There’s something about geospatial work that seems to break LLMs. I’ve experienced it a lot with latest versions of both Fable and Opus, and it’s the classic nightmare of confidently-spoken-but-wrong.
Yesterday, I had a relatively trivial bit of work in play using distance-weighting to measure stuff, and Claude did really well / but right at the end, despite having a success metric and lots of great results matching it, Claude insisted my maths was wrong and needed changing.
When i asked why, and pointed out that the suggested replacement would cancel out the distance-weighting entirely, it immediately agreed with me and asked to write its fifth project memory about it.
The other big ones it tends to mess up on without strict guardrails are coordinate systems; and measurements, often converting back and forth between a given nation grid and wgs84 latitude/longitude in ways that lose good amounts of precision; and then custom-coding haversine-equivalents to measure a distance that’s otherwise be a simple subtraction via the national grid coordinates. It’s baffling.
Yesterday, I had a relatively trivial bit of work in play using distance-weighting to measure stuff, and Claude did really well / but right at the end, despite having a success metric and lots of great results matching it, Claude insisted my maths was wrong and needed changing.
When i asked why, and pointed out that the suggested replacement would cancel out the distance-weighting entirely, it immediately agreed with me and asked to write its fifth project memory about it.
The other big ones it tends to mess up on without strict guardrails are coordinate systems; and measurements, often converting back and forth between a given nation grid and wgs84 latitude/longitude in ways that lose good amounts of precision; and then custom-coding haversine-equivalents to measure a distance that’s otherwise be a simple subtraction via the national grid coordinates. It’s baffling.