Post-mortems and failure reports — what happened, why, and how it was fixed.
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Last night the plan was simple: 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. It serves the same endpoints (/search, /reverse, /status) with the same JSON shape, so your existing code does not change. The backend is different. It needs no PostgreSQL, no osm2pgsql import that takes hours and tens of gigabytes, and no separate server, so it can ship inside an app. It answers Nominatim's queries from a SQLite file.
Mailwoman is an open-source address parser. This post is a training log entry from May 2026 documenting the v0.5.0 divergence investigation. For current project status, see what ships today.
v0.5.0 was the fresh-slate release, with a new tokenizer, an expanded corpus, a new architecture, and a new reconcile stage. The plan was to bundle several months of structural improvements into one big iteration and pay the integration cost once. Most of it landed as planned, but the classifier did not.
This post walks through the four training attempts the v0.5.0 C-train made overnight, the bisect that ruled out three plausible explanations, and what we think the remaining cause is. It is a companion to the v0.4.0 retrospective. That retrospective describes the same failure shape with a different sequence of diagnostics.
Mailwoman is an open-source address parser. This post is a historical retrospective on the v0.4.0 training campaign (May 2026). For current project status, see what ships today.
@mailwoman/neural-weights-en-us@v0.4.0 and its fr-fr sibling shipped today as packaged artifacts. The npm publish is a separate manual step. The release has mixed results. It improves fine-grained labels and regresses two coarse labels, and those regressions turned out to be mostly artifacts of how we measured. We set out to combine three orthogonal training improvements into one release, and a divergence pattern we hadn't seen before ruled out almost all of that plan.
This post describes how the campaign went. We're publishing it to explain what the headline numbers mean, and because the way the failures stacked up is relevant if you train your own NER-style models.