MindSync

Designed a discrepancy engine that compares two classifiers with incompatible label sets by folding both onto a shared six-emotion schema, projecting each channel's full confidence distribution into Russell's valence-arousal plane, and measuring the Euclidean distance between the weighted centroids.

Benchmarked 15 candidate models (8 text on the GoEmotions test split, 7 voice on all 1,440 RAVDESS clips) and found the backend's existing text model scored 0.172 macro-F1 below the best alternative.

Shipped the accuracy-per-parameter optimum rather than the top-ranked model: third place cost 0.007 macro-F1 and saved 3.2× the parameters, which matters when every cold start loads the weights.

Caught a false generalisation claim by checking a model's training corpus against the dataset's own provenance column instead of its model card—the card named one dataset, but that dataset aggregated RAVDESS, making the study's only apparent out-of-domain candidate in-domain like the rest.

Made the privacy claim structural rather than stated: no database, no cache, audio dropped when the handler returns, and FastAPI's default 422 handler replaced because its error payload echoes the rejected value—which on a journaling endpoint is the journal entry.

Handled Hive's silent failure on a wrong cipher (it recovers the box as empty, and the next write destroys the file) by verifying every key before opening a box and staging re-key migrations so an interrupted run is survivable.

Architected the Flutter client on MVVM and Repository with Riverpod—18 screens, ~16k lines of Dart—with three swappable sources behind each repository interface and a best-effort offline analysis queue with retry.