Oh don't get me wrong, it's a level-boosting monster even with that.

The worrying bit is if you're not keeping a close eye on it, very easy to think you're getting a solid system, but thanks to the internal madness it's used, your entire grid has in effect shifted 10 metres right, and any spatial measurements are far more approximate than it's letting on.

That was the bad old days for me. Nowadays I keep it locked down to specific systems for projections or actual spatial calculations, and that seems to keep it honest.

And the surprise is awesome when it happens! My favourite is all the QA steps you wish you'd had and that it reasons out on the raw data alone:

"oh hey, this one city has no data but it has a high population, oh right, that's because of this obscure lookup had a row chopped off, there you go, sorted."

I get to stand back and applaud it, honestly.

Oh absolutely. I just had flashbacks to getting outputs where a raster is completely wacko because the input coordinate systems were not the same and so the grids didn’t line up. It absolutely still has to be supervised closely, much more than a lot of more vanilla programming tasks I’ve experienced.

One thing that helped me a ton was making an adapter between my domain specific datasets/systems and QGIS. So I can watch and examine the intermediate steps and leverage that my brain is still pretty darn good at visual pattern matching.