Computer science master's student · software developer / software systems & AI research
I build software and study how AI systems behave.
My current interests are test-time training, interpretable AI, and long-context modelling. I also build tools for local workflows and reliable developer systems.
Selected projects
Things I have built.
A small selection of software I have shipped, maintained, or contributed to.
research implementation
cratemind
A local workflow for downloading Spotify playlists, analyzing audio for BPM, Camelot key, and genre, and organizing tracks into configurable DJ-library folders.offline-first learning tool
Marginalia
An Android tablet app for taking handwritten lecture notes beside course PDFs, keeping notes anchored to the page they belong to.application architecture
Flutter Base Template
A production-oriented Flutter foundation with BLoC, app flavors, Firebase Cloud Messaging, theming, routing, logging, error handling, and environment configuration.Writing
Notes from the workbench.
I write when the implementation exposes something worth explaining.
The Math Remembers: How to Catch a Model Lying About Being Original
Model-merge forensics using weight arithmetic to test claims of originality in supposedly homegrown AI systems.
Bounded Wrongness: A Field Guide to Probabilistic Data Structures
Bloom filters and related data structures, with attention to one-sided error, tunable false positives, and memory trade-offs.
The Rest of the Crate: Tempo, Key, and Never Stranding a File
Implementation notes for cratemind: tempo estimation, Camelot key detection, downloader reliability, and file organization.
Your Music Has No Genre: Reading It Off the Waveform
A case for audio-derived genre detection when playlist metadata is incomplete, unavailable, or too coarse.
Currently interested in