How to record how-to demo videos of a macOS native (SwiftUI) app for digital archives (semi-)automatically. I fully automate the CLI with vhs, and for the GUI I build a self-driving demo mode into the app itself, record per-window with screencapture, and cover narration/subtitle synchronization plus splitting into parts and reassembling them.
A record of implementing a native macOS app that builds SIPs from born-digital/digitized files in Swift instead of the upstream (BitCurator/Linux) tool, then signing, notarizing, and publishing it.
A record of submitting TEI Scanner — the macOS app whose Developer ID distribution I covered earlier — to the Mac App Store and getting it through review. The focus is on the diffs from the iOS submission flow: .pkg upload via the App Store Connect API, enabling App Sandbox, APP_DESKTOP screenshots, and filter[platform]=MAC_OS.
I built TEI Scanner, a macOS desktop app that runs Apple's Vision OCR over a folder of scanned page images and emits a single TEI/XML document. This post records the SwiftUI implementation, project management via xcodegen, notarization driven by an App Store Connect API key, and .dmg distribution through GitHub Releases.
How to handle crash monitoring, analytics, update prompts, and review requests for an iOS app without sending user data to external services — using only Apple-native tools and open-source libraries. Based on real-world experience with an App Store-published app.
A development log for "IIIF AR," an iOS app that places high-resolution IIIF Image API images at real-world scale on detected floor surfaces using ARKit, dynamically loading tiles as the camera approaches.
Investigation and fixes for a 6.7% crash rate in KotenOCR, an iOS OCR app using ONNX Runtime with 6 models totaling ~230MB on disk.
Fixing duplicate detection results in KotenOCR's modern-script OCR mode (NDLOCR-Lite) by adding NMS and filtering to line_* classes, achieving detection accuracy on par with the reference ndlocr-lite implementation.
KotenOCR v1.3.0 adds NDLOCR-Lite support for modern printed text OCR alongside the existing classical text (kuzushiji) mode, with one-tap mode switching and parallelized recognition for up to 6.7x speedup.
Using withThrowingTaskGroup to parallelize OCR recognition in an iOS app, achieving 1.4x to 6.7x speedup depending on the number of detected text regions.
KotenOCR is a free iOS app that runs the NDL Koten OCR-Lite model entirely on-device, enabling offline recognition of kuzushiji (classical Japanese cursive script) from photos.