Can a machine hear how you're doing?

A command line tool that turns an app screenshot into a rendered device image. The housing is generated procedurally from millimetre measurements and shaded by its own software rasteriser, so it needs no 3D assets and no GPU.

→

$ uvx mockframe render today.png --auto-device

or in Claude Code

“make me a hero image”

→ Screenshot in One command, either way Device image out

A local-first iOS app for substance-use self-monitoring and structured reduction. The feature set comes from the behaviour-change literature rather than from a feature list: self-monitoring alone barely shifts consumption, and the Drink Less factorial trial found an effect only where logging was paired with if-then action planning, so KLAR is built around that pairing. Everything stays on the device, with no account and no backend, so health data never leaves the phone.

Combined audio and text models (Wav2Vec 2.0, BERT) for interpretable emotion recognition, comparing several architectures including a cross-modal transformer and a custom domain model. Feeding one predicted intensity value into the decision fixed a quarter of the cases where happy speech was read as neutral, and beat the far heavier transformer. Graded 1.0, the top grade, at Björn Schuller's Chair of Health Informatics at TUM.

A clinical team project at TUM analysing parent-child dialogues for acoustic markers of ADHD. I built the classification pipeline and extracted the acoustic features with eGeMAPS. The clearest pattern was temporal: the children's voices drifted further apart from the control group the longer each conversation ran, while the parents' stayed put.