M.Sc. computer science / independent study and software
Test-time adaptation, interpretability, and long-context models.
I document independent study through technical writing, reproducible software, and structured reading notes.
Selected work
Software and technical tools.
Selected applications, tools, and contributions. Each entry links to its source repository and, where available, releases and automated checks.
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1
local audio tooling
cratemind
A local workflow for downloading Spotify playlists, analyzing audio for BPM, Camelot key, and genre, and organizing tracks into configurable DJ-library folders.
Python · spotdl · ffmpeg · local audio signals
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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.
Kotlin · Jetpack Compose · Room · PDFium · Android Ink
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3
Flutter application template
Flutter Base Template
A Flutter application template with BLoC, app flavors, Firebase Cloud Messaging, theming, routing, logging, error handling, and environment configuration.
Dart · BLoC · Firebase Cloud Messaging
Technical writing
Notes on systems, methods, and implementation.
I write explanatory essays and implementation notes about the systems I build and study.
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.
Independent study
Research questions I am currently working through.
My current process is literature mapping, technical writing, and small implementations. These are study topics, not claims of completed research results.
How models can adapt at inference time, what is learned during that process, and which trade-offs make adaptation reliable.
Methods for understanding internal model behavior, connecting mechanisms to outputs, and separating useful evidence from plausible stories.
Related writing: The Math Remembers: model-merge forensicsHiPPO, S4, Mamba-style models, polynomial projection, and the gap between long-context storage and long-context prediction.
Related writing: Memory as Polynomial Projection