> Often the model isn’t flaky at understanding the task. It’s flaky at expressing itself. You’re blaming the pilot for the landing gear.
> The model is the moat. The harness is the bridge. Burning bridges just means fewer people bother to cross. Treating harnesses as solved, or even inconsequential, is very short-sighted.
> The gap between “cool demo” and “reliable tool” isn’t model magic. It’s careful, rather boring, empirical engineering at the tool boundary.
Great post indeed but let me ask you, put yourself in the LLM shoes. Now instead of reading through coherent lines of code that is exclusively about solving problems, you now have random characters before every line that mean something (because the presence of the edit tool implies it) but not about your actual problem. Do you reckon the LLM will be distracted a little bit? The benchmark deliberately sidestep the actual intelligence of the model on the task at hand, so while the author feels successful at their subtask its very possible they've failed at the war. This seems to be the beauty of AI engineering. The smarter you think you are about something the bigger the fall.
> Often the model isn’t flaky at understanding the task. It’s flaky at expressing itself. You’re blaming the pilot for the landing gear.
> The model is the moat. The harness is the bridge. Burning bridges just means fewer people bother to cross. Treating harnesses as solved, or even inconsequential, is very short-sighted.
> The gap between “cool demo” and “reliable tool” isn’t model magic. It’s careful, rather boring, empirical engineering at the tool boundary.