omdesign portfolio



_Product Design_

2025

Radon Measurement Housing

Rad-Elec Offspring radon detector

Project: Two month design commission

Design, 3D Print Prototype
Liase with Engineering for fitment
Enable 3d print for in-house manufacture

Result: Revolutionary innovation in the market 1/10th the size of previous Recon device with 1/10th the budget of legacy manufacture and test

_Articles _

Several articles introduce original frameworks. My focus is on making new ideas usable through clear language, structure, and examples.

View all articles on Medium →]


_Technical Writing_

Blink
(an Amazon company)

support.blink.com →]

2019 – 2023

Customer Support Portal

Architected: Structure, Content, UX
Artwork: Product, Packaging, 3d renders
Advised: Translation, Feedback, Editorial, and Publishing user experiences.
Achieved: Two writers managed five languages, scaling from 250k -> 18million devices.
Site is largely intact five years later!
Invented: Blink Accessibility Guild, App Thumbnail Update, Enhanced opening tool, A11y Toolkit


IBM DataPower

Docs Portal Link →]

2013 – 2017

screenshot of IBM DataPower support website

Support Docs

Wrote: Quick Start, User Guides, Updates, Critical Alerts, Instructional Articles
Used: DITA, oXygen, Git, Jenkins, API, JIRA, Adobe Suite, Blender, Fusion 360
Led: Transition from Print to Web paradigm – continuous delivery saved team 40% effort!
Added L2 pipeline for product wisdom.


STAR Rescue Team

Wrote: Training Procedures
Trained: All environment rescue
Invented: Rescue Rigging Systems


US Air Force

Flawless Radar Operator Training Syllabi


_Other Projects_

Has-Needs Logo of a minimally stylized octagon with mirrored halves showing presence and absence

GitHub Link →]

Has-Needs

Disaster Logistics and Radical Sovereignty un-blockchain.

Framework Brief

BIAS Enhanced ░▒▓█

BIAS (Basic Institutional and Academic Stringency) a self-executing protocol that is sensitive to institutional, academic, or systemic influence on reported facts.
Using this protocol, AI agents may access and share vetted information reliably across federated networks.

Framework Brief

SATTVA ⚛

SATTVA is a biomimetic AI framework that organizes knowledge around semantic attractors: stable conceptual states where meaning emerges rather than statistical frequency.

Framework brief

The SATTVA model is currently trainable and can quickly identify semantic objects from multiple streams against a background of diverse inputs. Connections are discovered across domains that ‘resonate’ with established attractors. This cheap and effective solution tags items in a stream for further analysis by LLM.


_Graphic Design


_Commissioned AI Art_