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.
Same. AGENTS.md files and the like are quite effective. Especially if you’re reviewing the code and making the LLM help you update the markdown files when it makes a mistake to prevent the same type of mistake in the future. Having concrete examples of “good” vs. “bad” to illustrate each architectural rule goes a long way.
For any feature or bug fix that is “painting with the colors already in the tray”, it makes sense to let a LLM write the code. Humans will introduce new tech and new patterns out of boredom and turn the codebase into a big Frankenstein, but the LLM will just follow the architectural guidelines indefinitely.
Agree, I have them curated lessons.md anytime they make a mistake and have found that to be highly effective. Every now and then a lesson goes defunct and needs pruned, but I think that’s just part of the new swe skill set.
It’s possible to win lots of battles but still lose the war. You can ask Trump about that :)
I agree with your last paragraph. We had about 6 weeks of unlimited AI spend before the costs reached executive leadership, and in that time I saw the least experienced developers spend the most with the least to show for it.
But I will say that another factor is thinking that if you get 10% gains from a little AI, then a lot of AI will get you 100%.
But I find the article is right about repo-wide docs. At least on their own. I find having small markdowns (often in the form of skills/commands), focused on specific tasks reduces spend (especially when your execution agent is a low cost model, leaving the reasoning to dedicated agents) and gives better outcomes. Loading massive docs into every task reduces the attention to the task at hand and often confuses AI as the reasoning part of the model becomes overwhelmed and starts inferring wrong things confidently.
I suppose it heavily depends on the scale of the repo though. A large microservice with multiple upstream services it needs to call spends a lot tokens on API which is unnecessary for most tasks. And then it decides to use the wrong one… I have stories lol.
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.