studies show a clear trend – output is up (more code, more commits, bigger diffs), but outcomes don’t reflect that trend. If anything, the average team is taking longer to ship worse software
studies show a clear trend – output is up (more code, more commits, bigger diffs), but outcomes don’t reflect that trend. If anything, the average team is taking longer to ship worse software
Some of this does not line up with my lived experience pretty starkly.
Repo level markdown files with architectural guidance not working for example… I’ve found that works quite well.
Not perfectly well, but llms are designed specifically NOT to be perfect deterministic executioners. Still though, pretty well.
I have seen that in a jr engineers hands llms get to bad outcomes fast, and unintuitively (to leaders…) usage of llms in coding does not provide a path for a he engineer to upskill into a sr engineer. A sr engineer with llms though is almost always radically augmented regarding their output speed on task completion.
I’ve seen it become less and less effective as the size of the file(s) grew and as the codebase grew - they got increasingly more diluted or even lost in context compression. After several months of a 6 man team working on the project the rate at which they got ignored started affecting output a lot.