NinetyBytes

Selected software projects

Selected software projects spanning AI, health, and systems engineering.

NinetyBytes is a compact portfolio of private projects, prototypes, and technical demos. The work here ranges from on-device glucose prediction and AI-driven tooling to low-level systems software and local inference infrastructure.

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Projects

Selected Projects

Each section is short on purpose. The goal is to give enough context for a resume reference without turning this into a full showcase site.

Retired macOS app

GlucoGram

Dexcom Share, macOS menu bar utility

A retired macOS menu bar utility formerly distributed through the App Store for viewing live Dexcom glucose data without opening a separate app.

  • Pulls glucose data from Dexcom Share and shows current value and trend at a glance.
  • Built from a personal need to reduce context switching while working.
  • No longer maintained and retired from the App Store in 2024; included here as a concise project reference.

Screenshots

Selected screens from the retired macOS release.

GlucoGram 24 hour chart in normal range

01

24-hour view
GlucoGram 24 hour chart showing low glucose

02

Low alert state
GlucoGram 24 hour chart showing high glucose

03

High alert state
GlucoGram setup screen

04

Setup
GlucoGram general preferences screen

05

General preferences
GlucoGram graph preferences screen

06

Graph preferences
GlucoGram alert preferences screen

07

Alert preferences
GlucoGram secondary alert preferences screen

08

More alert preferences

Private iOS/watchOS AI app

Core ML and AI glucose prediction app

Core ML, CGM, prediction, pattern recognition, exercise impact, Apple Watch

A private iPhone and Apple Watch app focused on glucose prediction, therapy context, and forecast-driven decision support built around on-device ML.

  • Centers on on-device glucose forecasting with Core ML, personalization, retrieval, and forecast-confidence work.
  • Explores exercise-impact previews and what-if workflows that show how planned activity may affect glucose before a workout starts.
  • Includes pattern-recognition and insight work over historical glucose and therapy data, alongside phone, widget, Live Activity, and Apple Watch surfaces.
  • Includes insights and trend surfaces for forecast accuracy, recurring time-of-day patterns, exercise impact, and post-bolus response built from merged CGM and therapy history.

Demo slot

Paste prediction app demo here

Suggested demo: forecast screen, exercise-impact preview, insight/pattern view, and Apple Watch surfaces.

Screenshots

Additional screenshots can be added here as they are prepared.

01

Main predictor dashboard

02

Exercise impact preview

iPhone insights and trends screen

03

Pattern / insight view
Apple Watch glucose dashboard screen

04

Apple Watch surface

AI + systems project

Custom x86-64 operating system with local AI runtime

C, x86-64 assembly (arm64 port in progress), Qwen, GGUF, local inference

A long-running from-scratch operating system spanning bootloader, kernel, scheduler, virtual memory, VFS, PE-style program loading, and a growing native userland, with local AI integrated directly into the platform.

  • Boot path from loader to kernel bring-up and user-mode runtime.
  • Integrated local Qwen-based inference and agent tooling using GGUF models, native runtime components, and queue-driven workflows.
  • NT-style section mapping, demand paging, and file-backed memory to support heavier native workloads.
  • Desktop shell, native apps, and iterative systems debugging.

Demo slot

Paste OS walkthrough video here

Suggested demo: boot sequence, shell launch, Finder, Task Manager, app startup.

Supporting systems work

Networking, desktop UI, and native tooling

IPv4, ICMP, UDP, TCP, TLS, window manager, native apps

Supporting platform work built alongside the operating system: networking, TLS, windowing, task inspection, and first-party native apps.

  • e1000-based IPv4 networking with ICMP, UDP, and TCP support.
  • TLS client tooling for secure outbound requests.
  • Window manager, Finder, and Task Manager style native desktop apps.

Demo slot

Paste platform demo here

Suggested demo: ping output, TLS request flow, window movement, Finder navigation.

About

About This Site

NinetyBytes is a small portfolio of private projects across applied AI, health software, and systems engineering. The work here ranges from on-device prediction and native utility apps to operating systems and local inference infrastructure.

The scope is intentionally narrow. Each project is included as a concise technical reference: enough detail to show architecture, direction, and implementation depth without turning the page into a full case-study archive.

Contact

Contact

For follow-up, additional project context, or technical discussion, email is the simplest way to reach me.

Email