Order lunch on Uber Eats by tapping a picture
I had my AI agent create an app called “Lunch” to help me with decision fatigue and boost my productivity. Task: I had my AI agent create an app called “Lunch” to help me with decision fatigue and boost my productivity. It has a few pictures of things I might like to eat. Tapping one spawns a subagent to order that item on Uber Eats and 60-90 minutes later I’m eating. It’s 600 thousand lines of JavaScript.; tools: custom-built or unnamed agent. Human role: The original post does not say how a person stayed involved.; limitations: Prerequisites include Uber Eats API access and payment integration. The 600,000-line codebase suggests high maintenance complexity. Source reliability is low as it is a social media post without technical documentation. Human approval steps are undefined, posing risks for unauthorized spending..
Tools
custom-built or unnamed agent
Published on Agent Work Atlas:
Overview
I had my AI agent create an app called “Lunch” to help me with decision fatigue and boost my productivity.
Workflow
I had my AI agent create an app called “Lunch” to help me with decision fatigue and boost my productivity. It has a few pictures of things I might like to eat. Tapping one spawns a subagent to order that item on Uber Eats and 60-90 minutes later I’m eating. It’s 600 thousand lines of JavaScript.
Reported result
The source does not report measured outcomes, success rates, or specific productivity gains. It claims the user receives food 60-90 minutes after tapping. No verification of order accuracy or system stability is provided. Acceptance check: Verify order placement and delivery time for three distinct items.
Human role
The original post does not say how a person stayed involved.
Limitations
Prerequisites include Uber Eats API access and payment integration. The 600,000-line codebase suggests high maintenance complexity. Source reliability is low as it is a social media post without technical documentation. Human approval steps are undefined, posing risks for unauthorized spending.
Try it
I want to build a prototype 'Lunch' decision app. Use [replaceable: your preferred AI coding agent] to create a simple interface with 3 food images. When I tap an image, simulate an Uber Eats order request. Do not execute real payments. First, run a dry test with mock data and show me the JSON output of the simulated order. Wait for my confirmation before attempting any real API integration. If inputs are missing, ask for the specific food items and delivery address format. Ensure the code is modular and under 500 lines for this prototype.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.