Amid the chaos of a world in crisis, I’ve found hope in an unexpected place: coding. With tools like Claude.ai and MCP, I’ve been building a web app to help food pantries serve their communities better—automating inventory, breaking language barriers, and streamlining processes. This isn’t just about code; it’s about turning anxiety into action, using technology to create something meaningful. If you’ve ever wondered how AI can amplify human effort, this is a story for you.
Read MoreLearning Through Building: Adding Sort & Translation Features to a Food Pantry Web App
Today was a journey of wins and "learning opportunities" as I worked on improving William Temple House’s food pantry management system. The morning started great - implementing sortable table headers was surprisingly smooth — I’m continually impressed by the coding capabilities of Claude.ai. Working with both Claude and ChatGPT, we created a modular code structure, which made it easy to add sorting without breaking existing features.
But then came the interesting part. When I began tackling multi-language support, my API requests to OpenAI exploded into the tens of thousands. The goal seemed simple: translate food items into our client community's languages. William Temple House serves many non-English speaking clients: Spanish, Russian, Ukrainian, Chinese, Arabic, etc. I’ve integrated OpenAI's gpt-4o-mini API for translations, and everything worked beautifully... until it didn't.
Turns out a rate limit is easier to hit once the database of food items reaches critical mass. Who knew sending thousands of translation requests over a period of just a few hours would hit OpenAI's daily cap? Probably everyone who bothered to read the documentation first. :-)
It's actually kind of funny watching the error logs fill up with "please try again in 8.64 seconds" messages. Claude as an AI coding assistant was invaluable throughout this process. When I got stuck on implementing sort functionality, it helped refactor the code while maintaining the existing event-driven architecture. Later, when we hit the translation rate limits, it suggested implementing batch processing and queuing systems - solutions I wouldn't have considered immediately.
Key takeaways:
Small wins matter: The sorting feature works great in my Test UI
Read API docs before sending 10,000 requests
Sometimes the best solution is to wait 24 hours for rate limits to reset; I should be taking a break on a holiday weekend anyway
Having an AI pair programmer helps spot potential issues before they become problems
Next steps? Take a brief pause while OpenAI's rate limit resets, then return with a smarter approach to batch translations. As frustrating as the rate limit issue was, it's pushing us to build a more robust system.
I’m going to use some of this down time to reflect more on the process of building this system. I’ve learned a lot about how best to leverage AI for software development, and hope others will benefit from what I’ve found along the way.