Start with the reason
Define the user, friction and desired change before choosing the stack or drawing the interface.
The method behind the projects: concept, design, AI-assisted code, backend and the infrastructure that gets the work online.
Product thinking, design direction, implementation and iteration.
I use AI to explore, implement and debug faster, while keeping product intent and visual judgement human-led.
The process is not a fixed waterfall. I move between concept, prototype and implementation, using each working version to expose the next design question. The goal is always a coherent product, not a pile of generated screens.
Define the user, friction and desired change before choosing the stack or drawing the interface.
Figma and Stitch help establish hierarchy, tokens and reusable components before code multiplies decisions.
A deployed build reveals responsive, performance and content problems that static frames cannot.
Every phase produces something testable and feeds evidence back into the next decision.
Turn a loose idea into a clear problem, primary journey and realistic first release.
Use references, Figma or Stitch to define the visual language, responsive behaviour and component rules.
Build in small, reviewable slices; inspect output against the source rather than accepting generated defaults.
Choose Supabase, Neon, Firebase or a simpler static model based on the real product constraints.
Use Vercel or Cloudflare, then test the live result across routes, breakpoints and meaningful interactions.
Tokens, components and motion rules form the bridge between a design direction and maintainable code.
Next.js and React are common on the web; Flutter handles native product work. Supabase, Neon and Firebase cover different data and realtime needs, while Vercel and Cloudflare support deployment, edge logic and delivery.
The point
The best use of AI is to expand what I can execute while sharpening, not outsourcing, the choices that make a product coherent.
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