The Problem I Saw From the Floor, Not From a Laptop
Why I started building Materia AI before it was anywhere near finished. There's a specific kind of bad moment that happens on a restaurant floor. A table orders the salmon. You put it in. Ten minutes later the kitchen sends it back — we're out. Now you're the one walking back to that table to explain it, and if you're unlucky, you already told them how good it was. That moment isn't rare. It…
In the restaurant business, a particular moment of trouble frequently occurs. After a table places an order, it is sent to the kitchen, which then returns it after ten minutes due to an issue. As the waiter returns to the table, they must explain the situation, even if they had previously praised the food. This is a common occurrence and happens regularly throughout a shift.
The chefs are overwhelmed with managing inventory in real time and often learn of shortages only during service. By the time the kitchen realizes a dish is no longer available, the waiter has already presented it to the customer. For years, the reporter has worked on the restaurant floor while simultaneously writing code. Witnessing this same gap between the kitchen and waitstaff happen shift after shift, with the kitchen struggling to keep track of what’s left and the waitstaff left out of the loop until it’s too late, the reporter recognized this as a solvable problem.
This realization led to the birth of Materia AI, not as an idea from a hackathon or a dream in a blank editor, but as a solution to a problem seen in real life. Presently, Materia AI is still in its early stages. The backend, which converts menu items into easily understandable bilingual descriptions for customers, has been successfully implemented.
However, the frontend, the user interface, is still in development. Instead of waiting for the complete product, the reporter chose to release the backend on Gumroad as a working prototype. This move allows others to utilize the core solution while the frontend continues to evolve. The reporter chose not to wait until the full product was ready before sharing the story, opting instead for transparency and utility.
The reporter believes that the most valuable part of their project is not the finished product but the moment of recognizing a real problem that necessitates action. The reporter continues to work on Materia AI openly, starting with the backend and moving towards the frontend. If you're interested in the working portion of the solution, it can be found on Gumroad.
For those facing similar backend or AI integration challenges, the reporter is open to freelance and full-time work. The reporter's GitHub page offers additional insights into their work.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
