Having worked closely with junior engineers and with high-end models, I strongly disagree. In almost all cases the output of the juniors is on par or better (albeit slower) than the LLMs ,and unlike an LLM, a junior actually learns and becomes much more consistent than the AI over time.
Are you talking about a one shot from the model or using a harness? I agree a junior dev can do better then a one shot, but with a proper harness with adversarial review cycles I don’t think a junior dev could
junior actually learns and becomes much more consistent than the AI over time.
A proper harness will have memory and will get more consistent the more you use it. You can “teach” it by adding skills and having it write it’s learnings either locally or to repo context files.
A proper harness will have memory and will get more consistent the more you use it.
Yeah, by creating a bunch of .md files of questionable quality that get fed into an already-limited context window, on top of a pile of all sorts of other context cruft from the harness, the model provider, and whatever else is propping up the system to give the illusion of intelligence…
I’ve experimented with harnesses quite a bit and I don’t understand the hype. The difference in quality has been middling in my experience, but the token burn has been significantly more — which makes sense when you understand that the more of the context window you use, the worse most models perform.
Having worked closely with junior engineers and with high-end models, I strongly disagree. In almost all cases the output of the juniors is on par or better (albeit slower) than the LLMs ,and unlike an LLM, a junior actually learns and becomes much more consistent than the AI over time.
Are you talking about a one shot from the model or using a harness? I agree a junior dev can do better then a one shot, but with a proper harness with adversarial review cycles I don’t think a junior dev could
A proper harness will have memory and will get more consistent the more you use it. You can “teach” it by adding skills and having it write it’s learnings either locally or to repo context files.
Yeah, by creating a bunch of .md files of questionable quality that get fed into an already-limited context window, on top of a pile of all sorts of other context cruft from the harness, the model provider, and whatever else is propping up the system to give the illusion of intelligence…
I’ve experimented with harnesses quite a bit and I don’t understand the hype. The difference in quality has been middling in my experience, but the token burn has been significantly more — which makes sense when you understand that the more of the context window you use, the worse most models perform.