This thread hits very close to home for me. I'm engineering the frontend for a grocery list app as a capstone project right now and I'm handling a lot of the product and feature decisions, and the discussion about "just prompt Claude to build it" versus the reality of those decisions is something my team deals with constantly.

The example of reverse-engineering your grocery store's API and building a custom solution is awesome, and it's exactly the kind of thing that's now possible. But what I've found is that even with AI assistance, there are so many interconnected decisions that make this more than a one-shot prompt project.

I pushed for us to build a mobile app specifically to take advantage of portability (use it at home for planning, at the store for shopping) and the camera (image recognition with OpenAI and scanning barcodes with expo-camera). That sounds simple, but it cascades into hundreds of UX decisions about offline-first architecture, gesture patterns, camera permissions, and more.

The units and quantities problem mentioned in this thread is just the tip of the iceberg. I'm trying to figure out a data model that mirrors how people naturally think about groceries: how they categorize items, how they plan meals versus staples versus impulse buys, how they track what's running low. Modeling those mental models is genuinely hard.

What helps is that I worked as an ecommerce shopper at Whole Foods, and I learned that stores are meticulously organized with numbered bays and predetermined routes optimized for efficiency. Translating that knowledge into a system that can intelligently sort a shopping list based on store layout (which varies by location!) and typical shopping patterns is genuinely complex.

One of my teammates put it well: this is a simple idea, but it requires a level of care, expertise, and experience to get it right. AI's incredibly helpful for implementing solutions once we've made these decisions, but the decisions themselves require domain knowledge, user research, and taste. That's the part that's hard to automate, and it's what makes this a real engineering project rather than a weekend Claude experiment.