I build production-grade web products where interface quality, browser behavior, and real implementation detail matter. My work sits at the intersection of frontend engineering, browser extension architecture, AI-assisted systems, and cross-platform debugging — especially when a polished UI still has to survive real device constraints, service worker lifecycles, and production deployment realities.

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• Browser-platform engineering: Building Chrome Manifest V3 extensions, service-worker-driven workflows, isolated UI injection, and frontend systems that have to work inside real browser constraints.
• AI-integrated product systems: Using models as part of the product stack where they support workflow quality, reasoning, and UX — not as a substitute for real implementation or human evidence.
• Cross-platform debugging: Solving browser- and device-specific issues on physical hardware using Safari Web Inspector, ngrok, and live debugging workflows across iPhone, Safari, WebGL, and media/runtime edge cases.
• Release-minded frontend systems: Shipping with TypeScript, testing, CI, and production behavior in mind — treating maintainability, debuggability, and deployment quality as part of the product.

AptivAI is being re-centered around Signal Lab: a pre-send email testing workflow where users create email variants, generate reviewer links, capture human-response signals, compare outcomes, and improve copy before launch.

The Product Direction:
Instead of treating AI as the source of truth, AptivAI is moving toward a system where human evidence comes first. The strongest version of the product is not a generic AI email analytics platform. It is a structured experimentation product for trust, clarity, and action intent before an email is sent.

The Stack: React + TypeScript frontend, Node.js/Express backend, MongoDB, Firebase Auth, Gemini API, Google Cloud TTS, and MediaPipe.

What makes it hard:

• Signal Lab architecture: building tests, variants, reviewer sessions, public tokenized review links, event capture, survey collection, and comparison reporting without tearing apart the broader app
• Public reviewer session flow: secure token handling, expiration, stateful session tracking, and event capture without requiring reviewer authentication
• Additive product evolution: reshaping a broader analytics prototype into a sharper product while preserving working flows like the dashboard assistant and existing authenticated product areas
• Cross-platform complexity: earlier multimodal and browser/device-specific work introduced real debugging constraints, especially across Safari/iPhone behavior and client-side runtime handling

The Result:
AptivAI is evolving into a more credible software product: variant creation, reviewer sessions, human-response capture, comparison reporting, and later evidence-backed AI revision — with the broader assistant and analytics platform still supporting the product around it.

CI/CD: GitHub Actions — lint, typecheck, tests, and build on every PR.
Live Demo →

A Manifest V3 extension that shows upcoming Portland Timbers matches with a live countdown, TV/streaming info, and a fan confidence poll.

The Architecture: chrome.storage.local for state, alarm-based hourly refresh, runtime messaging between popup and service worker, and a three-tier data resolution strategy (live ESPN API → cache → bundled fallback). Safari support is handled through Xcode conversion with no polyfills required.

The Result: Published on the Chrome Web Store, with Safari support built into the codebase and workflow. GA4 telemetry via Measurement Protocol, Jest coverage, GitHub Actions CI, and Codecov integration.

GitHub →

A Manifest V3 Chrome extension that detects supported merchant domains in Google Search results and injects inline cashback banners next to matching results.

The Architecture: TypeScript + Vite build. Content script handles result link detection and domain normalization; background service worker resolves offers via a configuration-driven merchant registry with chrome.storage.local caching and JSON fallback data. Injected UI uses Shadow DOM for full style isolation from the host page.

The Result: A focused proof-of-concept for safe DOM orchestration, content script ↔ service worker messaging, and isolated UI injection in live search environments. Vitest coverage with GitHub Actions CI.

GitHub →

A client-side coupon generator for configurable discount codes, QR output, and persistent storage with no backend required.

The Architecture: Next.js 15 (App Router) + TypeScript + Tailwind. Coupon codes use crypto.getRandomValues for suffix generation. QR rendering via qrcode.react. localStorage persistence across sessions. Input validation for discount ranges, code length, and expiration dates.

The Result: Deployed on Vercel with Vitest unit coverage, ESLint, TypeScript type-checking, and GitHub Actions CI.

GitHub →

End-to-end design and build for a Portland-area landscaping company. Conversion-focused, responsive, and deployed on Vercel with Next.js, TypeScript, and Tailwind.

I treat AI tooling as an architectural constraint, not a substitute for engineering judgment.

That means:

• generated code should fit the actual system, not generic defaults
• repo-specific rules and conventions should keep implementation aligned with the product
• core projects should validate through linting, tests, and CI where the workflow calls for it
• browser behavior, runtime detail, and production reality matter just as much as clean code in the editor

I care about understanding what is actually running — especially when the debugging gets messy.

📍 Portland, OR · Portfolio · LinkedIn · Email