[everforward]

Seconded on smaller files. I feel like I tend to get better responses faster.

A notable flaw here is that I’ve not tried large vs small files in a large codebase. Most of my experimentation there has been on personal projects where even a small file contains a significant part of the project. I could see degradation when it has to load 5 files to figure out how something works.

Total LOC (tokens, really, literal lines probably don’t matter) is interesting as a factor. That might go some way towards explaining why LLMs are weirdly good at Clojure.

Eg last I checked Anthropics one-shot performance on Clojure was about the same as Python or Go despite almost certainly being less represented in training data. The combination of density and simple primitives might be easier for an LLM to wrangle, ameliorating the impact of a less popular language.

[torben-friis]

>Eg last I checked Anthropics one-shot performance on Clojure was about the same as Python or Go despite almost certainly being less represented in training data. The combination of density and simple primitives might be easier for an LLM to wrangle, ameliorating the impact of a less popular language.

There might be tons of confounding factors there. One that comes to mind is the quality of of data, it might perfectly be that the average clojure snippet is higher quality, due to the users demographics. Very few people start writing code with clojure, whether in college or during bootcamps.

[everforward]

Oh there absolutely are, I don’t mean to imply any certainty in that attribution.

Quality of data is totally one. Immutability may be another (it’s easier to reason about if you don’t have to track mutations to a variable). Another interesting one is Clojures emphasis on composability using basic primitives that are sort of hard to grok initially but unlock really cool stuff.

You can do some incredible stuff with recursive map and arrow functions in a few dozen characters.