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kristopherleads | 4 months ago
For example, I'm currently building a flow that connects device monitoring data to a central reporting structure, compares the incoming values against internal spec docs, and uses OpenAI to summarise overall operational status and any drift/out-of-spec issues. The summaries (along with the raw JSON objects) are then sent over MQTT for multi-site compliance and operations. Once you map it out, it's surprisingly straightforward to build.
What makes Node-RED and FlowFuse stand out on this particular use case IMO is that AI flows get treated just like any other message payload. That means your AI output becomes a first-class data object in the system - you can remix, transform, mutate, or extract from it the same way you would any other JSON payload, making it pretty portable and easy to integrate.
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