[infinityio]

> If you routinely pass in a dataset to AI, it can identify anomalies and watch to see if it settles or even reason why the anomaly occurred in the first place. You can provide the AI some guidelines to only alert when something needs attention if it meets a certain set of criteria. This ultimately increases the effectiveness of your on-call crew and lowers your TCO purely based on manpower.

> ...

> So you could use ChatGPT to identify activity trends of an individual to get a statistically high likelihood of reaching them online. Imagine this audit history

> [dataset of login and logout timestamps] `Return a json object with a "sendAtTime" property in ISO-8601 format that has the highest likelihood of reaching the person the next time they are online.`

This feels like it is slightly conflating the strengths of LLMs and AI as a whole.

There are certainly machine learning models that would assist in predicting when a user will be next online, and using an AI assistant to assist in writing such an implementation would likely be helpful.

However, passing JSON into an LLM, asking it to emulate a program to perform this functionality, and then parsing the response to extract other JSON doesn't feel like a stable approach (for example: I was unable to recreate their successful execution under ChatGPT v3), especially given this technique, if widely adopted, may end up including user-controlled fields that would make the system vulnerable to prompt injection.