docs: plan next linkedin posts and archive published ones

plans/post-4-brasilume.md

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+# Post 4 — Oct 21 · brasilume
+
+**Angle:** entity resolution and data quality, shown as a map
+
+---
+
+Brazil has 26.9 million active business establishments. Each one has an address in the federal company registry. Putting them on a map sounds trivial until you try.
+
+The registry addresses are typed by hand over decades: abbreviations, missing street titles ("Professora", "Coronel"), postcodes that are wrong or out of date. The usual shortcut is to drop every company on the centre of its postcode or street, which produces a nice-looking map that is mostly invented.
+
+**brasilume** does it the slow way: it matches each address against the 2022 Census address registry (IBGE's CNEFE) and only places a point when the street and the exact number or lot match. No postcode centroids, no street centroids. 21.4 million establishments made it, 80% of the active ones. The rest stay off the map instead of being guessed.
+
+Everything runs in the browser, with no server and no tile service: points stored as delta-encoded varints in Morton order, about 2.7× smaller than raw floats, a 2-million-point sample for the national view, and the full state loading when you zoom in. You can filter by economic sector or watch the country fill up by opening year.
+
+The matching rules are the part I'd reuse anywhere addresses, names or IDs don't line up — which is most real data.
+
+🔗 rafapolo.github.io/brasilume
+
+---
+
+**Before posting:**
+- [ ] Confirm the active/mapped totals on the live Brazil view (26,865,736 active · 21,400,627 mapped on 2026-10-06)
+- [ ] Attach a screen recording: zoom from Brazil into São Paulo, then scrub the opening-year timeline