Most of my recent product work is platform — powerful inside other products, hard to show directly. So I built one end to end: Morning Ready, an AI app that tells a parent what their kid needs each morning — concept to production in about three days.
Parents already know the basics: water bottle, backpack, snack. The scramble is the one detail buried across emails or WhatsApp messages: what does my child need today?
What to bring: Wear green, swimsuit, towel, class party items.
Lunch risk: Whether today's school lunch is likely to work for the child, based on allergies, dislikes, dietary rules, and past patterns.
This app answers this exact question.
When a child doesn't eat school lunch, the cascade is predictable:
The fix is knowing in advance. The core loop: parent uploads the monthly menu PDF → app parses the menu by date → compares each day’s lunch against this child’s allergies, dislikes, dietary rules, and past feedback → parent gets a useful alert only on risky days.
The same upload pattern powers Bring: paste a camp email or upload a schedule PDF to get tomorrow’s packing list.
Parents entering their child's allergies and dislikes need to trust the product. Two deliberate steps: the Google sign-in screen shows “Morning Ready” by name with Privacy Policy and Terms of Service linked — not a generic project ID. This requires going through Google's OAuth branding verification, which most demos skip. It's not hard, but it takes time and attention most builders don't spend — and it's exactly the kind of detail that separates something that feels real from something that feels like a side project. And the parent owns their data: one tap wipes the profile, all menus, and all scores.
A product tour runs before sign-in so the parent sees the output before committing. Onboarding ships with a pre-loaded menu so scoring works immediately — no data entry required.
Real auth, managed data, authenticated email, CI/CD, regression tests — the plumbing a prototype skips. Here as proof, not as the headline.
AI compressed the build from weeks into days. The point isn't the speed — it's where the saved time went: into the judgment a model can't make. A working product on day one, a deliberate design pass on day two, production hardening on day three — including two restructures (nav and onboarding) that were decided, not generated.
Anyone can prompt a model for a packing list. The work was building the product around it — the data model, the judgment calls, and the craft that keeps it from looking like something generated.
Morning Ready is live with a small group of alpha users (under five today). The near-term plan is to put it in front of 20–30 fellow parents as a beta, learn from how they actually use it, and improve from real feedback rather than assumptions.
Success isn't a big number — it's parents telling me it caught something they'd have missed, and coming back the next week.