A live workflow engine, not a screenshot.
This is the architecture behind IntegrateX, running in your browser. Drag the nodes, drag from one handle to another to rewire the graph — every change flows through a Zustand store, the same pattern that powers the production serialization adapter. Pipeline: Webhook Trigger → LLM Processor → Database Write.
React Flow + Zustand · drag nodes to reposition · drag between handles to connect
Serialization Adapter — live bench
● real bytes, measured from the canvas aboveDrag a node or wire a new edge above and watch both payloads reprice. The database stores what is true, not what is drawn — styles, dimensions, and handles are derived from the schema on load. The full story →
The Architecture is Everything.
AI handles the syntax; the engineer dictates the flow. Master the pipeline: Database → Backend → Frontend. An AI-equipped architect ships the volume of an entire engineering squad. Hold the reactor to see the difference.
~/lab
Infrastructure & Resiliency Lab
Interactive demonstrations of production-grade LLM cost governance and client-side network degradation recovery.
FinOps Cost Simulator
Live token economics: naive frontier-only inference vs. a semantic cache + model-cascade architecture, at 2,000 tokens per request.
Naive Stack
$3,000.00
100% frontier model · $15.00/1M tokens
Optimized Cascade Stack
$367.20
Cache hits free · 80% of misses → Flash ($0.075/1M) · 20% → Frontier
Monthly Savings
$2,632.80
▼ 87.8% infrastructure deficit reduction
Chaos Engineering Degrader
Inject a synthetic upstream failure and watch the client degrade gracefully: timeout → exponential backoff → local fallback cache. No crash, no blank screen.
›GET /api/inference → 200 OK · 120ms
›stream: telemetry packets flowing
~/observability
Every AI request is a trace.
A RAG pipeline you can't see span-by-span is a pipeline you can't debug or price. Run the query cold, then flip the semantic cache on and watch the same question collapse to a handful of milliseconds.
RAG query — trace waterfall
modeled on production trace shapesrun the query — with the cache off, then on — and compare the totals
~/guardrails
Go ahead — attack it.
Production agents survive adversarial input through layered, deterministic defenses — not polite prompting. Try an injection, leak some PII, ask something off-topic, and watch each layer rule on it.
Guardrail playground — try to break it
deterministic layers · runs in your browser · no model callspick an attack — or invent one — and watch each defense layer rule on it
~/vitals
Core Web Vitals — this page, your browser
● measured live via the web-vitals libraryGreen thresholds are Google's own (LCP ≤ 2.5s · CLS ≤ 0.1 · INP ≤ 200ms). If you're seeing green on a page running three interactive simulations, that's the performance engineering working.