What I Built
I started this project because I noticed something wrong with how bird identification works in my own town.
BirdNET is a genuinely impressive open-source project. Point it at a recording and it will confidently tell you what you heard. I tried it on some audio recorded on my hostel campus in Chidambaram, Tamil Nadu, and it told me about Rose-ringed Parakeet, Common Grackle, and Dark-eyed Junco. The grackle is a North American species. It has never been in India. It was never in my garden. The model was not confused - it was sure, and wrong.
That is the whole problem in one clip. A global classifier trained on global audio will happily tell you about birds that live four thousand kilometres away, because nothing in its architecture knows or cares where you are standing. There is no "not in my region" button.
chidambaram-birdnudge is that button. It runs the same open weights on your own machine, then filters every prediction through a species list built from real observations near you. It costs nothing to run, needs no account, works with no signal, and never sends your recordings anywhere.
A local Node.js pipeline that takes phone recordings, identifies birds with an open-weight audio model, filters the results to a regional species list, and tells you where to go next.
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