Context
PadiPay is a working Next.js marketplace connecting local sellers with delivery agents, with built-in trust infrastructure between the two sides — built across three iterative build cycles.
Problem
Working understanding, pending Victor's confirmation: local sellers and independent delivery agents lack a lightweight, trustworthy way to transact — sellers need a directory and a way to log handoffs/pickups, and there's no shared reliability signal between the two sides. This section will be updated with the exact problem statement in Victor's own words.
Opportunity
The opportunity was the trust gap itself, not just the absence of a directory — sellers and delivery agents who don't already know each other have no shared reliability signal to transact on, which is a different (and harder) problem than simply listing sellers.
Research
Discovery — persona and journey mapping for both sides of the marketplace — was documented before any code was written, covering both the seller's and the delivery agent's side of every transaction.
Decision-Making
Because implementation was directed rather than hand-coded, the decisions that mattered were about scope and sequencing: what each of the three build iterations should tackle, and when to let what the working product revealed override the original plan rather than executing a fixed spec blindly.
Prioritization
Dashboards, onboarding, and a basic directory were prioritized ahead of the trust/reliability mechanism, so the riskiest and least-proven piece of the product was built once the core transaction flow already existed to attach it to.
Execution
The build progressed incrementally across three directed iterations: dashboards, onboarding, a directory, transaction logging, and a trust/reliability mechanism between sellers and delivery agents.
Collaboration
Product owner and director. Problem discovery and user stories/journey maps were mine; I specified, reviewed, and re-scoped AI coding agents (Claude Code) across three build iterations rather than writing the application code by hand — a director-and-reviewer relationship, not a delegate-and-forget one.
Trade-offs
Directing rather than coding traded some low-level implementation control for iteration speed — the trade-off I actively managed was reviewing AI-generated output critically enough to catch scope drift, rather than rubber-stamping code that merely ran.
Outcome
A working three-iteration build exists with real seller- and agent-facing flows; the underlying discovery documentation and product requirements are shared directly on request rather than published in full here.
Reflection
The product manager's judgment remained the bottleneck and the value-add throughout this build — not the code generation itself. Reviewing AI-generated output critically, and being willing to redirect between iterations, mattered more than the speed of any individual cycle.
Lessons Learned
Speed from AI-directed builds is only useful if paired with the discipline to actually re-scope between iterations rather than accept whatever the previous cycle produced — the redirects, not the code generation, are where the product thinking lived.
Future Improvements
The honest next step is closing the gap flagged above: writing the original problem statement in my own words rather than a reconstructed one, then validating the trust mechanism against a handful of real sellers and delivery agents to confirm it addresses what they'd actually cite as the blocker.
My Contribution
Product owner and director — problem discovery, user stories, journey maps, and the scope/re-scope decisions across all three iterations were mine; implementation was directed through AI coding agents under my review.