Payload Doctor: a private, offline AI that explains failing integration payloads
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle What I Built Payload Doctor is a small tool for integration developers. You paste a failing JSON or XML payload and the error it produced, and a local AI model explains what went wrong and suggests a fix. I work
Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle Submission 馃捀
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
Payload Doctor is a small tool for integration developers. You paste a failing JSON or XML payload and the error it produced, and a local AI model explains what went wrong and suggests a fix.
I work with enterprise integrations day to day, and one of the most common time sinks is staring at a rejected payload: a wrong date format, a missing field, a namespace mismatch, an expired token. The obvious shortcut is pasting it into a cloud chatbot, but integration payloads contain business data (customer names, emails, order details, credentials), and that data usually isn't yours to send to a third party.
The people who would pay for this are freelance integration consultants and small dev shops that work with client data and can't, or won't, paste it into a cloud AI. Payload Doctor gives them the speed of an AI assistant without the data leaving their laptop.
A local, private assistant for integration developers. Paste a failing JSON/XML payload and its error, and an open-weight model running on your own machine explains what went wrong and how to fix it.
# 1. Install Ollama (https://ollama.com), then pull a model ollama pull qwen3.5:4b # or a smaller one: llama3.2:3b # 2. Run the app npm install npm start # http://127.0.0.1:3000 Enter fullscreen mode Exit fullscreen mode Options (environment variables): MODEL (default qwen3.5:4b ), PORT (default 3000 ), OLLAMA_HOST (default http://127.0.0.1:11434 ).