Now What
Now What
Now What
Now What? is an activity generator for parents of young children. Built entirely with AI tooling, it uses location, weather, safety alerts, and child age to deliver a single, developmentally appropriate suggestion.
Category
Category
Education
Education
My role
My role
Product Concept
Product Concept
Timeline
2 weeks
Problem
Parents have limited time to plan activities.
Parents of young children need activity ideas but rarely have the time or mental bandwidth to sift through Google searches or filter through local event sites.
Problem
Parents have limited time to plan activities.
Parents of young children need activity ideas but rarely have the time or mental bandwidth to sift through Google searches or filter through local event sites.
Solution
A simple activity generator built for decision fatigue.
Now What? eliminates the need for research and adds an element of discovery with a single suggestion. It required safety-constrained prompt design, a secure API architecture, and a graceful fallback system to address an unsolvable hyperlocal event-data gap.
Process
Product decisions
I gave the app an enthusiastic, authentic, and knowledgeable voice by training the model on explicit examples of what to avoid and include. To reduce friction, user settings persist across sessions so people don’t have to re-enter their information each time.
The data problem
The key assumption that reliable, free, hyperlocal data on family events existed proved false. To address this gap, I used a fallback hierarchy: Google Places for real venue references, defaulting to home/backyard activities when live event data isn’t available.
Technical architecture
Now What? pairs a React/Vite/Tailwind frontend with an Express proxy (to secure the API key) and a prompt-engineering layer. It integrates Open-Meteo, NWS, Google Places, and Figma MCP for weather, alerts, venue data, and design-to-code translation.
Solution
A simple activity generator built for decision fatigue.
Now What? eliminates the need for research and adds an element of discovery with a single suggestion. It required safety-constrained prompt design, a secure API architecture, and a graceful fallback system to address an unsolvable hyperlocal event-data gap.
Process
Product decisions
I gave the app an enthusiastic, authentic, and knowledgeable voice by training the model on explicit examples of what to avoid and include. To reduce friction, user settings persist across sessions so people don’t have to re-enter their information each time.
The data problem
The key assumption that reliable, free, hyperlocal data on family events existed proved false. To address this gap, I used a fallback hierarchy: Google Places for real venue references, defaulting to home/backyard activities when live event data isn’t available.
Technical architecture
Now What? pairs a React/Vite/Tailwind frontend with an Express proxy (to secure the API key) and a prompt-engineering layer. It integrates Open-Meteo, NWS, Google Places, and Figma MCP for weather, alerts, venue data, and design-to-code translation.
Main Flow
Main Flow
Prompting
Prompt Engineering & Safety
Parallel API integration made it hard to isolate issues, and desktop responsiveness would've been easier to address if tackled earlier rather than retrofitted after a mobile-first design. Despite that, the product shipped and improved through real use. User testing led to added context and fee transparency. AI tooling accelerates execution while product judgment defines what's worth building.
Prompting
Prompt Engineering & Safety
Parallel API integration made it hard to isolate issues, and desktop responsiveness would've been easier to address if tackled earlier rather than retrofitted after a mobile-first design. Despite that, the product shipped and improved through real use. User testing led to added context and fee transparency. AI tooling accelerates execution while product judgment defines what's worth building.
Reflections
What I learned
Parallel API integration made it hard to isolate issues, and desktop responsiveness would’ve been easier to address if tackled earlier rather than after a mobile-first design. Despite that, the product shipped and improved through real use. User testing led to added context and fee transparency.
Reflections
What I learned
Parallel API integration made it hard to isolate issues, and desktop responsiveness would’ve been easier to address if tackled earlier rather than after a mobile-first design. Despite that, the product shipped and improved through real use. User testing led to added context and fee transparency.
