audio: clipped phonemes, silence, background noise, half-finished words, and sentences that may still change direction.
