alt Hacker NewsI have an idea for a use case for this, and I'm wondering if you think it makes sense or if you have any thoughts on the approach.
I'm a big fan of OpenStreetMap, and I enjoy editing it from my computer. From my phone, I find it quite tedious trying to make sure I type in the phone number exactly correctly and double-check it, or find and select the right field from the large list of fields available in Upredor.
Generally, how it works is I see a restaurant, and there's a sign. I know that it says, "Cash only. Here's the phone number. Here's the opening hours." What would be really cool is if I could just speak to the phone and say, "Hey, here's the information about this place." It would automatically use your location to detect what places are nearby and maybe even detect which place you're talking about, and then tell you, "Okay, here are the changes I think you're proposing to make, or these things you stated are the ones that would create a diff." This would be limited to just perhaps the 20 most common keys in some predefined set of values for most of them. Like cuisine=x should just match to the most common not make up new ones.
Of course, this is something a large language model could do, but having it run on device would be a lot nicer and cheaper.
Makes a lot of sense! declare one record with the ~20 keys as fields, cuisine and friends as enums with the common values, and the grammar can't produce a value outside the set; fields with no evidence come back empty, so the output is exactly the diff.
For "which place", query nearby POIs from location in the app and pass the candidate names as an enum field, so Needle picks rather than guesses.
Two caveats: it's text-in, so you need on-device STT first, and opening_hours syntax is the risky bit, so either put the format in the description or capture the raw hours and normalise in code.