One authoring model
Generated content entered the same editable, typed structures as hand-authored content, so creators could generate, refine, publish, and measure without switching tools or mental models.
Case 03 / AI product · Frontend architecture
StakeStack
An AI course builder with reliability designed in.
Selected case / 03AI product · Frontend architecture
40%Fewer runtime errors
2 modesManual + AI authoring
End-to-endArchitecture to deploy
Real-timeEngagement tracking
01 / Context
StakeStack helps creators build, launch, and sell online courses. I led frontend development end to end, from architecture through deployment, while building the course builder and the shared type system at the core of the product.
02 / The challenge
The constraints shaped the system.
01
Make manual and AI-generated content feel like one coherent editing and publishing workflow.
02
Keep a fast-moving AI product reliable while APIs and generated content evolved.
03
Raise engineering quality across the team without slowing feature delivery.
03 / Decisions
How I approached it.
Type-safety as product reliability
I designed strongly typed APIs and shared frontend/backend interfaces so data-shape problems were caught during development instead of being discovered by customers.
Patterns that multiply a team
I coached developers on discriminated unions, utility types, and custom hooks, making the entire codebase safer for the team to extend.
Keep ownership continuous
Owning the frontend from architecture through deployment kept the experience coherent as the product and its AI capabilities expanded.
04 / Delivery
What I built and led.
01Unified manual and AI course builder
02Editing and publishing workflows
03Real-time learner engagement tracking
04Shared strongly typed API layer
05Reusable advanced TypeScript patterns
06Frontend architecture through deployment
Core stack
ReactNext.js App RouterTypeScriptTailwind CSSTanStack QueryAI content generation
05 / Outcome
Delivered the dual-authoring course builder and a shared type system that cut runtime errors by 40%. Over a 16-month engagement, I also raised team-wide TypeScript capability and made the frontend safer to extend.
What this project shows
Deep frontend architecture combined with real AI-product work: turning generated data into coherent product flows and designing systems that remain dependable as they scale.