Curriculum Vitae

Byron Johnson

Full-Stack Web Developer | AI-Native Builder
SaaS & Product Engineer

Contact GitHub LinkedIn byronj.me

01 / Summary

Summary

Frontend developer with 15+ years of industry experience, now building production SaaS products using modern AI-assisted development workflows. I design and ship complete web applications end-to-end: architecture, authentication, payments, SEO, GEO, and deployment. I use AI models (Claude, GitHub Copilot, Cursor) not as autocomplete but as development partners, managing context documents, agentic rules, and skill libraries to iterate faster and maintain higher quality than traditional development alone allows.

I'm currently building production sites using my favorite modern tools: Next.js App Router, React 19, TypeScript, Tailwind CSS v4, Clerk, Stripe, Supabase, and Vercel.

02 / Skills

Technical Skills

Core Stack

  • Next.js 16: App Router, SSR, SSG, serverless API routes
  • React 19: hooks, server and client components, dynamic imports, lazy loading
  • TypeScript 5: strict typing, interface design, utility types
  • Tailwind CSS v4: utility-first, theme tokens, CSS cascade layers
  • Vite 6: MPA builds, rollup config, legacy migration
  • Vanilla JavaScript / ES Modules: Canvas API, FileReader, custom animations
  • Three.js: 3D scene graph, particle systems, procedural geometry, WebGL rendering, scroll-driven cinematic experiences, reduced-motion accessibility
  • HTML5: semantic markup, Open Graph, Twitter Card, Apple meta, schema.org JSON-LD

SaaS Architecture

  • Clerk: authentication, sign-in and sign-up flows, post-auth redirects
  • Stripe: Checkout, subscriptions, billing portal, multi-tier pricing
  • Supabase: PostgreSQL schema design, storage, usage tracking
  • Vercel: serverless deployment, Instant Rollback, postbuild scripts

SEO & GEO

  • Technical SEO: dynamic XML sitemaps, per-page canonical URLs, noindex rules for private routes
  • Programmatic SEO: dynamic slug routing, 19+ topic hub pages per site, structured content modules
  • Structured Data: JSON-LD schema markup (WebApplication, Article, FAQPage, ItemList, BreadcrumbList, VideoObject, VideoClip)
  • Video SEO: video sitemap entries, VideoObject and Clip JSON-LD, per-video canonical metadata
  • Generative Engine Optimization: llms.txt and llms-full.txt for AI crawler discovery, GEO-optimized copy and content structure
  • IndexNow: automated post-build URL submission for indexing
  • Core Web Vitals: PageSpeed Insights, Lighthouse score analysis, CrUX field data interpretation
  • HTML Site Maps: crawl-discovery pages with internal link architecture for search and AI crawlers
  • On-Page SEO: keyword architecture, hub-and-spoke content models, internal linking patterns

Production

  • Hardened production defaults for billing events and subscription state
  • Content Security Policy headers, HSTS, environment variable hygiene
  • OWASP Top 10 awareness applied across server routes

Testing

  • Jest and React Testing Library: unit and component tests
  • TypeScript strict mode in CI
  • ESLint with Next.js config
  • Smoke tests via Playwright scripts
  • Pre-build sitemap and URL verification scripts

03 / Method

AI-Assisted Development Methodology

I build and maintain a structured agentic context system for each project that allows AI models to work accurately across large codebases without losing project-specific constraints.

Context architecture per project

  • AGENTS.md: single entry-point document routing agents to the correct context layer
  • PROJECT_BRAIN.md: architecture deep reference: stack, design decisions, incident history, integrations
  • Cursor rules: always-on and scoped rules that constrain agent behavior
  • Skills: repeatable workflow playbooks for hub pages, blog posts, Stripe tiers, and SEO content
  • Live inventory: auto-generated route and dependency map that stays current with the codebase
  • SEO/GEO master: keyword architecture, structured data templates, and GEO rules in one reference

What this enables

  • Consistent coding standards enforced across every AI-generated change
  • Rapid iteration: new hub pages, blog posts, server routes, and Stripe tiers scaffolded with correct patterns
  • Architectural guardrails that prevent regressions as the product grows
  • A living, version-controlled knowledge base that grows with the project

04 / Deliver

What I Deliver for Clients

New web app or SaaS

Full-stack Next.js product: auth, payments, database, deployment

SEO-first content site

Programmatic hub architecture, structured data, dynamic sitemaps, IndexNow

GEO / AI search visibility

llms.txt, AI-optimized content structure, schema markup for generative engines

Performance optimization

Core Web Vitals analysis via PSI, Lighthouse audits, CWV remediation

Design system & tokens

CSS custom properties, Tailwind v4 theme tokens, Figma variable exports

AI-augmented development

Agentic context systems that make iteration faster and safer over time

Privacy-conscious products

Client-side processing architecture, no unnecessary data collection

Subscription monetization

Stripe and Clerk integration, pricing tiers, billing portal

05 / Toward

Currently Building Toward

  • MCP (Model Context Protocol) server integrations: exposing product data and tools to AI agents programmatically
  • Expanded GEO strategy: structured content, citation-optimized writing, AI overview targeting
  • Agentic monetization: APIs and MCP endpoints that let AI assistants use SaaS products directly
  • Continuously iterating this resume with AI agents as new projects and skills are added

Last updated: August 2026.