Feed an address into a sequence labeler and part of it goes missing. The model tags the tokens it recognizes — house number, street, city — and the characters it doesn't understand fall on the floor. decodeAsJSON hands you back a tidy object with every field in its place, and nothing in that object tells you the model just shrugged at a fifth of the input.

So you ship that parser. It looks great on the addresses you tested it against. And you have no idea what it's dropping in production, because the output format was built to hide exactly that.

Type Vienna, Austria into our geocoder and, until this week, it answered with a confident set of coordinates: 39.32, −81.54. That's Vienna, West Virginia — population about ten thousand, a few miles up the Ohio River from Parkersburg. The capital of Austria is 7,500 kilometers and one ocean away, and the geocoder had no doubt whatsoever.

Last night the plan was straightforward: mailwoman was missing a pile of non-US coverage, and we were going to fix the parser to close the gap. By morning we'd shipped something better than planned, killed the original idea, and caught ourselves about to spend a GPU budget on a bug that lived in a symlink. Here's how a night of "fix the model" turned into "stop trusting your evals."

You can point a Nominatim client at Mailwoman now. Same endpoints — /search, /reverse, /status — same JSON shape, so your existing code doesn't change. The difference is what's behind it: no PostgreSQL, no osm2pgsql import that takes hours and tens of gigabytes, no server you can't ship inside an app. A geocoder that answers Nominatim's questions from a SQLite file.

You've got a hundred thousand addresses to reconcile. Two databases, the same clinics and providers scattered across both, each one spelled a dozen ways: abbreviated here, reordered there, a postcode dropped, a suite number glued to the street. You run them through a geocoder, match on the resolved coordinate, and it works. Mostly. Some fraction land on the wrong building, the wrong block, the wrong town, and the geocoder won't tell you which fraction. It hands back a pin for every row and the same silent confidence for all of them: none.

That's the gap we set out to close. Every geocoder chases accuracy, be-right-more-often, and so do we. The piece almost nobody hands you is the one underneath it: a number on each answer that tells you which ones to trust, so you can keep the good ones and send the rest to a human.

We had just watched our geocoder beat Nominatim across the United States by fifteen points, and we were feeling good about ourselves. So we pointed the same benchmark at Europe expecting a victory lap. Europe handed us a double-digit loss instead.

That sat badly. Not because losing is shameful — Nominatim is the bar, it carries the whole planet on community-contributed data, and clearing it anywhere is the goal. It sat badly because we didn't understand it. We knew our parser wasn't ten points worse in Europe than in America. So what was the gap actually made of?

This is the answer, the two fixes, and — because we'd be kidding you otherwise — the parts the fixes didn't reach. For the European leg we added a third system to grade against: Pelias, by way of geocode.earth, the hosted Elasticsearch stack a lot of people reach for. It turns out to be the real bar, and we'll be honest about where it still beats us.

Our parser scores 28% on Spanish addresses. By the metric that reads its labels, it is, on paper, broken in a country of forty-seven million people.

That metric is measuring the wrong thing. The moment we measured the right one, the panic fell apart.

Open the demo, type an address in Kabul, and watch nothing happen. Not a wrong pin a few streets off. Not a city-center fallback. Nothing — the gazetteer has never heard of the place. Try Hong Kong. Try Tirana, or Chişinău, or anywhere in the Democratic Republic of the Congo. Same silence. We had been calling this gazetteer "world coverage" for weeks, and it covered 97 of the world's ~195 countries. The other ninety-odd were simply not in the file.

The whole geocoder runs in your browser. You type an address, you get a rooftop coordinate, and no server ever sees your query — the gazetteer it resolves against is a SQLite database sitting on a CDN, and the page reads it with HTTP range requests, a few kilobytes at a time. It's a lovely trick. We were proud of it. Then we counted the requests it took to find a single city, and the number was 243.

So the questions for the day: why does looking up one name cost 243 round trips? What goes wrong when you search a database you can only read a slice at a time? And how do you get a global gazetteer — every country, region, county, and city we resolve against — down to about a dozen reads without putting a server back in the loop?

Overnight I taught the geocoder Canada. Or I thought I did. I pulled 843,000 Canadian postal codes, computed a centroid for every one, spot-checked the result — M5H 2N2, downtown Toronto, 43.652, −79.382, dead on — and validated the database every way I could read it: every postcode present, every coordinate right, nothing else disturbed. Green, top to bottom. This morning, before flipping it live, I asked the demo to find a Toronto address. It dropped the pin in Ohio.

So, the questions for the morning. How does a database that passes every check still land the answer 600 kilometres wrong? What does it actually mean to "validate" a thing? And why is Toronto in Ohio?