Scoring Model
No star punishment just because a place has a long wait. Some of the best food takes time. The real question is whether the wait is worth it.
A Smarter Food List Built by Neurodivergent Insight
The goal is to build a website and app that helps people decide where to eat using real behavior data, not hype. We want modern map discovery, better than typical review apps, while still using Google Maps location data for navigation.
Core Metrics
Food quality, food-per-dollar value, cleanliness, expected wait, and consistency will shape each score through weighted models.
Anti-Hype Focus
Designed to reduce viral hype and manipulated reviews by relying on structured, repeated signals instead of loud opinions.
Low-Friction Reviews
Less long-form text, more Q/A: how long you waited, what you ordered, food rating (1-5), and quick details people can compare.
Data Inputs
Receipt upload, menu parsing, and long-term account linking (like fast-food apps) to build more accurate real-world scoring over time.
Chain Store Notes
Instead of deep reviews for every chain location, surface practical notices: order correctness %, stale food %, restroom/play area availability.
Research + Marketing
Backed by research-driven product decisions and clear community marketing so this can become a useful daily tool, not another rating app.
Founder Note
This project exists because I am neurodivergent and I see a better way to score places. Not sure if I will get around to building it all, but this is the vision.
Tismlist is meant to turn everyday food choices into measurable, transparent decision support for people who want signal over noise.