This is going to be especially useful in vacation planning. :-)
We're already using ChatGPT and other tools to help plan a vacation overseas, but having good mapping data can help an agent figure out things like "does it make sense to go from A to B by car or train?"
With the right metadata, an agent can churn not only on the actual route planning (for a multi-city itinerary), but also things like "near this AirB&B, there are 5 public parking lots, so it'll be easy to park your car, if you do rent one"
I'm excited for this sort of spatial data to help with things like travel planning, or even "sprinkle these 5 errands into my calendar based on where you know i'll be"
https://github.com/oxidecomputer/typify may help for starters. Please create an issue if you need further help with integration!
I've used AFL but never managed to create a consistent fuzzing process using it.
This _should_ allow me to add some more extensive fuzz tests than I've currently been using, even though the grammar is binary since we're really just "parsing" and "rendering" text that happens to be [u8] rather than str.
I don't think there's been any real discussion of the Postgres message format philosophy, but it's a very regular syntax: messages are composed of primitives and lists. Lists are either length-prefixed or zero-suffixed. Everything else is just a combination of these building blocks.