What I build — in depth

I ship production App Router sites and products on Vercel — not demos. That means edge and Fluid compute, streaming UI, server actions, and realtime backends (often Supabase) that hold up under real traffic.

Across FoCoCo, OTW, Arete Minds, imaddhin, and THEVAULTLOSANGELES, the same pattern repeats: clear architecture, AI agents where they earn their keep, and deployment that stays fast to iterate.

I use Framer as the agentic design-to-publish surface — External Agents on the canvas, CMS collections structured for growth, and components agents can extend without breaking the brand.

When a site outgrows marketing CMS needs, I hand off cleanly to Next.js while keeping the Framer system as the source of visual truth for campaigns and product marketing.

I build and ship FlutterFlow apps end-to-end — UI, custom code, Firebase, payments, and store release — then push past the low-code ceiling with native Flutter when the product needs it.

As an early adopter and multi-year Official Expert, the practice is speed with production judgment: MCP AI in the workflow, clean architecture, and apps that survive App Store and Play review.

I deliver enterprise APIs, Blazor surfaces, and cloud-ready .NET services — typed C#, ASP.NET Core hosting, and real-time UI where the business needs it.

The focus is durable systems: clear domain models, auth, observability, and hosting that fits Azure or hybrid — not throwaway prototypes.

I architect client platforms on Cloudflare’s connectivity cloud with an agentic-first lens: stateful agents on the Cloudflare Agents SDK and Durable Objects, serverless inference via Workers AI, and unified routing through AI Gateway.

Security is engineered directly into the edge: Cloudflare Zero Trust replaces legacy VPNs, Turnstile shields forms without UX friction, and Next-Gen WAF blocks malicious bot traffic before it touches your application servers.

Backend workloads leverage zero-cold-start Workers, distributed D1 SQL, and R2 storage with zero egress fees, creating ultra-scalable architectures that cost a fraction of traditional hyperscalers.

I treat Rive state machines as a contract between design and engineering — number and boolean inputs driven by live telemetry, one .riv asset across Flutter, Swift, Kotlin, WebGL2, and C++ embedded displays.

The Home Energy Dashboard lab shows the pattern: high-fidelity motion owned by design, inputs owned by the backend, runtimes chosen per surface.