AI-Native Engineer · WebGL & Real-Time Systems · Deterministic AI Workflows

Most developers use AI as autocomplete.
I use AI as a structured, orchestrated development system capable of shipping enterprise-grade software faster and with fewer bugs.

Over the last year, I built MetaCurtis — a WebGL “consciousness theater” engine — with:

• 15,000+ GPU-driven particles at 60 FPS on integrated GPUs
• Real-time morphing constellations & narrative stages
• A deterministic single-writer rendering pipeline
• Contracts, guardrails, and evidence layers around all AI-generated code

All of it was created solo, using a methodology I designed for predictable, production-safe AI development.

Turning AI from “code suggestion” into a deterministic, multi-agent development pipeline.

I build systems where:

• Humans control architecture, contracts, event order, invariants
• AI handles implementation inside those boundaries
• Race conditions and drift are prevented at the pattern level
• Code remains stable even when 70–90% is generated by AI

• Particle engines
• GPU-based motion
• Interactive rendering pipelines
• R3F + GLSL optimization
• Architectures that run smoothly even on integrated GPUs

Shifting from:

• Human-first coding
  → AI-first, human-directed architecture
  → Deterministic engineering at scale

MetaCurtis is my flagship system — both a narrative engine and the technical proof of my AI-native methodology.

• 15k particle WebGL engine
• Stage-based morphing and “consciousness theater”
• Adaptive quality system
• Deterministic single-writer renderer
• Built entirely with AI as the development engine

🔗 Live Demo: https://metacurtis.com

I run a focused 4-week AI-Native Pilot for teams who:

• Use Copilot / Cursor / Claude extensively
• Build complex frontends, visual systems, or real-time UIs
• Are experiencing instability, regressions, or slowdowns
• Want to combine AI development with predictable reliability

• Pick one painful engineering problem
• Instrument and map system behavior
• Apply deterministic AI-native patterns (contracts, guardrails, tests)
• Implement a real fix in your codebase
• Leave your team with reusable processes

💡 Teams typically see 5–10× faster delivery with fewer regressions.

👉 Learn more: https://curtiswhorton.com

Public methodology repo (coming online soon):
👉 https://github.com/metacurtis/ai-native-engineering-patterns

Patterns include:

• Pattern S — Single Writer
• Pattern D — Deterministic Directives
• Pattern C — Contracts & Evidence
• Eventguard layers
• State constraints
• AI orchestration
• Quality shields & validation

• 🌐 Website: https://curtiswhorton.com
• 🧠 MetaCurtis Engine: https://metacurtis.com
• 💼 LinkedIn: https://linkedin.com/in/curtiswhorton3
• 🐦 Twitter: @CurtisWhorton

Always open to conversations about deterministic AI workflows, WebGL performance, and AI-native engineering.